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FaultGene: LLM-Assisted Fault-Augmented Test Generation for Hardware-in-the-Loop Validation of Autonomous Driving Systems

Chaoran Yuan, Fadi J Kurdahi · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29969 · cited by 0 works (OpenAlex) · open-access PDF

Korean

자율주행 사이버 물리적 시스템의 신뢰할 수 있는 컨텍스트 인증은 이름적 운전 행동뿐만 아니라 감지 및 내장 컴퓨팅의 결함으로 인한 실패를 테스트하는 것이 필요합니다. 물리적 도로 테스트는 현실적인 시스템 행동을 제공하지만 비용이 많이 들고 복제하기 어렵지만 시뮬레이션은 확장 가능한 평가를 가능하게하지만 완전히 하드웨어에 의존하는 운전 행동과 결함 확산을 캡처할 수 없습니다. 하드웨어-in-the-loop (HIL) 검증은 시뮬레이션 환경 내에서 실제 삽입 된 하드웨어에 드라이브 스택을 실행함으로써이 격차를 제거합니다. 그러나 HIL 테스트에 대한 결함 증가 시나리오를 준비하는 것은 HIL 시나리오

Machine translation (Meta M2M100); the English original follows.

Abstract

Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation. Physical road testing provides realistic system behavior but is costly and difficult to reproduce, while simulation enables scalable evaluation but cannot fully capture hardware-dependent behavior and fault propagation. Hardware-in-the-loop (HIL) validation bridges this gap by executing the driving stack on real embedded hardware within a simulated environment. However, preparing fault-augmented scenarios for HIL testing remains largely a manual process, limiting test coverage, scalability, and efficiency. We present FaultGene, an LLM-assisted framework for automated generation of executable fault-augmented tests for HIL fault-resilience validation. Given a baseline Scenic/CARLA scenario and a structured fault specification, FaultGene generates a fault-augmented test case that captures the effects of HIL-level fault injection. During offline refinement, semantic-consistency checks and execution-level feedback are used to correct candidates that fail to preserve the intended scenario context or produce the desired fault-induced degradation, and to construct high-quality supervision for fine-tuning. The resulting generator then produces fault-augmented tests directly at deployment time without requiring manually engineered reference executions or repeated refinement loops.

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Topics: Executable, Computer science, Scalability, Fault injection, Context (archaeology), Reliability engineering

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