# Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles

arXiv: https://arxiv.org/abs/2412.04341 · HTML: https://mlsanity.com/paper/2412.04341/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 84 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

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

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments](https://mlsanity.com/paper/1705.01693/) (2017)
- [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021)
- [Effective control of two-dimensional Rayleigh--B'enard convection: invariant multi-agent reinforcement learning is all you need](https://mlsanity.com/paper/2304.02370/) (2023)
- [Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning](https://mlsanity.com/paper/2301.10737/) (2023)
- [Multi-Agent Reinforcement Learning for Connected and Automated Vehicles Control: Recent Advancements and Future Prospects](https://mlsanity.com/paper/2312.11084/) (2023)
- [Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning](https://mlsanity.com/paper/1701.08832/) (2017)
- [A Fleet of Miniature Cars for Experiments in Cooperative Driving](https://mlsanity.com/paper/1902.06133/) (2019)
- [DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow](https://mlsanity.com/paper/2203.10639/) (2022)
- [Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach](https://mlsanity.com/paper/2103.15047/) (2021)
- [Coordinating Vehicle Platoons for Highway Bottleneck Decongestion and Throughput Improvement](https://mlsanity.com/paper/1907.13049/) (2019)
- [A Bi-Level Cooperative Driving Strategy Allowing Lane Changes](https://mlsanity.com/paper/1912.11495/) (2019)
- [Safety-Critical Traffic Control by Connected Automated Vehicles](https://mlsanity.com/paper/2301.04833/) (2023)
- [PDE-Based Feedback Control of Freeway Traffic Flow via Time-Gap Manipulation of ACC-Equipped Vehicles](https://mlsanity.com/paper/1812.08154/) (2018)
- [Pay to change lanes: A cooperative lane-changing strategy for connected/automated driving](https://mlsanity.com/paper/1903.04620/) (2019)
- [Output Feedback Control of Two-lane Traffic Congestion](https://mlsanity.com/paper/1905.01571/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives](https://mlsanity.com/paper/2503.13415/) (2025)

## Abstract (arXiv)

Lane change decision-making is challenging due to complex vehicle-vehicle and vehicle-infrastructure interactions. Existing lane-change control methods often rely on vehicles with some level of autonomy, limiting their applicability at low penetration rates of automated vehicles. To address this issue, we propose a lane-change regulation framework based on multi-agent reinforcement learning (MARL) to improve freeway traffic efficiency via connected vehicles. Regulation signals, such as allowing or prohibiting left or right lane changes, are computed at a traffic management center and broadcast to connected vehicles, while human-driven vehicles remain uncontrolled. Compared with vehicle-level maneuver control, the framework reduces communication and positioning requirements and avoids direct trajectory intervention. It combines microscopic traffic simulation with a macroscopic lane-grid representation: vehicle-level trajectories generate the realized traffic dynamics, while aggregated lane-grid states support low-cost grid-agent control. Based on a multi-lane macroscopic traffic model represented by partial differential equations (PDEs), lane changes are modeled as source-term exchanges between adjacent lanes and regulated through the MARL actions. Experiments across multiple traffic scenarios, demand levels, and connected-vehicle penetration rates show that the proposed method improves overall traffic efficiency with limited additional energy consumption while maintaining comparable driving safety.