A Conceptual Framework for Safety and Decision Justification in Autonomous Vehicles Using Explainable AI

Abstract

Autonomous Vehicles (AVs) have the potential to reduce road accidents and improve transportation efficiency significantly. However, safety concerns and the lack of transparency in decision-making remain major barriers to their widespread adoption. Modern AV systems rely heavily on complex Artificial Intelligence (AI) and Deep Learning models, which often function as black boxes, making it difficult to understand or justify their actions. This paper explores the critical role of safety mechanisms and decision justification in autonomous driving systems. We discuss the AV decision pipeline, identify safety challenges, and highlight the importance of Explainable Artificial Intelligence (XAI) techniques in improving trust, accountability, and regulatory compliance. The paper concludes by outlining open research challenges and future directions for safer and more transparent autonomous driving systems.