RISC-V is emerging as a viable platform for automotive-grade embedded computing, with recent ISO 26262 ASIL-D certifications demonstrating readiness for safety-critical deployment in autonomous driving systems. However, functional safety in automotive systems is fundamentally a certification problem rather than a processor problem. The dominant costs arise from diagnostic coverage analysis, toolchain qualification, fault injection campaigns, safety-case generation, and compliance with ISO 26262, ISO 21448 (SOTIF), and ISO/SAE 21434. This paper analyzes the role of RISC-V in automotive functional safety, focusing on ISA openness, formal verifiability, custom extension control, debug transparency, and vendor-independent qualification. We examine autonomous driving safety requirements and map them to RISC-V architectural challenges such as lockstep execution, safety islands, mixed-criticality isolation, and secure debug. Rather than proposing a single algorithmic breakthrough, we present an analytical framework and research roadmap centered on certification economics as the primary optimization objective. We also discuss how selected ML methods, including LLM-assisted FMEDA generation, knowledge-graph-based safety case automation, reinforcement learning for fault injection, and graph neural networks for diagnostic coverage, can support certification workflows. We argue that the strongest outcome is not a faster core, but an ASIL-D-ready certifiable RISC-V platform.

翻译：RISC-V正成为汽车级嵌入式计算的可行平台，近期获得的ISO 26262 ASIL-D认证表明其已具备在自动驾驶系统中进行安全关键部署的能力。然而，汽车系统中的功能安全本质上是一个认证问题而非处理器问题。主要成本源于诊断覆盖率分析、工具链认证、故障注入测试、安全案例生成以及符合ISO 26262、ISO 21448（SOTIF）和ISO/SAE 21434等标准的要求。本文分析了RISC-V在汽车功能安全中的作用，重点探讨了ISA开放性、形式化可验证性、自定义扩展控制、调试透明性以及供应商无关的认证。我们研究了自动驾驶安全需求并将其映射至RISC-V架构挑战，如锁步执行、安全岛、混合关键性隔离和安全调试。本文并非提出单一算法突破，而是提出一个以认证经济性为主要优化目标的分析框架与研究路线图。我们还讨论了如何选择ML方法（包括基于LLM的FMEDA自动生成、基于知识图谱的安全案例自动化、用于故障注入的强化学习以及用于诊断覆盖的图神经网络）来支持认证工作流。我们认为，最终的最优成果并非更快的核心，而是一个符合ASIL-D要求的可认证RISC-V平台。