Simulation is an indispensable tool in the development and testing of autonomous vehicles (AVs), offering an efficient and safe alternative to road testing by allowing the exploration of a wide range of scenarios. Despite its advantages, a significant challenge within simulation-based testing is the generation of safety-critical scenarios, which are essential to ensure that AVs can handle rare but potentially fatal situations. This paper addresses this challenge by introducing a novel generative framework, CaDRE, which is specifically designed for generating diverse and controllable safety-critical scenarios using real-world trajectories. Our approach optimizes for both the quality and diversity of scenarios by employing a unique formulation and algorithm that integrates real-world data, domain knowledge, and black-box optimization techniques. We validate the effectiveness of our framework through extensive testing in three representative types of traffic scenarios. The results demonstrate superior performance in generating diverse and high-quality scenarios with greater sample efficiency than existing reinforcement learning and sampling-based methods.

翻译：仿真在自动驾驶车辆（AV）的开发与测试中是不可或缺的工具，它通过探索广泛场景，为道路测试提供了高效且安全的替代方案。尽管具有诸多优势，仿真测试面临的一个关键挑战是如何生成安全关键场景——这些场景对于确保自动驾驶车辆能够应对罕见但可能致命的状况至关重要。本文通过引入一种新颖的生成框架CaDRE来应对这一挑战，该框架专门设计用于利用真实轨迹生成多样化且可控的安全关键场景。我们的方法通过采用一种独特的公式和算法，整合真实数据、领域知识与黑盒优化技术，同时优化场景的质量与多样性。我们通过在三种代表性交通场景类型中进行的广泛测试，验证了该框架的有效性。结果表明，与现有的强化学习和基于采样的方法相比，该方法能够以更高的样本效率生成多样化且高质量的场景。