# Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review

arXiv: https://arxiv.org/abs/2202.09457 · HTML: https://mlsanity.com/paper/2202.09457/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2022.

## Built on by (most cited first, 6 of 6)

- [HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving](https://mlsanity.com/paper/2401.03160/) (2024)
- [AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging](https://mlsanity.com/paper/2408.03624/) (2024)
- [Traffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control](https://mlsanity.com/paper/2408.17380/) (2024)
- [Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation](https://mlsanity.com/paper/2307.02663/) (2023)
- [RACE-SM: Reinforcement Learning Based Autonomous Control for Social On-Ramp Merging](https://mlsanity.com/paper/2403.03359/) (2024)
- [V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario](https://mlsanity.com/paper/2410.22987/) (2024)

## Abstract (arXiv)

On-ramp merging areas are typical bottlenecks in the freeway network, since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper presents a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field. The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows, and merging into multilane freeways. Relevant literature is reviewed regarding the required technologies, control decision level, applied methods, and impacts on traffic performance. More importantly, we identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.