arXiv cs.AI· Chaoran Yuan, Fadi Kurdahi·· 13 小时前AI 评分31
FaultGene：LLM 辅助的故障增强测试生成，用于自动驾驶系统 HIL 验证
FaultGene: LLM-Assisted Fault-Augmented Test Generation for Hardware-in-the-Loop Validation of Autonomous Driving Systems
AI 导读
FaultGene 是 LLM 辅助框架，可从基线 Scenic/CARLA 场景和结构化故障规范自动生成可执行的故障增强测试用例，用于 HIL 故障韧性验证。它通过语义一致性检查与执行级反馈进行离线精炼，纠正未保留场景上下文或未产生预期故障退化的候选，并构建微调监督数据。精炼后的生成器在部署时直接产出测试，无需人工构造参考执行或反复迭代。
正文 · 原文
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.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.29969 [cs.SE] |
| (or arXiv:2610.29969v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29969 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Chaoran Yuan [view email]
[v1]
Wed, 21 Oct 2026 16:20:57 UTC (3,348 KB)
来源：arXiv cs.AI · arxiv.org