Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks

Abstract

This paper outlines the implications and challenges that modern algorithms such as neural networks may have on the process of function development for highly automated driving. In this context, an approach is presented how synthetically generated data from a simulation environment can contribute to accelerate and automate the complex process of data acquisition and labeling for these neural networks. A concept of an exemplary implementation is shown and first results of the training of a convolutional neural network using these synthetic data are presented.