9783736974548 - analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions di schick, nico (5 risultati)
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
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EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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 s…ystem 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. 24 pp. Englisch.
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Da: moluna, Greven, Germaniamoluna
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EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextAbout 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 ca…rs for road traffic, .
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
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EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. 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 syste…m 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.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 29,88
EUR 60,24 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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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Da: preigu, Osnabrück, Germaniapreigu
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 29,88
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. Analysis and comparison of similarity measures for validation of generative algorithms in the context of probability density functions | Nico Schick | Taschenbuch | Englisch | 2021 | Cuvillier | EAN 9783736974548 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 490…78 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
