Lanes Are Not Enough: Enhancing Trajectory Prediction in Intralogistics Through Detailed Environmental Context

Abstract

Trajectory prediction is an essential component of the perception stack in autonomous mobile robots (AMRs). AMRs operate in complex environments where their movements are influenced by various environment elements, such as racks and storage locations. Therefore, accurate and efficient trajectory prediction for intralogistics requires detailed environment modeling that goes beyond the lane-based context mainly used in road traffic methods. We propose the addition of a new environment context encoder module that can be seamlessly integrated into state-of-the-art autonomous driving systems. Our approach, tailored to the specific challenges of intralogistics, achieves highly accurate predictions using compact and efficient baseline networks.