Safety assurance for automated driving is one of the major challenges in automotive research. Scenario-based testing has become a promising approach to tackle this issue to assess the risk of these functions. In this approach, the automated driving function is confronted with clearly defined scenarios instead of driving in real-world traffic. Despite its potential of significantly reducing the required testing effort compared to driving in the real-world, this approach comes with three major challenges: The proper definition of scenarios covering real-world traffic sufficiently and serving the safety assurance process, the management of those scenarios and underlying data, and the acquisition of traffic data to link scenarios to the real world.

    In the following, a holistic approach tackling those challenges is presented with references to other state of the art methods. For this, a methodology to abstract reality through scenarios is presented. Those are managed with a scenario database and the link to reality is established utilizing real-world data. Furthermore, a methodology is shown how to generate scenarios for simulations utilizing the scenario concept. Next to the methodology, practical solutions are shown with a scenario concept, the database scenario.center, and real-world data acquisition utilizing drones.


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    Title :

    Scenarios and Scenario Databases for AV Safety Assurance


    Additional title:

    Lect.Notes Mobility


    Contributors:

    Conference:

    Automated Road Transportation Symposium ; 2023 ; San Francisco, CA, USA July 09, 2023 - July 13, 2023


    Published in:

    Road Vehicle Automation 11 ; Chapter : 12 ; 126-148


    Publication date :

    2024-08-23


    Size :

    23 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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