The deployment of autonomous vehicles (AVs) has faced hurdles due to the dominance of rare but critical corner cases within the long-tail distribution of driving scenarios, which negatively affects their overall performance. To address this challenge, adversarial generation methods have emerged as a class of efficient approaches to synthesize safety-critical scenarios for AV testing. However, these generated scenarios are often underutilized for AV training, resulting in the potential for continual AV policy improvement remaining untapped, along with a deficiency in the closed-loop design needed to achieve it. Therefore, we tailor the Stackelberg Driver Model (SDM) to accurately characterize the hierarchical nature of vehicle interaction dynamics, facilitating iterative improvement by engaging background vehicles (BVs) and AV in a sequential game-like interaction paradigm. With AV acting as the leader and BVs as followers, this leader-follower modeling ensures that AV would consistently refine its policy, always taking into account the additional information that BVs play the best response to challenge AV. Extensive experiments have shown that our algorithm exhibits superior performance compared to several baselines especially in higher dimensional scenarios, leading to substantial advancements in AV capabilities while continually generating progressively challenging scenarios.

针对自动驾驶汽车在处理长尾分布的驾驶场景中的罕见但关键问题时面临的障碍，本文采用对抗生成方法并使用Stackelberg驾驶模型以及领导者-追随者建模来有效地生成安全关键场景，并实现自动驾驶策略的持续改进，从而在高维场景中取得了优于基准方法的性能，不断生成越来越具有挑战性的场景，推动自动驾驶汽车能力的进一步发展。

基于场景的闭环自动驾驶中的Stackelberg驾驶员模型的持续策略改进