Version 1
: Received: 16 October 2024 / Approved: 16 October 2024 / Online: 16 October 2024 (15:23:14 CEST)
How to cite:
Gutierrez-Moreno, R.; Barea, R.; López-Guillén, E.; Arango, F.; Sánchez-García, F.; Bergasa, L. M. Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control. Preprints2024, 2024101308. https://doi.org/10.20944/preprints202410.1308.v1
Gutierrez-Moreno, R.; Barea, R.; López-Guillén, E.; Arango, F.; Sánchez-García, F.; Bergasa, L. M. Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control. Preprints 2024, 2024101308. https://doi.org/10.20944/preprints202410.1308.v1
Gutierrez-Moreno, R.; Barea, R.; López-Guillén, E.; Arango, F.; Sánchez-García, F.; Bergasa, L. M. Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control. Preprints2024, 2024101308. https://doi.org/10.20944/preprints202410.1308.v1
APA Style
Gutierrez-Moreno, R., Barea, R., López-Guillén, E., Arango, F., Sánchez-García, F., & Bergasa, L. M. (2024). Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control. Preprints. https://doi.org/10.20944/preprints202410.1308.v1
Chicago/Turabian Style
Gutierrez-Moreno, R., Fabio Sánchez-García and Luis M. Bergasa. 2024 "Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control" Preprints. https://doi.org/10.20944/preprints202410.1308.v1
Abstract
The use of DL algorithms in the domain of DM for AVs has garnered significant attention in the literature in the last years, showcasing considerable potential. Nevertheless, most of the solutions proposed by the scientific community encounter difficulties in real-world applications. This paper aims to provide a realistic implementation of a hybrid DM module in an AD stack, integrating the learning capabilities from the experience of DRL algorithms and the reliability of classical methodologies. This work encompasses the implementation of concatenated scenarios in simulated environments, and the integration of AD modules. Specifically, the authors address the DM problem by employing a POMDP formulation and offer a solution through the use of DRL algorithms. Furthermore, an additional control module to execute the decisions in a safe and comfortable way through a hybrid architecture is presented. The proposed architecture is validated in the CARLA simulator by navigating through multiple concatenated scenarios, outperforming the CARLA Autopilot in terms of completion time, while ensuring both safety and comfort.
Keywords
Autonomous Driving; Deep Reinforcement Learning; Decision-Making; Vehicle Control; CARLA Simulator.
Subject
Engineering, Transportation Science and Technology
Copyright:
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.
