RISC-V Functional Safety for Autonomous Automotive Systems: An Analytical Framework and Research Roadmap for ML-Assisted Certification

TL;DR

This paper presents an analytical framework and research roadmap for achieving functional safety certification of RISC-V based autonomous automotive systems, emphasizing certification economics and ML-assisted methods.

Contribution

It introduces a comprehensive framework and research roadmap focusing on certification economics and ML techniques to support safety certification of RISC-V in autonomous vehicles.

Findings

RISC-V can meet ISO 26262 ASIL-D safety standards for automotive systems.

ML methods like LLM-assisted FMEDA and knowledge-graph safety case automation can aid certification.

The paper emphasizes certification economics over hardware speed improvements.

Abstract

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.