用大模型解释自动驾驶决策，让乘客看懂为什么变道或急刹。
An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving
- DRL+LLM双架构：强化学习控制驾驶，大模型生成安全解释
- 三种模式稳定切换：快、舒、停，全程遵守交通规则
- 适合关注安全透明度的乘客或测试人员，尤其在紧急干预时
自动驾驶虽有望提升出行安全与效率，但公众信任仍受限于决策过程不透明。本文结合深度强化学习（DRL）实现自适应驾驶控制，并引入大语言模型（LLM）解释模块，向乘客传达行为理由。DRL代理在仿真中采用双分支双深度Q网络训练，响应‘快’‘舒’‘停’三类驾驶请求，展现稳定学习能力、规则合规性及单次行程内模式可靠切换。同时，LLM模块解析乘客请求，判断解释时机并生成简洁、以安全为核心的说明。结果表明，该框架实现了决策安全性、适应性与可解释性的平衡，在请求延迟或因安全约束被覆盖时效果最佳。
原文摘要 · Abstract (English)
Autonomous vehicles offer the potential for safer and more efficient mobility, yet public trust remains limited due to the lack of transparency in their decision-making. This work addresses this issue by combining deep reinforcement learning (DRL) for adaptive driving control with large language model (LLM)-based explainability modules designed to communicate agent behavior to passengers. DRL agents were trained in simulation using a Dueling Double Deep Q-Network to follow distinct driving requests: \textit{fast}, \textit{comfort}, and \textit{stop}. They demonstrated stable learning, safe compliance with traffic rules, and reliable switching between modes within a single trip. In parallel, LLM modules were introduced to interpret passenger requests, determine when explanations were needed, and generate concise, safety-oriented justifications. Results show that this framework, serving as a proof of concept for integrating RL decision-making and LLMs, balances safety, adaptability, and explainability, and is most effective when requests are delayed or overridden due to safety constraints.
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