Autonomous driving represents a transformative shift in modern transportation, combining cutting-edge advancements in machine learning, robotics, and real-time systems engineering. By enabling vehicles to navigate and operate independently, this technology aims to enhance safety, reduce traffic congestion, and revolutionise mobility solutions. Central to autonomous driving is the development of algorithms and systems that allow vehicles to perceive their surroundings, interpret data from sensors, and make intelligent decisions. The field draws on interdisciplinary expertise, providing an exciting challenge for researchers and developers looking to shape the future of transportation.
| Course dates | 27 July - 7 August, 2026 (two weeks, 10 study days) |
|---|---|
| Course fee | 880 EUR |
| Course format | summer course |
| Study field | Computer Science, Artificial Intelligence |
| Language | English |
| Study group | master's, PhD students and lifelong learners |
| Location | Tartu University of Tartu Delta Centre, |
The course "Self-driving Cars" introduces basic concepts of self-driving cars using the software developed at the University of Tartu Autonomous Driving Lab as an example. The students implement core components of the modular autonomy stack from scratch in the Python programming language. They will test their software in the virtual Tartu simulation, which is a decimetre-level digital twin of the real city. The course ends with a demo ride in a self-driving Lexus vehicle that uses the very same software covered in the course.
| Course lecturer | Description |
|---|---|
| Tambet Matiisen | Tambet Matiisen runs the Autonomous Driving Lab at the University of Tartu. |
| Edgar Sepp | Edgar Sepp is a research engineer at the Autonomous Driving Lab. His background is in geoinformatics, and he is currently the lead engineer for the self-driving software. Live Demos |
| Karl-Johan Pilve | Karl-Johan Pilve is a research engineer at the Autonomous Driving Lab. Practical sessions |
| Dmytro Zabolotnii | Dmytro Zabolotnii is a doctoral researcher at the Autonomous Driving Lab. Practical sessions |
The highlight of the summer course was the demo ride on public streets. Seeing our implementations not only in simulation but also running on public streets, and the results they had in real life, was very precious to me.
Requirements:
Recommended:
NB! This is a preliminary programme. The final schedule will be sent to the participants two weeks before the course starts.
All lectures, practices and independent work will take place in computer classroom 2006 in the Delta building.
There are 25 computers available with Ubuntu and ROS pre-installed.
Day 1: Monday, 27 July
Introduction to ROS
2h lecture
2h guided practice
4h independent work
The students learn the main concepts in ROS: nodes, topics, and messages.
Assignment 1: implement a simple publisher and subscriber.
Day 2: Tuesday, 28 July
Localisation
2h lecture
2h guided practice
4h independent work
The students learn how the car determines its position on the map.
Assignment 2: implement GNSS localiser.
Day 3: Wednesday, 29 July
Control
2h lecture
2h guided practice
4h independent work
The students learn how the car follows a given trajectory by producing longitudinal and latitudinal commands.
Assignment 3: implement pure pursuit controller
Day 4: Thursday, 30 July
Global planner
2h lecture
2h guided practice
4h independent work
The students learn how the car plans a path from point A to point B.
Assignment 4: implement a global planner using Lanelet2 library.
Day 5: Friday, 31 July
Catchup day
4h tutors available
4h independent work
Saturday, 1 August: free day
Sunday, 2 August: free day
Day 6: Monday, 3 August
Obstacle detection
2h lecture
2h guided practice
4h independent work
The students learn how obstacle detection works with different sensors.
Assignment 5: implement a lidar-based object detector.
Day 7: Tuesday, 4 August
Local planner
2h lecture
2h guided practice
4h independent work
The students learn how the car reacts to the obstacles.
Assignment 6: implement a rule-based local planner.
Day 8: Wednesday, 5 August
Traffic light detection
2h lecture
2h guided practice
4h independent work
The students learn how the car detects the traffic lights and reacts to them.
Assignment 7: implement camera-based traffic light detector
Day 9: Thursday, 6 August
Validation & testing
2h lecture
2h guided practice
4h independent work
The students learn about different ways to validate and test self-driving cars.
Assignment 8: implement test scenario in Carla simulation.
Day 10: Friday, 7 August
Demo day
The students experience an autonomous ride around Tartu in the self-driving Lexus vehicle. The students will be divided into groups of 2-3 people. Each demo ride lasts 40 minutes. They can use the rest of the day to catch up with final assignment revisions.
Recommended reading:
Assignments:
Upon completion of the course, a student:
This course is fully booked and we are not accepting additional participants at the moment. Thank you for your interest. You are welcome to explore our other UniTartu Summer School courses.
Only fully completed applications, including all required annexes, received by the deadline (20 April) will be considered for selection. Applicants must submit the following:
The participants of the UniTartu Summer School courses are required to pay:
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