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.

翻译：安全关键场景生成对于评估自动驾驶系统至关重要。然而，现有方法往往难以平衡三个相互冲突的目标：对抗性临界性、物理可行性和行为真实性。为弥补这一差距，我们提出SaFeR：一种基于可行性约束令牌重采样的自动驾驶测试安全关键场景生成方法。我们首先将交通场景生成构建为离散的下一个令牌预测问题，采用基于Transformer的模型作为真实性先验，以捕捉自然驾驶分布。为在捕获复杂交互的同时有效抑制注意力噪声，我们在真实性先验中提出了一种新颖的差分注意力机制。基于此先验，SaFeR实施了一种新颖的重采样策略：在高概率信任区域内诱导对抗性行为以保持自然性，同时强制执行源自最大可行区域（LFR）的可行性约束。通过离线强化学习近似LFR，SaFeR有效避免了理论上不可避免的碰撞场景的生成。在Waymo Open Motion数据集和nuPlan上进行的闭环实验表明，SaFeR显著优于现有最先进的基线方法，在保持强对抗有效性的同时，实现了更高的求解率和更优的运动学真实性。