Objective: The aim of this study was to design and evaluate an algorithm for detecting drowsiness-related lane departures by applying a random forest classifier to steering wheel angle data. Background: Although algorithms exist to detect and mitigate driver drowsiness, the high rate of false alarms and missed detection of drowsiness represent persistent challenges. Current algorithms use a variety of data sources, definitions of drowsiness, and machine learning approaches to detect drowsiness. Methods: We develop a new approach for detecting drowsiness-related lane departures using steering wheel angle data that employ an ensemble definition of drowsiness and a random forest algorithm. Data collected from 72 participants driving the National Advanced Driving Simulator are used to train and evaluate the model. The model’s performance was assessed relative to a commonly used algorithm, percentage eye closure (PERCLOS). Results: The random forest steering algorithm had a higher classification accuracy and area under the receiver operating characteristic curve than PERCLOS and had comparable positive predictive value. The algorithm succeeds at identifying two key scenarios associated with the drowsiness detection task. These two scenarios consist of instances when drivers depart their lane because they fail to modulate their steering behavior according to the demands of the simulated road and instances when drivers correctly modulate their steering behavior according to the demands of the road. Conclusions: The random forest steering algorithm is a promising approach to detect driver drowsiness. The algorithm’s ties to consequences of drowsy driving suggest that it can be easily paired with mitigation systems.


    Zugriff

    Zugriff über TIB

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Steering in a Random Forest. Ensemble Learning for Detecting Drowsiness-Related Lane Departures


    Beteiligte:

    Erschienen in:

    Human Factors ; 56 , 5 ; 986-998


    Erscheinungsdatum :

    2014


    Format / Umfang :

    13 Seiten, 40 Quellen




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch




    Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

    McDonald, A.D. / Schwarz, C. / Lee, J.D. et al. | British Library Conference Proceedings | 2012


    Detecting lane departures from steering wheel signal

    Sandström, Max | Online Contents | 2017


    Detecting lane departures from steering wheel signal

    Sandström, Max / Lampsijärvi, Eetu / Holmström, Axi et al. | Elsevier | 2016