To simulate the low-level (‘microscopic’) behavior of human crowds, a local navigation algorithm computes how a single person (‘agent’) should move based on its surroundings. Many algorithms for this purpose have been proposed, each using different principles and implementation details that are difficult to compare.

When we are walking in crowds, we mainly use visual information to avoid collisions with other pedestrians. Thus, gaze activity should be considered to better understand interactions between people in a crowd. In this work, we use Virtual Reality (VR) to facilitate motion and gaze tracking, as well as to accurately control experimental conditions, in order to study the effect of crowd density on eye-gaze behavior. Our motivation is to better understand how interaction neighborhood (i.e., the subset of people actually influencing one’s locomotion trajectory) changes with density. To this end, we designed two experiments. The first one evaluates the biases introduced by the use of VR on the visual activity when walking among people, by comparing eye-gaze activity while walking in a real and virtual street. We then designed a second experiment where participants walked in a virtual street with different levels of pedestrian density. We demonstrate that gaze fixations are performed at the same frequency despite increases in pedestrian density, while the eyes scan a narrower portion of the street. These results suggest that in such situations walkers focus more on people in front and closer to them. These results provide valuable insights regarding eye-gaze activity during interactions between people in a crowd, and suggest new recommendations in designing more realistic crowd simulations.

This paper explores the use of Virtual Reality (VR) to study humanrobot interactions during navigation tasks by both immersing a user and a robot in a shared virtual spaces. VR combines the advantages of being safe (as robots and humans interacting by the means of VR but can physically be in remote places) and ecological (realistic environments are perceived by the robot and the human, and natural behaviors can be observed). Nevertheless, VR can introduce perceptual biases in the interaction and affect in some ways the observed behaviors, which can be problematic when used to acquire experimental data. In our case, not only human perception is concerned, but also the one of the robot which requires to be simulated to perceive the VR world. Thus, the contribution of this paper is twofold. It first provides a technical solution to perform human robot interactions in navigation tasks through VR: we describe how we combine motion tracking, VR devices, as well as robot sensors simulation algorithms to immerse together a human and a robot in a shared virtual space. We then assess a simple interaction task that we replicate in real and in virtual conditions to perform a first estimation of the importance of the biases introduced by the use of VR on both a Human and a robot. Our conclusions are in favor of using VR to study human-robot interactions, and we are developing directions for future work.

Simulating realistic interactions between virtual characters has been of interest to research communities for years, and is particularly important to automatically populate virtual environments. This problem requires to accurately understand and model how humans interact, which can be difficult to assess. In this context, Virtual Reality (VR) is a powerful tool to study human behaviour, especially as it allows assessing conditions which are both ecological and controlled. While VR was shown to allow realistic collision avoidance adaptations, in the frame of the ecological theory of perception and action, interactions between walkers can not solely be characterized through motion adaptations but also through the perception processes involved in such interactions. The objective of this paper is therefore to evaluate how different VR setups influence gaze behaviour during collision avoidance tasks between walkers. To this end, we designed an experiment involving a collision avoidance task between a participant and another walker (real confederate or virtual character). During this task, we compared both the partici-pant`s locomotion and gaze behaviour in a real environment and the same situation in different VR setups (including a CAVE, a screen and a Head-Mounted Display). Our results show that even if some quantitative differences exist, gaze behaviour is qualitatively similar between VR and real conditions. Especially, gaze behaviour in VR setups including a HMD is more in line with the real situation than the other setups. Furthermore, the outcome on motion adaptations confirms previous work, where collision avoidance behaviour is qualitatively similar in VR and real conditions. In conclusion, our results show that VR has potential for qualitative analysis of locomotion and gaze behaviour during collision avoidance. This opens perspectives in the design of new experiments to better understand human behaviour, in order to design more realistic virtual humans.

This paper presents a study performed in virtual reality on the effect of gaze interception during collision avoidance between two walkers. In such a situation, mutual gaze can be considered as a form of nonverbal communication. Additionally, gaze is believed to detail future path intentions and to be part of the nonverbal negotiation to achieve avoidance collaboratively. We considered an avoidance task between a real subject and a virtual human character and studied the influence of the character's gaze direction on the avoidance behaviour of the participant. Virtual reality provided an accurate control of the situation: seventeen participants were immersed in a virtual environment, instructed to navigate across a virtual space using a joystick and to avoid a virtual character that would appear from either side. The character would either gaze or not towards the participant. Further, the character would either perform or not a reciprocal adaptation of its trajectory to avoid a potential collision with the participant. The findings of this paper were that during an orthogonal collision avoidance task, gaze behaviour did not influence the collision avoidance behaviour of the participants. Further, the addition of reciprocal collision avoidance with gaze did not modify the collision behaviour of participants. These results suggest that for the duration of interaction in such a task, body motion cues were sufficient for coordination and regulation. We discuss the possible exploitation of these results to improve the design of virtual characters for populated virtual environments and user interaction.

Navigating in virtual environments requires using some locomotion interfaces, especially when the dimensions of the environment exceed the ones of the Virtual Reality system. Locomotion interfaces induce some biases both in the perception of the self-motion or in the formation of virtual locomotion trajectories. These biases have been mostly evaluated in the context of static environments, and studies need to be revisited in the new context of populated environments where users interact with virtual characters. We focus on a situation of collision avoidance between a real participant and a virtual character, and compared it to previous studies on real walkers. Our results show that, as in reality, the risk of future collision is accurately anticipated by participants, however with delay. We also show that collision avoidance trajectories formed in VR have common properties with real ones, with some quantitative differences in avoidance distances. More generally, our evaluation demonstrates that reliable results can be obtained for qualitative analysis of small scale interactions in VR. We discuss these results in the perspective of a VR platform for large scale interaction applications, such as in a crowd, for which real data are difficult to gather.

Abstract When navigating in crowds, humans are able to move efficiently between people. They look ahead to know which path would reduce the complexity of their interactions with others. Current navigation systems for virtual agents consider long‐term planning to find a path in the static environment and short‐term reactions to avoid collisions with close obstacles. Recently some mid‐term considerations have been added to avoid high density areas. However, there is no mid‐term planning among static and dynamic obstacles that would enable the agent to look ahead and avoid difficult paths or find easy ones as humans do. In this paper, we present a system for such mid‐term planning. This system is added to the navigation process between pathfinding and local avoidance to improve the navigation of virtual agents. We show the capacities of such a system using several case studies. Finally we use an energy criterion to compare trajectories computed with and without the mid‐term planning.

Abstract Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.

When avoiding a group, a walker has two possibilities: either he goes through it or around it. Going through very dense groups or around huge ones would not seem natural and could break any sense of presence in a virtual environment. This paper aims to enable crowd simulators to handle such situations correctly. To this end, we need to understand how real humans decide to go through or around groups. As a first hypothesis, we apply the Principle of Minimum Energy (PME) on different group sizes and density. According to this principle, a walker should go around small and dense groups whereas he should go through large and sparse groups. Such principle has already been used for crowd simulation; the novelty here is to apply it to decide on a global avoidance strategy instead of local adaptations only. Our study quantifies decision thresholds. However, PME leaves some inconclusive situations for which the two solutions paths have similar energetic costs. In a second part, we propose an experiment to corroborate PME decisions thresholds with real observations. As controlling the factors of an experiment with many people is extremely hard, we propose to use Virtual Reality as a new method to observe human behavior. This work represents the first crowd simulation algorithm component directly designed from a VR-based study. We also consider the role of secondary factors in inconclusive situations. We show the influence of the group appearance and direction of relative motion in the decision process. Finally, we draw some guidelines to integrate our conclusions to existing crowd simulators and show an example of such integration. We evaluate the achieved improvements.

In this paper, we present a new model to simulate following behavior. This model is based on a dynamic following distance that changes according to the follower's speed and to the leader's motion. The following distance is associated with a prediction of the leader's future position to give a following ideal position. We show the resulting following trajectory and detail the importance of the distance variation in different situations. The model is evaluated using real data. We demonstrate the capacity of our model to reproduce macroscopic patterns and show that it is also able to synthesize trajectories similar to real ones. Finally, we compare our results with other following models and point out the improvements.