An APF and MPC combined collaborative driving controller using vehicular communication technologies

Collaborative driving is a growing domain of Intelligent Transportation Systems (ITS) which aim to navigate traffic both efficiently and safely. Cooperation between vehicles heavily rely on the comprehensive information collected. With the development of vehicular communication technologies, information can be shared between vehicles or infrastructures through Vehicle-to-Vehicle (V2V)/Vehicle-to-Infrastructure (V2I) data exchange. By taking advantage of data sharing between vehicles, this paper proposes an Artificial Potential Field (APF) and Model Predictive Control (MPC) combined controller to implement collaborative driving in complex environments. Firstly, an APF model ​containing three components is developed to describe the mutual effect and collaboration properties between vehicles and surrounding environments. Afterwards, a MPC cost function for optimized control, considering both kinematic characteristics and environmental effect conveyed by APF, is presented to address the problem of collaborative driving. Such controller is designed from the perspective of multi-objective and multi-constraint optimization which takes the vehicle motion constraints, safety and comfort requirements into consideration. The prominent advantage of the proposed approach is that it can deal with the problems of route planning and manipulating simultaneously. To validate the proposed approach, a variety of scenario simulations are conducted in MATLAB, and the performance of the proposed method are verified.