Modelling social identification and helping in evacuation simulation

Social scientists have criticised computer models of pedestrian streams for their treatment of psychological crowds as mere aggregations of individuals. Indeed most models for evacuation dynamics use analogies from physics where pedestrians are considered as particles. Although this ensures that the results of the simulation match important physical phenomena, such as the deceleration of the crowd with increasing density, social phenomena such as group processes are ignored. In particular, people in a crowd have social identities and share those social identities with the others in the crowd. The process of self categorisation determines norms within the crowd and influences how people will behave in evacuation situations. We formulate the application of social identity in pedestrian simulation algorithmically. The goal is to examine whether it is possible to carry over the psychological model to computer models of pedestrian motion so that simulation results correspond to observations from crowd psychology. That is, we quantify and formalise empirical research on and verbal descriptions of the effect of group identity on behaviour. We use uncertainty quantification to analyse the model’s behaviour when we vary crucial model parameters. In this first approach we restrict ourselves to a specific scenario that was thoroughly investigated by crowd psychologists and where some quantitative data is available: the bombing and subsequent evacuation of a London underground tube carriage on July 7th 2005

Virtual reality crowd simulation: effects of agent density on user experience and behaviour

Agent-based crowd simulations are used for modelling building and space usage, allowing designers to explore hypothetical real-world scenarios, including extraordinary events such as evacuations. Existing work which engages virtual reality (VR) as a platform for crowd simulations has been primarily focussed on the validation of simulation models through observation; the use of interactions such as gaze to enhance a sense of immersion; or studies of proxemics. In this work, we extend previous studies of proxemics and examine the effects of varying crowd density on user experience and behaviour. We have created a simulation in which participants walk freely and perform a routine manual task, whilst interacting with agents controlled by a typical social force simulation model. We examine and report the effects of crowd density on both affective state and behaviour. Our results show a significant increase in negative affect with density, measured using a self-report scale. We further show significant differences in some aspects of user behaviours, using video analysis, and discuss how our results relate to VR simulation design for mixed human–agent scenarios

Motion Planning for Human Crowds: from Individuals to Groups of Virtual Characters

Virtual worlds, to become more lively and appealing, are typically populated by large crowds of virtual characters. One of the fundamental tasks that these characters have to perform is, on one hand, to plan their paths between different locations in the world and, on the other hand, to move toward their desired locations in a human-like manner avoiding collisions with each other and with the environment. This is the main topic of this thesis. Although the path planning problem has received considerable attention over the past thirty years, most path planning algorithms originate from robotics aiming at creating short and collision-free paths for one or a few robots having many degrees of freedom. In interactive virtual worlds, though, the requirements are different. Paths for hundreds of characters through complex environments should be planned simultaneously and in real-time using only a small percentage of the CPU time. In addition to being collision-free, the paths followed by the characters must also look plausible in order to retain the suspension of disbelief of the viewer. Such paths typically follow smooth curves, are short and keep a certain amount of clearance to obstacles. To address the aforementioned issues, in the first part of the thesis, we introduce the Indicative Route Method as a new path planning approach in interactive virtual worlds and games. We further combine the Indicative Route Method with techniques from Linear Programming to efficiently choreograph through space-time the motions of large heterogeneous groups of virtual characters. We also present simple techniques for creating variants of homotopic paths that virtual characters can follow given a path planning query. Such variation not only provides a more challenging and less predictable opponent for the user in a (serious) game, but also enhances the realism of a simulation allowing the characters to spread over the environment and take alternative routes. Besides demonstrating believable path planning behavior, the virtual characters should also be able to adapt their motions resolving a bewildering amount of local interactions and avoiding collisions with each other. This problem is very challenging, since real humans exhibit behaviors of enormous complexity and subtlety making their simulation a rather difficult task. In the second part of the thesis, we try to address some of these challenges. We first propose a physically-based model for solving interactions between virtual pedestrians that have converging trajectories. The proposed method is extremely fast, simple to implement and captures the emergence of self-organization phenomena allowing interactions to be solved more efficiently at a global scale. We also address the issue of realistic collision avoidance among virtual humans by exploiting experimental interactions data between real pedestrians. In the derived model, virtual characters take early and effort-efficient actions to avoid collisions by slightly adapting their directions and speeds. We further extend this technique to simulate the walking behavior of small groups of virtual pedestrians. Here, a novel algorithm is introduced ensuring that the group members will safely navigate toward their goals, while forming walking patterns similar to the ones observed in real-life

R.: Flexible path planning using corridor maps

Abstract. Path planning is a central problem in virtual environments and games. When computer-controlled characters move around in virtual worlds they have to plan their paths to desired locations. These paths must avoid collisions with the environment and with other moving characters. Also a chosen path must be natural, meaning that it is the kind of path a real human being could take. The algorithms for planning such paths must be able to handle hundreds of characters in real-time and must be flexible. The Corridor Map Method (cmm) was recently introduced as a flexible path planning method in interactive virtual environments and games. The method is fast and flexible and the resulting paths are reasonable. However, the paths tend to take unnatural turns when characters get close to other characters or small obstacles. In this paper we will improve on the cmm by decoupling collision avoidance with the environment and local steering behavior. The result is a method that keeps the advantages of the cmm but has much more natural steering. Also the method allows for more flexibility in the desired routes of the characters.

A mesoscopic model for the effect of density on pedestrian group dynamics

We introduce a mesoscopic model of pedestrian group behaviour, in which the internal group dynamics is modelled using a microscopic potential, while the effect of the environment is modelled using a harmonic term whose intensity depends on a macroscopic quantity, crowd density. We show that, in order to properly describe the behaviour of 2-person groups, the harmonic term is directed orthogonally to the walking direction, and its intensity grows linearly with density. We also show that, once calibrated on 2-person groups, the model correctly predicts the velocity and spatial extension of 3-person groups in the walking direction, while in order to describe properly also the abreast extension of 3-person groups a modification in the microscopic group dynamics has to be introduced. The model also correctly predicts the presence of a bifurcation phenomenon, namely the emergence of a stable 3-person Λ configuration at high densities, while only the V formation is stable at low densities

Attracted by light: vision-based steering virtual characters among dark and light obstacles

International audienc

Social Navigation with Human Empowerment Driven Deep Reinforcement Learning

Mobile robot navigation has seen extensive research in the last decades. The aspect of collaboration with robots and humans sharing workspaces will become increasingly important in the future. Therefore, the next generation of mobile robots needs to be socially-compliant to be accepted by their human collaborators. However, a formal definition of compliance is not straightforward. On the other hand, empowerment has been used by artificial agents to learn complicated and generalized actions and also has been shown to be a good model for biological behaviors. In this paper, we go beyond the approach of classical \acf{RL} and provide our agent with intrinsic motivation using empowerment. In contrast to self-empowerment, a robot employing our approach strives for the empowerment of people in its environment, so they are not disturbed by the robot's presence and motion. In our experiments, we show that our approach has a positive influence on humans, as it minimizes its distance to humans and thus decreases human travel time while moving efficiently towards its own goal. An interactive user-study shows that our method is considered more social than other state-of-the-art approaches by the participants