Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1\%, and the curvature is reduced by approximately 56.1\% compared with the CPF-LC method.

翻译：实现安全高效的换道操作是自动驾驶车辆在高速公路上确保安全行驶的关键功能之一。近年来，势场法因其简洁性和实时性被用于设计自动驾驶车辆的决策与规划模块。然而，通过势场法规划的换道轨迹通常较长，且会导致自车与障碍车辆横向平行且距离过近——当障碍车辆突然转向时极易引发危险工况。为降低该风险，本文提出一种基于势场与三次多项式的碰撞时间感知型换道策略，通过在优化曲线拟合中引入碰撞时间约束。利用MATLAB/Simulink在高速工况下进行对比驾驶场景仿真验证，结果表明：相较于传统基于势场的换道方法，所提方法能生成更短、更安全、更平滑的轨迹。其中换道轨迹长度缩短超过27.1%，曲率降低约56.1%。