The competition will use the Duckietown platform, a scaled-down affordable and accessible vision-based self-driving car platform used for autonomy education and research. This open-source project originated at MIT in 2016 and is now used by many institutions worldwide.
The AI Driving Olympics is presented in collaboration with 6 academic institutions: ETH Zurich (Switzerland), Université de Montréal (Canada), NCTU (Taiwan), TTIC (USA), Tsinghua (China) and Georgia Tech (USA), as well as two industry co-organizers: nuTonomy and Amazon Web Services (AWS).
The competition will comprise 5 challenges of increasing complexity: 1) Road following on an empty road; 2) Road following with obstacles; 3) Point-to-point navigation in a city network; 4) Point to point navigation in a city network with other vehicles; and 5) Fleet planning for a full autonomous mobility on demand system.
Competitors will have access to simulators, logs, reference implementations, and finally real environments (“Robotariums”) that will be remotely accessible for evaluation. The entries that score best in the robotariums will be run during the live event at NIPS 2018 to determine the winners.
The competition aims at directing academic research towards the hard problems of embodied AI, such as modularity of learning processes, and learning in simulation while deploying in reality. The competition also promotes the democratization of AI/robotics research by offering a common infrastructure available to everybody through the use of remote testing facilities.
Competitors can also build their own Duckiebots using provided DIY instructions, or buy Duckiebots and Duckietown hardware through a kickstarter campaign.
For rules and timeline, please see the site https://driving-olympics.ai/