arXiv subjects

Martin Singull

Publications and source records attributed to Martin Singull.

3 recordsLinked to original sources

arXiv2026

Effects of motion cueing on longitudinal acceleration perception in a driving simulator

The driveability of a new heavy-truck driveline is traditionally assessed using physical prototypes. Enabling early evaluation of the driving experience in a human-in-the-loop driving simulator using a virtual prototype has the potential to significantly improve development efficiency. To enable driveability assessment using a moving-base simulator, participants must be able to perceive small differences in longitudinal acceleration. The just-noticeable difference (JND) was therefore evaluated for two variants of the classical motion-cueing algorithm (MCA) tuned specifically for tip-in/launch tests and compared to a more general variant in a driving simulator with a long linear track. Psychometric functions were fitted to responses obtained using a weighted staircase procedure and analysed using a generalized linear model. No significant differences in JND were found between the motion cueing variants. The mean JND across all participants and MCA variants was 5.4%. The mean point of subjective equality in the JND experiment was -1.9%, suggesting that participants perceived the acceleration as higher in the second stimulus of a pair. In a subjective comparison, most participants preferred the motion cueing variants that were tuned for launch manoeuvres over the general variant.

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arXiv2026

Asymptotic Normality and Convergence Rates for Tsallis Entropy Estimators via Stabilization Techniques

We study nearest-neighbor-based estimators of Tsallis entropy associated with Poisson and binomial point processes on general metric measure spaces. Using stabilization techniques based on flexible add-one cost operators together with second-order Poincar\'e inequalities, we establish asymptotic normality and derive explicit convergence rates for the Kolmogorov distance. Our analysis avoids explicit score-function decompositions and instead relies on flexible localizations of add-one costs, which simplify the treatment of higher-order terms. Under natural stabilization and moment conditions, the resulting bounds recover the classical normal approximation rates $s^{-1/2}$ and $n^{-1/2}$ and extend corresponding results for Shannon and R\'enyi entropy estimators. We further illustrate the scope of the framework through examples involving Tsallis entropy functionals, weighted $k$-nearest-neighbor Shannon entropy estimators. The examples provided highlight the benefits of stabilization-based normal approximations for non-parametric statistical inference in complex spatial and high-dimensional settings.

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arXiv2026

A New Estimator of Kullback--Leibler Divergence via Shannon Entropy

We examine the estimation of the Kullback-Leibler (KL) divergence and the use of the goodness-of-fit test for multivariate continuous distributions. Our starting point is the maximum entropy principle for Shannon entropy: among all distributions with a fixed mean vector and covariance matrix, the multivariate Gaussian distributions uniquely maximize entropy. As a result, the KL divergence from a moment-matched Gaussian distribution to an unknown density can then be written as the \emph{entropy difference}, which is a suitable information-theoretic measure of divergence from the Gaussian distribution. To estimate, we use $k$-nearest neighbor (kNN) estimators based on Shannon entropy and KL divergence derived from the Kozachenko-Leonenko approach and subsequent improvements, along with the consistency and $L^{2}$-convergence results established for these estimators. Motivated by previous entropy-based goodness-of-fit ideas developed for R\'enyi-type functionals under generalized Gaussian and Student-type models, we describe a KL-based test statistic as being the difference between (i) the entropy of a Gaussian model fitted to the sample mean and covariance and (ii) the KL divergence between the unknown entropy and the kNN estimate. The statistic converges to zero under multivariate normality and converges to a strictly positive bound under non-Gaussian alternatives. Results from Monte Carlo simulations on various dimensions and sample sizes indicate that the proposed procedure achieves accurate Type I error control and accurate, generally superior power compared to conventional multivariate tests of normality, particularly at medium and high dimensions.

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