Abstract— The purpose of this paper is to propose a method to
abstract and classify vehicle data collected from vision sensors
into road scenarios. The classified scenarios can be played back
on specialized hardware designed to handle these scenarios to
characterize its performance. Since the majority of existing
automotive computer vision systems mandate real-time results,
this study aims to introduce the utilization of Graphics
Processing Units (GPUs) as a prototype to perform these
classification and abstraction tasks. This paper evaluates the
ability of the GPU architecture to handle these tasks. It also
discusses the suitability of GPUs for integrating navigation data
with data from vision and RADAR sensors for aiding Visual
Simultaneous Localization and Mapping