The real‐time simulation of human crowds has many applications. Simulating how the people in a crowd move through an environment is an active and ever‐growing research topic. Most research focuses on ...microscopic (or ‘agent‐based’) crowd‐simulation methods that model the behavior of each individual person, from which collective behavior can then emerge.
This state‐of‐the‐art report analyzes how the research on microscopic crowd simulation has advanced since the year 2010. We focus on the most popular research area within the microscopic paradigm, which is local navigation, and most notably collision avoidance between agents. We discuss the four most popular categories of algorithms in this area (force‐based, velocity‐based, vision‐based, and data‐driven) that have either emerged or grown in the last decade. We also analyze the conceptual and computational (dis)advantages of each category. Next, we extend the discussion to other types of behavior or navigation (such as group behavior and the combination with path planning), and we review work on evaluating the quality of simulations.
Based on the observed advancements in the 2010s, we conclude by predicting how the research area of microscopic crowd simulation will evolve in the future. Overall, we expect a significant growth in the area of data‐driven and learning‐based agent navigation, and we expect an increasing number of methods that re‐group multiple ‘levels’ of behavior into one principle. Furthermore, we observe a clear need for new ways to analyze (real or simulated) crowd behavior, which is important for quantifying the realism of a simulation and for choosing the right algorithms at the right time.
We present a novel framework to evaluate multi‐agent crowd simulation algorithms based on real‐world observations of crowd movements. A key aspect of our approach is to enable fair comparisons by ...automatically estimating the parameters that enable the simulation algorithms to best fit the given data. We formulate parameter estimation as an optimization problem, and propose a general framework to solve the combinatorial optimization problem for all parameterized crowd simulation algorithms. Our framework supports a variety of metrics to compare reference data and simulation outputs. The reference data may correspond to recorded trajectories, macroscopic parameters, or artist‐driven sketches. We demonstrate the benefits of our framework for example‐based simulation, modeling of cultural variations, artist‐driven crowd animation, and relative comparison of some widely‐used multi‐agent 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 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.
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. Following is frequently observed when people walk in corridors or when they queue. Typical macroscopic stop‐and‐go waves emerge under such traffic conditions. Our contributions are, first, an experimental study on following behaviors, second, a numerical model for simulating such interactions, and third, its calibration, evaluation and applications. Through an experimental approach, we elaborate and calibrate a model from microscopic analysis of real kinematics data collected during experiments. We carefully evaluate our model both at the microscopic and the macroscopic levels. We also demonstrate our approach on applications where following interactions are prominent.
When navigating in crowds, humans are able to move efficiently between people. They look ahead to know which path would reduce the complexity of their interactions with others. Current navigation ...systems for virtual agents consider long‐term planning to find a path in the static environment and short‐term reactions to avoid collisions with close obstacles. Recently some mid‐term considerations have been added to avoid high density areas. However, there is no mid‐term planning among static and dynamic obstacles that would enable the agent to look ahead and avoid difficult paths or find easy ones as humans do. In this paper, we present a system for such mid‐term planning. This system is added to the navigation process between pathfinding and local avoidance to improve the navigation of virtual agents. We show the capacities of such a system using several case studies. Finally we use an energy criterion to compare trajectories computed with and without the mid‐term planning.
When navigating in crowds, humans are able to move efficiently between people. They look ahead to know which path would reduce the complexity of their interactions with others. Current navigation systems for virtual agents consider long‐term planning to find a path in the static environment and short‐term reactions to avoid collisions with close obstacles. Recently some mid‐term considerations have been added to avoid high density areas. However, there is no mid‐term planning among static and dynamic obstacles that would enable the agent to look ahead and avoid difficult paths or find easy ones as humans do. In this paper, we present a system for such mid‐term planning.
The real‐time simulation of human crowds has many applications. In a typical crowd simulation, each person ('agent') in the crowd moves towards a goal while adhering to local constraints. Many ...algorithms exist for specific local ‘steering’ tasks such as collision avoidance or group behavior. However, these do not easily extend to completely new types of behavior, such as circling around another agent or hiding behind an obstacle. They also tend to focus purely on an agent's velocity without explicitly controlling its orientation. This paper presents a novel sketch‐based method for modelling and simulating many steering behaviors for agents in a crowd. Central to this is the concept of an interaction field (IF): a vector field that describes the velocities or orientations that agents should use around a given ‘source’ agent or obstacle. An IF can also change dynamically according to parameters, such as the walking speed of the source agent. IFs can be easily combined with other aspects of crowd simulation, such as collision avoidance. Using an implementation of IFs in a real‐time crowd simulation framework, we demonstrate the capabilities of IFs in various scenarios. This includes game‐like scenarios where the crowd responds to a user‐controlled avatar. We also present an interactive tool that computes an IF based on input sketches. This IF editor lets users intuitively and quickly design new types of behavior, without the need for programming extra behavioral rules. We thoroughly evaluate the efficacy of the IF editor through a user study, which demonstrates that our method enables non‐expert users to easily enrich any agent‐based crowd simulation with new agent interactions.
We derive a hierarchy of kinetic and macroscopic models from a noisy variant of the heuristic behavioral Individual-Based Model of Ngai et al. (Disaster Med. Public Health Prep. 3:191–195,
2009
) ...where pedestrians are supposed to have constant speeds. This IBM supposes that pedestrians seek the best compromise between navigation towards their target and collisions avoidance. We first propose a kinetic model for the probability distribution function of pedestrians. Then, we derive fluid models and propose three different closure relations. The first two closures assume that the velocity distribution function is either a Dirac delta or a von Mises-Fisher distribution respectively. The third closure results from a hydrodynamic limit associated to a Local Thermodynamical Equilibrium. We develop an analogy between this equilibrium and Nash equilibria in a game theoretic framework. In each case, we discuss the features of the models and their suitability for practical use.
Modelling crowd behavior is essential for the management of mass events and pedestrian traffic. Current microscopic approaches consider the individual's behavior to predict the effect of individual ...actions in local interactions on the collective scale of the crowd motion. Recent developments in the use of virtual reality as an experimental tool have offered an opportunity to extend the understanding of these interactions in controlled and repeatable settings. Nevertheless, based on kinematics alone, it remains difficult to tease out how these interactions unfold. Therefore, we tested the hypothesis that gaze activity provides additional information about pedestrian interactions. Using an eye tracker, we recorded the participant's gaze behavior whilst navigating through a virtual crowd. Results revealed that gaze was consistently attracted to virtual walkers with the smallest values of distance at closest approach (DCA) and time to closest approach (TtCA), indicating a higher risk of collision. Moreover, virtual walkers gazed upon before an avoidance maneuver was initiated had a high risk of collision and were typically avoided in the subsequent avoidance maneuver. We argue that humans navigate through crowds by selecting only few interactions and that gaze reveals how a walker prioritizes these interactions. Moreover, we pose that combining kinematic and gaze data provides new opportunities for studying how interactions are selected by pedestrians walking through crowded dynamic environments.
•Time until- and distance at-closest approach can be combined using a Pareto ranking.•Gaze fixations are drawn to walkers that pose the highest risk of collision.•The fixation prior to an avoidance maneuver is likely to have triggered this response.•Gaze is intricately linked to locomotor avoidance and can be used to study the interaction neighborhood.
Group Modeling: A Unified Velocity‐Based Approach Ren, Z.; Charalambous, P.; Bruneau, J. ...
Computer graphics forum,
December 2017, 2017-12-00, 20171201, 2017-12, Letnik:
36, Številka:
8
Journal Article
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Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual ...crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.
Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.
We introduce “Crowd Sculpting”: a method to interactively design populated environments by using intuitive deformation gestures to drive both the spatial coverage and the temporal sequencing of a ...crowd motion. Our approach assembles large environments from sets of spatial elements which contain inter‐connectible, periodic crowd animations. Such a “Crowd Patches” approach allows us to avoid expensive and difficult‐to‐control simulations. It also overcomes the limitations of motion editing, that would result into animations delimited in space and time. Our novel methods allows the user to control the crowd patches layout in ways inspired by elastic shape sculpting: the user creates and tunes the desired populated environment through stretching, bending, cutting and merging gestures, applied either in space or time. Our examples demonstrate that our method allows the space‐time editing of very large populations and results into endless animation, while offering real‐time, intuitive control and maintaining animation quality.
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