Obstacle detection is crucial to the operation of autonomous driving systems, which rely on multiple sensors, such as cameras and LiDARs, combined with code logic and deep learning models to detect obstacles for time-sensitive decisions. Consequently, obstacle detection latency is critical to the safety and effectiveness of autonomous driving systems. However, the latency of the obstacle detection module and its resilience to various changes in the LiDAR point cloud data are not yet fully understood. In this work, we present the first comprehensive investigation on measuring and modeling the performance of the obstacle detection modules in two industry-grade autonomous driving systems, i.e., Apollo and Autoware. Learning from this investigation, we introduce ADPerf, a tool that aims to generate realistic point cloud data test cases that can expose increased detection latency. Increasing latency decreases the availability of the detected obstacles and stresses the capabilities of subsequent modules in autonomous driving systems, i.e., the modules may be negatively impacted by the increased latency in obstacle detection. We applied ADPerf to stress-test the performance of widely used 3D obstacle detection modules in autonomous driving systems, as well as the propagation of such tests on trajectory prediction modules. Our evaluation highlights the need to conduct performance testing of obstacle detection components, especially 3D obstacle detection, as they can be a major bottleneck to increased latency of the autonomous driving system. Such an adverse outcome will also further propagate to other modules, reducing the overall reliability of autonomous driving systems.

翻译：障碍物检测对自动驾驶系统的运行至关重要，该系统依赖摄像头与激光雷达等多种传感器，结合代码逻辑与深度学习模型进行障碍物检测，以实现对时间敏感的关键决策。因此，障碍物检测延迟对自动驾驶系统的安全性和有效性具有决定性影响。然而，障碍物检测模块的延迟及其对激光雷达点云数据各类变化的鲁棒性尚未得到充分理解。本研究首次对两个工业级自动驾驶系统（即Apollo与Autoware）中障碍物检测模块的性能测量与建模进行了全面调查。基于此项调查，我们提出了ADPerf工具，该工具旨在生成能够暴露检测延迟增加的真实点云数据测试用例。延迟增加会降低已检测障碍物的可用性，并对自动驾驶系统中后续模块的处理能力造成压力，即这些模块可能因障碍物检测延迟的增加而受到负面影响。我们应用ADPerf对自动驾驶系统中广泛使用的三维障碍物检测模块进行压力测试，并评估此类测试对轨迹预测模块的传导效应。实验结果表明，有必要对障碍物检测组件（尤其是三维障碍物检测）开展性能测试，因其可能成为导致自动驾驶系统延迟增加的主要瓶颈。此类不利影响还将进一步传导至其他模块，从而降低自动驾驶系统的整体可靠性。