This is NO LONGER THE MAIN information page of the competition
Main information page on ADL site
ADL Minicar Challenge, is a yearly software competition held by the Autonomous Driving Lab (ADL) and the Insitute of Computer Science. The task in this competition series is to create a software solution that can autonomously drive a toy car based on visual inputs.
Most of the solutions apply some form of data science, very often machine learning and artificial neural networks to solve the task. However, any software can be used, including hand-written rules and commands. We call it a software competition because upgrading the hardware is not allowed.
We strongly believe that self-driving with toy cars is an awesome way to learn the entire life cycle of data-driven solutions. In common data science student projects the data acquisition, cleaning and model deployment phases are omitted. In here, the student will be responsible for the entire process from data collection to multiple iterations of deployment and learning from failures. Having a solution successfully deployed in the real world looks good on any CV.
Competition in January 2023
Results of the 2023 competition (videos to be added soon):
1st place: Yevhen Pankevych and Volodymyr Savchuk, score 0.068, github
2nd place: Mihkel Lepson and Artur Tuttar, score 0.054 github
3rd place: Pavlo Pyvovar, score 0.020
github
The year 2023 competition will be held on Wednesday 25.01.2023. The hardware platform will still be DonkeyCar.
The rules are comprehensive and give a good idea of the task: read the rules
The main task will be driving in a toy town, with the challenges of avoiding pedestrians and static vehicles. Three new elements will be introduced in addition to Task1 of 2022 completion (see video of 2022 winner's performance below):
1) giving way on a pedestrian crossing
2) giving way to a vehicle on the right on a T-shaped intersection
3) adhering to "entering one-directional street" and "stop" signs
To participate, please register by sending an email to ardi.tampuu@ut.ee.
Participation in the competition can yield 6EAP if you register to Autonomous Vehicles Project LTAT.06.012, link: https://courses.cs.ut.ee/2022/AutVehProj/fall . It can also serve as a course project in various courses, e.g. Machine Learning, Neural Networks and Introduction to Data Science.
We will use the DonkeyCar S1 platform. These cars come equipped with a frontal camera which is the main sensor here. They also have an IMU (inertial measurement unit), that can but doesn't need to be used. The car is a 10:1 scale compared to a real car. Changing car hardware is not allowed in this competition, this is a software competition. The existing codebase is really nice and lets you get to collecting data in 10 minutes and training a neural network model in less than an hour.
Competition in January 2022 In January 2022 the second competition was held with 1:10 scale cars called DonkeyCar. There were two tasks: object avoidance and route completion. The main prizes for each tasks were 1000 euros.
The rewarded teams were:
Task 1: object avoidance and lane following promo video
1st place: Rustam Abdumalikov and Aral Açıkalın winners run
2nd place: Mike Camara
3rd place: Kristjan Roosild
Task 2: route completion pr video
1st place: Rustam Abdumalikov and Aral Açıkalın
2nd place: not awarded
3rd place: not awarded
Competition in January 2021
In January 2021 the first competition was held with 1:25 scale cars on a racing track. The winning solution completed the track in both directions with a clean driving and great speed. The rewarded teams were:
1st place: Leo Schoberwalter (video1, video2, video from onboad camera)
2nd place: Enlik and Handy Kurniawan (video1, video2)
3rd place: Thamara Luup, Mykyta Baliesnyi, Aleksasha Krylov (video1, video2)