Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving - Softcover
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Approximately 3700 people die in traffic accidents each day. The mostfrequent cause of accidents is human error. Autonomous driving can significantly reduce thenumber of traffic accidents. To prepare autonomous vehicles for road traffic, the software andsystem components must be thoroughly validated and tested. However, due to their criticality, thereis only a limited amount of data for safety-critical driving scenarios. Such driving scenarios canbe represented in the form of time series. These represent the corresponding kinematic vehiclemovements by including vectors of time, position coordinates, velocities, and accelerations. Thereare several ways to provide such data. For example, this can be done in the form of a kinematicmodel. Alternatively, methods of artificial intelligence or machine learning can be used. These arealready being widely used in the development of autonomous vehicles. For example, generativealgorithms can be used to generate safety-critical driving data. A novel taxonomy for the generationof time series and suitable generative algorithms will be described in this paper. In addition, agenerative algorithm will be recommended and used to demonstrate the generation of time seriesassociated with a typical example of a driving-critical scenario.
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Taschenbuch. Zustand: 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 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. 30 pp. Englisch. Bestandsnummer des Verkäufers 9783736974531
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Kartoniert / Broschiert. Zustand: 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. Bestandsnummer des Verkäufers 555816404
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Cuvillier, Cuvillier Jun 2021, 2021
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ISBN 13: 9783736974531
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Taschenbuch. Zustand: 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 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.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 28 pp. Englisch. Bestandsnummer des Verkäufers 9783736974531
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Taschenbuch. Zustand: 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 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. Bestandsnummer des Verkäufers 9783736974531
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Analysis of suitable generative algorithms for the generation of safety-critical driving data in the field of autonomous driving
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Taschenbuch. Zustand: 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: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 120474622
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