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<p>Deep Reinforcement Learning has shown immense potential in autonomous driving decision-making; however, its application in safety–critical scenarios such as highways still faces significant challenges in data efficiency and safety performance. To address this, this paper proposes an LLM-guided Safety-Aware DQN (LSA-DQN) framework. This framework first utilizes a multi-head attention network to enhance the comprehension of complex traffic scenarios. Building on this, we design a method combining physics-based pre-screening and Large Language Model (LLM) arbitration to accurately classify experience data. Furthermore, we devise a hybrid safety regularization method that integrates Conservative Q-Learning with a Margin-based Contrastive Penalization (MCP) to learn explicit safety boundaries. Experimental results demonstrate that, compared to baseline algorithms, LSA-DQN reduces the collision rate to 0.9% while maintaining high traffic efficiency, proving its high robustness and reliability in complex and dynamic highway environments.</p>

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LLM-guided deep reinforcement learning with contrastive safety regularization for autonomous driving

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月30日

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  • Haochen Ren,
  • Yuelin Xing

摘要

Deep Reinforcement Learning has shown immense potential in autonomous driving decision-making; however, its application in safety–critical scenarios such as highways still faces significant challenges in data efficiency and safety performance. To address this, this paper proposes an LLM-guided Safety-Aware DQN (LSA-DQN) framework. This framework first utilizes a multi-head attention network to enhance the comprehension of complex traffic scenarios. Building on this, we design a method combining physics-based pre-screening and Large Language Model (LLM) arbitration to accurately classify experience data. Furthermore, we devise a hybrid safety regularization method that integrates Conservative Q-Learning with a Margin-based Contrastive Penalization (MCP) to learn explicit safety boundaries. Experimental results demonstrate that, compared to baseline algorithms, LSA-DQN reduces the collision rate to 0.9% while maintaining high traffic efficiency, proving its high robustness and reliability in complex and dynamic highway environments.