标题： 基于大语言模型的现实安全关键驾驶视频生成 显示英文标题

标题： LLM-based Realistic Safety-Critical Driving Video Generation

摘要： 设计多样且安全关键的驾驶场景对于评估自动驾驶系统至关重要。 在本文中，我们提出了一种新框架，利用大型语言模型（LLMs）进行少量样本代码生成，以在CARLA模拟器中自动合成驾驶场景，该模拟器在场景脚本方面具有灵活性，能够高效地基于代码控制交通参与者，并实施真实的物理动力学。 给定几个示例提示和代码样本，LLM会生成涉及交通参与者行为和位置的安全关键场景脚本，特别关注碰撞事件。 为了弥合仿真与现实外观之间的差距，我们集成了使用Cosmos-Transfer1和ControlNet的视频生成管道，将渲染的场景转换为逼真的驾驶视频。 我们的方法实现了可控的场景生成，并促进了罕见但关键的边缘案例的创建，例如遮挡下的行人横穿或突然的车辆切入。 实验结果表明，我们的方法在生成广泛、多样且安全关键的场景方面是有效的，为自动驾驶汽车的基于仿真的测试提供了一个有前景的工具。 显示英文摘要

摘要： 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.