G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction System

arXiv:2210.09846v3 Announce Type: replace-cross
Abstract: Navigating dynamic physical environments without obstructing or damaging human assets is of quintessential importance for social robots. In this work, we solve autonomous drone navigation's sub-problem of predicting out-of-domain human and agent trajectories using a deep generative model. Our method: General-PECNet or G-PECNet observes an improvement of 9.5\% on the Final Displacement Error (FDE) on 2020's benchmark: PECNet through a combination of architectural improvements inspired by periodic activation functions and synthetic trajectory (data) augmentations using …

arxiv cs.ai cs.cv cs.lg cs.ro pedestrian prediction trajectory type