考虑HV-AV 交互的动态换道轨迹规划方法

Accurately predicting the potential changes in the control behavior of nearby vehicles is crucial for autonomous vehicles (AVs) to understand their driving environments, prevent safety hazards, and enhance lane-changing safety. Existing lane-changing trajectory-planning methods do not sufficiently consider the uncertainty of nearby vehicle control behaviors, which is particularly prominent in mixed traffic environments that include human-driven vehicles (HVs). Hence, a dynamic lane-changing trajectory-planning method that considers the interaction between HVs and AVs was proposed to improve the adaptability and safety of AV lane-changing behaviors. This method comprises three key models. First, a driving-style-identification model based on the LightGBM algorithm was established to accurately identify the driving styles of nearby vehicle drivers. Subsequently, the full velocity-difference model was used to simulate and analyze the dynamic control feedback of nearby vehicles under different driving styles during the lane-changing process of the AV. Finally, considering the interaction effects between an HV and AV, lane-changing trajectories and acceleration curves were dynamically generated to ensure the safety and smoothness of the operations. The results show that this method can effectively adjust lane-changing strategies based on real-time traffic conditions, thereby mitigating collision risks. Compared with conventional lane-changing trajectory planning methods, the proposed method demonstrates higher safety and adaptability in actual lane-changing scenarios.