Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

Development of a method to predict crash risk using trend analysis of driver behavior changes over time

“…vertical neck bending angle), the increasing trend was detected 16 times in a row between about 550 s and 700 s. The discussion suggests that it is necessary to use a multi-metric assessment system (multiple evaluation measures entered into the trend analysis), and synthesize the trend analysis results of each measure and predict in advance the point in time with high risk of virtual accident before the occurrence of virtual accident.This study is different from past studies such asMcDonald et al(2012),Murata et al(2011a),and Murata et al(2011b) as follows McDonald et al(2012). showed that steering-angle can be used to assess and predict drowsiness related lane-departures in advance.…”

“…McDonald, Schwartz, Lee, and Brown (2012) showed that steering wheel angle could be used to predict drowsiness related lane-departures six seconds before they occurred. Hanowski, Bowman, Alden, Wierewille, and Carrol (2008) made an attempt to assess driver drowsiness using an eye-closure measure and lane deviation performance, and showed that a multi-metric assessment system was more robust and effective than a single-metric assessment system using only an eye-closure measurement.…”

“…Past studies(McDonald et al, 2012;Hanowski, 2008) have shown that the point in time with high risk of crash can be identified to some extent using the steering wheel angle. When the following two conditions were simultaneously satisfied, we judged that the participant surely would have been encountered a serious accident with fairly high probability if continued driving.…”

Detection of drowsy driving based on driving information

“…They tried to reduce false alarm through the real driving test on the real road. In [3], they proposes a new approach for detecting drowsiness related lane departures six seconds before they occur using only unfiltered steering wheel angle data and a random forest algorithm. The mean accuracy of the RF-steering-model was 24% higher than PERCLOS.…”

Wristband-Type Driver Vigilance Monitoring System Using Smartwatch

“…Thus, an obtrusive and inexpensive method is essential to monitor the driver drowsiness in real-time. Reports in [3] also implied that drivers are enthusiastic in new technologies that could alert them once a signature of low vigilance is detected.…”