@article{ref1,
title="An AI-driven system for identifying dangerous driving vehicles",
journal="Lecture notes on data engineering and communications technologies",
year="2024",
author="Tanaka, Hibiki and Shimomura, Kazuki and Tanaka, Naoki and Ikeda, Makoto and Barolli, Leonard and Barolli, Leonard",
volume="AINA 2024",
number="",
pages="395-400",
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.<p /> <p>Language: en</p>",
language="en",
issn="2367-4512",
doi="10.1007/978-3-031-57942-4_38",
url="http://dx.doi.org/10.1007/978-3-031-57942-4_38"
}