Automatic learning-based data optimization method for autonomous driving

Abstract

One of the major reasons for the explosion of autonomous driving in recent years is the great development of computer vision. As the number of model parameters increases, many large autonomous driving models need to be deployed in the cloud. The increasing data on the vehicle side has put huge pressure on transmission bandwidth. In order to reduce data transmission consumption and improve the quality of uploaded data, we design a data optimization method based on reinforcement learning. Through this method, the collected data will be filtered and only those data that are valuable for model training will be uploaded, thereby saving communication resources. The results show that this method can reduce the collected data to 42.9% by implementing data optimization, while increasing the convergence speed by 46% in model training.