Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
Language: English
Published by Springer Gabler, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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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
- Title
- Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
- Author
- Kun Gao
- Publisher
- Springer Gabler
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 3658507896
- ISBN 13
- 9783658507893
- Item weight
- 236 grams
- Dimensions
- 210x148x10 mm
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.
"Synopsis" may belong to another edition of this title.
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 the title" may belong to another edition of this title.
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