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This paper proposes a novel approach by integrating sensor fusion with deepreinforcement learning, specifically the Soft Actor-Critic (SAC) algorithm, todevelop an optimal control policy for self-driving cars. Our system employs atwo-branch fusion method for vehicle image and tracking sensor data, leveragingthe strengths of residual structures and identity mapping to enhance agenttraining. Through comprehensive comparisons, we demonstrate the efficacy ofinformation fusion and establish the superiority of our selected algorithm overalternative approaches. Our work advances the field of autonomous driving anddemonstrates the potential of reinforcement learning in enabling intelligentvehicle decision-making.

Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning