Human crowds often bear a striking resemblance to interacting particle systems, and this has prompted many researchers to describe pedestrian dynamics in terms of interaction forces and potential energies. The correct quantitative form of this interaction, however, has remained an open question. Here, we introduce a novel statistical-mechanical approach to directly measure the interaction energy between pedestrians. This analysis, when applied to a large collection of human motion data, reveals a simple power law interaction that is based not on the physical separation between pedestrians but on their projected time to a potential future collision, and is therefore fundamentally anticipatory in nature. Remarkably, this simple law is able to describe human interactions across a wide variety of situations, speeds and densities. We further show, through simulations, that the interaction law we identify is sufficient to reproduce many known crowd phenomena.In terms of its large-scale behaviors, a crowd of pedestrians can look strikingly similar to many other collections of repulsively-interacting particles [1][2][3][4]. These similarities have inspired a variety of pedestrian crowd models, including cellular automata and continuum-based approaches [5][6][7][8], as well as simple particle or agent-based models [9][10][11][12][13][14][15]. Many of these models conform to a longstanding hypothesis that humans in a crowd interact with their neighbors through some form of "social potential" [16], analogous to the repulsive potential energies between physical particles. How to best determine the quantitative form of this interaction potential, however, has remained an open question, with most previous researchers employing a simulation-driven approach.Previously, direct measurement of the interaction law between pedestrians has been confounded by two primary factors. First, each individual in a crowd experiences a complex environment of competing forces, making it difficult to isolate and robustly quantify a single pairwise interaction. Secondly, a pedestrian's motion is strongly influenced not just by the present position of neighboring pedestrians, but by their anticipated future positions [17][18][19][20][21], a fact which has influenced recent models [22][23][24][25]. Consider, for example, two well-separated pedestrians walking into a head-on collision (Fig. 1a). These pedestrians typically exhibit relatively large acceleration as they move to avoid each other, as would result from a large repulsive force. On the other hand, pedestrians walking in parallel directions exhibit almost no acceleration, even when their mutual separation is small (Fig. 1b).Here, we address both of the aforementioned factors using a data-driven, statistical mechanics-based analysis that accounts properly for the anticipatory nature of human interactions. This approach allows us to directly and robustly measure the interaction energy between pedestrians. The consistency of our measurements across a variety of settings suggests a simple and u...

No abstract

Recent advancements in local methods have significantly improved the collision avoidance behavior of virtual characters. However, existing methods fail to take into account that in real life pedestrians tend to walk in small groups, consisting mainly of pairs or triples of individuals. We present a novel approach to simulate the walking behavior of such small groups. Our model describes how group members interact with each other, with other groups and individuals. We highlight the potential of our method through a wide range of test-case scenarios. We evaluate the results from our simulations using a number of quantitative quality metrics, and also provide visual and numerical comparisons with video footages of real crowds.

An important challenge in virtual environment applications is to steer virtual characters through complex and dynamic worlds. The characters should be able to plan their paths and move toward their desired locations, avoiding at the same time collisions with the environment and with other moving entities. In this paper we propose a general method for realistic path planning, the Indicative Route Method (irm). In the irm, a so-called indicative route determines a global route for the character, whereas a corridor around this route is used to handle a broad range of other path planning issues, such as avoiding characters and computing smooth paths. As we will show, our method can be used for real-time navigation of many moving characters in complicated environments. It is fast, flexible and generates believable paths.

REFERENCETEST 1. DATA INPUT 2. ANOMALY DETECTION 3. EVALUATION Displacement NN Distance NN Diff in Velocity … Curvature w Figure 1: System Overview (1) Reference data from real crowds or relevant simulations are compared against testing data from simulations the user wishes to analyze.(2) Using a variety of metrics our framework automatically detects outliers using anomaly detection techniques such as Pareto Depth Analysis.(3) The resulting analysis is then shown as set of different visualizations (including heatmaps, histograms, 2D/3D animation players and a 2D outlier browsing tool) that characterize the test data compared to the reference data -here the most anomalous data are shown in red. AbstractWe present a novel approach for analyzing the quality of multi-agent crowd simulation algorithms. Our approach is data-driven, taking as input a set of user-defined metrics and reference training data, either synthetic or from video footage of real crowds. Given a simulation, we formulate the crowd analysis problem as an anomaly detection problem and exploit state-of-the-art outlier detection algorithms to address it. To that end, we introduce a new framework for the visual analysis of crowd simulations. Our framework allows us to capture potentially erroneous behaviors on a per-agent basis either by automatically detecting outliers based on individual evaluation metrics or by accounting for multiple evaluation criteria in a principled fashion using Principle Component Analysis and the notion of Pareto Optimality. We discuss optimizations necessary to allow real-time performance on large datasets and demonstrate the applicability of our framework through the analysis of simulations created by several widely-used methods, including a simulation from a commercial game.