HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways

• URL: http://arxiv.org/abs/2507.11621v1
• Date: Tue, 15 Jul 2025 18:01:29 GMT
• Title: HCOMC: A Hierarchical Cooperative On-Ramp Merging Control Framework in Mixed Traffic Environment on Two-Lane Highways
• Authors: Tianyi Wang, Yangyang Wang, Jie Pan, Junfeng Jiao, Christian Claudel,
• Abstract summary: A cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem.<n>While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow.<n>This paper proposes a HCOMC framework, consisting of a hierarchical cooperative planning model based on the modified virtual vehicle model, a discretionary lane-changing model based on game theory, and a multi-dominated optimization model.
• Score: 6.838130893718755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Highway on-ramp merging areas are common bottlenecks to traffic congestion and accidents. Currently, a cooperative control strategy based on connected and automated vehicles (CAVs) is a fundamental solution to this problem. While CAVs are not fully widespread, it is necessary to propose a hierarchical cooperative on-ramp merging control (HCOMC) framework for heterogeneous traffic flow on two-lane highways to address this gap. This paper extends longitudinal car-following models based on the intelligent driver model and lateral lane-changing models using the quintic polynomial curve to account for human-driven vehicles (HDVs) and CAVs, comprehensively considering human factors and cooperative adaptive cruise control. Besides, this paper proposes a HCOMC framework, consisting of a hierarchical cooperative planning model based on the modified virtual vehicle model, a discretionary lane-changing model based on game theory, and a multi-objective optimization model using the elitist non-dominated sorting genetic algorithm to ensure the safe, smooth, and efficient merging process. Then, the performance of our HCOMC is analyzed under different traffic densities and CAV penetration rates through simulation. The findings underscore our HCOMC's pronounced comprehensive advantages in enhancing the safety of group vehicles, stabilizing and expediting merging process, optimizing traffic efficiency, and economizing fuel consumption compared with benchmarks.

Related papers

• Heterogeneous Multi-Agent Reinforcement Learning for Distributed Channel Access in WLANs [47.600901884970845]
  This paper investigates the use of multi-agent reinforcement learning (MARL) to address distributed channel access in wireless local area networks.<n>In particular, we consider the challenging yet more practical case where the agents heterogeneously adopt value-based or policy-based reinforcement learning algorithms to train the model.<n>We propose a heterogeneous MARL training framework, named QPMIX, which adopts a centralized training with distributed execution paradigm to enable heterogeneous agents to collaborate.
  arXiv  Detail & Related papers  (2024-12-18T13:50:31Z)
• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• Multi-agent Path Finding for Mixed Autonomy Traffic Coordination [7.857093164418706]
  We propose a Behavior Prediction Kinematic Priority Based Search (BK-PBS) to forecast HDV responses to CAV maneuvers. Our work is directly applicable to many scenarios of multi-human multi-robot coordination.
  arXiv  Detail & Related papers  (2024-09-05T19:37:01Z)
• Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks [43.443526528832145]
  We propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method.<n>In order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model.
  arXiv  Detail & Related papers  (2024-06-17T06:16:07Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Adaptive Kalman-based hybrid car following strategy using TD3 and CACC [5.052960220478617]
  In autonomous driving, the hybrid strategy of deep reinforcement learning and cooperative adaptive cruise control (CACC) can significantly improve the performance of car following. It is challenging for the traditional hybrid strategy based on fixed coefficients to adapt to mixed traffic flow scenarios. A hybrid car following strategy based on an adaptive Kalman Filter is proposed by regarding CACC and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms.
  arXiv  Detail & Related papers  (2023-12-26T10:51:46Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach [6.475252042082737]
  This paper proposes an eco-driving framework for electric connected vehicles (CVs) based on reinforcement learning (RL) We show that our strategy can significantly reduce energy consumption by learning proper action schemes without any interruption of other human-driven vehicles (HDVs)
  arXiv  Detail & Related papers  (2022-06-24T04:11:28Z)
• Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections [7.860567520771493]
  Cooperative coordination at unsignalized road intersections aims to improve the safety driving traffic throughput for connected and automated vehicles. We introduce a model-free Markov Decision Process (MDP) and tackle it by a Twin Delayed Deep Deterministic Policy (TD3)-based strategy in the deep reinforcement learning framework. We show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve control in the realistic continuous flow.
  arXiv  Detail & Related papers  (2022-05-03T02:56:02Z)
• Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections [3.401874022426856]
  Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency. HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy. Experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.
  arXiv  Detail & Related papers  (2022-01-19T19:31:12Z)
• Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles [109.71532364079711]
  A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs) A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs. A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
  arXiv  Detail & Related papers  (2021-02-05T19:57:47Z)
• Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication [53.47785498477648]
  This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
  arXiv  Detail & Related papers  (2020-01-22T08:51:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.