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This study presents an approach to formulating the optimal control policy for self-driving cars. Our method integrates sensor fusion with deep reinforcement learning, specifically utilizing the Soft Actor-Critic (SAC) algorithm. By leveraging identity mapping and residual structures, we improve agent training through a two-branch fusion technique for vehicle image and tracking sensor data. We introduce a Non-linear Auto-Regressive model with Exogenous inputs (NARX) within the sensor fusion architecture. This model incorporates actions from previous time steps to capture temporal dependencies in the sensor data, providing a deeper understanding of the environment for the Reinforcement Learning (RL) agent and enhancing decision-making. Our research demonstrates this information fusion approach’s effectiveness and highlights our method’s advantages through comprehensive comparisons. It illustrates the practical application of reinforcement learning in improving intelligent vehicle decision-making and contributes to the evolving field of autonomous driving. This advancement can positively affect how self-driving cars operate, potentially fostering more trust in the progress of autonomous driving technology.

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Enhancing Autonomous Vehicle Control Through Sensor Fusion, NARX-Based Reinforcement Learning with Soft Actor-Critic (SAC) in CARLA Simulator

  • 会议论文

  • 发表日期：

    2025年

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  • Seyed Ali Abdollahian,
  • Sina Fazel,
  • Mohadeseh Rezaei Hadadi,
  • Amin Jalal Aghdasian,
  • Soroush Sadeghnejad

摘要

This study presents an approach to formulating the optimal control policy for self-driving cars. Our method integrates sensor fusion with deep reinforcement learning, specifically utilizing the Soft Actor-Critic (SAC) algorithm. By leveraging identity mapping and residual structures, we improve agent training through a two-branch fusion technique for vehicle image and tracking sensor data. We introduce a Non-linear Auto-Regressive model with Exogenous inputs (NARX) within the sensor fusion architecture. This model incorporates actions from previous time steps to capture temporal dependencies in the sensor data, providing a deeper understanding of the environment for the Reinforcement Learning (RL) agent and enhancing decision-making. Our research demonstrates this information fusion approach’s effectiveness and highlights our method’s advantages through comprehensive comparisons. It illustrates the practical application of reinforcement learning in improving intelligent vehicle decision-making and contributes to the evolving field of autonomous driving. This advancement can positively affect how self-driving cars operate, potentially fostering more trust in the progress of autonomous driving technology.