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<p>End-to-end autonomous driving technology breaks the constraints of traditional modular pipeline approaches by integrating perception, prediction, and planning within a single framework, achieving global optimization. Current end-to-end frameworks typically rely on deep learning planning, which requires extensive offline data for training. Deep reinforcement learning (DRL) algorithms are also popular, as they allow agents to adapt to environmental changes through reward functions. However, these frameworks cannot implement back-propagation with the perception module. Each approach has its strengths and weaknesses. This paper combines both frameworks by developing a bird’s eye view (BEV) feature extraction network to capture key traffic flow information, creating an end-to-end DRL planning framework based on BEV features. This shift transforms the technology from data-driven to behavior-driven. To improve training speed and quality, we propose an advanced imitation learning algorithm, validated through simulations in the CARLA simulator. Experimental results show that our approach outperforms other frameworks, enhancing the agent’s safety and efficiency.</p>

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BEV-Fused Imitation and Reinforcement Learning for Autonomous Driving Planning

  • 期刊论文

  • 发表日期：

    2025年10月14日

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  • Jie Xia,
  • Xiaodong Wu,
  • Min Xu

摘要

End-to-end autonomous driving technology breaks the constraints of traditional modular pipeline approaches by integrating perception, prediction, and planning within a single framework, achieving global optimization. Current end-to-end frameworks typically rely on deep learning planning, which requires extensive offline data for training. Deep reinforcement learning (DRL) algorithms are also popular, as they allow agents to adapt to environmental changes through reward functions. However, these frameworks cannot implement back-propagation with the perception module. Each approach has its strengths and weaknesses. This paper combines both frameworks by developing a bird’s eye view (BEV) feature extraction network to capture key traffic flow information, creating an end-to-end DRL planning framework based on BEV features. This shift transforms the technology from data-driven to behavior-driven. To improve training speed and quality, we propose an advanced imitation learning algorithm, validated through simulations in the CARLA simulator. Experimental results show that our approach outperforms other frameworks, enhancing the agent’s safety and efficiency.