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

Nico Schick

ISBN 10: 373697454X / ISBN 13: 9783736974548

Published by Cuvillier, Cuvillier Jun 2021, 2021

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 24 pp. Englisch.

Seller Inventory # 9783736974548

Bibliographic Details

• Title: Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions
• Publisher: Cuvillier, Cuvillier Jun 2021
• Publication Date: 2021
• Language: English
• ISBN 10: 373697454X
• ISBN 13: 9783736974548
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 210W x 148H x 2D millimeters 210 millimeters width by 148 millimeters height by 2 millimeters depth
• Weight: 47 grams

About this title

Synopsis

About 3700 people die in traffic accidents every day. Human error is the number one causeof accidents. Autonomous driving can greatly reduce the occurrence of traffic accidents. Torelease self-driving cars for road traffic, the system including software must be validated and testedefficiently. However, due to their criticality, the amount of data corresponding to safety-criticaldriving scenarios are limited. These driving scenes can be expressed as a time series. They representthe corresponding movement of the vehicle, including time vector, position coordinates, speed andacceleration. 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 havebeen widely used in the development of autonomous vehicles. For example, generative algorithmscan be used to generate such safety-critical driving data. However, the validation of generativealgorithms is a challenge in general. In most cases, their quality is assessed by means of expertknowledge (qualitative). In order to achieve a higher degree of automation, a quantitative validationapproach is necessary. Generative algorithms are based on probability distributions or probabilitydensity 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 definedevaluation criteria. With respect to the use case mentioned, a recommended similarity measure isimplemented and validated for an example of a typical safety-critical driving scenario.