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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    Title :

    Sim2Real Autonomous Driving Using Convolutional Neural Network for Urban Environments


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

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



    Publication date :

    2024-08-06


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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