Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-driven approaches. However, without an appropriate upper bound for adversariality, the scenarios might exhibit excessive adversariality, potentially leading to unavoidable collisions. In this paper, we introduce FREA, a novel safety-critical scenarios generation method that incorporates the Largest Feasible Region (LFR) of AV as guidance to ensure the reasonableness of the adversarial scenarios. Concretely, FREA initially pre-calculates the LFR of AV from offline datasets. Subsequently, it learns a reasonable adversarial policy that controls the scene's critical background vehicles (CBVs) to generate adversarial yet AV-feasible scenarios by maximizing a novel feasibility-dependent adversarial objective function. Extensive experiments illustrate that FREA can effectively generate safety-critical scenarios, yielding considerable near-miss events while ensuring AV's feasibility. Generalization analysis also confirms the robustness of FREA in AV testing across various surrogate AV methods and traffic environments.

翻译：生成安全关键场景是评估自动驾驶车辆（AV）鲁棒性的有效方法，但此类场景至关重要且难以大规模采集。现有方法侧重于在保持场景自然性的同时优化对抗性，旨在通过数据驱动方法实现平衡。然而，若缺乏适当的对抗性上限，生成的场景可能表现出过度对抗性，导致不可避免的碰撞风险。本文提出FREA，一种新颖的安全关键场景生成方法，该方法引入自动驾驶车辆的最大可行区域（LFR）作为引导，以确保对抗场景的合理性。具体而言，FREA首先从离线数据集中预计算AV的LFR；随后通过学习合理的对抗策略，控制场景中的关键背景车辆（CBVs），通过最大化新型的可行性依赖对抗目标函数，生成具有对抗性且AV可行的场景。大量实验表明，FREA能有效生成安全关键场景，在保证AV可行性的同时产生大量险态事件。泛化分析也验证了FREA在不同替代AV方法与交通环境下的AV测试中具有鲁棒性。