Modeling Stochastic Data Using Copulas For Application in Validation of Autonomous Driving

TL;DR

This paper presents a copula-based modeling approach to accurately capture dependencies in stochastic data from real-world measurements, enhancing the validation process of autonomous vehicles in virtual environments.

Contribution

It introduces a novel application of copula models to drone measurement data for generating realistic dependent stochastic samples in autonomous vehicle validation.

Findings

Copula models effectively capture interdependencies in real-world data.

Generated samples reflect real-time measurement dependencies.

Application improves virtual validation accuracy for autonomous driving.

Abstract

Verification and validation of fully automated vehicles is linked to an almost intractable challenge of reflecting the real world with all its interactions in a virtual environment. Influential stochastic parameters need to be extracted from real-world measurements and real-time data, capturing all interdependencies, for an accurate simulation of reality. A copula is a probability model that represents a multivariate distribution, examining the dependence between the underlying variables. This model is used on drone measurement data from a roundabout containing dependent stochastic parameters. With the help of the copula model, samples are generated that reflect the real-time data. Resulting applications and possible extensions are discussed and explored.