Cooperative Driving between Autonomous Vehicles and Human-driven Vehicles Considering Stochastic Human Input and System Delay

Abstract

We investigate the coordination of autonomous vehicles (AVs) and intelligent human vehicles (IHVs) for merging on a two-lane road. An IHV is equipped with an automated system with advisory directives to the human driver to optimize its maneuver while communicating and collaborating with other vehicles. For optimal coordination of the two vehicles, modeling and incorporating stochasticity of the human driver’s actions in the IHV is important. We introduce a method of cooperative driving that considers multiple stochastic human parameters in the IHV, such as human intent and human input transitions. We also model the system to account for computational delays and the driver’s ability to follow advisory directives. The coordination actions for the AV and the IHV are generated in a stochastic model predictive control (sMPC) framework. Using simulated results, we demonstrate that the model considering stochastic effects of the human driver’s actions performs better and can mitigate the effect due to the driver’s inattentiveness while merging