Platoon-centered control for eco-driving at signalized intersection built upon hybrid MPC system, online learning and distributed optimization part I: Modeling and solution algorithm design

Abstract

Inspired by connected and autonomous vehicle (CAV) technologies, extensive studies have developed open-loop vehicle-level trajectory planning or speed advisory to promote eco-driving at traffic intersections. But few studies work on platoon-level closed-loop trajectory control, which can better sustain stream traffic smoothness and efficiency. Motivated by this research gap, this study developed a system optimal platoon-centered control for eco-driving (PCC-eDriving), which can guide a platoon mixed with connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) to smoothly approach, split as needed, and then sequentially pass signalized intersections, while reducing or even avoiding sharp deceleration and red idling. The effort is separated to Part I and Part II to prevent a lengthy article. Specifically, Part I of this study modeled the PCC-eDriving as a hybrid Model Predictive Control (MPC) system. It involves three MPC controllers for platoon trajectory control and a mixed-integer nonlinear program (MINLP) for optimal splitting decisions. Each MPC controller is integrated with robust vehicle dynamics and an online adaptive curve learning algorithm to factor control and vehicle driving uncertainties. An active-set-based optimal condition decomposition algorithm (AS-OCD) was developed to efficiently solve the MPC controllers' large-scale optimizers in a distributed manner. The numerical experiments built upon the field and simulated data indicated that the PCC-eDriving could significantly improve traffic smoothness and efficiency while reducing energy consumption and emission at urban signalized intersections. Part II will analyze and prove the sequential feasibility and the Input-to-State stability of the hybrid MPC system, as well as the convergence of the AS-OCD solution approach to theoretically sustain the performance of the hybrid MPC system.