系统梳理自动驾驶极端场景识别生成方法，助力安全提升
Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety
- 从1673篇论文中筛选110篇，分类分析极端场景技术
- 发现现有方法多聚焦单一场景，缺乏跨场景整合能力
- 适合自动驾驶研发、测试及政策制定者参考
近年来自动驾驶技术取得显著进展，但缺乏充分证据证明其实际安全性，易引发公众信任危机，阻碍产业发展与社会应用。为提升自动驾驶安全性，研究提出利用虚拟仿真中的合成数据，尤其是高风险的极端场景（Corner Cases, CCs），以暴露并改进系统缺陷。本文开展系统性文献综述，从1673篇论文中依据标准筛选出110篇，通过多维度分类回答八个相互关联的研究问题，全面分析当前极端场景识别与生成方法。研究指出当前方法存在碎片化、缺乏跨场景协同等问题，并建议产业界、学术界与监管机构建立更紧密的协作机制，推动安全导向的集成式开发。
原文摘要 · Abstract (English)
In recent years, there has been significant development of autonomous vehicle (AV) technologies. However, despite the notable achievements of some industry players, a strong and appealing body of evidence that demonstrate AVs are actually safe is lacky, which could foster public distrust in this technology and further compromise the entire development of this industry, as well as related social impacts. To improve the safety of AVs, several techniques are proposed that use synthetic data in virtual simulation. In particular, the highest risk data, known as corner cases (CCs), are the most valuable for developing and testing AV controls, as they can expose and improve the weaknesses of these autonomous systems. In this context, the present paper presents a systematic literature review aiming to comprehensively analyze methodologies for CC identifi cation and generation, also pointing out current gaps and further implications of synthetic data for AV safety and reliability. Based on a selection criteria, 110 studies were picked from an initial sample of 1673 papers. These selected paper were mapped into multiple categories to answer eight inter-linked research questions. It concludes with the recommendation of a more integrated approach focused on safe development among all stakeholders, with active collaboration between industry, academia and regulatory bodies.
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