Testing Nvidia Drive for small cars in toy town

Abstract

The aim of this bachelor’s thesis is to analyze and evaluate the capabilities of neural network based image processing in Nvidia Drive in conditions far from its original operating domain. The data for the models is gathered from a toy town made of wood and populated with toy cars and toy pedestrians. This town is used for self-driving student competitions, teaching and public demonstrations. If applicable to this data, tools from NVIDIA Drive could be used for achieving self-driving with toy cars in this toy town, similar to how NVIDIA drive can be used on real cars in the real world. Three different Nvidia Drive models are tested in this thesis, which are DriveNet, PathNet, and OpenRoadNet. The performance of these models is evaluated and a conclusion is made.