The upcoming market introduction of highly automated driving functions and associated requirements on reliability and safety require new tools for the virtual test coverage to lower development expenses. In this contribution, a computationally efficient and accurate simulation environment for the vehicle’s lateral dynamics is introduced. Therefore, an analytic single track model is coupled with a long-short-term-memory neural network to compensate modelling inaccuracies of the single track model. This ‘Hybrid Vehicle Model’ is parameterized with selected training batches obtained from a complex simulation model serving as a reference to simplify the data acquisition. The single track model is parameterized using given catalogue data. Thereafter, the long-short-term-memory network is trained to cover for the single track model’s shortcomings compared to the ground truth in a closed-loop setup. The evaluation with measurements from the real vehicle shows that the hybrid model can provide accurate long-term predictions with low computational effort that outperform results achieved when using the models isolated.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A fast and accurate hybrid simulation model for the large-scale testing of automated driving functions


    Beteiligte:
    Fraikin, Nicolas (Autor:in) / Funk, Kilian (Autor:in) / Frey, Michael (Autor:in) / Gauterin, Frank (Autor:in)


    Erscheinungsdatum :

    2020-03-01


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Vehicle simulation model chain for virtual testing of automated driving functions and systems*

    Bartolozzi, R. / Landersheim, V. / Stoll, G. et al. | IEEE | 2022


    Hybrid Testing: A Vehicle-in-the-Loop Testing Method for the Development of Automated Driving Functions

    Reckenzaun, Jakob / Mischinger, Marlies / Rudigier, Martin et al. | SAE Technical Papers | 2021


    Comparing two systematic approaches for testing automated driving functions

    Felbinger, Hermann / Kluck, Florian / Li, Yihao et al. | IEEE | 2019


    Highly immersive driving simulator for scenario based testing of automated driving functions

    Prokop, Günther / Tüschen, Thomas / Eisenköck, Norman et al. | Springer Verlag | 2022


    Enhancing CARLA Traffic Simulation with Pedestrian Animation for Testing Perception Functions in Automated Driving

    de Oliveira, Lucas Fonseca Alexandre / Bernhard, Johannes / Schories, Lars et al. | IEEE | 2023