# Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment

arXiv: https://arxiv.org/abs/1912.02149 · HTML: https://mlsanity.com/paper/1912.02149/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2303.01212/) (2023)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Multi-Agent Variational Occlusion Inference Using People as Sensors](https://mlsanity.com/paper/2109.02173/) (2021)
- [Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments](https://mlsanity.com/paper/2507.18808/) (2025)
- [Social Occlusion Inference with Vectorized Representation for Autonomous Driving](https://mlsanity.com/paper/2303.10385/) (2023)

## Abstract (arXiv)

This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real-world environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the spatial uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. Experiments performed using LIDAR data verified the real-time performance of the algorithm.