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

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@article{1709937,
      author = {Raphael Ugboko, Oluwafemi Oloruntoba},
      title = {Explainable Artificial Intelligence in Autonomous Vehicles: Methodologies, Challenges, and Prospective Directions},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {1578-1593},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709937.pdf},
      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},
      month = {April},
  }