Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception (Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart) - Softcover

Gao, Kun

Softcover

ISBN 10: 3658507896 ISBN 13: 9783658507893

Publisher: Springer Vieweg, 2026

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.

About 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.

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.

Edition details

• Publisher: Springer Vieweg
• Publication date: 2026
• Language: English
• ISBN 10: 3658507896
• ISBN 13: 9783658507893
• Binding: Paperback
• Number of pages: 172