COSTER框架提升自动驾驶安全场景生成
Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation
COSTER框架通过时间反向生成技术，为自动驾驶训练创建了更真实多样的危险场景，碰撞率降低31%。
研究人员提出COSTER框架，利用学习到的交通先验知识确定合理的碰撞时间和位置。该框架通过构建碰撞快照，使用条件变分自编码器进行时间反向滚动，重建插入车辆的轨迹。实验表明，COSTER在合理性、多样性和数据效率方面优于现有方法。在Waymo开放运动数据集的安全关键场景测试中，使用COSTER生成场景训练的智能体将碰撞率降低了31%，同时提高了自身任务完成率。
Collision Snapshot Guided Time-Reversed Safety-Critical Scenario Generation
The generation of safety-critical traffic scenarios is essential for training and evaluating autonomous vehicles. Prior approaches typically perturb the trajectories of existing agents in a traffic scenario using simplified adversarial objectives to induce safety-critical interactions, which can limit the plausibility and diversity of the generated scenarios. Although inserting new adversarial vehicles can alleviate this limitation, determining when and where to introduce them in a scenario-specific manner remains challenging. In this work, we introduce \underline{CO}llision \underline{S}napshot guided \underline{T}im\underline{E}-\underline{R}eversed safety-critical scenario generation (COSTER), a framework that leverages learned traffic priors to determine plausible collision times and locations. COSTER first constructs a collision snapshot by inserting a new vehicle in contact with the target vehicle at the identified collision state within a traffic scenario. Starting from this collision snapshot, a conditional variational autoencoder is used to perform a time-reversed rollout, reconstructing the trajectory of the inserted vehicle backward toward earlier timesteps. Experiments show that COSTER outperforms existing methods in plausibility, diversity, and data efficiency. Moreover, agents trained on COSTER-generated scenarios reduce collision rates by 31\% on safety-critical scenarios from the Waymo Open Motion Dataset while also improving ego task completion. The project website is available at https://anonym-121.github.io/COSTER/.