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

TL;DR

This paper proposes an explainable testing methodology for autonomous driving systems that integrates transparency and interpretability into validation and verification processes, aiming to improve diagnostic efficiency and user trust.

Contribution

It introduces a novel framework combining LLM-generated explainable test scenarios, real-time validation, and stakeholder-informed requirements for enhanced V&V of ADS.

Findings

Framework includes test oracle, explanation generation, and test chatbot

Empirical studies planned to evaluate diagnostic improvements

Aims to streamline V&V and increase transparency

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.