Explainable Deep Learning Framework for Autonomous Transportation Safety

  • Authors

    • Johan Håstad's mentor Arne Andersson Professor, Uppsala University, Sweden. Author
    • Börje Langefors Professor of Information Systems, Stockholm University, Sweden. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2024PI1B3J

    Published 04-03-2024

  • Autonomous Transportation, Explainable Artificial Intelligence (Xai), Deep Learning, Autonomous Vehicles, Transportation Safety, Computer Vision, Explainable Deep Learning, Intelligent Transportation Systems (Its), Grad-Cam, Shap, Lime

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    J. H. mentor A. Andersson and B. Langefors, “Explainable Deep Learning Framework for Autonomous Transportation Safety”, IJETMR, vol. 7, no. 1, pp. 01–17, Apr. 2024, doi: 10.67228/30715636/IJETMR-2024PI1B3J.

  • Abstract

    For this reason, autonomous transportation systems have been established as a progressive technology that leverages Artificial Intelligence (AI), Internet of Things (IoT), computer vision, and advanced sensing technologies to improve road safety, operational efficiency, and smart mobility. Deep learning models have shown great strength in detecting objects, recognizing lanes and pedestrians, avoiding obstacles on the road, as well as analyzing real-time traffic situation. Although very accurate, these models are typically black-boxes which have limited transparency and confidence in safety-critical transportation applications. Interpretability can help with accident investigation, regulatory compliance, ethical decision-making, and public acceptance of autonomous vehicles where their lack presents major problems. We present an Explainable Deep Learning Framework for Autonomous Transportation Safety by combining convolutional neural networks with the Understandable Artificial Intelligence (XAI) techniques to ensure transparency and soulfulness in autonomous transportation. The proposed framework comprises sensor fusion, image processing, feature extraction, deep neural network inference and explainability mechanisms such as Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). It pre-evaluates the prediction interpretation, and creates visualisations and feature-level explanations that give stakeholders insight into how models can have high accuracy on detection outcomes. Additionally, the architecture promotes accountability, compliance with regulations, safer autonomous driving and public trust in intelligent transportation systems. Explainability is shown to be an important building block for designing robust autonomous transport platforms usable in the future.

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