The Shared Control (SC) cooperation scheme, where the driver and automated driving system control the vehicle together, has been gaining attention through the years as a promising option to improve road safety. As a result, advanced interaction methods can be investigated to enhance user experience, acceptance, and trust. Under this perspective, not only the development of algorithms and system applications are needed, but it is also essential to evaluate the system with real drivers, assess its impact on road safety, and understand how drivers accept and are willing to use this technology. In this sense, the contribution of this work is to conduct an experimental study to evaluate if a previously developed shared control system can improve overtaking performance on roads with oncoming traffic. The evaluation is performed in a Driver-in-the-Loop (DiL) simulator with 13 real drivers. The system based on SC is compared against a vehicle with conventional SAE-L2 functionalities. The evaluation includes both objective and subjective assessments. Results show that SC proved to be the best solution for assisting the driver during overtaking in terms of safety and acceptance. The SC’s longer and smoother control transitions provide benefits to cooperative driving. The System Usability Scale (SUS) and the System Acceptance Scale (SAS) questionnaire show that the SC system was perceived as better in terms of usability, usefulness, and satisfaction.


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    Titel :

    Can shared control improve overtaking performance? Combining human and automation strengths for a safer maneuver


    Beteiligte:
    Mauricio Marcano (Autor:in) / Fabio Tango (Autor:in) / Joseba Sarabia (Autor:in) / Silvia Chiesa (Autor:in) / Joshué Pérez (Autor:in) / Sergio Díaz (Autor:in)

    Erscheinungsdatum :

    23.11.2022


    Anmerkungen:

    oai:zenodo.org:7354389
    MDPI Sensors Sensors 2022, 22(23), 9093(Special Issue Human Machine Interaction in Automated Vehicles)



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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