Liu, Qichao (Southeast University), Ngoduy, Dong (Monash University), Yu, Weijie (School of Civil and Transportation Engineering, Hebei University), Wang, Jian (Southeast University)

Model Predictive Control for Coordinated Lane Assignment and Longitudinal Coordination in Intersection Lane Expansion Zones

Scheduled for presentation during the Regular Session "S40a-Cooperative and Connected Autonomous Systems" (FR-LM-T40), Friday, November 21, 2025, 11:50−12:10, 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

Abstract

This paper proposes a Coordinated Lane Assignment and Longitudinal Control (CLALC) strategy for Connected and Autonomous Vehicles (CAVs) in intersection lane expansion zones. To address spatial constraints and short maneuvering windows, the proposed method decouples lane allocation from trajectory planning and formulates a centralized Mixed Integer Linear Programming (MILP) model within a Model Predictive Control (MPC) framework. The model explicitly incorporates vehicle arrival dynamics, lateral-longitudinal coupling, and inter-vehicle constraints. Simulation experiments under varying traffic demands demonstrate that the CLALC strategy consistently reduces average delay by up to 6.6% and increases throughput by up to 8.8%. Moreover, the system adaptively adjusts lane utilization patterns based on demand intensity, favoring asymmetric allocation under low flow and balanced use under saturated conditions. These results validate the strategy's efficiency, adaptability, and scalability in complex urban scenarios.