
Ontology for Scenarios for the Assessment of Automated Vehicles
The development of assessment methods for the performance of Automated V...
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RealWorld Scenario Mining for the Assessment of Automated Vehicles
Scenariobased methods for the assessment of Automated Vehicles (AVs) ar...
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Procedure for the Safety Assessment of an Autonomous Vehicle Using RealWorld Scenarios
The development of Autonomous Vehicles (AVs) has made significant progre...
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A flexible method of estimating luminosity functions via Kernel Density Estimation
We propose a flexible method for estimating luminosity functions (LFs) b...
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A flexible method for estimating luminosity functions via Kernel Density Estimation
We propose a flexible method for estimating luminosity functions (LFs) b...
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From Functional to Logical Scenarios: Detailing a KeywordBased Scenario Description for Execution in a Simulation Environment
Scenariobased development and test processes are a promising approach f...
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Reduction of the Number of Variables in Parametric Constrained LeastSquares Problems
For linearly constrained leastsquares problems that depend on a vector ...
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Constrained Sampling from a Kernel Density Estimator to Generate Scenarios for the Assessment of Automated Vehicles
The safety assessment of automated vehicles (AVs) is an important aspect of the development cycle of AVs. A scenariobased assessment approach is accepted by many players in the field as part of the complete safety assessment. A scenario is a representation of a situation on the road to which the AV needs to respond appropriately. One way to generate the required scenariobased test descriptions is to parameterize the scenarios and to draw these parameters from a probability density function (pdf). Because the shape of the pdf is unknown beforehand, assuming a functional form of the pdf and fitting the parameters to the data may lead to inaccurate fits. As an alternative, Kernel Density Estimation (KDE) is a promising candidate for estimating the underlying pdf, because it is flexible with the underlying distribution of the parameters. Drawing random samples from a pdf estimated with KDE is possible without the need of evaluating the actual pdf, which makes it suitable for drawing random samples for, e.g., Monte Carlo methods. Sampling from a KDE while the samples satisfy a linear equality constraint, however, has not been described in the literature, as far as the authors know. In this paper, we propose a method to sample from a pdf estimated using KDE, such that the samples satisfy a linear equality constraint. We also present an algorithm of our method in pseudocode. The method can be used to generating scenarios that have, e.g., a predetermined starting speed or to generate different types of scenarios. This paper also shows that the method for sampling scenarios can be used in case a Singular Value Decomposition (SVD) is used to reduce the dimension of the parameter vectors.
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