Gradient‐based steering for vision‐based crowd simulation algorithms

• Published in: Computer 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
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2017; Wiley
• Subjects: 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
• DOI: 10.1111/cgf.13130

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.