SaFeR: Safety-Critical Scenario Generation for Autonomous Driving Test via Feasibility-Constrained Token Resampling

AI 总结

• 将交通场景生成建模为离散的下一token预测任务，使用Transformer作为真实驾驶分布先验。
• 提出差分注意力机制以增强多体交互建模能力并降低注意力噪声。
• 设计可行性约束重采样策略，在高概率信任域内注入对抗行为以保持自然性。
• 基于离线强化学习近似最大可行区域（LFR），避免生成理论上不可避免碰撞场景。
• 在Waymo Open Motion Dataset与nuPlan闭环实验中，相比SOTA实现更高解率、更好运动学真实度且保持强对抗性。

内容摘录

Safety-critical scenario generation is crucial for evaluating autonomous driving systems. However, existing approaches often struggle to balance three conflicting objectives: adversarial criticality, physical feasibility, and behavioral realism. To bridge this gap, we propose SaFeR: safety-critical scenario generation for autonomous driving test via feasibility-constrained token resampling. We first formulate traffic generation as a discrete next token prediction problem, employing a Transformer-based model as a realism prior to capture naturalistic driving distributions. To capture complex interactions while effectively mitigating attention noise, we propose a novel differential attention mechanism within the realism prior. Building on this prior, SaFeR implements a novel resampling strategy that induces adversarial behaviors within a high-probability trust region to maintain naturalism, while enforcing a feasibility constraint derived from the Largest Feasible Region (LFR). By approximating the LFR via offline reinforcement learning, SaFeR effectively prevents the generation of theoretically inevitable collisions. Closed-loop experiments on the Waymo Open Motion Dataset and nuPlan demonstrate that SaFeR significantly outperforms state-of-the-art baselines, achieving a higher solution rate and superior kinematic realism while maintaining strong adversarial effectiveness.