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An AI-Driven System for Identifying Dangerous Driving Vehicles

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Advanced Information Networking and Applications (AINA 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 204))

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Abstract

As the world faces aging population, traffic accidents caused by elderly drivers have become a social issue. The decline in driving skills and judgment can lead to deviation of vehicles from their lanes while they are in motion resulting in accidents. In this paper, we propose an intelligent safety drive assisting system that identifies dangerous vehicles. The proposed system has an AI application that identifies vehicles engaging in risky driving and notifies both the control center and the driver. We present the detection performance of four different datasets. From the evaluation results, we observed that datasets based on remote-controlled cars and actual vehicle images reduced misrecognition rate and improved detection accuracy.

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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 & Naoki Tanaka

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

    Kazuki Shimomura, Makoto Ikeda & Leonard Barolli

Authors
  1. Hibiki Tanaka
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  2. Kazuki Shimomura
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  3. Naoki Tanaka
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  4. Makoto Ikeda
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  5. 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., Shimomura, K., Tanaka, N., Ikeda, M., Barolli, L. (2024). An AI-Driven System for Identifying Dangerous Driving Vehicles. In: Barolli, L. (eds) Advanced Information Networking and Applications. AINA 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 204. Springer, Cham. https://doi.org/10.1007/978-3-031-57942-4_38

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