Using Explainable Artificial Intelligence (X-AI) To Address The “Black-Box” Dilemma in Autonomous Driving

Abstract

Deep learning-based autonomous vehicles (AVs) demonstrate significant potential in reducing traffic accident rates and enhancing transportation efficiency. However, the “black-box” nature of artificial intelligence systems such as deep neural networks (DNNs) has raised widespread concerns regarding the explainability, transparency, and safety of their decision-making processes. This paper focuses on the application of explainable artificial intelligence (XAI) in the autonomous driving domain as an effective approach to address current technological challenges. The paper first reviews two fundamental architectures of autonomous driving systems and provides a basic overview of X-AI technologies, explaining their significance within these systems. It then systematically outlines the classification framework of X-AI and its critical importance in high-risk domains. Through analysis of multiple cutting-edge research frameworks—including the SafeX framework for modular architectures, the XAI integration framework for end-to-end systems, and the XAI-ADS system for cybersecurity— This paper delves into how X-AI enhances the safety, regulatory compliance, and user trust of autonomous driving systems. Finally, it outlines future development directions for X-AI in autonomous driving technology and proposes research recommendations for building explainable, safe, and responsible autonomous driving systems.