Highlights Autonomous simulated vehicles can be controlled to generate predefined scenarios. Scenarios are generated by reconstructing driving context, not action sequences solely. Participants can be actively engaged in scenarios by limiting their driving choices. Realistic and repeatable driving simulation scenario can be orchestrated. The algorithm can be used for target pursue and vehicle-driver collaboration.

    Abstract In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.


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    Title :

    Orchestration of driving simulator scenarios based on dynamic actor preparation and automated action planning


    Contributors:


    Publication date :

    2015-02-05


    Size :

    12 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English







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