WarpDriver

IEEE Transactions on Visualization and Computer Graphics, 2011

We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents towards desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with most current local collision-avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.

International Journal of Advanced Computer Science and Applications, 2016

We address the problem of simulating pedestrian crowd behaviors in real time. To date, two approaches can be used in modeling and simulation of crowd behaviors, i.e. macroscopic and microscopic models. Microscopic simulation techniques can capture the accuracy simulation of individualistic pedestrian behavior while macroscopic simulations maximize the efficiency of simulation; neither of them assures the two goals at the same time. In order to achieve the strengths of the two classes of crowd modeling, we propose a hybrid architecture for defining the complex behaviors of crowd at two levels, the individual behaviors, and the aggregate motion of pedestrian flow. It consists of combining a microscopic and a macroscopic model in an unified framework, we simulate individual pedestrian behaviors in regions of low density by using a microscopic model, and we use a faster continuum model of pedestrian flow in the remainder regions of the simulation environment. We demonstrate the flexibility and scalability of our interactive hybrid simulation technique in a large environment. This technique demonstrates the applicability of hybrid techniques to the efficient simulation of large-scale flows with complex dynamics.

2014

Crowd simulation is a large complex system that visualizes the behavior of crowd entities' movement and their interactions with the virtual environment. Crowd model is usually integrated into a virtual environment to make the environment alive. In the context of agent-based simulation (as in crowd simulation), it encompasses collision checking between moving agents that are present in the same environment. Hence, it is important to design an efficient and yet effective collision detection in crowd simulation. This is to ensure that it is cost effective toward computational processing usage and still produce a believable behavior. This paper presents a study of collision detection techniques in crowd models, and recent advancement to accelerate the process so that in turn, these efforts could also improve the performance and outcome of crowd model in virtual environment applications.

SIMULATION, 2021

Computer simulation of dense crowds is finding increased use in event planning, congestion prediction, and threat assessment. State-of-the-art particle-based crowd methods assume and aim for collision-free trajectories. That is an idealistic yet not overly realistic expectation, as near-collisions increase in dense and rushed settings compared with typically sparse pedestrian scenarios. Centroidal particle dynamics (CPD) is a method we defined that explicitly models the compressible personal space area surrounding each entity to inform its local pathing and collision-avoidance decisions. We illustrate how our proposed agent-based method for local dynamics can reproduce several key emergent dense crowd phenomena at the microscopic level with higher congruence to real trajectory data and with more visually convincing collision-avoidance paths than the existing state of the art. We present advanced models in which we consider distraction of the pedestrians in the crowd, flocking behavi...

2016

Real-time crowd simulation is a challenging task that demands a careful consideration of the classic trade-off between accuracy and efficiency. Existing particle-based methods have seen success in simulating crowd scenarios for various applications in the architecture, military, urban planning, robotics, and entertainment (film and gaming) industries. In this paper we focus on local dynamics and present an area-based penalty force that captures the infringement of each entity's personal space. This method does not necessitate a costly nearest-neighbor search and allows for an inherently data-parallel implementation that is capable of simulating thousands of entities at interactive frame-rates. The algorithm successfully reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.

Computer Graphics Forum, 2017

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.

The International Series in Video Computing, 2013

We present a system to simulate the movement of individual agents in large-scale crowds performing the Tawaf. The Tawaf serves as a unique test case; the large crowd consists of a heterogeneous set of pilgrims, varying in both physical capacity and activity. Furthermore, the density of the crowd reaches extremely high levels (up to eight people/m 2 .) This extreme density can place impractical constraints on simulation parameters. We use a velocity-space-based pedestrian model which exhibits consistent results even under extreme density: reciprocal velocity obstacles (RVO). Furthermore, we extend RVO to include priority and right of way-agents respond to potential collisions asymmetrically depending on context; one agent may yield, to varying degrees, to another. Our system uses a finite state machine to specify the behavior of the agents at each time step, to model the varied behaviors seen during the Tawaf. The finite-state machine, used in conjunction with RVO, generates collision-free trajectories for tens of thousands of agents in the performance of the Tawaf. The overall system can model agents with varying age, gender and behaviors, supporting the heterogeneity observed in the performance of the Tawaf, even at high densities.

2012

Abstract While walking through a crowd, a pedestrian experiences a large number of interactions with his neighbors. The nature of these interactions is varied, and it has been observed that macroscopic phenomena emerge from the combination of these local interactions. Crowd models have hitherto considered collision avoidance as the unique type of interactions between individuals, few have considered walking in groups. By contrast, our paper focuses on interactions due to the following behaviors of pedestrians.

2016 IEEE Virtual Reality (VR), 2016

We present an adaptive data-driven algorithm for interactive crowd simulation. Our approach combines realistic trajectory behaviors extracted from videos with synthetic multi-agent algorithms to generate plausible simulations. We use statistical techniques to compute the movement patterns and motion dynamics from noisy 2D trajectories extracted from crowd videos. These learned pedestrian dynamic characteristics are used to generate collision-free trajectories of virtual pedestrians in slightly different environments or situations. The overall approach is robust and can generate perceptually realistic crowd movements at interactive rates in dynamic environments. We also present results from preliminary user studies that evaluate the trajectory behaviors generated by our algorithm.

2010

We present a new algorithm for simulating large-scale crowds at interactive rates based on the Principle of Least Effort. Our approach uses an optimization method to compute a biomechanically energy-efficient, collision-free trajectory that minimizes the amount of effort for each heterogeneous agent in a large crowd. Moreover, the algorithm can automatically generate many emergent phenomena such as lane formation, crowd compression, edge and wake effects ant others. We compare the results from our simulations to data collected from prior studies in pedestrian and crowd dynamics, and provide visual comparisons with real-world video. In practice, our approach can interactively simulate large crowds with thousands of agents on a desktop PC and naturally generates a diverse set of emergent behaviors.