Synthetic Datasets for Autonomous Driving: A Survey

arXiv:2304.12205v2 Announce Type: replace-cross
Abstract: Autonomous driving techniques have been flourishing in recent years while thirsting for huge amounts of high-quality data. However, it is difficult for real-world datasets to keep up with the pace of changing requirements due to their expensive and time-consuming experimental and labeling costs. Therefore, more and more researchers are turning to synthetic datasets to easily generate rich and changeable data as an effective complement to the real world and to improve the performance of algorithms. …

abstract arxiv autonomous autonomous driving costs cs.cv data datasets driving eess.iv experimental labeling quality quality data requirements researchers survey synthetic type world