# V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario

arXiv: https://arxiv.org/abs/2410.22987 · HTML: https://mlsanity.com/paper/2410.22987/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 71 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [OSQP: An Operator Splitting Solver for Quadratic Programs](https://mlsanity.com/paper/1711.08013/) (2017)
- [Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic](https://mlsanity.com/paper/2105.05701/) (2021)
- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks](https://mlsanity.com/paper/2208.01890/) (2022)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2406.07213/) (2024)
- [Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNets](https://mlsanity.com/paper/2406.07996/) (2024)
- [A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communications](https://mlsanity.com/paper/1807.07301/) (2018)
- [Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G](https://mlsanity.com/paper/2402.01665/) (2024)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network](https://mlsanity.com/paper/1909.05665/) (2019)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08458/) (2024)
- [Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMM](https://mlsanity.com/paper/2301.04386/) (2023)
- [Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systems](https://mlsanity.com/paper/2401.07224/) (2024)
- [Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing](https://mlsanity.com/paper/2404.08444/) (2024)
- [Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review](https://mlsanity.com/paper/2202.09457/) (2022)
- [Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approach](https://mlsanity.com/paper/2103.15047/) (2021)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)
- [Decentralized Resource Allocation via Dual Consensus ADMM](https://mlsanity.com/paper/1809.07376/) (2018)

## Abstract (arXiv)

This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance.