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Explainable Artificial Intelligence in Autonomous Vehicles: Methodologies, Challenges, and Prospective Directions
Subject area: Science,Engineering and Technology · Area of research: Explainable Artificial Intelligence
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
How to cite this paper
@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},
}