This paper presents a planning and control pipeline for an autonomous race car to drive around a track that may not be previously known for three laps. In the case of a limited perception range in the first lap, boundary detection and motion planning separately extract and optimize the trajectory to minimize the trajectory curvature. After finishing the mapping, multi-strategy NMPC is used to optimize or track the trajectory. We use the real-world map data from Formula Student Autonomous China 2019 for experiments. The experiment shows that under the same vehicle model, the system can significantly improve the performance of the race car.


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    Titel :

    Real-Time Motion Planning and Control for a Formula Student Driverless Car


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Chen, Tairan (Autor:in) / Gao, Xinyu (Autor:in) / Huang, Chenrui (Autor:in) / Li, Xiang (Autor:in) / Yang, Shaokun (Autor:in) / Gong, Hailong (Autor:in) / Feng, Yunji (Autor:in)


    Erscheinungsdatum :

    2022-01-13


    Format / Umfang :

    17 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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    Startklar fuer die Formula Student Driverless

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    AUTONOMOUS DRIVING SYSTEM DESIGN FOR FORMULA STUDENT DRIVERLESS RACECAR

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    Autonomous Driving System Design for Formula Student Driverless Racecar

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