# Situation-Aware Pedestrian Trajectory Prediction with Spatio-Temporal Attention Model

arXiv: https://arxiv.org/abs/1902.05437 · HTML: https://mlsanity.com/paper/1902.05437/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 10 of 10)

- [Interpretable Social Anchors for Human Trajectory Forecasting in Crowds](https://mlsanity.com/paper/2105.03136/) (2021)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2112.02459/) (2021)
- [Relation-based Motion Prediction using Traffic Scene Graphs](https://mlsanity.com/paper/2212.02503/) (2022)
- [Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation](https://mlsanity.com/paper/2103.11471/) (2021)
- [Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network](https://mlsanity.com/paper/2404.19218/) (2024)
- [Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach](https://mlsanity.com/paper/2211.10226/) (2022)
- [SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation](https://mlsanity.com/paper/2409.00774/) (2024)
- [A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction](https://mlsanity.com/paper/2501.13973/) (2025)
- [MamMA: A Mamba-Based Pedestrian Trajectory Prediction Algorithm Considering Occupancy Map and Pedestrian Awareness States](https://mlsanity.com/paper/2609.08041/) (2026)

## Abstract (arXiv)

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving and robot navigation. However, predicting a pedestrian's trajectory in crowded environments is non-trivial as it is influenced by other pedestrians' motion and static structures that are present in the scene. Such human-human and human-space interactions lead to non-linearities in the trajectories. In this paper, we present a new spatio-temporal graph based Long Short-Term Memory (LSTM) network for predicting pedestrian trajectory in crowded environments, which takes into account the interaction with static (physical objects) and dynamic (other pedestrians) elements in the scene. Our results are based on two widely-used datasets to demonstrate that the proposed method outperforms the state-of-the-art approaches in human trajectory prediction. In particular, our method leads to a reduction in Average Displacement Error (ADE) and Final Displacement Error (FDE) of up to 55% and 61% respectively over state-of-the-art approaches.