Although Deep Reinforcement Learning (DRL) and Large Language Models (LLMs) each show promise in addressing decision-making challenges in autonomous driving, DRL often suffers from high sample complexity, while LLMs have difficulty ensuring real-time decision making. To address these limitations, we propose TeLL-Drive, a hybrid framework that integrates an Teacher LLM to guide an attention-based Student DRL policy. By incorporating risk metrics, historical scenario retrieval, and domain heuristics into context-rich prompts, the LLM produces high-level driving strategies through chain-of-thought reasoning. A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness across diverse driving conditions. Our experimental results, evaluated across multiple traffic scenarios, show that TeLL-Drive outperforms existing baseline methods, including other LLM-based approaches, in terms of success rates, average returns, and real-time feasibility. Ablation studies underscore the importance of each model component, especially the synergy between the attention mechanism and LLM-driven guidance. These findings suggest that TeLL-Drive significantly enhances both the adaptability and safety of autonomous driving systems, while offering a more efficient and scalable approach for policy learning. Full validation results are available on our website.

本研究解决了深度强化学习在自动驾驶决策中面临的高样本复杂度和实时决策难题。提出的TeLL-Drive框架结合了教师大型语言模型与关注机制的学生强化学习策略，通过上下文丰富的提示生成高水平驾驶策略，并加速策略收敛和增强鲁棒性。实验结果显示TeLL-Drive在各种交通场景中超越了现有方法，显著提高了自动驾驶系统的适应性和安全性。

TeLL-Drive：通过教师大型语言模型指导深度强化学习提升自动驾驶能力