International Conference on Computer and Knowledge Engineering

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XAI for Transparent Autonomous Vehicles: A New Approach to Understanding Decision-Making in Self-driving Cars

Authors :

Maryam Sadat Hosseini Azad¹ Amir Abbas Hamidi Imani² Shahriar Baradaran Shokouhi³

1- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran 2- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran 3- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Keywords :

XAI،Explainable Deep Driving،Natural Language Explanation،Autonomous Vehicles،Attention Mechanism

Abstract :

While numerous advancements have been achieved in deep learning-based autonomous driving systems, the lack of transparency, interpretability, and user acceptance remains a significant challenge. Researchers argue that without the ability to explain decision-making behavior, deep learning models cannot be practically implemented in various real-world scenarios. This is vital in decision-making networks, since inappropriate outputs could lead to severe traffic accidents. To address this problem, we propose an innovative approach that integrates the Convolutional Block Attention Module with Deep Connected Attention (CBAM-DCA) with a state-of-the-art (SOTA) decision maker and textual explainer model. This integration results in a more precise and comprehensive explainable system. To quantitatively evaluate the performance of our model, we used the standard F1 score and applied it to the BDD object-induced action (BDD-OIA) dataset. Our proposed technique outperforms the current SOTA model and demonstrates significant improvement in explainability. This research advances future explainable autonomous vehicles and contributes to creating more transparent and trustworthy self-driving systems.