TITLE:
Stereoscopic Camera-Sensor Model for the Development of Highly Automated Driving Functions within a Virtual Test Environment
AUTHORS:
René Degen, Martin de Fries, Alexander Nüßgen, Marcus Irmer, Mats Leijon, Margot Ruschitzka
KEYWORDS:
Sensor Model, Virtual Test Environment, Stereoscopic Camera, Unreal Engine, OpenCV, ADAS/AD
JOURNAL NAME:
Journal of Transportation Technologies,
Vol.13 No.1,
January
31,
2023
ABSTRACT: The need for efficient and reproducible development processes for sensor
and perception systems is growing with their increased use in modern vehicles.
Such processes can be achieved by using virtual test environments and virtual
sensor models. In the context of this, the present paper documents the
development of a sensor model for depth estimation of virtual three-dimensional
scenarios. For this purpose, the geometric and algorithmic principles of
stereoscopic camera systems are recreated in a virtual form. The model is
implemented as a subroutine in the Epic Games Unreal Engine, which is one of
the most common Game Engines. Its architecture consists of several independent
procedures that enable a local depth estimation, but also a reconstruction of a
whole three-dimensional scenery. In addition, a separate programme for
calibrating the model is presented. In addition to the basic principles, the
architecture and the implementation, this work also documents the evaluation of
the model created. It is shown that the model meets specifically defined
requirements for real-time capability and the accuracy of the evaluation. Thus,
it is suitable for the virtual testing of common algorithms and highly
automated driving functions.