Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms. One critical challenge for their massive deployment in the real world is their safety evaluation. Most existing driving systems are still trained and evaluated on naturalistic scenarios collected from daily life or heuristically-generated adversarial ones. However, the large population of cars, in general, leads to an extremely low collision rate, indicating that the safety-critical scenarios are rare in the collected real-world data. Thus, methods to artificially generate scenarios become crucial to measure the risk and reduce the cost. In this survey, we focus on the algorithms of safety-critical scenario generation in autonomous driving. We first provide a comprehensive taxonomy of existing algorithms by dividing them into three categories: data-driven generation, adversarial generation, and knowledge-based generation. Then, we discuss useful tools for scenario generation, including simulation platforms and packages. Finally, we extend our discussion to five main challenges of current works -- fidelity, efficiency, diversity, transferability, controllability -- and research opportunities lighted up by these challenges.

翻译：自动驾驶系统近年来凭借机器学习驱动的感知与决策算法取得了显著发展，其大规模部署于真实世界的关键挑战在于安全性评估。现有驾驶系统大多仍基于日常采集的自然场景或启发式生成的对抗场景进行训练与评估。然而，由于汽车总量庞大，碰撞率普遍极低，导致安全关键场景在真实世界数据中极为罕见。因此，人工生成场景的方法对于风险度量与成本降低至关重要。本综述聚焦于自动驾驶中安全关键场景生成算法：首先，通过将现有算法划分为数据驱动生成、对抗生成与基于知识生成三大类别，建立系统性分类体系；其次，讨论场景生成的有用工具（包括仿真平台与工具包）；最后，针对当前工作的五大核心挑战——保真性、效率、多样性、可迁移性与可控性——及其催生的研究机遇展开深入探讨。