mlsanityData to 7 Oct 2026

HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model

arXiv 2405.13547 ↗First posted 22 May 2024cs.RO cs.AIcited by 9

On the map: End-to-end autonomous driving → Large language models for driving

Abstract (arXiv)

Autonomous driving is a complex task which requires advanced decision making and control algorithms. Understanding the rationale behind the autonomous vehicles' decision is crucial to ensure their safe and effective operation on highway driving. This study presents a novel approach, HighwayLLM, which harnesses the reasoning capabilities of large language models (LLMs) to predict the future waypoints for ego-vehicle's navigation. Our approach also utilizes a pre-trained Reinforcement Learning (RL) model to serve as a high-level planner, making decisions on appropriate meta-level actions. The HighwayLLM combines the output from the RL model and the current state information to make safe, collision-free, and explainable predictions for the next states, thereby constructing a trajectory for the ego-vehicle. Subsequently, a PID-based controller guides the vehicle to the waypoints predicted by the LLM agent. This integration of LLM with RL and PID enhances the decision-making process and provides interpretability for highway autonomous driving.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Placed in Large language models for driving, one of 9 directions in End-to-end autonomous driving. Builds on 16 of its 26 references that are in the map. First posted on arXiv 22 May 2024.

Builds on

16 of 26 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model2 Oct

Other references · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2023Mistral 7Bcited by 3,065
  6. 2017Billion-scale similarity search with GPUscited by 2,477
  7. 2022Planning-oriented Autonomous Drivingcited by 1,068
  8. 2023GPT-Driver: Learning to Drive with GPTcited by 344
  9. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  10. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  11. 2019Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Drivingcited by 52
  12. 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 20
3 more
  1. 2019Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learningcited by 14
  2. 2022Prediction Based Decision Making for Autonomous Highway Drivingcited by 4
  3. 2020Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freewayscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 1.

9 papers.

  1. 2024VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Drivingcited by 49
  2. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  3. 2025HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Drivingcited by 4
  4. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspectivecited by 2
  5. 2024Behavioral Cloning Models Reality Check for Autonomous Drivingcited by 1
  6. 2024The Context of Crash Occurrence: A Complexity-Infused Approach Integrating Semantic, Contextual, and Kinematic Featurescited by 1
  7. 2025GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matchingcited by 1
  8. 2025VIVA+: Human-Centered Situational Decision-Makingcited by 0
  9. 2026Textual Explanations and Their Evaluations for Reinforcement Learning Policycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving
  2. 2023DriveMLM: Aligning Multi-Modal Large Language Models with Behavioral Planning States for Autonomous Driving
  3. 2024Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving
  4. 2024Generating and Evolving Reward Functions for Highway Driving with Large Language Models
  5. 2025LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving
  6. 2023LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
  7. 2025ADRD: LLM-Driven Autonomous Driving Based on Rule-based Decision Systems
  8. 2025TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning