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.
@article{arxiv.2211.07545,
title = {NeurIPS 2022 Competition: Driving SMARTS},
author = {Amir Rasouli and Randy Goebel and Matthew E. Taylor and Iuliia Kotseruba and Soheil Alizadeh and Tianpei Yang and Montgomery Alban and Florian Shkurti and Yuzheng Zhuang and Adam Scibior and Kasra Rezaee and Animesh Garg and David Meger and Jun Luo and Liam Paull and Weinan Zhang and Xinyu Wang and Xi Chen},
journal= {arXiv preprint arXiv:2211.07545},
year = {2022}
}