Sequencing-Enabled Hierarchical Cooperative On-Ramp Merging Control for Connected and Automated Vehicles

Abstract

This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging multi-scale control framework. The proposed framework consists of a two-layer design: the upper level control sequences the vehicles to balance the density difference between mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation. Based on this, the lower-level control employs a longitudinal distributed model predictive controller (MPC) with a virtual car-following (CF) concept, to ensure constrained multi-objective optimality by actively generating merging gaps, ensuring safe merging and further smooth traffic. Additionally, an auxiliary lateral control is developed to maintain lane-keeping and merging smoothness while accommodating ramp geometric curvature. To validate the proposed framework, multiple numerical experiments are conducted. The results indicate that our upper-level controller significantly outperforms distance-based sequencing method. Furthermore, the results demonstrate the effectiveness of the lower-level control by rendering smooth control inputs, merging with safe spacing, and empirical local and string stability.