用大模型指导强化学习，让自动驾驶更安全高效。
SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
- 用大模型+检索增强生成替代随机探索，决策更可靠
- 融合注意力判别器和碰撞预测模块，显著降低事故率
- 适合关注自动驾驶安全与智能决策的研究者
确保自动驾驶系统在决策中的安全与效率仍是根本挑战。传统深度强化学习（DRL）存在不安全的随机探索和收敛缓慢问题，而大语言模型（LLMs）在实时推理中又存在固有延迟。为此，本文提出SARAD，一种融合LLMs与DRL的安全感知混合框架。SARAD以动态专家知识库中的RAG增强型大模型决策替代DRL的随机探索，并设计注意力判别器将大模型先验知识融入策略优化；同时，基于历史碰撞数据微调的碰撞预测模块进一步提升车辆安全性。大量实验表明，SARAD在Highway-Env模拟器中取得显著性能提升，验证了该模型在自动驾驶中的有效性。
原文摘要 · Abstract (English)
Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large Language Models (LLMs) demonstrate inherent latency in real-time inference operations. To address these limitations, this paper proposes SARAD, a novel safety-aware hybrid framework that synergizes LLMs and DRL for autonomous driving. SARAD substitutes the random exploration of DRL with Retrieval-Augmented Generation (RAG)-enhanced, LLM-guided decisions sourced from a dynamic expert knowledge repository. An attention discriminator is proposed to integrate the prior knowledge of LLMs into DRL policy optimization. A collision predictor module, fine-tuned with historical collision data, is further designed to improve vehicle safety. Extensive experiments show that SARAD achieves significant performance improvements in the Highway-Env simulator, validating the effectiveness of the proposed model in autonomous driving.
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