Most road traffic accidents are caused due to human errors and lead to fatal injuries, hospitalization, and even deaths. Autonomous vehicles pose to be a good option to minimize the possibility of such human errors. But autonomous systems today are not capable of handling extreme uncertainties that an everyday driver has to face on the road. Researchers are trying to study new ways of making autonomous vehicles better and safe. In this paper, we have discussed some of the most important parts of a modular autonomous vehicle: Sensor fusion, lane detection, and drivable area segmentation. We also present a detailed survey of existing and state-of-the-art approaches for these modules. Understanding these techniques and how they work, can lay a proper foundation for the planning and acting phase of autonomous vehicle systems.


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    Titel :

    Autonomous Vehicles: A Survey on Sensor Fusion, Lane Detection and Drivable Area Segmentation


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Nayak, Janmenjoy (Herausgeber:in) / Behera, H.S. (Herausgeber:in) / Naik, Bighnaraj (Herausgeber:in) / Vimal, S. (Herausgeber:in) / Pelusi, Danilo (Herausgeber:in) / Morkar, Tejas (Autor:in) / Sonawane, Suyash (Autor:in) / Mahajan, Aditya (Autor:in) / Shinde, Swati (Autor:in)


    Erscheinungsdatum :

    2022-05-07


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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