Evaluating the safety of autonomous vehicles (AVs) requires diverse, safety-critical scenarios, with collisions being especially important yet rare and unsafe to collect in the real world. Therefore, the community has been focusing on generating safety-critical scenarios in simulation. However, controlling attributes such as collision type and time-to-accident (TTA) remains challenging. We introduce a new task called controllable collision scenario generation, where the goal is to produce trajectories that realize a user-specified collision type and TTA, to investigate the feasibility of automatically generating desired collision scenarios. To support this task, we present COLLIDE, a large-scale collision scenario dataset constructed by transforming real-world driving logs into diverse collisions, balanced across five representative collision types and different TTA intervals. We propose a framework that predicts Collision Pattern, a compact and interpretable representation that captures the spatial configuration of the ego and the adversarial vehicles at impact, before rolling out full adversarial trajectories. Experiments show that our approach outperforms strong baselines in both collision rate and controllability. Furthermore, generated scenarios consistently induce higher planner failure rates, revealing limitations of existing planners. We demonstrate that these scenarios fine-tune planners for robustness improvements, contributing to safer AV deployment in different collision scenarios.

翻译：评估自动驾驶车辆（AVs）的安全性需要多样化且安全关键的场景，其中碰撞场景尤为重要，但在现实世界中既罕见又难以安全采集。因此，学术界一直致力于在仿真环境中生成安全关键场景。然而，控制碰撞类型和事故前时间（TTA）等属性仍具挑战性。本文提出了一项称为可控碰撞场景生成的新任务，其目标是生成满足用户指定碰撞类型和TTA的轨迹，以探究自动生成预期碰撞场景的可行性。为支持此任务，我们构建了COLLIDE——一个通过将真实世界驾驶日志转化为多样化碰撞场景而构建的大规模碰撞场景数据集，该数据集在五种代表性碰撞类型及不同TTA区间内保持平衡。我们提出一个预测碰撞模式的框架，该模式是一种紧凑且可解释的表征，能在展开完整对抗轨迹前捕捉主车与对抗车辆在碰撞时的空间构型。实验表明，我们的方法在碰撞率和可控性方面均优于强基线模型。此外，生成的场景能持续诱发更高的规划器故障率，揭示了现有规划器的局限性。我们证明这些场景可通过微调提升规划器的鲁棒性，从而促进自动驾驶车辆在不同碰撞场景中更安全地部署。