Incorporating Stochastic Human Driving States in Cooperative Driving Between a Human-Driven Vehicle and an Autonomous Vehicle

Modeling a human-driven vehicle is a difficult subject since human drivers have a variety of stochastic behavioral components that influence their driving styles. We develop a cooperative driving framework to incorporate dif-ferent human behavior aspects, including the attentiveness of a driver and the tendency of the driver following advising commands. To demonstrate the framework, we consider the merging coordination between a human-driven vehicle and an autonomous vehicle (AV) in a connected environment. We propose a stochastic model predictive controller (sMPC) to address the stochasticity in human driving behavior and design coordinated merging actions to optimize the AV input and influence human driving behavior through advising commands. Simulation and human-in-the-loop (HITL) experimental results show that our formulation is capable of accommodating a distracted driver and optimizing AV inputs based on human driving behavior recognition.