Using Simulations and Domain Randomization for Autonomous Driving
- 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 autonomousIn 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.…
Author: | Stefan LeiprechtORCiD |
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URN: | urn:nbn:de:bvb:859-16586 |
Parent Title (English): | Technical Reports in Computing Science |
Series (Serial Number): | Technical Reports in Computing Science – Schriftenreihe Informatik der HS Kempten (CS-02-2020) |
Publisher: | Hochschule Kempten, Fakultät Informatik |
Place of publication: | Kempten |
Document Type: | Preprint |
Language: | English |
Year of first Publication: | 2020 |
Publishing Institution: | Hochschule für angewandte Wissenschaften Kempten |
Issue: | July 2020 |
Number of pages: | 4 Seiten |
First Page: | 1 |
Last Page: | 4 |
Original Publication / Source: | Leiprecht, Stefan: Using Simulations and Domain Randomization for Autonomous Driving, In: Technical Reports in Computing Science, July 2020, http://dx.doi.org/10.13140/RG.2.2.30272.61447 |
Institutes: | IFA – Institut für Innovative Fahrzeugantriebe |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme |
Open Access: | open_access |
Research focus: | FSP2: Mobilität |
Publication Lists: | Leiprecht, Stefan |
Publication reviewed: | nicht begutachtet |
Licence (German): | ![]() |
Release Date: | 2023/03/01 |