With multi-sensor-based collision avoidance systems (CASs) being adopted in today’s automobiles, a new method that enables collaborative decision-making with preceding vehicle detection under various external environments is needed. In this paper, spatial–temporal correlations of multi-channel signals that are collected by multiple sensors on the host vehicle are considered, and a multi-channel detection technique with a stochastic model is introduced for automobile collision avoidance. We propose an accurate and robust multi-channel, generalized likelihood ratio test (GLRT)-based detection and collaborative decision-making scheme, with a vehicle kinematic analysis for avoiding rear-end collisions. The results of simulations and physical experiments demonstrated that our detector expands the detection range with a high detection rate and that our proposed scheme obtains good performance under varying operating and environmental conditions.


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    Titel :

    Rear-End Collision Avoidance-Based on Multi-Channel Detection


    Beteiligte:
    Xiang, Yu (Autor:in) / Huang, Sida (Autor:in) / Li, Min (Autor:in) / Li, Jin (Autor:in) / Wang, Wenyong (Autor:in)


    Erscheinungsdatum :

    2020-08-01


    Format / Umfang :

    2090534 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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