Minimizing Collision among Social Groups in Wide-Open Spaces

Our paper introduces a novel approach for collision avoidance (CA) among social groups that uses the collective gaze-movement angle (CGMA) between groups as the primary approach for collision prediction. Rules are used for handling the CA steering such that it mimics how real human groups travel together in wide-open walk able areas. We also show how to apply reciprocal velocity obstacle (RVO) based CA to groups as an alternative, and we use this alternative as a baseline for comparison in our approach for group-to-group CA. In an immersive evaluation using a head-mounted display, a group of people determined that our CA approach was much more believable than the RVO based CA approach for groups.

I. Introduction

Virtual worlds with human avatars can be found in media such as games, simulated training, architecture designs, and even online worlds that feature virtual shopping malls and social networking services. Great strides in research have been done to make virtual humans look real, but moving like real human beings in a virtual space is another matter. There have been far more works for motion planning of individual agents compared to agents moving together in groups. For most prior crowd simulations with groups, members of these groups are randomly positioned in a cluster and do not travel in a social way. References [1], [2] reported that real people walk together in small groups from two to six but range in size in order according to a Poisson distribution that highly favors dyads. The people also tend to organize themselves in a shoulder-to-shoulder formation when going somewhere in order to communicate to each other better even if it is less efficient to move around together to their destination.