Driver Behavior Classification in Crash and Near-Crash Events Using 100-CAR Naturalistic Data Set

Abdalla Abdelrahman, Najah Abu-Ali, Hossam S. Hassanein

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

    10 Citations (Scopus)

    Abstract

    Recently, several car insurance companies got interested in classifying the behavior of drivers. Usage-based insurance (UBI), such as Pay-How-you- Drive (PHYD) scheme, is an innovative idea in which the insurance premium changes based on the driving behavior. This behavior is usually evaluated in terms of vehicle-related variables such as distance, speed, and acceleration to determine the expected risk profile for drivers. In this paper, an additional level of classification in the hierarchy of profiling is proposed. Using the 100-CAR naturalistic driving study (NDS) data set, five different Hidden Markov Models (HMMs) are trained to determine the fault responsibility of a Subject Vehicle (SV) in a crash or near-crash events. Two specific driving situations, which are conflicts with leading and following vehicles, are investigated in this study. Results show that these models can achieve a reasonable classification accuracy.

    Original languageEnglish
    Title of host publication2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-6
    Number of pages6
    ISBN (Electronic)9781509050192
    DOIs
    Publication statusPublished - Jul 1 2017
    Event2017 IEEE Global Communications Conference, GLOBECOM 2017 - Singapore, Singapore
    Duration: Dec 4 2017Dec 8 2017

    Publication series

    Name2017 IEEE Global Communications Conference, GLOBECOM 2017 - Proceedings
    Volume2018-January

    Other

    Other2017 IEEE Global Communications Conference, GLOBECOM 2017
    Country/TerritorySingapore
    CitySingapore
    Period12/4/1712/8/17

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Safety, Risk, Reliability and Quality

    Fingerprint

    Dive into the research topics of 'Driver Behavior Classification in Crash and Near-Crash Events Using 100-CAR Naturalistic Data Set'. Together they form a unique fingerprint.

    Cite this