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.
Sim2Real Autonomous Driving Using Convolutional Neural Network for Urban Environments
Springer Proceedings in Advanced Robotics
International Symposium on Experimental Robotics ; 2023 ; Melia, Chiang Mai, Thailand November 26, 2023 - November 30, 2023
2024-08-06
10 pages
Article/Chapter (Book)
Electronic Resource
English