Compete in the 5th AI Driving Olympics (AI-DO)
The 5th edition of the Artificial Intelligence Driving Olympics (AI-DO 5) has officially started!
The AI-DO serves to benchmark the state of the art of artificial intelligence in autonomous driving by providing standardized simulation and hardware environments for tasks related to multi-sensory perception and embodied AI.
Duckietown hosts AI-DO competitions biannually, with finals events held at machine learning and robotics conferences such as the International Conference on Robotics and Automation (ICRA) and the Neural Information Processing Systems (NeurIPS).
Urban driving league challenges
This year’s Urban League includes a traditional AI-DO challenge (LF) and introduces two new ones (LFP, LFVM).
Lane Following (LF)
The most traditional of AI-DO challenges: have a Duckiebot navigate a road loop without intersection, pedestrians (duckies) or other vehicles. The objective is traveling the longest path in a given time while staying in the lane.
Lane following with Pedestrian (LFP)
The LFP challenge is new to AI-DO. It builds upon LF by introducing static obstacles (duckies) on the road. The objectives are the same as for lane following, but do not hit the duckies!
Lane Following with Vehicles, multi-body (LFVM)
In this traditional AI-DO challenge, contestants seek to travel the longest path in a city without intersections nor pedestrians, but with other vehicles on the road. Except this year there’s a twist. In this year’s novel multi-body variant, all vehicles on the road are controlled by the submission.
Getting started: the webinars
We offer a short webinar series to guide contestants through the steps for participating: from running our baselines in simulation as well as deploying them on hardware. All webinars are 9 am EST and free!
Learn about the Duckietown project and the Artificial Intelligence Driving Olympics.
How to run and build upon the “traditional” Robotic Operation System (ROS) baseline.
On the workflow for developing and deploying to Duckiebots, for hardware-based testing.
Learn how to use the Pytorch template for reinforcement learning approaches.
Introduction to the Tensorflow template, use of logs and simulator for imitation learning.
Advanced sensing league challenges
Previous AI-DO editions featured: detection, tracking and prediction challenges around the nuScenes dataset.
For the 5th iteration of AI-DO we have a brand new lidar segmentation challenge.
The challenge is based on the recently released lidar segmentation annotations for nuScenes and features an astonishing 1,400,000,000 lidar points annotated with one of 32 labels.
We hope that this new benchmark will help to push the boundaries in lidar segmentation. Please see https://www.nuscenes.org/lidar-segmentation for more details.
Furthermore, due to popular demand, we will organize the 3rd iteration of the nuScenes 3d detection challenge. Please see https://www.nuscenes.org/object-detection for more details.
AI-DO 5 Finals event
Thank you to our generous sponsors!
The Duckietown Foundation is grateful to its sponsors for supporting this fifth edition of the AI Driving Olympics!