A Comparison of autonomous vehicle navigation simulators under regulatory and reinforcement learning constraints

Abstract

The transition from conventional vehicles to autonomous vehicles is regulated thorough ADAS (Advanced Driver Assistance Systems) functionalities. The combination of different ADAS functions allows vehicles navigate on a highway autonomously, but at the same time, following the traffic rules and regulations requirements, and also guaranteeing safety on the road. The practical objective in this article is to implement a Reinforcement Learning method whose actions are based in these regulated functions for autonomous vehicles navigation. With this aim, a study of the state-of-the-art of autonomous vehicles simulators has been completed. Hence, the algorithm will be tested using a five-lane highway simulator, previously selected. Results and performance of the model through experimentation will be presented and evaluated using the simulator for different network architectures.