Explainable Artificial Intelligence (Xai) In Self-Driving Cars: A Review Of Techniques For Transparent Perception, Prediction and Decision-Making

Self-driving cars use cameras, radar, LiDAR and deep learning to detect objects, recognize traffic signs, identify pedestrians and make driving decisions without direct human control. Most of these models behave as black boxes, making it difficult to understand why a vehicle braked, changed lanes or failed to detect an obstacle. This lack of transparency creates problems for safety assurance, debugging, user trust, accountability and regulation. Explainable Artificial Intelligence (XAI) addresses these problems by providing understandable reasons for AI decisions. This paper reviews XAI for autonomous driving across the perception, prediction and planning stages. It examines post-hoc methods such as LIME, SHAP and gradient-based saliency maps, visual attention and VisualBackProp for end-to-end steering models, textual explanations that describe driving actions in natural language, object-induced action explanations, and inherently interpretable designs. Stakeholder needs of developers, passengers, safety assessors and regulators are analysed. A layered XAI-enabled autonomous driving architecture is proposed, and research gaps, objectives and a comparison of explanation techniques are presented. The review concludes that integrating explainability throughout the driving stack, and evaluating explanations for faithfulness and usefulness, is essential for safe, reliable and trustworthy autonomous transportation.