A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook

arXiv:2401.01454v2 Announce Type: replace
Abstract: Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys either focused on a limited number or lacked detailed investigation of dataset characteristics. To this end, we present an exhaustive study of 265 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions. We introduce a novel metric …

abstract advances algorithms annotation arxiv autonomous autonomous driving cs.cv dataset datasets deep learning deep learning techniques driving fundamental future hardware investigation outlook performance quality statistics survey surveys type