REARL: A Closed-loop Autonomous Driving Simulation Enhancement Framework with Real Traffic Data and Large Language Models

arXiv:2609.19903 · cs.LG · Submitted 2026-09-17 · Read on arXiv

cs.LG

Submitted: 2026-09-17

Updated: 2026-09-24

Comments: 14 pages, 8 figures, 3 tables. Corresponding author: Yiwen Sun. This work was supported by the National Natural Science Foundation of China (Grant No. 62503015)

Code: https://github.com/eleurent/highway-env

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 35× in this paper · explained in 2 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Fidelity — 9× in this paper · explained in 2 episodes
• Action Space — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Adaptability — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Core — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Admissibility — 2× in this paper · explained in 2 episodes
• Bayesian Optimization — 2× in this paper · explained in 3 episodes
• IEEE Intelligent Vehicles Symposium — 2× in this paper
• Mean Absolute Percentage Error — 2× in this paper
• Proximal Policy Optimization — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Acquisition Function — 1× in this paper · explained in 1 episode
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Intelligent Driver Model — 1× in this paper
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Minimizing Overall Braking Induced by Lane Changes — 1× in this paper
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Proximal Policy Optimization (PPO) — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Bringing Diversity to Autonomous Vehicles: An Interpretable Multi-vehicle Decision-making and Planning Framework
• Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation
• DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
• A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
• A Survey of Large Language Models
• Language Models of Code are Few-Shot Commonsense Learners
• Large and Small Model Collaboration for Air Interface
• LinguaSim: Interactive Multi-Vehicle Testing Scenario Generation via Natural Language Instruction Based on Large Language Models
• A Multi-Agent LLM Framework for Design Space Exploration in Autonomous Driving Systems
• Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving
• Proximal Policy Optimization Algorithms

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