The AI Driving Olympics

The AI Driving Olympics (AI-DO) is a set of competition with the objective of evaluating the state of the art for ML/AI for embodied intelligence.

The Duckietown Foundation hosts the AI-DO competitions twice per year, at ICRA (International Conference on Robotics and Automation) and NeurIPS (Neural Information Processing Systems Conference).

The current edition is AI-DO 5 in conjunction with NeurIPS 2020.
There are two leagues:

  • The Urban Driving League uses the Duckietown Platform. Read on to know all about it.
  • The Advanced Perception League uses the nuScenes dataset/challenges and is organized by Motional.

AI-DO sponsors

Learn about the AI-DO

First webinar for AI-DO 5

Second webinar for AI-DO 5

Third webinar for AI-DO 5

Fourth webinar for AI-DO 5

Fifth webinar for AI-DO 5

AI-DO 3 @ ICRA 2019

Quotes from the experts

“It is great to see Duckietown host the AI Driving Olympics at ICRA and NIPS. What a fun way to demonstrate the real challenges in building and deploying self driving cars!”
John Leonard
Massachusetts Institute of Technology

“Understanding the behavior of AVs is pivotal
to assess their riskiness: Swiss Re enthusiastically
supports the Duckietown and AI Driving Olympics
Luigi Di Lillo
Swiss Re

“The AI Driving Olympics offer a glimpse of the challenges of creating self-driving cars. A playful but rigorous competition on a smaller and safer testbed is the best way to develop the creativity needed to make progress in this field.”
Emilio Frazzoli
ETH Zurich / Motional

“The AI Driving Olympics is a great way to push the limits of deep learning on physically embodied systems.”
Joshua Bengio
University of Monreal

Urban Driving League

It is based on the Duckietown platform, and includes a series of tasks of increasing complexity.

The Challenges

There are 3 challenges for AI-DO 5:

  1. Lane Following (LF), in which you need to follow a lane.
  2. Lane Following with Pedestrians Vehicles (LFP), in which you need to avoid the duckie-pedestrians.
  3. Lane Following with other Vehicles, multibody (LFV_multi), in which your agent is embodied in multiple vehicles.

Everyone can compete

Participants will not need to be physically present at any stage of the competition.

Competitors submit their solutions to specific challenges in the form of agents packaged as a Docker container..

The agents are evaluated first in simulation (remotely, locally and/or in the cloud), and then the same code is tested on physical robots in a Duckietown Autolab. The technical infrastructure supporting the AI-DO Urban Driving League is described here.

Resources available

We provide tools for competitors to use in the form of simulatorslogscode templatesbaseline implementations and low-cost access to robotic hardware.

Challenge server

The challenges server allows to control one’s submissions, and to see the leaderboards.

Get Started with the AI-Driving Olympics

(Hidden) Webinars


AI-DO 5 (NeurIPS 2020) Webinar 1
  • Introduction to AI-DO 5

AI-DO 5 (NeurIPS 2020) Webinar 2
  • ROS + Duckietown baseline

AI-DO 5 (NeurIPS 2020) Webinar 3
  • Local development on Duckiebot (DB19)

AI-DO 5 (NeurIPS 2020) Webinar 4
  • Reinforcement learning baseline

AI-DO 5 (NeurIPS 2020) Webinar 5
  • Imitation learning baseline

AI-DO 3 (2019)

AI-DO 3 (2019) Webinar 1
  • Introduction to AI-DO 3
  • Minimal agent template

AI-DO 3 (2019) Webinar 2
  • ROS template 
  • Duckietown baseline

AI-DO 3 (2019) Webinar 3
  • TensorFlow template
  • Imitation learning from logs and using a simulator

AI-DO 3 (2019) Webinar 4
  • PyTorch template
  • Reinforcement learning baseline

AI-DO 3 (2019) Webinar 5

  • Training in the cloud with SageMaker

AI-DO 3 (2019) Webinar 6

  • Local development: deploying on the Duckiebot (DB18)

(Hidden) Previous Webinars




Recorded Video

Thurs. Oct 31

  • Introduction
  • Description of the challenges
  • Minimal agent template

Fri. Nov 1

  • Classic template (ROS)
  • Duckietown baseline

Mon. Nov 4

  • Tensorflow template
  • IL logs
  • IL simulator

Tues. Nov. 5

  • Pytorch template
  • RL

Wed. Nov. 6

  • Training in the cloud

Thurs. Nov 14

  • Running on the Duckiebot


Do I need to attend the conference to compete?

No! If you are not present at the conference where the finals will be hosted we will run your submission on your behalf.

How do I get help?

Join the Duckietown international Slack community and ask away!

How do I get the hardware to test on a real robot?

Specially crafted hardware kits for each challenge are available here. For any question, you can reach out to hardware@duckietown.com. 

Past Competitions

The first edition of the AI-DO took place in December 2018, at the 2018 Neural Information Processing Systems (NeurIPS), the premiere machine learning conference, in Montréal. This was the first ever competition with real robots to take place at NeurIPS. AI-DO 1 only had the Urban league, and only the Lane Following challenge. There were over 1600 submissions from 58 unique participants.

Read a summary of the event here.

The second edition of AI-DO took place at the 2019 International Conference on Robotics and Automation (ICRA), with finals held in Montréal, Canada, in May 2019. 

AI-DO 2 comprised again only the Urban league, but additional challenges were added, such as Lane following with other vehicles (LFV) and Lane following with other Vehicles and Intersections (LFVI). The number of submissions to AI-DO 2 was similar to the number for AI-DO 1.

Find out who won!

The third edition was held at NeurIPS 2019 with finals held in Vancouver, Canada. In AI-DO 3 we introduced the advanced perception and racing leagues. AI-DO 3 received over 2000 submissions across all of the leagues.

Read all about it!

The fourth edition was scheduled for ICRA 2020 in Paris, France, but was unfortunately cancelled as a result of the COVID-19 outbreak. 

The fifth edition of AI-DO is in conjunction with NeurIPS 2020. Due to the COVID-19 pandemic, the conference and are held virtually. AI-DO 5 will comprise two leagues: Urban Driving and Advanced Perception with novel challenges in each.