Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to-vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.

翻译：路径规划对于自动驾驶车辆（AVs）在考虑约束和目标的同时确定最优路线至关重要。势场（PF）方法因其结构简单、计算效率高而成为路径规划中的主流方法。然而，当前用于自动驾驶车辆的PF方法仅关注自车的路径生成，同时假设周围障碍车辆在未使用基于势场的路径规划器的情况下以预设行为行驶，这忽略了自车的势场也可能影响障碍车辆路径生成的事实。为解决这一问题，我们提出了一种基于PF的路径规划方法，其中本地路径通过车对车（V2V）通信在自车与障碍车辆之间共享。通过将该共享本地路径整合到目标函数中，设计了一种名为交互式速度优化（ISO）的新优化函数，以确保自车和障碍车辆的行驶安全与舒适性。在紧急并道场景中，使用MATLAB/Simulink对所提方法进行仿真评估，并与传统方法进行了对比。仿真结果表明，所提方法能够通过提前减速来缓解其他自动驾驶车辆势场的影响，有效减少自车和障碍自动驾驶车辆的速度振荡。