Lane following in the Duckietown environment under extreme conditions


Deep Reinforcement Learning (DRL) is a field of machine learning which enables intelligent software agents in an environment to attain their goal. They utilize deep neural networks to learn the best possible actions in each state. This technique has been successfully applied to beat world champions in different board and computer games, for example, Go¹ or StartCraft II².


I used the Duckietown environment³ to implement and test my method. It is an open, inexpensive, and flexible platform for small autonomous vehicles. The platform consists of two parts: the Duckietowns and the Duckiebots.


The overview of the method can be seen in the following picture.

Overview of the method


The performance of the agent is tested both in the simulated and the real-world environment. In each test case, the vehicle was started from a randomly chosen position on the track. The test case was considered successful if it was able to drive at least one complete lap without leaving the right lane — after this, it has successfully passed all parts of the track, so we can expect it to be able to continue its journey for more laps as well. These tests were carried out on four simulated and one real-world map, and the results are summarized in the following table — the agent was successful in most cases both in the simulator and in the real world.

Extreme tests include driving in night mode. This provides a significantly different and thus a more challenging environment for the vehicle compared to daytime.
Extreme test cases include recovery from irregular starting positions: starting from the oncoming lane or perpendicularly to the road.



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Deep Learning and AI solutions from Budapest University of Technology and Economics.