  Long-term human motion prediction (LHMP) is essential for safely operating
autonomous robots and vehicles in populated environments. It is fundamental for
various applications, including motion planning, tracking, human-robot
interaction and safety monitoring. However, accurate prediction of human
trajectories is challenging due to complex factors, including, for example,
social norms and environmental conditions. The influence of such factors can be
captured through Maps of Dynamics (MoDs), which encode spatial motion patterns
learned from (possibly scattered and partial) past observations of motion in
the environment and which can be used for data-efficient, interpretable motion
prediction (MoD-LHMP). To address the limitations of prior work, especially
regarding accuracy and sensitivity to anomalies in long-term prediction, we
propose the Laminar Component Enhanced LHMP approach (LaCE-LHMP). Our approach
is inspired by data-driven airflow modelling, which estimates laminar and
turbulent flow components and uses predominantly the laminar components to make
flow predictions. Based on the hypothesis that human trajectory patterns also
manifest laminar flow (that represents predictable motion) and turbulent flow
components (that reflect more unpredictable and arbitrary motion), LaCE-LHMP
extracts the laminar patterns in human dynamics and uses them for human motion
prediction. We demonstrate the superior prediction performance of LaCE-LHMP
through benchmark comparisons with state-of-the-art LHMP methods, offering an
unconventional perspective and a more intuitive understanding of human movement
patterns.
