Velocity-based modeling of physical interactions in dense crowds

We present an interactive algorithm to model physics-based interactions in dense crowds. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We combine velocity-based collision-avoidance algorithms with external physical forces. The overall formulation produces various effects of forces acting on agents and crowds, including balance recovery motion and force propagation through the crowd. We further extend our method to model more complex behaviors involving social and cultural rules. We use finite-state machines to specify a series of behaviors and demonstrate our approach on many complex scenarios. Our algorithm can simulate a few thousand agents at interactive rates and can generate many emergent behaviors.     

Directing crowd simulations using navigation fields

We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents toward 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 the 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.     

Right of way

Pedestrian models typically represent interactions between agents in a symmetric fashion. In general, these symmetric relationships are valid for a large number of crowd simulation scenarios. However, there are many cases in which symmetric responses between agents are inappropriate, leading to unrealistic behavior or undesirable simulation artifacts. We present a novel formulation, called right of way, which provides a well-disciplined mechanism for modeling asymmetric relationships between pedestrians. Right of way is a general principle, which can be applied to different types of pedestrian models. We illustrate this by applying right of way to three different pedestrian models (two based on social forces and one based on velocity obstacles) and show its impact in multiple scenarios. Particularly, we show how it enables simulation of the complex relationships exhibited by pilgrims performing the Islamic religious ritual, the Tawaf.     

人群应急疏散可视仿真研究进展和问题

人群应急疏散可视仿真是用智能体来模拟具有自主感知、情绪和行为能力的人群个体,并采用3维可视的方式来直观呈现人群应急疏散情景,可以为制定人群应急预案提供形象直观的分析方法。本文从人群仿真数据的来源、人群导航模型的构建、人群行为模型、人群情绪感染、人群渲染5个方面概述目前研究的进展,然后从仿真模型的可验证性、人群疏散导航模型的构建、人与环境的物理模型、动物逃生实验与仿真、疏散中的社会行为表现以及人群情绪的可视计算6个角度讨论需要进一步研究的问题。针对需要深入研究的问题,指出借助于紧急事件的视频监控分析和虚拟人群情景的用户调查,有助于完善人群仿真模型。结合物理模型,可以更准确地描述人群应急疏散场景。开展动物逃生实验分析,有助于完善人群运动导航算法。建立人群社会行为模型,可以更详细描述疏散中人群行为的多样性。构建基于多通道感知的人群情绪感染计算方法,可以详尽描述情绪感染的过程。人群应急疏散行为的可视仿真研究在城市的安全管理方面具有重要的应用前景,但其研究仍存在很多亟待解决的问题,综合地运用多学科知识,完善实验手段是进一步推动研究的关键所在。     

SITUATED CELLULAR AGENTS APPROACH TO CROWD MODELING AND SIMULATION

The article describes a multi-agent approach to crowd modeling and simulation. After a brief introduction of the Situated Cellular Agents model, the guidelines to the crowd modeling approach is introduced as a way to support the communication among the different actors that are part of the simulation project team. The approach is then applied to describe a complex scenario providing a blend of competitive and cooperative behavior for pedestrian agents: an underground station. A module supporting the effective 3D visualization of simulated crowd dynamics is finally introduced, as an instrument for the communication of simulation results to decision makers and nonexperts in crowd phenomena.     

ProactiveCrowd: Modelling Proactive Steering Behaviours for Agent‐Based Crowd Simulation

How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.     

人群应急疏散中一种多智能体情绪感染仿真模型

为了从情绪的视角分析紧急情境下人群的疏散行为,梳理了现有情绪感染的研究工作,总结了人群紧急状况下行为特点.采用智能体描述人群个体,提出一种多智能体情绪感染模型.其主体框架分为感知层、情绪层、感染层、行为层和行动层.归纳了产生情绪感染现象的3个条件及情绪感染的3个规则,提出了情绪感染的算法,考虑个体的个性和个体间距离因素,采用情绪强度和人群紧密度来计算个体疏散速度.用C#语言编制了仿真实验,采用真实的地震疏散案例,验证了仿真疏散时间和实际观测的基本一致.通过与以往基于传染病思路的情绪感染模型对比,所提出的模型可以更好地描述情绪感染从局部到整体的过程.实验结果表明,所提出的模型可以推演情绪驱动下的群体聚集行为,有望为制定应急疏散预案提供一种可视化分析方法.     

建筑物火灾中的人群疏散研究

建筑物火灾是我国频发的安全事故,所以应研究建筑物火灾人群安全疏散问题。由于在建筑物火灾中,人群疏散时出现拥堵,存在不安全因素,造成人员伤亡。针对在现有的研究中未考虑人员行为的影响,提出了智能体(Agent)的人群行为建模技术在建筑物火灾中的人群疏散仿真中的应用方法。仿真结果显示基于Agent的行为模型可以仿真出人员特性及决策过程对人群疏散的影响,弥补现有的人群疏散模型的不足。仿真结果证明,Agent的行为建模技术具有仿真火灾全过程中人员疏散行为的功能,适用于建筑物火灾中的人群优化疏散策略。     

Real-time path planning in dynamic virtual environments using multiagent navigation graphs

We present a novel approach for efficient path planning and navigation of multiple virtual agents in complex dynamic scenes. We introduce a new data structure, Multi-agent Navigation Graph (MaNG), which is constructed using first- and second-order Voronoi diagrams. The MaNG is used to perform route planning and proximity computations for each agent in real time. Moreover, we use the path information and proximity relationships for local dynamics computation of each agent by extending a social force model [Helbing05]. We compute the MaNG using graphics hardware and present culling techniques to accelerate the computation. We also address undersampling issues and present techniques to improve the accuracy of our algorithm. Our algorithm is used for real-time multi-agent planning in pursuit-evasion, terrain exploration and crowd simulation scenarios consisting of hundreds of moving agents, each with a distinct goal.     

Shadow obstacle model for realistic corner-turning behavior in crowd simulation

This paper describes a novel model known as the shadow obstacle model to generate a realistic corner-turning behavior in crowd simulation. The motivation for this model comes from the observation that people tend to choose a safer route rather than a shorter one when turning a corner. To calculate a safer route, an optimization method is proposed to generate the corner-turning rule that maximizes the viewing range for the agents. By combining psychological and physical forces together, a full crowd simulation framework is established to provide a more realistic crowd simulation. We demonstrate that our model produces a more realistic corner-turning behavior by comparison with real data obtained from the experiments. Finally, we perform parameter analysis to show the believability of our model through a series of experiments.     

Crowds by Example

We present an example-based crowd simulation technique. Most crowd simulation techniques assume that the behavior exhibited by each person in the crowd can be defined by a restricted set of rules. This assumption limits the behavioral complexity of the simulated agents. By learning from real-world examples, our autonomous agents display complex natural behaviors that are often missing in crowd simulations. Examples are created from tracked video segments of real pedestrian crowds. During a simulation, autonomous agents search for examples that closely match the situation that they are facing. Trajectories taken by real people in similar situations, are copied to the simulated agents, resulting in seemingly natural behaviors.     

Optimal crowd editing

Simulating realistic crowd behaviors is a challenging problem in computer graphics. Yet, several satisfying simulation models exhibiting natural pedestrians or group emerging behaviors exist. Choosing among these model generally depends on the considered crowd density or the topology of the environment. Conversely, achieving a user-desired kinematic or dynamic pattern at a given instant of the simulation reveals to be much more tedious. In this paper, a novel generic control methodology is proposed to solve this crowd editing issue. Our method relies on an adjoint formulation of the underlying optimization procedure. It is independent to a certain extent of the choice of the simulation model, and is designed to handle several forms of constraints. A variety of examples attesting the benefits of our approach are proposed, along with quantitative performance measures.     

Urban Walkability Design Using Virtual Population Simulation

We present a system to generate a procedural environment that produces a desired crowd behaviour. Instead of altering the behavioural parameters of the crowd itself, we automatically alter the environment to yield such desired crowd behaviour. This novel inverse approach is useful both to crowd simulation in virtual environments and to urban crowd planning applications. Our approach tightly integrates and extends a space discretization crowd simulator with inverse procedural modelling. We extend crowd simulation by goal exploration (i.e. agents are initially unaware of the goal locations), variable‐appealing sign usage and several acceleration schemes. We use Markov chain Monte Carlo to quickly explore the solution space and yield interactive design. We have applied our method to a variety of virtual and real‐world locations, yielding one order of magnitude faster crowd simulation performance over related methods and several fold improvement of crowd indicators.     

Guidance path scheduling using particle swarm optimization in crowd simulation

In this paper, we propose a method for using particle swarm optimization (PSO) to compute optimal guidance paths for various crowd densities in an agent‐based crowd simulation. The inputs of our system are guidance paths that provide hints for the movement directions of agents. Input guidance paths may not be located correctly (e.g., leading to congestion or high traveling cost); therefore, our method adjusts the guidance paths by using PSO. We consider several factors for evaluating the quality of a guidance path, including the average traveling time and interaction distance between agents. We apply our method in several examples. Experimental results show that our method can compute adaptive guidance paths for various crowd densities. Our system can simulate organized crowds that move in directions specified by the guidance paths. Copyright © 2015 John Wiley & Sons, Ltd.     

MODEL CHECKING A KNOWLEDGE EXCHANGE SCENARIO

We are interested in applying model checking techniques to the verification of communication protocols that require safe communication. Typically, in such scenarios, one desires to demonstrate that one party can reliably communicate information to another party without a third party being able to determine this information. Our approach involves using the modal logic of knowledge, which has only relatively recently been studied in the context of security protocols. We demonstrate our approach by means of a detailed case study: the Russian cards problem. This is an example of a security protocol with nontrivial requirements on the knowledge of the agents involved. Using the Russian cards problem as an example, it is shown how the satisfaction of properties involving knowledge can be verified in a standard model checker, which in our case is SPIN.     

Pedestrian Reactive Navigation for Crowd Simulation: a Predictive Approach

This paper addresses the problem of virtual pedestrian autonomous navigation for crowd simulation. It describes a method for solving interactions between pedestrians and avoiding inter-collisions. Our approach is agent-based and predictive: each agent perceives surrounding agents and extrapolates their trajectory in order to react to potential collisions. We aim at obtaining realistic results, thus the proposed model is calibrated from experimental motion capture data. Our method is shown to be valid and solves major drawbacks compared to previous approaches such as oscillations due to a lack of anticipation. We first describe the mathematical representation used in our model, we then detail its implementation, and finally, its calibration and validation from real data.     

Learning behavior patterns from video for agent-based crowd modeling and simulation

This paper proposes a novel data-driven modeling framework to construct agent-based crowd model based on real-world video data. The constructed crowd model can generate crowd behaviors that match those observed in the video and can be used to predict trajectories of pedestrians in the same scenario. In the proposed framework, a dual-layer architecture is proposed to model crowd behaviors. The bottom layer models the microscopic collision avoidance behaviors, while the top layer models the macroscopic crowd behaviors such as the goal selection patterns and the path navigation patterns. An automatic learning algorithm is proposed to learn behavior patterns from video data. The learned behavior patterns are then integrated into the dual-layer architecture to generate realistic crowd behaviors. To validate its effectiveness, the proposed framework is applied to two different real world scenarios. The simulation results demonstrate that the proposed framework can generate crowd behaviors similar to those observed in the videos in terms of crowd density distribution. In addition, the proposed framework can also offer promising performance on predicting the trajectories of pedestrians.     

Teaching communication skills with virtual humans

At the University of Florida, our research team has worked on applying virtual humans as partners in interpersonal communication scenarios. Our goal is to teach communication skills using virtual humans (VHs). The simulation of an interaction between people to facilitate teaching, training, and testing of communication skills would be a powerful new VR application. Our approach is to employ natural methods of interaction with the VH. We are striving to create an experience similar to two (and eventually more) people talking.