A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and
  Outlook
Mingyu Liu Ekim Yurtsever Xingcheng Zhou Jonathan Fossaert Yuning Cui Bare Luka Zagar Alois C. Knoll
Autonomous driving has rapidly developed and shown promising performance with
recent advances in hardware and deep learning methods. High-quality datasets
are fundamental for developing reliable autonomous driving algorithms. Previous
dataset surveys tried to review the datasets but either focused on a limited
number or lacked detailed investigation of the characters of datasets. To this
end, we present an exhaustive study of over 200 autonomous driving datasets
from multiple perspectives, including sensor modalities, data size, tasks, and
contextual conditions. We introduce a novel metric to evaluate the impact of
each dataset, which can also be a guide for establishing new datasets. We
further analyze the annotation process and quality of datasets. Additionally,
we conduct an in-depth analysis of the data distribution of several vital
datasets. Finally, we discuss the development trend of the future autonomous
driving datasets.