A major challenge in cooperative sensing is to weight the measurements taken from the various sources to get an accurate result. Ideally, the weights should be inversely proportional to the error in the sensing information. However, previous cooperative sensor fusion approaches for autonomous vehicles use a fixed error model, in which the covariance of a sensor and its recognizer pipeline is just the mean of the measured covariance for all sensing scenarios. The approach proposed in this paper estimates error using key predictor terms that have high correlation with sensing and localization accuracy for accurate covariance estimation of each sensor observation. We adopt a tiered fusion model consisting of local and global sensor fusion steps. At the local fusion level, we add in a covariance generation stage using the error model for each sensor and the measured distance to generate the expected covariance matrix for each observation. At the global sensor fusion stage we add an additional stage to generate the localization covariance matrix from the key predictor term velocity and combines that with the covariance generated from the local fusion for accurate cooperative sensing. To showcase our method, we built a set of 1/10 scale model autonomous vehicles with scale accurate sensing capabilities and classified the error characteristics against a motion capture system. Results show an average and max improvement in RMSE when detecting vehicle positions of 1.42x and 1.78x respectively in a four-vehicle cooperative fusion scenario when using our error model versus a typical fixed error model.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Accurate Cooperative Sensor Fusion by Parameterized Covariance Generation for Sensing and Localization Pipelines in CAVs


    Beteiligte:


    Erscheinungsdatum :

    08.10.2022


    Format / Umfang :

    876184 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    SYSTEMS AND METHODS FOR COOPERATIVE SENSOR FUSION BY PARAMETERIZED COVARIANCE GENERATION IN CONNECTED AUTONOMOUS VEHICLES

    ANDERT EDWARD / SHRIVASTAVA AVIRAL | Europäisches Patentamt | 2025

    Freier Zugriff

    Cooperative LiDAR-Aided Self-localization of CAVs in Real Urban Scenario

    Adas, Akif / Barbieri, Luca / Morri, Pietro et al. | Springer Verlag | 2024

    Freier Zugriff

    Virtual Platoon based CAVs Cooperative Driving at Unsignalized Intersection

    Cong, Xiangyue / Yang, Bo / Gao, Fengkun et al. | IEEE | 2022


    CAVS 2019 Reviewers

    IEEE | 2019

    Freier Zugriff

    CAVS 2019 Keynotes

    IEEE | 2019

    Freier Zugriff