Glance behavior is significant because whether and how the driver is scanning and observing the driving scene is closely related to driving safety. This paper aims to improve the accuracy of glance behavior modeling and realize the spatiotemporal representation and visualization of glance behavior. Forty subjects were recruited to perform a freeway driving task using a driving simulator. The vehicle data were collected by the simulator. Drivers’ gaze points were collected by an eye tracker. The prior knowledge on gaze points obtained through a statistical analysis were provided for K-means (KM) to form a semi-supervised K-means (SSKM), which classifies gaze points into different fixation zones. The classification results were compared with the results of KM. Furthermore, a clustering center-based piecewise aggregate representation (CCPAR) was proposed to characterize glance behavior. Maneuvers identification was taken as a case to evaluate the proposed method. The k-nearest neighbour (KNN) based on the similarity of CCPAR identified driving maneuvers into lane-keeping, left lane change, and right lane change. The identification results were compared with the results of the Hidden Markov Model (HMM). The average classification accuracies of KM and SSKM were 55.28% and 94.75%, respectively. The accuracies of maneuvers identified by CCPAR-KNN and by HMM were 87.50% and 85.83%, respectively. The results indicate that SSKM and CCPAR are feasible for glance behavior modeling. SSKM eliminates the randomness of initial cluster center selection and improves the accuracy of gaze points classification. CCPAR is intuitive and convenient to describe and visualize the spatiotemporal characteristics of glance behavior.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Driver Glance Behavior Modeling Based on Semi-Supervised Clustering and Piecewise Aggregate Representation


    Beteiligte:
    Huang, Jianling (Autor:in) / Long, Yan (Autor:in) / Zhao, Xiaohua (Autor:in)


    Erscheinungsdatum :

    01.07.2022


    Format / Umfang :

    3208177 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    PREDICTING DRIVER STATUS USING GLANCE BEHAVIOR

    LIU KE / HECHT RON / GARNETT NOA et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Predicting driver status using glance behavior

    LIU KE / HECHT RON / GARNETT NOA et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Observed Driver Glance Behavior at Roadside Advertising Signs

    Beijer, Daan / Smiley, Alison / Eizenman, Moshe | Transportation Research Record | 2004


    Driver Demand: Eye Glance Measures

    Seaman, Sean / Hsieh, Li / Young, Richard | British Library Conference Proceedings | 2016


    Driver Demand: Eye Glance Measures

    Hsieh, Li / Young, Richard / Seaman, Sean | SAE Technical Papers | 2016