To gain reliable and accurate perception is essential for autonomous driving in complex traffic environments, which often requires the cooperative perceptions based on on-board and roadside sensors. However, fusion of roadside sensors is rarely studied. Moreover, road-side perception is not only essential for autonomous driving, but also helpful for traffic management. In this chapter, we first give a detailed introduction on cooperative perceptions, along with core tasks and data sources of multi-sensor fusion for perception in complex traffic environments. We then propose a more reasonable division rule, which divides multi-sensor fusion methodologies into three categories including decision-level fusion, decision-feature-level fusion, and feature-level fusion. Furthermore, this chapter will give some recommendations on the forward research directions to address the problems that have been encountered in the field of multi-sensor fusion.


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    Titel :

    Multi-sensor Fusion for Perception in Complex Traffic Environments


    Beteiligte:
    Zhu, Yongdong (Herausgeber:in) / Cao, Yue (Herausgeber:in) / Hua, Wei (Herausgeber:in) / Xu, Lexi (Herausgeber:in) / Huang, Qian (Autor:in) / Zhu, Kainan (Autor:in) / Wu, Kan (Autor:in) / Hua, Wei (Autor:in) / Zhu, Yongdong (Autor:in)


    Erscheinungsdatum :

    01.11.2023


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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