A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook

• DOI: 10.48550/arxiv.2401.01454
• Publication Date: 2024-01-01
• Authors: Mingyu Liu; Ekim Yurtsever; Xingcheng Zhou; Jonathan Fossaert; Yuning Cui; Bare Luka Žagar; Alois Knoll
• Keywords: Statistic; Modalities

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