Schick Nico (18 results)
Language: German
Series: PROKNOME - Meister der Gefühle, Book 1 of 1. Book 1 of 1 - PROKNOME - Meister der Gefühle
- Softcover
Seller: medimops, Berlin, Germanymedimops
Contact seller5-star sellerCondition: Used - Good
US$ 7.05
US$ 11.43 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.
- Softcover
Seller: moluna, Greven, , Germanymoluna
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 55.98 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New.
- Softcover
- Print on Demand
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
Contact seller5-star sellerCondition: New
US$ 30.57
Free ShippingShips within U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
Contact seller5-star sellerCondition: New
US$ 30.81
US$ 4.36 shippingShips from United Kingdom to U.S.A.Quantity: Over 20 available
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 26.28 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Autonomous driving is one of the key disciplines in the automotive field and currently under intensive development, especially with the objective of saving more people's lives on the roads due to significant reductions in the number… of traffic accidents. Therefore, the software components within autonomous cars must be tested efficient and precisely. One of the most challenging aspects of autonomous cars are the safety-critical driving scenarios. Their criticality has seldom been measured in terms of further forensic analysis or software solutions in the field of artificial intelligence. Therefore, data related to safety-critical driving scenarios must be obtained another way. In this context, kinematic models can be used to represent these scenes by describing the vehicle's movements based on defined boundary constraints as well as providing synthesized data through the simulation of a model for the training and validation of the underlying machine learning algorithms, such as neural networks or generative algorithms. In this paper, three of the most significant safety-critical driving scenarios, namely emergency braking, turning, and overtaking, are modeled accordingly. 26 pp. Englisch.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 26.28 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 t…raffic, 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. 30 pp. Englisch.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 26.28 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: 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 sy…stem 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.
- Softcover
- Print on Demand
Seller: moluna, Greven, , Germanymoluna
Contact seller5-star sellerCondition: New
US$ 29.31
US$ 55.98 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnApproximately 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.
- Softcover
- Print on Demand
Seller: moluna, Greven, , Germanymoluna
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 55.98 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: 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 car…s for road traffic, .
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
US$ 29.31
US$ 68.56 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 traff…ic, 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.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 28 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
US$ 29.31
US$ 68.86 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. 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 traffi…c, 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.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 68.56 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Autonomous driving is one of the key disciplines in the automotive field and currently under intensive development, especially with the objective of saving more people's lives on the roads due to significant reductions in the number of…traffic accidents. Therefore, the software components within autonomous cars must be tested efficient and precisely. One of the most challenging aspects of autonomous cars are the safety-critical driving scenarios. Their criticality has seldom been measured in terms of further forensic analysis or software solutions in the field of artificial intelligence. Therefore, data related to safety-critical driving scenarios must be obtained another way. In this context, kinematic models can be used to represent these scenes by describing the vehicle's movements based on defined boundary constraints as well as providing synthesized data through the simulation of a model for the training and validation of the underlying machine learning algorithms, such as neural networks or generative algorithms. In this paper, three of the most significant safety-critical driving scenarios, namely emergency braking, turning, and overtaking, are modeled accordingly.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 26 pp. Englisch.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 68.83 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: 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.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 68.85 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Autonomous driving is one of the key disciplines in the automotive field and currently under intensive development, especially with the objective of saving more people's lives on the roads due to significant reductions in the number of t…raffic accidents. Therefore, the software components within autonomous cars must be tested efficient and precisely. One of the most challenging aspects of autonomous cars are the safety-critical driving scenarios. Their criticality has seldom been measured in terms of further forensic analysis or software solutions in the field of artificial intelligence. Therefore, data related to safety-critical driving scenarios must be obtained another way. In this context, kinematic models can be used to represent these scenes by describing the vehicle's movements based on defined boundary constraints as well as providing synthesized data through the simulation of a model for the training and validation of the underlying machine learning algorithms, such as neural networks or generative algorithms. In this paper, three of the most significant safety-critical driving scenarios, namely emergency braking, turning, and overtaking, are modeled accordingly.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 68.56 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: 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 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.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
US$ 29.31
US$ 79.99 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving | Nico Schick | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Cuvillier | EAN 9783736974531 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonnenstieg 8…, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 79.99 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: Neu. Modeling of specific safety-critical driving scenarios for data synthesis in the context of autonomous driving software | Nico Schick | Taschenbuch | Englisch | 2020 | Cuvillier | EAN 9783736972469 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, ma…il[at]preigu[dot]de | Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
US$ 35.17
US$ 79.99 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: 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, 4907…8 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
