educational program

MiniRacecar is an impressive course which guides students through the programming in Python and assembling of a racecar which will be able to autonomously run through a track with a certain set of parameters and obstacles. It is provided online and is asynchronous.

MiniRacecar is offered at no cost by BWSI and is in English. The course can be fully completed through BWSI’s miniRacecar course software simulation and doesn’t require a physical car (for the teams who wish to acquire a car-kit, the car-kit is estimated to cost approximately €1,200 using the physical car’s specifications provided in the BWSI course). The 2nd Racecar Challenge does not require teams to use a physical car either.

Students and educators registering for miniRacecar will also have free access to BWSI Python Core course, an optional Python tutorial which students may want to complete first before starting miniRacecar, depending on their level in Python.

You can find miniRacecar’s curriculum here and BWSI Python Core’s curriculum here .


Their objective is to teach introductory topics in Python, Robotics and computer vision with OpenCV framework.



Continuous support is provided to teachers by the OpenEdx platform through the Piazza Forum, as well as by the Greek team supportRACECAR Greece.






A program that develops




Computer vision



Η κάμερα

O αισθητήρας LIDAR



Use the start-update paradigm to create a program which can run on the RACECAR.

Use the drive module to move the RACECAR.

Use the controller module to respond to input from the keyboard in real time.


Learn how to use Jupyter Notebooks.

Gain familiarity with OpenCV’s image processing functions.

Use contours to identify the size and locations of important objects.


Use depth images to calculate distances between objects and detect the closest one.

Reduce image noise with functions.

Use colored and depth images to detect and calculate the distance of an object.


Compare the advantages of using the depth sensing and LIDAR and identify situations where each would be best.

Convert raw LIDAR data into meaningful information about the surrounding environment.

Understand and implement rudimentary path planning.


Identify the corner location, orientation, color and ID of AR Markers.

Make decisions based on information provided by AR markers.

Learn about and use Python.