Explainable Artificial Intelligence in Autonomous Vehicles: Methodologies, Challenges, and Prospective Directions

• Author(s): Raphael Ugboko ; Oluwafemi Oloruntoba
• Paper ID: 1709937
• Page: 1578-1593
• Published Date: 06-08-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 10 April-2025

Abstract

The increasing complexity of autonomous vehicle (AV) decision-making systems driven by deep learning and black-box models has intensified the need for explainable artificial intelligence (XAI). This paper explores the integration of XAI within AV systems, focusing on methodologies that enhance interpretability without compromising real-time performance and safety. We provide a structured taxonomy of XAI approaches, comparing post-hoc techniques such as LIME and SHAP with inherently interpretable models like decision trees and linear classifiers. The paper also investigates causal reasoning, human-machine trust, ethical concerns, and regulatory implications. Through analysis of current challenges and emerging solutions including inherently interpretable neural networks and standardized XAI benchmarks we offer a roadmap for future research. Our findings underscore the critical role of XAI in fostering trust, accountability, and safe deployment of autonomous systems.

Keywords

Explainable Artificial Intelligence (XAI), Autonomous Vehicles (AVs), Model Interpretability, Human-AI Trust, Safety-Critical AI Systems

Citations

IRE Journals:
Raphael Ugboko , Oluwafemi Oloruntoba "Explainable Artificial Intelligence in Autonomous Vehicles: Methodologies, Challenges, and Prospective Directions" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 1578-1593

IEEE:
Raphael Ugboko , Oluwafemi Oloruntoba "Explainable Artificial Intelligence in Autonomous Vehicles: Methodologies, Challenges, and Prospective Directions" Iconic Research And Engineering Journals, 8(10)