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Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning

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Mobile Web and Intelligent Information Systems (MobiWIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12814))

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Abstract

Accurate object detection (e.g., buildings, vehicles, road signs and pedestrians) is essential to the success of the idea of autonomous and self-driving cars. Various object detection techniques have been proposed to enable Autonomous Vehicles (AVs) to achieve reliable safe driving. Most of these techniques are adequate for normal weather conditions, such as sunny or overcast days, but their effectiveness drops when they are exposed to inclement weather conditions, such as days with heavy snowfall or foggy days. In this paper, we propose an object detection system over AVs that capitalizes on the You Only Look Once (YOLO) emerging convolutional neural network (CNN) approach, together with a Federated Learning (FL) framework with the aim of improving the detection accuracy in adverse weather circumstances in real-time. We validate our system on the Canadian Adverse Driving Conditions (CADC) dataset. Experiments show that our solution achieves better performance than traditional solutions (i.e. Gossip decentralized model, and Centralized model).

Supported by NSERC and Innovation for Defence Excellence and Security (IDEaS), The Department of National Defence, Canada.

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Notes

  1. 1.

    https://cadcd.uwaterloo.ca.

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Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada

    Gaith Rjoub, Jamal Bentahar & Ahmed Saleh Bataineh

  2. Department of Computer Science and Engineering, Université du Québec en Outaouais, Quebec, Canada

    Omar Abdel Wahab

Authors
  1. Gaith Rjoub
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  2. Omar Abdel Wahab
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  3. Jamal Bentahar
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  4. Ahmed Saleh Bataineh
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Corresponding author

Correspondence to Jamal Bentahar .

Editor information

Editors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Jamal Bentahar

  2. University of Bradford, Bradford, UK

    Irfan Awan

  3. Oxford Brookes University, Oxford, UK

    Muhammad Younas

  4. Kristiania University College, Oslo, Norway

    Tor-Morten Grønli

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Rjoub, G., Wahab, O.A., Bentahar, J., Bataineh, A.S. (2021). Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning. In: Bentahar, J., Awan, I., Younas, M., Grønli, TM. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2021. Lecture Notes in Computer Science(), vol 12814. Springer, Cham. https://doi.org/10.1007/978-3-030-83164-6_10

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  • DOI: https://doi.org/10.1007/978-3-030-83164-6_10

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