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...

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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
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Abstract 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.
AbstractList 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.
Author Paduraru, Ciprian
Paduraru, Miruna
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  givenname: Miruna
  surname: Paduraru
  fullname: Paduraru, Miruna
  email: mpaduraru@ea.com
  organization: Electronic Arts and University of Bucharest
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Snippet 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....
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SubjectTerms Correlation
Deep learning
Games
Manuals
motion forecasting
pedestrians
Predictive models
Real-time systems
Semantics
simulation software
video games
Title Pedestrian motion in simulation applications using deep learning
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