To operate safely, an automated vehicle (AV) must anticipate how the environment around it will evolve. For that purpose, it is important to know which prediction models are most appropriate for every situation. Currently, assessment of prediction models is often performed over a set of trajectories without distinction of the type of movement they capture, resulting in the inability to determine the suitability of each model for different situations. In this work we illustrate how standardized evaluation methods result in wrong conclusions regarding a model's predictive capabilities, preventing a clear assessment of prediction models and potentially leading to dangerous on-road situations. We argue that following evaluation practices in safety assessment for AVs, assessment of prediction models should be performed in a scenario-based fashion. To encourage scenario-based assessment of prediction models and illustrate the dangers of improper assessment, we categorize trajectories of the Waymo Open Motion dataset according to the type of movement they capture. Next, three different models are thoroughly evaluated for different trajectory types and prediction horizons. Results show that common evaluation methods are insufficient and the assessment should be performed depending on the application in which the model will operate.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Scenario-based Evaluation of Prediction Models for Automated Vehicles


    Beteiligte:


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    983304 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Cut-in Scenario Prediction for Automated Vehicles

    Remmen, Floris / Cara, Irene / de Gelder, Erwin et al. | IEEE | 2018


    Scenario-Based Safety Assessment Framework for Automated Vehicles

    Ploeg, J. / de Gelder, E. / Slavík, M. et al. | ArXiv | 2021

    Freier Zugriff

    Simulation Testing Scenario Generation for Comfort Evaluation of Automated Vehicles

    Yang, Yuhang / Wang, Yafei / Yin, Chengliang et al. | IEEE | 2021