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Human motion prediction is important for mobile service robots andintelligent vehicles to operate safely and smoothly around people. The moreaccurate predictions are, particularly over extended periods of time, thebetter a system can, e.g., assess collision risks and plan ahead. In thispaper, we propose to exploit maps of dynamics (MoDs, a class of generalrepresentations of place-dependent spatial motion patterns, learned from priorobservations) for long-term human motion prediction (LHMP). We present a newMoD-informed human motion prediction approach, named CLiFF-LHMP, which is dataefficient, explainable, and insensitive to errors from an upstream trackingsystem. Our approach uses CLiFF-map, a specific MoD trained with human motiondata recorded in the same environment. We bias a constant velocity predictionwith samples from the CLiFF-map to generate multi-modal trajectory predictions.In two public datasets we show that this algorithm outperforms the state of theart for predictions over very extended periods of time, achieving 45% moreaccurate prediction performance at 50s compared to the baseline.

CLiFF-LHMP: Using Spatial Dynamics Patterns for Long-Term Human Motion Prediction