Stochastic Model Predictive Control for Coordination of Autonomous and Human-driven Vehicles

Abstract

In this paper, we investigate coordination of an autonomous vehicle (AV) and an intelligent human vehicle (IHV). The IHV is a human-driven vehicle that can communicate and collaborate with other vehicles while also providing advisory directives to the driver to optimize its maneuver. The objective is to optimize control inputs for the AV and advisory directives for the driver on the IHV to coordinate their motions. We consider a coordinated lane merging example where the two vehicles need to reach a prescribed separation before the lane merging maneuver. We model the motion of the IHV and the AV using a Discrete Hybrid Stochastic Automata (DHSA) and formulate a model predictive control (MPC) problem to generate optimal inputs to the two vehicles. In particular, the input to the IHV is advisory commands that stochastically transition the human state. Since solving the MPC involves mixed-integer programming (MIP), we leverage a machine learning approach to predict optimal integer values, thereby reducing the computational time of the optimization. Preliminary simulation results and experimental findings from a driving simulator reveal successful coordination between the IHV and the AV and enhanced merging performance when compared to the ‘no advising’ scenario.