Model Predictive Longitudinal Control for Autonomous Driving

In this paper, longitudinal control is implemented for autonomous driving using Model Predictive Control (MPC) algorithm. Initially, the longitudinal dynamic model of the vehicle is established then the influence of vehicle safe driving and driver comfort is taken into account. In the vehicle following mode Adaptive Cruise Control (ACC) system based on the Model predictive control is performing a longitudinal operation with the help of relative distance and the safe distance between vehicles. To control the longitudinal motion of the vehicle, the control has been divided into upper-level and lower-level control. To determine the desired acceleration, the higher-level control uses the model predictive control technique and the desired acceleration calculated by the high controller is converted by the lower controller into throttle opening and braking pressure using the vehicle inverse longitudinal dynamics model. This paper focuses only on the high-level control based on the model predictive controller, and calculates desired acceleration for a vehicle to move. Low-level control is based on the inverse longitudinal dynamics where the lower controller and vehicle are integrated and reduced to a first-order system with time ‘
$\tau$
’ and gain ‘
$k$
’. Different driving modes have been created using state flow and based on a relative and safe distance between the front and ego vehicle. Lastly, MATLAB/Simulink is used to test the longitudinal motion controller's reliability in various operating scenarios and results are carried out which ensure the safety and comfort of occupants and performed longitudinal control by selecting different driving modes.