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

2025-07-15

AI Summary

To address recurrent congestion and accidents in on-ramp merging zones under mixed two-lane highway traffic, this paper proposes a Hierarchical Cooperative On-ramp Merging Control (HCOMC) framework. The framework integrates virtual vehicle modeling, game-theoretic autonomous lane-changing decision-making, and multi-objective optimization (NSGA-II) to coordinate connected and autonomous vehicles (CAVs) with human-driven vehicles in heterogeneous traffic flows. Innovatively, it combines an enhanced Intelligent Driver Model (IDM) with a quintic polynomial lane-changing model and incorporates cooperative adaptive cruise control (CACC). Simulation results demonstrate that HCOMC significantly improves merging safety and stability across varying traffic densities and CAV penetration rates. Specifically, it achieves a 12.6% increase in throughput efficiency and a 9.3% reduction in average fuel consumption, outperforming existing benchmark methods in comprehensive performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

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.

Problem

• Develops a control framework for on-ramp merging in mixed traffic environments
• Addresses traffic congestion and accidents in two-lane highway merging zones
• Optimizes safety and efficiency for both human-driven and automated vehicles

Innovation

• Hierarchical cooperative planning with virtual vehicles
• Game theory-based discretionary lane-changing model
• Multi-objective optimization using genetic algorithm