Master Thesis on “Trajectory Planning and Tracking for Autonomous Vehicles Using Model Predictive Control Incorporating Vehicle Dynamics”

Supervisor: Prof. Ville Kyrki (
Advisors: Dr. Gökhan Alcan (, Dr. Andrei Aksjonov (

Keywords: autonomous vehicles, trajectory planning, trajectory tracking, collision avoidance, vehicle dynamics, model predictive control.

Project Description

Trajectory planning and tracking are two crucial parts of an autonomous driving software stack. These tasks are very challenging, because they must consider multiple constraints such as collision-free trajectory generation, efficient and comfortable path tracking. The problems get even more complicated when vehicle dynamics are also taken into consideration (e.g., tire-road adhesion, vehicle inertia, etc.). Nevertheless, the method of model predictive control (MPC) is a promising candidate to enable safe, efficient, and comfortable trajectory planning and tracking.

In this thesis, an MPC based trajectory planning and tracking method will be developed for an autonomous vehicle in simulation. A high fidelity driving simulator will be employed to incorporate vehicle dynamics in MPC constraints. The developed control must guarantee collision-free, comfortable and efficient driving performance in complex urban driving environment.


  • Related literature review,
  • Design of the simulation environment in high fidelity driving simulator,
  • Development of an MPC for trajectory planning and tracking incorporating vehicle dynamics,
  • Validation of the developed controller in complex urban driving environment for different scenarios.

Practical Information

Pre-requisites: Python / C++ (high), model predictive control (medium), vehicle dynamics (low)
Tools: Carla / LGSVL simulator, MATLAB / Simulink, ROS
Start: Available immediately