Modeling Crowd and Trained Leader Behavior during Building Evacuation

This article considers animating evacuation in complex buildings by crowds who might not know the structure\u27s connectivity, or who find routes accidently blocked. It takes into account simulated crowd behavior under two conditions: where agents communicate building route knowledge, and where agents take different roles such as trained personnel, leaders, and followers

Controlling Individual Agents in High-Density Crowd Simulation

Simulating the motion of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either resemble particle simulations (where agents lack orientation controls) or are conservative in the range of human motion possible (agents lack psychological state and aren’t allowed to ‘push’ each other). Our HiDAC system (for High-Density Autonomous Crowds) focuses on the problem of simulating the local motion and global wayfinding behaviors of crowds moving in a natural manner within dynamically changing virtual environments. By applying a combination of psychological and geometrical rules with a social and physical forces model, HiDAC exhibits a wide variety of emergent behaviors from agent line formation to pushing behavior and its consequences; relative to the current situation, personalities of the individuals and perceived social density

Animation Fidelity in Self-Avatars: Impact on User Performance and Sense of Agency

The use of self-avatars is gaining popularity thanks to affordable VR headsets. Unfortunately, mainstream VR devices often use a small number of trackers and provide low-accuracy animations. Previous studies have shown that the Sense of Embodiment, and in particular the Sense of Agency, depends on the extent to which the avatar's movements mimic the user's movements. However, few works study such effect for tasks requiring a precise interaction with the environment, i.e., tasks that require accurate manipulation, precise foot stepping, or correct body poses. In these cases, users are likely to notice inconsistencies between their self-avatars and their actual pose. In this paper, we study the impact of the animation fidelity of the user avatar on a variety of tasks that focus on arm movement, leg movement and body posture. We compare three different animation techniques: two of them using Inverse Kinematics to reconstruct the pose from sparse input (6 trackers), and a third one using a professional motion capture system with 17 inertial sensors. We evaluate these animation techniques both quantitatively (completion time, unintentional collisions, pose accuracy) and qualitatively (Sense of Embodiment). Our results show that the animation quality affects the Sense of Embodiment. Inertial-based MoCap performs significantly better in mimicking body poses. Surprisingly, IK-based solutions using fewer sensors outperformed MoCap in tasks requiring accurate positioning, which we attribute to the higher latency and the positional drift that causes errors at the end-effectors, which are more noticeable in contact areas such as the feet.Comment: Accepted in IEEE VR 202

Generating Plausible Individual Agent Movements From Spatio-Temporal Occupancy Data

We introduce the Spatio-Temporal Agent Motion Model, a datadriven representation of the behavior and motion of individuals within a space over the course of a day. We explore different representations for this model, incorporating different modes of individual behavior, and describe how crowd simulations can use this model as source material for dynamic and realistic behaviors

Crowd Simulation Incorporating Agent Psychological Models, Roles and Communication

We describe a new architecture to integrate a psychological model into a crowd simulation system in order to obtain believable emergent behaviors. Our existing crowd simulation system (MACES) performs high level wayfinding to explore unknown environments and obtain a cognitive map for navigation purposes, in addition to dealing with low level motion within each room based on social forces. Communication and roles are added to achieve individualistic behaviors and a realistic way to spread information about the environment. To expand the range of realistic human behaviors, we use a system (PMFserv) that implements human behavior models from a range of ability, stress, emotion, decision theoretic and motivation sources. An architecture is proposed that combines and integrates MACES and PMFserv to add validated agent behaviors to crowd simulations

Multi-Domain Real-Time Planning in Dynamic Environments

This paper presents a real-time planning framework for multicharacter navigation that enables the use of multiple heterogeneous problem domains of differing complexities for navigation in large, complex, dynamic virtual environments. The original navigation problem is decomposed into a set of smaller problems that are distributed across planning tasks working in these different domains. An anytime dynamic planner is used to efficiently compute and repair plans for each of these tasks, while using plans in one domain to focus and accelerate searches in more complex domains. We demonstrate the benefits of our framework by solving many challenging multi-agent scenarios in complex dynamic environments requiring space-time precision and explicit coordination between interacting agents, by accounting for dynamic information at all stages of the decision-making process

Crowd simulation incorporating agent psychological models, roles and communication

We describe a new architecture to integrate a psychological model into a crowd simulation system in order to obtain believable emergent behaviors. Our existing crowd simulation system (MACES) performs high level wayfinding to explore unknown environments and obtain a cognitive map for navigation purposes, in addition to dealing with low level motion within each room based on social forces. Communication and roles are added to achieve individualistic behaviors and a realistic way to spread information about the environment. To expand the range of realistic human behaviors, we use a system (PMFserv) that implements human behavior models from a range of ability, stress, emotion, decision theoretic and motivation sources. An architecture is proposed that combines and integrates MACES and PMFserv to add validated agent behaviors to crowd simulations

Modeling realistic high density autonomous agent crowd movement: Social forces, communication, roles and psychological influences

The simulation of realistic, large, dense crowds of autonomous agents is still a challenge for the computer graphics community. Typical approaches either look like particle simulations (where agents \u27vibrate\u27 back and forth) or are conservative in the range of motion possible (agents aren\u27t allowed to \u27push\u27 each other). Our HiDAC system (High Density Autonomous Crowds) focuses on the problem of simulating the local motion behaviors of crowds moving in a natural manner within dynamically changing virtual environments. This is achieved by applying a combination of psychological and geometrical rules layered on top of a social forces model. The results show: elimination of agent \u27shaking\u27 behavior, fast perception, and a wide variety of emergent behaviors including: bi-directional flows, overtaking, emergent queuing with different line widths, agents being \u27pushed\u27 and \u27falling\u27, and panic propagation. These behaviors emerge based on the current situation, agent personality and perceived density of the crowd. To accurately simulate crowds in large, complex environments, it is not enough to only model local motion; agents must also have the ability to navigate the unknown virtual environment. We therefore address the problems that arise during crowd navigation where not all individuals have complete knowledge of the building\u27s internal structure. In addition, we simulate the effects of communication on the behavior of autonomous agents while exploring the building. We have developed a system called MACES (Multi-Agent Communication for Evacuation Simulation) which combines local motion with wayfinding using inter-agent communication and different roles. Together they automatically augment an agent\u27s mental map of the environment to produce empirically better maze evacuation performance. We study the emergent behavior during building evacuation under different conditions such as agents using communication to share their knowledge of the building routes and hazards, psychological factors driving different navigation skills, and agents taking different roles such as trained personnel, leaders and followers. The experimental results show significant improvements in evacuation rates with inter-agent communication and demonstrate that only a relatively small percentage of trained leaders yield evacuation rates comparable to the case where all are trained. The framework presented in this dissertation combines decision making, including communication and roles (MACES), with local motion (HiDAC). The two systems interact in real-time while being driven by a set of psychological and physiological parameters that allow the user to have control over the final behavior exhibited by the crowd