ITSC 2025 Paper Abstract

Paper FR-EA-T38.3

Yumori, Torachika (The University of Osaka), Sawa, Fuma (The University of Osaka), Nishikawa, Hiroki (The University of Osaka), Taniguchi, Ittetsu (The University of Osaka), Onoye, Takao (The University of Osaka)

How Far Should We Coordinate?: Exploring Spatial Coordination Strategies for Scalable Autonomous Intersection Management

Scheduled for presentation during the Regular Session "S38b-Towards Scalable and Trustworthy AI in Connected Mobility" (FR-EA-T38), Friday, November 21, 2025, 14:10−14:30, Coolangata 2

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 Infrastructure Requirements for Connected and Automated Vehicles, Multi-vehicle Coordination for Autonomous Fleets in Urban Environments, Cooperative Driving Systems and Vehicle Coordination in Multi-vehicle Scenarios

Abstract

Autonomous Intersection Management (AIM) systems coordinate Connected and Autonomous Vehicles (CAVs) without traffic signals. These systems offer the potential for improved safety and efficiency. However, their scalability remains a major challenge, especially in determining the appropriate spatial extent of coordination, which is referred to as the control range and strongly affects computational cost. This paper investigates how both the design of the control range and the strategy for selecting vehicles to re-plan influence the trade-off between coordination performance and computation. We define the control range as a tunable parameter and propose a selection method that focuses only on vehicles that are likely to interact through potential collisions. Through simulation under various traffic densities, ranging from 5 to 20 vehicles, and different control range lengths from 25 to 150 m, we show that the proposed method approximately reduces intersection delays by up to 30% and lowers computation time by as much as 90%. Furthermore, we observe that both metrics tend to saturate when the control range exceeds approximately 100 m. These findings suggest that scalable coordination can be achieved by adaptively choosing which vehicles to re-plan and how far ahead to consider, based on traffic conditions.