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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart) - Tapa blanda
Sinopsis
Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them.
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Acerca del autor
Kun Gao is a research assistant at the Institute for Automotive Engineering Stuttgart (IFS) at the University of Stuttgart, Germany, where he also earned his doctorate. His research focuses on AI-based perception systems for automated driving.
De la contraportada
Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them.
About the Author
Kun Gao is a research assistant at the Institute for Automotive Engineering Stuttgart (IFS) at the University of Stuttgart, Germany, where he also earned his doctorate. His research focuses on AI-based perception systems for automated driving.
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Springer, Berlin, Springer Vieweg, 2026
ISBN 10: 3658507896
ISBN 13: 9783658507893
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them. 145 pp. Englisch. Nº de ref. del artículo: 9783658507893
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Paperback)
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Springer Fachmedien Wiesbaden, Weisbaden, 2026
ISBN 10: 3658507896
ISBN 13: 9783658507893
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Paperback. Condición: new. Paperback. Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them. background: white;">Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9783658507893
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them. Nº de ref. del artículo: 9783658507893
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
Publicado por
Springer Vieweg Jan 2026, 2026
ISBN 10: 3658507896
ISBN 13: 9783658507893
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Automated driving requires robust and reliable perception systems, but rare and dangerous scenarios are often missing from real-world data. Kun Gao proposes an approach to scene data augmentation that combines real and virtual data to improve the performance of perception systems in complex environments. The goal is to reduce the limitations caused by insufficient training data for AI models. The method first analyzes important risk factors that influence perception performance. A scene data augmentation framework is then developed, integrating the realism of real data with the flexibility of virtual data. Using computer graphics and reinforcement learning, the approach generates a large number of challenging scenes and efficiently explores high-risk parameter combinations. The experimental results show that the proposed method improves robustness in rare and hazardous situations and increases the performance of AI-based object detection. The study also demonstrates that combining real and virtual data helps reduce the domain gap between them.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch. Nº de ref. del artículo: 9783658507893
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Taschenbuch. Condición: Neu. Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception | Kun Gao | Taschenbuch | xxvii | Englisch | 2026 | Springer Vieweg | EAN 9783658507893 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 134424856
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart)
Librería: Revaluation Books, Exeter, Reino Unido
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Paperback. Condición: Brand New. 172 pages. 5.83x0.40x8.27 inches. In Stock. Nº de ref. del artículo: x-3658507896
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