Generating adversarial safety-critical scenarios is a pivotal method for testing autonomous driving systems, as it identifies potential weaknesses and enhances system robustness and reliability. However, existing approaches predominantly emphasize unrestricted collision scenarios, prompting non-player character (NPC) vehicles to attack the ego vehicle indiscriminately. These works overlook these scenarios' authenticity, rationality, and relevance, resulting in numerous extreme, contrived, and largely unrealistic collision events involving aggressive NPC vehicles. To rectify this issue, we propose a three-layer relative safety region model, which partitions the area based on danger levels and increases the likelihood of NPC vehicles entering relative boundary regions. This model directs NPC vehicles to engage in adversarial actions within relatively safe boundary regions, thereby augmenting the scenarios' authenticity. We introduce AuthSim, a comprehensive platform for generating authentic and effective safety-critical scenarios by integrating the three-layer relative safety region model with reinforcement learning. To our knowledge, this is the first attempt to address the authenticity and effectiveness of autonomous driving system test scenarios comprehensively. Extensive experiments demonstrate that AuthSim outperforms existing methods in generating effective safety-critical scenarios. Notably, AuthSim achieves a 5.25% improvement in average cut-in distance and a 27.12% enhancement in average collision interval time, while maintaining higher efficiency in generating effective safety-critical scenarios compared to existing methods. This underscores its significant advantage in producing authentic scenarios over current methodologies.

翻译：生成对抗性安全关键场景是测试自动驾驶系统的关键方法，因其能识别潜在弱点并增强系统的鲁棒性与可靠性。然而，现有方法主要侧重于无限制碰撞场景，促使非玩家角色（NPC）车辆无差别攻击自车。这些工作忽视了场景的真实性、合理性与相关性，导致产生大量涉及攻击性NPC车辆的极端、人为且高度不现实的碰撞事件。为纠正此问题，我们提出一种三层相对安全区域模型，该模型依据危险等级划分区域并提高NPC车辆进入相对边界区域的可能性。该模型引导NPC车辆在相对安全的边界区域内执行对抗行为，从而增强场景的真实性。我们提出AuthSim——一个通过将三层相对安全区域模型与强化学习相结合来生成真实有效安全关键场景的综合平台。据我们所知，这是首次全面解决自动驾驶系统测试场景真实性与有效性的尝试。大量实验表明，AuthSim在生成有效安全关键场景方面优于现有方法。值得注意的是，与现有方法相比，AuthSim在保持更高有效安全关键场景生成效率的同时，实现了平均切入距离5.25%的提升和平均碰撞间隔时间27.12%的改善。这突显了其在生成真实场景方面相较于当前方法的显著优势。