2023/11/25 by Sixu Li, Yang Zhou, Li, Sixu +7 · 1 citation
Engineering · #FOS: Electrical engineering #Systems and Control (eess.SY) #Traffic control and management #Vehicle Dynamics and Control Systems #Vehicle emissions and performance #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.14924
openalex publication_date 2023/11/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper develops a sequencing-enabled hierarchical connected automated vehicle (CAV) cooperative on-ramp merging control framework. The proposed framework consists of a two-layer design: the upper level control sequences the vehicles to harmonize the traffic density across mainline and on-ramp segments while enhancing lower-level control efficiency through a mixed-integer linear programming formulation. Subsequently, the lower-level control employs a longitudinal distributed model predictive control (MPC) supplemented by a virtual car-following (CF) concept to ensure asymptotic local stability, l2 norm string stability, and safety. Proofs of asymptotic local stability and l2 norm string stability are mathematically derived. Compared to other prevalent asymptotic local-stable MPC controllers, the proposed distributed MPC controller greatly expands the initial feasible set. 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. Results indicate a notable outperformance of our upper-level controller against a distance-based sequencing method. Furthermore, the lower-level control effectively ensures smooth acceleration, safe merging with adequate spacing, adherence to proven longitudinal local and string stability, and rapid regulation of lateral deviations.