Using High Performance Computing in Vehicles to Create Image Datasets for Deep Learning

Modern vehicles are equipped with multiple sensors that help engineers to automate driving functions as much as possible. Among other types of sensors, these vehicles are equipped with multiple cameras that enable capturing the video data of the surrounding vehicles. These video data can be later used in training deep neural networks that are typically used within the vehicles to detect objects of interest around them. However, before we can use the video data for training, we must annotate them. Nowadays, the annotation process is typically semi-automated, where the initial annotations are added in a datacenter and then fine-tuned by human annotators. In this paper, we investigate the possibility to utilize high performance computing inside the vehicles in order to add initial annotations at the edge, instead of using the datacenter. With this approach, we expect a more scalable overall solution for the annotation process, because we utilize the computing power that is already available in the vehicles. For this investigation, we focus on heterogeneous automotive platforms of Intel® and analyze their potential for the annotation tasks at the automotive edge.