Improved Method of Driving Safety Field Modeling by Considering the Abnormal Transfer of Energy Between Vehicles

Abstract

This study presents a Driving Safety Field (DSF) modeling method for quantifying the risks faced by autonomous vehicles, aiming to address the real-time and precise quantification of risks posed by other road participants during the driving process. The proposed modeling method is based on the concept that accidents involve abnormal energy transfer. By considering the interactions between the ego vehicle and other road participants, the model introduces reduced mass and vector velocity and utilizes coordinate transformations to ensure that risks are distributed along the direction of relative velocity, thereby satisfying Newton’s third law of motion regarding interactions between vehicles. Compared to previous methods, the proposed DSF modeling method offers higher interpretability and more direct quantification of instantaneous risks. Specifically, any change in the speed or position of any road participant in the environment leads to significant changes in the DSF force map. By simulation analyzing three risk-avoidance paths—driving straight, turning right, and making a U-turn—and comparing the distribution of driving risk field forces across 18 different scenarios, the results show that the proposed model can effectively identify driving risks. The U-turn strategy to the opposite lane is the most optimal risk-avoidance solution, reducing overall risk by 73.93% when the vehicle speed is 6 m/s. This method provides an intuitive and comprehensive safety situational awareness capability for autonomous vehicles. This ability is of great significance for improving the safety of autonomous driving and reducing the occurrence of traffic accidents.