This paper examines how people jointly coordinate their decisions and actions with risky vs. safe human and agent road-crossing partners (Fig. 1 ). The task for participants was to physically cross a steady stream of traffic in a large-screen virtual environment without getting hit by a car. The computer-generated (CG) agent was programmed to be either safe (taking only large gaps) or risky (also taking small gaps). The human partners were classified as safe (taking more large gaps) or risky (also taking some small gaps) based on their average gap size selection. We found that participants in all four conditions preferred to cross with their partner. As a consequence, the riskiness of the partner (both human and agent) influenced the riskiness of participants' gap choices. We also found that participants tightly synchronized their movement with both human and agent partners. The largest differences in performance between those paired with agent vs. human partners occurred on trials when participants crossed different gaps than their partners. This study demonstrates the potential for studying how people interact with CG agents when performing whole-body joint actions using large-screen immersive virtual environments.

We investigated how two people jointly coordinate their decisions and actions in a co-occupied, large-screen virtual environment. The task for participants was to physically cross a virtual road with continuous traffic without getting hit by a car. Participants performed this task either alone or with another person (see Figure 1). Two separate streams of non-stereo images were generated based on the dynamic locations of the two viewers’ eye-points. Stereo shutter glasses were programmed to display a single image stream to each viewer so that they saw perspectively correct non-stereo images for their eyepoint. We found that participant pairs often crossed the same gap together and closely synchronized their movements when crossing. Pairs also chose larger gaps than individuals, presumably to accommodate the extra time needed to cross through gaps together. These results demonstrate how two people interact and coordinate their behaviors in performing whole-body, joint motions in a co-occupied virtual environment. This study also provides a foundation for future studies examining joint actions in shared VEs where participants are represented by graphic avatars.

This investigation compared how people performed a complex perception-action task - crossing traffic-filled roadways - in a CAVE vs. an HMD virtual environment. Participants physically crossed a virtual roadway with continuous cross traffic in either a CAVE-like or an HTC Vive pedestrian simulator. The 3D model and traffic scenario were identical in both simulators, allowing for a direct comparison between the two display systems. We found that participants in the Vive group accepted smaller gaps for crossing than participants in the CAVE group. They also timed their entry into the gap more precisely and tended to cross somewhat more quickly. As a result, participants in the Vive group had a somewhat larger margin of safety when they exited the roadway than those in the CAVE group. The results provide a foundation for future studies of pedestrian behavior and other tasks involving full-body motion using HMD-based VR.

Optical motion capture is based on estimating the three-dimensional positions of markers by triangulation from multiple cameras. Successful performance depends on points being visible from at least two cameras and on the accuracy of the triangulation. Triangulation accuracy is strongly related to the positions and orientations of the cameras. Thus, the configuration of the camera network has a critical impact on performance. A poor camera configuration may result in a low quality three-dimensional (3D) estimation and consequently low quality of tracking. This paper introduces and compares two methods for camera placement. The first method is based on a metric that computes target point visibility in the presence of dynamic occlusion from cameras with "good" views. The second method is based on the distribution of views of target points. Efficient algorithms, based on simulated annealing, are introduced for estimating the optimal configuration of cameras for the two metrics and a given distribution of target points. The accuracy and robustness of the algorithms are evaluated through both simulation and empirical measurement. Implementations of the two methods are available for download as tools for the community.

We investigated how people jointly coordinate their decisions and actions with a computer-generated character (agent) in a large-screen virtual environment. The task for participants was to physically cross a steady stream of traffic on a virtual road without getting hit by a car. Participants performed this task with another person or with a computer-generated character (Fig. 1). The character was programmed to be either safe (taking only large gaps) or risky (also taking relatively small gaps). We found that participants behaved in many respects similarly with real and virtual partners. They maintained similar distances between themselves and their partner, they often crossed the same gap with their partner, and they synchronized their crossing with their partner. We also found that the riskiness of the character influenced the gap choices of participants. This study demonstrates the potential for using large-screen virtual environments to study how people interact with CG characters when performing whole-body joint actions.

We investigated how two people jointly coordinate their decisions and actions in a co-occupied, large-screen virtual environment. The task for participants was to physically cross a virtual road with continuous traffic without getting hit by a car. Participants performed this task either alone or with another person (see Fig.1). We found that pairs often crossed the same gap together and closely synchronized their movements when crossing. Pairs also chose larger gaps than individuals to accommodate the extra time needed to cross through gaps together. These results reveal how two people interact and coordinate their behaviors in performing whole-body, joint motions. This study also provides a foundation for future studies examining joint actions in shared VEs where participants are represented by graphic avatars.

This paper presents an experiment conducted in a large-screen immersive virtual environment to evaluate how texting pedestrians respond to permissive traffic alerts delivered via their cell phone. We developed a cell phone app that delivered information to texting pedestrians about when traffic conditions permit safe crossing. We compared gap selection and movement timing in three groups of pedestrians: texting, texting with alerts, and no texting (control). Participants in the control and alert groups chose larger gaps and were more discriminating in their gap choices than participants in the texting group. Both the control and alert groups had more time to spare than the texting group when they exited the roadway even though the alert group timed their entry relative to the lead car less tightly than the control and texting groups. By choosing larger gaps, participants in the alert group were able to compensate for their poorer timing of entry, resulting in a margin of safety that did not differ from those who were not texting. However, they also relied heavily on the alert system and paid less attention to the roadway. The discussion focuses on the potential of assistive technologies based on Vehicle-to-Pedestrian (V2P) communications technology for mitigating pedestrian-motor vehicle crashes.

Optical motion capture is based on estimating the three-dimensional positions of markers by triangulation from multiple cameras. Successful performance depends on points being visible from at least two cameras and on the accuracy of the triangulation. Triangulation accuracy is strongly related to the positions and orientations of the cameras. Thus, the configuration of the camera network has a critical impact on performance. A poor camera configuration may result in a low quality three-dimensional (3D) estimation and consequently low quality of tracking. This paper proposes a camera configuration metric that is based on a probabilistic model of target point visibility from cameras with "good" views. An efficient algorithm, based on simulated annealing, is introduced for estimating the optimal configuration of an arbitrary set of cameras for a given distribution of target points. The accuracy and robustness of the algorithm are evaluated through both simulation and empirical measurement. An implementation of the method is available for download as a tool for the community [Rahimian and Kearney 2015] (Figure 1).