A deep learning based model for driving risk assessment

Yiyang Bian, Chang Heon Lee, Yibo Wang, J. Leon Zhao

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In this paper a novel multilayer model is proposed for assessing driving risk. Studying aggressive behavior via massive driving data is essential for protecting road traffic safety and reducing losses of human life and property in smart city context. In particular, identifying aggressive behavior and driving risk are multi-factors combined evaluation process, which must be processed with time and environment. For instance, improper time and environment may facilitate abnormal driving behavior. The proposed Dynamic Multilayer Model consists of identifying instant aggressive driving behavior that can be visited within specific time windows and calculating individual driving risk via Deep Neural Networks based classification algorithms. Validation results show that the proposed methods are particularly effective for identifying driving aggressiveness and risk level via real dataset of 2129 drivers' driving behavior.

    Original languageEnglish
    Title of host publicationProceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
    EditorsTung X. Bui
    PublisherIEEE Computer Society
    Pages1294-1303
    Number of pages10
    ISBN (Electronic)9780998133126
    Publication statusPublished - 2019
    Event52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 - Maui, United States
    Duration: Jan 8 2019Jan 11 2019

    Publication series

    NameProceedings of the Annual Hawaii International Conference on System Sciences
    Volume2019-January
    ISSN (Print)1530-1605

    Conference

    Conference52nd Annual Hawaii International Conference on System Sciences, HICSS 2019
    Country/TerritoryUnited States
    CityMaui
    Period1/8/191/11/19

    ASJC Scopus subject areas

    • Engineering(all)

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