宝马首个L3级自动驾驶系统安全框架，系统性降低风险至可接受水平。
Safety integrity framework for automated driving
- 融合多领域方法，系统识别并量化危险场景不确定性
- 通过随机模拟与敏感性分析，评估系统残余风险
- 为自动驾驶系统开发提供透明可靠的全流程安全保障
本文描述了支撑宝马首款SAE Level 3自动驾驶系统开发、发布流程及监管审批的全面安全框架。该框架创新性地整合了系统工程、工程风险分析、贝叶斯数据分析、实验设计与统计学习等领域的定性与定量方法。通过系统识别和量化与危害场景相关的不确定性，并基于设计实验、实车数据与专家知识构建冗余系统，将硬件、软件故障、性能局限及规范不足带来的风险降至可接受水平，实现正向风险平衡。系统残余风险通过随机模拟估算，并经敏感性分析评估。该框架融入V模型，统一并补充现有汽车安全标准，为自动驾驶系统的开发与部署提供全面、严谨且透明的安全保障流程。
原文摘要 · Abstract (English)
This paper describes the comprehensive safety framework that underpinned the development, release process, and regulatory approval of BMW's first SAE Level 3 Automated Driving System. The framework combines established qualitative and quantitative methods from the fields of Systems Engineering, Engineering Risk Analysis, Bayesian Data Analysis, Design of Experiments, and Statistical Learning in a novel manner. The approach systematically minimizes the risks associated with hardware and software faults, performance limitations, and insufficient specifications to an acceptable level that achieves a Positive Risk Balance. At the core of the framework is the systematic identification and quantification of uncertainties associated with hazard scenarios and the redundantly designed system based on designed experiments, field data, and expert knowledge. The residual risk of the system is then estimated through Stochastic Simulation and evaluated by Sensitivity Analysis. By integrating these advanced analytical techniques into the V-Model, the framework fulfills, unifies, and complements existing automotive safety standards. It therefore provides a comprehensive, rigorous, and transparent safety assurance process for the development and deployment of Automated Driving Systems.
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