Dynamic Acceleration-Aware Risk Potential Field Car-Following Model for Heterogeneous Individuals

In the developing phase of connected autonomous driving, roads are populated with heterogeneous individuals, i.e., connected and unconnected vehicles, autonomous driving and human driving, resulting in intricate interactions and significant uncertainty in their motion trajectories. At present, the research of modeling under mixed traffic scenarios has the following challenges: 1) Traditional path planning ignores dynamic risks from the third-order vehicle state, i.e., acceleration; 2) Existing microscopic models lack the descriptions for the diverse decision-making patterns among heterogeneous individuals, which increases the uncertainty and complexity of the scene. In this paper, we propose a novel Risk Potential Field car-Following Model (RPFFM) tailored for heterogeneous individuals. It employs differentiated parameter adjustments, integrating forward propulsion with potential field repulsion to calculate vehicle acceleration. RPFFM achieves proactive and safe car-following by perceiving the change of dynamic obstacle acceleration through dynamic factors and converting it into the variation of the dynamic potential field. Experimental results based on the Next Generation Simulation (NGSIM) dataset demonstrate that the RPFFM surpasses both the Intelligent Driver Model (IDM) and Optimal Velocity Model (OVM) in terms of effectiveness, safety, and providing enhanced car-following comfort.