  In this paper a self-developed controller algorithm is presented with the
goal of handling a basic parking maneuver. One of the biggest challenges of
autonomous vehicle control is the right calibration and finding the right
vehicle models for the given conditions. As a result of many other research,
model predictive control (MPC) structures have started to become the most
promising control technique. During our work we implemented an MPC function
from white paper. Considering the low-speed conditions of a parking maneuver we
use a kinematic bicycle model as the basis of the controller. The algorithm has
two main inputs: a planned trajectory and the vehicle state feedback signals.
The controller is realized as a Simulink model, and it is integrated into a
complete autonomous control system using ROS framework. The results are
validated through multiple steps: using Simulink only with a pure kinematic
bicycle plant model; using LGSVL simulation framework containing a real vehicle
model and the entire software chain; the controller is prepared for real
vehicle tests.

Robotics