Vehicle control and drowsiness - VTI

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3 Variables looked at to detect drowsinessSince the literature study focused on methods to automatically detect drowsiness,the following variables were of main interest: time-to-line crossing, lateralposition of the car and related measures, steering wheel measures (see also definitionsin appendix 4). Other measurement variables of possible interest are:longitudinal position deviation, eye blink frequency, physiological variables, anddriving performance measures (other than lateral position).16 VTI meddelande 922A
4 Methods to identify drowsiness in drivers4.1 Physiological measuresPhysiological measures have been frequently used to detect drowsiness. The mostpromising measures are electroencephalogram measures (EEG) aimed at detectingbrain waves typical for fatigue. There is good evidence that rising alpha (8–11Hz)and theta (4–7 Hz) EEG activities indicate increasing sleepiness and thus thepotential for lapses in attention and behaviour [18].Renner and Mehring [19] used EEG and eye parameters to capture driverimpairment (mainly caused by fatigue): slow eye movements were used toidentify moments of "insufficient perceptual capabilities", and increase of slowwave EEG to identify "deficient information processing". The authors write thatthis combination appears to be the most promising way to obtain a completepicture of the drivers state related to alertness.The following figure (figure 1) shows the correlation coefficients r forsubjectively evaluated drowsiness and a) an algorithm based on signals fromlateral vehicle position, steering wheel angle and longitudinal speed (vehicle stateand driver inputs); b) only driver inputs (such as steering angle); c) only vehiclestate variables (such as lateral vehicle position).single vehicle state input (best result)single driver input (best result)driver inputs combinationvehicle state inputs combinationalgorithm w ith both driver inputs andvehicle state inputs0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8correlation coefficient rFigure 1 Correlation coefficients for different methods of drowsiness prediction,based on a study in the Daimler Benz simulator with 20 professional truck drivers[19]. The algorithm itself is based on retrospective six minute moving averagesusing one-minute intervals. Driver inputs are for example steering wheel angle;vehicle state inputs are measures like lateral position and TLC.The heart rate variability is also reported to be able to determine the alertnessstate of the driver [20, 21]. It is though challenging to obtain this measure withouthaving to place electrodes on the driver.VTI meddelande 922A 17
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: 2 Terms and definitions“Fatigue:
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 and 38: 5.5 Companies that are planning to
Page 39 and 40: 7 Problems related to automatic dro
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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