Conditioned Human Trajectory Prediction using Iterative Attention Blocks

Aleksey Postnikov, Aleksander Gamayunov, Gonzalo Ferrer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Human motion prediction is key to understand social environments, with direct applications in robotics, surveil-lance, etc. We present a simple yet effective pedestrian trajectory prediction model aimed at pedestrians' positions prediction in urban-like environments conditioned by the environment: map and surround agents. Our model is a neural-based architecture that can run several layers of attention blocks and transformers in an iterative sequential fashion, allowing to capture the important features in the environment that improve prediction. We show that without explicit introduction of social masks, dynamical models, social pooling layers, or complicated graph-like structures, it is possible to produce on par results with SoTA models, which makes our approach easily extendable and configurable, depending on the data available. We report results performing similarly with SoTA models on publicly available and extensible-used datasets with uni-modal prediction metrics ADE and FDE.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Robotics and Automation, ICRA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4599-4604
Number of pages6
ISBN (Electronic)9781728196817
DOIs
Publication statusPublished - 2022
Event39th IEEE International Conference on Robotics and Automation, ICRA 2022 - Philadelphia, United States
Duration: 23 May 202227 May 2022

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference39th IEEE International Conference on Robotics and Automation, ICRA 2022
Country/TerritoryUnited States
CityPhiladelphia
Period23/05/2227/05/22

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