Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning
School of Architecture, Engineering and Design, Universidad Europea de Madrid, Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1536; https://doi.org/10.3390/electronics8121536
Received: 1 November 2019 / Revised: 9 December 2019 / Accepted: 10 December 2019 / Published: 13 December 2019
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. This paper proposes an approach based on the use of the Q-learning algorithm to train an autonomous vehicle agent to learn how to appropriately navigate roundabouts. The proposed learning algorithm is implemented using the CARLA simulation environment. Several simulations are performed to train the algorithm in two scenarios: navigating a roundabout with and without surrounding traffic. The results illustrate that the Q-learning-algorithm-based vehicle agent is able to learn smooth and efficient driving to perform maneuvers within roundabouts.
View Full-Text
Keywords:
reinforcement learning; Q-learning; autonomous driving; roundabouts; machine learning; simulation environment; driving behavior; environment perception
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
García Cuenca, L.; Puertas, E.; Fernandez Andrés, J.; Aliane, N. Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning. Electronics 2019, 8, 1536.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
