Real-time perception and motion planning are two crucial tasks for autonomous driving. While there are many research works focused on improving the performance of perception and motion planning individually, it is still not clear how a perception error may adversely impact the motion planning results. In this work, we propose a joint simulation framework with LiDAR-based perception and motion planning for real-time automated driving. Taking the sensor input from the CARLA simulator with additive noise, a LiDAR perception system is designed to detect and track all surrounding vehicles and to provide precise orientation and velocity information. Next, we introduce a new collision bound representation that relaxes the communication cost between the perception module and the motion planner. A novel collision checking algorithm is implemented using line intersection checking that is more efficient for long distance range in comparing to the traditional method of occupancy grid. We evaluate the joint simulation framework in CARLA for urban driving scenarios. Experiments show that our proposed automated driving system can execute at 25 Hz, which meets the real-time requirement. The LiDAR perception system has high accuracy within 20 meters when evaluated with the ground truth. The motion planning results in consistent safe distance keeping when tested in CARLA urban driving scenarios.

翻译：实时感知与运动规划是自动驾驶的两项关键任务。尽管已有大量研究分别聚焦于提升感知和运动规划的性能，但感知误差如何对运动规划结果产生不利影响仍不明确。本研究提出了一种基于激光雷达的感知与运动规划联合仿真框架，用于实现实时自动驾驶。通过引入带有加性噪声的CARLA模拟器传感器输入，我们设计了一个激光雷达感知系统，用于检测和跟踪周围所有车辆，并提供精确的朝向与速度信息。随后，我们提出了一种新的碰撞边界表示方法，以降低感知模块与运动规划器之间的通信成本。同时实现了一种基于线段交叉检测的新型碰撞检测算法，与传统占用网格方法相比，该算法在长距离范围内具有更高效率。我们在CARLA城市驾驶场景下对该联合仿真框架进行了评估。实验表明，所提出的自动驾驶系统能以25 Hz的频率运行，满足实时性要求。激光雷达感知系统在20米范围内与真实数据对比时具有高精度。在CARLA城市驾驶场景测试中，运动规划结果能够保持一致的车辆安全距离。