studies on the detection of road surface states using tire noise from ...

STUDIES ON THE DETECTION OF ROAD SURFACESTATES USING TIRE NOISE FROM PASSINGVEHICLESWUTTIWAT KONGRATTANAPRASERTTHE UNIVERSITY OF ELECTRO-COMMUNICATIONSSEPTEMBER 2010

STUDIES ON THE DETECTION OF ROAD SURFACESTATES USING TIRE NOISE FROM PASSINGVEHICLESbyWUTTIWAT KONGRATTANAPRASERTA Dissertation Submitted in Partial Fulfillment of theRequirements for the Degree ofDOCTOR OF ENGINEERINGatTHE UNIVERSITY OF ELECTRO-COMMUNICATIONSSEPTEMBER 2010

STUDIES ON THE DETECTION OF ROAD SURFACESTATES USING TIRE NOISE FROM PASSINGVEHICLESApproved by the supervisory committee:Chairperson: ProfessorTomoo KamakuraMember: ProfessorMember: ProfessorMember: ProfessorKazushi NakanoTetsuo KirimotoSeiichi ShinMember: Associate ProfessorHisaaki Tanaka

CopyrightbyWUTTIWAT KONGRATTANAPRASERT2010

走 行 車 のタシ゜ヤ音 をを 利 用 した 路 面 状 況 の 検 出 に 関 する 研 究コングラッタナプラサート ワゥッティワット論 文 概 要気 象 条 件 によって 時 々 刻 々 変 化 する 道 路 状 況 ををチラ゜トーヴに 的 確 に 情 報 提供 することは 事 故 をを 未 然 に 防 ぐうえから 重 要 である。 本 論 文 は, 圧 雪 や 湿 潤等 の 路 面 状 況 をを 的 確 に 判 別 するため, 車 の 走 行 時 に 発 生 するタシ゜ヤ 音 ををマブ゜クェロュビンルで 受 音 し,その 信 号 をを 適 切 に 解 析 して, 路 面 状 況 の 情 報 をを 得 ることをを 目 的 としている。マブ゜クェロュビンルからの 信 号 にはエ゠ンルジンル 音 や 風 切 り 音 が 含まれるが,それらをを 除 去 し, 対 象 とするタシ゜ヤ 音 のみをを 取 り 出 すために,ロューヴカィッセトネ゛ャタシで 信 号 の 前 処 理 ををする。 次 に, 音 圧 は 車 の 種 類 や 走 行 状 態で 異 なるので, 周 波 数 スヒクェトャの 規 格 化 をを 行 う。この 規 格 化 で 得 たスヒクェトャの 度 数 分 布 関 数 および 自 己 相 関 関 数 等 から6 個 の 指 標 をを 取 り 出 し,それぞれの 指 標 で 路 面 状 況 の 数 値 化 をを 行 った。また,ニッポーヴラャネヅッセトワョーヴクェ 解析 の 導 入 をを 試 みた。10 日 間 の 圧 雪 をを 含 むタシ゜ヤ 音 のデーヴタシから, 音 だけでも,路 面 映 像 と 音 をを 利 用 した 従 来 型 のハ゜ノリッセト 方 式 とほぼ 同 じ 90 %の 精 度 で路 面 状 況 が 識 別 できた。i

Studies on the Detection of Road Surface States UsingTire Noise from Passing VehiclesWuttiwat KongrattanaprasertAbstractInformation on road surface states is important and helpful for road users such asautomobile drivers, particularly in the snowy season. In practice, the surface statesdepend greatly on the weather, road users, location, and other relevant factors. Thisdissertation is concerned with the reliable detection of the surface states using tire noisefrom road vehicles. The tire/road noise emitted from moving vehicles variesmomentarily depending on the road surface properties. Then, it may be possible topassively and easily detect the state of the road surface: i.e., dry, wet, snowy, or otherstate. To detect tire noise, the author used a commercially available microphone as anacoustic sensor, which enabled us to easily reduce the cost and size in realizing apractical system for detecting road surface states. Several features in the frequency andthe time domain of noise signals are proposed to successfully classify the road statesinto several categories and to improve the classification accuracy by combining theindicator of the feature obtained with the standard deviation of the cumulativedistribution curves. Furthermore, this dissertation proposes a new processing methodfor automatically detecting the states of road surface. The method is based on artificialneural networks. The proposed classification is carried out in multiple neural networksusing learning vector quantization. The outcomes of the networks are then integrated bythe voting decision-making scheme. From the experimental results obtained in snowyseason demonstrated that an accuracy of approximately 90% can be attained forpredicting road surface states using only tire noise data.ii

AcknowledgementsFirst of all, the author would like to express my deep and sincere gratitude tomy research supervisor, Professor Tomoo Kamakura, for his guidance, supervision, andmotivating discussions throughout my doctoral study, as well as his constructivecomments and encouragement in writing up this dissertation.Special thanks to Professor Kazushi Nakano, Professor Tetsuo Kirimoto,Professor Seiichi Shin, and Associate Professor Hisaaki Tanaka for kindly serving asthe committee members of this dissertation and their valuable comments andsuggestions.The author would like to thank Professor Koji Ueda of the Department ofInformation Systems, Daido University for providing us tire noise data that wereobserved near Sapporo city.The author also would like to acknowledge the Department of Electrical andTelecommunication Engineering, Faculty of Engineering, Rajamangala University ofTechnology Krungthep (RMUTK), Thailand for financial support during my doctoralstudy.Sincerely thanks to all the members of Kamakura’s Laboratory for theirfriendship, human support, advice, and valuable contributions to this dissertation.Especially, the author would like to thank Assistant Professor Hideyuki Nomura for hishelpful discussions. Finally, the author would like to express my gratitude to my dearestparents for providing me the best educational opportunities and to my elder brothers andmy wife for their continuous support, encouragement, and understanding.iii

Table of ContentsAbstract in Japanese........................................................................................................iAbstract...........................................................................................................................iiAcknowledgements.........................................................................................................iiiTable of Contents............................................................................................................ivList of Figures................................................................................................................viiList of Tables...................................................................................................................xi1 INTRODUCTION.....................................................................................................11.1 Introduction to road weather information in winter............................................11.2 Detection of road surface states..........................................................................41.3 Organization of this dissertation.........................................................................72 THE OVERVIEWS OF TIRE/ROAD NOISE........…….............................……...82.1 The sources of vehicle noise………………….…………..………...………….82.1.1 Wind turbulence noise…...……………....…………………….……......92.1.2 Power unit noise…...............………………………………….……......92.1.3 Tire/road noise...............…...…………………………..……….……...102.2 The generation and propagation of tire noise............…….…...........……….122.2.1 The mechanisms of tire noise generation...............................................122.2.2 The mechanisms of studless tire noise generation.................….……....162.2.3 Tire noise propagation...................……………............……….……....172.3 The influence of the road surface on tire noise...................................……….192.3.1 Road surface influence on tire noise…......…….…….........…………...192.3.2 Texture and absorption characteristics of a road surface........................202.3.3 Tire influence on tire noise..........………….….............……….……....212.3.4 Tangential and lateral forces on the tire..................................................232.3.5 Temperature............................................................................................242.3.6 Weather...................................................................................................252.4 Summary..........................................................................................………….26iv

5.4 Application of LVQ to classification............................................................…765.4.1 Classification structure…….................................……......…….……....765.4.2 Experimental results and discussions...............……......…….……........785.5 Summary….................................................................………...…………...…836 CONCLUSIONS.......................……………………........................................…...86REFERENCES.......................................................…........…………………………...89APPENDIX A. Cumulative distribution curves……..........................................……94A.1 Ambulance noise…........……..........................................……...……94A.2 Snow removal noise…........……....................................……………97A.3 Studless tire noise…....................................……...........…………101APPENDIX B. LVQ training...........................................………….......……………105B.1 Training set…................……............................................…………105B.2 Parameters of LVQ network.........................……...........…..………105B.3 An example of LVQ training...........................................……..……106B.4 Decision-making scheme........……................................…..………110APPENDIX C. New features….……………………………....……......……………112C.1 Noise signal analysis.........................................................…………112APPENDIX D. Publications………………………………....……........……………119vi

List of Figures1.1 Change of national snow and ice control (Budget of Japan for year 2009).. 21.2 Example of road weather information system............................................... 31.3 Flow chart of the detection of road surface states from tire noise usingneural network analysis................................................................................. 62.1 Noise emission sources in a road vehicle...................................................... 82.2 Construction principle of the radial tire [14]................................................. 112.3 Mechanisms of tire/road noise generation.................................................... 132.4 Example of studded tire (a) and studless tire (b)........................................... 162.5 Geometry for a source and receiver in the vicinity of a ground plane,which is reflective surface (a) and porous surface (b).................................. 182.6 Absorption and reflection of the road........................................................... 192.7 Influence of surface texture on the characterization of road surfaces........... 212.8 Tire forces at which relate to tire slip angle and camber angle..................... 232.9 Effect of temperature on noise emission....................................................... 252.10 Typical spectrograms of acoustic signals from passing vehicles forvarious road conditions [10]. Spectrum in red indicates high soundpressure level and that in blue indicates the low level.................................. 263.1 Experimental location near UEC................................................................... 293.2 Porous asphalt pavement near UEC.............................................................. 303.3 Experimental setup for detecting tire noise from passing vehicles (UEC)... 303.4 Frequency response of the electret condenser microphone of a PCMrecorder [36].................................................................................................. 303.5 Hardware system of the measurement we performed when the road wasdry (a) and wet due to rain (b) near UEC...................................................... 313.6 Study site location near an elemental school in Minami-ku, Sapporo city... 333.7 Porous asphalt pavement at study site located near an elemental school inMinami-ku, Sapporo city............................................................................... 333.8 Experimental setup for detecting tire noise from passing vehicles(Sapporo city)................................................................................................ 343.9 Frequency response of the electret condenser microphone of a soundrecording system (Sapporo city) ................................................................. 34vii

3.10 Hardware (a) and schematic diagram (b) of the measurement system at anobservation site near Sapporo city............................................................. 353.11 Microphones covered with windscreens. Observation site near UEC (a)and Sapporo city (b)...................................................................................... 363.12 Definition and typical pictures of actual road surfaces................................. 374.1 Examples of tire noise signals of 1.5 s from passing sedan type cars........... 404.2 Power spectrum p(f) converted from tire noise signals in Fig. 4.1............... 414.3 Peak frequencies in the power spectra of tire noises..................................... 434.4 Peak frequencies averaged over every 20 vehicles....................................... 444.5 Peak frequencies averaged over every 20 small cars.................................... 454.6 Typical blocking time series of a tire noise signal from passing vehiclesfor 5 min observation into frames................................................................. 474.7 Spectrum components, p ( f') of 5-minute sound signals.......................... 484.8 Typical Cumulative distribution curves of the PSD of tire noises fromPassing vehicles for five-minutes.................................................................. 494.9 Time histories of the “amplitude at 1.5 kHz” for 50-minute observationnear UEC (a) and Sapporo city (b)................................................................ 504.10 Time histories of the “frequency at 0.5” for 50-minute observation nearUEC (a) and Sapporo city (b)........................................................................ 514.11 One-day observation near Sapporo city. The amplitude at 1.5 kHz (a) andthe frequency at 0.5 are presented. The observation started at 0 a.m. andended the next day at 0 a.m........................................................................... 524.12 Transition diagram for the different surface states. F l and F h are thethreshold frequencies for the frequency at 0.5. Specially, F l = 1.70 kHzand F h = 2.07 kHz in the present experiment................................................ 534.13 Time histories of the frequency at 0.5 for the three-day observation nearSapporo city. Data (b) is the same as in Fig. 4.10(b). Data (a) to (c) weretaken over three days at the same observation location................................ 554.14 Flowchart for a simple method of classifying the road surface states usingthe feature frequency at 0.5........................................................................... 564.15 Typical cumulative distribution curves for four kinds of surface states........ 564.16 Typical five cumulative curves of power spectrum in 5 min for the dryand slushy states............................................................................................ 574.17 Standard deviation for the second day observation near Sapporo city......... 58viii

4.18 Flowchart for an advanced method of classifying road surface statesusing the frequency at 0.5. The information of the standard deviation isincluded........................................................................................................ 594.19 Typical autocorrelation curves of the PSD of tire noises from passingvehicles for five-minutes............................................................................ 614.20 One-day observation near Sapporo city of features. The observationstarted at 0 a.m. and ended the next day at 0 a.m. (a) ACF at lag 0.2 ms,(b) time lag at 0.5, and (c) peak frequency................................................. 645.1 Architecture of the LVQ network.............................................................. 735.2 Block diagram of the automatic detection of road surface states............... 765.3 Results of detection for the 6th and 7th day............................................... 815.4 Results of detection for the 8th day............................................................ 82A.1 Ambulance and snow vehicle..................................................................... 94A.2 Ambulance noise signal included in the recorded tire noise data for thewet state in 5 minutes and its p ( f') ........................................................ 95A.3 Ambulance noise signal included in the recorded tire noise data for thedry state in 5 minutes and its p ( f') ......................................................... 95A.4 Ambulance noise signal included in the recorded tire noise data for thesnowy state in 5 minutes and its p ( f') .................................................... 96A.5 Cumulative curves obtained from the tire noise data for three states in 5min included in ambulance noise signals................................................... 96A.6 Snow removal noise included in the recorded tire noise data for thesnowy state in 5 minutes and its p ( f') .................................................... 97A.7 Cumulative curves obtained from tire noise data for 5 min included withsnow removal noise.................................................................................... 98A.8 Spectral peak obtained from tire noise data for three states in 5 minincluded with unwanted noise.................................................................... 99A.9 Cumulative curves obtained from tire noise data for three states in 5 minwhich ambulance noises are removed........................................................ 100A.10 Cumulative curve obtained from tire noise data for 5 min which snowremoval noises are removed....................................................................... 101A.11 Experimental setup for detecting tire noise from passing vehicles withdifferent tires.............................................................................................. 102A.12 Tire noise data from summer tires for dry state in 40 s and its p ( f') ..... 102ix

A.13 Tire noise data from studless tires for dry state in 40 s and itsp( f')..... 103A.14 Cumulative curve of the PSD from two types of tire for the dry state in40 s (UEC).................................................................................................. 103B.1 Typical outputs of each classifier team for detecting the road surfacestates........................................................................................................... 111C.1 Residual vectors of tire noise from passing sedan type cars (UEC)..................... 114C.2 Residual vectors averaged over every 20 sedan type cars (UEC).......................... 114C.3 Time history of the residual vector d and D (a), skewness (b) for one-dayobservation near Sapporo city................................................................................ 116C.4 Typical distribution curves of the mean of the adjusted spectrum from tirenoises for five minutes.................................................................................... 117x

List of Tables4.1 Experimental results of detecting the road surface states over three daysusing 5-minute sound signals........................................................................ 574.2 Improved results by introducing the standard deviation of detection ofroad surface states over three days using 5-minute sound signals................ 594.3 One-day experimental results of detecting road surface states using5-minute sound signals.................................................................................. 655.1 Matching routine in each state of Team 1 for classifying the surface states. 775.2 Feature data set of detecting the road surface states over 10 days using5-minute sound signals.................................................................................. 795.3 Results of automatic detection of the road surface states over 10 daysusing 5-minute sound signals........................................................................ 795.4 Data numbers of the incorrectly judged states for every 2 hours over 10days using 5-minute sound signals................................................................ 805.5 Verification results for the 8th day are shown by data numbers of thecorrectly and incorrectly judged states.......................................................... 825.6 Items comparison of the related report [10] with the proposedclassification method..................................................................................... 85B.1 Total number of noises recorded from the first seven days for LVQtraining and dividing into each team............................................................. 105B.2 Number of hidden neurons and training time of each LVQ in three Teams 110C.1 Experimental results using residual vector, D and skewness for detectingthe road surface states over three days.......................................................... 118C.2 Improved experimental results using the standard deviation for detectingthe road surface states................................................................................... 118xi

CHAPTER1INTRODUCTION1.1 Introduction to road weather information in winterWinter driving conditions in snowy areas are extremely severe due to snow frozenroad surfaces, snow storms and impaired visibility. As empirical <strong>studies</strong> in countries ofweather-related road collision risk, especially, when the road surface was wet or snowystates, Andrey et al. [1] report that injury rates increase during snowfall relative tonormal driving conditions and risk appears to be greatest for freezing rain/sleet and thefirst snowfalls of the season. Thordarson and Olafsson [2] state that the causes of thestudy on all the 53 accidents investigated the relationship between traffic safety,weather, user information on road weather and driving conditions, and wintermaintenance operations. According to police reports, causes are due to the fact thatwhen the road surface states are slippery, icy or snowy drivers do not pay attention tocareful driving on these road states. They suggest that the monitoring and providingcorrect information of the road surface states to drivers and/or roadway users in realtime is important because it can reduce the occurrence of accidents on the road.In addition, information of the actual road conditions is prior data for roadadministrators to make more thorough judgments on locations where they shouldconduct road management work and formulate more effective action plans. Because thedecision of whether there should be a winter road maintenance operation or not, is ofimportance. If many unnecessary callouts are made throughout the winter when icystates do not occur, much money and resources are wasted. On the other hand, if theturnout is made too late, or not all, it can lead to accidents and/or traffic jams which arecostly for society. To find the optimal timing for the callout and the right methods foraddressing snow and ice control [3], many countries commonly use information aboutroad surface states for forecasting slippery types. They also highlight the importance ofsharing information on road surface states with drivers, traffic information centers andvarious media organizations for improving safety and smooth running of the roadnetwork. For example, Japan has a relatively small land area and high population1

density (>300 inhabitants/km 2 ). The road network density in Japan is high. Moreover,some areas receive much snowfall than any other areas in the world. Road traffic insnowy areas is often paralyzed. Roads are damaged by freezing in extremely coldregions. The annual snow and ice control budget on national highways and prefecturalroads subject to the national snow and ice control projects in Sapporo city amounts tonearly 150 billion yen, although it fluctuates from year to year depending on thesnowfall [4], as shown in Fig.1.1.Fig. 1.1Change of national snow and ice control(Budget of Japan for year 2009).Real-time road weather information and critical observations such as actual roadconditions, information on the weather environmental roads, visibility and regulationsas a public service are assembled and are distributed by a unique system ofmeteorological and roadside monitoring devices (weather condition detectors) locatedalongside the highways, as shown in Fig. 1.2. Generally, these and other weatherinformation (local observation data of road users and patrol cars [5, 6] on a communalopen server) can be gainfully adopted to support effective management procedures ofroad maintenance officials. For instance [7], surface images using cameras, road surfacetemperatures and database of specific characteristics with different road surface statesfrom pavement sensors are used for early detecting of the commencement of freezing ofroad surface and formulating the application of freezing inhibitors at an appropriatetime.2

At the same time [1, 5 and 6], near real-time road information, weather warningand work-related decisions have been broadly provided for general road users via avariety of sources such as variable message boards, internet-based road informationprovision, television, radio broadcasts, mobile phones and email, and so on. When theroad users got correct information of the road surface states (i.e., slippery, icy or withsnow) of paths before driving, they can take appropriate countermeasures such ashaving winter tires or detouring the routes, and prepare for careful driving on snowy andicy roads [8]. Also, the area’s road administrators can share information on roads (suchas road works, traffic regulation, snow removal operations, etc.) via an intranettechnology-based platform for use in their respective road management operations [5].Therefore, the role of road surface information is priority and extremely importantto achieve assistance for safe driving and to increase efficiency in winter roadmanagement.Road information sharingbased on Internet technologiesOrder for dispatchof maintenance staffServerControl andmonitoringGeneral weatherinformationAdministratorSnow removalstationsRoad surfaceInformationWeather conditiondetectorsRoad maintenanceofficialsRoadinformationprovisionRoadsurfaceInformationRoad weatherinformationRoad usersMeteorological officesInformationdisplayTreeat roadsideTreeStreet

1.2 Detection of road surface statesIt is substantial challenge to remotely obtain information about road surface stateswith sufficient prediction accuracy. In particular, in the snowy season, prior informationabout the road states such as an icy state, helps road users or automobile drivers toobviate serious traffic accidents. To predict road surface states 3 hours and 24 hoursahead, Saegusa and Fujiwara recently proposed a promising method using weatherforecast data and field data [9]. They achieved a great improvement in the accuracy ofpredicting the surface states, particularly dry and frozen states, compared with almostthe same methods previously reported. Different views and treatments were adopted inthe past by McFall and Niittula [10], who used images of road surfaces and traffic noisefrom vehicles to classify road surface states without weather data [11]. Unfortunately,their approach is suffered from systematic problems of high cost and unstable accuracy.For cost reduction, the detection of the surface states using tire noise emitted fromvehicles has so far been performed. Kubo et al. presented a frequency spectrum methodthat enables the determination of the frequency components and sound pressure levelsof tire noise [12]. However, the pressure levels depend greatly on, for example, the sizeof the vehicle and its engine sound. Hence, it is not always stable in the detection of thestates using only the magnitude of the spectrum. To reduce such instability, Ueda et al.introduced the normalized power spectrum based on the ratio of the frequency spectrumto the total power [13]. Experimentally, they found that the cut-off frequency of ahigh-pass filter for classifying dry, wet, and snowy states is 2 kHz in the normalizedpower spectrum. The thus determined frequency seems to change depending onmeasurement locations.This dissertation is basically in line with the approach of Ueda et al. To knowgeneral tendencies of the power spectrum, the author recorded a number of tire noises atdifferent locations. The received signals from microphones are processed through ahigh-pass filter with a cut off frequency of 300 Hz to remove the engine noises ofpassing road vehicles and wind noises. They are then converted into power spectrausing the fast Fourier transform (FFT). After that, the author determined the frequencyat which the power spectrum of each time history record reaches the maximum. In thefrequency domain of the noise signal, our predicting approach relies on the normalizedmagnitude of the spectrum at a frequency of 1.5 kHz and at a frequency at which thenormalized magnitude takes a value of 0.5. The author refers to these as the “peakfrequency,” the “amplitude at 1.5 kHz,” and the “frequency at 0.5,” respectively. Theeffectiveness of these three classification indicators is verified by noise data samples4

obtained at an experimental location near Sapporo city and are compared with visualinspections of actual road surfaces.Our experimental results reveal that classification accuracy reaches approximately70% at the maximum when using the frequency at 0.5, which is the last indicator of thethree features mention above. Interesting, the accuracy in classification is improved byas much as 81% by combining the indicator of the frequency at 0.5 with the standarddeviation of the cumulative distribution curves. It is therefore necessary to continuouslydevelop practical classification methods with the goal of remotely predicting roadsurface states as accurately as possible.Additionally, the author extracts signals features in the time domain from recordedtire noise. The features are based on the autocorrelation function of tire noises. In thetime domain of noise signals, our predicting approach relies on the magnitude of theautocorrelation at 0.2 ms, where the largest differences in magnitude appear and thetime lag at which the magnitude takes a value of 0.5. The author refers to these as the“ACF at lag 0.2 ms,” and the “time lag at 0.5,” respectively. The effectiveness of thefeature indicators proposed is verified by noise data samples obtained at anexperimental location near Sapporo city and are compared with visual inspections ofactual road surfaces.Furthermore, to improve classification accuracy, our approach now uses anartificial neural network (ANN), which is widely used to model involved relationshipsbetween input and output data. In related work, the application of ANN to theclassification task of road surface states was performed by McFall and Niitula [6]. Theycaptured road surfaces with both a video camera for the visual road states and amicrophone for the tire noises, and fed these 51 signal features into their ANN system.The hybrid system that combines the surface images and tire noises generated correctclassifications at a high accuracy of more than 90%. However, the system did not workwell during the hours of darkness and experienced difficulty in identifying dry surfaceroads. Our ANN system shown in Fig. 1.3 is composed of sets of multiple neuralnetworks, and a final decision about road surface states is reached by integrating theoutcomes of the networks using a decision-making scheme. The author improves theaccuracy by means of only tire noise data as well as a small number of input data intothe ANN system.5

Fig. 1.3 Flow chart of the detection of road surface states from tire noise using neuralnetwork analysis.6

1.3 Organization of this dissertationThe remainder of this dissertation is organized into the following chapters:Chapter 2: This chapter provides the background information which describes thevarious noise sources associated with vehicle noise emission, the generation andpropagation of tire/road noise and the influence of important parameters on tire/roadnoise.Chapter 3: This chapter explains hardware systems including experimental setup andprocedure for recording tire noises emitted from moving vehicles at two experimentallocations: i.e., near the campus of The University of Electro- Communications and nearSapporo city, especially in the snow season. Winter weather, traffic problems inSapporo city and effect of wind noise on measuring microphones are also described.Chapter 4: Power spectral density functions of vehicle noise signals are obtained in thischapter using FFT when the road surface is wet, dry and snowy. Simple classificationmethods based on a few signal features that are extracted in the frequency and timedomain are presented to successfully classify the states of the surface into fourcategories. Experimental results as well as careful discussions for all the proposedmethods are also given.Chapter 5: Basic concepts, theories, and learning algorithms concerning ANNs at afundamental level are described in this chapter. A new processing method based on setsof multiple neural networks and decision-making scheme are utilized for automaticallydetecting the states from the tire noise of passing vehicles. To evaluate the performanceof the proposed automatic detection method, the author examined the noise data offifteen days at the observation location near Sapporo city. Experimental results and thediscussions of correctly and incorrectly judged states are also shown.Chapter 6: This chapter presents major conclusions that are deduced from the presentresearch results are provided. Additionally, some recommendations for furtherinvestigation are remarked.7

CHAPTER2THE OVERVIEWS OF TIRE/ROAD NOISERoad traffic noise is the accumulation of noise emissions from all vehicles inthe traffic stream. Each vehicle has a number of different noise sources which give thetotal vehicle noise emission. This chapter provides the background information whichdescribes the various noise sources associated with vehicle noise emissions. Animportant consideration is to understand the factors which influence noise emissionsfrom these various sources. In particular, noise sources associated with the interaction ofthe vehicle tires with the road surface often referred to as tire/road noise, will behighlighted including its generation, propagation and its significance on overall trafficnoise. Noise emissions can be affected by meteorological factors such as weather andtemperature.2.1 The sources of Vehicle NoiseFig. 2.1 Noise emission sources in a road vehicle.Vehicle’s acoustic signal consists of a combination of various noise signalsgenerated by the engine, the tires, the exhaust system, aerodynamic effects, andmechanical effects: i.e., axle rotation, break pads and suspension. Hence, the spectralcontents of vehicle’s signals include wideband processes as well as harmonic8

components. It also has a spatial distribution because the noise sources are at differentlocations on the vehicle. The mixture weighting of these spectral components at anygiven location depends on the vehicle’s speed, whether the vehicle is accelerating,decelerating, turning and whether the vehicle is in good mechanical condition. Ingeneral, one can approximate vehicle’s signals as consisting of three main categories[14], as illustrated in Fig. 2.1.2.1.1 Wind turbulence noiseVehicle induced turbulence can become an important factor in the overallperceived loudness of a vehicle as the vehicle speed increases. This noise is due to airflow generated by the boundary layer of the vehicle and is prominent immediately afterthe vehicle passes by the sensor. Wind turbulence noise depends on the vehicleaerodynamics as well as the ambient wind speed and orientation [15]. Aerodynamicnoise sources are not important for exterior vehicle noise (at least not for “legal” speeds,i.e., below around 120 km/h) due to the effective aerodynamic design which isnecessary to meet fuel consumption requirements. The frequency spectrum of steadywind noise is typically broadband and is heavily biased toward the low frequencies(31.5 to 63 Hz). Cross-wind on a highway is a type of aerodynamic noise due tofluctuation of exterior varying wind conditions. It has spectral contents at higherfrequencies (above 300 Hz). However, it may be quite important for interior noise. Infact, this problem is a clear commercial argument and is one of the reasons why windturbulence noise is kept low outside the vehicle [14].2.1.2 Power unit noiseNoise emission from the units of the vehicle that take part in propulsion of thevehicle (this includes all mechanical noise sources of the vehicle except the tires). It isrelated to the engine, fan and exhaust as well as the deterministic harmonic train(transmission) and a stochastic component similar to human speech [16]. Those sourcesare the “most obvious” for many people who are not professionally involved inautomotive science. The power unit as the major noise source in the mind of commonpeople has historical origin and may nowadays be prejudice. Noise from the power unitis composed of several sub-sources where engine, exhaust, fan and intake systems playthe most important rule.In general, the frequency components of the engine noise are mainly determinedby the type of engine and its working revolutions per minute. For a conventional4-cylinder engine used in most cars, it can be inferred that the fundamental frequency of9

induction noise is twice of rotation frequency, according to the mechanisms of enginenoise generation. At engine speeds from 1260 to 5550 r/min, the engine noise isdominant in the frequency ranges between 42-185 Hz [17]. However, at speeds of 70-90km/h for cars, power unit noise is known to be negligible [14].2.1.3 Tire/road noiseThe terminology related to a modern passenger car ‘radial tire’ is made of severaldifferent materials including steel, fabric and of course numerous rubber compounds asshown in Fig. 2.2. Truck tires are basically similar, but due to their higher loads andgreater risk for damage they often have some additional elements. Various approacheson tire noises have been performed by many tire makers and researchers for decades. Infact, the tire noise is more often called “tire/road noise”. The tire/road noise is definedas the noise emitted from the rolling tire as a result of its interaction with the roadsurface. Tire noise and drive train noise are the most prominent contributors to high waynoise. In the recent years, manufactures have been successful in producing vehicles withgreatly reduced power and drive train noise such as an electric vehicle and a hybrid car.If a vehicle is in a good operating condition and has an effective exhaust system,tire/road interaction will dominate the overall noise level of a vehicle at moderate tohigh speeds. Moderate to high speed on dry roads is defined to be 30-50 km/h forautomobiles and 40-70 km/h for trucks [14].The tire noise consists of two components: vibration noise and air noise [13]. Thevibration component is caused by the contact between the tire treads and the pavementtexture. Its spectrum frequency range is most dominant between 100-1000 Hz. The airnoise is generated by the air being sucked-in or forced out of the rubber blocks of a tireand is dominant in the frequency range of 1000-3000 Hz. In the driving direction of thevehicle, the road and the tire forms a geometrical structure that amplifies the noisegenerated by the tire/road interaction [14, 18]. This effect is called the “horn effect” andhas a directional pattern. It is a fundamental part in the tire radiation and mostprominent at coast-by or pass-by conditions in the range of 600-2000 Hz, although thepeak frequency depends very much on the location of the sound source in the horngeometry [18]. Tire noise becomes important because a megaphone effect (horn effect)is created between the reflecting surfaces of the tire and the road surface.Apart from the influence of vehicle speed, an important factor which influencestire noise is the design of the tire where tread pattern, materials and constructiontogether with the overall width are important contributing elements. In particular, whiletire design and vehicle operation affect the levels of noise generated, the design and10

construction of the road surface can affect both the generation and propagationinvolving several complex mechanisms. The principal factors are the roughness ortexture of the surface, the texture pattern and the degree of porosity of the surfacestructure. The latter governs the degree of sound absorption.The following section describes the mechanisms associated with the generationand propagation of tire noise.Fig. 2.2 Construction principle of the radial tire [14].11

2.2 The generation and propagation of tire noiseTire noise is the result of a complex interaction between the rolling tire and theroad surface. It is a major cause of noise from road traffic particularly for vehicles’moving at moderate to high speed as illustrated in the previous section. In order to studythe principle of basic information about tire noise, it is necessary to obtain a thoroughunderstanding of the mechanisms governing the generation and propagation of tire noise.Obviously, these mechanisms have been well understood and much progress has beenmade in the recent years to optimize the acoustic benefits of low-noise surfaces [14].2.2.1 The mechanisms of tire noise generationThe design of the tire and the road surface play a part in the generation of tirenoise. References include knowledge of the mechanisms of tire noise generation and agood understanding of the relative importance of the different sources of the tire noise.It is considered that tire noise results from a combination of physical processesthat are categorized by convention into three distinct classes of mechanism.(1) Impacts and shocks caused by the variation of the interaction forces betweenthe tire tread and the road surface including the vibration response of the tirecarcass (causing mainly radial vibration).(2) Aerodynamic processes occurred in/out displacement of air in cavities orbetween and/or within the tire tread and road surface.(3) Adhesion and micro-movement effects of tread rubber on the road surface(causing mainly tangential vibration).The various stages of tread pattern rotation and the different noise generationeffects at each stage of the process are shown in Fig. 2.3. It is regarded that for regularrolling conditions the tire noise is mainly composed of “impacts and shocks” noise and“air pumping” noise, with the first mainly occurring below 1000 Hz and the secondmainly occurring above 1000 Hz.12

RollingdirectionAs the tire rotates, there are no forces actingupon the tread block under observation.TreadblockRoadsurfaceLeadingedgeAs the tread block impacts with the roadsurface, shocks are sent through the blockwhich generates vibrations. Air caughtbetween individual tread blocks iscompressed.BlockimpactRollingdirectionRadialvibrationsRepaid aircompressionThe air trapped between the tread blocks iscompressed and decompressed as the tirepasses over the road surface. This is knownas “air pumping”. Organ pipe resonanceoccurs in the longitudinal tire grooves.Friction forces acting on the tread blocks incontact with the road surface cause the“stick-slip” effect.As the tread block leaves the contact patch,compressed air in the tread cavity is expelledrapidly, resulting in the “air pumping”effect. The tread block is returned to its nondeflectedrolling radius position by “snapout”from the compressed state in the contactpatch.Tread blockcompressionRollingdirectionAirpumpingAirpumpingRollingdirectionStick-slipeffectTread blockslipBlocksnap-outNoise generated at the contact patch isamplified by the geometry of the tire androad surface (the “horn effect”). The treadblock returns to its steady state as the tirerotates.TrailingedgeRollingdirectionHorneffectFig. 2.3Mechanisms of tire/road noise generation.13

(1) Impacts and shocksThis mechanism essentially consists of the excitation of the tire tread elements asthey come into contact with the road surface, the vibration response of the tire carcass,and the subsequent radiation of sound by an area of the vibrating tire [19].Vibrations in vehicle tires are the result of a complex interaction between treadblocks with the road surface. As a tread block entering the contact patch impacts theroad surface, generating radial vibrations which are driven into the tire. The tensionexerted on the tread block then decreases and increases depending on the frictionalforces between the tire and the road surface, while the block is passing through thecontact patch. As the trailing edge of the block leaves the contact patch, it is releasedfrom this tension and rapidly returns to its non-deflected rolling radius. The rapidmovement occurring during this process, known as block “snap-out”, excites both radialand tangential vibration modes in the tire structure [20].Noise that is generated by the tire as a result of vibrations caused by tire impactsand snap-out effects tends to occur towards the lower end of the frequency range (below1000 Hz) attributed to tire noise. This has also recently been confirmed by Hamet [21]and Kim [22]. At below 80 Hz, the tire behaves like a spring-mass system (the sidewallacts as a spring, while the tread band acts as mass). In the frequency range 80-300 Hz,the tire behaves like a beam elastically supported by a sidewall spring. The strongdependency on the tire width and tread stiffness is mainly obvious in the frequencyrange of around 400-800 Hz. It is the range where the horn effect starts to act. A reasonis the tire acts as a low-pass filter, effectively attenuating the radiation of noise at higherfrequencies.(2) Aerodynamic processesNoise is generated by several mechanisms related to the movement of air in thecavities of the tread pattern. These occur principally in the region of the contact patch.Of these processes the most commonly cited is referred to as “air pumping”.The original air pumping theory was described by Hayden [23]. This processinvolves the sudden outflow of air trapped in the grooves of the tread pattern or roadsurface texture when the tire comes into contact with the road surface. The air pressuremodulations caused by these processes have been shown theoretically to causesignificant levels of tire noise, particularly when the surface is non-porous and relativelysmooth [24]. The provision of air paths in the road surface layer (i.e., porous andsemi-porous surfaces) can help to dissipate air trapped in the tread grooves andtherefore largely prevent air pumping occurring.14

Sandberg and Ejsmont have discussed the possibility of noise generation beingaffected by air resonance in the cavities of the tread pattern [14]. The phenomenonoccurs when the dimension of the cavities are small in comparison to the wavelength ofsound and is analogous to the resonance of a mechanical system. In general, noisegenerated by aerodynamic mechanism tends to be important in the range of frequenciesbetween 1000 and 2000 Hz.(3) Adhesion mechanismsA further noise generation mechanism is caused by tire vibrations induced by thefrictional forces created in the contact patch between the tire and the road surface. Whenthe tire flattens in the contact patch, the continually changing radial deflection producestangential forces between the tire and the road. (force variation axes are shown in Fig2.7) These forces are resisted by friction and tire stiffness, and any residual forces aredissipated by slip of the tread material over the road surface.Forces comprised of hysteresis and adhesion components control friction betweenthe tread and the road surface. The adhesion component has its origins at a molecularlevel and is governed to a large extent by the small-scale roughness characteristics, ormicrotexture of the road surface. During relative sliding between the tire and the roadsurface, the adhesion bonds that have been formed between the tire and the road surfacebegin to rupture and break apart so that contact is effectively lost and the tire element isthen free to slip across the road surface. Contact may be regained as these residualforces are dissipated. The hysteresis force is due to a bulk phenomenon that also acts atthe sliding surface. The hysteresis component of tire surface friction in largely controlby the surface macrotexture, which comprises texture wavelengths corresponding to thesize of the aggregate used in the surface material. (see Section 2.3.2.)Obviously, the slippage of tread elements only cannot give rise to tangentialvibration excitation of the tire. It is the combination of two mechanisms. They aresurface adhesion and hysteresis through the deformation of the tread as it interacts withthe roughness on the road surface. Both the mechanisms depend on a small amount ofslip occurring as a “stick-slip” process in the contact patch. This process provides thevibration excitation of the tire. Tire vibration and noise generated by these mechanismshave been related to the slip velocity of the tread elements [25]. The highest velocitiestend to be found to the rear of the contact patch and may contribute to block snap-outeffects as the tread elements are released from the compressed state in the contact patchand return to their non-deflected rolling radius.15

2.2.2 The mechanisms of studless tire noise generationStudded tires are used on winter tires in a number of countries such as Sweden,Finland, Norway, Russia, mountainous areas in Europe, Canada, some states in the USAand parts of Japan. The construction details of studs, winter tires with typical studmounting and studded tire generation mechanisms are presented in [14]. Figure 2.4(a)shows an example of studded tire with typical stud mounting. They result in veryspecial tire noise emission.Fig. 2.4Example of studded tire (a) and studless tire (b).The use of studded tires will increase rolling noise significantly. This is due to theimpact of the metal studs with the road surface and the resulting vibrations set up in thetire. At speeds between approximately 70-90 km/h, the effect of the studs produces anoise increase approximately 2-6 dBA in the frequency range 500-5000 Hz and 5-15dBA above 5000 Hz. At lower speeds, the stud influence is mostly even higher. In thebest measurements of actual traffic, the noise effect of studs was made by using amicrophone height of 2 m. This should not influence the difference between the studdedand non-studded cases.Starting in the early 1960’s, studded tires had been popular with motorists insnowy areas, in Japan. However, studded tires were abolished in 1994 due to seriousenvironmental problems, such as dust and noise pollution [26]. In order to run on ice orsnow safely, modern winter tires called “studless tires” have been developed and sold.Nowadays, many studless tires are available in the market. An example of studless isshown in Fig. 2.4(b). Studless tires contain millions of uniformly distributedmicroscopic pores constantly being exposed as tread surface wears and gripping likesuction cups. These pores help wick away the thin layer of water that often develops ontop of snow-compacted and icy roads. In addition, thousands of miniature biting edges16

provide to better adhere to the surface for more traction. The friction mechanismsbetween a studless tire and ice is related to the characteristics of the ice; i.e., the crystalstructure, the size, the dielectric constant and the concentration of impurities areimportant factors [27].Sandberg and Ejsmont [14] have also identified that studless tires areapproximately equal to modern summer tires with regard to exterior noise emission.They might even be somewhat less noisy. However, the quietest winter tires are studlesstires.2.2.3 Tire noise propagationThe road surface can play an important part in affecting how sound generated bythe tire propagates to the roadside. This mechanism involves the complex interferencebetween sound reflected from the surface and sound directly radiated to the receiverposition. The process is demonstrated in Fig. 2.5. The figure shows a simplegeometrical representation of a source (tire) and receiver located above a reflective andan absorptive road surface. It should be noted that when the surface is porous thenadditional propagation occurs in the surface itself. The diagram clearly shows that thesound pressure arriving at the receptor depends upon the combination of the direct andreflected sound waves which, in turn, depends upon the phase and amplitude of thecomponent waves. When the phase of the two main components differs then destructiveinterference can occur and the resulting sound level is reduced.Generally, tire noise propagating over reflecting surfaces, this destructiveinterference effect occurs at relatively high frequencies (typically over 8 kHz). However,propagation over porous surfaces the additional phase shift that occur as a result ofpropagation in the surface layer give rise to destructive interference effects at muchlower frequencies: i.e., typically at about 800-1000 Hz. These are the frequencies wheremost of the acoustic energy generated by vehicle tires is located. The beneficial effectscaused by these phase interactions coupled with the lack of air pumping are the primaryreasons why porous road surfaces have been shown to be associated with significantlylower tire noise levels than non-porous road surfaces.Noise generated near the contact patch can be exaggerated due to the shape of theregion between the tire and the road surface immediately to the rear (or front) of thecontact patch. In this region multiple reflections between the tire and the road surfaceoccur which focus the sound. The process is referred to as the “horn effect” [28]. Theinfluence of horn effect is investigated by measuring the noise levels from anomni-directional impulsive noise source placed close to the rear of the contact patch of a17

stationary tire. The measurements were then repeated with different tire types. Thelargest amplifications were reported to occur in the region of 2000 Hz. Amplification ofthe noise levels measured at this frequency and to the rear of the contact patch, wherethe influence was found to be greatest, was found to be 22 dBA. It was found thatsubstantial amplification occurred at frequencies from 1000 Hz up to approximately 10kHz [18]. It follows that the porous road surfaces help to reduce the amplificationsproduced by the horn effect as reflections from the road surface are reduced.Fig. 2.5Geometry for a source and receiver in the vicinity of a ground plane,which is reflective surface (a) and porous surface (b).18

2.3 The influence of the road surface on tire noise2.3.1 Road surface influence on tire noiseDifferent parameters influence the radiation of the tire noise. One main parameteris the road surface. The road influences the tire noise: i.e., reflecting the vehicle noise,building a part of the horn effect between the tire and the road surface and exciting tirevibrations by the means of the texture of the road.The often used term “Low Noise Road Surface” is defined in [14]. A low noiseroad surface is a road surface which, when interacting with a rolling tire, influencesvehicle noise such a way as to cause at least 3 dBA (half power) lower vehicle noisethan that obtained on conventional and “most common” road surface. This could be alittle different in various countries. However, the type of surface that is very commonand conventional in most industrialized countries, in particular in densely populatedareas where noise problems are common, is an asphalt pavement with a maximumaggregate size between 11 and 16 mm.On porous roads, sound energy is absorbed by the road surface due to its porosity.Sound waves enter the upper layer of the road surface and are partly reflected and partlyabsorbed. “Absorbed” means that sound energy is transformed into another kind ofenergy. In roads this is mainly due to two effects:• By viscous losses as the pressure wave pumps air in and out of the cavitiesin the road.• By thermal elastic damping.A porous road does not absorb all sound that is incident as shown in Fig 2.6. Theabsorption is dependent on frequency. The influence of parameters on the roadabsorption is thickness of the porous layer, flow resistivity (indirectly determined by thestone grading) and porosity (amount of accessible air cavities). Furthermore, theabsorption is influenced by the angle of incidence of the sound waves on the surface.Fig. 2.6Absorption and reflection of the road.19

2.3.2 Texture and absorption characteristics of a road surfaceThe surface parameters are important in characterizing a road surface and whichnot only influence the tire noise but also tire rolling resistance and skidding resistance[14]. As an introduction, the road surface profile can be visualized as a continuousseries of peaks and troughs which may typically be randomized or alternatively welldefined as in the case of transverse textured surfacing. Nevertheless, any type of profileshape can be described as the summation of a number of sinusoidal variations differingin both amplitude and wavelength. This process of reducing a complex profile shapeinto its component cyclic waveforms is known as “Fourier analysis,” each waveformhas associated with a texture amplitude and texture wavelength.It was found that it is convenient to divide the range of texture wavelengths intofour regions, as shown in Fig. 2.7. These texture regions have been defined by thetexture wavelength of the surface irregularities as follows:(1) Microtexture is the texture with wavelengths shorter than 0.5 mm. It refers toirregularities in the surfaces of the stone particles (fine-scale texture) thataffect adhesion. These irregularities are what make the stone particles feelsmooth or harsh to the touch. The magnitude of microtexture depends oninitial roughness on the aggregate surface and the ability of the aggregate toretain this roughness against the polishing action of traffic.(2) Macrotexture is the texture with wavelengths in the range 0.5 to 50 mm. Itrefers to the larger irregularities in the surfaces (coarse-scale texture) thataffect hysteresis. These larger irregularities are associated with voids betweenstone particles. The magnitude of this component will depend on severalfactors. The initial macrotexture on a pavement surface will be determined bysize, shape, and gradation of coarse aggregates used in the pavementconstruction, as well as the particular construction techniques used in theplacement of the pavement surface layer. Macrotexture is also essential inproviding escape channels to water in the tire/road interaction. It provides airpaths between the tire and the road, reduces air pumping and favor absorption.(3) Megatexture is the texture with wavelengths in the range 50 to 500 mm. Itdescribes irregularities that can result from rutting, potholes, patching, surfacestone loss, and major joints and cracks. It affects the tire vibrations, rollingresistance and interior noise levels.20

(4) Roughness refers to surface irregularities larger than megatexture that alsoaffects rolling resistance, in addition to ride quality and vehicle operatingcosts.Fig. 2.7Influence of surface texture on the characterization of road surfaces.The tire noise energy is mainly reflected by dense road surface, while it is mainlyabsorbed by a porous road surface [29]. The volume of air flow in the contact patch andthe amplification by the horn effect are reduced on a porous surface. Due to the roughsurface the vibration noise may be increased. Low frequency tire noise is associatedwith tire vibrations induced by the scrolling of the road profile under the tire. Anincrease of the texture wavelength results in an increase of the tire noise at lowfrequencies. And an increase of the road texture level at small wavelength results in adecrease of the tire noise at high frequencies. However, a low noise road surface,texture wavelengths in the macrotexture and megatexture range between 20 to 160 mmare important for controlling tire noise.2.3.3 Tire influence on tire noiseIn explaining tire influence on tire noise the most important tire designcharacteristics are tread area features, casing construction features and the rubbercompound. These are the results of a number of balanced objectives (price, rollingresistance, wet traction, hydroplaning, snow traction, comfort, noise, weight, etc.). Thecontribution of the different noise generation mechanisms to the total tire noise can beanalyzed by simulations using tire models.21

The geometry of the tread pattern has an important influence on tire noise.Uniformly spaced tread elements lead to a highly tonal character. A randomization ofthe tread reduces the tonal character. The greatest influence on pattern noise is crossgrooves. Therefore, they should be narrow and have a small angle to the circumferencedirection.In practice, aggressive treads lead to marginal noise level increases of 1-2 dBA.However, owing to the fact that sound radiation is generated even by smooth tires, onlya limited reduction in the tire noise can be achieved by changing the tread pattern. Theinfluence of the blocks and ribs depends on their geometry and on the road texture. Onthe one hand, the presence of the tread blocks may cause higher noise levels in thelow-frequency range on very smooth roads. On the other hand, smooth tires emit morenoise than standard tires on rough-textured pavements.Additionally, the tread pattern of the tire is intended for the grip of the tire on theroad surface, especially on the wet state by expelling water from the contact patch.Therefore, it is important to have a sufficient density of channels. But if the number ofchannels is too high, the tread elements get mechanically unstable. For passenger cartires, the number of the tread on the circumference therefore will be 45 to 70 elements[14].Tire design and conditions that affect the noise emission in several ways are:• Tire wear and ageing influences tire noise.• In general, harder rubber compounds cause higher noise levels than softerones, especially for aggressive tread patterns. The elastic modulus of thetread has often a much larger influence than that of the sidewall.• Studded winter tires show very significant increases in noise levelscompared to the same tires without studs.• In general, noise emission increases with tire width.• The tire’s inner structure influences tire noise. Radial tires are somewhatless noisy than bias tires. A decrease of the belt stiffness can increase tirenoise. Increases of carcass stiffness of truck tires can result in reductions oftire noise.• The tire’s sidewall affects the whole tire vibration due to road megatextureas described in Section 2.3.2. The sidewall design can also change thelevel of the sound that radiates away from the tire.• Small improvements seem to be obtainable by fitting absorbing material onthe rim inside the tire and by filling the tire with solid materials.• Tire load and inflation pressure can also influence the tire noise.22

Some new developments are porous treads and run-flat tires [30]. Porous treadscould absorb sound within their structure. Run-flat tires are designed to retain theirstability even when a perforation and loss of inflation pressure occurs. This is achievedby increased stiffness of the sidewalls, which may lead to increased noise emission.2.3.4 Tangential and lateral forces on the tireWhen a tire rolls on the road, several loads and stresses are imparted onto the tire.The tire serves four basic functions on a vehicle; (1) it supports the vertical load wheelcushioning against road shocks, (2) it develops longitudinal forces (or the resultant forceexerted on the tire by the road) for acceleration and braking, (3) it enables directionalchange of the vehicle, and (4) it develops lateral forces (or the component in the groundplane) for braking. Three forces acting on the tire from the ground are shown in Fig.2.8.Fig. 2.8 Tire forces at which relate to tire slip angle and camber angle.An increased force leads to an increased slip of the tire. Then, an increasing slipleads to an increasing sound level. This is basically due to the fact that these forcesincrease the level of the tire noise [14]. Therefore, the tire noise generation is directlyrelated to the slip and camber angles of the tire [31].23

The slip angle characterizes the deviation of the rolling direction of the tire fromthe traveling direction. This angle causes the lateral force which is perpendicular tolongitudinal axis of the wheel. The relationship between the lateral force and the slipangle is of fundamental importance to the directional control and stability of the roadvehicles. If the slip angle is 0, the lateral force of the tire is zero.The camber angle of wheel is the inclination of the tire compared to the verticaldirection. Generally, the lateral force is developed at the tire/road contact patch whenthe camber angle of the wheel is 0. The tire noise levels for patterned tires are least ifthe lateral force is zero. Especially, in the high frequency range around 2-10 kHz the tirenoise is influenced by the lateral force [14].2.3.5 TemperatureOne parameter influencing tire noise is temperature. In general, the level of the tirenoise decreases with increasing temperature. Sandberg and Ejsmont propose to use theroad surface temperature or the air temperature for measuring the temperaturedependence of the tire noise, because they correlate well with the noise levels and thetire temperature [14]. The effect of temperature is clearly frequency-dependent. Thereason is that various generation mechanisms may be influenced by temperature inconflicting or different ways. Anfosso-Ledee and Pichaud support this finding [32].Sandberg and Ejsmont reported the largest effect occurs in the range of 1-4 kHz, but theeffects also occur at rather low frequencies. Anfosso-Ledee and Pichaud presented theeffect of temperature on noise emission and pointed out that it is important at the lowfrequency range (below 500 Hz), and in the high frequency range from 1.6 to 5 kHz forroad surfaces containing asphalt. It is minimal in the medium frequency range between500 to 1.25 kHz.Figure 2.9 shows effects of temperature on noise emission at the frequency ranges.The effect at low frequencies could be explained by reducing the road and the tirestiffness when temperature increases, and thus reducing the excitation of the tire. Theeffect at high frequencies could be explained by adhesion sensitivity to temperature.This means that stick-slip and consequently air pumping mechanisms are affected bytemperature. The low effect at medium frequencies indicates that high frequencyvibrations and low frequency air pumping phenomena are not significantly affected bytemperature.24

Fig. 2.9Effect of temperature on noise emission.2.3.6 WeatherOn porous road surfaces, the wet state leads to a higher tire noise level than thedry state [14]. Particularly in the frequency range above 1 kHz, there are differences upto 15 dBA. On the wet road state, the high frequency range of the tire noise is amplified.McFall and Niittula [10] support these findings using spectrograms of the noise signalrecorded from passing vehicles for various road conditions, as shown in Fig. 2.10. Thespectrogram is quite simple for the dry state; the frequency contents increase as thevehicle approaches and then drops sharply as it passes. For the wet state, thespectrogram is the similar tendency in the beginning, but decrease in strength relativelyslowly with time, as the water is caught in the vehicle’s wake while it passes. Obviously,the frequencies are lowest for the snowy state. The frequency contents may be absorbedby characteristics of snow (air pockets or pores in snow, soft and flat surface) when theroad surface is covered by a layer of snow [33]. Moreover, the snow can also block thegrooves of tire, as smooth tire or patternless tread [34]. Snow on the road gives a softerride and lower sound [14].25

Fig. 2.10 Typical spectrograms of acoustic signals from passing vehicles forvarious road conditions [10]. Spectrum in red indicates high sound pressure level andthat in blue indicates the low level.A light humidity on road surface has little effect on pass-by noise levels. Theentering of the tread elements into the contact patch and therefore the impact to thewater surface is the important factor for generation of the tire noise on the wet surface.In addition, the displacement of water in the front of the contact patch leads to tire noise.Tire noise is generated by compressing the water in the tread pattern channels, resultingin a jet spraying out of the contact patch. At the trailing edge, the braking of adhesionbonds between rubber and water results in the tire noise.For non-porous surfaces, the effect of the passenger cars at a speed of 50 km/hproduces a noise increase approximately 0.9 dBA at 1250 Hz.For light vehicles, the noise increase is highest at low speed, but for heavy trucksthe opposite appears to be the case or unreliable.2.4 SummaryIn this chapter, the author has given the overviews of noise sources associated withthe interaction of the vehicle tires with the road surface from road traffic particularly forvehicles traveling at moderate to high road speeds. For high speeds, there are alsoaerodynamically caused sound sources, but they are unimportant compared to thetire/road noise. Of the sources that contribute to vehicle noise, the noise generated byseveral mechanisms of the tires with the road surface has become the dominant noisesource. The process followed is generally a qualitative one. For tire noise below 1 kHz,complex vibrations of the tire wall caused by collisions between the tread blocks andthe texture of the top road surface layer are important. Tire noise above 1 kHz is causedby aerodynamic mechanisms within the cavities such as air pumping, tangentialvibrations of tread blocks and air resonant radiation effect.26

Tire noise propagation is influenced by the properties of the grooves, the profile ofthe tire and the porosity of the road. The substantial amplification occurs at frequenciesfrom 1000 Hz up to approximately 10 kHz due to horn effect, which is created betweenthe reflecting surfaces of the tire and the road surface near the contact patch.For studless tire, noise generation was approximately equal to modern summertires with regard to exterior noise emission.In addition, the influence of parameters on tire noise was provided to give thereader a basic introduction regarding the major influences on tire noise emission.• The road influences the tire noise; i.e., reflecting the vehicle noise,building a part of the horn effect and exciting tire vibrations by the meansof the texture of the road.• Road texture profile plays a fundamental role in the tire noise generation.On porous surfaces, sound energy is absorbed by several mechanismsrelated to surface porosity.• Generally, the tread pattern of the tire is intended for the grip of the tire onthe road surface, especially on the wet state. The geometry of the treadpattern also has an important influence on tire noise in the low-frequencyrange. The greatest influence on pattern noise is cross grooves.• In high frequency range around 2-10 kHz the tire noise is influenced by thelateral force.• The level of the tire noise decreases with increasing temperature due to thedifferent generation mechanisms. For an asphalt road, the effect oftemperature on noise emission is important at the low (below 500 Hz) andhigh (1.6 to 5 kHz) frequency range. But in the medium frequency range(500 to 1.25 kHz), it is minimal.• The wet surfaces lead to a higher tire noise level than the dry surfaces,particularly in the frequency range above 1 kHz. For the snowy state, thefrequencies are lowest in comparison with the other states.The tire noise emitted from moving vehicles varies momentarily depending on theroad surface properties such as texture and porosity.27

CHAPTER3EXPERIMENTAL CONDITIONSThe detection of road surface states is an important process for efficient roadmanagement. It is a substantial challenge to remotely obtain information about thesurface states with sufficient prediction accuracy. In particular, in the snowy season,prior information such as an icy state helps road users or automobile drivers to avoidserious traffic accidents. In practice, road surface states depend greatly on weather, roadusers, location, and other relevant factors. This chapter explains the hardware system ofexperimental locations and provides a description of the measurement setup at each siteas well as how real traffic data was collected to evaluate the effectiveness of theproposed classification methods in this dissertation. To detect tire noise, the author usesa commercially available microphone as an acoustic sensor, which enables us to easilyreduce the cost and size in realizing a practical detection system.Two locations were investigated in this dissertation. The first location is locatednear the campus of The University of Electro-Communications. The second is locatednear Sapporo city, especially in the snowy season.3.1 Near The University of Electro-Communications3.1.1 Observation site descriptionThis experimental location is a sidewalk of a city road (Musashi Sakai Dori) nearthe campus of The University of Electro-Communications as shown in Fig. 3.1. Theauthor calls this location “UEC” for short. The road has a two-lane; the noise signalspicked up at the sidewalk of the road seem to come from the tire sounds of individualvehicles. The road is a porous asphalt (PA) pavement as shown in Fig. 3.2. The size ofstones in the surface is approximately 1 cm. The spaces or pores between stones areprovided for drainage of surface water, air paths between the tire and the road, andreduces air pumping, as described in Chapter 2. It is currently used for noise reduction,in Japan [35].28

3.1.2 Experimental setupTire noise emitted from moving vehicles on the road surface by variousmechanisms such as vibration impact and air pumping was recorded using a PCMrecorder (PCM-D1, Sony) [36], which was set on a tripod on the side-walk as shown inFig. 3.3. The recording level of the recorder is set into an invariant level (level 4) bychecking this level on both the peak meter of the display and the analog level meters(Adjust the level closer to -12 dB into appropriate range). Vehicles usually pass by at 40km/h on average. A built-in electret condenser microphone was set with the PCMrecorder at a 2 m height from the road surface level in order to avoid the noise effect ofstuds (as described in Section 2.2.2) so that it was directed towards passenger vehiclesat 45 o with respect to the road surface. Figure 3.4 shows the frequency response of thecondenser microphone the author used [36]. The response is almost uniform fromseveral hundred Hz to 10 kHz, although it slightly depends on the direction of anincident sound. The recorder digitally sampled sound signals at a frequency of 22.05kHz with 16 bit quantization.Fig. 3.1Experimental location near UEC.29

Fig. 3.2Porous asphalt pavement near UEC.Fig. 3.3Experimental setup for detecting tire noise from passing vehicles (UEC).Level [dB/Pa]Fig. 3.4 Frequency response of the electret condenser microphone of a PCM recorder [36].30

3.1.3 Noise data descriptionAt the UEC observation site, the author obtained noise data for vehicles withsummer or normal tires only when the road was dry or wet due to rain. Tire noise dataof dry state on fine days were collected on August 27, 2007 between 13.30 p.m. and16.10 p.m. and September 18, 2007 between 13.30 p.m. and 16.10 p.m. And tire noisedata when the road surface was wet state were collected on September 5, 2007 between14.00 p.m. and 16.30 p.m., and November 12, 2007 between 15.52 p.m. and 17.00 p.m.Figure 3.5 shows the hardware system of the measurement, yielding a total of about1000 vehicles signatures that passed by the UEC observation point.Fig. 3.5Hardware system of the measurement we performed when the road was dry (a)and wet due to rain (b) near UEC.31

3.2 Near Sapporo city3.2.1 Winter weather and traffic problems in Sapporo citySapporo has usually a heavy snowfall. Among cities of the world, Sapporo is theonly one with a population of more than 2 million and an annual snowfall of more than5 m. This variation greatly affects winter road management. Annually, Sapporo has 123days of snowfall on average. This means that the author has snowy days for about onethird of the year. In general, Sapporo is covered with snow for four months, from lateDecember to late March. The normal monthly temperature in Sapporo, which is thepolitical and economic center of Hokkaido, is about -7.7 ºC during the coldest months,January and February. It is the only city in the world where such a great number ofpeople live amidst such extreme snowfall [1].Since low visibility occurs frequently on national roads or downtown, theproblems of slippery road surfaces caused by compacted snow and ice and of skiddingaccidents and traffic congestion have become serious social issues. A higher level ofroad management is needed for the winter road traffic environment in Sapporo city. Onthe roads, even careful drivers find it difficult to start and stop, and skidding accidentsand congestion often hinder traffic. Traffic accidents in Sapporo city have increasedrapidly, with the increase of number of vehicles and traveling distance. Rear endaccidents, head on accidents and single vehicle accidents are the major accident types innational roads of Sapporo city in the period from 1989 to2001 [37]. Occurrence of thesetraffic dangers in snowy season is closely connected to the weather conditions. Thus,the role of weather information is very important for developing and increasingefficiency in winter road management. Prior information about the road conditions, suchas an icy state, helps road users or automobile drivers to obviate serious traffic accidents.That is why Sapporo city is an interesting observation site of the detection of roadsurface state in this dissertation.3.2.2 Observation site descriptionTo detect road surface conditions every day that may change with time andweather in the snowy season, the tire noise signals were picked up at an observation siteon the side of a four-lane national road near an elemental school in Minami-ku, Sapporocity as shown in Fig. 3.6. The road is a PA pavement as shown in Fig. 3.7. Apparently,almost the same size of stones, but the porosity of the road surface is less than incomparison with the pavement at the observation site near UEC.32

Fig. 3.6Study site location near an elemental school in Minami-ku, Sapporo city.Fig. 3.7Porous asphalt pavement at observation site located near an elemental schoolin Minami-ku, Sapporo city.33

3.2.3 Experimental setupA microphone was setup on a post at a height of 4 m from the road surface leveland was directed towards passenger vehicles at 45 o with respect to the road surface, asshown in Fig. 3.8. It is used as an acoustic sensor for detecting tire noise emitted frompassing vehicles on the road surface. Figure 3.9 shows the frequency response of anelectret condenser microphone (ECM) of the sound record system. The response isalmost uniform over frequencies from several hundred Hz to 4 kHz. The sampling rateof system was 22.05 kHz. All passenger vehicles seemed to have winter or studless tires.The vehicles were running at 60 km/h to 80 km/h on average.Fig. 3.8Experimental setup for detecting tire noise from passing vehicles (Sapporo city).Level [dB/Pa]Fig. 3.9Frequency response of the electret condenser microphone of a sound recordingsystem (Sapporo city).34

Actual road surface states were monitored visually using a video camera. Tirenoise signals and images of the road surface measured were collected using a personalcomputer (PC) and were recorded on a hard disk drive (HDD) which was installed in acontroller box, as shown in Fig. 3.10. The recording level of the sound recorder is set toan invariant level in the same way as the PCM recorder (UEC). That was adjusted to bethe level closer to -12 dB.Fig. 3.10Hardware (a) and schematic diagram (b) of the measurement systemat an observation site near Sapporo city.3.2.4 Noise data descriptionAt an elemental school observation site near Sapporo city, the author obtained tiresound data of moving vehicles on the roads in snowy area. Tire noise data werecollected at all hours of the day and night on different two days using the measurement35

system as shown in Fig. 3.10. At the first time, the author recorded continuously thenoise data of three-day observation on January 11 to 12, 2007. At the second time, tirenoise was recorded continuously during seventeen days, on January 25 to February 10,2007.3.3 Effect of wind noise on a microphoneWind noise to a microphone of the measurement system influences the acousticmeasurements especially in the low frequency band. The wind noise is wind-borneturbulence in the vicinity of the microphone. It is generated by the temporal and spatialfluctuations of wind velocity across the microphone. The spectrum of this component isconsidered to be dominated by low frequency components from the experimental resultsof wind turbulence measurements. For example, measurements of wind noise in thefrequency range from 0.05 to 50 Hz have been made for wind speeds from 4 to 7 m/s atthree different sites by using a three-axis orthogonal microphone array [38].Generally, the wind noise can be readily reduced by using windscreen in recordingstep, which is well known and widely used to reduce the effect of wind noise on themicrophone. At each location, the author prepared for windscreens such as ones shownin Fig. 3.11 and covered the microphone with them before recording. The built-inmicrophones is covered with the supplied windscreen. An ECM windscreen is made ofpolyurethane foam.Fig. 3.11 Microphones covered with windscreens. Observation sitenear UEC (a) and Sapporo city (b).36

3.4 Several categories of the road surface stateAs a rule of thumb in this dissertation, the author classifies the surface states intothree principle categories by visual observation, as shown in Fig. 3.12. Definition ofeach surface state shows relation with water on the road, fusion caused by the change intemperature and the snow accumulated on the road.(1) Dry A road surface with no water,that is truly dry.(2) Wet A road that is covered withwater and remains wet. Vehiclessplash water as they pass by and thetire tracks remain for a while. Thiscondition includes slushy waterfrom melted snow.(3) Snow-compacted A snowy surfacethat has become compacted owingto passing vehicles. The roadsurface looks completely white,including the wheel tracks.Fig. 3.12Definition and typical pictures of actual road surfaces.37

3.5 SummaryThe author has explained our hardware system and measurement setup at eachobservation site. The road surfaces are all porous asphalt pavement. The vehicles wererunning at moderate to high speeds (approximately > 30 km/h, tire/road interaction willdominate the overall noise level of a vehicle). Real traffic data were collected toevaluate the effectiveness of the proposed classification methods in this dissertation. Atthe same time, actual road surface states were monitored visually using a video camera.Actually, the tire signals the author observed included those from types of vehicle, suchas small car, truck, bus, and motorcycle.At an observation site on the sidewalk of two-lane near UEC, vehicles usuallypass by at 40 km/h on average. PCM recorder was used for collecting noise data withsummer or normal tires only when the road was dry or wet due to rain for four days.Siren noise from ambulance included in the collected noise data when it passed byobservation point.At an observation site on the side of a four-lane national road near Sapporo city,the vehicles were running at 60 km/h to 80 km/h on average. All passing vehiclesseemed to have winter or studless tires. In the snowy season, the tire noise signals fortwenty days at all hours of the day and night were picked up at an observation site nearSapporo city using a commercially available microphone as an acoustic sensor. In theearly morning from 2 to 4 a.m., not many vehicles passed through the observation site.When the great amount of snow on the road, snow-compacted and ice, snow removal onroadways is conducted to maintain the traffic ability of national highways and topromote interregional exchanges and living activities [1]. Therefore, the noise datarecorded at observation site near Sapporo city, noise from snow removal is also pickedup when it passed by.38

CHAPTER4EXTRACTION OF SIGNAL FEATURESTire noises from passing vehicles are collected at two different locations. Theauthor usually observes that the timbre of the tire noise is dependent on the road surfacestates. When a road has water on its surface, for example, the pressure level of tire noisegenerally is increased because of water splashing. Tire noise signals vary momentarilydepending on road surface properties. Then, it may be possible to passively and readilydetect the state of the road surface.This chapter considers the frequency spectra of vehicle noise signals when theroad surface is wet, dry and snowy using the fast Fourier transform (FFT). It thenpresents simple classification methods based on a few signal features that are extractedin the frequency and time domain from the recorded tire noises in order to successfullyclassify the states into four categories: snowy, slushy, wet, and dry states.4.1 Sound signal analysisTo study characteristics of the distribution of power spectrum, tire noise signalsfrom passing vehicles are extracted manually using a free sound engine program [39]from time history records observed over about one hour. The individual waveforms lastfor 1.5 s. Typical waveforms for the wet, dry, and snowy states are shown in Fig. 4.1.By applying FFT, the individual waveforms are converted into the frequencydomain from the time domain to obtain the frequency spectrum. The best frequencyresolution can be attained by transforming the entire sample in a single analysis. This isa convenient and popular method of spectral decomposition. Therefore, the authortransforms the individual waveform that last for 1.5 s into a set of power spectrum in thefrequency domain without any pre-processing.In this research approach, the author uses Parseval’s theorem [40], which statesthat the power of a sound signal represented by a real function f(t) is the same whethercomputed in signal space or frequency (transform) space; that is,39

Fig. 4.1Examples of tire noise signals of 1.5 s from passing sedan type cars.+∞∫−∞f2( t)dt =+∞∫−∞F(f )2df(4.1)The power spectral density (PSD) p(f) (f is frequency) of f(t) represents thedistribution of the energy as a function of frequency, is given byp ( f ) = F(f ) , −∞ ≤ f2≤ +∞(4.2)40

p( f )p( f )p( f )Fig. 4.2 Power spectrum p(f) converted from tire noise signals in Fig. 4.1.Figure 4.2 shows the power spectrum density (PSD) of tire noise signals in Fig.4.1 when the road state is dry, wet and snowy. The power spectrum of all states increaseas the vehicle approaches and drops sharply as it passes. In general, the main advantageof spectral analysis of signals is the possibility to study their frequency-specificoscillations. As described in Chapters 2 and 3, tire noise signal is generated by severalmechanisms related to complex interaction between the rolling tire and the road surface(porous pavement), the influence of parameters on tire noise emission and effect ofwind noise on a microphone. To determine a cut-off frequency of a high-pass filter fromthe obtained PSD, the author divides consideration into three frequency ranges as41

follows:(A) The frequency spectrum (below 300 Hz) comes from the results of complexvibrations caused by the contact between the tire treads and the pavement, tire impacts,stick-slip and snap-out effects, the sidewall acts as a spring, air or road temperature,mechanisms of engine noise generation and wind noise in the vicinity of themicrophone. These are unwanted noise or artifact noise data for detecting the roadsurface states on the basis of the frequency spectrum.(B) The frequency spectrum (300 to 600 Hz) is obtained from results of snap-outeffects, a few influence of temperature on tire noise emission. And it is reinforced by aneffect that depends on the tire width and treads stiffness. That is the reason why themagnitudes of PSD in this range are somewhat low in comparison with the magnitudesin other frequency ranges. However, the predominant magnitudes of frequencyspectrum are declared in this range for the snowy state which may be independent oflocation as well as kinds of tires. These different results may permit to detect the state ofroad surface into several categories.(C) The frequency spectrum (600 Hz to 10 kHz) is obtained from the results of airnoise such as aerodynamic mechanisms, air pumping and horn effect which the largestamplification occurs in this range. It was also affected by the lateral force andtemperature. That is the reason why the magnitudes of PSD in this range are higher thanthose in the frequency range (B).Apparently, the PSD analysis provides the ability to distinguish between differentfrequencies spectra and the associated types of vehicle noise sources, the mechanisms oftire noise generation and influences on tire noise. Additionally, there are differences ofthe distribution of PSD for three states. Especially, when the road surface is wet, thefrequency components of the tire noise seem to be auditory increased on the wholecompared with those of dry surface and especially snowy surface.However, to extract distinct differences among various states of road surfaces isvery important information. Therefore, in our method, the unwanted noise or artifactnoise data are removed from initial tire noise signals by employing a high-pass filterwith a cut-off frequency of 300 Hz. The author therefore focuses on the extraction ofsignal features in the frequency spectrum between 300 Hz to 10 kHz to classifysuccessfully the road surface states using only tire noise data.42

4.2 Peak frequenciesFig. 4.3Peak frequencies in the power spectra of the tire noises.43

Tire noise signals recorded with a microphone are fed to a high-pass filter with acut-off frequency of 300 Hz to remove unnecessary signals such as engine noise andwind noise, as discussed in Section 4.1. Then, FFT is applied to obtain a PSD for tirenoise signal from each vehicle. The author then first focuses attention on the frequencyat which each tire noise attains a peak in its PSD. All spectra of interest are obtained byexecuting FFT on the signal waveform that lasts for 1.5 s.Figure 4.3 shows the peak frequencies of about 1500 vehicles that passed by theUEC and Sapporo city observation point. Three different conditions, the dry, wet andsnowy states on the road, are the targets of classification. Obviously, the frequencyvaries from vehicle to vehicle, and it seems difficult to obtain information on the roadsurface conditions from these randomly scattered frequencies. However, by taking theaverage of the frequencies over every 20 vehicles, a definite difference appears. As canbe seen in Fig. 4.4, the peak frequencies are within the range of 0.8 kHz to 1 kHz for thewet state and are about 0.3 kHz higher than those for dry state that are concentratedaround 0.6 kHz. Likewise, the peak frequencies for the dry state are also approximately0.1 kHz higher than those for snowy state, which take a frequency of 0.5 kHz onaverage. This observation supports our auditory sense. Besides, the results of PSDapparently indicate the same patterns as those described above.Frequency [kHz.]Fig. 4.4Peak frequencies averaged over every 20 vehicles.44

Tire noises from trucks and buses are generally louder than those from theremaining small cars, and difference in signal characteristic might exist between the twovehicle groups. Actually, the tire signals the author observed included those from 20small cars, 10 trucks, 2 buses, and 2 motorcycles in the first 5 minutes. From all the data,the author picked out only the signals from small cars using the software of the soundengine program [39]. Figure 4.5 shows the time history records of the peak frequenciesaveraged over every 20 small cars. Apparently, almost the same patterns are obtainedfor the three different road states: i.e., the peak frequencies for the wet state are 0.2 kHzand 0.4 kHz or much higher than those for the dry and snowy state, respectively.Fig. 4.5Peak frequencies averaged over every 20 small cars.Consequently, the following two important findings are obtained. First, it is ofgreat necessity to execute averaging for the data obtained from vehicles in order toextract distinct difference among various states of road surfaces. In fact, such averagingshould be done over time rather than the number of vehicles because road surfaceconditions are time-varying as they depend on the weather. Empirically, 5-minuteaveraging is recommended. Second, there is no great difference between the data of thepeak frequencies measured from all the vehicles and from only small cars. There seemsto be no need for the pre-processing that extracts data of only small cars.45

4.3 Cumulative distribution analysisSince the sound pressure level depends greatly on, for example, the size of avehicle, its engine and tire noise, it is not sufficiently stable for detecting the roadsurface states on the basis of only the magnitude of the spectrum.Generally, a tire noise is a type of stochastic signal and can be considered to be astationary signal or quasi-stationary process if the running conditions of a vehicle do notoften change. Unfortunately, the author often encounters obstacle signals that comefrom unwanted noise sources such as siren noise from ambulance or noise from snowremoval, see Appendix A.1. One common difficulty in analyzing and classifying roadsurface states using tire noise data for 5 minutes is to remove such unwanted noisesources.To remove unwanted noise automatically, sound noise data for 5 minutes issegmented into multiple sound frames continuously using a time window with a 22.05kHz sampling rate. There is no exact or definite rule governing the window size of tirenoise data analysis using FFT. Generally, selecting a window size appropriate toapplication, it depends on how precise our frequency and time estimations need to be.For a such problem, Wu et al. [41] proposed a method based on short-time Fouriertransform (STFT) for extracting vehicle sound signatures from tire noise data for 5 susing a window size of 0.186 s with continuously overlapping. The analysis with a shortwindow is very accurate in identifying the sound signatures. However, much time isneeded in computing even when tire noise data for 5 minutes is executed.In the present research, the short window with non-overlapping is movedincrementally over the segment and a set of PSD components is calculated in eachposition. This process reduces the amount of computation time required and removalprocedures for unwanted noise signals. To obtain N short-time series {X 1 , X 2 , ..., X n },the sound data of 5 minutes are segmented into multiple sound frames of 110250samples (or 5 second/frame), where N = 60 frames, X ni (n = 1, 2, ..., N, i = 0, 1, ...,110250), as shown in Fig. 4.6. Traditionally, STFT is used to transform the each frameof the data into a set of spectrum components in frequency domain, p n (f). These resultsare in a 55125-dimentional FFT-based spectrum components at a frequency resolutionof 0.2 Hz with the information of frequencies up to 11.025 kHz. Without the sets ofspectrum components of unwanted noise, the remaining sets of those, M are then46

summarized into a set of spectrum components, p ( f') , see Appendix A.1.Unfortunately, signal data may be mixed with windows of unwanted noise. That cannotbe completely avoided as long as the present framing method of using the window sizeis employed.AmplitudeFig. 4.6 Typical blocking time series of a tire noise signal from passing vehiclesfor 5 min observation into frames.Figure 4.7 shows typical spectrum components, p ( f') of five minutes soundsignals when the state is dry, wet, and snowy. The components of all states first increasetheir magnitudes relatively abruptly with frequency. For the wet and the dry state, thep( f') drops sharply at approximately 600 or 700 Hz due to the effect of temperature onnoise emission as explained in Section 2.3.5. And then, they are increased slowly near 1kHz. After that, the p ( f') in the wet state decrease its strength relatively slowly withfrequency owing to water splashing. This means that the high-frequency components ofthe tire noise are increased when the road has water on its surface, as a whole incomparison with those of the dry surfaces and especially snowy surfaces.Prior to taking the next step, the author introduces the following cumulativedistribution function of PSD P ( f ) that can be defined asP(f ) =f∫flf h∫flp(fp(f'') df) df''(4.3)47

Fig. 4.7 Spectrum components, p ( f') of 5-minute sound signals.where f l = 300 Hz is the low-cut frequency. Generally, tire noise does not significantlycontain frequency components greater than 10 kHz. Consequently, the upper limit ofintegration with respect to frequency is determined to be f h = 10 kHz.Typical cumulative distribution curves obtained from passing vehicles for afive-minute signal are presented in Fig. 4.8. All three curves in the snowy, dry, and wetstates first increase in magnitude relatively slowly as the frequency increases, and thenthe rates of the increase become abrupt near 1 kHz. After that, they slow down again atapproximately 4 or 5 kHz. Additionally, the magnitudes in the wet state are lower thanthose in the dry and snowy states at all frequencies. This means that the wet surfacestate predominates at high frequencies in comparison with the other two states.48

P( f)Fig. 4.8 Typical Cumulative distribution curves of the PSD of tire noisesfrom passing vehicles for five-minutes.From the cumulative curves in Fig. 4.8, the author proposes two classificationindicators in accordance with the fact that easy classification of the states is feasiblewhen the appearance of distinct differences between the three distribution curves. Onefeature indicator is the normalized magnitude of P ( f ) at a frequency of 1.5 kHz. Theother indicator is the frequency at which the normalized magnitude of P ( f ) takes avalue of 0.5. Hereafter, the author refers to these as the “amplitude at 1.5 kHz” and the“frequency at 0.5,” respectively. In Fig. 4.8, the amplitude at 1.5 kHz is 0.73, 0.5 and0.27 for the snowy, dry and wet states, respectively. The frequency at 0.5 is 1.15 kHz,1.5 kHz and 2.2 kHz for the respective states.As an example of siren noise from an ambulance, their PSD and the cumulativedistribution curve are described in Appendix A.1. In addition, the author has to payattention to studless tire influence on tire noise for detecting the road surface states. Thecumulative distribution curves obtained from signal data of the studless tire and normaltire are also described for comparison in Appendix A.2.49

4.4 Effectiveness of the features from cumulative curvesTo evaluate the effectiveness of the proposed classification methods, the authorexecuted signal processing using tire noise detected near UEC and Sapporo city. All thesignals last continuously for 50 minutes. Additional data for 3 days were obtained at thesame observation location near Sapporo city.4.4.1 Observation for 50 minutesWithout changing the observation location, the author detected tire noise signalsfor at least 50 minutes for the road surface in different states. Figure 4.9 shows the timehistories of the “amplitude at 1.5 kHz” for three different states. These data are theaveraged magnitudes over every 5 minutes. It is demonstrated that the proposed featureexplicitly exhibits differences in magnitudes for the three surface states, although themagnitude of the feature itself changes somewhat from location to location.Amplitude at 1.5 kHz0.80.70.60.50.40.3SnowyWetDry0.21 2 3 4 5 6 7 8 9 10Number of data processings [every 5 min](a)0.8Amplitude at 1.5 kHz0.70.60.50.40.30.21 2 3 4 5 6 7 8 9 10Number of data processings [every 5 min](b)Fig. 4.9 Time histories of the “amplitude at 1.5 kHz” for 50-minuteobservation near UEC (a) and Sapporo city (b).50

For example, the magnitude remains within the range of 0.5 to 0.6 for the dry stateand within the range of 0.3 to 0.4 for the wet state near UEC. In contrast, the values aresmaller near Sapporo city, being around 0.4 and within the range of 0.2 to 0.3,respectively. Even so, the tendencies of change in magnitude that depend on the roadsurface states may be independent of locations as well as the kinds of tires. Obviously,the indicator takes the largest value for the snowy state and the smallest value for thewet state.Data processing has been executed by the second classification method, using the“frequency at 0.5”, for the same tire noise data. The results are shown in Fig. 4.10. Theorder of magnitude is interchanged compared with Fig. 4.9: i.e., the wet surface takesthe highest frequency and the snowy surface the lowest. The frequencies for the drystate remain within the intermediate range of the three states. Using the observed data,accurate classification into three states seems to be feasible by employing eitherindicator because the states are perfectly separable on the basis of certain thresholdvalues.3.53.02.52.01.51.0SnowyWetDry0.51 2 3 4 5 6 7 8 9 10Number of data processings [every 5 min](a)3.53.02.52.01.51.00.51 2 3 4 5 6 7 8 9 10Number of data processings [every 5 min](b)Fig. 4.10 Time histories of the “frequency at 0.5” for 50-minuteobservation near UEC (a) and Sapporo city (b).51

4.4.2 Observation for 24 hoursIt is of interest to extend the present methods to the daily observation of roadsurface states that may be change with time and weather. To know whether the featurescan actually indicate the changeable surface states, the author examined typical one-daysound data of a long-time observation, which include all three states of road surfaces atlocation site near Sapporo city.0.8SnowyWetDry0.70.60.50.40.30.2Number of data processings [every 5 min](a)3.53.02.52.01.51.00.5Number of data processings [every 5 min](b)Fig. 4.11 One-day observation near Sapporo city.The amplitude at 1.5 kHz (a) and the frequency at 0.5 (b) are presented.The observation started at 0 a.m. and ended the next day at 0 a.m.52

Figure 4.11 shows the time histories of the two features. The data collectionstarted at 0 a.m. and ended the next day at 0 a.m. These surface states were monitoredvisually using a video camera. It should be noted that both the features indeed representthe changes of the surface states. Overall, the sample data are scattered in the earlymorning from 2 to 4 a.m., probably because the number of vehicles passing through theobservation site was small. However, the data obtained using the frequency at 0.5 showsrelatively less scattering results throughout the day than the data obtained using theamplitude at 1.5 kHz.Interestingly, even if the observation with the camera is unavailable, it can beroughly expected, from the figures, that the road surface changed from the snowy stateto the wet state in the morning, remained wet until 2 p.m., and subsequently changed tothe dry state. At any rate, the frequency at 0.5 takes a frequency of 1.46 kHz on averagefor the snowy state from 0 a.m. to 9:30 a.m. Likewise, for the wet and dry surfaces, ittakes 2.20 kHz and 1.94 kHz, respectively.The author then proposes the threshold frequencies of classification usingarithmetic averaging as follows: the frequencies are F l = (1.46 + 1.94) / 2 = 1.70 kHzbetween the snowy and dry states, and F h = (1.94 + 2.20) / 2 = 2.07 kHz between thedry and wet states.4.4.3 Classification into three stateshlFig. 4.12 Transition diagram for the different surface states.F l and F h are the threshold frequencies for the frequency at 0.5.Specially, F l = 1.70 kHz and F h = 2.07 kHz in the present experiment.53

Figure 4.12(a) shows the natural transition process between the three differentstates. The snowy state may change to the wet state depending on temperature elevationand to the dry state by water evaporation when the temperature is further elevated. It isalso allowed to change from dry to wet and wet to snowy. However, the direct transitionfrom the snowy state to the dry state is not allowed: i.e., the wet state always exists inthe process. Unfortunately, the order of the states in the indicators the author proposes isdifferent from such a natural transition order just mentioned. As shown in Fig. 4.12(b),the wet and dry states are interchanged, i.e., for the wet state, the frequency at 0.5 hasthe highest frequency of the three, while the wet state in the natural process existsbetween the other two states. This interchanged situation makes it difficult to classifysuccessfully the three states. In fact, at around 10 a.m. in Fig. 4.11(b), the feature takesalmost the same frequencies as those in the dry state after 2 p.m., but it does not meanthat the road surface is dry in the former time zone: the frequency happens totemporarily take a value of 2 kHz in the transition process from the snowy state to thewet state. This is important information for classifying the dry and slushy states.Three-day observation data for the frequency at 0.5, including sound signals fromthe preceding day and the following day of the day corresponding to Fig. 4.11, areshown in Fig. 4.13. Data (b) is the same as that in Fig. 4.11(b). Using a simpleclassification method based on only the threshold frequencies and the flowchart shownin Fig. 4.14, the author attempted to classify the road surface into the three states. Table4.1 shows the results, where the results of the other two methods of using the amplitudeat 1.5 kHz and the peak frequency of the tire noise spectrum are listed for comparison.Although the accuracy of correct detection is low overall, the use of the frequency at 0.5gives the highest accuracy, and the peak-frequency method has the lowest accuracy. Themain reason why relatively high detection errors arise is that the road was covered withslushy water at around 10 a.m. and was incorrectly predicted to be dry in the transitionprocess from the snowy state to the wet state, as shown in Fig. 4.12(b).As is described in Section 3.4, a slushy surface is expediently categorized into thewet state. Actual surfaces for such roads are not always covered with slush: i.e., parts ofthe surface are still snowy and other parts are already dry owing to water evaporation.Additionally, not all vehicles pass over the slushy surfaces. Figure 4.15 shows typicalcumulative distributions for an observation time of about 30 minutes. Four kinds of roadsurfaces, wet, dry, snowy, and slushy states, are exhibited. It is noted that the curves forthe slushy road are more obviously scattered than the curves of the remaining threestates. Therefore, it seems to be feasible to discriminate the dry and slushy surfaces byintroducing some statistical measures, such as the standard deviation σ.54

3.5SnowySlushyWetDry1st dayFrequency at 0.5 [kHz]3.02.52.01.51.00.50.002.004.006.008.0010.0012.0014.0016.0018.0020.00Number of data processings [every 5 min]22.0024.00(a)3.52nd dayFrequency at 0.5 [kHz]3.02.52.01.51.0Frequency at 0.5 [kHz]0.53.53.02.52.01.51.00.002.004.006.008.0010.0012.0014.0016.0018.0020.00Number of data processings [every 5 min]22.0024.00(b)3rd day0.50.002.004.006.008.0010.0012.0014.0016.0018.0020.00Number of data processings [every 5 min]22.0024.00(c)Fig. 4.13 Time histories of the frequency at 0.5 for the three-dayobservation near Sapporo city. Data (b) is the same as in Fig. 4.11(b).Data (a) to (c) were taken over three days at the same observation location.55

lhhlFig. 4.14Flowchart for a simple method of classifying the road surface statesusing the feature frequency at 0.5.11P ( f )P( f )0.5010.5Frequency [kHz]SnowyP( f ))P( f0.5010.5Wet10 0 10 1Frequency [kHz]010 0 10 1 SlushyDry10 0 10 1Frequency [kHz]010 0 10 1Frequency [kHz]Fig. 4.15 Typical cumulative distribution curves for four kinds of surface states.56

Table 4.1 Experimental results of detecting the road surface states over three days using5-minute sound signals.To obtain the standard deviation of tire noise for 5 minutes, the author summarizesthe remaining sets of spectrum components of each minute, p ( f ) , (k = 1, 2, ..., 5).After that, the author determines cumulative curves using equation (4.3). Additionally,the standard deviation is computed based on the frequency at 0.5 of each curve, Skcan be defined as follows:k'that⎡ 51σ = ⎢ −⎢ 4∑(S k S )⎣ k= 12⎤⎥⎥⎦12(4.4)where S is the mean of Sk . Figure 4.16 shows the obtained five cumulative curves ofPSD in 5 minutes for the dry and slushy states. It can be note that the curves of the drystate are potentially scatters in a random manner.P( f)P( f)Fig. 4.16 Typical five cumulative curves of power spectrum in 5 minfor the dry and slushy states.57

Fig. 4.17Standard deviations for the second day observation near Sapporo city.Figure 4.17 shows the time histories of standard deviation for the second dayobservation near Sapporo city. Overall, the sample data are scattered in various periods,the early morning from 2 to 4 a.m., probably because less vehicles passed through theobservation site. From 12 a.m. to 2 p.m., the wet state change to the dry state, the roadsurface is not always covered with water: i.e., parts of the surface are still water andother parts are already dry due to water evaporation when temperature is furtherelevated. Therefore, not all vehicles pass over the water surfaces. The sample data fordry state show relatively less scattering results in comparison with the other states.At any rate, the standard deviation takes a frequency of 213 Hz on average for theslushy state at around 10 a.m. Likewise, for the dry state from 2 p.m. to the end of thenext day, it takes 89 Hz. That is sample data for the slushy state higher than the mostdata for the dry state. The author then proposes a threshold value of classificationbetween the slushy and dry state using a simple arithmetic averaging (213 + 89)/2 =151Hz. The flowchart in Fig. 4.18 is an advanced classification method that makes use ofthe statistical factor σ.Table 4.2 shows the results obtained using the advanced classification method. Itis already shown in Table 4.1, for example, when the frequency at 0.5 takes a valuebetween 1.7 kHz and 2.07 kHz, the road surface is either dry or slushy. In this case, theauthor uses the standard deviation σ for judging the classification. If σ < 151 Hz, thesurface is dry. Otherwise, the surface is in the slushy state. Table 4.1 demonstrates thatthe accuracy in classification is improved by introducing the standard deviation.58

lhhlFig. 4.18Flowchart for an advanced method of classifying road surface states using thefrequency at 0.5. The information of the standard deviation is included.Table 4.2 Improved results by introducing the standard deviation of detection of road surfacestates over three days using 5-minute sound signals.σ tAt the present time, for classifying road surface conditions using only the tiresound noise emitted from moving vehicles, a couple of detection features wereproposed: the magnitude of the cumulative distribution curve with a frequency of 1.5kHz and the frequency at which the magnitude takes a value of 0.5. From experimentalresults using three-day observation data, it was found that the two features have almostthe same classification accuracy. It was also demonstrated that averaging of the noisedata is important in order to extract distinct differences among various states of the roadsurfaces. Attractively, the accuracy in classification was improved by as much as 81%by combining the indicator of the frequency at 0.5 with the standard deviation of thecumulative distribution curves.59

However, our main aim in increasing capabilities, accuracy and reliability formaking a good decision on detection of road states is to additionally extract the relevantfeatures, based on only tire noise signals emitted from passing vehicles. Therefore, inthe next section, the author proposes feature indicators in the time domain based on theautocorrelation function of the recorded tire noises to successfully classify the roadstates into four categories.4.5 Autocorrelation analysisAs is mentioned in Section 4.3, tire noise is a type of stochastic signal and can beconsidered to be a stationary signal or quasi-stationary process if the running conditionsof a vehicle do not often change. The autocorrelation function (ACF), ρ ff (τ ) , (τ is timelag) for a stationary signal is a measure of the time-related properties in data that isdelayed by a fixed time. ACF tells us more about the signal, such as whether significantcorrelation between the time series exists and whether the similarity tendency of thesame state remains from one observation to another. For example, suppose the signalconsists of some pulse which occurs at random times but is always followed, say asecond later, by a second pulse of half the intensity of original pulse. In the ACF, thesecond pulse would appear as a sharp line at a lag of one second, yet this line would bespread out overall frequencies in the PSD and might become much more difficult todetect. The main reason why the author focuses on both the PSD and ACF is that theyare sensitive to different characteristics of the time series.From one useful property of the Fourier transform pairs based on the fact thatconvolution in the time domain is equivalent to multiplication in frequency domain.Thus the author has the important result that for a finite energy signal, ACF and PSDcontain the same information and constitute a Fourier transform pair, i.e.'ρ ff ( τ ) ↔ p(f )(4.5)Taking the inverse Fourier transform of the PSD generates the ACF according tothe Wiener-Khinchin theorem [38], that is'ρ ( τ ) = F [ p(f )](4.6)ff-160

The author focuses here on the autocorrelation function that is readily calculatedfrom the power spectrum using FFT to extract new signal features in the recorded tirenoises f(t), then its ACF is defined as+ α∫ρ ff ( τ ) = f ( t)f ( t + τ ) dt−α(4.7)The autocorrelation sequence calculated using the FFT produces autocorrelationpoints at lags up to one-half of the data length. An autocorrelation plot is often restrictedto fewer points to better show values at smaller lags. In this research, the authorconfines attention to the first main lobe in each curve, that is in a 20-dimensionalACF-based the autocorrelation coefficient, ρ (j = 1, 2, ..., 21) at the time resolution of0.05 ms based on the input sampling frequency.Since the magnitude of PSD is not sufficiently stable for detecting the conditionsas described previously, the autocorrelation coefficients need to be normalized beforeany further processing. The author uses a conventional normalization method, that isr jmaxjρ j − ρmin=ρ − ρ , j=1, 2 , ..., 21 (4.8)minFig. 4.19 Typical autocorrelation curves of the PSD of tire noisesfrom passing vehicles for 5 minutes.61

Autocorrelation curves for 5 minutes are shown in Fig. 4.19. As can be seen, thethree curves decrease in magnitude relatively abruptly with time lags. However, greatdifferences exist in their shapes: the magnitude for the wet state is entirely lower thanthose for the dry and snowy states. This result is somewhat expected from the fact thatthe magnitude of the high-frequency components in the tire signal emitted from the roadsurface in the wet state is dominant in comparison with the magnitudes in the other twostates. Since the high frequencies are equivalent in short time periods, the correlation inthe wet state becomes small as the time lag is increased. To the contrary, when roadsurfaces are snowy and low-frequency components predominate, a relatively strongcorrelation should remain at even short time lags.Two feature indicators are indeed inferred from the autocorrelation data in Fig.4.19. One indicator is the magnitude of the autocorrelation at 0.2 ms, where the largestdifferences in magnitude appear. The other indicator is the time lag at which themagnitude takes a value of 0.5. The author henceforth refers to these indicators as the“ACF at lag 0.2 ms” and the “time lag at 0.5,” respectively. In Fig. 4.19, the ACF at lag0.2 ms is determined to be 0.76 ms, 0.5 ms and 0.04 ms for the snowy, dry and wetstates, respectively. Whereas, the time lag at 0.5 is determined to be 0.34 ms, 0.2 ms and0.08 ms for the respective states.4.5.1 One-day classificationTo determine whether both the proposed features of the ACFs can actuallyindicate changeable surface states, the author first examines typical one-day sound datathat were collected on the second day of the three-day observation in Section 4.4.2. Thereason why the author uses such data is that the data include all four different states.Figure 4.20 shows the time histories of the three features; the ACF at lag 0.2 ms,the time lag at 0.5 and the peak frequency. The observation started at 0 a.m. and endedthe next day at 0 a.m. At the same time as the sound recording, the author visuallymonitored the surface states with a video camera. As can be seen in Fig. 4.11 and 4.20,overall, the sample data are scattered in the early morning from 2 a.m. to 4 a.m.,probably because less vehicles passed through the observation site.It should be noted that all the features actually represent the changes of the surfacestates. The results of the wet data obtained using the ACF at lag 0.2 ms show the mostscattering. Classifying, however, does not seem to be difficult because the wet dataexhibit distinct differences in magnitude from the other remaining states. In contrast, thedry data obtained using the ACF at lag 0.2 ms (a) and time lag at 0.5 (b) indicate lessscattering results than those obtained using the peak frequency and the amplitude at 1.562

kHz, as shown in Fig. 4.20(c), and 4.11(a). It is noted as well that the data obtained forthe time lag at 0.5 indicate relatively less scattering results after approximately 10 a.m.than the data obtained using the ACF at lag 0.2 ms. Of the five features, the data for thefrequency at 0.5, see in Fig. 4.11(b), indicate the least scattering results, and the dataobtained using the peak frequency (c), indicate the highest scattering. From the data inFig. 4.20(a) and (b), even if observation with cameras is unavailable, the author canmore or less assume that the road surface changed from the snowy state to slushy statebefore changing to the wet state in the morning, remained wet until 2 p.m., and afterthat changed to the dry state. Therefore, accurate classification into the snowy, wet, anddry states seems to be feasible by employing either feature on the basis of certainthreshold values.At any rate, the time lag at 0.5 takes a time lag of 0.28 ms on average for thesnowy state from 0 a.m. to 9:30 a.m. Similarly, for the wet and dry surfaces, it takes0.18 ms and 0.22 ms, respectively. The author then proposes the threshold values ofclassification using arithmetic averaging as follows: the time lags are (0.28 + 0.22) / 2 =0.25 kHz between the snowy and dry states, and (0.22 + 0.18) / 2 = 0.20 kHz betweenthe dry and wet states.Using the same classification method as in Section 4.43, when the time lag at 0.5takes a value of 0.2 ms to 0.25 ms, the road surface is either dry or slushy. In this case,the author uses the standard deviation σ for judging the classification. If σ < 151 Hz, thesurface is dry. Otherwise, the surface state is decided to be slushy.Table 4.3 shows the experimental results based on the ACF at lag 0.2 ms and timelag at 0.5, where the data using the peak frequency, the amplitude at 1.5 kHz, and thefrequency at 0.5 of the tire noise spectrum are also listed for comparison. Obviously, theclassification ability by using both features of ACF shows high precision, and theyachieve a classification accuracy rate of 93%. The use of the frequency at 0.5 gives thehighest accuracy, and that of the peak frequency method gives the lowest accuracy. Theaccuracies of correct detection for the features in the ACF remain above 74%, which isthe lowest accuracy of the peak frequency method.63

0.80.7Snowy Slushy Wet Dry2nd day0.60.50.40.30.20.350.30Number of data processings [every 5 min](a)2nd day0.250.200.150.100.051.41.2Number of data processings [every 5 min](b)2nd day1.00.80.60.40.2Number of data processings [every 5 min](c)Fig. 4.20 One-day observation near Sapporo city of features.The observation started at 0 a.m. and ended the next day at 0 a.m.(a) ACF at lag 0.2 ms, (b) time lag at 0.5, and (c) peak frequency.64

Table 4.3 One-day experimental results of detecting road surface states using 5-minutesound signals.σ t4.5.2 SummaryIn this section, the author proposed a couple of simple detection methods toclassify road surface conditions into several categories of state and to improve theclassification accuracy. One feature is the magnitude of the autocorrelation at 0.2 ms.The other feature is the time lag at which the magnitude takes a value of 0.5. From theexperimental results using one-day observation data obtained in snowy area, it has beendemonstrated that an accuracy of up to approximately 93% is attained in predicting theroad surface states using only tire noise data. At the present time, the accuracy ofcorrect detection is relatively high as a whole. However, it suffered from systematicproblems for automatization. Therefore, it is necessary to continuously develop practicalclassification methods with the goal of automatically detecting the states of roadsurfaces from tire noises.65

CHAPTER5CLASSIFICATION BASED ONARTIFICIAL NEURAL NETWORKIt seems somewhat cumbersome to correctly detect the road surface states fromthe time records of the various indicators in Chapter 4 because they involve a broadrange of the surface state categories or classes. Therefore, the decision criteria of theproposed features may overlap. The ultimate goal for our simple identification andclassification work is to correctly label the unknown objects such as signals andprocesses according to their actual categories. To avoid such problems for classifyingthe surface states, this research employs an artificial neural network (ANN), which iswidely used to model involved relationships between input and output data.This chapter proposes a new processing method for automatically detecting thestates from the tire noise of passing vehicles. The noise signals vary momentarilydepending on road surface properties. Then, it may be possible to passively and readilydetect the state of the road surface. Our ANN system is composed of sets of multipleneural networks, and a final decision about road surface states is reached by integratingthe outcomes of the networks using a decision-making scheme.Prior to taking the next step, the author basically describes concepts, theories, andlearning algorithms concerning ANN. Knowledge about the learning vectorquantization (LVQ) network will be provided only at a fundamental level, which shouldbe sufficient for the readers to understand how ANN operates to solve generalclassification problems.5.1 Artificial neural networkThe artificial neural network is one of the emerging and exciting developments insolving engineering problems such as computer vision, control and speech recognition.They can learn and recognize patterns in an autonomous manner by imitating thelearning process of the human brain. They consist of a number of simple neurons, whichare also called nodes or units. These neurons are connected by links, which have66

strength or “weights” that are learned during a training step by the network. Thedirection of connections within a network can be one-way, two-way, or a combinationof both. Distinct organizations of those components, along with distinct learningmechanisms, are developed for particular purposes or tasks. Most neural networkmodels that have been created are either two-layer or multilayer networks.ANNs have been implemented to solved several complicates problems in bothscientific and nonscientific tasks. ANNs possess promising characteristics andproperties that are suitable for those tasks [43] as following.• First, ANNs possess parallel processing capability. Each neuron within anetwork behaves like a single independent processor. However, all neuronscan be operated at the same time or in parallel. Parallel processingtherefore enables an ANN to perform complex tasks much faster thantraditional computation methods.• Second, ANNs have like an associative memory. This is the ability torecognize, recall, and draw inferences or associations among variousinformation items. In other words, if we provide an ANN for only a portionof the entire set of input data, it should give us back the related completeoutput by recollecting to the past experiences or knowledge of the sametype of data.• Third, ANNs are fault-tolerant systems. The remarkable architecture ofANNs creates robust systems that effectively handle any malfunction ofsome neurons or incomplete data sets and noises. Since the knowledge isevenly distributed overall individual storage elements and links, a loss of afew data items will cause only a small degradation of performance qualityof an ANN.To make ANN models effectively work for particular tasks, users have to makesure that the selected architecture, learning algorithm, and related parameters areappropriate. User also must consider the appropriate type and format of input and outputdata. If carefully planned, will greatly improve the performance of the neural networkmodels and ensure more accurate results. At the same time, they will make analysis andinterpretation of the outcomes more meaningful to a decision-making scheme.67

5.1.1 Learning within ANNThe most remarkable characteristic of ANN that is hardly found within traditionalcomputing systems is their ability to learn from their experience. ANN basicallyconsists of a collection of intercommunicating neurons. The knowledge an ANN haslearned will be stored in its individual neurons and in connections among the neurons. Agiven learning algorithm trains an ANN to recognize patterns of the data in any domain.This learning process technically changes a value of parameters within each neuron andits connected weights. There are basically two distinct mechanisms of learning [43, 44,and 46].(1) Supervised learningThis learning mechanism is sometimes referred to as learning with the help of ateacher. To learn with in this mechanism, an ANN requires a pair of an input vector andtarget output vector for individual observation of the whole training data set. The targetoutput vector represents the correct or desired results that ANN is supposed to produce.At the initial stage, given an input vector, the network computes and produces its ownoutput vector using the initial values of its parameters. The produced output vector isthen compared with the corresponding target output vector. The difference, if any,between those two vectors will be fed back to the network. During the feedback process,the values of connected weights and probably the values of some parameters withinneurons are adjusted. The changes in those values are to minimize the error between theproduced outputs and the target ones. The learning and adjustment process will besequentially applied to individual training vectors over and over again, until thedifference for the entire training set has reached an acceptable level.(2) Unsupervised learningThis learning mechanism has been recognized to be a close resemblance of theactual learning mechanism and environment within the brain. A neural network with thislearning mechanism does not require a target output vector for a particular input vector.The network learns to recognize patterns of input vectors by extracting their statisticalproperties, grouping the vectors of similar properties together and then assigning theminto a distinct class. It is expected that the network would produce the same pattern ofoutputs for a subset of similar or closely related input vectors. The outputs from thisANN, however, are up to the process of learning and are difficult to determinebeforehand their specific patterns. The outputs generally need some transformation,68

visualization, and interpretation to make them become more comprehensible andmeaningful to users.5.1.2 Input data to ANNANN can handle various types of data from simple linearly correlated to complexnonlinearly correlated data. However, researchers generally agree that ANN approachwill be a very efficient and useful technique for finding relationships within a set of datathat can not be handled successfully by other methods or techniques. To enhance thecapability of ANN, the appropriate formats and properties of input data should beprepared. Yale suggested some guidelines for preparing the right data for training neuralnetworks [45]. The size of sampled input data should be substantially large to provide ahigh level of confidence that an ANN will finally converge. The large sample size willalso ensure that all possible patterns or scenarios of input data are provides for trainingANN. The range of measurable values should be kept as tight as possible. This tightrange reduces the chances of getting stuck into a local minimum. In case of categoricalvariables, the numerical values just behave like labels and do not convey any meaning.It is more efficient for ANN to learn if the categorical data are represented as a set ofbinary values of all corresponding possible categories. Finally, the training data setshould be uniformly distributed. There should be relatively an equal number of inputdata for each possible scenario.5.2 Selection of learning vector quantization networkObviously, according to the above characteristics of ANN, applying ANN todetection of road surface states from tire noise is a good choice. In the related research,the application of ANN to the classification task of road surface states was performed byMcFall and Niitula [10], as discussed in Chapter 1. The extracted 51 signal featuresfrom both the surface images and tire noise are fed into their ANN system based onbackpropagation (BP). The BP learning algorithm is classified as a supervised learningmechanism. Although BP neural networks have been extensively utilized to solveseveral classification and prediction problems, it has been shown that an often-superiormethod exist such as learning vector quantization (LVQ) network. The LVQ network isa hybrid network which uses both unsupervised and supervised learning to formclassifications [43, 46]. The hybrid network first finds the hidden structures orrelationships within a data set by its competitive learning method. This findingsimplifies the problem by reducing the dimensionality of the data. The supervised69

learning portion then makes a classification decision by easily matching the clusteredlow dimensional data with the desirable classes.Several publications in different fields of science have already proved thatvector-based neural networks, especially LVQ network [47 - 49], outperform BP in thefield of supervised pattern recognition [50-52]. It is difficult to evaluate and comparethese <strong>studies</strong> because both databases and the experimental conditions are different.However, the LVQ network has promising potential for solving classification problems.McDermott and Katagiri [50] presented a simple method using the LVQ instead ofBP as a basis for phoneme recognition architecture. One phoneme token consisted of 15time frames of 16 mel-scale spectrum channels, with a frame rate of 10 ms. Inputspeech was sampled at 12 kHz, hamming-windowed using a window size of 20 ms, anda 256 point FFT computed every 5 ms. Mel-scale coefficients were generated from thepower spectrum and adjacent coefficients in time were collapsed. The coefficients werethen normalized between 1 and -1 with the average at 0. These tokens were drawn froma database of 5250 common Japanese words, uttered in the soundproof booth by a maleprofessional announcer. This is the same database as used in the BP-based system. Thedatabase was split into a training set and a testing set of 2620 utterances, from which thephoneme tokens were then extracted using manually selected phonetic labels. Phonemerecognition rates indicated that the LVQ-based system is as high as those of BP-basedsystem. On the whole, the LVQ is endowed with great simplicity, speed, and powerfulclassification ability.Dieterlr et al. [51] presented advantages and drawbacks of the LVQ comparedwith the popular BP in respect to an implementation in a medical decision supportsystem. Spot urine samples were collected from 85 female breast cancer patients 1 daybefore they underwent tumor removal at Tuebingen Frauenklinik. The diagonosis“breast cancer” was confirmed in each case by histopathological examination of thetumor tissue. After collection, the urine samples were immediately frozen without anypreservatives and stored at -20 o C. Before analyzing, the samples were thawed at roomtemperature. Twelve different major and minor ribonucleosides of each pattern weredetermined by a high performance liquid chromatography procedure, as features of eachsample. The total numbers of urine samples to train and test both networks (LVQ- andBP-based method) are 85 and 206 samples, respectively. Both classification methodsperformed very well on the calibration and prediction of the data set into the categories70

“cancer” and “healthy”. The results of the LVQ networks are more reproducible, as theinitialization is deterministic. Based on the results of this research, it is recommended touse the LVQ implementation for a further extended study due to the following reasons:• The LVQ is much easier to understand with prototype patterns beingformulated during training and with classifying by finding the most relatedprototype pattern.• The LVQ only needs the set of few parameters by users, while the BPneeds lots of decisions to be made by users for training, like choosing thenumber of hidden layers, the number of neurons, the type of activationfunction, parameters for cross-validation, several parameters for thelearning function and several parameters for a possible pruning step.• The random initialization of the weights of the BP results in differentoutputs for each run even when using the same datasets for training andprediction and thus complicates the reproducibility and comparability.• The implementation of the LVQ into software is easy, as the algorithms arevarying simple, whereas the algorithms for the BP networks are oftenhighly sophisticated.• The LVQ, which is based on classifying by distance, can easily beconfigured to reject patterns that are not within a certain distance of theprototype patterns. It is more difficult for the BP because it needs thenotion of an object’s classification margin.• The LVQ is faster than the BP in computation because the BP needssignificantly much time for training. Using several hundred times morepatterns, the time needed for training become an important problem, as thetime increases dramatically with the number of patterns.Wu et al. [52] extracted an original feature set from spectrum images of thesurface defects of cold rolled strips by FFT, sum of valid pixels and its optimized centerregion, which concentrates nearly all energies. An optimized feature set with 51 featurescan be achieved using a genetic algorithm to optimize the feature set, and use them asinput vectors of the LVQ and the BP neural networks to run surface defect recognition.Training set is 51 features which are extracted from images obtained by a surface defectonline inspection system of a certain product line. Meanwhile, the test set is originalfeature set, including 240 features and the optimized feature set including 51 features.The results of their research, recognition rates are improved with the input feature being71

optimized, and the BP neural network can not work very well under multi-input-featuresand multi-defect-types because of the time complexity of the BP neural network.Generally, the BP and the LVQ networks possess features and capabilities that arecapable of handling the data classification and prediction. The major difference betweenthem is in their learning algorithms, which adopt different concepts of pattern detectionand recognition. The BP algorithm adopts the gradient descent method that calculatesthe derivative of transfer function to adjust connected weights within the network. Thisattempt is aimed at minimizing the ultimate squared errors of outputs from the network.The LVQ algorithm adopts the Euclidean distance method that diminishes the higherdimension of data to the lower dimension, usually one or two of data. This lowerdimensional data are much easier for the fine-tuned classification process of thealgorithm to assign them into the right groups. In the next section, knowledge aboutarchitecture and learning algorithm of the LVQ network are discussed.5.3 Learning vector quantizationThe original LVQ algorithm was developed by Kohonen in 1989 as a classificationmethod [43]. As its name indicates, it is based on vector quantization which is themapping of an n-dimensional vector into one belonging to a finite set of representativevectors. That is, vector quantization involves clustering input samples around apredetermined number of weight vectors of neurons (the reference vectors). Learning inan LVQ network essentially consists of finding those reference vectors.The classification of input values into clusters is conducted on the basis of thenearest neighborhood, and the smallest distance between the input vector and referencevectors is calculated. Reinforcement learning with the “winner-take-all” learningstrategy is adopted. This means that, each learning iteration, the network is only toldwhether its output is correct or incorrect and only the reference vector of that neuronwhich wins the competition by being closet to the input vector is activated and isallowed to modify its connection weights.72

5.3.1 Architecture of learning vector quantizationAccording to Fig 5.1, an LVQ network can be described as a three-layer neuralnetwork [43, 46].Input layerCompetitive layer(Subclasses)Linear layer(Classes)p 1p 2IIw i 1,w i,2w i,RHHHW − P 1W − P 2W i − PCOOOutput, Yp RI1W( S × R)HHW S− P 12W2 1( S × S )Where R is number of elements in the input vector, P.1S2Sis number of competitive neurons.is number of linear neurons.O1Fig. 5.1 Architecture of the LVQ network.1) The input layer has as many neurons as the pattern variables and behaves likefeeders. They receive input vector P directly from outside the network andonly convey it to the network.2) The competitive layer performs actual information processing to classify itsinput vectors into target classes chosen by the user. The classes learned by thecompetitive layer are referred to as “subclasses”. However, the classes that thecompetitive layer finds are dependent only on the distance measure betweensubclass vectors (input weight matrix, W ) and input vector P. The competitivelayer is also called Kohonen layer or Hidden layer. The network is fullyconnected between the input and competitive layers and partially connectedbetween the competitive and linear layers.173

3) The linear layer transforms the competitive layer’s classes into targetclassifications defined by the user and pass to the world outside the network.The classes of the linear layer are referred to as “target classes”.Both the competitive and linear layers have one neuron per (sub or target) class.The number of competitive neurons, S 1 will therefore always be at least as large as thenumber of linear neurons S 2 and will usually be larger.The classification process inside the LVQ network may be briefly described asfollows. Each neuron (designated as “H”) in the competitive layer of the networkcomputes the Euclidean distance between the given input vector P and a prototypicalsubclass vector W (template pattern of a specific subclass). For instance, the ith neuronsin the competitive layer compute d[ p p ... ] TP 1 2p Ri= W − P , where Wi= [ wi,1 wi,2 ... wi,R] T andi= , where R is number of input elements, are a prototypicalsubclass vector and input vector, respectively. Subsequently, the competitive layer(designed as “C”) assigns 1 to the closet subclass to the given input vector and 0 to allother subclasses represented in the network. The linear layer combines the givenidentified subclass into a (target) class.5.3.2 Algorithm of learning vector quantizationThe dimension of each input vector is the same as that of the weight vector. Thebasic steps of the algorithm are listed as follows:Step: 1 Initialize weight vectors and decide the parameter of an LVQ structure suchas the number of inputs P n and outputs Y n , learning rate α , the number ofhidden neurons for each output class hd and training epoch TR epoch , seeAppendix B.2.Step: 2 Start learning while a stopping condition or stopping criterion is satisfied(Repeat steps 3-10). Here, repeating until all patterns are correctly classified(stopping criterion).Step: 3 For each input vector P n , repeat steps 4-7.Step: 4 Present an input vector from training data set, which consists of both theinput vectors P n and their corresponding target classes T n .Step: 5 The class regions in the input space are defined by the nearest-neighborcomparison method. This method computes Euclidean distances betweencompetitive neurons and input vector p r ;74

Step: 6Step: 7Step: 8⎡ R⎤2d i = Wi− Pn= ⎢ − ⎥⎢∑( wi, r ( t)pr)⎥(5.1)⎣ r = 1⎦where p r is the r th input vector.W i 1 (t) is the i th weight vector at time t.Find a winner neuron i with the minimum distance.12C = arg min d i (5.2)The class of the winning neuron will be compared with the target class of thetraining vector. If the classes are similar, the weights of the winning neuronwill be adjusted in the direction that makes them move closer to the inputvector. However, if the class of the winning neuron is different from that ofthe input vector, two sets of weights, belonging to two different neurons, willbe adjusted.Update weight vector w i (t) as follows :When T = C i or the winning neuron is in the correct output class(that is the class which the input vector is known to belong to)w i (t+1) = w i (t)+α(t)×[P(t)-w i (t)] (5.3)When T ≠ C i or the winning neuron is in the incorrect class.w i (t+1) = w i (t)-α(t)×[P(t)-w i (t)] (5.4)where T is a target class for learning vector.C iis a class represented by the i th output unit.α(t) is a learning rate at time t.Update a learning rate α(t), which is a monotonically decreasing functionof time.Step: 9 Increase the iteration number t = t + 1.Step:10 Check a stopping condition.75

5.4 Application of LVQ to classification5.4.1Classification structureThe construction of the cumulative curves and ACF plots for a neural classifier isbased on the conceptual block diagram presented in Fig. 5.2. The schematic blockdiagram consists of preprocessing, processing, and post-processing.Fig. 5.2Block diagram of the automatic detection of road surface states.Input data for the neural network are the pre-processed tire noise signals with thefollowing six features extracted from the noise signals: the peak frequency, thefrequency at 0.5, the amplitude at 1.5 kHz, the standard deviation, the ACF at lag 0.276

ms, and the time lag at 0.5, as described in Chapter 4. The processing phase usesmultiple neural networks with these six features. The author employs three teams in theANNs, in which each team consists of 12 LVQ networks. For example, Team 1 containsthree groups, A, B and C, and the individual group with four LVQs from #1 to #4 areclassifiers for each surface state.In this dissertation, each LVQ is trained using the features of two classes of roadsurface state as the input vectors in order to reduce computing time and simplify thestructures of the LVQ networks. For example, the work of LVQ#1 of each group isallocated for specifying the snowy state, see Appendix B.3. At the same time, the inputdata of LVQ#2 to #4 are provided for classifying the slushy, wet, and dry states,respectively. Therefore, a matching routine in each state is provided for classifying thesurface states, as shown in Table 5.1. The output data of the processing phase are thefour types of surface states.Table 5.1 Matching routine in each state of Team 1 for classifying the surface states.When multiple neural networks are utilized, post-processing phase is required tocombine the outcomes of the multiple neural networks for making a decision on roadsurface states and to provide a level of confidence for the decision. The output of eachteam is then combined to produce the final decision about the states with one of thedecision-making schemes.LVQs must be trained using known road surface states before they are used as partof a classifier. Each of the LVQs is trained separately and their weight vectors areinitialized independently. After the training process, the individually different weightvectors are determined definitely. In the testing phase, the states are examined alongwith all the other prespecified ones. The schematic diagram for the testing phase is thesame as the one shown in Fig. 5.2. The use of multiple sets of neural networks arisesfrom the need to achieve a higher accuracy rate and provides a way of determining adegree of confidence for each identified state. The voting scheme is the simplest method77

of combining the output of multiple neural networks. A decision is made based onwhich type of road surface states receives the most votes [53]. (see Appendix B.4)5.4.2 Experimental results and discussionsTo evaluate the performance of the present automatic detection method, the authorexamined the noise data of seventeen days at the observation location near Sapporo city.The data of the first seven days (January 25 to 31, 2007) are the training set and theremaining data of ten days (February 1 to 10, 2007) are the testing set. Table 5.2 showsthe numbers of tire noise records required for each surface state. The total number offeature data for each day is 288 by 24 × 60 min/5 min. For a ten-day testing observation,the total number of data is 2880. The total number of noises recorded for training theclassifiers is 400 per team. (see Appendix B.1)There is no exact or definite rule governing the amount of input data in thetraining phase. Generally, when more data are available for training, the classifier worksmore effectively. Unfortunately, using more data in training of the network may resultsin a significant increase of computing time. A classifier with a small data set is veryaccurate in identifying the training data set. However, it can potentially be unable toidentify other data sets. This phenomenon is known as overfitting [46].The ANN method in this study is performed using a MATLAB program (Version7.5.0.342, R2007b). To test the neural networks, 288 recorded tire noise signals for eachday are used. Table 5.3 summarizes the verification results. The results show theperformance of the automatic classification of the four types of surface states. Forexample, the total number of tire noise signals for the snowy state is 1170. Of these data,1124 signals are correctly recognized as the snowy state. Therefore, the accuracy rate is96%. It can be noted that the error rate changed daily, although the changes are slight.In our analysis, the highest accuracy of 96.5% is attained on the 6th day, while theaccuracy on the 8th day gives the lowest rate of 73%. The accuracies in the remainingdays are greater than 82% and the average value for the entire ten days is approximately89%. It is also noted that the classification of the snowy state gives the highest accuracyrate. The classification of the slushy state gives the lowest accuracy rate.78

Table 5.2 Feature data set of detecting the road surface states over 10 days using 5-minute sound signals.Table 5.3 Results of automatic detection of the road surface states over 10 days using 5-minute sound signals.79

Table 5.4 Data numbers of the incorrectly judged states for every 2 hours over 10 days using 5-minute sound signals.80

Additionally, the author examined data numbers of the incorrectly classified statesfor every 2 hours throughout the day as shown in Table 5.4. Overall, the data numbers inthe early morning from midnight to 6 a.m. indicate relatively more the incorrectclassifications in comparison with the other remaining periods and especially frommidday to 2 p.m. There is the most number of the misjudged states from 2 a.m. to 4 a.m.according to the time histories of the features in Chapter 4; they show more scatteringresults in this period, because less vehicles passed through the observation site. This isan important source in misjudgment in this period of daily observation. Figure 5.3(b)shows the results of typical fine-day which include all four different states. In the earlymorning from 3 to 5 a.m., the most number of the misjudged states appear when theroad was covered with slushy water. Nevertheless, it is not a problem for detecting theroad surface state when it is snowy, as shown in Fig 5.3(a).0.002.004.006.008.0010.0012.0014.0016.0018.0020.0022.0024.00Detectedresults0.002.004.006.008.0010.0012.0014.0016.0018.0020.0022.0024.00DetectedresultsFig. 5.3Results of detection for the 6th and 7th day.81

Table 5.5 Verification results for the 8th day are shown by data numbers of the correctlyand incorrectly judged states.0.002.004.006.008.0010.0012.0014.0016.0018.0020.0022.0024.00DetectedresultsFig. 5.4Results of detection for the 8th day.To seek the reason why the low accuracy rate appears when the state is slushy, theauthor examines the data of the 8th day in detail. Table 5.5 shows the relationshipbetween the four visually classified states and the detected states from tire noise throughour signal processing technique according to Fig. 5.4. Obviously, our ANN classifiermostly misjudges the wet state as the slushy one and the slushy state as the dry one. Theformer misjudgement is probably due to the unavoidable fact that the wet road surfaceincludes slushy water from melted snow. Whereas, the latter misjudgement is attributedto almost the same magnitudes of the sound features, as Fig.4.11 shows. Other reasonsare probably our classification operation between the snowy and slushy states does notwork well and the involved transition process of the road surface state that should becontinuous with time. Unfortunately, it is not easy to completely avoid these factors by82

only the present classification method. However, by including more appropriate soundfeatures as input data that specify road surface states and additional meteorologicalinformation such as road surface temperature, the classification accuracy must beincreased.5.5 SummaryIn this chapter, the author proposed a new processing method for automaticallydetecting road surface states from the tire noise of passing vehicles. Our proposedmethod was carried out in sets of multiple neural networks using a learning vectorquantization (LVQ) network. Input data for the neural network are the pre-processedtire noise signals with the following six features extracted from the noise signals: thepeak frequency, the frequency at 0.5, the amplitude at 1.5 kHz, the standard deviation,the ACF at lag 0.2 ms, and the time lag at 0.5. In the processing phase, the authoremployed three teams in the artificial neural networks, in which each team consisted of12 LVQ networks. The outcomes of the multiple neural networks were combined formaking a decision on road surface states in the post-processing.By comparing tire noise data samples obtained near Sapporo city with visualinspection data of the actual road surfaces, the author evaluated the automaticclassification capability at all hours of the day and night using only the noise signals.Typical ten-day sound data and sufficient training data demonstrated that the four typesof road surface states can be classified with a high classification accuracy of 89% onaverage which is almost the same accuracy rate stated in the previous related report [10],as shown in Table 5.6. They captured road surfaces with both the visual road states andthe tire noises, and fed these 51 signal features into their ANN system. Theclassification system that combines the surface images and tire noises generated correctclassifications at a high accuracy of 90%. However, using a few number of test data foreach state, the system did not work well during the hours of darkness and experienceddifficulty in identifying dry surface roads. Therefore, the present study leads us tobelieve that six signal features together with the neural network structure offer greatpotential for the automatic detection of road surface states.In our analysis, the classification results of the snowy state gave the highestaccuracy rate, while those of the slushy state gave the lowest accuracy rate. The mostpossible misjudgment of our ANN classifier occurred from two sources. The first sourceis the unavoidable fact that the wet road surface includes slushy water from melted snow.The other one is the magnitudes of the sound features for slushy and dry state was83

attributed to almost the same. Probably, our classification operation between the snowyand slushy states does not work well and the incorrectly judged states have happenedbetween adjacent states.Nevertheless, the present study leads us to believe that six signal features togetherwith the neural network structure offer great potential for the automatic detection ofroad surface states. Additionally, it is shown that with enough training data and carefulunwanted noise control, the present method has potential for achieving accuracies in theremaining days (except for the result on the 8th day) are greater than 82%.84

Table 5.6classification method.Items comparison of the related report [10] with the proposed85

CHAPTER 6CONCLUSIONSThis chapter presents the major conclusions that have been obtained from thepresent research and provides some remarks and recommendations for furtherinvestigation.Based on our literature review presented in this dissertation, it is clear that manycountries affected by wintry road conditions are looking for innovative technologies andmore automated means of addressing snow and ice control. For road users who live insnowy areas, it is necessary to know real road surface states before driving to obviateserious traffic accidents.In this dissertation, the author has implemented the detection of road surface statesusing only the tire noises emitted from passing vehicles. The tire noise signals wererecorded with microphones at two different observation sites. One of them was on asidewalk of two-lane city road near the campus of the University of Electro-Communications (UEC), and the author obtained noise data from vehicles with regulartires only when the road was dry or wet due to rain for four days during the periods. Theother site was on the sides of four-lane national road near Sapporo city. At that location,tire noise data for twenty days at all hours of the day and night were collected in thesnowy season. In the meantime, actual road surface states were monitored visuallyusing a video camera.The underlying approach of the proposed methods was to carry out the automaticdetection using multiple neural networks. To do this, the author extracted features thatseem to be appropriate from recorded tire noises.In frequency domain: the frequency at which the noise power spectrum reachesthe maximum (peak frequency), the magnitude of the cumulative distribution curve witha frequency of 1.5 kHz (amplitude at 1.5 kHz) and the frequency at which themagnitude takes a value of 0.5 (frequency at 0.5) and the frequency at 0.5 basedstandard deviation.In time domain: the magnitude of the autocorrelation (ACF) plot with a time lag of0.2 ms (ACF at lag 0.2 ms) and the time lag at which the magnitude takes a value of 0.5(time lag at 0.5).Form various field experiments in Section 4.4, it was found that the peak86

frequency method has the lowest accuracy. The amplitude at 1.5 kHz and frequency at0.5 have almost the same classification accuracy. It was also demonstrated thataveraging of the noise data is important in order to extract distinct differences amongvarious states of the road surfaces. Attractively, the accuracy in classification of allfeatures was improved by combining each feature with the standard deviation of thecumulative distribution curves.From one-day experimental results in Section 4.5, the use of the frequency at 0.5gave the highest accuracy, and that of the peak frequency method gave the lowestaccuracy. They achieved the classification accuracy rates of 96% and 74%, respectively.Obviously, the classification ability of the surface states revealed that the ACF at lag 0.2ms and time lag at 0.5 could be classified with a high classification accuracy rate of93%. These results imply that they are essential for the automatic detection and accurateclassification of the surface states.The author has advanced the research of multiple neural network analysis usingboth the four features extracted in the frequency domain and two signal features of ACF.By comparing tire noise data samples obtained near Sapporo city with visual inspectiondata of the actual road surfaces, the author evaluated the automatic classificationcapability at all hours of the day and night using only tire noise signals. Typical ten-daysound data and sufficient training data have demonstrated that the four types of roadsurface states can be classified with a high classification accuracy of 89% on average,which is almost the same accuracy rate that is stated in the previous related report [10].The present study leads us to believe that six signal features together with the neuralnetwork structure offer great potential for the automatic detection of road surface states.On the basis of the research results obtained in this dissertation, the accuracy maybe improved by incorporating new features, additional data from existing infrastructuresuch as visible images and meteorological information (road temperature) that is widelyused to predict road weather conditions, and by using more advanced neural networks.In the near future, to increase the efficiency of road management and secure safetransport, the author shall apply these methods to the actual detection of road surfacestates. Therefore, further research efforts in accurate detection of the surface states fromtire noise signal are needed to investigate the following items:For extracting more accurate feature sets from tire noise signals which play animportant role on road states detection, new methods based on the principal componentanalysis are presented in appendix C. The short time Fourier transform is primarily used87

for feature extraction. The normalization is powerful for extracting spectral featurevectors, being useful for the dimension reduction of the feature vectors. In addition,road surfaces states can be readily predicted by introducing the skewness value of theadjusted spectrum from tire noise data.Furthermore, the use of wavelet transform should be explored to performmultilevel recognition based on the present method to get a better classification effect.Because it is a mathematical tool that decomposes a signal into different scales withdifferent levels of resolution and provides a local representation (in both time andfrequency) of a given signal [54]. But Fourier transform gives a global representation ofa signal.In fact, the snowy areas have the serve environment of snow-induced visibilityhindrance and ice roads at those paths. Sometimes drivers have difficulties in passingsuch paths and/or observation site, especially in the early morning. When there is novehicle that passes through the observation site, the author can not use the proposedmethod. The author therefore shall study the use of visible image and road temperatureto assist in accurately judging the road surface states.At the same time, a more advanced neural network must be studied to overcomethe problems of the surface states which depend greatly on the weather, road users,traffic volume, location, and relevant factors. Nobody knows what will happen nexttime in transition process of the surface states that should be continuous with time. Theneural networks for classifying the road surface states are required to learn gradually theknowledge in operating process, and to have adaptive function expanding theknowledge continuously without the loss of the previous knowledge during learningnew knowledge. A human brain is able to learn many new events without necessarilyforgetting events that occurred in the past. For example, Yang et al. [55] proposed a newneural network based on the theory of adaptive resonance theory (ART) and the learningstrategy of Kohonen neural network (KNN) for learning new events in a continuousmanner as a human brain. The accuracy rate of ART-KNN can reach 100%, while therate of LVQ was 93%. From this example, we can see that ART-KNN can performon-line learning without forgetting previous patterns.As a consequence, our future works would include the applications of ART-KNNto obtain a higher accuracy based on the feature indicators in this dissertation and newfeatures which play an important role in detection of road surface states as a few signalfeatures shown in appendix C.88

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Appendix A.Cumulative distribution curvesIn real traffic conditions, there are other noise sources which are unwanted for ourclassification method. These sources are siren noises from an ambulance car and from asnow vehicle as shown in Fig. A.1. To obtain the effectiveness of a proposed simplemethod for removing unwanted noises automatically based on short-time Fouriertransform (STFT), the author executed signal processing using the tire noise detectednear Sapporo city. In this Appendix, typical examples of spectrum components, p ( f') ,peak of each set of spectrum components, p n (f),(n = 1,2,...,N ), before and after thecumulative curves with removing unwanted noise signals recorded from passingvehicles when the road state was dry, wet and snowy for 5 minutes are presented.(a)(b)Fig. A.1 Ambulance and snow vehicle.A.1 Ambulance noiseUnwanted noises include in the recorded tire noise data for 5 minutes when theroad state is wet, dry and snowy are segmented into multiple sound frames of 110250samples (or 5 s/frame), where N = 60 frames, as shown in Fig. A.2(a), A.3(a) and A.4(a),respectively. All the sound frames are processed through a high-pass filter with a cut-offfrequency of 300 Hz. STFT is used to transform the each frame of sound data into a setof spectrum components in the frequency domain p ( f') . All the sets of p n (f) are thensummarized into a set of p ( f') for respective states, as illustrated in Fig. A.2(b),A.3(b) and A.4(b).94

p ( f')AmplitudeFig. A.2 Ambulance noise signal included in the recorded tire noise datafor the wet state in 5 minutes and its p ( f') .p ( f')Fig. A.3 Ambulance noise signal included in the recorded tire noise datafor the dry state in 5 minutes and its p ( f') .95

p ( f')Fig. A.4Ambulance noise signal included in the recorded tire noise datafor the snowy state in 5 minutes and its p ( f') .10.8)P( f0.60.40.20SnowyDryWet10 0 10 1Frequency [kHZ]Fig. A.5Cumulative curves obtained from the tire noise data for three states in5 minutes included in ambulance noise signals.96

Figure A.5 shows the obtained three cumulative curves using equation (4.3), Iwhich tendencies are clearly different in comparison with the magnitudes in Fig 4.7. Allcurves first increase in magnitude relatively slowly with frequency, then the rates ofincrease become very abrupt or almost the same vertical line for two frequency ranges;between 1.5 to 1.6 kHz and 1.8 to 2 kHz. At approximately 2 or 3 kHz, it becomes slowdown again. Obviously, there are no differences from curve to curve, and it appearsdifficult to obtain information on the road surface states from such unreliable curves.A.2 Snow removal noiseIn countries affected by wintry road conditions, the problems of slippery roadsurfaces caused by compacted snow and ice and of skidding accidents occur frequentlyon national roads. A higher level of road management is needed for the winter roadtraffic environment. Snow removal on the roadways is conducted around the clock tomaintain the traffic ability of national highways and principal prefectural roads and topromote interregional exchanges and living activities [1]. That is why snow removalnoise may be picked up by microphones at an observation site on the side of a four-lanenational road near Sapporo city in the snowy season.p ( f')Fig. A.6Snow removal noise included in the recorded tire noise datafor the snowy state in 5 minutes and its p ( f') .97

Snow removal noise include in the recorded tire noise data for 5 minutes aresegmented into multiple sound frames of 110,250 samples (or 5 second/frame), where N= 60 frames, as shown in Fig. A.6(a). All sound frames are fed to a high-pass filter witha cut-off frequency of 300 Hz. The respective frame of sound data is transformed into aset of spectrum components in the frequency domain pn( f ) based on STFT. All sets ofp n (f) are then summarized into a set of p ( f') , as shown in Fig. A.6(b).From Figure A.6(b), the frequency components of snow removal noise aredominant in the frequency ranges between 400-600 Hz. They directly affectedtendencies of the cumulative curve in Fig. A. 7 by equation (4.3). Cumulative curveappears clearly in different tendencies in comparison with the magnitudes of curve forsnowy state in Fig 4.7.After calculating p n (f) as described above, the author focuses attention on themagnitude at which each frame attains a peak in its PSD because peak of PSD forframes of ambulance and snow removal noises are much higher than those for othersound frames, as shown in Fig. A. 8. The magnitudes for ambulance noise are higherthan those for snow removal noise. This means that the ambulance noise predominatesover the snow removal noise in the frequency domain and especially tire noise data.10.8)P( f0.60.40.20Snowy10 0 10 1Frequency [kHZ]Fig. A.7Cumulative curves obtained from tire noise data for 5 minincluded with snow removal noise.98

Fig. A.8Spectral peak obtained from tire noise datafor three states in 5 min included with unwanted noise.These unwanted noise data samples were inspected by listening. It should be notedthat the spectral peaks indeed represent the frames of the unwanted noise. At any rate,the spectral peak takes a magnitude of 0.08 at minimum for unwanted noise frame. In99

contrast, the spectral peak obtained using other sound frames takes a maximum value of0.06. The author then proposes a threshold value of removing unwanted noise frameusing arithmetic averaging, which is (0.08 + 0.06) / 2 = 0.07). Therefore, the authorremoves the unwanted noise frames when constructing the PSD components as the basisfor a threshold of 0.07. For example, the remaining frames M are 48, as shown in Fig.A.8(a). The remaining part is summarized into a set of p ( f') .Consequently, a set of p ( f') is calculated based on equation (4.3). From figureA.9 and A.10, all cumulative distribution curves have almost the same tendencies as inFig 4.7. Apparently, the curves obtained do not seem to be difficult to classify the roadsurface into the three states by employing two classification indicators, as described inSection 4.3.P( f)Fig. A.9 Cumulative curves obtained from tire noise data for three states in 5 minwhich ambulance noises are removed.100

P( f)Fig. A.10 Cumulative curve obtained from tire noise data for 5 minwhich snow removal noises are removed.A.3 Studless tire noiseTo know general tendencies of PSD ansd cumulative curve of the tire noiserecorded from summer and studless tires, the author recorded tire noise for the dry stateemitted from moving vehicles which use two types of tire with a PCM recorder in thecampus of UEC. Figure A.11 shows the experimental scenery. The recorder digitallysampled sound signals at a frequency of 22.05 kHz with 16 bit quantization. And it wasset on a tripod on the side-walk as described in Chapter 3. The road is a porous asphaltpavement. Vehicles passed by at 40 km/h on average. Experimental trials are 13 times.Tire noise data is captured by the author using the PCM recorder when the vehicleclosely passes by the observation site. Each set of tire noise data is approximately 40 s,as shown in Fig. A.12 (a) and A.13(a).Tire noise data for 40 s is segmented into multiple sound frames of 110,250samples (or 5 s/frame), where N = 8 frames. All sound frames are fed to a high-passfilter with a cut-off frequency of 300 Hz. The each frame of sound data is transformedinto a set of spectrum components in the frequency domain p n (f) based on STFT. Allsets of p n (f) are then summarized into a set of p ( f') , which are shown in Fig. A.12 (b)and A.13(b).101

Fig. A.11 Experimental setup for detecting tire noise from passing vehicleswith different tires.p ( f')AmplitudeFig. A.12 Tire noise data from summer tires for dry state in 40 sand its p ( f') .102

p ( f')Fig. A.13 Tire noise data from studless tires for dry state in 40 sand its p ( f') .P( f)Fig. A.14 Cumulative curve of the PSD from two types of tirefor the dry state in 40 s (UEC).103

The spectral peak of both types obtained from tire noise for 40 s exists near 1 kHzwith relatively wide skirts. The sound energy for summer and studless tires isinterchanged, i.e., for the studless tire, the magnitudes of PSD at below 1 kHz hashigher than those for the summer tire, while the sound energy above 1 kHz for studlesstire is less than and decrease in magnitude relatively slowly with frequency near 3 kHz.They directly affected the tendencies of the obtained cumulative curve in Fig. A. 14, byequation (4.3). Both curves have a few different magnitudes in two ranges. This remarkabout the information of characteristics in the frequency domain of the summer andstudless tires are found by Kent Seki [42]. Tire sounds generated from “Tyre UniformityMachine (TUM)” are recorded by a microphone and extracted characteristics byvisualization of tire sounds with the wavelet transform. The spectral peak of both typesexists near 1 kHz. Large proportion of the sound energy concentrates around 6 to 9 kHzfor summer tire. But most of the sound energy of studless tire concentrates below 4 kHz.However, it does not seem to be difficult to classify the road surface states using twoclassification indicators, as described in Section 4.3.104

Appendix B.LVQ trainingB.1 Training setThe training data set for LVQ networks is the noise data of the first seven days on(January 25 to 31, 2007) at the observation location near Sapporo city. The total numberof noises recorded is sequentially divided into each state of three teams, as shown inTable B.1. Therefore, the classifiers of each team are trained using the different noisedata.Table B.1 Total number of noises recorded from the first seven days for LVQ training anddividing into each team.B.2 Parameters of LVQ networkTo train the LVQ network for detection of road surface states, the parameters ofthe algorithm are represented by• The number of inputs: n = 200• The number of outputs: n = 200• Learning rate: α = 0.1:-0.01:0.01• The number of hidden neurons for each output class: hd = 10:10:100• Training epoch: TR epoch = 100:100:1000The number of noises recorded for each state in training data set is 100. Inputvectors to the LVQ, P n is the total number of two states which are matched forclassifying the surface states (see Table 5.1), that is 100+100 = 200. The dimension ofoutput vectors is the same as that of the input vectors.105

An epoch is one sweep through all the records in the training set. For each epoch,all training vectors (or sequences) are individually presented once in a different randomorder, with the network based on training function “trainr” [46] and weight and biasvalues updated after the individual presentation. Training occurs according to trainingparameters of trainr, a default value of the maximum number of epochs to the train is100.How many subclasses (hidden neurons in the competitive layer) will make up eachof the two classes? Using excessive hidden neurons will cause overfitting, which meansthat the neural networks over-estimate the complexity of the target problem. If a fewhidden neurons are used, the network will be unable to model complex data, resulting ina poor fit [43]. Here, the author starts with 10 hidden neurons and training the network.If training the network fails to converge after a reasonable period, the number of hiddenneurons continues to increase by the step value (On each iteration through the loop, stepvalue is +10), it is possible that the training has begun to fit the sample in the trainingdata, and overfitting occurs.Learning rate, if we make it too large the algorithm will become unstable; theoscillations will increase instead of decaying [43]. It is recommended that α(t)shouldinitially be rather small, smaller than 0.1, and α (t)continues decreasing to 0.01 [43,46]. These are reasons why the parameters of the algorithm are specified as presentedabove.B.3 An example of LVQ trainingThe author would like to train LVQ#1 of group A of Team 1 as an example ofLVQ training for classifying snowy and wet state classes.Snowy class :⎧ ⎡ 725.2 ⎤⎪ ⎢ ⎥⎪ ⎢1549.2⎥⎪ ⎢0.4626⎥⎨P1 = ⎢ ⎥,P⎪ ⎢161.33⎥⎪ ⎢0.2681⎥⎪ ⎢ ⎥⎪⎩⎢⎣0.6129⎥⎦2⎡ 516.8 ⎤⎢ ⎥⎢1211.2⎥⎢ 0.5691 ⎥= ⎢ ⎥,...,P⎢289.4612⎥⎢ 0.2361 ⎥⎢ ⎥⎢⎣0.5102 ⎥⎦100⎡ 385 ⎤⎫⎢ ⎥⎪⎢1476.1⎥⎪⎢0.5134⎥⎪= ⎢ ⎥⎬⎢58.176⎥⎪⎢0.2927⎥⎪⎢ ⎥⎪⎢⎣0.6687⎥⎦⎪⎭106

Wet class :⎧⎪⎪⎪⎨P⎪⎪⎪⎪⎩101⎡ 765.8 ⎤⎢ ⎥⎢2326.9⎥⎢0.2764⎥= ⎢ ⎥,P⎢309.56⎥⎢0.1615⎥⎢ ⎥⎢⎣0.2744⎥⎦102⎡ 598.6 ⎤⎢ ⎥⎢2029.9⎥⎢0.3561⎥= ⎢ ⎥,...,P⎢115.05⎥⎢0.1873⎥⎢ ⎥⎢⎣0.3693⎥⎦200⎡ 645.4 ⎤⎫⎢ ⎥⎪⎢2359⎥⎪⎢0.2490⎥⎪= ⎢ ⎥⎬⎢354.47⎥⎪⎢0.1622⎥⎪⎢ ⎥⎪⎢⎣0.2812⎥⎦⎪⎭The author begins by assigning target classes to each input vector:⎧ ⎡ 725.2 ⎤ ⎫⎪ ⎢ ⎥ ⎪⎪ ⎢1549.2⎥ ⎪⎪ ⎢0.4626⎥⎡1⎤⎪⎨P 1 = ⎢ ⎥,T 1 = ⎢ ⎥⎬,⎪ ⎢161.33⎥⎣0⎦⎪⎪ ⎢0.2681⎥⎪⎪ ⎢ ⎥ ⎪⎪⎩⎢⎣0.6129⎥⎦⎪⎭⎧⎪⎪⎪⎨⎪⎪⎪⎪⎩⎡ 516.8 ⎤⎢ ⎥⎢1211.2⎥⎢ 0.5691 ⎥= ⎢ ⎥,⎢289.4612⎥⎢ 0.2361 ⎥⎢ ⎥⎢⎣0.5102 ⎥⎦⎫⎪⎪⎡1⎤⎪= ⎢ ⎥⎬⎣0⎦⎪⎪⎪⎪⎭P 2 T 2 , ...,⎧⎪⎪⎪⎨P⎪⎪⎪⎪⎩⎡ 385 ⎤⎢ ⎥⎢1476.1⎥⎢0.5134⎥= ⎢ ⎥,⎢58.176⎥⎢0.2927⎥⎢ ⎥⎢⎣0.6687⎥⎦100 T 100⎫⎪⎪⎡1⎤⎪= ⎢ ⎥⎬⎣0⎦⎪⎪⎪⎪⎭⎧ ⎡ 765.8 ⎤ ⎫⎪ ⎢ ⎥ ⎪⎪ ⎢2326.9⎥ ⎪⎪ ⎢0.2764⎥⎡0⎤⎪⎨P 101 = ⎢ ⎥,T 101 = ⎢ ⎥⎬,⎪ ⎢309.56⎥⎣1⎦⎪⎪ ⎢0.1615⎥⎪⎪ ⎢ ⎥ ⎪⎪⎩⎢⎣0.2744⎥⎦⎪⎭⎧⎪⎪⎪⎨⎪⎪⎪⎪⎩⎡ 598.6 ⎤⎢ ⎥⎢2029.9⎥⎢0.3561⎥= ⎢ ⎥,⎢115.05⎥⎢0.1873⎥⎢ ⎥⎢⎣0.3693⎥⎦⎫⎪⎪⎡0⎤⎪= ⎢ ⎥⎬⎣1⎦⎪⎪⎪⎪⎭P 102 T 102 , ...,⎧⎪⎪⎪⎨P⎪⎪⎪⎪⎩⎡ 645.4 ⎤⎢ ⎥⎢2359⎥⎢0.2490⎥= ⎢ ⎥,⎢354.47⎥⎢0.1622⎥⎢ ⎥⎢⎣0.2812⎥⎦200 T 200⎫⎪⎪⎡0⎤⎪= ⎢ ⎥⎬⎣1⎦⎪⎪⎪⎪⎭Here, the author lets each class be the union of two subclasses. The author willend up with ten neurons in the competitive layer. The linear layer weight matrix will be2W⎡1= ⎢⎣0101010W 2 connects hidden neurons in the competitive layer (1 to 5) to first output neuron(Class 1). Also, it connects hidden neurons (6 to10) to second output neuron (Class 2).Each class will be made up of two convex regions.The row vectors in W 1 are initially set to random values. The values for theseweights are10010101010⎤⎥1⎦⎡486.773⎤⎢ ⎥⎢1405.6⎥⎢ ⎥1 0.5362w 1 = ⎢ ⎥ ,⎢186.605⎥⎢ 0.2674 ⎥⎢ ⎥⎢⎣0.6037 ⎥⎦⎡799.063⎤⎢ ⎥⎢1710.8⎥⎢ ⎥1 0.4190w 2 = ⎢ ⎥ ,⎢123.145⎥⎢ 0.2457 ⎥⎢ ⎥⎢⎣0.5482 ⎥⎦⎡725.600⎤⎢ ⎥⎢1990.6⎥⎢ ⎥1 0.5634w 3 = ⎢ ⎥ ,⎢317.332⎥⎢ 0.2871 ⎥⎢ ⎥⎢⎣0.5667 ⎥⎦⎡519.778⎤⎢ ⎥⎢1753.5⎥⎢ ⎥1 0.4101w 4 = ⎢ ⎥ ,⎢158.552⎥⎢ 0.2331 ⎥⎢ ⎥⎢⎣0.5135 ⎥⎦⎡710.916⎤⎢ ⎥⎢1893.2⎥⎢ ⎥1 0.6317w 5 = ⎢ ⎥⎢373.617⎥⎢ 0.3147 ⎥⎢ ⎥⎢⎣0.6339 ⎥⎦107

⎡988.089⎤⎢ ⎥⎢2639.5⎥⎢ ⎥1 0.2160w 6 = ⎢ ⎥ ,⎢231.766⎥⎢ 0.1492 ⎥⎢ ⎥⎢⎣0.2168 ⎥⎦⎡705.195⎤⎢ ⎥⎢2341.9⎥⎢ ⎥1 0.2496w 7 = ⎢ ⎥ ,⎢180.009⎥⎢ 0.1636 ⎥⎢ ⎥⎢⎣0.2851 ⎥⎦⎡ 1003.4 ⎤⎢ ⎥⎢2233.1⎥⎢ ⎥1 0.2560w 8 = ⎢ ⎥ ,⎢177.453⎥⎢ 0.1745 ⎥⎢ ⎥⎢⎣0.3245 ⎥⎦⎡687.683⎤⎢ ⎥⎢2582.7⎥⎢ ⎥1 0.2303w 9 = ⎢ ⎥ ,⎢228.770⎥⎢ 0.1531 ⎥⎢ ⎥⎢⎣0.2280 ⎥⎦w110⎡644.902⎤⎢ ⎥⎢2134.2⎥⎢ 0.2913 ⎥= ⎢ ⎥⎢133.445⎥⎢ 0.1808 ⎥⎢ ⎥⎢⎣0.3526 ⎥⎦At each iteration of training process, the author presents an input vector, find itsresponse, and then adjust the weights. In this case, the author will begin by presentingP 1.From (5.1);d1⎛ ⎡ 1⎜ w⎢1 − P1⎜⎢ 1⎜ w⎢ 2 − P1⎜⎢ 1⎜⎢w 3 − P1⎜⎢⎜1⎢ w 3 − P1⎜⎢⎜ 1⎢ w5− P1= ⎜⎢⎜ 1⎢ w −⎜ 6 P1⎢⎜ 1⎢ w −⎜ 7 P1⎢⎜ 1⎢ w −⎜ 8 P1⎢⎜⎢1⎜w9− P1⎢⎜⎢ 1w10− P1⎝ ⎣⎤ ⎞⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎥⎟⎦ ⎠⎛ ⎡⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢d⎜1 = ⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎜⎢⎝ ⎣TT[ 486.773 1405.6 . . . 0.6037] − [ 725.2 1549.2 . . . 0.6129]⎤⎞⎥TT[ 799.063 1710.8 . . . 0.5482] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT ⎥[ 725.600 1990.6 . . . 0.5667] − [ 725.2 1549.2 . . . 0.6129]⎥TT ⎥[ 519.778 1753.5 . . . 0.5135] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT[ 710.916 1893.2 . . . 0.6339] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT[ 988.089 2639.5 . . . 0.2168] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT[ 705.195 2341.9 . . . 0.2851] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT[ 1003.40 2233.1 . . . 0.3245] − [ 725.2 1549.2 . . . 0.6129]⎥⎥TT[ 687.683 2582.7 . . . 0.2280] − [ 725.2 1549.2 . . . 0.6129]⎥⎥⎥[ ] [ ] ⎟ ⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟ TT644.902 2134.2 . . . 0.3526 − 725.2 1549.2 . . . 0.6129 ⎥⎦⎠⎛ ⎡0.0092⎤⎞⎜⎢ ⎥⎟⎜⎢0.0380⎥⎟⎜⎢⎟⎜ 0.0690⎥⎢ ⎥⎟⎜⎢0.0070⎥⎟⎜⎢⎟⎜ 0.0851⎥⎥⎟= ⎢⎜⎢0.2827⎥⎟⎜⎢ ⎥⎟⎜⎢0.1853⎥⎟⎜⎢ ⎥⎟⎜0.2406⎢ ⎥⎟⎜⎢0.1922⎥⎟⎜⎢ ⎥⎟⎝ ⎣0.2219⎦⎠108

From (5.2);⎛ ⎡0⎤⎞⎜⎢ ⎥⎟⎜⎢0⎥⎟⎜⎢⎟⎜ 0⎥⎢ ⎥⎟⎜⎢1⎥⎟⎜⎢⎟⎜ 0⎥⎟C = ⎢ ⎥⎜⎢0⎥⎟⎜⎢ ⎥⎟⎜⎢0⎥⎟⎜⎢ ⎥⎟⎜0⎢ ⎥⎟⎜⎢0⎥⎟⎜⎢ ⎥⎟⎝ ⎣0⎦⎠The neuron of linear layer has closet a weight vector to P 1 . In order to determinewhich class this neuron belongs to, the author multiply C by W 2Y1 =W2C⎡1= ⎢⎣01010101001010101⎡0⎤⎢ ⎥⎢0⎥⎢0⎥⎢ ⎥⎢1⎥0⎤⎢0⎥⎥⎢⎥1⎦⎢0⎥⎢ ⎥⎢0⎥⎢0⎥⎢ ⎥⎢0⎥⎢ ⎥⎣0⎦⎡1⎤= ⎢ ⎥⎣ 0 ⎦This output indicates that P 1 is a member of class 1. This is correct, so w 1 isupdated by moving it toward P 1 .w ( ) = w ( 0 ) + α ( 0 )* [ P ( 0 ) w ( 0 )]1 1 11 − 1⎡486.773⎤⎛ ⎡ 725.2 ⎤ ⎡486.773⎤⎞⎢ ⎥⎜⎢ ⎥ ⎢ ⎥⎟⎢1405.6⎥⎜⎢1549.2⎥ ⎢1405.6⎥⎟⎢⎜⎥ ⎢ ⎥⎟0.5362 ⎥ ⎢⎜ 0.4626 0.5362= ⎢ ⎥ + 0.1 ⎢ ⎥ − ⎢ ⎥⎟⎢186.605⎥⎜⎢161.33⎥⎢186.605⎥⎟⎢ ⎥⎜⎢ ⎥ ⎢ ⎥⎟0.2674⎢ ⎥⎜ 0.2681 0.2674⎢ ⎥ ⎢ ⎥⎟⎢ ⎥⎜⎟⎣ 0.6037 ⎦ ⎝ ⎢⎣0.6129⎥⎦⎢⎣0.6037 ⎥⎦⎠⎡510.615⎤⎢ ⎥⎢1419.96⎥⎢ 0.5288 ⎥= ⎢ ⎥⎢184.775⎥⎢ 0.2674 ⎥⎢ ⎥⎢⎣0.6012 ⎥⎦Next, the remaining LVQ are trained with training data set specified based on thesame algorithm and desktop computer, which its specification is AMD Athlon(tm) 64Processor, 991 MHz, 448 MB RAM. The different results of the number of hiddenneurons for each output class, hd and training time of 36 LVQ are presented in Table109

B.2. The training may take a considerable amount of time depending on computingpower, the training data set and increase of neuron to finally converge. There are only 3LVQ that solve the classification problem by using the number of neuronsapproximately 60 to 70, and require the training time around 30,000 s and 70,000 s.,probably because the sound features have almost the same magnitudes. However, thenumber of hidden neurons in the remaining LVQs is less than 40 neurons and theamount of training time is less than 17,000 s with a 991 MHz desktop computer.Table B.2 Number of hidden neurons and training time of each LVQ in three Teams.B.4 Decision-making schemeOnce the training phase is completed, the testing phase is very straightforwardusing the same desktop computer mentioned above. The classifier is capable ofidentifying one tire sound data for 5 minutes within one to two seconds, using thevoting scheme [46]. The confidence of the decision made is represented as an agreementlevel defined as the ration of the total number of votes received to the total number ofvotes. In this research, there are three groups for a classifier team, and since there arethree classifier teams. Thus there are 9 total votes. Typical outputs of each team areillustrated in Fig. B.1. From the figure, the different types of the snowy, slushy, wet anddry surface states receive 9, 2, 1 and 1 votes, respectively. Therefore, the type of thestate will be classified as the snowy state with an agreement level 9/9 = 100%.110

LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4LVQ 1 - LVQ 4Input signal(Automobile tire sound)(all cars, for 5 min.)Fast Fourier transformABCABCABC1 0 0 0 1 0 0 0 1 0 1 01 1 0 0 1 0 0 0 1 0 0 01 0 0 0 1 0 0 1 1 1 0 0Team 1Team 2Team 33 0 1 03 1 0 0 3 1 0 1Decision-making schemeSnowy Slushy Wet DryVotes :9 2 1 1Agreement level [%] : 9/9 = 100 2/9 = 22.2 1/9 = 11.1 1/9 = 11.1Type of road surface state isSnowyFig. B.1 Typical outputs of each classifier team for detecting the road surface states.111

Appendix C.New featuresIn this dissertation, the idea for the automatic detection of road surface states is touse artificial neural networks based on a few signal features that are readily extracted inthe frequency and the time domain of the noise signals. The classification results havebeen shown to be agreeable accuracy and reliability by applying noise data samplesobtained near Sapporo city. Incidentally, our main aim in increasing capabilities,accuracy and reliability for better decision making of the automatic detection of roadstates is to apply techniques to the additional extraction of relevant features, based ononly tire noise signals emitted from passing vehicles.In reference [41], Wu et al. proposed a new method based on the short-timeFourier transform (STFT) and the principal component analysis (PCA) for extractingvehicle sound signatures. The basic idea of their method is to use together the mean ofthe adjusted sound spectrum and key eigenvectors of covariance matrix to characterizeacoustic signatures. Their technique has been efficient for the dynamic acoustic featuredetection that is quite low on dimension and effective for producing good classificationresults. However, no experimental performance was shown in their report.The study in this appendix is basically in line with the approach of Wu et al. Theprimary objective of our study is to develop effective PCA and to use to extract robustfeatures for the passive and simple classification of the surface states into severalcategories using tire noises from passing vehicles. To achieve the objective, the authorproposes a normalizing method by our empirical data, reduce the dimension of thespectrum components, and estimate its reliable performance and implementation basedon PCA. In addition, the mean of the adjusted spectrum components are calculated toobtain the skewness statistics for the easy classification of the road states. Theeffectiveness of the proposed features is verified by noise data samples obtained at thetwo experimental locations and are compared with the visual inspections of the actualroad surfaces.C.1 Noise signal analysisTo obtain N short-time series {X 1 , X 2 , ..., X N }}, using the window with112

continuously overlapping of 512 samples, each sound signal for 1.5 s is segmented intomultiple sound frames of 4096 samples, where N = 9 frames, x n,i , (n = 1,2, ..., N, i = 0,1, ..., 4095). The Hamming window is added to each sample as a filter to depress theGibbs’ effect. Traditionally, STFT is used to transform the each processed frame ofsound data into a set of spectrum features in the frequency domain X ( f ) . These resultsare in a 2048-dimensional FFT-based spectrum feature at frequency resolution of 5.38Hz with information for frequencies up to 11,025 Hz.In this appendix, the author introduces the following function for normalizing 97bands of 100 Hz from 300 Hz to 10 kHz that can be defined asn'Xn( f ) =f + 100∫∫ffhflXXnn( f( f'') df) df'',f = 300, 400, ..., 9900 (C.1)where f l = 300 Hz is the low-cut frequency. As described in Chapters 4, tire noises donot significantly contain frequency components greater than 10 kHz. Consequently, theupper limit of integration with respect to frequency is determined to be f h = 10 kHz. Itmeans that the dimension of spectrum feature is reduced from 2048 to 97 using thisnormalizing method. Then, the author adjusts the normalized spectrum distribution assuggested by Wu et al. [41].The covariance matrix C of a training set is readily calculated by starting from themean of given training set Ψ of the adjusted spectrum samples. The eigenspace modelis also calculated by solving the eigenvalue decomposition of C. The data isconcentrated in a linear subspace, and this provides a way to represent data withoutlosing information and simplifying the consideration on the overall shape of tire noisespectrum distribution in the frequency domain. All eigenvectors v, {v 1 , v 2 , ..., v k }, (k =97) correspond to the eigenvalues λ , { λ1≥λ 2≥... λ k } of C.C.1.1 Classification for individual vehiclesTo compute the residual vector magnitude for detecting road surface states usingPCA procedure, the tire noise of about 1000 sedan cars that passed by the UEC113

observation location is projected onto the above processes as a test set to obtain thecorresponding set of the adjusted spectrum vectors Γ n , (n = 1, 2, ..., N), being comparedwith the set of Ψ and v in the training set.First, Γnare projected onto 97 orthonormal eigenvectors in the eigenspace and findout the difference weight vectors, w n,k . Consequently, the residual vector magnitude dcan be calculated byjεn= Γn− Ψ − ∑ ( wn,k * vk)(C.2)k=1d = ε n(C.3)Fig. C.1Residual vectors of tire noise from passing sedan type cars (UEC).Fig. C.2Residual vectors averaged over every 20 sedan type cars (UEC).114

Figure C.1 shows the norm d of the tire noise in training and test set that wereobtained near the campus of UEC. Two different conditions, the wet and dry states onthe road, are the target of classification. Obviously, d is different from vehicle to vehicle,and difficulty appears for obtaining information on the road surface states from theserandomly scattered d magnitudes. However, upon averaging of d over every 20 vehicles,definite differences appear. As can be seen in Fig. C.2, the values of d are within therange of 500 to 700 for the ‘dry’ state, and are about 200 lower than the values for the‘wet’ state. Therefore, it seems to be feasible to correctly classify the dry and wetsurfaces by employing d on the basis of certain threshold values.C.1.2 Classification for vehicles in 5 minuteTo evaluate whether the proposed d based on PCA can actually indicatechangeable surface states, the author examines the typical one-day sound data thatinclude all four different states; dry, wet, snowy, and slushy states. The observationstarted at 0 a.m. and ended the next day at 0 a.m. near Sapporo city. At the same time asthe sound recording, the author visually monitored surface states with a video camera.Tire noise data for 5 min were segmented into multiple sound frames of 33072 samples(or 1.5 s/frame), N = 228 frames. In the framing step 12.5% overlapping betweenadjacent windows were taken based on the input sampling frequency. After adjusting thenormalized spectrum distribution as described above, the author found that the sum ofthe power spectrum in the frequency domain of some frames in which noise signals arevery close to zero. Then, the author removed them when constructing the feature vectorsas the basis for a threshold of 0.0001. The remaining frames, M are 216.The total number of feature data for one-day is 288 by 24*60 min/5 min. A set of24 tire noise signals of the dry state at 5 p.m. to 7 p.m. is used as the training set and theremaining part is used as the test set. Before classification, the training set was used toextract the mean of the adjusted spectrum samples and eigenvector using the proposedmethod. Then, the adjusted spectrum vectors of the test set are projected onto the 97orthonormal eigenvector in eigenspace, to compute d magnitude, as shown in Fig C.3(a). In the snowy state, d takes almost the same magnitudes as those in the other threestates. This situation makes it difficult to classify successfully the four states. Toimprove the identification of d into changeable four surface states continuously, theauthor calculated the mean of d of the training set based on arithmetic averaging to useas a reference value for separating two different categories. It is 3642.9, as shown in Fig.C.3(a). Let D be a new residual vector, the author can use the category label to adjust dof each test noise data that can be defined as follows:115

⎧⎪d⎪D = ⎨⎪3642.9−(d −3642.9)⎪⎩ififMsign[∑εn ] = 1n=1Msign[∑εn ] =−1n=1(C.4)7000Snowy Slushy Wet Dry6000Residual vector500040003642.930002000100000.002.004.006.008.0010.0012.0014.00d : Snowy 2nd dayD : SnowyDry: in training setDry: in test set16.0018.0020.00Number of data processings [every 5 min](a)0.002.0022.0024.004.006.008.0010.0012.0014.0016.0018.0020.0022.0024.00SkewnessFig. C.3 Time history of the residual vector d and D (a), skewness (b)for one-day observation near Sapporo city.116

300200Ψ100SnowyDryWet0 1 2 3 4 5 6 7 8 9 10Frequency [kHz]Fig. C.4 Typical distribution curves of the mean of the adjusted spectrumfrom tire noises for five minutes.Figure C.3(a) shows the time history of D. Overall, the sample data are scatteredin the early morning from 0 a.m. to 4 a.m. and after evening at 8 p.m. to the next day at0 a.m., probably because few vehicles passed through the observation site. However,classification does not seem to be difficult after 4 a.m. because many vehicles passedby.By extracting spectral feature vectors by means of the present method, the authorfound that the distributions of Ψ of the three states tend to be asymmetrical, decreasein magnitude relatively slowly with frequency, and has different mass, as shown in Fig.C.4. The magnitudes for the wet state are higher than those for the other states over thefrequency range from 3 to 10 kHz. This means that the wet state predominates at highfrequencies in comparison with the other two states.Therefore, the author proposes an additional feature corresponding to a basicstatistic for characterizing quantitatively the shape of the distribution. It is the skewnessstatistic of the mean of the adjusted spectrum. The results are shown in Fig. C.3(b). Theorder of magnitude is interchanged compared with Fig. C.3(a). Interestingly, it can beroughly expected that skewness indeed represents the changes of the surface states, theroad surface changed from the snowy state to the wet state in the morning, remained wetuntil 2 p.m., and subsequently changed to the dry state. At any rate, the skewness takesa value of 0.4 on average for the snowy state from 0 a.m. to 9:30 a.m. Similarly, for thewet and dry surfaces, it takes -0.06 and 0.11, respectively. The skewness for the wetstate takes negative value as a whole.Table C.1 shows the results, where the results of the proposed two methods ofusing D and skewness are listed for comparison. Although, the accuracy of correctdetection is low overall, the use of the skewness gives the highest accuracy. Moreover,117

the author used statistical measures such as standard deviation to discriminate the dryand slushy states which were suggested in chapter 4. The classification ability of usingboth features is improved to high precision, as shown in Table C.2. The classificationaccuracy rates are achieved to be 76.3 % and 78.4 %, respectively. This result leads usto believe that both the features offer a great potential for the detection of road surfacestates.Table C.1Experimental results using residual vector, D and skewness for detecting theroad surface states over three days.Table C.2 Improved experimental results using the standard deviation for detecting the roadsurface states.σ t118

PublicationsAcademic journal papers:1. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Detection of RoadSurface Conditions Using Tire Noise from Vehicles”, The institute of ElectricalEngineers of Japan, IEEJ Transactions on Industry Applications, Vol.129, No.7 2009,pp.761-767, July 2009.2. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Detection of RoadSurface States from Tire Noise Using Neural Network Analysis”, The institute ofElectrical Engineers of Japan, IEEJ Transactions on Industry Applications, Vol.130,No.7 2010, pp.920-925, July 2010.Conference and meeting papers:1. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Detection of RoadSurface Conditions Using Automobile Tire Sounds,” The Institute of Electronics,Information and Communication Engineers, IEICE Technical Report,No.EA2007-111 (2008-1), pp.59-64, Jan.29, 2008.2. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “AutomaticDetection of Road Surface Conditions Using Tire Noise from Vehicles,” TheInstitute of Electronics, Information and Communication Engineers, IEICETechnical Report, No.EA2008-125 (2009-1), pp.55-60, Jan. 29-30, 2009.3. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Wavelet-basedneural networks applied to automatic detection of road surface conditions using tirenoise from vehicles,” The Journal of the Acoustical Society of America, ASA157th,Portland, Oregon, 125 No.4, Pt.2, pp.2730, May 18-22, 2009.4. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Application ofNeural Network Analysis to Automatic Detection of Road Surface ConditionsUtilizing Tire Noise from Vehicles,” ICCAS-SICE 2009, Fukuoka, Japan, pp. 175,Aug. 18-21, 2009.5. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “PrincipalComponent Analysis Applied to Detection of Road Surface Conditions using TireNoise from Passing Vehicles,” The Institute of Electronics, Information andCommunication Engineers, IEICE Technical Report, No.EA2009-111 (2010-1),pp.73-76, Jan. 25-26, 2010.6. W. Kongrattanaprasert, H. Nomura, T. Kamakura, and K. Ueda, “Automatic119

Detection of Road Surface States from Tire Noise Using Neural Network Analysis,”Proceeding of 20 th International Congress on Acoustics, ICA 2010, Sydney, Aug.22-27, 2010.120