Autonomous systems, such as self-driving vehicles, quadrupeds, and robot manipulators, are largely enabled by the rapid development of artificial intelligence. However, such systems involve several trustworthy challenges such as safety, robustness, and generalization, due to their deployment in open-ended and real-time environments. To evaluate and improve trustworthiness, simulations or so-called digital twins are largely utilized for system development with low cost and high efficiency. One important thing in virtual simulations is scenarios that consist of static and dynamic objects, specific tasks, and evaluation metrics. However, designing diverse, realistic, and effective scenarios is still a challenging problem. One straightforward way is creating scenarios through human design, which is time-consuming and limited by the experience of experts. Another method commonly used in self-driving areas is log replay. This method collects scenario data in the real world and then replays it in simulations or adds random perturbations. Although the replay scenarios are realistic, most of the collected scenarios are redundant since they are all ordinary scenarios that only consider a small portion of critical cases. The desired scenarios should cover all cases in the real world, especially rare but critical events with extremely low probability. Critical scenarios are rare but important to test autonomous systems under risky conditions and unpredictable perturbations, which reveal their trustworthiness.

翻译：自主系统，例如自动驾驶车辆、四足机器人和机械臂，主要得益于人工智能的快速发展。然而，由于此类系统部署于开放、实时的环境中，它们面临着安全性、鲁棒性和泛化能力等多个可信挑战。为评估和提升可信性，仿真（即所谓的数字孪生）因其低成本和高效率而被广泛应用于系统开发。虚拟仿真中的一个关键要素是场景，其包含静态与动态物体、特定任务及评估指标。然而，设计多样化、真实且有效的场景仍是一个难题。一种直接的方法是通过人工设计创建场景，但这种方式耗时且受限于专家经验。另一种常用于自动驾驶领域的方法是日志回放，即采集真实世界的场景数据，然后在仿真中回放或添加随机扰动。尽管回放场景真实，但绝大多数采集到的场景是冗余的，因为它们均为普通场景，仅覆盖了极小部分关键情况。理想的场景应涵盖真实世界中的所有情况，尤其是那些概率极低但至关重要的罕见事件。关键场景虽罕见，但对于在危险条件和不可预测扰动下测试自主系统至关重要，能够揭示其可信性。