# A Data-Efficient Approach for Long-Term Human Motion Prediction Using Maps of Dynamics

arXiv: https://arxiv.org/abs/2306.03617 · HTML: https://mlsanity.com/paper/2306.03617/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Risk-aware Trajectory Prediction by Incorporating Spatio-temporal Traffic Interaction Analysis](https://mlsanity.com/paper/2407.10639/) (2024)
- [Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets](https://mlsanity.com/paper/2510.03776/) (2025)

## Abstract (arXiv)

Human motion prediction is essential for the safe and smooth operation of mobile service robots and intelligent vehicles around people. Commonly used neural network-based approaches often require large amounts of complete trajectories to represent motion dynamics in complex semantically-rich spaces. This requirement may complicate deployment of physical systems in new environments, especially when the data is being collected online from onboard sensors. In this paper we explore a data-efficient alternative using maps of dynamics (MoD) to represent place-dependent multi-modal spatial motion patterns, learned from prior observations. Our approach can perform efficient human motion prediction in the long-term perspective of up to 60 seconds. We quantitatively evaluate its accuracy with limited amount of training data in comparison to an LSTM-based baseline, and qualitatively show that the predicted trajectories reflect the natural semantic properties of the environment, e.g. the locations of short- and long-term goals, navigation in narrow passages, around obstacles, etc.