Real-time driving risk assessment based on the psycho-physical field

Abstract

Real-time Driving Risk Assessment (RDRA) is a critical component of traffic safety and is influenced by confounding impacts from drivers, surrounding vehicles, and roadway conditions. Previous studies simplified the RDRA based on kinematic characteristics. However, drivers perceived the risks not only through kinematic characteristics but also by anticipating the behaviors of surrounding participants and execute collision-preventive maneuvers. In this study, an innovative RDRA framework based on the Psycho-Physical Field (PPF) is proposed. Specifically, the PPF of the subjective vehicle exerts repulsive forces on intrusive risk sources in different directions, of which the two-dimensional field distribution is determined by physical collision-related measures and regulated by the psychological characteristics of behavior anticipations and risk perception abilities. The proposed method was first analyzed through theoretical feasibility analyses and verified for over 450 high-risk events from naturalistic driving data, including three typical types of scenarios: car-following, lane-changing, and being cut-in/off. Moreover, the adaptability was further validated through three cases and compared with the risk warning functions of Mobileye. The results showed that the proposed method can provide accurate risk evaluations, and identify potential hazards about 2 s in advance for high-risk cut-in events.