Nico Schick's Books

Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions

By: Roberto Corlito,Nico Schick

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Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions

By: Roberto Corlito,Nico Schick

About 3700 people die in traffic accidents every day. Human error is the number one cause of accidents. Autonomous driving can greatly reduce the occurrence of traffic accidents. To release self-driving cars for road traffic, the system including software must be validated and tested efficiently. However, due to their criticality, the amount of data corresponding to safety-critical driving scenarios are limited. These driving scenes can be expressed as a time series. They represent the corresponding movement of the vehicle, including time vector, position coordinates, speed and acceleration. Such data can be provided on different ways. For example, in the form of a kinematic model. Alternatively, artificial intelligence or machine learning methods can be used. They have been widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate such safety-critical driving data. However, the validation of generative algorithms is a challenge in general. In most cases, their quality is assessed by means of expert knowledge (qualitative). In order to achieve a higher degree of automation, a quantitative validation approach is necessary. Generative algorithms are based on probability distributions or probability density functions. Accordingly, similarity measures can be used to evaluate generative algorithms. In this publication, such similarity measures are described and compared on the basis of defined evaluation criteria. With respect to the use case mentioned, a recommended similarity measure is implemented and validated for an example of a typical safety-critical driving scenario.

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Cuvillier Verlag

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ISBN 10

3736964544

ISBN 13

9783736964549

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

By: Nico Schick

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

By: Nico Schick

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.

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Cuvillier Verlag

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3736964536

ISBN 13

9783736964532

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

By: Nico Schick

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

By: Nico Schick

Average ratings

N/A

Write a review

Solve jigsaw puzzle

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Publisher

Cuvillier Verlag

Published Date

ISBN 10

3736964536

ISBN 13

9783736964532

Book's by Nico Schick

Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions

Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions

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

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

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

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

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