TrajPRed: Trajectory Prediction with Region-based Relation Learning

arXiv:2404.06971v1 Announce Type: cross
Abstract: Forecasting human trajectories in traffic scenes is critical for safety within mixed or fully autonomous systems. Human future trajectories are driven by two major stimuli, social interactions, and stochastic goals. Thus, reliable forecasting needs to capture these two stimuli. Edge-based relation modeling represents social interactions using pairwise correlations from precise individual states. Nevertheless, edge-based relations can be vulnerable under perturbations. To alleviate these issues, we propose a region-based relation learning paradigm that models social interactions …

abstract arxiv autonomous autonomous systems correlations cs.ai cs.cv cs.lg edge forecasting fully autonomous future human interactions major mixed modeling prediction safety social stochastic systems traffic trajectory type