Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88476
Title: Comparing dead reckoning algorithms for distributed car simulations
Authors: Chen, Youfu
Liu, Elvis S.
Keywords: Dead Reckoning
Distributed Virtual Environments
Engineering::Computer science and engineering
Issue Date: 2018
Source: Chen, Y., & Liu, E. S. (2018). Comparing dead reckoning algorithms for distributed car simulations. Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. doi:10.1145/3200921.3200939
Conference: Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
Abstract: Dead reckoning is an important technique used in distributed virtual environments (DVEs) to mitigate the bandwidth consumption of frequent state updates and the negative effects of network latency. This paper proposes a novel dead reckoning approach for common DVE applications such as multiplayer online games. Unlike traditional dead reckoning approaches that estimate the movements of remote entities with pure kinematic models, the new approach performs extrapolations with the considerations of environmental factors and human behaviours. We have performed experiments, based on a distributed car simulator, to compare the the new approach with representative existing dead reckoning approaches. The results show that the new approach gives more accurate predictions with an acceptable overhead.
URI: https://hdl.handle.net/10356/88476
http://hdl.handle.net/10220/49837
DOI: 10.1145/3200921.3200939
Schools: Interdisciplinary Graduate School (IGS) 
Organisations: Multi-plAtform Game Innovation Centre
Rights: © 2018 Association for Computing Machinery (ACM). All rights reserved. This paper was published in Proceedings of the 2018 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation and is made available with permission of Association for Computing Machinery (ACM).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IGS Conference Papers

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