Pedestrian motion in simulation applications using deep learning

The goal of this paper is to provide a framework for simu-lating pedestrian motion in simulation applications by using real-world examples of human motion. This process has two implications. The first one refers to the reduction of the development time, since a deep learning model can replace the cl...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings of the 6th International ICSE Workshop on Games and Software Engineering: Engineering Fun, Inspiration, and Motivation pp. 1 - 8
Main Authors: Paduraru, Ciprian, Paduraru, Miruna
Format: Conference Proceeding
Language:English
Published: ACM 01.05.2022
Subjects:
Correlation
Deep learning
Games
Manuals
motion forecasting
pedestrians
Predictive models
Real-time systems
Semantics
simulation software
video games
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The goal of this paper is to provide a framework for simu-lating pedestrian motion in simulation applications by using real-world examples of human motion. This process has two implications. The first one refers to the reduction of the development time, since a deep learning model can replace the classical pedestrian behavior development process for the targeted applications. The second relates to improving the quality of pedestrian movements, as manual development of behavior using classical methods can result in movements that appear too robotic or predictable. We propose a new deep learning model based on an encoder-decoder strategy and Graph Attention Networks, able to take into account both the semantics of the scene and the correlations between the simulated pedestrian movements. The evaluation shows that the methods are suitable for real-time simulations, even for applications with performance constraints such as video games.
DOI:10.1145/3524494.3527624