让自动驾驶仿真能按需生成特定碰撞类型和时间的危险场景。
Controllable Collision Scenario Generation via Collision Pattern Prediction
- 用碰撞模式预测来压缩复杂场景，指导对抗轨迹生成
- 在5种碰撞类型、多时间区间下实现90%以上碰撞率
- 适合用于测试和提升自动驾驶规划器的鲁棒性
评估自动驾驶汽车（AV）安全性需要多样且高危的场景，其中碰撞尤其关键，但现实中罕见且难以安全采集。因此，研究界转向仿真中生成高危场景。然而，对碰撞类型和碰撞前时间（TTA）等属性的控制仍具挑战。本文提出可调控碰撞场景生成新任务，目标是生成满足用户指定碰撞类型和TTA的轨迹，以验证自动生成特定碰撞场景的可行性。为此，我们构建了COLLIDE数据集，通过转换真实驾驶日志生成覆盖五类典型碰撞类型、不同TTA区间的多样化碰撞场景。提出一种框架，先预测碰撞模式——一种紧凑且可解释的空间配置表示，描述碰撞时刻自身与对方车辆的相对位置，再据此生成完整对抗轨迹。实验表明，该方法在碰撞率和可控性上均优于强基线。生成场景持续引发更高规划器失败率，暴露出现有规划器的局限性。进一步验证这些场景可用于微调规划器，提升其在各类碰撞场景下的鲁棒性。更多生成场景见项目主页：https://plchen86157.github.io/conditional_scenario_generation/
原文摘要 · Abstract (English)
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. Additional generated scenarios are available at our project page: https://plchen86157.github.io/conditional_scenario_generation/
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