用大模型生成逼真自动驾驶测试场景，支持罕见高危情况模拟。
LLM-based Realistic Safety-Critical Driving Video Generation
- 通过大模型少量示例自动生成驾驶场景代码
- 生成包含碰撞等关键事件的多样化真实场景
- 适合自动驾驶安全测试与边缘案例研究
设计多样且具有安全关键性的驾驶场景对于评估自动驾驶系统至关重要。本文提出一种新框架，利用大语言模型（LLM）进行少样本代码生成，自动在CARLA模拟器中合成驾驶场景。该框架具备场景脚本灵活性、基于代码的交通参与者高效控制以及真实物理动力学约束能力。给定少量示例提示和代码样本，LLM可生成聚焦于碰撞事件的场景脚本，明确指定交通参与者的行为与位置。为弥合仿真与真实视觉之间的差距，集成基于Cosmos-Transfer1与ControlNet的视频生成流水线，将渲染画面转换为逼真驾驶视频。本方法支持可控场景生成，可有效创建罕见但关键的边缘案例，如遮挡下的行人横穿或突然变道切入。实验结果表明，该方法能生成广泛、真实、多样的安全关键场景，为自动驾驶系统的仿真测试提供有力工具。
原文摘要 · Abstract (English)
Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) for few-shot code generation to automatically synthesize driving scenarios within the CARLA simulator, which has flexibility in scenario scripting, efficient code-based control of traffic participants, and enforcement of realistic physical dynamics. Given a few example prompts and code samples, the LLM generates safety-critical scenario scripts that specify the behavior and placement of traffic participants, with a particular focus on collision events. To bridge the gap between simulation and real-world appearance, we integrate a video generation pipeline using Cosmos-Transfer1 with ControlNet, which converts rendered scenes into realistic driving videos. Our approach enables controllable scenario generation and facilitates the creation of rare but critical edge cases, such as pedestrian crossings under occlusion or sudden vehicle cut-ins. Experimental results demonstrate the effectiveness of our method in generating a wide range of realistic, diverse, and safety-critical scenarios, offering a promising tool for simulation-based testing of autonomous vehicles.
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