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.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 firstname.lastname@example.org
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 email@example.com