Rule-adaptive lane-changing trajectory planning method for autonomous vehicles driven by dynamic risk information

Rapid advancements in autonomous driving technology have highlighted the challenges of ensuring vehicle safety and driving efficiency in complex dynamic traffic environments. Current approaches typically define potential risks as safety constraints for compliance and use them in trajectory planning. However, the risks predefined in these constraints are often fixed, reducing driving efficiency. To address this limitation, we proposed a dynamic risk-information-driven adaptive trajectory planning method for autonomous vehicles (AVs). This study dynamically adjusted safety constraints using risk assessment results to improve driving efficiency without compromising safety. Firstly, considering the influence of vehicle suspension characteristics on driving safety, collision, and instability risk assessment indices were designed using a three-way-coupled dynamic model to assess driving safety risks. Next, we used the safety risk assessment module to evaluate specific potential risks and adaptively adjusted the safety constraints for constraint-based adaptive trajectory planning. Furthermore, considering trajectory traversal constraints, trajectory selection and optimization were performed on pre-planned trajectories using the cost function to determine the optimal driving trajectory. Lane-changing trajectory planning experiments showed that the method adaptively adjusts safety constraints based on risk assessment results. Under the premise of ensuring driving safety, driving efficiency improved by 55.9% in the preset instability constraint scenario and 27.86% in the preset collision constraint scenario.