This paper focuses on the automated learning of driver braking “signature” in the presence of road anomalies. Our motivation is to improve driver experience using preview information from navigation maps. Smartphones facilitate, due to their unprecedented market penetration, the large-scale deployment of advanced driver assistance systems. On the other hand, it is challenging to exploit smartphone sensor data because of the fewer and lower quality signals, compared to the ones on board. Methods for detecting braking behavior using smartphones exist, however, most of them focus only on harsh events. Additionally, only a few studies correlate longitudinal driving behavior with the road condition. In this paper, a new method, based on deep neural networks and the sliding correlation coefficient, is proposed for the spatio-temporal correlation of road anomalies and driver behavior. A unique deep neural network structure, that requires minimum tuning, is proposed. Extensive field trials were conducted and vehicle motion was recorded using smartphones and a data acquisition system, comprising an inertial measurement unit and differential GPS. The proposed method was validated using the probabilistic Receiver Operating Characteristics method. The method proves to be a robust and flexible tool for self-learning driver behavior.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study




    Erscheinungsdatum :

    01.01.2019


    Format / Umfang :

    2638081 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Driver behavior profiling using smartphones

    Castignani, German / Frank, Raphael / Engel, Thomas | IEEE | 2013


    AUTO EMERGENCY BRAKING METHOD USING ROAD FRICTION COEFFICIENT

    HWANG YUN HYOUNG / LEE HYUCK KEE / SHIN SEONG GEUN et al. | Europäisches Patentamt | 2016

    Freier Zugriff

    Auto Emergency Braking Method Using Road Friction Coefficient

    Europäisches Patentamt | 2018

    Freier Zugriff


    Anomaly Detection in Road Networks Using Sliding-Window Tensor Factorization

    Xu, Ming / Wu, Jianping / Wang, Haohan et al. | IEEE | 2019