  Driving SMARTS is a regular competition designed to tackle problems caused by
the distribution shift in dynamic interaction contexts that are prevalent in
real-world autonomous driving (AD). The proposed competition supports
methodologically diverse solutions, such as reinforcement learning (RL) and
offline learning methods, trained on a combination of naturalistic AD data and
open-source simulation platform SMARTS. The two-track structure allows focusing
on different aspects of the distribution shift. Track 1 is open to any method
and will give ML researchers with different backgrounds an opportunity to solve
a real-world autonomous driving challenge. Track 2 is designed for strictly
offline learning methods. Therefore, direct comparisons can be made between
different methods with the aim to identify new promising research directions.
The proposed setup consists of 1) realistic traffic generated using real-world
data and micro simulators to ensure fidelity of the scenarios, 2) framework
accommodating diverse methods for solving the problem, and 3) baseline method.
As such it provides a unique opportunity for the principled investigation into
various aspects of autonomous vehicle deployment.
