Cooperative Ramp Merging in Mixed Traffic: Hybrid Model Predictive Control and Real Time Computations

Abstract

This paper focuses on the problem of decision making in mixed traffic with conventional human-operated vehicles (HVs) and connected automated vehicles (CAVs). To better handle the stochasticity in HV behaviors, the paper applies a closed-loop optimal control approach to extend the previously developed deterministic cooperative decision-making framework for mixed traffic (Deterministic-CDMMT). The proposed framework is illustrated using a ramp merging example. This framework can be described as an optimization problem in which a merge sequencing problem and a trajectory planning problem are embedded and solved by a bi-level hybrid centralized-decentralized model predictive control (HMPC) approach. The upper-level merge sequencing problem is solved using a dynamic-programming-based approach. Considering real-time computational performance, three solution methods, including dynamic programming (DP), dynamic matrix predictive control (DMC), and simplified discrete control (SDC), are developed to solve the lower-level trajectory planning problem. Simulation results show that compared to open-loop control, the proposed framework can effectively address the uncertainty caused by the stochastic driving behaviors of HVs. In addition, it is found that hybrid centralized-decentralized control can reduce the computational time to the millisecond-level and provide a similar, system-efficient performance compared to centralized control, potentially meeting real-time computational requirements.

Keywords: Cooperative decision-making for mixed traffic (CDMMT), Mixed traffic, Model predictive control (MPC), Hybrid centralized-decentralized control, Ramp merging.