Multi-modal behaviors exhibited by surrounding vehicles (SVs) can typically lead to traffic congestion and reduce the travel efficiency of autonomous vehicles (AVs) in dense traffic. This paper proposes a real-time parallel trajectory optimization method for the AV to achieve high travel efficiency in dynamic and congested environments. A spatiotemporal safety module is developed to facilitate the safe interaction between the AV and SVs in the presence of trajectory prediction errors resulting from the multi-modal behaviors of the SVs. By leveraging multiple shooting and constraint transcription, we transform the trajectory optimization problem into a nonlinear programming problem, which allows for the use of optimization solvers and parallel computing techniques to generate multiple feasible trajectories in parallel. Subsequently, these spatiotemporal trajectories are fed into a multi-objective evaluation module considering both safety and efficiency objectives, such that the optimal feasible trajectory corresponding to the optimal target lane can be selected. The proposed framework is validated through simulations in a dense and congested driving scenario with multiple uncertain SVs. The results demonstrate that our method enables the AV to safely navigate through a dense and congested traffic scenario while achieving high travel efficiency and task accuracy in real time.

翻译：周围车辆所展现的多模态行为通常会导致交通拥堵，并降低自主驾驶车辆在密集交通中的行驶效率。本文提出一种实时并行轨迹优化方法，使自主驾驶车辆能够在动态拥堵环境中实现高行驶效率。针对周围车辆多模态行为导致的轨迹预测误差，我们开发了一个时空安全模块，以促进自主驾驶车辆与周围车辆之间的安全交互。通过利用多重打靶法和约束转录技术，我们将轨迹优化问题转化为非线性规划问题，从而能够使用优化求解器和并行计算技术并行生成多条可行轨迹。随后，这些时空轨迹被输入到考虑安全与效率目标的多目标评估模块中，以选择与最优目标车道相对应的最优可行轨迹。通过在包含多个不确定周围车辆的密集拥堵驾驶场景中进行仿真验证，结果表明，该方法能够使自主驾驶车辆在安全穿越密集拥堵交通场景的同时，实时实现高行驶效率与任务精确性。