Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving

Language: English

Published by Cuvillier, Cuvillier, 2021

3736974531 / 9783736974531

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nach der Bestellung gedruckt Neuware - Printed after ordering - Approximately 3700 people die in traffic accidents each day. The most frequent cause of accidents is human error. Autonomous driving can significantly reduce the number of traffic accidents. To prepare autonomous vehicles for road traffic, the software and system components must be thoroughly validated and tested. However, due to their criticality, there is only a limited amount of data for safety-critical driving scenarios. Such driving scenarios can be represented in the form of time series. These represent the corresponding kinematic vehicle movements by including vectors of time, position coordinates, velocities, and accelerations. There are several ways to provide such data. For example, this can be done in the form of a kinematic model. Alternatively, methods of artificial intelligence or machine learning can be used. These are already being widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate safety-critical driving data. A novel taxonomy for the generation of time series and suitable generative algorithms will be described in this paper. In addition, a generative algorithm will be recommended and used to demonstrate the generation of time series associated with a typical example of a driving-critical scenario.

Seller Inventory # 9783736974531

Title
Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
Author
Nico Schick
Publisher
Cuvillier, Cuvillier
Publication year
2021
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3736974531
ISBN 13
9783736974531
Item weight
52 grams
Dimensions
210x148x3 mm

AHA-BUCH GmbH

Einbeck, Germany

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AbeBooks seller since August 14, 2006

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