Vehicle control and drowsiness - VTI

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6 PatentsA patent for an “alertness and drowsiness detection and tracking system” wasawarded in 1998 (US Patent 5,813,993). The system analyses the EEG, moreprecisely the rhythmic signal components above 30 Hz. “One or more analogueEEG signals are collected from a subject, appropriately filtered, converted intodigital form, and subjected to frequency analysis. Selected signal componentsfrom the frequencies above 30 Hz, which have a high correlation with subjectdrowsiness, are isolated, and their amplitude, energy, or power contribution to theEEG signal is used to produce a continuous output measure reflecting the subject'salertness or drowsiness. The output measure may be compared with a threshold toprovide an indication of whether or not the subject is excessively drowsy.” [73]Another patent for a “Driver's drowsiness detection method of drowsy drivingwarning system” was awarded in 1999 to Jung-Hack Yeo. The US patent with thenumber 6,243,015 describes the method of detection as based on monitoring ofeyelid closure by means of a CCD camera [74].There is a number of other patents that concentrate on drowsiness detection(see table 7 for a short list, for the patent details go to [74]).Table 7 Patents related to drowsiness warning for drivers. Search terms usedalone or in combination with each other: drowsiness – detection – alertness –driving – alarm.US Patent NumberName6,016,103 Sleep-detecting driving gloves4,144,531 Drowsiness detecting apparatus4,450,438 Drowsiness alarm system for a vehicle4,564,833 Dozing warning system for a vehicle4,928,090 Arousal level judging apparatus and method5,568,127 Drowsiness warning device and neck support6,154,123 Driver alertness monitoring system6,097,295 Apparatus for determining the alertness of a driver4,967,186 Method and apparatus for fatigue detection4,585,011 Conjugate eye movement detector for sleep analysisNote: US patents are listed here. The list of patents is not complete and is onlyrecorded as a summary.38 VTI meddelande 922A
7 Problems related to automatic drowsinessdetectionProblems with automatic drowsiness detection are: what is ‘normal’ or safetycritical ‘abnormal’ variability for measures such as lane deviation? What is therange of ‘normal’ variability of these measures in the driving population? Could aperfectly safe driver be classified as ‘abnormal’ on occasions, for example, score afalse positive (see also chapter 1.3 Validation). How has the threshold of‘abnormal’ driving behaviour been selected? With rare exceptions these questionsare not answered in the product descriptions of commercially available products.Thus these technologies also fail to provide satisfactory answers to the problem ofsuccessful validation [11].7.1 Detecting vs. predicting drowsinessClearly the goal has to be the prediction of drowsiness onset, since the meredetection of it may already be too late to prevent an accident. The onset ofdrowsiness is slow, and there is no single physiological or physical measure that isa good indicator. The problem is emphasized in Renner’s and Mehring's paper:”Drowsiness is not easy to recognize especially on a pure visual basis. There aremoments in which the driver still looks awake (eyes wide open) but does notprocess the information anymore. These „looking without seeing“ moments aredangerous and need to be captured by an effective system.In order to identify moments of both „insufficient perceptual capabilities“(slow eye closures) and „deficient information processing“ (increase of slowwave EEG) we have chosen to define drowsiness as a combination of brain andeye function. This appears to be the most promising way to obtain a completepicture of the drivers state. Therefore our efforts to predict drowsiness werefocused on modelling the time course of a complex drowsiness parameter ratherthan concentrating on a single aspect of alertness.” [19, page 2]The prediction of drowsiness represents a greater problem then the detection ofit. Many drowsiness detectors and more general methods to detect drowsinessidentify when the driver has fallen asleep, which in many cases is too late to avoidaccidents [75].Folkard and Åkerstedt [76] simulated the changes of daytime alertness by thecombined action of a homeostatic process, a circadian process and a sleep inertiaprocess. This model was recently validated [77] and applied for predicting sleeplatency and sleep duration (which is related to drowsiness in some extent),however, it will not be treated in depth here, since it belongs to the mathematicalmodels of sleep-alertness dynamics, which are not part of the report.7.2 Gender and age of the driverInvolvement in drowsy driver crashes is strongly related to the gender and age ofthe driver [78]: young males are for example the most endangered group when iscomes to such accidents.VTI meddelande 922A 39
Page 1 and 2: VTI meddelande 922A • 2002Vehicle
Page 3 and 4: Publisher:Publication:VTI Meddeland
Page 5 and 6: ForewordThis study was commissioned
Page 7 and 8: ContentPageSummary 7Sammanfattning
Page 9 and 10: Vehicle Control and Drowsinessby Al
Page 11 and 12: Part one: Literature Study1 Introdu
Page 13 and 14: 1.3 ValidationA relevant problem in
Page 15 and 16: 2 Terms and definitions“Fatigue:
Page 17 and 18: 4 Methods to identify drowsiness in
Page 19 and 20: ight light conditions, when people
Page 21 and 22: Micro-correction in steeringMicro-c
Page 23 and 24: in response to conditions in the ro
Page 25 and 26: driving performance measures. Some
Page 27 and 28: SD lateral positionFigure 10 Standa
Page 29 and 30: Different measures have been used i
Page 31 and 32: Figure 12 Prediction results for 50
Page 33 and 34: 1: AVEOBS, which is an observer rat
Page 35 and 36: 5 Commercially available fatigue mo
Page 37: 5.5 Companies that are planning to
Page 41 and 42: 8 Further literature of interestThe
Page 43 and 44: 9.3 Other physiological measuresPhy
Page 45 and 46: Note: the summary only illustrates
Page 47 and 48: Figure 16 Lateral position of a dri
Page 49 and 50: Example 2, subject number 17Figure
Page 51 and 52: 11 Analysis of drowsiness ratingIn
Page 53 and 54: 0,60,5SDLP (meters)0,40,30,2TiredAl
Page 55 and 56: As described in the previous sectio
Page 57 and 58: 109Tiredness876Predicted YTiredAler
Page 59 and 60: 14 Times driver change directionA s
Page 61 and 62: A large number of previous studies
Page 63 and 64: The two groups where compared in or
Page 65 and 66: 16 Frequency AnalysisAn analysis of
Page 67 and 68: The large power variation between i
Page 69 and 70: Fourier transform on the two groups
Page 71 and 72: 16.2 Power Spectrum estimate via Bu
Page 73 and 74: Table 9 Average value of dependent
Page 75 and 76: 18.4 Time to Line Crossing - TLCIn
Page 77 and 78: 19 References1. Wierwille, WW & Wre
Page 79 and 80: 29. PERCLOS: A Valid Psychophysiolo
Page 81 and 82: 54. Chiu-Geng, Lin: Lane Sensing an
Page 83 and 84: 87. Haworth, NL & Vulcan, AP: Testi
Page 85 and 86: Appendix 1Page 1 (2)TLC formulaeVTI
Page 87 and 88: TLC calculationAppendix 2Page 1 (1)
Page 89 and 90:
Appendix 4Page 1 (2)Definition of t
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