Model-based design and processor-in-the-loop validation of a model predictive control for coupled longitudinal-lateral vehicle dynamics of connected and automated vehicles

This paper proposes a Model Predictive Control (MPC) strategy for Connected and Automated Vehicles (CAVs), able to work in intelligent cooperative driving environments and to provide Advanced Driver Assistance Systems (ADAS) at SAE automation level L4 (High Automation). The control strategy is implemented from a vehicle point of view, interfaced with a Smart Road infrastructure in order to: i) receive Cooperative-Intelligent Transportation Systems (C-ITS) services such as dynamic speed limits by Intelligent Speed Assistance (ISA); ii) provide ADAS functionalities such as Adaptive Cruise Control (ACC), Emergency Electronic Brake (EEB) and Lane Keeping System (LKS). The MPC design is based on nonlinear bicycle model for coupled longitudinal-lateral vehicle dynamics, extended with additional information related to an ahead vehicle. The MPC provides wheel torque and steering angle (at the wheels). We used MATLAB/Simulink (R2020b) to develop and verify model and control. The MPC was compiled for STM32 MCU to run Processor In the Loop (PIL) testing on an ST NUCLEO-H743ZI2. PIL provides information about memory usage of MPC and execution time on MCU. The main objective is to verify that the cycle execution time of the MPC is lower than 10 ms, the typical cycle time of CAN messages. The proposed methodology is general and can be applied to any kind of vehicle and for different road conditions.