基于扩展Scenic DSL的LLM驱动场景生成流水线用于自动驾驶安全验证
An LLM-driven Scenario Generation Pipeline Using an Extended Scenic DSL for Autonomous Driving Safety Validation
AI总结：
本文提出基于扩展Scenic DSL和LLM的场景生成流水线，实现高精度自动驾驶安全验证。
摘要：
现实世界碰撞报告，结合文本摘要和草图，是自动驾驶系统（ADS）基于场景测试的重要数据来源。然而，当前方法无法有效将这种多模态数据转化为精确可执行的模拟场景，阻碍了ADS安全验证的可扩展性。在本工作中，我们提出了一种可扩展且可验证的流水线，利用大型语言模型（GPT-4o mini）和概率中间表示（扩展Scenic领域特定语言）自动从碰撞报告中提取语义场景配置并生成相应的模拟准备场景。与早期方法如ScenicNL和LCTGen（直接从文本生成场景）或TARGET（从交通规则使用确定性映射）不同，我们的方法引入了中间Scenic DSL层，将高层语义理解与低层场景渲染分离，从而减少错误并捕捉现实世界的变异性。我们评估了该流水线在NHTSA CIREN数据库中的案例。结果表明在知识提取方面具有高准确性：环境和道路网络属性的正确性为100%，而对oracle和actor轨迹的正确性分别为97%和98%，与人工推导的地面真实值相比。我们使用CARLA模拟器和Autoware驾驶堆栈执行了生成的场景，并在2000种场景变体中一致触发了预期的交通规则违规（如对向车道穿越和闯红灯）。这些发现表明，所提出的流水线提供了一种法律基础、可扩展且可验证的ADS安全验证方法。
英文摘要：
Real-world crash reports, which combine textual summaries and sketches, are valuable for scenario-based testing of autonomous driving systems (ADS). However, current methods cannot effectively translate this multimodal data into precise, executable simulation scenarios, hindering the scalability of ADS safety validation. In this work, we propose a scalable and verifiable pipeline that uses a large language model (GPT-4o mini) and a probabilistic intermediate representation (an Extended Scenic domain-specific language) to automatically extract semantic scenario configurations from crash reports and generate corresponding simulation-ready scenarios. Unlike earlier approaches such as ScenicNL and LCTGen (which generate scenarios directly from text) or TARGET (which uses deterministic mappings from traffic rules), our method introduces an intermediate Scenic DSL layer to separate high-level semantic understanding from low-level scenario rendering, reducing errors and capturing real-world variability. We evaluated the pipeline on cases from the NHTSA CIREN database. The results show high accuracy in knowledge extraction: 100% correctness for environmental and road network attributes, and 97% and 98% for oracle and actor trajectories, respectively, compared to human-derived ground truth. We executed the generated scenarios in the CARLA simulator using the Autoware driving stack, and they consistently triggered the intended traffic-rule violations (such as opposite-lane crossing and red-light running) across 2,000 scenario variations. These findings demonstrate that the proposed pipeline provides a legally grounded, scalable, and verifiable approach to ADS safety validation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
