Papers › Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

27 Dec 2023arXiv:2312.16620archive 2025-07-28

Amin Jalal Aghdasian, Amirhossein Heydarian Ardakani, Kianoush Aqabakee, Farzaneh Abdollahi

This paper proposes a novel approach by integrating sensor fusion with deep reinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, to develop an optimal control policy for self-driving cars. Our system employs a two-branch fusion method for vehicle image and tracking sensor data, leveraging the strengths of residual structures and identity mapping to enhance agent training. Through comprehensive comparisons, we demonstrate the efficacy of information fusion and establish the superiority of our selected algorithm over alternative approaches. Our work advances the field of autonomous driving and demonstrates the potential of reinforcement learning in enabling intelligent vehicle decision-making.

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CMVS-Lab/SAC-Residual-Fusion-CARLA official report

aliansgp/RL-SAC-CARLA report

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Autonomous Driving Decision Making Deep Reinforcement Learning Reinforcement Learning Self-Driving Cars Sensor Fusion reinforcement-learning

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