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Influence of Weather Features in Determining Sudden Braking

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Abstract

Understanding conditions and situations causing abnormal driving behaviors like sudden braking or sudden acceleration is important for preventing traffic accidents. Previous studies have used probe vehicle data to detect risky situations where sudden braking frequently occurred. However, they have mainly focused on location and vehicle-related factors. In this paper, we build models which discriminate sudden braking using a machine learning method. The models use weather-related information as well as probe data. To investigate how weather-related factors help to determine sudden braking, we conducted extensive experiments using probe data obtained from dashboard cameras and two types of weather-related information obtained from meteorological observatories (MO) and AMeDAS. Experimental results illustrate that using weather-related information improves performance in determining sudden braking and that the temporally and spatially denser characteristics of weather-related factors from AMeDAS help to compensate for insufficiencies in the model with MO data.

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Notes

  1. http://www.data.jma.go.jp/gmd/risk/obsdl/index.php

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Acknowledgments

This work is partly supported by JSPS KAKENHI Grant Numbers JP19KK0257 and JP20H01728.

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Authors and Affiliations

  1. Department of Advanced Information Technology, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Yuta Sato & Takuya Kawatani

  2. Department of Advanced Information Technology, Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan

    Tsunenori Mine

Authors
  1. Yuta Sato
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  2. Takuya Kawatani
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  3. Tsunenori Mine
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Correspondence to Yuta Sato.

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Sato, Y., Kawatani, T. & Mine, T. Influence of Weather Features in Determining Sudden Braking. Int. J. ITS Res. 19, 366–377 (2021). https://doi.org/10.1007/s13177-021-00253-6

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  • DOI: https://doi.org/10.1007/s13177-021-00253-6

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