A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook
This survey addresses the need for comprehensive dataset analysis in autonomous driving research, which is incremental but provides valuable benchmarking and standardization insights for researchers and practitioners.
The authors conducted an exhaustive survey of 265 autonomous driving datasets, analyzing their characteristics, annotation quality, and impact on system performance, while introducing a novel metric to evaluate dataset impact and guide future dataset creation.
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 to evaluate the impact of datasets, which can also be a guide for creating new datasets. Besides, we analyze the annotation processes, existing labeling tools, and the annotation quality of datasets, showing the importance of establishing a standard annotation pipeline. On the other hand, we thoroughly analyze the impact of geographical and adversarial environmental conditions on the performance of autonomous driving systems. Moreover, we exhibit the data distribution of several vital datasets and discuss their pros and cons accordingly. Finally, we discuss the current challenges and the development trend of the future autonomous driving datasets.