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An Enhanced AI-Based Vehicular Driver Support System Considering Hyperparameter Optimization

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Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 177))

Abstract

In general, the accidents are caused by declining of driving skills and the lack of attention due to the increasing number of elderly people. With the expansion of infotainment functions, concentration while driving is also hindered. In this paper, we focus on this problem and propose a enhanced intelligent driving support system to detect distracted driving behaviors. In the proposed system, the object detection is computed by YOLOv5m considering hyperparameter optimization. The proposed system can detect multiple distracted driving behaviors by considering driver’s hand movements. From the evaluation results, we found that the proposed system detected the hand manipulating of IVI with 100% accuracy. While, the cell phone usage was detected with a probability over 90%.

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Authors and Affiliations

  1. Graduate School of Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Hibiki Tanaka & Masahiro Miwata

  2. Department of Information and Communication Engineering, Fukuoka Institute of Technology, 3-30-1 Wajiro-Higashi, Higashi-Ku, Fukuoka, 811-0295, Japan

    Makoto Ikeda & Leonard Barolli

Authors
  1. Hibiki Tanaka
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  2. Masahiro Miwata
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  3. Makoto Ikeda
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  4. Leonard Barolli
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Corresponding author

Correspondence to Makoto Ikeda .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

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Tanaka, H., Miwata, M., Ikeda, M., Barolli, L. (2023). An Enhanced AI-Based Vehicular Driver Support System Considering Hyperparameter Optimization. In: Barolli, L. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 177. Springer, Cham. https://doi.org/10.1007/978-3-031-35836-4_1

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