Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable, if such driving maneuvers are detected beforehand and the driver is assisted accordingly. In fact, various recent research has focused on the automated prediction of driving maneuver based on hand-crafted features extracted mainly from in-cabin driver videos. Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers’ intention based on both in-cabin and traffic scene videos. More specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder to extract motion features from the out-cabin traffic, (2) train a classifier which considers motions from both in- and outside of the cabin jointly for maneuver intention anticipation, (3) experimentally prove that the in- and outside image features have complementary information. Our evaluation based on the publicly available dataset Brain4cars shows that our framework achieves a prediction with the accuracy of 83.98% and $F_{1}-$ score of 84.3%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring


    Contributors:


    Publication date :

    2020-09-20


    Size :

    2643662 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring

    Rong, Yao / Akata, Zeynep / Kasneci, Enkelejda | ArXiv | 2020

    Free access

    Analytic method of driver driving intention

    JIN ZHENHUA / LI YIWEN / LU QINGCHUN et al. | European Patent Office | 2015

    Free access

    Driver Anticipation in Car Following

    Deng, Hui / Zhang, H. Michael | Transportation Research Record | 2012


    Driver Anticipation in Car Following

    Deng, Hui | Online Contents | 2012


    Driver intention-based lane assistant system for autonomous driving vehicles

    ZHU FAN / KONG QI | European Patent Office | 2020

    Free access