# Human Trajectory Prediction using Spatially aware Deep Attention Models

arXiv: https://arxiv.org/abs/1705.09436 · HTML: https://mlsanity.com/paper/1705.09436/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2017.

## Built on by (most cited first, 12 of 12)

- [Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction](https://mlsanity.com/paper/2207.09953/) (2022)
- [EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecasting](https://mlsanity.com/paper/2307.09306/) (2023)
- [SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model](https://mlsanity.com/paper/2403.18452/) (2024)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Continuous Locomotive Crowd Behavior Generation](https://mlsanity.com/paper/2504.04756/) (2025)
- [ManiCast: Collaborative Manipulation with Cost-Aware Human Forecasting](https://mlsanity.com/paper/2310.13258/) (2023)
- [Adaptive Human Trajectory Prediction via Latent Corridors](https://mlsanity.com/paper/2312.06653/) (2023)
- [Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation](https://mlsanity.com/paper/2103.11471/) (2021)
- [Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?](https://mlsanity.com/paper/2106.15321/) (2021)
- [Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments](https://mlsanity.com/paper/2306.00575/) (2023)
- [EMT: A Visual Multi-Task Benchmark Dataset for Autonomous Driving](https://mlsanity.com/paper/2502.19260/) (2025)

## Abstract (arXiv)

Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence. Thanks to a large number of applications like predicting abnormal events, navigation system for the blind, etc. there have been many approaches to attempt learning patterns of motion directly from data using a wide variety of techniques ranging from hand-crafted features to sophisticated deep learning models for unsupervised feature learning. All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects. We propose an end to end deep learning model to learn the motion patterns of humans using different navigational modes directly from data using the much popular sequence to sequence model coupled with a soft attention mechanism. We also propose a novel approach to model the static artefacts in a scene and using these to predict the dynamic trajectories. The proposed method, tested on trajectories of pedestrians, consistently outperforms previously proposed state of the art approaches on a variety of large scale data sets. We also show how our architecture can be naturally extended to handle multiple modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.