This chapter delves into the planning and control module and introduces routing planning algorithms, behavioral planning algorithms, motion planning algorithms, and feedback control algorithms. It presents a real‐world case study of Apollo's Iterative Expectation–Maximization Planner, which was designed for L4 autonomous driving passenger vehicles. After a route plan has been found, the autonomous vehicle must be able to navigate the selected route and interact with other traffic participants according to driving conventions and rules of the road. In addition, the chapter introduces PerceptIn's planning and control framework, which was developed to enable low‐speed autonomous driving in controlled environments, such as university campuses, entertainment parks, and industrial parks. PerceptIn's planning and control framework consists of a mission planner, a behavior planner, a motion planner, and a vehicle controller.


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    Titel :

    Planning and Control


    Beteiligte:
    Liu, Shaoshan (Autor:in)


    Erscheinungsdatum :

    2020-04-13


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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