提升自动驾驶残余风险量化验证效率，优化实路测试方法。
Towards more efficient quantitative safety validation of residual risk for assisted and automated driving
- 基于ISO 21448标准构建通用与基础模型，明确安全验证框架。
- 评估多种减量方法在可量化性、有效性等方面表现，发现普遍存漏洞。
- 强调实路测试仍不可替代，适合自动驾驶安全验证研究者参考。
高级驾驶辅助系统（ADAS）和自动驾驶系统（ADS）的安全验证亟需高效可靠的残余风险量化方法，符合国际标准ISO 21448。传统上，实地运行测试（FOT）是实现至SAE 2级自动化功能宏观安全验证的核心手段。然而，现有基于FOT的实证安全演示方法常导致不切实际的测试负担，尤其在更高自动化层级下更为显著。即使在低层级，高昂成本也促使探索提升FOT效率的途径。本文系统识别并评估了当前FOT减量方法（RAs），包括文献中报道的新方法。基于ISO 21448分析，推导出一个通用模型以捕获标准论证要素，并建立一个基础模型，以自动紧急制动（AEB）系统为例，确立真实道路环境中残余风险定量安全验证（QSVRR）的基准要求。随后，依据可量化性、有效性威胁、遗漏环节和黑箱兼容性四项标准评估各方法，揭示其潜在优势与固有局限，指出关键研究方向。评估表明，尽管部分方法具潜力，但均存在缺失环节或重大缺陷，且无一能完全替代FOT，凸显其在ADAS/ADS安全验证中的核心地位。
原文摘要 · Abstract (English)
The safety validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) increasingly demands efficient and reliable methods to quantify residual risk while adhering to international standards such as ISO 21448. Traditionally, Field Operational Testing (FOT) has been pivotal for macroscopic safety validation of automotive driving functions up to SAE automation level 2. However, state-of-the-art derivations for empirical safety demonstrations using FOT often result in impractical testing efforts, particularly at higher automation levels. Even at lower automation levels, this limitation - coupled with the substantial costs associated with FOT - motivates the exploration of approaches to enhance the efficiency of FOT-based macroscopic safety validation. Therefore, this publication systematically identifies and evaluates state-of-the-art Reduction Approaches (RAs) for FOT, including novel methods reported in the literature. Based on an analysis of ISO 21448, two models are derived: a generic model capturing the argumentation components of the standard, and a base model, exemplarily applied to Automatic Emergency Braking (AEB) systems, establishing a baseline for the real-world driving requirement for a Quantitative Safety Validation of Residual Risk (QSVRR). Subsequently, the RAs are assessed using four criteria: quantifiability, threats to validity, missing links, and black box compatibility, highlighting potential benefits, inherent limitations, and identifying key areas for further research. Our evaluation reveals that, while several approaches offer potential, none are free from missing links or other substantial shortcomings. Moreover, no identified alternative can fully replace FOT, reflecting its crucial role in the safety validation of ADAS and ADS.
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