No description available.

No description available.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 117-118). Also available via the Internet.

Thesis (M.S. in Astronautical Engineering and Astronautical Engineer)--Naval Postgraduate School, December 2004. / Thesis advisor(s): I. Michael Ross, D. A. Danielson. Includes bibliographical references (p. 93-94). Also available online.

Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Paredis, Chris; Committee Member: Bras, Bert; Committee Member: Burkhart, Roger; Committee Member: Choi, Seung-Kyum.

Location-aware devices are one of the examples of variety of systems that can provide trajectory data. The formal definition of a trajectory is the path of a moving object in space as a function of time. Surveillance systems can now automatically detect moving objects and provide a useful dataset for further analysis. Clustering moving objects in a given scene can provide vital information about the trajectory patterns and outliers. The trajectory of an object may contain extended data at each position where the object was detected such as size, colour, etc. The focus of this work is to find an efficient trajectory clustering solution given the most fundamental trajectory data, namely position and time. The main challenge of clustering trajectory data is to handle the length of a single trajectory. The length of a trajectory can be extremely long in some cases. Hence it may cause problems to keep trajectories in main memory or it may be very inefficient to process them. Preprocessing trajectories and simplifying them will help minimize the effects of such issues. We will use some algorithms taken from literature in conjunction with some of our own algorithms in order to cluster trajectories in an efficient manner. In an attempt to accomplish this, we have designed a representation of a trajectory Furthermore, we have designed and implemented algorithms to simplify and evaluate distances between these trajectories. Moreover, we proved that our distance function obeys triangulation properties which is beneficial for clustering algorithms. Our distance function is a variation of the Fréchet distance proposed in 1906 by Maurice René Fréchet. Additionally, we will illustrate how our work can be integrated with an incremental clustering algorithm to cluster trajectories.

Note:

Vehicle trajectory data has played a critical role in the recent history of traffic flow and CAV operations-related studies. However, available trajectories have limited coverage, either spatial or temporal. The implementation of CAV technology is expected to produce a large-scale trajectory dataset. However, at the initial implementation level, the trajectory data produced is expected to have gaps in terms of completeness. This research develops a data model for large-scale trajectory data that can be built on CAV-collected trajectories and easily manipulated to produce traffic parameters for CAV control and operation research. A benchmarking process has been applied to test a trajectory reconstruction approach to develop a population database from partial trajectories to fill the expected data gap in CAV feedback. The large-scale trajectory data is then used in CAV operations-related studies focusing on CAV's integration with human drivers and developing performance matrices for CAV-controlled optimized trajectories. This research used large-scale vehicle trajectory data from Wide Area Motion Imagery (WAMI) developed by PVLabs for modeling and analyzing traffic characteristics as a surrogate of CAV-collected trajectories. This timestamped location data capture provides trajectory information at an interval of one second. Trajectories from an approximate area of four-square kilometers in downtown Hamilton, Canada, are used to develop a data model to extract and store traffic characteristics. The video data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like other moving object detection-based algorithms, this data suffers from false-positive detection, false-negative detection, and other positional inaccuracies caused by faulty image registration. A context-based trajectory filtering algorithm has been developed and validated against ten minutes of vehicle counts from actual WAMI images. The filtered data provides a sample of trajectories over the area, including complete and partial vehicle trajectories, excluding undetected ones. The missing trajectory reconstruction process using a dynamic state estimation process is developed to reconstruct partial and missing trajectories. A data analytics approach predicts the number of missing trajectories between two successive detections in the traffic stream on a roadway lane. A benchmarking test of the performance of the missing trajectory prediction algorithm is conducted using the NGSIM I80 database. A frame-by-frame learning method is developed to join the identified missing trajectories. This data analytics approach preserves the naturalistic property of the trajectory, which was a concern of previous traffic-flow model-based approaches. Joining partial/split trajectories provides a more comprehensive picture of the trajectory population. Due to data structure similarities, including the nature of the split and missing trajectories, the methods developed in this study to recover trajectories can be adopted for future CAV feedback data in a mixed traffic scenario. The applicability of using the large-scale trajectory data model is explored in two performance areas of CAV operations. The first is a scenario-based testing process, which evaluates the "intelligence" of a CAV in handling interactions with Human driven Vehicles (HV) by artificially replacing an HV in the traffic stream with a CAV. Scenario-based testing is conducted for a particular Operational Design Domain (ODD). The ODD is defined as operating conditions under which particular driver assistance or automated control systems are designed to function. Existing literature on scenario-based testing primarily focuses on CAV-HV interaction on highways as large-scale naturalistic trajectory data are available to facilitate such studies. This research explores car-following and lane-changing aspects of arterial CAV testing. The large-scale trajectory data model generates testing scenarios and calibrates the surrogate model for CAV operation. The modification to the trajectory data model to accommodate the scenario-based testing is illustrated. The second consists of using the large-scale trajectory data model to estimate a new trajectory smoothness parameter that can indicate the impact of intersection stop-and-go movement on the smoothness of the entire trajectory. This smoothness parameter can be applied as an optimization variable in future trajectory control-based intersection management. Long-duration trajectories from the large-scale trajectory data are used to estimate the spectral arc length parameter for trajectory smoothness. This research only estimates smoothness parameters for human-driven vehicles to illustrate its applicability for vehicle trajectories. This research developed a framework for applying expected partial trajectories from CAV technology in estimating near-complete trajectories. The large-scale data application process in two CAV operations-related studies is also provided. / Doctor of Philosophy / The decision-making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior for prevailing and future traffic conditions. The analytical tools for policy, design, decision-making, and safety analysis use aggregated and disaggregated traffic parameters. Traffic parameters are information about the dynamic state of the traffic. In the case of a vehicle, the dynamic state information can be location, speed, acceleration, heading, and spacing with other vehicles in the traffic stream. The sequence of these dynamic parameters is called vehicle trajectories in a broader term. The trajectory information is collected using several direct and indirect collection systems. The implementation of CAV technologies is expected to provide a new source of vehicle trajectory information. Trajectory data are integral to CAV safety, operational evaluation, and optimization control algorithms. Trajectory data are also used to develop, calibrate, and validate the models representing a particular aspect of human driver behavior, and the recent development of CAV has elevated the necessity and application of trajectory data. As a result, a significant demand exists in academia and industry for the procedure to create trajectories of the vehicle population in the traffic stream. The trajectory population represents the dynamic properties of all the vehicles moving over the data collection area. The primary goal of this research is to develop and apply a large-scale trajectory population database. Trajectories are typically stored in a Moving Object Database (MOD). This research leverages a MOD database collected by a new generation of Wide-Area Motion Imagery (WAMI). The WAMI system collects images from a high-altitude moving aerial platform with high-definition cameras at a fixed time interval, which captures the trajectories of vehicles in the collection area. However, validating the created trajectories for completeness and data noise revealed continuity and consistency gaps in trajectories. A multistep data mining process is undertaken to filter, process, and extract sample trajectories with reduced data noise. A trajectory reconstruction task is undertaken to reduce the data gap. A benchmarking performance test for trajectory reconstruction is conducted using NGSIM I80 data because it has been validated in multiple studies and contains trajectories of all vehicles during the collection period (i.e., trajectory population). The trajectory reconstruction methodology developed in this research can be adapted for future CAV-collected partial trajectory data. The development of the trajectory reconstruction methodology and training data created from NGSIM I80 is one of the main contributions of this research in the field of trajectory reconstruction. Several traffic flow measures are then estimated from the sample trajectories that outline the analytical requirements to integrate trajectory data with roadway infrastructure. A data model is developed to store and manipulate dynamic trajectory parameters efficiently. The resulting data processing and integration process can be applied to CAV-collected trajectories to create an analytical trajectory database. The large-scale trajectory database is used to illustrate its capability in evaluating CAV operating models, specifically the car-following and lane-changing models on an arterial network. The car-following model mimics the longitudinal movement of real-world drivers following another vehicle. The lane-changing model predicts lane-changing behavior due to path-planning requirements and navigating surrounding traffic conditions. The overall operational model evaluation process is called accelerated evaluation, in which the naturalistic vehicle movement data is used to measure CAV's operational and safety performance. For a second application of the large-scale trajectory data, long-duration trajectories are used to develop a trajectory smoothness performance measure that can be used to test different trajectory control approaches for intersection movement management. This research is one of the early attempts to leverage large-scale vehicle trajectory datasets in transportation engineering applications. Its primary contribution is the development of a comprehensive trajectory validation methodology that can be applied to future CAV feedback to produce a trajectory population database with enhanced analytical capability. The secondary output of this research is benchmarking results for different analytical methodologies to develop the trajectories that can be used in future research and development as a reference.

Aujourd'hui, l'inspection des ouvrages d'art est réalisée de façon visuelle par des contrôleurs sur l'ensemble de la structure. Cette procédure est couteuse et peut être particulièrement dangereuse pour les intervenants. Pour cela, le développement du système de vision embarquée sur des drones est privilégié ces jours-ci afin de faciliter l'accès aux zones dangereuses.Dans ce contexte, le travail de cette thèse porte sur l'obtention des méthodes originales permettant la planification, la génération des trajectoires de référence, et le suivi de ces trajectoires par une plate-forme aéroportée stationnaire autonome. Ces méthodes devront habiliter une automatisation du vol en présence de perturbations aérologiques ainsi que des obstacles. Dans ce cadre, nous nous sommes intéressés à deux types de véhicules aériens capable de vol stationnaire : le dirigeable et le quadri-rotors.Premièrement, la représentation mathématique du véhicule volant en présence du vent a été réalisée en se basant sur la deuxième loi de Newton. Deuxièmement, la problématique de génération de trajectoire en présence de vent a été étudiée : le problème de temps minimal est formulé, analysé analytiquement et résolu numériquement. Ensuite, une stratégie de planification de trajectoire basée sur les approches de recherche opérationnelle a été développée.Troisièmement, le problème de suivi de trajectoire a été abordé. Une loi de commande non-linéaire robuste basée sur l'analyse de Lyapunov a été proposée. En outre, un pilote automatique basée sur les fonctions de saturations pour un quadri-rotors a été développée.Les méthodes et algorithmes proposés dans cette thèse ont été validés par des simulations. / Today, the inspection of structures is carried out through visual assessments effected by qualified inspectors. This procedure is very expensive and can put the personal in dangerous situations. Consequently, the development of an unmanned aerial vehicle equipped with on-board vision systems is privileged nowadays in order to facilitate the access to unreachable zones.In this context, the main focus in the thesis is developing original methods to deal with planning, reference trajectories generation and tracking issues by a hovering airborne platform. These methods should allow an automation of the flight in the presence of air disturbances and obstacles. Within this framework, we are interested in two kinds of aerial vehicles with hovering capacity: airship and quad-rotors.Firstly, the mathematical representation of an aerial vehicle in the presence of wind has been realized using the second law of newton.Secondly, the question of trajectory generation in the presence of wind has been studied: the problem of minimal time was formulated, analyzed analytically and solved numerically. Then, a strategy of trajectory planning based on operational research approaches has been developed.Thirdly, the problem of trajectory tracking was carried out. A nonlinear robust control law based on Lyapunov analysis has been proposed. In addition, an autopilot based on saturation functions for quad-rotor crafts has been developed.All methods and algorithms proposed in this thesis have been validated through simulations.

This thesis developed a Rudder Augmented Trajectory Correction (RATC) method for small unmanned aerial vehicles. The goal of this type of controller is to minimize the lateral image errors of body-fixed non-gimbaled cameras. This is achieved through both aggressive trajectory following and elimination of the roll angle present in current aileron only trajectory correction autopilots. The analytical derivation of the rudder augmented trajectory correction controller is presented. Using estimated aerodynamic derivatives of the Aerosonde UAV, RATC, produced a stable and controllable system. This control algorithm was integrated into the AggieAir Minion-class UAV using the Paparazzi open source autopilot. Flight results are presented that show significant reduction in the roll angle present during trajectory correction. This is shown using both inertial measurement nit sensor data as well as payload imagery collected over a selected region of interest. The conclusion of this thesis is that the RATC algorithm is a viable solution to minimize lateral image errors for body-fixed cameras in realm of aerial surveying.