Automated vehicles rely on their environment model, usually generated by a tracking module using sensor data, to make decisions. Therefore, estimating the accuracy of the tracking module is vital for the safe and reliable operation of the vehicle. This work makes a step towards this goal by providing a detection probability estimation method with a self-monitored quality assessment for the labeled multi-Bernoulli filter. We demonstrate the significance of the proposed quality index by comparing it with the actual estimation error calculated with ground truth data. This shows that the developed index is a meaningful value that can be computed online without ground truth data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Self-Monitored Detection Probability Estimation for the Labeled Multi-Bernoulli Filter


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    683932 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Interaction-Aware Labeled Multi-Bernoulli Filter

    Ishtiaq, Nida / Gostar, Amirali Khodadadian / Bab-Hadiashar, Alireza et al. | IEEE | 2023


    A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation

    Kropfreiter, Thomas / Meyer, Florian / Hlawatsch, Franz | IEEE | 2020

    Freier Zugriff