arXiv 2503.237082025-04-07cs.ROcs.AI

面向安全关键场景下自动驾驶安全性与鲁棒性基准测试和评估的研究

Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios

Zhongguancun Laboratory（中关村实验室）；Beihang University（北京航空航天大学）；Institute of Data Space, Hefei Comprehensive National Science Center（合肥综合性国家科学中心数据空间研究院）；Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；A*STAR（新加坡科技研究局）

Jingzheng Li, Xianglong Liu, Shikui Wei, Zhijun Chen, Bing Li, Qing Guo, Xianqi Yang, Yanjun Pu, Jiakai Wang

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AI总结：

本研究针对自动驾驶在安全关键场景下的安全性与鲁棒性评估不足的问题，明确了安全关键场景定义，开发了涵盖感知与系统级评估的自动驾驶安全测试平台，为行业建立标准化测试框架和降低实际道路部署风险提供技术支持。

摘要：

自动驾驶在学术界和工业界均取得了显著进展，包括感知任务的性能提升以及端到端自动驾驶系统的开发。然而，自动驾驶的安全性和鲁棒性评估尚未得到充分重视。当前对自动驾驶的评估通常在自然驾驶场景中进行。然而，许多事故往往发生在边缘情况中，也被称为安全关键场景。这些安全关键场景难以收集，且目前对于什么构成安全关键场景尚无明确定义。在本研究中，我们探讨了自动驾驶在安全关键场景下的安全性和鲁棒性。首先，我们给出了安全关键场景的定义，包括静态交通场景（如对抗攻击场景和自然分布偏移）以及动态交通场景（如事故场景）。然后，我们开发了一个自动驾驶安全测试平台，以全面评估自动驾驶系统，不仅涵盖感知模块的评估，还包括系统级评估。我们的工作系统地构建了自动驾驶的安全验证流程，为行业建立标准化测试框架并降低实际道路部署中的风险提供了技术支持。

英文摘要：

Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically conducted in natural driving scenarios. However, many accidents often occur in edge cases, also known as safety-critical scenarios. These safety-critical scenarios are difficult to collect, and there is currently no clear definition of what constitutes a safety-critical scenario. In this work, we explore the safety and robustness of autonomous driving in safety-critical scenarios. First, we provide a definition of safety-critical scenarios, including static traffic scenarios such as adversarial attack scenarios and natural distribution shifts, as well as dynamic traffic scenarios such as accident scenarios. Then, we develop an autonomous driving safety testing platform to comprehensively evaluate autonomous driving systems, encompassing not only the assessment of perception modules but also system-level evaluations. Our work systematically constructs a safety verification process for autonomous driving, providing technical support for the industry to establish standardized test framework and reduce risks in real-world road deployment.

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