Automated driving motion planning algorithms require a high degree of real-time performance. In a closed structured road environment, making full use of the road information helps to reduce the computational effort and improve the real-time performance of the algorithm. Therefore, this paper proposes a real-time automated driving motion planning process suitable for closed structured road environments. Based on the environmental information of the structured road, the key factors affecting the motion decision are extracted. The scenarios are classified according to the key factors, and decision rules are established for the vehicle based on the headway. In addition, based on a pure tracking algorithm, a motion planning method based on dynamic target points is proposed, which takes into account both single-cycle trajectory planning and vehicle motion continuity. Finally, a real-vehicle test is conducted under two typical driving scenarios. The results show that the proposed motion planning process has a good real-time performance and adaptability in real traffic scenarios.

自动驾驶运动规划算法需要高度的实时性能。在封闭的结构化道路环境中，充分利用道路信息有助于减少计算量，提高算法的实时性。因此，本文提出了一种适用于封闭结构化道路环境的实时自动驾驶运动规划过程。基于结构化道路的环境信息，提取影响运动决策的关键因素。根据关键因素对场景进行分类，并根据车头时距为车辆建立决策规则。此外，在纯跟踪算法的基础上，提出了一种基于动态目标点的运动规划方法，它同时考虑了单周期轨迹规划和车辆运动连续性。最后，在两种典型的驾驶场景下进行了实车测试。结果表明，所提出的运动规划过程在实际交通场景中具有良好的实时性和适应性。