Driving is a highly dynamic activity where drivers' awareness to the traffic environment plays the essential role for successful performance. Easy, smooth driving depends on drivers' awareness to develop situation-specific expectations, where infrequent or unexpected situations are not taken into account. Understanding these unexpected situations provides important insight on driver situation awareness and accident prevention. This study takes advantage of recent development of wearable devices and uses driver physiological signals to identify such unexpected situations during driver hard brake. Based on a naturalistic driving dataset, we define two types of hard brake behavior: reactive and intended hard brake. The reactive hard brake relates to drivers reacting to unexpected situations that usually leads to deviated physiological signals due to stress. The intended hard brake relates to planned maneuver implementation that consists of stable physiological signals. By using the human evaluation, we identified the different situations in which these two types of hard brake occurs. Clear difference is observed between these two groups of situations. Our goal is to identify features that are representative of these two type of road environment, especially the situations where unexpected reactive hard brake happens. Following this direction, we extracted features from Lidar depth scanner to represent the road scene, and applied both lasso regression and logistic regression classifier for feature analysis. The regression model achieves high correlation of 0.77 between the prediction and the ground truth while the classification achieves F-score of 0.76. The selected Lidar features can serve as high level road scene representation that facilitate next generation advanced driver assistance systems (ADAS) to prevent accident in unexpected traffic scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Understand driver awareness through brake behavior analysis: Reactive versus intended hard brake


    Contributors:
    Li, Nanxiang (author) / Misu, Teruhisa (author) / Tao, Fei (author)


    Publication date :

    2017-06-01


    Size :

    633024 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Understand Driver Awareness through Brake Behavior Analysis: Reactive versus Intended Hard Brake

    Li, Nanxiang / Misu, Teruhisa / Tao, Fei | British Library Conference Proceedings | 2017


    UNDERSTANDING DRIVER AWARENESS THROUGH BRAKE BEHAVIOR ANALYSIS

    LI NANXIANG / MISU TERUHISA / TAO FEI | European Patent Office | 2018

    Free access

    Understanding driver awareness through brake behavior analysis

    LI NANXIANG / MISU TERUHISA / TAO FEI | European Patent Office | 2019

    Free access

    BRAKE LAMPS TO UNDERSTAND BRAKE FORCE

    MURAKAMI SHIGERU | European Patent Office | 2016

    Free access