Ped-Air: A Simulator for Loading, Unloading, and Evacuating Aircraft

AbstractWe present Ped-Air, a pedestrian simulation system to model the loading, unloading, and evacuation of commercial aircraft. We address the challenge of simulating passenger movement in constrained spaces (e.g., aisles and rows), along with complex, coordinating behaviors between the passengers. Ped-Air models different categories of passengers and flight crew, capturing their unique behaviors and complex interactions. We exhibit Ped-Airs capabilities by simulating passenger movements on two representative aircraft: a single-aisle Boeing 737, and a double-aisle Boeing 777. We are able to simulate the following behaviors: stress, luggage placement, flight staff assisting passengers, obstructed exits for evacuation

Menge: A Modular Framework for Simulating Crowd Movement

We present Menge, a cross-platform, extensible, modular framework for simulating pedestrian movement in a crowd.  Menge's architecture is inspired by an implicit decomposition of the problem of simulating crowds into component subproblems.  These subproblems can typically be solved in many ways; different combinations of subproblem solutions yield crowd simulators with likewise varying properties.  Menge creates abstractions for those subproblems and provides a plug-in architecture so that a novel simulator can be dynamically configured by connecting built-in and bespoke implementations of solutions to the various subproblems.  Use of this type of framework could facilitate crowd simulation research, evaluation, and applications by reducing the cost of entering the domain, facilitating collaboration, and making comparisons between algorithms simpler.  We show how the Menge framework is compatible with many prior models and algorithms used in crowd simulation and illustrate its flexibility via a varied set of scenarios and applications

EVAQ: Person-Specific Evacuation Simulation for Large Crowd Egress Analysis

Timely crowd evacuation in life-threatening situations such as fire emergency or terrorist attack is a significant concern for authorities and first responders. An individual’s fate in this kind of situation is highly dependent on a host of factors, especially (i) agent dynamics: how the individual selects and executes an egress strategy, (ii) hazard dynamics: how hazards propagate (e.g., fire and smoke spread, lone wolf attacker moves) and impair the surrounding environment with time, (iii) intervention dynamics: how first responders intervene (e.g., firefighters spread repellents) to recover environment. This thesis presents EVAQ, a simulation modeling framework for evaluating the impact of these factors on the likelihood of survival in an emergency evacuation. The framework captures the effect of personal traits and physical habitat parameters on occupants’ decision-making. In particular, personal (i.e., age, gender, disability) and interpersonal (i.e., agent-agent interactions) attributes, as well as an individual’s situational awareness are parameterized in a deteriorating environment considering different exit layouts and physical constraints. Further, the framework supports a variety of hazard propagation schemes (e.g., fire spreading in a given direction, lone wolf attacker targeting individuals), and intervene schemes (e.g., firefighters spreading repellents, police catch the attacker) to support a wide range of emergency evacuation scenarios. The application of EVAQ to crowd egress planning in an airport terminal and a shopping mall in the fire emergency is presented in this thesis, and results are discussed. Result shows that the likelihood of survival decreases with a decrease in availability of the nearest exits and a resulting increase in congestions in the environment. Also, it is observed that the incorporation of group behavior increases the likelihood of survival for children, as well as elderly and disabled people. In addition, several verifications and validation tests are performed to assess the reliability and integrity of EVAQ in comparison with existing evacuation modeling tools. As personalized sensing and information delivery platforms are becoming more ubiquitous, findings of this work are ultimately sought to assist in developing and executing more robust and adaptive emergency mapping and evacuation plans, ultimately aimed at promoting people’s lives and wellbeing

Interactive Tracking, Prediction, and Behavior Learning of Pedestrians in Dense Crowds

The ability to automatically recognize human motions and behaviors is a key skill for autonomous machines to exhibit to interact intelligently with a human-inhabited environment. The capabilities autonomous machines should have include computing the motion trajectory of each pedestrian in a crowd, predicting his or her position in the near future, and analyzing the personality characteristics of the pedestrian. Such techniques are frequently used for collision-free robot navigation, data-driven crowd simulation, and crowd surveillance applications. However, prior methods for these problems have been restricted to low-density or sparse crowds where the pedestrian movement is modeled using simple motion models. In this thesis, we present several interactive algorithms to extract pedestrian trajectories from videos in dense crowds. Our approach combines different pedestrian motion models with particle tracking and mixture models and can obtain an average of $20\%$ improvement in accuracy in medium-density crowds over prior work. We compute the pedestrian dynamics from these trajectories using Bayesian learning techniques and combine them with global methods for long-term pedestrian prediction in densely crowded settings. Finally, we combine these techniques with Personality Trait Theory to automatically classify the dynamic behavior or the personality of a pedestrian based on his or her movements in a crowded scene. The resulting algorithms are robust and can handle sparse and noisy motion trajectories. We demonstrate the benefits of our long-term prediction and behavior classification methods in dense crowds and highlight the benefits over prior techniques. We highlight the performance of our novel algorithms on three different applications. The first application is interactive data-driven crowd simulation, which includes crowd replication as well as the combination of pedestrian behaviors from different videos. Secondly, we combine the prediction scheme with proxemic characteristics from psychology and use them to perform socially-aware navigation. Finally, we present novel techniques for anomaly detection in low-to medium-density crowd videos using trajectory-level behavior learning.Doctor of Philosoph

Velocity-Space Reasoning for Interactive Simulation of Dynamic Crowd Behaviors

The problem of simulating a large number of independent entities, interacting with each other and moving through a shared space, has received considerable attention in computer graphics, biomechanics, psychology, robotics, architectural design, and pedestrian dynamics. One of the major challenges is to simulate the dynamic nature, variety, and subtle aspects of real-world crowd motions. Furthermore, many applications require the capabilities to simulate these movements and behaviors at interactive rates. In this thesis, we present interactive methods for computing trajectory-level behaviors that capture various aspects of human crowds. At a microscopic level, we address the problem of modeling the local interactions. First, we simulate dynamic patterns of crowd behaviors using Attribution theory and General Adaptation Syndrome theory from psychology. Our model accounts for permanent, stable disposition and the dynamic nature of human behaviors that change in response to the situation. Second, we model physics-based interactions in dense crowds by combining velocity-based collision avoidance algorithms with external forces. Our approach is capable of modeling both physical forces and interactions between agents and obstacles, while also allowing the agents to anticipate and avoid upcoming collisions during local navigation. We also address the problem at macroscopic level by modeling high-level aspects of human crowd behaviors. We present an automated scheme for learning and predicting individual behaviors from real-world crowd trajectories. Our approach is based on Bayesian learning algorithms combined with a velocity-based local collision avoidance model. We further extend our method to learn time-varying trajectory behavior patterns from pedestrian trajectories. These behavior patterns can be combined with local navigation algorithms to generate crowd behaviors that are similar to those observed in real-world videos. We highlight their performance for pedestrian navigation, architectural design and generating dynamic behaviors for virtual environments.Doctor of Philosoph

Interactive Motion Planning for Multi-agent Systems with Physics-based and Behavior Constraints

Man-made entities and humans rely on movement as an essential form of interaction with the world. Whether it is an autonomous vehicle navigating crowded roadways or a simulated pedestrian traversing a virtual world, each entity must compute safe, effective paths to achieve their goals. In addition, these entities, termed agents, are subject to unique physical and behavioral limitations within their environment. For example, vehicles have a finite physical turning radius and must obey behavioral constraints such as traffic signals and rules of the road. Effective motion planning algorithms for diverse agents must account for these physics-based and behavior constraints. In this dissertation, we present novel motion planning algorithms that account for constraints which physically limit the agent and impose behavioral limitations on the virtual agents. We describe representational approaches to capture specific physical constraints on the various agents and propose abstractions to model behavior constraints affecting them. We then describe algorithms to plan motions for agents who are subject to the modeled constraints. First, we describe a biomechanically accurate elliptical representation for virtual pedestrians; we also describe human-like movement constraints corresponding to shoulder-turning and side-stepping in dense environments. We detail a novel motion planning algorithm extending velocity obstacles to generate collisionfree paths for hundreds of elliptical agents at interactive rates. Next, we describe an algorithm to encode dynamics and traffic-like behavior constraints for autonomous vehicles in urban and highway environments. We describe a motion planning algorithm to generate safe, high-speed avoidance maneuvers using a novel optimization function and modified control obstacle formulation, and we also present a simulation framework to evaluate driving strategies. Next, we present an approach to incorporate high-level reasoning to model the motions and behaviors of virtual agents in terms of verbal interactions with other agents or avatars. Our approach leverages natural-language interaction to reduce uncertainty and generate effective plans. Finally, we describe an application of our techniques to simulate pedestrian behaviors for gathering simulated data about loading, unloading, and evacuating an aircraft.Doctor of Philosoph

Pedestrian velocity obstacles: pedestrian simulation through reasoning in velocity space

We live in a populous world. Furthermore, as social animals, we participate in activities which draw us together into shared spaces -- office buildings, city sidewalks, parks, events (e.g., religious, sporting, or political), etc. Models that can predict how crowds of humans behave in such settings would be valuable in allowing us to analyze the designs for novel environments and anticipate issues with space utility and safety. They would also better enable robots to safely work in a common environment with humans. Furthermore, credible simulation of crowds of humans would allow us to populate virtual worlds, helping to increase the immersive properties of virtual reality or entertainment applications. We propose a new model for pedestrian crowd simulation: Pedestrian Velocity Obstacles (PedVO). PedVO is based on Optimal Reciprocal Collision Avoidance (ORCA), a local navigation algorithm for computing optimal feasible velocities which simultaneously avoid collisions while still allowing the agents to progress toward their individual goals. PedVO extends ORCA by introducing new models of pedestrian behavior and relationships in conjunction with a modified geometric optimization planning technique to efficiently simulate agents with improved human-like behaviors. PedVO introduces asymmetric relationships between agents through two complementary techniques: Composite Agents and Right of Way. The former exploits the underlying collision avoidance mechanism to encode abstract factors and the latter modifies the optimization algorithm's constraint definition to enforce asymmetric coordination. PedVO further changes the optimization algorithm to more fully encode the agent's knowledge of its environment, allowing the agent to make more intelligent decisions, leading to a better utilization of space and improved flow. PedVO incorporates a new model, which works in conjunction with the local planning algorithm, to introduce a ubiquitous density-sensitive behavior observed in human crowds -- the so-called "fundamental diagram." We also provide a physically-plausible, interactive model for simulating walking motion to support the computed agent trajectories. We evaluate these techniques by simulating various scenarios, such as pedestrian experiments and a challenging real-world scenario: simulating the performance of the Tawaf, an aspect of the Muslim Hajj.Doctor of Philosoph

Developing an agent-based evacuation simulation model based on the study of human behaviour in fire investigation reports

Fire disasters happen every day all over the world. These hazardous events threaten people's lives and force an immediate movement of people wanting to escape from a dangerous area. Evacuation drills are held to encourage people to practise evacuation skills and to ensure they are familiar with the environment. However, these drills cannot accurately represent real emergency situations and, in some cases, people may be injured during practice. Therefore, modelling pedestrian motion and crowd dynamics in evacuation situations has important implications for human safety, building design, and evacuation processes. This thesis focuses on indoor pedestrian evacuation in fire disasters. To understand how humans behave in emergency situations, and to simulate more realistic human behaviour, this thesis studies human behaviour from fire investigation reports, which provide a variety details about the building, fire circumstance, and human behaviour from professional fire investigation teams. A generic agent-based evacuation model is developed based on common human behaviour that indentified in the fire investigation reports studied. A number of human evacuation behaviours are selected and then used to design different types of agents, assigning with various characteristics. In addition, the interactions between various agents and an evacuation timeline are modelled to simulate human behaviour and evacuation phenomena during evacuation. The application developed is validated using three specific real fire cases to evaluate how closely the simulation results reflected reality. The model provides information on the number of casualties, high-risk areas, egress selections, and evacuation time. In addition, changes to the building configuration, number of occupants, and location of fire origin are tested in order to predict potential risk areas, building capacity and evacuation time for different situations. Consequently, the application can be used to inform building designs, evacuation plans, and priority rescue processes