An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving

• URL: http://arxiv.org/abs/2605.00291v1
• Date: Thu, 30 Apr 2026 23:27:39 GMT
• Title: An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
• Abstract summary: We propose a multi-scale attention-based model in which driving decisions are fed into the reasoning component to provide case-specific explanations for each decision simultaneously.<n>The results demonstrate the superiority of our reasoning network over the classic and state-of-the-art models.
• Score: 0.6662501454516897
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of computer vision is gradually increasing across various domains. They employ deep learning models with a black-box nature. Without the ability to explain the behavior of neural networks, especially their decision-making processes, it is not possible to recognize their efficiency, predict system failures, or effectively implement them in real-world applications. Due to the inevitable use of deep learning in fully automated driving systems, many methods have been proposed to explain their behavior; however, they suffer from flawed reasoning and unreliable metrics, which have prevented a comprehensive understanding of complex models in autonomous vehicles and hindered the development of truly reliable systems. In this study, we propose a multi-scale attention-based model in which driving decisions are fed into the reasoning component to provide case-specific explanations for each decision simultaneously. For quantitative evaluation of our model's performance, we employ the F1-score metric, and also proposed a new metric called the Joint F1 score to demonstrate the accurate and reliable performance of the model in terms of Explainable Artificial Intelligence (XAI). In addition to the BDD-OIA dataset, the nu-AR dataset is utilized to further validate the generalization capability and robustness of the proposed network. The results demonstrate the superiority of our reasoning network over the classic and state-of-the-art models.

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