用生成式AI提升自动驾驶测试效率与多样性
Generative AI for Testing of Autonomous Driving Systems: A Survey
- 基于生成式AI构建多样化驾驶场景，增强测试覆盖
- 系统梳理91项研究，归纳六类核心应用场景
- 适合自动驾驶安全验证与测试工具研发者参考
自动驾驶系统（ADS）是当前研究热点，有望为社会带来显著效益。但在大规模公开道路部署前，需在多样驾驶条件下进行充分测试以验证其功能与安全性。为此，亟需高效、有效的测试方法，而现有方法仍面临挑战。近年来，生成式AI因其上下文理解、复杂任务推理和多样化输出生成能力，被广泛应用于自动驾驶测试。本文系统分析了91篇相关研究，将其成果归纳为六大应用类别，主要聚焦于基于场景的测试。同时，综述了所用的数据集、仿真工具、自动驾驶系统、评估指标与基准测试，并识别出27项现存局限。本综述为生成式AI在自动驾驶测试中的应用提供了全面概述与实践洞察，揭示当前挑战并指明未来研究方向。
原文摘要 · Abstract (English)
Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on public roads, extensive testing is necessary to validate their functionality and safety under diverse driving conditions. Therefore, different testing approaches are required, and achieving effective and efficient testing of ADS remains an open challenge. Recently, generative AI has emerged as a powerful tool across many domains, and it is increasingly being applied to ADS testing due to its ability to interpret context, reason about complex tasks, and generate diverse outputs. To gain a deeper understanding of its role in ADS testing, we systematically analyzed 91 relevant studies and synthesized their findings into six major application categories, primarily centered on scenario-based testing of ADS. We also reviewed their effectiveness and compiled a wide range of datasets, simulators, ADS, metrics, and benchmarks used for evaluation, while identifying 27 limitations. This survey provides an overview and practical insights into the use of generative AI for testing ADS, highlights existing challenges, and outlines directions for future research in this rapidly evolving field.
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