A Comparison of autonomous vehicle navigation simulators under regulatory and reinforcement learning constraints
Document typeConference lecture
Rights accessRestricted access - publisher's policy
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.
CitationCabañeros, A.; Angulo, C. A Comparison of autonomous vehicle navigation simulators under regulatory and reinforcement learning constraints. A: International Conference of the Catalan Association for Artificial Intelligence. "Artificial Intelligence Research and Development vol. 319". IOS Press, 2019, p. 115-124.