TFG: Autonomous driving for racing cars based on RL and using AWS Deep Racer and Gazebo
Luis Miguel López has successfully presented his final degree project named “Conducción Autónoma de coches de carreras basado en Reinforcement Learning con AWS DeepRacer y Gazebo”.
| Information and Resources | |
|---|---|
| Work | |
| Slides | |
| Git Repository | Link |
| Tutor | Roberto Calvo Palomino |
The automotive world does not escape the advances of robotics and AI. one of the great dreams in the history of humanity Since cars circulate in our world, autonomous cars are becoming increasingly popular every day closer to being a reality. In some countries, autonomous cars are already going circulate through large cities, even doing taxi services. The world of autonomous driving is also related to the racing motorsport, with the existence of some competitions in which one one of the most important elements, if not the most, of a racing car, which is the pilot is not a person but a computer capable of understanding and analyzing the car environment to drive himself as fast as possible around track.
This TFG focuses on the development of autonomous driving behaviors based on reinforcement learning in racing circuits, in order to obtain reactive and appropriate behaviors in different scenarios using a robot with the necessary characteristics to carry out these behaviors, the AWS Deepracer.
The AWS Deepracer league is a competition where participants try to build an autonomous behavior in a small racing car using artificial intelligence. In this competition, participants are provided with a small robot with autonomous capabilities, the AWS Deepracer promoted by Amazon, together with a simulated environment in which the participants must program the robot with one goal: be the fastast one!
Check the video below to see more details about the system: