Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations

arXiv:2403.13261v1 Announce Type: new
Abstract: The perception of motion behavior in a dynamic environment holds significant importance for autonomous driving systems, wherein class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming. Therefore, several annotation-efficient methods have been proposed to address this challenge. Although effective, these methods rely on weak annotations or additional multi-modal data like images, and …

abstract arxiv autonomous autonomous driving autonomous driving systems behavior class cloud cloud data cs.cv data driving dynamic environment importance labeling perception prediction spatial supervised learning systems temporal type