An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving

• URL: http://arxiv.org/abs/2606.20640v1
• Date: Tue, 02 Jun 2026 15:15:05 GMT
• Title: An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving
• Abstract summary: This work combines deep reinforcement learning (DRL) for adaptive driving control with large language model (LLM)-based explainability modules.<n>DRL agents were trained in simulation using a Dueling Double Deep Q-Network to follow distinct driving requests.<n>LLM modules were introduced to interpret passenger requests, determine when explanations were needed, and generate concise, safety-oriented justifications.
• Score: 2.0646127669654826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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