Servicio al cliente

Areas
Serie de libros (90)	1258
Letra	2244
Ciencias Naturales	5308
Matemática	223
Informática	310
Física	971
Química	1346
Geociencias	130
Medicina humana	241
Estomatología	10
Veterinaria	98
Farmacia	147
Biología	812
Bioquímica, biología molecular, tecnología genética	113
Biofísica	24
Nutrición	44
Agricultura	992
Silvicultura	202
Horticultura	18
Ecología y conservación de la tierra	145
Ciencias Ingeniería	1710
General	91
Leitlinien Unfallchirurgie 5. Auflage bestellen

Erweiterte Suche

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

Impresion
EUR 29,88

E-Book
EUR 21,48

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

Nico Schick (Autor)

Previo

Lectura de prueba, PDF (1,1 MB)

ISBN-13 (Impresion)	9783736974531
ISBN-13 (E-Book)	9783736964532
Idioma	Inglés
Numero de paginas	30
Laminacion de la cubierta	mate
Edicion	1.
Lugar de publicacion	Göttingen
Fecha de publicacion	21.06.2021
Clasificacion simple	Libro de divulgacion
Area	Informática
Palabras claves	artificial intelligence, machine learning, mobility, autonomous driving, autonomous car, safety, safety-critical, safety-critical driving, safety-critical driving scenario, safety-critical driving scene, safety-critical driving maneuver, time series, time series data, vehicle data, criticality, kinematic, kinematic model, taxonomy, probability distribution, unsupervised learning, supervised learning, generative algorithm, Generative Adversarial Network, GAN, Restricted Boltzmann Machine, RBM, Recurrent Neural Network, RNN, Conditional Restricted Boltzmann Machine, CRBM, Factored Conditional Restricted Boltzmann Machine, FCRBM, Recurrent Temporal Restricted Boltzmann Machine, RTRBM, Variational-Autoencoder Generative Adversarial Network, VAE-GAN, Recurrent-Conditional Generative Adversarial Network, RCGAN, Time-Series Generative Adversarial Network, TimeGAN, Künstliche Intelligenz, Maschinelles Lernen, Mobilität, Autonomes Fahren, autonomes Auto, Sicherheit, sicherheitskritisches Fahren, sicherheitskritisches Fahrszenario, sicherheitskritische Fahrszene, sicherheitskritisches Fahrmanöver, Zeitreihe, Zeitreihendaten, Fahrzeugdaten, Kritikalität, Kinematik, kinematisches Modell, Taxonomie, Wahrscheinlichkeitsverteilung, unüberwachtes Lernen, überwachtes Lernen, generativer Algorithmus, Generative Adversarial Network, GAN, Restricted Boltzmann Machine, RBM, Recurrent Neural Network, RNN, Notbremsung, Conditional Restricted Boltzmann Machine, CRBM, Factored Conditional Restricted Boltzmann Machine, FCRBM, Recurrent Temporal Restricted Boltzmann Machine, RTRBM, Variational-Autoencoder Generative Adversarial Network, VAE-GAN, Recurrent-Conditional Generative Adversarial Network, RCGAN, Time-Series Generative Adversarial Network, TimeGAN, Netzwerk, Straßenverkehr, road traffic, Positionskoordinaten, position coordinates, Geschwindigkeit, velocitie, acceleration, Verkehrsunfall, traffic accident, Unfallursache, cause of accident, road traffic, Straßenverkehr, Notbremsszenario, emergency braking, emergency braking scenario,
URL para pagina web externa	www.fernuni-hagen.de

Descripcion

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.