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

arXiv 2410.22987 ↗First posted 30 Oct 2024eess.SY cs.LG cs.NIcited by 0

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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.

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.

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References · 25

  1. 2021Convex optimizationcited by 2,515
  2. 2017OSQP: An Operator Splitting Solver for Quadratic Programscited by 161
  3. 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 41
  4. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  5. 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
  6. 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
  7. 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
  8. 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
  9. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  10. 2024Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learningcited by 17
  11. 2024Semantic-Aware Resource Allocation Based on Deep Reinforcement Learning for 5G-V2X HetNetscited by 17
  12. 2018A Swarming Approach to Optimize the One-hop Delay in Smart Driving Inter-platoon Communicationscited by 16
13 more
  1. 2024Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6Gcited by 15
  2. 2024Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computingcited by 15
  3. 2019Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Networkcited by 14
  4. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 13
  5. 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
  6. 2024Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learningcited by 11
  7. 2023Decentralized iLQR for Cooperative Trajectory Planning of Connected Autonomous Vehicles via Dual Consensus ADMMcited by 10
  8. 2024Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systemscited by 9
  9. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
  10. 2022Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Reviewcited by 6
  11. 2021Connected and Automated Vehicle Distributed Control for On-ramp Merging Scenario: A Virtual Rotation Approachcited by 5
  12. 2024Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocationcited by 5
  13. 2018Decentralized Resource Allocation via Dual Consensus ADMMcited by 1

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