让自动驾驶测试能区分事故是系统缺陷还是不可避免的交通冲突。
Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles
- 在闭环仿真中结合责任归属，生成可解释的碰撞场景。
- 在多个国家交通数据集上实现高责任判定率与物理可行性。
- 适合自动驾驶安全验证团队和法规合规研究人员使用。
建立自动驾驶系统（ADS）可信赖的安全保障，需要证明事故源于可避免的系统缺陷而非不可避免的交通冲突。现有对抗性仿真方法虽能高效暴露碰撞，但普遍缺乏区分这两种根本不同故障模式的机制。本文提出CARS（上下文感知、责任归属场景生成）框架，将责任归属直接整合进对抗性场景生成过程。CARS结合上下文感知的对手选择与闭环仿真中优化的生成对抗策略，构建出既物理可行又可诊断归因的碰撞场景。在涵盖异构国家交通环境的基准数据集上，CARS在多种法规规定的谨慎且称职驾驶员模型下，持续发现具有高责任判定率的可行碰撞场景。通过将对抗生成与规范性责任评估相结合，CARS使仿真测试从单纯碰撞发现，迈向可解释、符合法规的安全证据构建，支持可扩展的ADS验证。
原文摘要 · Abstract (English)
Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies rather than unavoidable traffic conflicts. Current adversarial simulation methods can efficiently expose collisions, but generally lack mechanisms to distinguish these fundamentally different failure modes. Here we present CARS (Context-Aware, Responsibility-attributed Scenario generation), a framework that integrates responsibility attribution directly into adversarial scenario generation. CARS combines context-aware adversary selection with a generative adversarial policy optimized in closed-loop simulation to construct collision scenarios that are both physically feasible and diagnostically attributable. Across benchmark datasets spanning heterogeneous national traffic environments, CARS consistently discovers feasible collision scenarios with high attribution rates under multiple regulation-prescribed careful and competent driver models. By coupling adversarial generation with normative responsibility assessment, CARS moves simulation testing beyond collision discovery toward the construction of interpretable, regulation-aligned safety evidence for scalable ADS validation.
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