668 research outputs found
Activity choice modeling for pedestrian facilities
This thesis develops models of activity and destination choices in pedestrian facilities from WiFi traces. We adapt the activity-based travel demand analysis of urban mobility to pedestrians and to digital footprints. We are interested in understanding the sequence of activities and destinations of a pedestrian using discrete choice models and localization data from communication antennas. Activity and destination choice models are needed by pedestrian facilities for decision aid when building new infrastructure, modifying existing infrastructures, or locating points of interest. Understanding demand for activities is particularly important when facing an increasing number of visitors or when developing new activities, such as shopping or catering. Data from existing sensors, such as WiFi access points, are cheap and cover entire facilities, but are imprecise and lack semantics to describe moving, stopping, destinations or activities carried out at destinations. Thus, understanding pedestrian behavior first requires to observe the actual behavior and detect stops at destinations, and second to model the behavior. Part I of this thesis focuses on activity-episode sequence detection. We develop a Bayesian approach to merge raw localization data with other data sources in order to take into account the imprecision and describe activity-episode sequences. This approach generates several activity-episode sequences for a single individual. Each activity-episode sequence is associated with a probability of being the true sequence. The prior represents the attractivity of the different points of interest surrounding the measurement and allows the use of a priori information from other sources of data (register data, point-of-sale data, counting sensors, etc.). Part II proposes models for activity and destination choices. The joint choice of activity type and activity timing is modeled by seeing a sequence of activity episodes as a path in an activity network. Time is considered as discrete. Unlike traditional models, our model is not tour-based, starting and ending at the home location, since the daily ``home''activity is meaningless in our context. The choice set contains all combinations of activity types and time intervals. The number of different paths is thus very large (increasing with time resolution and disaggregation of types of activities). Inspired by route choice models, we use a Metropolis-Hastings algorithm for the sampling of paths to generate the choice set. An importance sampling correction of the utility allows the estimation of unbiased model parameters without enumerating the full choice set. While the activity path model describes the choice of an activity type in time, the location where the activity is performed is modeled with a destination choice model conditional on the activity type. Our approach accounts for the panel nature of the data and deals with serial correlation between error terms. Using real WiFi data collected on the EPFL campus, we detect pedestrian activity-episode sequences, estimate an activity path choice model and develop a destination choice model for a specific activity type: eating. Knowing that the individual has decided to eat, which restaurant does she choose? This conditional destination choice model includes in its utility the cost of menus, available types of foods and drinks, distance from a previous activity episode, socioeconomic characteristics and habits
Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition
The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future
Control and communication systems for automated vehicles cooperation and coordination
Mención Internacional en el título de doctorThe technological advances in the Intelligent Transportation Systems (ITS) are exponentially
improving over the last century. The objective is to provide intelligent and innovative services
for the different modes of transportation, towards a better, safer, coordinated and smarter
transport networks. The Intelligent Transportation Systems (ITS) focus is divided into two
main categories; the first is to improve existing components of the transport networks, while
the second is to develop intelligent vehicles which facilitate the transportation process. Different
research efforts have been exerted to tackle various aspects in the fields of the automated
vehicles. Accordingly, this thesis is addressing the problem of multiple automated vehicles
cooperation and coordination. At first, 3DCoAutoSim driving simulator was developed
in Unity game engine and connected to Robot Operating System (ROS) framework and
Simulation of Urban Mobility (SUMO). 3DCoAutoSim is an abbreviation for "3D Simulator
for Cooperative Advanced Driver Assistance Systems (ADAS) and Automated Vehicles
Simulator". 3DCoAutoSim was tested under different circumstances and conditions, afterward,
it was validated through carrying-out several controlled experiments and compare
the results against their counter reality experiments. The obtained results showed the efficiency
of the simulator to handle different situations, emulating real world vehicles. Next
is the development of the iCab platforms, which is an abbreviation for "Intelligent Campus
Automobile". The platforms are two electric golf-carts that were modified mechanically, electronically
and electrically towards the goal of automated driving. Each iCab was equipped
with several on-board embedded computers, perception sensors and auxiliary devices, in
order to execute the necessary actions for self-driving. Moreover, the platforms are capable
of several Vehicle-to-Everything (V2X) communication schemes, applying three layers of
control, utilizing cooperation architecture for platooning, executing localization systems,
mapping systems, perception systems, and finally several planning systems. Hundreds of
experiments were carried-out for the validation of each system in the iCab platform. Results
proved the functionality of the platform to self-drive from one point to another with minimal
human intervention.Los avances tecnológicos en Sistemas Inteligentes de Transporte (ITS) han crecido de forma
exponencial durante el último siglo. El objetivo de estos avances es el de proveer de sistemas
innovadores e inteligentes para ser aplicados a los diferentes medios de transporte, con el fin
de conseguir un transporte mas eficiente, seguro, coordinado e inteligente. El foco de los ITS
se divide principalmente en dos categorías; la primera es la mejora de los componentes ya
existentes en las redes de transporte, mientras que la segunda es la de desarrollar vehículos
inteligentes que hagan más fácil y eficiente el transporte. Diferentes esfuerzos de investigación
se han llevado a cabo con el fin de solucionar los numerosos aspectos asociados con
la conducción autónoma. Esta tesis propone una solución para la cooperación y coordinación
de múltiples vehículos. Para ello, en primer lugar se desarrolló un simulador (3DCoAutoSim)
de conducción basado en el motor de juegos Unity, conectado al framework Robot Operating
System (ROS) y al simulador Simulation of Urban Mobility (SUMO). 3DCoAutoSim ha
sido probado en diferentes condiciones y circunstancias, para posteriormente validarlo con
resultados a través de varios experimentos reales controlados. Los resultados obtenidos
mostraron la eficiencia del simulador para manejar diferentes situaciones, emulando los
vehículos en el mundo real. En segundo lugar, se desarrolló la plataforma de investigación
Intelligent Campus Automobile (iCab), que consiste en dos carritos eléctricos de golf, que
fueron modificados eléctrica, mecánica y electrónicamente para darle capacidades autónomas.
Cada iCab se equipó con diferentes computadoras embebidas, sensores de percepción y
unidades auxiliares, con la finalidad de transformarlos en vehículos autónomos. Además,
se les han dado capacidad de comunicación multimodal (V2X), se les han aplicado tres
capas de control, incorporando una arquitectura de cooperación para operación en modo
tren, diferentes esquemas de localización, mapeado, percepción y planificación de rutas.
Innumerables experimentos han sido realizados para validar cada uno de los diferentes sistemas
incorporados. Los resultados prueban la funcionalidad de esta plataforma para realizar
conducción autónoma y cooperativa con mínima intervención humana.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Francisco Javier Otamendi Fernández de la Puebla.- Secretario: Hanno Hildmann.- Vocal: Pietro Cerr
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Development and Performance Evaluation of Urban Mobility Applications and Services
L'abstract è presente nell'allegato / the abstract is in the attachmen
Direct communication radio Iinterface for new radio multicasting and cooperative positioning
Cotutela: Universidad de defensa UNIVERSITA’ MEDITERRANEA DI REGGIO CALABRIARecently, the popularity of Millimeter Wave (mmWave) wireless networks has increased due to their capability to cope with the escalation of mobile data demands caused by the unprecedented proliferation of smart devices in the fifth-generation (5G). Extremely high frequency or mmWave band is a fundamental pillar in the provision of the expected gigabit data rates. Hence, according to both academic and industrial communities, mmWave technology, e.g., 5G New Radio (NR) and WiGig (60 GHz), is considered as one of the main components of 5G and beyond networks. Particularly, the 3rd Generation Partnership Project (3GPP) provides for the use of licensed mmWave sub-bands for the 5G mmWave cellular networks, whereas IEEE actively explores the unlicensed band at 60 GHz for the next-generation wireless local area networks. In this regard, mmWave has been envisaged as a new technology
layout for real-time heavy-traffic and wearable applications.
This very work is devoted to solving the problem of mmWave band communication system while enhancing its advantages through utilizing the direct communication radio interface for NR multicasting, cooperative positioning, and mission-critical applications. The main contributions presented in this work include: (i) a set of mathematical frameworks and simulation tools to characterize multicast traffic delivery in mmWave directional systems; (ii) sidelink
relaying concept exploitation to deal with the channel condition deterioration of dynamic multicast systems and to ensure mission-critical and ultra-reliable low-latency communications; (iii) cooperative positioning techniques analysis for enhancing cellular positioning accuracy for 5G+ emerging applications that require not only improved communication characteristics but also precise localization.
Our study indicates the need for additional mechanisms/research that can be utilized: (i) to further improve multicasting performance in 5G/6G systems; (ii) to investigate sideline aspects, including, but not limited to, standardization perspective and the next relay selection strategies; and (iii) to design cooperative positioning systems based on Device-to-Device (D2D) technology
Robot Mapping and Navigation in Real-World Environments
Robots can perform various tasks, such as mapping hazardous sites, taking part in search-and-rescue scenarios, or delivering goods and people. Robots operating in the real world face many challenges on the way to the completion of their mission. Essential capabilities required for the operation of such robots are mapping, localization and navigation. Solving all of these tasks robustly presents a substantial difficulty as these components are usually interconnected, i.e., a robot that starts without any knowledge about the environment must simultaneously build a map, localize itself in it, analyze the surroundings and plan a path to efficiently explore an unknown environment. In addition to the interconnections between these tasks, they highly depend on the sensors used by the robot and on the type of the environment in which the robot operates. For example, an RGB camera can be used in an outdoor scene for computing visual odometry, or to detect dynamic objects but becomes less useful in an environment that does not have enough light for cameras to operate. The software that controls the behavior of the robot must seamlessly process all the data coming from different sensors. This often leads to systems that are tailored to a particular robot and a particular set of sensors. In this thesis, we challenge this concept by developing and implementing methods for a typical robot navigation pipeline that can work with different types of the sensors seamlessly both, in indoor and outdoor environments. With the emergence of new range-sensing RGBD and LiDAR sensors, there is an opportunity to build a single system that can operate robustly both in indoor and outdoor environments equally well and, thus, extends the application areas of mobile robots. The techniques presented in this thesis aim to be used with both RGBD and LiDAR sensors without adaptations for individual sensor models by using range image representation and aim to provide methods for navigation and scene interpretation in both static and dynamic environments. For a static world, we present a number of approaches that address the core components of a typical robot navigation pipeline. At the core of building a consistent map of the environment using a mobile robot lies point cloud matching. To this end, we present a method for photometric point cloud matching that treats RGBD and LiDAR sensors in a uniform fashion and is able to accurately register point clouds at the frame rate of the sensor. This method serves as a building block for the further mapping pipeline. In addition to the matching algorithm, we present a method for traversability analysis of the currently observed terrain in order to guide an autonomous robot to the safe parts of the surrounding environment. A source of danger when navigating difficult to access sites is the fact that the robot may fail in building a correct map of the environment. This dramatically impacts the ability of an autonomous robot to navigate towards its goal in a robust way, thus, it is important for the robot to be able to detect these situations and to find its way home not relying on any kind of map. To address this challenge, we present a method for analyzing the quality of the map that the robot has built to date, and safely returning the robot to the starting point in case the map is found to be in an inconsistent state. The scenes in dynamic environments are vastly different from the ones experienced in static ones. In a dynamic setting, objects can be moving, thus making static traversability estimates not enough. With the approaches developed in this thesis, we aim at identifying distinct objects and tracking them to aid navigation and scene understanding. We target these challenges by providing a method for clustering a scene taken with a LiDAR scanner and a measure that can be used to determine if two clustered objects are similar that can aid the tracking performance. All methods presented in this thesis are capable of supporting real-time robot operation, rely on RGBD or LiDAR sensors and have been tested on real robots in real-world environments and on real-world datasets. All approaches have been published in peer-reviewed conference papers and journal articles. In addition to that, most of the presented contributions have been released publicly as open source software
- …