为高阶自动驾驶设计安全架构，保障AI系统可靠运行
Key Safety Design Overview in AI-driven Autonomous Vehicles
- 构建支持ASIL D级安全的软硬件一体化设计框架
- 提出AI失效应对策略与全生命周期可靠性保障机制
- 适合智能驾驶系统安全研发人员参考
随着高级别自动驾驶（SAE L3/L4）中人工智能软件的广泛应用，其带来的复杂技术挑战要求具备高水平的功能安全与稳健的软件设计。本文探讨了面向汽车软硬件的安全架构与系统性方法，涵盖最高安全完整性等级ASIL D的故障降级处理、人工智能与机器学习在汽车安全体系中的集成。针对日益增长的AI驱动型汽车软件所面临独特挑战，提出了多种缓解策略与安全失效分析方法，以确保汽车软件的安全性与可靠性，并阐明了人工智能在数据生命周期各阶段对软件可靠性的作用。研究覆盖先进驾驶辅助系统（ADAS）应用场景及电子控制单元（ECU）等核心组件。
原文摘要 · Abstract (English)
With the increasing presence of autonomous SAE level 3 and level 4, which incorporate artificial intelligence software, along with the complex technical challenges they present, it is essential to maintain a high level of functional safety and robust software design. This paper explores the necessary safety architecture and systematic approach for automotive software and hardware, including fail soft handling of automotive safety integrity level (ASIL) D (highest level of safety integrity), integration of artificial intelligence (AI), and machine learning (ML) in automotive safety architecture. By addressing the unique challenges presented by increasing AI-based automotive software, we proposed various techniques, such as mitigation strategies and safety failure analysis, to ensure the safety and reliability of automotive software, as well as the role of AI in software reliability throughout the data lifecycle. Index Terms Safety Design, Automotive Software, Performance Evaluation, Advanced Driver Assistance Systems (ADAS) Applications, Automotive Software Systems, Electronic Control Units.
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。