TY - INPR A1 - Leiprecht, Stefan T1 - Using Simulations and Domain Randomization for Autonomous Driving T2 - Technical Reports in Computing Science N2 - In the last decade autonomous driving has evolved from a science fictional dream to an everyday reality. With the advance of more and more companies bringing their versions of self-driving cars on the street it is just a matter of time before the majority of transportation will be in the hand of computers. But with the deadly car accident involving a self-driving Uber car back in 2018 there is also the question about how reliable autonomous driving really is and how we can validate and test the safety of this new road user 1. An uprising approach towards creating robust and adaptable neural networks is called domain randomization. This paper explores the possibility of using this method to create training data with driving simulations. It will propose a list of important criteria and factors affecting the selection of a fitting simulation. Furthermore it will present a track generator which is able to create useful tracks and export them to a format which can be used by several common simulations used in the field of autonomous driving research. T3 - Technical Reports in Computing Science – Schriftenreihe Informatik der HS Kempten - CS-02-2020 Y1 - 2020 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/1658 UR - https://nbn-resolving.org/urn:nbn:de:bvb:859-16586 IS - July 2020 SP - 1 EP - 4 PB - Hochschule Kempten, Fakultät Informatik CY - Kempten ER -