The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. Improving safety at intersections has also been identified as high priority due to the large number of intersection related fatalities. This paper focuses on developing algorithms for estimating driver behavior at road intersections. It introduces two classes of algorithms that can classify drivers as compliant or violating. They are based on 1) Support Vector Machines (SVM) and 2) Hidden Markov Models (HMM), two very popular machine learning approaches that have been used extensively for classification in multiple disciplines. The algorithms are successfully validated using naturalistic intersection data collected in Christiansburg, VA, through the US Department of Transportation Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) initiative.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Behavior classification algorithms at intersections and validation using naturalistic data


    Contributors:


    Publication date :

    2011-06-01


    Size :

    463514 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Behavior Classification Algorithms at Intersections and Validation Using Naturalistic Data

    Aoude, G. / Desaraju, V. / Stephens, L. et al. | British Library Conference Proceedings | 2011



    Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set

    Aoude, Georges S. / Desaraju, Vishnu R. / Stephens, Lauren H. et al. | IEEE | 2012


    System and method for providing driver behavior classification at intersections and validation on large naturalistic data sets

    AOUDE GEORGES / DESARAJU VISHNU RAJESWAR / HOW JONATHAN P et al. | European Patent Office | 2015

    Free access

    Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

    Hao, Haiyan / Gibbons, Ronald B. / Medina, Alejandra | Transportation Research Record | 2021