Enhancing Autonomous Driving in Urban Scenarios: A Hybrid Approach with Reinforcement Learning and Classical Control

Rodrigo Gutierrez-Moreno, Rafael Barea, Elena López-Guillén, Felipe Arango, Fabio Sánchez-García, Luis M. Bergasa

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.

External IDs:doi:10.20944/preprints202410.1308.v1