Deep Neural Networks trained in a fully supervised fashion are the dominant technology in perception-based autonomous driving systems. While collecting large amounts of unlabeled data is already a major undertaking, only a subset of it can be labeled by humans due to the effort needed for high-quality annotation. Therefore, finding the right data to label has become a key challenge. Active learning is a powerful technique to improve data efficiency for supervised learning methods, as it aims at selecting the smallest possible training set to reach a required performance. We have built a scalable production system for active learning in the domain of autonomous driving. In this paper, we describe the resulting high-level design, sketch some of the challenges and their solutions, present our current results at scale, and briefly describe the open problems and future directions.
@article{arxiv.2004.04699,
title = {Scalable Active Learning for Object Detection},
author = {Elmar Haussmann and Michele Fenzi and Kashyap Chitta and Jan Ivanecky and Hanson Xu and Donna Roy and Akshita Mittel and Nicolas Koumchatzky and Clement Farabet and Jose M. Alvarez},
journal= {arXiv preprint arXiv:2004.04699},
year = {2020}
}