Risk assessment and observation of driver with pedestrian using instantaneous heart rate and HRV
Authors: Riku Kikuta, Daniel Carruth, John Ball, Reuben Burch, Ichiro Kageyama
Abstract: Currently, human drivers outperform self-driving vehicles in many conditions such as collision avoidance. Therefore, understanding human driver behaviour in these conditions will provide insight for future autonomous vehicles. For understanding driver behaviour, risk assessment is applied so far as one of the approaches by using both subjective and objective measurement. Subjective measurement methods such as questionnaires may provide insight into driver risk assessment but there is often significant variability between drivers. Physiological measurements such as heart rate (HR), electroencephalogram (EEG), and electromyogram (EMG) provide more objective measurements of driver risk assessment. HR is often used for measuring driver’s risk assessment based on observed correlations between HR and risk perception. Previous work has used HR to measure driver’s risk assessment in self-driving systems, but pedestrian dynamics is not considered for the research. In this study, we observed driver’s behaviour in certain scenarios which have pedestrian on driving simulator. The scenarios have safe/unsafe situations (i.e., pedestrian crosses road and vehicle may hit pedestrian in one scenario), HR analysis in time/frequency domain is processed for risk assessment. As a result, HR analysis in frequency domain shows certain reasonability for driver risk assessment when driver has pedestrian in its traffic.
Keywords: Self, driving, Human factors, Risk evaluation, Wearable device, Simulation
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