Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control

This study proposes a novel mixed motion planning and tracking (MPT) control framework for autonomous vehicles (AVs) based on model predictive control (MPC), which is made up of an MPC-based longitudinal motion planning module, a feed-forward longitudinal motion tracking module, and an MPC-based integrated lateral motion planning and tracking module. First, given the global reference path and the surroundings information obtained from onboard devices and V2X network, the longitudinal motion planning based on a vehicle kinematics model is applied to determine the local target path, the desired longitudinal acceleration, and velocity considering the longitudinal safety priority. Then, based on the planned target path and longitudinal velocity, the integrated lateral MPT module based on a 2 degree-of-freedom vehicle model is developed to determine the optimal steering angle while satisfying the multiple kinematics and dynamics constraints. Finally, based on the desired longitudinal acceleration and the steering angle, the longitudinal forces of tires are determined. More importantly, co-simulations under several typical scenarios between MATLAB/Simulink and CarSim are conducted, and the results demonstrate excellent performance of the proposed mixed framework in both planning and tracking and also its real-time implementation.