Processing Driving Simulator Data Before Statistical Analysis by Means of Interpolation and an Integral Formula

2022 ◽  
Author(s):  
Caroline Ariën ◽  
Giovanni Vanroelen ◽  
Veerle Ross ◽  
Yanchao Song ◽  
Tom Brijs ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Driving Simulator ◽  
Integral Formula

scholarly journals Validation of a driving simulator for research into human factors issues of automated vehicles

2019 ◽  
Vol 30 (2) ◽  
pp. 37-44
Author(s):  
Nebojsa Tomasevic ◽  
Tim Horberry ◽  
Brian Fildes
Keyword(s):  
Statistical Analysis ◽  
Human Factors ◽  
Driving Simulator ◽  
Research Centre ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Simulated Driving ◽  
Automated Vehicle ◽  
Perception Of Safety ◽  
Accident Research

This study evaluated the behavioural validity of the Monash University Accident Research Centre automation driving simulator for research into the human factors issues associated with automated driving. The study involved both on-road and simulated driving. Twenty participants gave ratings of their willingness to resume control of an automated vehicle and perception of safety for a variety of situations along the drives. Each situation was individually categorised and ratings were processed. Statistical analysis of the ratings confirmed the behavioural validity of the simulator, in terms of the similarity of the on-road and simulator data.

Validation of a Driving Simulator for Work Zone Design

2005 ◽  
Vol 1937 (1) ◽  
pp. 136-144 ◽  
Author(s):  
Francesco Bella
Keyword(s):  
Virtual Reality ◽  
Statistical Analysis ◽  
Road Safety ◽  
Driving Simulator ◽  
Work Zone ◽  
Real Situation ◽  
Driving Experience ◽  
The Real ◽  
Transition Area ◽  
Matched Samples

The research project aimed at calibrating and validating the driving simulator of the European Interuniversity Research Center for Road Safety to enable its use for design and verification of the effectiveness of temporary traffic signs on highways. The research was developed through the following steps: ( a) a survey of speed measurements on highways next to a work zone of medium duration, ( b) reconstruction in virtual reality of the real situation by using the driving simulator and subsequent running of a series of driving tests, and ( c) statistical analysis of the field speeds and of the speeds from driving simulations for validation of the simulator. The surveyed work zone was located on Highway A1 from Milan to Naples, Italy. Speed measurements were conducted with a laser speed meter in the transition area, the activity area, and the termination area, and in the advance warning area speeds were shot with a camera from an overpass. Speed data from the field and the simulator were analyzed by using the bilateral Z-test for non-matched samples to determine whether drivers responded differently in the simulator compared with their response during the real driving experience. The activity carried out revealed that differences between the speeds observed in the real situation and those measured with the simulator were not statistically significant.

Statistical analysis of classification

1966 ◽  
Vol 24 ◽  
pp. 188-189
Author(s):  
T. J. Deeming
Keyword(s):  
Multivariate Analysis ◽  
Statistical Analysis ◽  
Classification Scheme ◽  
Analytical Procedure ◽  
Narrow Band ◽  
Scientific Research ◽  
Maximum Amount ◽  
Amount Of Information

If we make a set of measurements, such as narrow-band or multicolour photo-electric measurements, which are designed to improve a scheme of classification, and in particular if they are designed to extend the number of dimensions of classification, i.e. the number of classification parameters, then some important problems of analytical procedure arise. First, it is important not to reproduce the errors of the classification scheme which we are trying to improve. Second, when trying to extend the number of dimensions of classification we have little or nothing with which to test the validity of the new parameters.Problems similar to these have occurred in other areas of scientific research (notably psychology and education) and the branch of Statistics called Multivariate Analysis has been developed to deal with them. The techniques of this subject are largely unknown to astronomers, but, if carefully applied, they should at the very least ensure that the astronomer gets the maximum amount of information out of his data and does not waste his time looking for information which is not there. More optimistically, these techniques are potentially capable of indicating the number of classification parameters necessary and giving specific formulas for computing them, as well as pinpointing those particular measurements which are most crucial for determining the classification parameters.

Quantitative parallel EELS spectrum imaging developed as a new tool in AEM

1992 ◽  
Vol 50 (2) ◽  
pp. 1202-1203
Author(s):  
Gianluigi Botton ◽  
Gilles L'espérance
Keyword(s):  
Statistical Analysis ◽  
Spatial Resolution ◽  
Personal Computer ◽  
Systematic Study ◽  
Present Author ◽  
Chemical Elements ◽  
Detection Limits ◽  
Research Groups ◽  
Quantitative Performance ◽  
Spectrum Imaging

As interest for parallel EELS spectrum imaging grows in laboratories equipped with commercial spectrometers, different approaches were used in recent years by a few research groups in the development of the technique of spectrum imaging as reported in the literature. Either by controlling, with a personal computer both the microsope and the spectrometer or using more powerful workstations interfaced to conventional multichannel analysers with commercially available programs to control the microscope and the spectrometer, spectrum images can now be obtained. Work on the limits of the technique, in terms of the quantitative performance was reported, however, by the present author where a systematic study of artifacts detection limits, statistical errors as a function of desired spatial resolution and range of chemical elements to be studied in a map was carried out The aim of the present paper is to show an application of quantitative parallel EELS spectrum imaging where statistical analysis is performed at each pixel and interpretation is carried out using criteria established from the statistical analysis and variations in composition are analyzed with the help of information retreived from t/γ maps so that artifacts are avoided.

Statistical analysis related to fetal heart rate patterns

1979 ◽  
Vol 135 (1) ◽  
pp. 168
Author(s):  
H. William Perlis ◽  
John F. Huddleston
Keyword(s):  
Heart Rate ◽  
Statistical Analysis ◽  
Fetal Heart Rate ◽  
Fetal Heart

Statistical Analysis of Stochastic Processes in Time

2004 ◽  
Author(s):  
J. K. Lindsey
Keyword(s):  
Statistical Analysis ◽  
Stochastic Processes

Intraindividual Variability in Development Within and Between Individuals

2001 ◽  
Vol 6 (3) ◽  
pp. 187-193 ◽  
Author(s):  
John R. Nesselroade
Keyword(s):  
Statistical Analysis ◽  
Research Design ◽  
Intraindividual Variability ◽  
Factor Analytic

A focus on the study of development and other kinds of changes in the whole individual has been one of the hallmarks of research by Magnusson and his colleagues. A number of different approaches emphasize this individual focus in their respective ways. This presentation focuses on intraindividual variability stemming from Cattell's P-technique factor analytic proposals, making several refinements to make it more tractable from a research design standpoint and more appropriate from a statistical analysis perspective. The associated methods make it possible to study intraindividual variability both within and between individuals. An empirical example is used to illustrate the procedure.

Review of Basic FORTRAN for Statistical Analysis.

1967 ◽  
Vol 12 (9) ◽  
pp. 467-467
Author(s):  
JOHN C. LOEHLIN
Keyword(s):  
Statistical Analysis
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