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.

中文翻译：

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基于自动学习的自动驾驶数据优化方法

近年来自动驾驶爆发的主要原因之一是计算机视觉的巨大发展。随着模型参数数量的增加，许多大型自动驾驶模型需要部署在云端。车辆侧数据的不断增加，给传输带宽带来了巨大的压力。为了减少数据传输消耗，提高上传数据质量，我们设计了一种基于强化学习的数据优化方法。通过这种方法，会对收集到的数据进行过滤，只上传对模型训练有价值的数据，从而节省通信资源。结果表明，该方法通过实施数据优化，可以将采集数据减少到42.9%，同时模型训练的收敛速度提高46%。