用新标准评估电动车AI电池电量估算的安全性
AI Safety Assurance in Electric Vehicles: A Case Study on AI-Driven SOC Estimation
- 结合ISO 26262与ISO/PAS 8800，构建AI安全独立评估框架
- 通过故障注入测试验证AI组件对传感器扰动的鲁棒性
- 为汽车AI功能安全提供可落地的评估方法，适合车企研发人员
将人工智能技术融入电动汽车带来独特的安全保证挑战，尤其在遵循汽车领域功能安全标准ISO 26262的背景下。传统评估方法难以适用于AI功能，亟需更新标准与实践。本文探讨了在结合ISO 26262与最新发布的ISO/PAS 8800（涵盖道路车辆人工智能安全）的前提下，如何对电动汽车中的AI组件进行独立评估。以基于AI的电池荷电状态（SOC）估计算法为例，识别出该扩展评估方法的关键特征。评估过程中，通过故障注入实验系统性地引入扰动传感器输入，检验该组件对输入变化的抗干扰能力。
原文摘要 · Abstract (English)
Integrating Artificial Intelligence (AI) technology in electric vehicles (EV) introduces unique challenges for safety assurance, particularly within the framework of ISO 26262, which governs functional safety in the automotive domain. Traditional assessment methodologies are not geared toward evaluating AI-based functions and require evolving standards and practices. This paper explores how an independent assessment of an AI component in an EV can be achieved when combining ISO 26262 with the recently released ISO/PAS 8800, whose scope is AI safety for road vehicles. The AI-driven State of Charge (SOC) battery estimation exemplifies the process. Key features relevant to the independent assessment of this extended evaluation approach are identified. As part of the evaluation, robustness testing of the AI component is conducted using fault injection experiments, wherein perturbed sensor inputs are systematically introduced to assess the component's resilience to input variance.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。