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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computer graphics forum Ročník 36; číslo 2; s. 337 - 348
Hlavní autori: Dutra, T. B., Marques, R., Cavalcante‐Neto, J.B., Vidal, C. A., Pettré, J.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Blackwell Publishing Ltd 01.05.2017
Wiley
Predmet:
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
ISSN:0167-7055, 1467-8659
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13130