Properties of pedestrians walking in line - Fundamental diagrams

We present experimental results obtained for a one-dimensional flow using high precision motion capture. The full pedestrians' trajectories are obtained. In this paper, we focus on the fundamental diagram, and on the relation between the instantaneous velocity and spatial headway (distance to the predecessor). While the latter was found to be linear in previous experiments, we show that it is rather a piecewise linear behavior which is found if larger density ranges are covered. Indeed, our data clearly exhibits three distinct regimes in the behavior of pedestrians that follow each other. The transitions between these regimes occur at spatial headways of about 1.1 and 3 m, respectively. This finding could be useful for future modeling.Comment: 9 figures, 3 table

Properties of pedestrians walking in line without density constraint

This article deals with the study of pedestrian behaviour in one-dimensional traffic situations. We asked participants to walk either in a straight line with a fast or slow leader, or to form a circle, without ever forcing the conditions of density. While the observed density results from individual decisions in the line case, both density and velocity have to be collectively chosen in the case of circle formation. In the latter case, interestingly, one finds that the resulting velocity is very stable among realizations, as if collective decision was playing the role of an average. In the line experiment, though participants could choose comfortable headways, they rather stick to short headways requiring a faster adaption - a fact that could come from a ``social pressure from behind''. For flows close to the jamming transition, the same operating point is chosen as in previous experiments where it was not velocity but density that was imposed. All these results show that the walking values preferred by humans in following tasks depend on more factors than previously considered.Comment: Main paper (11 pages, 13 figures) + Suppl. Mat. (8 pages, 9 figures

Vision-based macroscopic pedestrian models

International audienceWe propose a hierarchy of kinetic and macroscopic models for a system consisting of a large number of interacting pedestrians. The basic interaction rules are derived from earlier work where the dangerousness level of an interaction with another pedestrian is measured in terms of the derivative of the bearing angle (angle between the walking direction and the line connecting the two subjects) and of the time-to-interaction (time before reaching the closest distance between the two subjects). A mean-field kinetic model is derived. Then, three different macroscopic continuum models are proposed. The first two ones rely on two different closure assumptions of the kinetic model, respectively based on a monokinetic and a von Mises-Fisher distribution. The third one is derived through a hydrodynamic limit. In each case, we discuss the relevance of the model for practical simulations of pedestrian crowds

Reward Function Design for Crowd Simulation via Reinforcement Learning

Crowd simulation is important for video-games design, since it enables to populate virtual worlds with autonomous avatars that navigate in a human-like manner. Reinforcement learning has shown great potential in simulating virtual crowds, but the design of the reward function is critical to achieving effective and efficient results. In this work, we explore the design of reward functions for reinforcement learning-based crowd simulation. We provide theoretical insights on the validity of certain reward functions according to their analytical properties, and evaluate them empirically using a range of scenarios, using the energy efficiency as the metric. Our experiments show that directly minimizing the energy usage is a viable strategy as long as it is paired with an appropriately scaled guiding potential, and enable us to study the impact of the different reward components on the behavior of the simulated crowd. Our findings can inform the development of new crowd simulation techniques, and contribute to the wider study of human-like navigation

Understanding reinforcement learned crowds

Simulating trajectories of virtual crowds is a commonly encountered task in Computer Graphics. Several recent works have applied Reinforcement Learning methods to animate virtual agents, however they often make different design choices when it comes to the fundamental simulation setup. Each of these choices comes with a reasonable justification for its use, so it is not obvious what is their real impact, and how they affect the results. In this work, we analyze some of these arbitrary choices in terms of their impact on the learning performance, as well as the quality of the resulting simulation measured in terms of the energy efficiency. We perform a theoretical analysis of the properties of the reward function design, and empirically evaluate the impact of using certain observation and action spaces on a variety of scenarios, with the reward function and energy usage as metrics. We show that directly using the neighboring agents' information as observation generally outperforms the more widely used raycasting. Similarly, using nonholonomic controls with egocentric observations tends to produce more efficient behaviors than holonomic controls with absolute observations. Each of these choices has a significant, and potentially nontrivial impact on the results, and so researchers should be mindful about choosing and reporting them in their work.Comment: Accepted for publication at MIG 202

Mesh Alignment using Grid based PCA

We present an algorithm for mesh alignment by performing Principal Components Analysis (PCA) on a set of nodes of a regular 3D grid. The use of a 3D lattice external to both inputs increases the robustness of PCA, particularly when dealing with meshes of different and possibly uneven vertex density. The proposed algorithm was tested on meshes that have undergone standard mesh processing operations such as smoothing, simplification and remeshing. In several cases the results indicate an improved robustness compared to performing PCA directly on mesh vertices

WarpDriver: context-aware probabilistic motion prediction for crowd simulation

Microscopic crowd simulators rely on models of local interaction (e.g. collision avoidance) to synthesize the individual motion of each virtual agent. The quality of the resulting motions heavily depends on this component, which has significantly improved in the past few years. Recent advances have been in particular due to the introduction of a short-horizon motion prediction strategy that enables anticipated motion adaptation during local interactions among agents. However, the simplicity of prediction techniques of existing models somewhat limits their domain of validity. In this paper, our key objective is to significantly improve the quality of simulations by expanding the applicable range of motion predictions. To this end, we present a novel local interaction algorithm with a new context-aware, probabilistic motion prediction model. By context-aware, we mean that this approach allows crowd simulators to account for many factors, such as the influence of environment layouts or in-progress interactions among agents, and has the ability to simultaneously maintain several possible alternate scenarios for future motions and to cope with uncertainties on sensing and other agent's motions. Technically, this model introduces "collision probability fields" between agents, efficiently computed through the cumulative application of Warp Operators on a source Intrinsic Field. We demonstrate how this model significantly improves the quality of simulated motions in challenging scenarios, such as dense crowds and complex environments

Simulation of Past Life: Controlling Agent Behaviors from the Interactions between Ethnic Groups

International audienceMany efforts have been carried out in preserving the history and culture of Penang and also other regions of Malaysia since George Town was elected as a UNESCO living heritage city. This paper presents a method to simulate life in a local trading port in the 1800s, where various populations with very different social rules interacted with each other. These populations included Indian coolies, Malay vendors, British colonists and Chinese traders. The challenge is to model these ethnic groups as autonomous agents, and to capture the changes of behavior due to inter-ethnic interactions and to the arrival of boats at the pier. Agents from each population are equipped with a specific set of steering methods which are selected and parameterized according to predefined behavioral patterns (graphs of states). In this paper, we propose a new formalism where interactions between the different ethnics groups and with the boats can be either activated globally or locally. Global interactions cause changes of states for all the agents belonging to the target population, while local interactions only take place between specific agents, and result in changes of states for these agents only. The main contributions of our method are: i) Applying microscopic crowd simulation to the complex case of a multi-ethnic trading port, involving different behavioral patterns; ii) Introducing a high-level control method, through the inter- ethnic interactions formalism. The resulting system generates a variety of real-time animations, all reflecting the adequate social behaviors. Such a system would be particularly useful in a virtual tour application

Social Ways: Learning Multi-Modal Distributions of Pedestrian Trajectories with GANs

International audienc