用大模型从事故报告生成可模拟的交通事故场景。
CrashAgent: Crash Scenario Generation via Multi-modal Reasoning
- 通过多模态大模型解析事故报告，自动构建道路布局与车辆行为。
- 生成的场景碰撞率高且多样性好，真实还原事故关键特征。
- 适合研究自动驾驶安全算法的团队使用，提升对危急情况的应对能力。
自动驾驶算法的训练与评估需要多样化的交通场景，但现有数据集主要包含人类驾驶员的正常驾驶行为，导致安全事故场景稀缺。这种长尾分布限制了算法从高风险或故障场景中学习的能力，而这些场景对人类高效掌握驾驶技能至关重要。为此，我们利用多模态大语言模型，将真实交通事故报告转化为可直接在仿真环境中执行的结构化场景格式。具体提出 CrashAgent，一个用于理解多模态交通事故报告的多智能体框架，可自动生成道路布局、自车及周边车辆的行为。我们在布局重建准确性、碰撞率和场景多样性等多个维度对生成场景进行综合评估。所构建的高质量、大规模事故数据集将公开发布，以支持安全驾驶算法在危急情境下的开发。
原文摘要 · Abstract (English)
Training and evaluating autonomous driving algorithms requires a diverse range of scenarios. However, most available datasets predominantly consist of normal driving behaviors demonstrated by human drivers, resulting in a limited number of safety-critical cases. This imbalance, often referred to as a long-tail distribution, restricts the ability of driving algorithms to learn from crucial scenarios involving risk or failure, scenarios that are essential for humans to develop driving skills efficiently. To generate such scenarios, we utilize Multi-modal Large Language Models to convert crash reports of accidents into a structured scenario format, which can be directly executed within simulations. Specifically, we introduce CrashAgent, a multi-agent framework designed to interpret multi-modal real-world traffic crash reports for the generation of both road layouts and the behaviors of the ego vehicle and surrounding traffic participants. We comprehensively evaluate the generated crash scenarios from multiple perspectives, including the accuracy of layout reconstruction, collision rate, and diversity. The resulting high-quality and large-scale crash dataset will be publicly available to support the development of safe driving algorithms in handling safety-critical situations.
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