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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart) - Softcover
Synopsis
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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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.
From the Back Cover
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.
"About this title" may belong to another edition of this title.
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Taschenbuch. Condition: 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. Seller Inventory # 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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Paperback. Condition: 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. Seller Inventory # 9783658507893
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Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universit?t Stuttgar)
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Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universit?t Stuttgar)
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart)
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Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universit?t Stuttgar)
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Springer Vieweg Jan 2026, 2026
ISBN 10: 3658507896
ISBN 13: 9783658507893
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Taschenbuch. Condition: 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. Seller Inventory # 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. Condition: 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. Seller Inventory # 134424856
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Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
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Taschenbuch. Condition: 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. Seller Inventory # 9783658507893