Walk-sharing - A smarter way to improve pedestrian safety and safety perception in urban spaces

Fortbewegung zu Fuß ist nachweislich der körperlichen und geistigen Gesundheit der Menschen zuträglich und gilt als Schlüssel zu nachhaltigem und lebenswertem städtischem Leben. Der Anteil der Fußgänger am Verkehrsaufkommen ist allerdings mit der rasanten Motorisierung und Verstädterung auf der ganzen Welt rückläufig. Darüber hinaus halten fußgängerunfreundliche Umgebungen Menschen davon ab, zu Fuß zu gehen. Die Angst vor Kriminalität wurde als wichtigstes Hindernis genannt. Sie macht das Zufußgehen zu kritischen Tageszeiten unattraktiv, selbst wenn es nach allen anderen Maßstäben bequem wäre. Die Furcht vor Kriminalität beeinflusst die Wahl des Weges und der Verkehrsmittel. Sie motiviert die Menschen dazu, kostspieligere Alternativen zu nutzen, zum Beispiel sinnvolle Umwege zu gehen oder ganz auf das Gehen zu verzichten und auf andere, meist motorisierte Verkehrsmittel umzusteigen. Die Angst vor Kriminalität verringert die allgemeine Begehbarkeit eines Stadtgebiets, reduziert die Zeit, die zu Fuß verbracht wird, und verhindert damit die Vorteile, die das Zufußgehen geboten hätte. Herkömmliche Ansätze zur Verringerung der Furcht vor Kriminalität in Außenbereichen umfassen städtebauliche Verbesserungen und Infrastrukturüberholungen. Sie sind teuer, lokal begrenzt und erfordern einen erheblichen Zeit- und Personalaufwand. Andere, neuere, ortsgestützte IT-Ansätze, die zum Beispiel sichere Routenempfehlungssysteme beinhalten, leiden unter einer starken Abhängigkeit von Kriminalitäts- und anderen Daten und sind dafür bekannt, dass sie Gesellschaften durch die Erstellung von Profilen sozioökonomischer Gruppen segregieren. Um die Herausforderungen der bestehenden Methoden zu überwinden, wird in dieser Arbeit das Walk-Sharing (wörtlich: gemeinsames Gehen) eingeführt. Walk-Sharing ist ein neuartiger Service in der Kategorie der geteilten Mobilität, die darauf abzielt, Menschen dazu zu ermutigen, zu Fuß zu gehen, anstatt andere Verkehrsmittel zu nutzen, wenn dies möglich ist. Da sich Menschen sicherer fühlen, wenn sie in Begleitung gehen, bringt Walk-Sharing Menschen mit ähnlichen räumlichen und zeitlichen Mobilitätsbedürfnissen zusammen, die bereit sind, zu Fuß zu ihren jeweiligen Zielen zu gehen. Durch das gemeinsame Gehen für einen Teil oder die gesamte Strecke verbessert das Walk-Sharing die aktive natürliche Wachsamkeit und verringert so die Angst vor Kriminalität. Durch die Verringerung der Angst vor Kriminalität während des Gehens hat Walk-Sharing das Potenzial, das Gehen attraktiver zu machen und damit den Anteil des Fußverkehrs auf kurzen Strecken zu erhöhen und folglich den motorisierten Verkehr zu reduzieren, was wiederum zu einer Verringerung der Emissionen und der Verkehrsbelastung führt. In dieser Arbeit werden die Grundlagen des Walk-Sharing erörtert, seine Gemeinsamkeiten und Unterschiede zu bestehenden geteilten Mobilitätsformen herausgearbeitet und ein konzeptionelles Modell vorgeschlagen, das eine abstrakte Darstellung eines möglichen Walk-Sharing-Systems darstellt. Basierend auf der Logik dieses konzeptionellen Modells wird in dieser Arbeit ein agentenbasiertes Simulationsmodell vorgestellt, um die Leistung von Walk-Sharing unter plausiblen Szenarien objektiv zu messen. Anhand theoretischer Simulationen wird das Sensitivitätsverhalten des Walk-Sharing-Modells dargestellt, was auch die logische Funktion des Modells selbst zeigt. Danach werden begründeter Annahmen über menschliche Präferenzen herangezogen, um eine Simulation des Walk-Sharing auf einem Universitätscampus vorzustellen. Diese Simulation zeigt bis zu 80% Effektivität in Bezug auf die Verbesserung der Sicherheit. Schließlich werden in dieser Arbeit eine Umfrage und deren Ergebnisse vorgestellt, die die tatsächlichen räumlich-zeitlichen Präferenzen, die sozialen Präferenzen und die allgemeine Wahrscheinlichkeit der Teilnahme an Walk-Sharing aufzeigen. Mit diesen Erkenntnissen wird eine kalibrierte, ausgefeiltere und fundiertere Simulation des Walk-Sharing vorgestellt. Die Ergebnisse zeigen, dass gemeinsames Gehen bis zu 60% zur Verbesserung der Sicherheit beiträgt und gleichzeitig räumlich-zeitliche Kosten verursacht, die im Rahmen der von der befragten Gruppe bevorzugten Standards liegen. Walk-Sharing überwindet die Nachteile der bestehenden Ansätze zur Verringerung der Kriminalitätsfurcht, indem es proaktiv (unabhängig von Kriminalitäts- und stellvertretenden soziodemographischen Daten) und kostengünstig ist (keine größeren infrastrukturellen Veränderungen oder erheblicher menschlicher Aufwand erforderlich). Es ist skalierbar und übertragbar (kann überall angewendet werden und ist für die Gesellschaft angesichts der gegenwärtigen Verbreitung von Smartphones leicht zugänglich). Im Zeitalter des ubiquitären Computings, des Internets der Dinge, effizienter standortbezogener Dienste, und Smartphones könnte Walk-Sharing die intelligentere Lösung sein, die das Zufußgehen als sicherere Mobilitätsform für räumlich und zeitlich günstige Wege fördert und somit Fortschritte in Richtung eines nachhaltigeren städtischen Lebens macht, indem sie die aktive Mobilität erhöht und den motorisierten Verkehr reduziert

Multi-scale Pedestrian Navigation and Movement in Urban Areas

Sustainable transport planning highlights the importance of walking to low-carbon and healthy urban transport systems. Studies have identified multiple ways in which vehicle traffic can negatively impact pedestrians and inhibit walking intentions. However, pedestrian-vehicle interactions are underrepresented in models of pedestrian mobility. This omission limits the ability of transport simulations to support pedestrian-centric street design. Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. This thesis presents a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network based decisions at the neighbourhood level. The framework is based on Construal Level Theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. The route choice framework is implemented in a spatial agent-based simulation in which pedestrian and vehicle agents complete trips in an urban environment. Global sensitivity analysis is used to explore the behaviour produced by the multi-scale pedestrian route choice model. Finally, simulation experiments are used to explore the impacts of restrictions to pedestrian movement. The results demonstrate the potential insights that can be gained by linking street scale movement and interactions with neighbourhood level mobility patterns

Optimizing Simulated Crowd Behaviour

In the context of crowd simulation, there is a diverse set of algorithms that model steering, the ability of an agent to navigate between spatial locations, while avoiding static and dynamic obstacles. The performance of steering approaches, both in terms of quality of results and computational efficiency, depends on internal parameters that are manually tuned to satisfy application-specific requirements. This work investigates the effect that these parameters have on an algorithm's performance. Using three representative steering algorithms and a set of established performance criteria, we perform a number of large scale optimization experiments that optimize an algorithm's parameters for a range of objectives. For example, our method automatically finds optimal parameters to minimize turbulence at bottlenecks, reduce building evacuation times, produce emergent patterns, and increase the computational efficiency of an algorithm. Our study includes a statistical analysis of the correlations between algorithmic parameters, and performance criteria. We also propose using the Pareto Optimal Front as an efficient way of modelling optimal relationships between multiple objectives, and demonstrate its effectiveness by estimating optimal parameters for interactively defined combinations of the associated objectives. The proposed methodologies are general and can be applied to any steering algorithm using any set of performance criteria

Autonomous Robot Navigation through a Crowded and Dynamic Environment: Using A Novel form of Path Planning to Demonstrate Consideration towards Pedestrians and other Robots

This thesis presents a novel path planning algorithm for robotic crowd navigation through a pedestrian environment. The robot is designed to negotiate its way through the crowd using considerate movements. Unlike many other path planning algorithms, which assume cooperation with other pedestrians, this algorithm is completely independent and requires only observation. A considerate navigation strategy has been developed in this thesis, which utilises consideration as an directs an autonomous mobile robot. Using simple methods of predicting pedestrian movements, as well as simple relative distance and trajectory measurements between the robot and pedestrians, the robot can navigate through a crowd without causing disruption to pedestrian trajectories. Dynamic pedestrian positions are predicted using uncertainty ellipses. A novel Voronoi diagram-visibility graph hybrid roadmap is implemented so that the path planner can exploit any available gaps in between pedestrians, and plan considerate paths. The aim of the considerate path planner is to have the robot behave in specific ways when moving through the crowd. By predicting pedestrian trajectories, the robot can avoid interfering with them. Following preferences to move behind pedestrians, when cutting across their trajectories; to move in the same direction of the crowd when possible; and to slow down in crowded areas, will prevent any interference to individual pedestrians, as well as preventing an increase in congestion to the crowd as a whole. The effectiveness of the considerate navigation strategy is evaluated using simulated pedestrians, multiple mobile robots loaded with the path planning algorithm, as well as a real-life pedestrian dataset. The algorithm will highlight its ability to move with the aforementioned consideration towards each individual dynamic agent

An Agent-Based Variogram Modeller: Investigating Intelligent, Distributed-Component Geographical Information Systems

Geo-Information Science (GIScience) is the field of study that addresses substantive questions concerning the handling, analysis and visualisation of spatial data. Geo- Information Systems (GIS), including software, data acquisition and organisational arrangements, are the key technologies underpinning GIScience. A GIS is normally tailored to the service it is supposed to perform. However, there is often the need to do a function that might not be supported by the GIS tool being used. The normal solution in these circumstances is to go out and look for another tool that can do the service, and often an expert to use that tool. This is expensive, time consuming and certainly stressful to the geographical data analyses. On the other hand, GIS is often used in conjunction with other technologies to form a geocomputational environment. One of the complex tools in geocomputation is geostatistics. One of its functions is to provide the means to determine the extent of spatial dependencies within geographical data and processes. Spatial datasets are often large and complex. Currently Agent system are being integrated into GIS to offer flexibility and allow better data analysis. The theis will look into the current application of Agents in within the GIS community, determine if they are used to representing data, process or act a service. The thesis looks into proving the applicability of an agent-oriented paradigm as a service based GIS, having the possibility of providing greater interoperability and reducing resource requirements (human and tools). In particular, analysis was undertaken to determine the need to introduce enhanced features to agents, in order to maximise their effectiveness in GIS. This was achieved by addressing the software agent complexity in design and implementation for the GIS environment and by suggesting possible solutions to encountered problems. The software agent characteristics and features (which include the dynamic binding of plans to software agents in order to tackle the levels of complexity and range of contexts) were examined, as well as discussing current GIScience and the applications of agent technology to GIS, agents as entities, objects and processes. These concepts and their functionalities to GIS are then analysed and discussed. The extent of agent functionality, analysis of the gaps and the use these technologies to express a distributed service providing an agent-based GIS framework is then presented. Thus, a general agent-based framework for GIS and a novel agent-based architecture for a specific part of GIS, the variogram, to examine the applicability of the agent- oriented paradigm to GIS, was devised. An examination of the current mechanisms for constructing variograms, underlying processes and functions was undertaken, then these processes were embedded into a novel agent architecture for GIS. Once the successful software agent implementation had been achieved, the corresponding tool was tested and validated - internally for code errors and externally to determine its functional requirements and whether it enhances the GIS process of dealing with data. Thereafter, its compared with other known service based GIS agents and its advantages and disadvantages analysed

Proceedings of the 4th Symposium on Management of Future Motorway and Urban Traffic Systems 2022

The 4th Symposium on Management of Future Motorway and Urban Traffic Systems (MFTS) was held in Dresden, Germany, from November 30th to December 2nd, 2022. Organized by the Chair of Traffic Process Automation (VPA) at the “Friedrich List” Faculty of Transport and Traffic Sciences of the TU Dresden, the proceedings of this conference are published as volume 9 in the Chair’s publication series “Verkehrstelematik” and contain a large part of the presented conference extended abstracts. The focus of the MFTS conference 2022 was cooperative management of multimodal transport and reflected the vision of the professorship to be an internationally recognized group in ITS research and education with the goal of optimizing the operation of multimodal transport systems. In 14 MFTS sessions, current topics in demand and traffic management, traffic control in conventional, connected and automated transport, connected and autonomous vehicles, traffic flow modeling and simulation, new and shared mobility systems, digitization, and user behavior and safety were discussed. In addition, special sessions were organized, for example on “Human aspects in traffic modeling and simulation” and “Lesson learned from Covid19 pandemic”, whose descriptions and analyses are also included in these proceedings.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the FutureDas 4. Symposium zum Management zukünftiger Autobahn- und Stadtverkehrssysteme (MFTS) fand vom 30. November bis 2. Dezember 2022 in Dresden statt und wurde vom Lehrstuhl für Verkehrsprozessautomatisierung (VPA) an der Fakultät Verkehrswissenschaften„Friedrich List“ der TU Dresden organisiert. Der Tagungsband erscheint als Band 9 in der Schriftenreihe „Verkehrstelematik“ des Lehrstuhls und enthält einen Großteil der vorgestellten Extended-Abstracts des Symposiums. Der Schwerpunkt des MFTS-Symposiums 2022 lag auf dem kooperativen Management multimodalen Verkehrs und spiegelte die Vision der Professur wider, eine international anerkannte Gruppe in der ITS-Forschung und -Ausbildung mit dem Ziel der Optimierung des Betriebs multimodaler Transportsysteme zu sein. In 14 MFTS-Sitzungen wurden aktuelle Themen aus den Bereichen Nachfrage- und Verkehrsmanagement, Verkehrssteuerung im konventionellen, vernetzten und automatisierten Verkehr, vernetzte und autonome Fahrzeuge, Verkehrsflussmodellierung und -simulation, neue und geteilte Mobilitätssysteme, Digitalisierung sowie Nutzerverhalten und Sicherheit diskutiert. Darüber hinaus wurden Sondersitzungen organisiert, beispielsweise zu „Menschlichen Aspekten bei der Verkehrsmodellierung und -simulation“ und „Lektionen aus der Covid-19-Pandemie“, deren Beschreibungen und Analysen ebenfalls in diesen Tagungsband einfließen.:1 Connected and Automated Vehicles 1.1 Traffic-based Control of Truck Platoons on Freeways 1.2 A Lateral Positioning Strategy for Connected and Automated Vehicles in Lane-free Traffic 1.3 Simulation Methods for Mixed Legacy-Autonomous Mainline Train Operations 1.4 Can Dedicated Lanes for Automated Vehicles on Urban Roads Improve Traffic Efficiency? 1.5 GLOSA System with Uncertain Green and Red Signal Phases 2 New Mobility Systems 2.1 A New Model for Electric Vehicle Mobility and Energy Consumption in Urban Traffic Networks 2.2 Shared Autonomous Vehicles Implementation for a Disrupted Public Transport Network 3 Traffic Flow and Simulation 3.1 Multi-vehicle Stochastic Fundamental Diagram Consistent with Transportations Systems Theory 3.2 A RoundD-like Roundabout Scenario in CARLA Simulator 3.3 Multimodal Performance Evaluation of Urban Traffic Control: A Microscopic Simulation Study 3.4 A MILP Framework to Solve the Sustainable System Optimum with Link MFD Functions 3.5 On How Traffic Signals Impact the Fundamental Diagrams of Urban Roads 4 Traffic Control in Conventional Traffic 4.1 Data-driven Methods for Identifying Travel Conditions Based on Traffic and Weather Characteristics 4.2 AI-based Multi-class Traffic Model Oriented to Freeway Traffic Control 4.3 Exploiting Deep Learning and Traffic Models for Freeway Traffic Estimation 4.4 Automatic Design of Optimal Actuated Traffic Signal Control with Transit Signal Priority 4.5 A Deep Reinforcement Learning Approach for Dynamic Traffic Light Control with Transit Signal Priority 4.6 Towards Efficient Incident Detection in Real-time Traffic Management 4.7 Dynamic Cycle Time in Traffic Signal of Cyclic Max-Pressure Control 5 Traffic Control with Autonomous Vehicles 5.1 Distributed Ordering and Optimization for Intersection Management with Connected and Automated Vehicles 5.2 Prioritization of an Automated Shuttle for V2X Public Transport at a Signalized Intersection – a Real-life Demonstration 6 User Behaviour and Safety 6.1 Local Traffic Safety Analyzer (LTSA) - Improved Road Safety and Optimized Signal Control for Future Urban Intersections 7 Demand and Traffic Management 7.1 A Stochastic Programming Method for OD Estimation Using LBSN Check-in Data 7.2 Delineation of Traffic Analysis Zone for Public Transportation OD Matrix Estimation Based on Socio-spatial Practices 8 Workshops 8.1 How to Integrate Human Aspects Into Engineering Science of Transport and Traffic? - a Workshop Report about Discussions on Social Contextualization of Mobility 8.2 Learning from Covid: How Can we Predict Mobility Behaviour in the Face of Disruptive Events? – How to Investigate the Mobility of the Futur

Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications

In recent years, there has been enormous public interest in autonomous vehicles (AV), with more than 80 billion dollars invested in self-driving car technology. However, for the foreseeable future, self-driving cars will interact with human driven vehicles and other human road users, such as pedestrians and cyclists. Therefore, in order to ensure safe operation of AVs, there is need for computational models of humans traffic behaviour that can be used for testing and verification of autonomous vehicles. Game theoretic models of human driving behaviour is a promising computational tool that can be used in many phases of AV development. However, traditional game theoretic models are typically built around the idea of rationality, i.e., selection of the most optimal action based on individual preferences. In reality, not only is it hard to infer diverse human preferences from observational data, but real-world traffic shows that humans rarely choose the most optimal action that a computational model suggests. The thesis makes a set of methodological contributions towards modelling sub-optimality in driving behaviour within a game theoretic framework. These include solution concepts that account for boundedly rational behaviour in hierarchical games, addressing challenges of bounded rationality in dynamic games, and estimation of multi-objective utility aggregation from observational data. Each of these contributions are evaluated based on a novel multi-agent traffic dataset. Building on the game theoretic models, the second part of the thesis demonstrates the application of the models by developing novel safety validation methodologies for testing AV planners. The first application is an automated generation of interpretable variations of lane change behaviour based on Quantal Best Response model. The proposed model is shown to be effective for generating both rare-event situations and to replicate the typical behaviour distribution observed in naturalistic data. The second application is safety validation of strategic planners in situations of dynamic occlusion. Using the concept of hypergames, in which different agents have different views of the game, the thesis develops a new safety surrogate metric, dynamic occlusion risk (DOR), that can be used to evaluate the risk associated with each action in situations of dynamic occlusion. The thesis concludes with a taxonomy of strategic interactions that maps complex design specific strategies in a game to a simpler taxonomy of traffic interactions. Regulations around what strategies an AV should execute in traffic can be developed over the simpler taxonomy, and a process of automated mapping can protect the proprietary design decisions of an AV manufacturer

Practical and conceptual issues in the use of agent-based modelling for disaster management

Application of agent-based modelling technology (ABM) to disaster management has to date been limited in nature. Existing research has concentrated on extending the model structures and agent architectures of complex algorithms to test robustness and extensibility of this simulation approach. Less attention has been brought to bear on testing the current state-of-the-art in ABM for modelling real-life systems. This thesis aims to take first steps in remedying this gap. It focuses on identifying the practical and conceptual issues which preclude wider utilisation of ABM in disaster management. It identifies that insufficient attention is put on incorporating real-life information and domain knowledge into model definitions. This research first proposes a methodology by which some of these issues may be overcome, and consequently tests and evaluates it through implementation of InSiM (Incident Simulation Model), which depicts reaction of pedestrians to a CBRN (chemical, biological, radiological or nuclear) explosion in a city centre. A number of steps are conducted to obtain real-life information related to human response to CBRN incidents. This information is then used for design and parameterisation of InSiM which is implemented in three configurations. In order to identify the effects use of real-life data have on the simulation results each configuration incorporates the information at different level of complexity. The effects are assessed by comparison of the generated dispersion patterns of agents along the city centre. However, use of conventional statistical goodness-of-fit tests for assessing the degree of the difference was challenged by inhomogeneous nature of the data. Hence, alternative approaches are also adopted so that results can be qualitatively assessed. Nevertheless, the evaluation reveals significant differences at global and local level. This research highlights that incorporation of real-life information and domain knowledge into ABM is not without problems. Each time a problem was addressed, additional issues began to emerge. Most of these challenges were related to generalisation of the complex real-life systems that the model represents. Therefore, further investigations are needed at every methodological step before ABM can fully realise its potential to support disaster management

Prédiction de trajectoires humaines pour la navigation de robots

Our lives are becoming increasingly influenced by robots. They are no longer limited to working in factories and increasingly appear in shared spaces with humans, to deliver goods and parcels, ferry medications, or give company to elderly people. Therefore, they need to perceive, analyze, and predict the behavior of surrounding people and take collision-free and socially-acceptable actions. In this thesis, we address the problem of (short-term) human trajectory prediction, to enable mobile robots, such as Pepper, to navigate crowded environments. We propose a novel socially-aware approach for prediction of multiple pedestrians. Our model is designed and trained based on Generative Adversarial Networks, which learn the multi-modal distribution of plausible predictions for each pedestrian. Additionally, we use a modified version of this model to perform data-driven crowd simulation. Predicting the location of occluded pedestrians is another problem discussed in this dissertation. Also, we carried out a study on common human trajectory datasets. A list of quantitative metrics is suggested to assess prediction complexity in those datasets.Nos vies sont de plus en plus influencées par les robots. Ils ne se limitent plus à travailler dans les usines et apparaissent de plus en plus dans des espaces partagés avec les humains, pour livrer des biens et des colis, transporter des médicaments ou tenir compagnie à des personnes âgées. Par conséquent, ils doivent percevoir, analyser et prévoir le comportement des personnes qui les entourent et prendre des mesures sans collision et socialement acceptables des actions sans collision et socialement acceptables. Dans cette thèse, nous abordons le problème de la prédiction de la trajectoire humaine (à court terme), afin de permettre aux robots mobiles, tels que Pepper, de naviguer dans des environnements bondés. Nous proposons une nouvelle approche socialement consciente pour la prédiction de plusieurs piétons. Notre modèle est conçu et entraîné sur la base de réseaux adversariaux génératifs, qui apprennent la distribution multimodale des prédictions plausibles pour chaque piéton. De plus, nous utilisons une version modifiée de ce modèle pour effectuer une simulation de foule basée sur des données. La prédiction de l’emplacement des piétons occultés est un autre problème abordé dans cette thèse. Nous avons également réalisé une étude sur des jeux de données courants de trajectoires humaines. Une liste de métriques quantitatives est proposée pour évaluer la complexité de la prédiction dans ces jeux de données