We introduce an autonomous driving framework that employs convolutional neural networks. This framework utilizes forward-facing stereo camera images, vehicle speed, traffic light status, and higher-level navigation commands to predict future waypoints for the vehicle’s trajectory. The model was trained on a dataset collected from the CARLA Simulator and underwent testing in both simulation and real-world settings without any additional fine-tuning on real-world datasets.

    In simulation testing, the model successfully navigated previously unseen maps and weather conditions, covering a distance of 3000 m without encountering collisions or traffic light violations. Real-world testing on a differential drive vehicle demonstrated the model’s ability to navigate without lane invasions.


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    Titel :

    Sim2Real Autonomous Driving Using Convolutional Neural Network for Urban Environments


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Ang Jr, Marcelo H. (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Nambiar, Karthik (Autor:in) / Sujit, P. B. (Autor:in)

    Kongress:

    International Symposium on Experimental Robotics ; 2023 ; Melia, Chiang Mai, Thailand November 26, 2023 - November 30, 2023


    Erschienen in:

    Experimental Robotics ; Kapitel : 49 ; 547-556


    Erscheinungsdatum :

    06.08.2024


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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