From individual characters to large crowds: augmenting the believability of open-world games through exploring social emotion in pedestrian groups

Crowds of non-player characters improve the game-play experiences of open-world video-games. Grouping is a common phenomenon of crowds and plays an important role in crowd behaviour. Recent crowd simulation research focuses on group modelling in pedestrian crowds and game-designers have argued that the design of non-player characters should capture and exploit the relationship between characters. The concepts of social groups and inter-character relationships are not new in social psychology, and on-going work addresses the social life of emotions and its behavioural consequences on individuals and groups alike. The aim of this paper is to provide an overview of current research in social psychology, and to use the findings as a source of inspiration to design a social network of non-player characters, with application to the problem of group modelling in simulated crowds in computer games

Psychological Model for Animating Crowded

This paper proposes a psychological model forsimulating pedestrian behaviors in a crowdedspace. Our decision-making scheme controlsplausible avoidance behavior depending onthe positional relations among surroundingpersons, on the basis of a two-stage personalspace and a virtual memory structure asproposed in social psychology. Our systemdetermines pedestrian walking speed withthe crowd density to imitate the measureddata in urban engineering, and automaticallygenerates plausible motions of the individualpedestrian by composing a locomotion graphwith motion capture data. Our approachbased on psychology and a variety of actualmeasurements can increase the accuracy ofsimulation at both the micro and macro levels

Environmental effect on egress simulation

Abstract. Evacuation and egress simulations can be a useful tool for studying the effect of design decisions on the flow of agent movement. This type of simulation can be used to determine before hand the effect of design decisions and enable exploration of potential improvements. In this work, we study at how agent egress is affected by the environment in real world and large scale virtual environments and investigate metrics to analyze the flow. Our work differs from many evacuation systems in that we support grouping restrictions between agents (e.g., families or other social groups traveling together), and model scenarios with multiple modes of transportation with physically realistic dynamics (e.g., individuals walk from a building to their own cars and leave only when all people in the group arrive).

Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

Parallelized Egocentric Fields for Autonomous Navigation

In this paper, we propose a general framework for local path-planning and steering that can be easily extended to perform high-level behaviors. Our framework is based on the concept of affordances: the possible ways an agent can interact with its environment. Each agent perceives the environment through a set of vector and scalar fields that are represented in the agent’s local space. This egocentric property allows us to efficiently compute a local space-time plan and has better parallel scalability than a global fields approach. We then use these perception fields to compute a fitness measure for every possible action, defined as an affordance field. The action that has the optimal value in the affordance field is the agent’s steering decision. We propose an extension to a linear space-time prediction model for dynamic collision avoidance and present our parallelization results on multicore systems. We analyze and evaluate our framework using a comprehensive suite of test cases provided in SteerBench and demonstrate autonomous virtual pedestrians that perform steering and path planning in unknown environments along with the emergence of high-level responses to never seen before situations