A novel lane-changing model of connected and automated vehicles: Using the safety potential field theory

Abstract

In order to adequately characterize the driving risks that vehicles face during the lane change process and ensure that vehicles perform safer lane change decisions, a vehicle lane change model based on the safe potential field theory is established in this paper. Firstly, the driving risk encountered during the vehicle lane-changing process is evaluated, and the spatial distribution of the safety potential field under different motion states during the vehicle driving process is given based on the potential field theory. Secondly, the critical distances between vehicles at the end of the lane-change process are summarized according to the distribution of different safety potential fields of relevant vehicles during the lane change process. Compared with the traditional critical distance calculation model, the method proposed in this paper can dynamically characterize the trend of the critical distance of the vehicle under different velocity and acceleration conditions. Based on this, according to the characteristics that various types of vehicle movement status can be perceived in real-time under the CAVs environment, the safety-critical time required for lane change under various motion states of the vehicle is summarized, and the minimum safety distance lane change model based on the safety potential field theory is finally established. Numerical simulation analysis of the model shows that the model can characterize the effects of various motion parameters on the lane change results. The research results can provide some theoretical support for related researches such as vehicle lane changing, vehicle autonomous driving, and vehicle group optimization control in the intelligent networked environment in the future.