ITSC 2025 Paper Abstract

Paper TH-EA-T25.3

Cheng, Ming (Univ. of Jilin), Hu, Hongyu (Jilin University), Li, Zhengyi (Jilin University, China), Jin, Sheng (Zhejiang University)

Platoon Formation Strategy Considering CAV-HDV Co-Opetition Interactions in Mixed Traffic Environments

Scheduled for presentation during the Regular Session "S25b-Cooperative and Connected Autonomous Systems" (TH-EA-T25), Thursday, November 20, 2025, 14:10−14:30, Cooleangata 4

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, Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety, Data Analytics and Real-time Decision Making for Autonomous Traffic Management

Abstract

This paper addresses the challenge of collaborative platooning between connected automated vehicles (CAVs) and human-driven vehicles (HDVs) in mixed traffic environments. It proposes a platoon formation strategy based on quantifying co-opetition relationships and using a self-attention mechanism. By constructing an ICI-enhanced cross-vehicle matrix (ICI-CVM), the approach comprehensively evaluates the degree of cooperation and competition between CAVs and HDVs, quantifying indicators such as following intention, lane-changing competitiveness, stable following behavior, and interaction friendliness to form an integrated co-opetition index (ICI). Building on this foundation, the paper designs a CAV leadership qualification assessment mechanism that considers factors such as traffic density, reverse influence area length, platoon capacity, and positional advantages to screen potential lead vehicles. It also introduces a self-attention mechanism to optimize the platoon selection process, achieving global cooperative decision-making. Simulation experiments demonstrate that the proposed method of self-attention mechanism based on ICI (ICI-ATT) significantly outperforms traditional methods in terms of traffic capacity, fuel economy, and safety. With a CAV penetration rate of 70%, traffic capacity increases by 15.5% and fuel consumption decreases by 18.4%, while also achieving optimal platoon size and stability. This research provides theoretical support and technical approaches for efficient coordination in mixed traffic flows.