利用真实事故记录生成自动驾驶测试场景，提升测试效率与多样性。
Scenario Generation for Testing of Autonomous Driving Systems Using Real-World Failure Records
- 基于历史事故文本，用大模型生成可适配测试约束的合成场景。
- 在20个场景内发现系统在4种道路、3类车辆行为下的异常表现。
- 适合自动驾驶安全测试人员快速构建高价值测试用例。
为确保自动驾驶系统（ADS）上路安全，部署前的测试与故障发现至关重要。当前基于仿真的测试方法多依赖数学模型高效搜索最优场景，但假设场景表示固定；而真实世界测试则需大量人工设计测试模板，涵盖不同车辆运动、地图类型等失败场景。历史事故记录是真实失败条件的可靠来源，可用于场景生成。本文提出一种基于自然语言格式历史记录的场景生成流程，结合类别与上下文信息，采用模块化大模型生成符合特定系统测试约束的合成场景。我们在Metadrive仿真器上应用该方法，基于NHTSA ADS事故记录生成多样化测试场景。结果生成包含4种道路类型、3类非自车运动模式的场景，含施工区等道路异常，且在仅20个场景的有限预算下，成功揭示系统潜在缺陷。代码已开源。
原文摘要 · Abstract (English)
To ensure safe on-road behavior, pre-deployment testing and failure discovery of Autonomous Driving Systems (ADS) is crucial. Present day simulation based testing methods focus largely on mathematical models for efficient search of optimal scenarios, assuming a fixed scenario representation. On the other hand, real-world testing involves substantial manual effort to design scenario templates for testing. These templates represent distinct failure scenarios consisting of pre-deployment vehicle movements, map types, etc. Historical failure records for ADS are a reliable source of real-world failure conditions, which can be used for scenario generation. In this work, we propose a scenario generation pipeline using categorical and contextual information available from historical records in natural language format. Our approach consists of modular LLM based synthetic scenario generation, compatible with the testing constraints of a given system. We successfully apply our method to generate a diverse set of scenarios for testing autonomous navigation on Metadrive simulator using the NHTSA ADS crash records. Our approach results in accurate and diverse scenario generation with a combination of 4 road types, 3 non ego vehicle movement types, including on road anomalies in the form of working zones. Generated scenarios align with the provided testing conditions, and reveals interesting failures of the system within a limited testing budget of 20 scenarios. Code is available at https://github.com/anjaliParashar/crash2scenario.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。