Drivers’ Manoeuvre Classification for Safe HRI

Erwin José López Pulgarín, Guido Herrmann, Ute Leonards

    Research output: Other contributionpeer-review


    Ever increasing autonomy of machines and the need to interact with them creates challenges to ensure safe operation. Recent technical and commercial interest in increasing autonomy of vehicles has led to the integration of more sensors and actuators inside the vehicle, making them more like robots. For interaction with semi-autonomous cars, the use of these sensors could help to create new safety mechanisms. This work explores the concept of using motion tracking (i.e. skeletal tracking) data gathered from the driver whilst driving to learn to classify the manoeuvre being performed. A kernel-based classifier is trained with empirically selected features based on data gathered from a Kinect V2 sensor in a controlled environment. This method shows that skeletal tracking data can be used in a driving scenario to classify manoeuvres and sets a background for further work.
    Original languageEnglish
    PublisherSpringer Nature
    Number of pages9
    ISBN (Print)9783319641065
    Publication statusPublished - 20 Jul 2017


    • Classification
    • Driver actions
    • HRI
    • Machine learning
    • Semi-autonomous
    • vehicles
    • Vehicles


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