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

arXiv: https://arxiv.org/abs/2405.13547 · HTML: https://mlsanity.com/paper/2405.13547/

## Key facts

- Cited by 9 papers in the map (data through 14 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.

## Placed in

- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [DriveGPT4: Interpretable End-to-end Autonomous Driving via Large Language Model](https://mlsanity.com/paper/2310.01412/) (2023)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions](https://mlsanity.com/paper/2112.11561/) (2021)
- [Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving](https://mlsanity.com/paper/1905.02680/) (2019)
- [Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment](https://mlsanity.com/paper/1909.11538/) (2019)
- [Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.10171/) (2019)
- [Prediction Based Decision Making for Autonomous Highway Driving](https://mlsanity.com/paper/2209.02106/) (2022)
- [Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways](https://mlsanity.com/paper/2007.14032/) (2020)

## Built on by (most cited first, 9 of 9)

- [VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving](https://mlsanity.com/paper/2412.15544/) (2024)
- [Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions](https://mlsanity.com/paper/2501.04437/) (2025)
- [HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving](https://mlsanity.com/paper/2505.15793/) (2025)
- [A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective](https://mlsanity.com/paper/2503.23650/) (2025)
- [GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching](https://mlsanity.com/paper/2505.13669/) (2025)
- [Behavioral Cloning Models Reality Check for Autonomous Driving](https://mlsanity.com/paper/2409.07218/) (2024)
- [The Context of Crash Occurrence: A Complexity-Infused Approach Integrating Semantic, Contextual, and Kinematic Features](https://mlsanity.com/paper/2411.17886/) (2024)
- [VIVA+: Human-Centered Situational Decision-Making](https://mlsanity.com/paper/2509.23698/) (2025)
- [Textual Explanations and Their Evaluations for Reinforcement Learning Policy](https://mlsanity.com/paper/2601.02514/) (2026)

## 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.