For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (RDD) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data


    Contributors:


    Publication date :

    2020-09-10


    Size :

    649332 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Model-based safety validation of the automated driving functio highway pilot

    Beglerovic, Halil / Ravi, A. / Wikström, N. et al. | Springer Verlag | 2017


    Real World Scenarios for the Safety Validation and Development of Highly Automated Driving Functions

    Moers, T. / Klas, C. / Zlocki, A. et al. | British Library Conference Proceedings | 2022


    SENSE—ASSESS—EXPLAIN (SAX): BUILDING TRUST IN AUTONOMOUS VEHICLES IN CHALLENGING REAL-WORLD DRIVING SCENARIOS

    Gadd, Matthew / De Martini, Daniele / Marchegiani, Letizia et al. | British Library Conference Proceedings | 2020


    Sense-Assess-eXplain (SAX): building trust in autonomous vehicles in challenging real-world driving scenarios

    Gadd, M / de Martini, D / Marchegiani, M et al. | BASE | 2020

    Free access

    Sense–Assess–eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios

    Gadd, Matthew / de Martini, Daniele / Marchegiani, Letizia et al. | IEEE | 2020