This paper describes a dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles (CAVs). The proposed dynamic lane-changing trajectory planning (DLTP) model adopts vehicle-to-vehicle (V2V) communication to generate an automated lane-changing maneuver with avoiding potential collisions and rollovers during the lane-changing process. The novelty of this method is that the DLTP model combines a detailed velocity planning strategy and considers more complete driving environment information. Besides, a lane-changing safety monitoring algorithm and a lane-changing starting-point determination algorithm are presented to guarantee the lane-changing safety, efficiency and stability of automated vehicles. Moreover, a trajectory-tracking controller based on model predictive control (MPC) is introduced to make the automated vehicle travel along the reference trajectory. The field traffic data from NGSIM are selected as the target dataset to simulate a real-world lane-changing driving environment. The simulations are performed in CarSim-Simulink platform and the experimental results show that the proposed method is effective for lane-changing maneuver.

本文介绍了一种用于连接和自动驾驶汽车（CAV）的变道操纵的动态轨迹规划方法。拟议的动态车道变换轨迹规划（DLTP）模型采用车到车（V2V）通信，以生成自动的车道变换动作，避免在车道变换过程中发生潜在的碰撞和翻车。该方法的新颖之处在于DLTP模型结合了详细的速度计划策略，并考虑了更完整的驾驶环境信息。提出了变道安全监控算法和变道起点确定算法，以保证自动驾驶汽车变道安全性，效率和稳定性。此外，引入了基于模型预测控制（MPC）的轨迹跟踪控制器，以使自动车辆沿参考轨迹行驶。来自NGSIM的现场交通数据被选为目标数据集，以模拟现实世界中的变道驾驶环境。在CarSim-Simulink平台上进行了仿真，实验结果表明，该方法对于变道机动是有效的。