Modeling and Recognizing Driver Behavior Based on Driving Data: A Survey

In recent years, modeling and recognizing driver behavior have become crucial to understanding intelligence transport systems, human-vehicle systems, and intelligent vehicle systems. A wide range of both mathematical identification methods and modeling methods of driver behavior are presented from the control point of view in this paper based on the driving data, such as the brake/throttle pedal position and the steering wheel angle, among others. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems, and the evaluation and verification of vehicle systems based on the driver model are described

Extension of effort for lunar flight handbook detailed technical report

Lunar flight handbook - orbital departure windows, libration points, and lunar flight orbit estimation, theory, and operation

On-orbit transfer trajectory methods using high fidelity dynamic models

A high fidelity trajectory propagator for use in targeting and reference trajectory generation is developed for aerospace applications in low Earth and translunar orbits. The dominant perturbing effects necessary to accurately model vehicle motion in these dynamic environments are incorporated into a numerical predictor-corrector scheme to converge on a realistic trajectory incorporating multi-body gravitation, high order gravity, atmospheric drag, and solar radiation pressure. The predictor-corrector algorithm is shown to reliably produce accurate required velocities to meet constraints on the final position for the dominant perturbation effects modeled. Low fidelity conic state propagation techniques such as Lambert's method and multiconic pseudostate theory are developed to provide a suitable initial guess. Feasibility of the method is demonstrated through sensitivity analysis to the initial guess for a bounding set of cases

A generic rigorous model for a long track stereo satellite sensors.

The aim of this thesis is to develop a generic rigorous sensor model for high resolution optical satellite sensors, with along track stereoscopic capabilities, in order to orientate directly and simultaneously all the along track stereo images. In other words, the idea is to determine the orbit of the satellite platform covering the time acquisition of all images, using satellite photogrammetry in combination with astrodynamics, thus finding common exterior orientation parameters for all images directly or indirectly. As a result, the number of unknown parameters is reduced and also the correlation between them, thus giving a more stable solution. Moreover, the simultaneous solution extends the narrow field of view of each satellite image because all along track images are treated as one iconic image, with the field of view equal to the angle between the first and the last image. Great effort is made in order to define the essential forces which are involved in the acquisition of the pushbroom images, according to the needed accuracy and the data provided. The fundamental assumptions is that Kepler motion is maintained along the acquisition time of all the along track images. Various versions of the model are developed, based on different orbit determination-propagation methods. The first one, based on the Kepler problem (orbit propagation), can be used for more than two along track images. The second one is based on Gauss-Lambert method which can be used only for two along track images like SPOT-HRS and TERRA-ASTER. The final one is based on Herrick-Gibbs method which is combined with the Gauss-Lambert method in order to be used in the case of more than two along track images. An accuracy assessment is made of the above different orbit determination-propagation methods. It is possible to extract the exterior orientation of all images together directly, without Ground Control Points using the metadata information, with accepted accuracy. The model is evaluated using TERRA-ASTER and SPOT5-HRS imagery with precision close to pixel size. Finally the accuracy of the along track model is compared with the accuracy of single image sensor model and of a commercial sensor model (Leica Photogrammetry Suite)

Essays on long memory time series and fractional cointegration

The dissertation considers an indirect approach for the estimation of the cointegrating parameters, in the sense that the estimators are jointly constructed along with estimating other nuisance parameters. This approach was proposed by Robinson (2008) where a bivariate local Whittle estimator was developed to jointly estimate a cointegrating parameter along with the memory parameters and the phase parameters (discussed in chapter 2). The main contributions of this dissertation is to establish, similar to Robinson (2008), a joint estimation of the memory, cointegrating and phase parameters in stationary and nonstationary fractionally cointegrated models in a multivariate framework. In order to accomplish such task, a general shape of the spectral density matrix, first noted in Davidson and Hashimzade (2008), is utilised to cover multivariate jointly dependent stationary long memory time series allowing more than one cointegrating relation (discussed in chapter 3). Consequently, the notion of the extended discrete Fourier transform is adopted based on the work of Phillips (1999) to allow for the multivariate estimation to cover the non stationary region (explained in chapter 4). Overall, the estimation methods adopted in this dissertation follows the semiparametric approach, in that the spectral density is only specified in a neighbourhood of zero frequency. The dissertation is organised in four self-contained chapters that are connected to each other, in additional to this introductory chapter: • Chapter 1 discusses the univariate long memory time series analysis covering different definitions, models and estimation methods. Consequently, parametric and semiparametric estimation methods were applied to a univariate series of the daily Egyptian stock returns to examine the presence of long memory properties. The results show strong and significant evidence of long memory in the Egyptian stock market which refutes the hypothesis of market efficiency. • Chapter 2 expands the analysis in the first chapter using a bivariate framework first introduced by Robinson (2008) for long memory time series in stationary system. The bivariate model presents four unknown parameters, including two memory parameters, a phase parameter and a cointegration parameter, which are jointly estimated. The estimation analysis is applied to a bivariate framework includes the US and Canada inflation rates where a linear combination between the US and Canada inflation rates that has a long memory less than the two individual series has been detected. • Chapter 3 introduces a semiparametric local Whittle (LW) estimator for a general multivariate stationary fractional cointegration using a general shape of the spectral density matrix first introduced by Davidson and Hashimzade (2008). The proposed estimator is used to jointly estimate the memory parameters along with the cointegrating and phase parameters. The consistency and asymptotic normality of the proposed estimator is proved. In addition, a Monte Carlo study is conducted to examine the performance of the new proposed estimator for different sample sizes. The multivariate local whittle estimation analysis is applied to three different relevant examples to examine the presence of fractional cointegration relationships. • In the first three chapters, the estimation procedures focused on the stationary case where the memory parameter is between zero and half. On the other hand, the analysis in chapter 4, which is a natural progress to that in chapter 3, adjusts the estimation procedures in order to cover the non-stationary values of the memory parameters. Chapter 4 expands the analysis in chapter 3 using the extended discrete Fourier transform and periodogram to extend the local Whittle estimation to non stationary multivariate systems. As a result, the new extended local Whittle (XLW) estimator can be applied throughout the stationary and non stationary zones. The XLW estimator is identical to the LW estimator in the stationary region, introduced in chapter 3. Application to a trivariate series of US money aggregates is employed

The method of quasilikelihood

The methods of “ Maximum Likelihood ” (ML) and “ Least Squares” (LS) are two central methods in the theory of inference. They have being developed and studied for a long time, and they still play an important role in statistics. However, as time goes on, more and more understanding of natural phenomena has been obtained and more diverse requirements have been introduced into statistical practice. Sometimes those phenomena and requirements cause some difficulties in the use of the ML and LS methods. Therefore statisticians have to look in detail into the advantages and weaknesses of each of the methods

Gravity gradient stabilization system for the applications technology satellite Second quarterly progress report, 1 Oct. - 31 Dec. 1964

Systems analysis, boom subsystem, combination passive damper, and attitude sensor subsystem in gravity gradient stabilization system of applications technology satellit

Statistical evaluation of diagnostic tests under verification bias

The use of diagnostic tests to discriminate between disease classes is becoming more and more popular in medicine, which leads to the urgent need for assessing accuracy of diagnostic tests before their implementation. To do that, a common tool is receiver operating characteristic (ROC) analysis. More precisely, the ROC curve and the area under the ROC curve (AUC) are commonly employed when two disease classes (typically, non-diseased and diseased) are considered, whereas the ROC surface and the volume under the ROC surface (VUS) are frequently used when the disease status has three categories (e.g., non-diseased, intermediate and diseased). In estimating such parameters, we assume that the true disease status of each patient can be determined by means of a gold standard test. In practice, unfortunately, the true disease status could be unavailable for all study subjects, due to the expensiveness or invasiveness of the gold standard test. Thus, often only a subset of patients undergoes disease verification. Statistical evaluations of diagnostic accuracy of a test based only on data from subjects with verified disease status are typically biased. This bias is known as verification bias. Various methods have been developed to adjust for verification bias in estimation of the ROC curve and its area for tests with binary or ordinal or continuous results. For the ROC surface and its volume, verification bias correction methods exist for tests with ordinal responses, but not for continuous tests. In this thesis, we propose several bias--corrected methods for estimating the ROC surface and the VUS of continuous diagnostic tests in presence of verification bias. In particular, these methods are constructed based on imputation and re--weighting techniques, and work well when the missingness mechanism of the true disease status is missing at random or missing not at random. The asymptotic behaviors of the estimators are also studied. To illustrate how to use the methods in real applications, two datasets dealing with epithelial ovarian cancer are considered. To support researchers in carrying out the ROC surface analysis in presence of verification bias, an R package and the corresponding Shiny web application have been created

Advanced Syncom - Syncom II summary report

Spacecraft systems design, reliability, support equipment, alternate configurations, and radiation instrumentation payload for Syncom I