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ICSE 2022
Sun 8 - Fri 27 May 2022
Fri 20 May 2022 15:12 - 15:37 at GAS room - Paper Session: Game Design & Simulation Chair(s): Dayi Lin

Paper Abstract: The goal of this paper is to provide a framework for simulating 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.

Fri 20 May

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14:30 - 15:45
Paper Session: Game Design & SimulationGAS at GAS room
Chair(s): Dayi Lin Centre for Software Excellence, Huawei, Canada
Towards Self-Adaptive Game Logic
Byron Devries Grand Valley State University, Erik Fredericks Grand Valley State University, Jared Moore Grand Valley State University
Developing Games with Data-Oriented Design
Jessica Bayliss Rochester Institute of Technology
Pedestrian motion in simulation applications using deep learning
Ciprian Paduraru University of Bucharest, Miruna Gabriela Paduraru University of Bucharest

Information for Participants
Fri 20 May 2022 14:30 - 15:45 at GAS room - Paper Session: Game Design & Simulation Chair(s): Dayi Lin
Info for room GAS room:

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