Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existing driving video generation methods may produce visually realistic videos, they often fail to deliver physically realistic simulations because they lack the capability to generate accurate post-collision trajectories. In this paper, we introduce AccidentSim, a novel framework that generates physically realistic vehicle collision videos by extracting and utilizing the physical clues and contextual information available in real-world vehicle accident reports. Specifically, AccidentSim leverages a reliable physical simulator to replicate post-collision vehicle trajectories from the physical and contextual information in the accident reports and to build a vehicle collision trajectory dataset. This dataset is then used to fine-tune a language model, enabling it to respond to user prompts and predict physically consistent post-collision trajectories across various driving scenarios based on user descriptions. Finally, we employ Neural Radiance Fields (NeRF) to render high-quality backgrounds, merging them with the foreground vehicles that exhibit physically realistic trajectories to generate vehicle collision videos. Experimental results demonstrate that the videos produced by AccidentSim excel in both visual and physical authenticity.

翻译：为自动驾驶研究收集真实世界车辆事故视频具有挑战性，原因在于其稀有性与复杂性。现有的驾驶视频生成方法虽能产生视觉上逼真的视频，但由于缺乏生成准确碰撞后轨迹的能力，往往无法提供物理真实的模拟。本文提出AccidentSim，一种新颖的框架，通过提取并利用真实世界车辆事故报告中可用的物理线索与上下文信息，生成物理真实的车辆碰撞视频。具体而言，AccidentSim利用一个可靠的物理模拟器，根据事故报告中的物理与上下文信息复现碰撞后车辆轨迹，并构建一个车辆碰撞轨迹数据集。该数据集随后用于微调一个语言模型，使其能够响应用户提示，并根据用户描述预测各种驾驶场景下物理一致的碰撞后轨迹。最后，我们采用神经辐射场（NeRF）渲染高质量背景，并将其与展现物理真实轨迹的前景车辆融合，以生成车辆碰撞视频。实验结果表明，AccidentSim生成的视频在视觉真实性与物理真实性方面均表现出色。