Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems

• URL: http://arxiv.org/abs/2506.16876v1
• Date: Fri, 20 Jun 2025 09:55:56 GMT
• Title: Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
• Authors: Halit Eris, Stefan Wagner,
• Abstract summary: This vision paper presents a methodology that integrates explainability, transparency, and interpretability into validation and verification processes.<n>We propose refining V&V requirements through literature reviews and stakeholder input, generating explainable test scenarios via large language models (LLMs), and enabling real-time validation in simulation environments.<n>Our goal is to streamline V&V, reduce resources, and build user trust in autonomous technologies.
• Score: 2.7143159361691227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. We propose refining V&V requirements through literature reviews and stakeholder input, generating explainable test scenarios via large language models (LLMs), and enabling real-time validation in simulation environments. Our framework includes test oracle, explanation generation, and a test chatbot, with empirical studies planned to evaluate improvements in diagnostic efficiency and transparency. Our goal is to streamline V&V, reduce resources, and build user trust in autonomous technologies.

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