Enhancing Autonomous Driving Navigation Using Soft Actor-Critic

Badr Ben Elallid, Nabil Benamar, Miloud Bagaa and Yassine Hadjadj-Aoul ()
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Badr Ben Elallid: School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Nabil Benamar: School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Miloud Bagaa: Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada
Yassine Hadjadj-Aoul: Department of Computer Science, University of Rennes, Inria, CNRS, IRISA, 35000 Rennes, France

Future Internet, 2024, vol. 16, issue 7, 1-16

Abstract: Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on Deep Reinforcement Learning (DRL) show potential for tackling urban driving situations, they suffer from slow convergence, especially in complex scenarios with high mobility. In this paper, we present a new approach based on the Soft Actor-Critic (SAC) algorithm to control the autonomous vehicle to enter roundabouts smoothly and safely and ensure it reaches its destination without delay. For this, we introduce a destination vector concatenated with extracted features using Convolutional Neural Networks (CNN). To evaluate the performance of our model, we conducted extensive experiments in the CARLA simulator and compared it with the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) models. Qualitative results reveal that our model converges rapidly and achieves a high success rate in scenarios with high traffic compared to the DQN and PPO models.

Keywords: autonomous driving; deep reinforcement learning; navigation (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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