用概率网格法验证自动驾驶车辆在复杂环境下的安全风险。
Probabilistic Safety Verification for an Autonomous Ground Vehicle: A Situation Coverage Grid Approach
- 构建环境情景网格，系统化覆盖车辆运行条件
- 通过测试数据建模概率转移，量化异常行为风险
- 支持法规合规，适合高安全要求的自动驾驶系统
随着工业级自动驾驶地面车辆在高安全要求场景中日益普及，确保其在多种条件下安全运行至关重要。本文提出一种基于系统化情景提取、概率建模与验证的新方法。构建情景覆盖网格，全面枚举影响车辆运行的环境配置；结合基于情景的系统测试获取的定量概率数据，补充网格以捕捉情景间的概率转移。进而生成编码正常与非正常系统行为动态的概率模型，并利用时序逻辑形式化的安全属性，通过概率模型检验进行验证。结果表明，该方法能有效识别高风险情景，提供可量化的安全保证，支持符合监管标准，有助于提升自动驾驶系统的可靠部署。
原文摘要 · Abstract (English)
As industrial autonomous ground vehicles are increasingly deployed in safety-critical environments, ensuring their safe operation under diverse conditions is paramount. This paper presents a novel approach for their safety verification based on systematic situation extraction, probabilistic modelling and verification. We build upon the concept of a situation coverage grid, which exhaustively enumerates environmental configurations relevant to the vehicle's operation. This grid is augmented with quantitative probabilistic data collected from situation-based system testing, capturing probabilistic transitions between situations. We then generate a probabilistic model that encodes the dynamics of both normal and unsafe system behaviour. Safety properties extracted from hazard analysis and formalised in temporal logic are verified through probabilistic model checking against this model. The results demonstrate that our approach effectively identifies high-risk situations, provides quantitative safety guarantees, and supports compliance with regulatory standards, thereby contributing to the robust deployment of autonomous systems.
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