ITSC 2025 Paper Abstract

Paper TH-EA-T24.2

Wang, Tianyi (The University of Texas at Austin), Wang, Yangyang (Tongji University), Pan, Jie (Tsinghua University), Jiao, Junfeng (the University of Texas at Austin), Claudel, Christian (UT Austin)

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

Scheduled for presentation during the Invited Session "S24b-Traffic Control and Connected Autonomous Vehicles: benefits for efficiency, safety and beyond" (TH-EA-T24), Thursday, November 20, 2025, 13:50−14:10, Coolangata 3

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Keywords Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios, Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Cooperative Vehicle-to-Vehicle Data Sharing for Safe and Efficient Traffic Flow

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.