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2019 | OriginalPaper | Buchkapitel

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

verfasst von: Raphael Pfeffer, Patrick Ukas, Eric Sax

Erschienen in: Fahrerassistenzsysteme 2018

Verlag: Springer Fachmedien Wiesbaden

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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.

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Titel: Potential of Virtual Test Environments for the Development of Highly Automated Driving Functions Using Neural Networks
verfasst von: Raphael Pfeffer
Patrick Ukas
Eric Sax
Verlag: Springer Fachmedien Wiesbaden
Buch: Fahrerassistenzsysteme 2018
Print ISBN: 978-3-658-23750-9

Electronic ISBN: 978-3-658-23751-6

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-658-23751-6_18

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