Agora : unified framework for crowd simulation research

Crowd simulation focuses on modeling the movements and behaviors of large groups of people. This area of study has become increasingly important because of its several applications in various fields such as urban planning, safety, and entertainment. In each of these domains, the presence of virtual agents exhibiting realistic behavior greatly enhances the quality of the simulations. However, the inherently multifaceted and intricate nature of human behavior presents a unique challenge, necessitating the effective combination of multiple behavior models. This thesis introduces a novel theoretical framework for modeling human behavior in crowd simulations, addressing the unresolved issue of combining a plethora of behavior models, often developed in isolation. The proposed framework decomposes human behavior into fundamental driving stimuli, which are then represented graphically through the heatmap paradigm. Subsequently, the agent behavior is influenced by the heatmaps, which guide them toward attractive areas and steer them away from repulsive locations based on the encoded stimuli. A key advantage of this approach lies in the ability to combine heatmaps using well-defined color operations, effectively integrating different aspects of human behavior. Furthermore, the heatmap paradigm facilitates objective comparison of simulation output with real-world data, employing image similarity metrics to evaluate model accuracy. To realize this framework, the thesis presents a modular software architecture designed to support various tasks involved in crowd simulation, emphasizing the separation of concerns for each task. This architecture comprises a collection of abstract modules, which are subsequently implemented using appropriate software components to realize the underlying features, resulting in the Agora framework. To assess the ability of Agora to support the various tasks involved in crowd simulation, two case studies are implemented and analyzed. The first case study simulates tourists visiting Þingvellir national park in Iceland, examining how their behavior is influenced by the visibility of the surrounding environment. The second case study employs Agora to model the thermal and density comfort levels of virtual pedestrians in an urban setting. The results demonstrate that Agora successfully supports the development, combination, and evaluation of crowd simulation models against real-world data. The authoring process, assisted by Agora, is significantly more streamlined compared to its native counterpart. The integration of multiple models is achieved by combining the heatmaps, resulting in plausible behavior, and the model assessment is made convenient through the evaluator within the framework. The thesis concludes by discussing the implications of these findings for the field of crowd simulation, highlighting the contributions and potential future directions of the Agora framework.Mannfjöldahermun fæst við gerð líkana af hreyfingu og hegðun stórra hópa af fólki. Mikilvægi þessa rannsóknasviðs hefur vaxið stöðugt vegna hagnýtingar á margvíslegum vetvangi, eins og til dæmis á vetvangi borgarskipulags, öryggis og afþreyingar. Þegar sýndarmenni hegða sér á sannfærandi hátt, leiðir það til betri hermunar fyrir þessi notkunarsvið. En mannleg hegðun er í eðli sínu margbrotin og flókin og því er það sérstök áskorun við smíði sýndarmenna að sameina, með áhrifaríkum hætti, mörg mismunandi hegðunarlíkön. Þessi ritgerð kynnir nýja fræðilega umgjörð líkanasmíði mannlegrar hegðunar fyrir mannfjöldahermun, sem tekur á þeim óleysta vanda að sameina fjölda hegðunarlíkana, sem oft eru þróuð með aðskildum hætti. Umgjörðin brýtur mannlega hegðun niður í grundvallar drifáreiti, sem eru sett fram grafískt útfrá hugmyndafræði hitakorta. Sýndarmennin hegða sér síðan undir áhrifum frá hitakortunum, sem vísa þeim í áttina að aðlaðandi svæðum og stýra þeim burt frá fráhrindandi svæðum, útfrá hinu umritaða áreiti. Lykilkostur þessarar nálgunar er sá eiginleiki að geta blandað saman hitakortum með vel skilgreindum litaaðgerðum, sem eru þá í raun samþætting mismunandi hliða mannlegrar hegðunar. Hitakortshugmyndafræðin auðveldar ennfremur hlutlægan samanburð hermunarúttaks og raungagna með notkun myndsamanburðarmælinga, til að meta nákvæmni líkana. Varðandi útfærslu, þá kynnir þessi ritgerð einingadrifna hugbúnaðarhögun sem er hönnuð til að styðja við ýmsa ferla mannfjöldahermunar, með áherslu á aðskilnað helstu viðfangsefna hvers ferlis. Þessi högun inniheldur safn huglægra eininga, sem síðan eru útfærðar með viðeigandi hugbúnaðarhlutum, sem raungera undirliggjandi eiginleika. Útkoman er sjálf Agora umbjörðin. Tvö sýnidæmi eru útfærð og greind til að meta getu Agoru til að styðja við ýmis mannfjöldahermunarverkefni. Fyrra dæmið hermir eftir ferðamönnum sem heimsækja Þingvallaþjóðgarð, og skoðar hvernig hegðun þeirra verður fyrir áhrifum sýnileika umhverfisins sem umleikur þá. Seinna dæmið nýtir Agoru til að smíða líkan af hitauppstreymis- og þéttleikaþægindum hjá sýndarvegfarendum í borgarumhverfi. Niðurstöðurnar sýna góðan árangur Agoru við að styðja þróun, samþættingu og mat mannfjöldahermunarlíkana gagnvart raungögnum. Þróunarferlið er verulega þjálla með Agoru en með hefðbundnum aðferðum. Samþætting margra líkana tókst með blöndun hitakorta, möguleg hegðun var framkölluð og mat á líkönunum varð þægilegra með umgjörðinni. Ritgerðinni lýkur með því að fjalla um áhrif þessara niðurstaðna á svið mannfjöldahegðunar, með áherslu á nýstálegt framlag þessarar rannsóknar og mögulega framtíðarþróun Agora umgjarðarinnar

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Walking with virtual humans : understanding human response to virtual humanoids' appearance and behaviour while navigating in immersive VR

In this thesis, we present a set of studies whose results have allowed us to analyze how to improve the realism, navigation, and behaviour of the avatars in an immersive virtual reality environment. In our simulations, participants must perform a series of tasks and we have analyzed perceptual and behavioural data. The results of the studies have allowed us to deduce what improvements are needed to be incorporated to the original simulations, in order to enhance the perception of realism, the navigation technique, the rendering of the avatars, their behaviour or their animations. The most reliable technique for simulating avatars’ behaviour in a virtual reality environment should be based on the study of how humans behave within the environment. For this purpose, it is necessary to build virtual environments where participants can navigate safely and comfortably with a proper metaphor and, if the environment is populated with avatars, simulate their behaviour accurately. All these aspects together will make the participants behave in a way that is closer to how they would behave in the real world. Besides, the integration of these concepts could provide an ideal platform to develop different types of applications with and without collaborative virtual reality such as emergency simulations, teaching, architecture, or designing. In the first contribution of this thesis, we carried out an experiment to study human decision making during an evacuation. We were interested to evaluate to what extent the behaviour of a virtual crowd can affect individuals' decisions. From the second contribution, in which we studied the perception of realism with bots and humans performing just locomotion or varied animations, we can conclude that the combination of having human-like avatars with animation variety can increase the overall realism of a crowd simulation, trajectories and animation. The preliminary study presented in the third contribution of this thesis showed that realistic rendering of the environment and the avatars do not appear to increase the perception of realism in the participants, which is consistent with works presented previously. The preliminary results in our walk-in-place contribution showed a seamless and natural transition between walk-in-place and normal walk. Our system provided a velocity mapping function that closely resembles natural walk. We observed through a pilot study that the system successfully reduces motion sickness and enhances immersion. Finally, the results of the contribution related to locomotion in collaborative virtual reality showed that animation synchronism and footstep sound of the avatars representing the participants do not seem to have a strong impact in terms of presence and feeling of avatar control. However, in our experiment, incorporating natural animations and footstep sound resulted in smaller clearance values in VR than previous work in the literature. The main objective of this thesis was to improve different factors related to virtual reality experiences to make the participants feel more comfortable in the virtual environment. These factors include the behaviour and appearance of the virtual avatars and the navigation through the simulated space in the experience. By increasing the realism of the avatars and facilitating navigation, high scores in presence are achieved during the simulations. This provides an ideal framework for developing collaborative virtual reality applications or emergency simulations that require participants to feel as if they were in real life.En aquesta tesi, es presenta un conjunt d'estudis els resultats dels quals ens han permès analitzar com millorar el realisme, la navegació i el comportament dels avatars en un entorn de realitat virtual immersiu. En les nostres simulacions, els participants han de realitzar una sèrie de tasques i hem analitzat dades perceptives i de comportament mentre les feien. Els resultats dels estudis ens han permès deduir quines millores són necessàries per a ser incorporades a les simulacions originals, amb la finalitat de millorar la percepció del realisme, la tècnica de navegació, la representació dels avatars, el seu comportament o les seves animacions. La tècnica més fiable per simular el comportament dels avatars en un entorn de realitat virtual hauria de basar-se en l'estudi de com es comporten els humans dins de l¿entorn virtual. Per a aquest propòsit, és necessari construir entorns virtuals on els participants poden navegar amb seguretat i comoditat amb una metàfora adequada i, si l¿entorn està poblat amb avatars, simular el seu comportament amb precisió. Tots aquests aspectes junts fan que els participants es comportin d'una manera més pròxima a com es comportarien en el món real. A més, la integració d'aquests conceptes podria proporcionar una plataforma ideal per desenvolupar diferents tipus d'aplicacions amb i sense realitat virtual col·laborativa com simulacions d'emergència, ensenyament, arquitectura o disseny. En la primera contribució d'aquesta tesi, vam realitzar un experiment per estudiar la presa de decisions durant una evacuació. Estàvem interessats a avaluar en quina mesura el comportament d'una multitud virtual pot afectar les decisions dels participants. A partir de la segona contribució, en la qual estudiem la percepció del realisme amb robots i humans que realitzen només una animació de caminar o bé realitzen diverses animacions, vam arribar a la conclusió que la combinació de tenir avatars semblants als humans amb animacions variades pot augmentar la percepció del realisme general de la simulació de la multitud, les seves trajectòries i animacions. L'estudi preliminar presentat en la tercera contribució d'aquesta tesi va demostrar que la representació realista de l¿entorn i dels avatars no semblen augmentar la percepció del realisme en els participants, que és coherent amb treballs presentats anteriorment. Els resultats preliminars de la nostra contribució de walk-in-place van mostrar una transició suau i natural entre les metàfores de walk-in-place i caminar normal. El nostre sistema va proporcionar una funció de mapatge de velocitat que s'assembla molt al caminar natural. Hem observat a través d'un estudi pilot que el sistema redueix amb èxit el motion sickness i millora la immersió. Finalment, els resultats de la contribució relacionada amb locomoció en realitat virtual col·laborativa van mostrar que el sincronisme de l'animació i el so dels avatars que representen els participants no semblen tenir un fort impacte en termes de presència i sensació de control de l'avatar. No obstant això, en el nostre experiment, la incorporació d'animacions naturals i el so de passos va donar lloc a valors de clearance més petits en RV que treballs anteriors ja publicats. L'objectiu principal d'aquesta tesi ha estat millorar els diferents factors relacionats amb experiències de realitat virtual immersiva per fer que els participants se sentin més còmodes en l'entorn virtual. Aquests factors inclouen el comportament i l'aparença dels avatars i la navegació a través de l'entorn virtual. En augmentar el realisme dels avatars i facilitar la navegació, s'aconsegueixen altes puntuacions en presència durant les simulacions. Això proporciona un marc ideal per desenvolupar aplicacions col·laboratives de realitat virtual o simulacions d'emergència que requereixen que els participants se sentin com si estiguessin en la vida realPostprint (published version

Physical crowds and psychological crowds: applying self-categorization theory to computer simulation of collective behaviour

Computer models are used to simulate pedestrian behaviour for safety at mass events. Previous research has indicated differences between physical crowds of co-present individuals, and psychological crowds who mobilise collective behaviour through a shared social identity. This thesis aimed to examine the assumptions models use about crowds, conduct two studies of crowd movement to ascertain the behavioural signatures of psychological crowds, and implement these into a theoretically-driven model of crowd behaviour. A systematic review of crowd modelling literature is presented which explores the assumptions about crowd behaviour being used in current models. This review demonstrates that models portray the crowd as either an identical mass with no inter-personal connections, unique individuals with no connections to others, or as small groups within a crowd. Thus, no models have incorporated the role of self-categorisation theory needed to simulate collective behaviour. The empirical research in this thesis aimed to determine the behavioural effects of self-categorisation on pedestrian movement. Findings from a first study illustrate that, in comparison to a physical crowd, perception of shared social identities in the psychological crowd motivated participants to maintain close proximity with ingroup members through regulation of their speed and distance walked. A second study showed that collective self-organisation seemed to be increased by the presence of an outgroup, causing ingroup members to tighten formation to avoid splitting up. Finally, a computer model is presented which implements the quantified behavioural effects of self-categorisation found in the behavioural studies. A self-categorisation parameter is introduced to simulate ingroup members self-organising to remain together. This is compared to a physical crowd simulation with group identities absent. The results demonstrate that the self-categorisation parameter provides more accurate simulation of psychological crowd behaviour. Thus, it is argued that models should implement self-categorisation into simulations of psychological crowds to increase safety at mass events

Social Perception of Pedestrians and Virtual Agents Using Movement Features

In many tasks such as navigation in a shared space, humans explicitly or implicitly estimate social information related to the emotions, dominance, and friendliness of other humans around them. This social perception is critical in predicting others’ motions or actions and deciding how to interact with them. Therefore, modeling social perception is an important problem for robotics, autonomous vehicle navigation, and VR and AR applications. In this thesis, we present novel, data-driven models for the social perception of pedestrians and virtual agents based on their movement cues, including gaits, gestures, gazing, and trajectories. We use deep learning techniques (e.g., LSTMs) along with biomechanics to compute the gait features and combine them with local motion models to compute the trajectory features. Furthermore, we compute the gesture and gaze representations using psychological characteristics. We describe novel mappings between these computed gaits, gestures, gazing, and trajectory features and the various components (emotions, dominance, friendliness, approachability, and deception) of social perception. Our resulting data-driven models can identify the dominance, deception, and emotion of pedestrians from videos with an accuracy of more than 80%. We also release new datasets to evaluate these methods. We apply our data-driven models to socially-aware robot navigation and the navigation of autonomous vehicles among pedestrians. Our method generates robot movement based on pedestrians’ dominance levels, resulting in higher rapport and comfort. We also apply our data-driven models to simulate virtual agents with desired emotions, dominance, and friendliness. We perform user studies and show that our data-driven models significantly increase the user’s sense of social presence in VR and AR environments compared to the baseline methods.Doctor of Philosoph