Zexuan Jia

Autonomous driving significantly benefits from data-driven deep neural networks that require large-scale and high-quality datasets for training. Several well-known datasets such as nuScenes, CADC, and SUSCape have been applied to train deep models for autonomous driving. However, these datasets generally have many redundant and low-quality frames that take extra training time and affect the performance of the trained models. In this work, we propose a novel three-layer method by considering scene complexity, instance quality, and sensing uncertainty to remove redundant data and maintain valuable data for optimizing autonomous driving datasets with smaller data sizes and equivalent effectiveness. We apply the method to optimize four well-known datasets nuScenes, CADC, SUSCape, and Carla-4Scenes, which are optimized with 76.5%, 71.4%, 76.5%, and 81.2% original size. Furthermore, we retrain the well-known perception algorithms on these optimized datasets. The experimental results show that these retrained perception algorithms on the optimized datasets perform equivalent even better accuracy with less training time. We conclude that our three-layer method successfully optimizes autonomous driving datasets with smaller sizes and equivalent effectiveness, which contributes to faster training with lower hardware requirements.