Sensor-Level Fault Diagnosis for Automotive Software Validation Using Large Language Models

2026-08-06

AI Summary

This study addresses the challenge of root-cause localization in automotive software testing, where high-dimensional sensor data generated during hardware-in-the-loop (HIL) simulations render traditional threshold-based methods ineffective. Existing data-driven approaches often require extensive labeled data and lack interpretability, failing to meet ISO 26262 traceability requirements. To overcome these limitations, the authors propose a two-stage diagnostic framework: first, safety requirements are automatically verified on a dSPACE real-time platform to filter anomalous test records; then, sliding windows of sensor signals are abstracted into statistical, relational, and contextual descriptors, which are fed as fixed prompts to an open-source large language model fine-tuned with 4-bit low-rank adaptation (LoRA). Evaluated on six fault types injected into a gasoline engine, the approach achieves 81.6% accuracy with a minimal 2B-parameter model—comparable to larger models—while operating entirely on a single consumer-grade GPU. This work pioneers the use of instruction-tuned large language models for sensor-level automotive fault diagnosis, demonstrating that diagnostic performance hinges more on task-specific adaptation convergence than on model scale, thereby achieving high accuracy, data efficiency, and explainable decision-making.

Abstract

The pre-series validation of automotive software on hardware-in-the-loop (HIL) platforms produces large volumes of multivariate sensor recordings whose assessment against functional safety requirements exceeds what manual review can sustain at campaign scale. Threshold-based tooling reports that a deviation has occurred but neither identifies its nature nor locates its source, while data-driven classifiers, although accurate, rely on large labelled datasets and return opaque decisions that sit uneasily with the traceability demanded by ISO 26262. This study examines whether open-source instruction-tuned large language models (LLMs), given a textual description of sensor behaviour, can serve as data-efficient and interpretable engines for fault detection and diagnosis inside the validation loop. A two-phase framework is proposed: automated requirement checking on a dSPACE real-time platform first isolates the recordings that violate a safety requirement, and only these are inspected, with sliding windows of the signals reduced to statistical, relational, and contextual descriptors, embedded in a fixed prompt, and mapped by a 4-bit low-rank-adapted LLM to a fault location accompanied by a written justification. Four model families ranging from two to eight billion parameters were adapted and tested on a gasoline-engine case study spanning six injected fault classes. The smallest model matched the largest at 81.6\% accuracy, whereas a comparably sized model failed to converge, indicating that diagnostic competence under task-specific adaptation follows convergence rather than parameter count, with the entire adapt-and-evaluate cycle fitting on a single commodity accelerator.

Problem

• sensor-level fault diagnosis
• automotive software validation
• hardware-in-the-loop
• functional safety
• ISO 26262

Innovation

• large language models
• fault diagnosis
• hardware-in-the-loop
• low-rank adaptation
• automotive software validation