Evaluation of Driver Overconfidence in Automotive Driving Using Physiological Data

Abstract

This research aims to explore a method for the real-time evaluation of overconfidence during car driving. Overconfidence can lead to dangerous driving behaviors, making real-time detection crucial to reduce traffic accidents. In this study, a driving simulation environment was created using virtual reality (VR), and a right-turning scenario was used to encourage overconfidence during the experiment. The driving behavior data (accelerator, brake, and steering), physiological data (skin conductance and electrocardiogram (ECG)), and driving footage were recorded simultaneously. Overconfidence was measured by having participants watch the driving footage they created, obtaining both self-assessment from their own perspective and from an external perspective, as well as the difference between the two. The relationship between the measured overconfidence and the feature-extracted driving behavior and physiological data was analyzed. The results show that, when considering the period from the pedestrian crossing before the right turn until the increase in speed as the "pre-turn phase," and from the increase in speed until passing the pedestrian crossing on the turning side as the "turning phase," all subjects completed the turn and exhibited similar driving behavior during the turning phase. A feature-based analysis of the time-series data showed strong correlations between overconfidence and several features. In the driving behavior data, a significant negative correlation was observed between the minimum accelerator value during the turning phase (r = –0.718, p = 0.013). Furthermore, significant negative correlations were found between the average change in accelerator data in the turning phase (r = –0.676, p = 0.022), minimum slope of the accelerator in the turning phase (r = –0.644, p = 0.032), and minimum steering angle during the pre-turn phase (r = –0.622, p = 0.041). For physiological data, a significant negative correlation was found with the standard deviation of skin conductance (r = –0.662, p = 0.027). These results suggest the possibility of using driving behavior and physiological data to evaluate overconfidence in real time.

Keywords: Overconfidence, Biometric Data, Driving Skills, Virtual Reality

DOI: 10.54941/ahfe1006056