Vehicle localization (ground vehicles) is an important task for intelligent vehicle systems and vehicle cooperation may bring benefits for this task. A new cooperative multi-vehicle localization method using split covariance intersection filter is proposed in this paper. In the proposed method, each vehicle maintains an estimate of a decomposed group state and this estimate is shared with neighboring vehicles; the estimate of the decomposed group state is updated with both the sensor data of the ego-vehicle and the estimates sent from other vehicles; the covariance intersection filter which yields consistent estimates even facing unknown degree of inter-estimate correlation has been used for data fusion. A comparative study based simulations demonstrate the effectiveness and the advantage of the proposed cooperative localization method.1


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    Titel :

    Cooperative multi-vehicle localization using split covariance intersection filter


    Beteiligte:
    Li, Hao (Autor:in) / Nashashibi, Fawzi (Autor:in)


    Erscheinungsdatum :

    2012-06-01


    Format / Umfang :

    414554 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Cooperative Multi-Vehicle Localization Using Split Covariance Intersection Filter

    Li, H. / Nashashibi, F. / Institute of Electrical and Electronics Engineers | British Library Conference Proceedings | 2012





    Data Fusion with Split Covariance Intersection for Cooperative Perception

    Lima, Antoine / Bonnifait, Philippe / Cherfaoui, Veronique et al. | IEEE | 2021