Gradient‐based steering for vision‐based crowd simulation algorithms

Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new percepti...

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Published inComputer graphics forum Vol. 36; no. 2; pp. 337 - 348
Main Authors Dutra, T. B., Marques, R., Cavalcante‐Neto, J.B., Vidal, C. A., Pettré, J.
Format Journal Article
LanguageEnglish
Published Oxford Blackwell Publishing Ltd 01.05.2017
Wiley
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Online AccessGet full text
ISSN0167-7055
1467-8659
DOI10.1111/cgf.13130

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Abstract Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision‐based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent's situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision‐based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases.
AbstractList Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision‐based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent's situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision‐based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases.
Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic simulation of the way humans navigate according to their perception of the surrounding environment. In this paper, we propose a new perception/motion loop to steering agents along collision free trajectories that significantly improves the quality of vision-based crowd simulators. In contrast with solutions where agents avoid collisions in a purely reactive (binary) way, we suggest exploring the full range of possible adaptations and retaining the locally optimal one. To this end, we introduce a cost function, based on perceptual variables, which estimates an agent’s situation considering both the risks of future collision and a desired destination. We then compute the partial derivatives of that function with respect to all possible motion adaptations. The agent then adapts its motion by following the gradient. This paper has thus two main contributions: the definition of a general purpose control scheme for steering synthetic vision-based agents; and the proposition of cost functions for evaluating the perceived danger of the current situation. We demonstrate improvements in several cases. T. B. Dutra acknowl1edges CAPES for the fellowship (PDSE Proc. 0130/13-3) and J. Ondˇrej for helping in the implementation of his model. R. Marques acknowledges the projects Percolation (ANR- 13-JS02-0008), and Kristina (H2020-RIA-645012) for partially financing this research.
Author Vidal, C. A.
Pettré, J.
Cavalcante‐Neto, J.B.
Dutra, T. B.
Marques, R.
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ContentType Journal Article
Copyright 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
2017 The Eurographics Association and John Wiley & Sons Ltd.
info:eu-repo/semantics/openAccess This is the peer reviewed version of the following article: Dutra TB, Marques R, Cavalcante-Neto JB, Vidal CA, Pettré J. Gradient-based steering for vision-based crowd simulation algorithms. Comput Graph Forum. 2017;36:337-48, which has been published in final form at http://dx.doi.org/10.1111/cgf.13130 This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Distributed under a Creative Commons Attribution 4.0 International License
Copyright_xml – notice: 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.
– notice: 2017 The Eurographics Association and John Wiley & Sons Ltd.
– notice: info:eu-repo/semantics/openAccess This is the peer reviewed version of the following article: Dutra TB, Marques R, Cavalcante-Neto JB, Vidal CA, Pettré J. Gradient-based steering for vision-based crowd simulation algorithms. Comput Graph Forum. 2017;36:337-48, which has been published in final form at <a href="http://dx.doi.org/10.1111/cgf.13130">http://dx.doi.org/10.1111/cgf.13130</a> This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
– notice: Distributed under a Creative Commons Attribution 4.0 International License
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Snippet Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic...
Most recent crowd simulation algorithms equip agents with a synthetic vision component for steering. They offer promising perspectives through a more realistic...
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SubjectTerms Algorithms
Animation
Categories and Subject Descriptors (according to ACM CCS)
Collision avoidance
Collision dynamics
Computer Science
Computer simulation
Cost function
Crowds
Enhanced vision
Graphics
Hazards
I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—Animation
I.6.5 [Simulation and Modeling]: Types of Simulation—Animation
Navigation
Perception
Simulation
Simulators
Steering
Three-dimensional graphics and realism
Types of simulation
Vision systems
Title Gradient‐based steering for vision‐based crowd simulation algorithms
URI https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fcgf.13130
https://www.proquest.com/docview/1901405968
https://recercat.cat/handle/2072/333612
https://inria.hal.science/hal-01653590
Volume 36
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