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PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X

arXiv 2501.12656 ↗First posted 22 Jan 2025cs.NI cs.LGcited by 0

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Abstract (arXiv)

On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 32 of its 91 references that are in the map. First posted on arXiv 22 Jan 2025.

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32 of 91 references.

References · 32

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2020Age of Information: An Introduction and Surveycited by 64
  3. 2022Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learningcited by 28
  4. 2022High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networkscited by 26
  5. 2022Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Methodcited by 23
  6. 2023Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoonscited by 23
  7. 2023URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computingcited by 22
  8. 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
  9. 2021AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learningcited by 17
  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
20 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. 2024Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networkscited by 12
  4. 2024Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learningcited by 11
  5. 2024Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learningcited by 11
  6. 2024Vehicle Selection for C-V2X Mode 4 Based Federated Edge Learning Systemscited by 9
  7. 2024Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computingcited by 9
  8. 2024Mobility-Aware Federated Self-supervised Learning in Vehicular Networkcited by 9
  9. 2024DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoVcited by 7
  10. 2024DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computingcited by 7
  11. 2024Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communicationscited by 6
  12. 2024DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoVcited by 6
  13. 2022Asynchronous Federated Learning for Edge-assisted Vehicular Networkscited by 5
  14. 2023Deep Reinforcement Learning Based Vehicle Selection for Asynchronous Federated Learning Enabled Vehicular Edge Computingcited by 5
  15. 2022Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communicationscited by 4
  16. 2023Enhanced C-V2X Mode 4 to Optimize Age of Information and Reliability for IoVcited by 3
  17. 2024Blockchain-Enabled Variational Information Bottleneck for IoT Networkscited by 3
  18. 2023Deep Reinforcement Learning Based Power Allocation for Minimizing AoI and Energy Consumption in MIMO-NOMA IoT Systemscited by 2
  19. 2019Performance Analysis of C-V2X Mode 4 Communication Introducing an Open-Source C-V2X Simulatorcited by 1
  20. 2020Age of Information Optimized MAC in V2X Sidelink via Piggyback-Based Collaborationcited by 1

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