Institute of Computer Science
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  2. 2024/25 fall
  3. Autonomous Vehicles Project (LTAT.06.012)
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Autonomous Vehicles Project 2024/25 fall

  • Overview
  • First Meeting
  • Autoware Mini Course
  • 1:10 scale car track
  • Useful Links

Autoware Mini Course

The goal of the course is to give an overview how a self-driving car works using Autoware Mini as an example. Autoware Mini is a minimalistic Python-based autonomy software developed in-house by UT Autonomous Driving Lab. It has been field-tested with our Lexus RX450h vehicle on the streets of Tartu. Passing this course should give you a good enough background to start contributing to Autoware Mini or to start using it in your research.

The course consists of two parts:

  • Lectures and practicals introducing Autoware Mini.
  • A project to implement a change in Autoware Mini from an idea to a pull request.

Lectures and practicals take place for the first 8 weeks every Tuesday 12:15-15:45 in computer class 2006 in the Delta building. The first half focuses on introducing an Autoware Mini module and the second half involves implementing this module in Python.

In the later project you choose an issue from the Autoware Mini Gitlab, implement a fix and get it through the code review. It is also meant to introduce you to the industry's best practices in code maintenance and software quality.

The full course is worth 6 ECTS. It is possible to also do only the first part (Autoware Mini lectures and practicals) and earn 3 ECTS.

Course schedule

Schedule for lectures and practicals:

03.09.2024Introduction to ROS(slides)(practice)
10.09.2024Localizer(slides)(practice)
17.09.2024Controller(slides)(practice)
24.09.2024Global planner(slides)(practice)
01.10.2024Obstacle detection(slides)(practice)
08.10.2024Local planner(slides)(practice)
22.10.2024Traffic light detection(slides)(practice)
29.10.2024Validation(slides)(practice)

Schedule for projects:

29.10.2024Choose a project (Gitlab issue)
05.11.2024Submit an initial design (Gitlab issue comment)
12.11.2024Get a review for the initial design (Gitlab issue comment)
19.11.2024Implementation
26.11.2024Implementation
03.12.2024Code submitted for review (Gitlab merge request)
10.12.2024Get code review feedback (Gitlab merge request comments)
17.12.2024Final code submitted (updated Gitlab merge request, all comments closed)

Prerequisites

You should be fairly comfortable in:

  • Linux command line
  • Python programming, including Numpy

It is helpful if you know:

  • ROS (Robot Operating System)
  • General robotics, i.e. what are transforms
  • General machine learning, e.g. nearest neighbors search

Deliverables

To get 3 ECTS for lectures and practicals you need to present:

  • Working solutions (code) for all 8 practicals.

To get additional 3 ECTS for the project you need to present:

  • Final code as a merge request with all outstanding code review comments closed.

Contacts

Course instructors:

  • Tambet Matiisen, tambet.matiisen@ut.ee
  • Dmytro Zabolotnii, dmytro.zabolotnii@ut.ee

They can be found in Autonomous Driving Lab (room 3095 in Delta, behind Sandbox).

  • Institute of Computer Science
  • Faculty of Science and Technology
  • University of Tartu
In case of technical problems or questions write to:

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