Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-50789-3 (ISBN)
- Noch nicht erschienen - erscheint am 01.02.2026
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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.
Kun Gao is a research assistant at the Research Institute of Automotive Engineering and Vehicle Engines Stuttgart (IFS), where he also earned his doctorate. His research focuses on AI-based perception systems for automated driving.
Scene Risk Factors Analysis for Perception Systems.- Real-Virtual Scene Augmentation and Reinforcement Learning Exploration.- Implementation and Experiments.- Discussion on Reinforcement Learning-Based Scene Data Augmentation.
| Erscheint lt. Verlag | 1.2.2026 |
|---|---|
| Reihe/Serie | Wissenschaftliche Reihe Fahrzeugtechnik Universität Stuttgart |
| Zusatzinfo | XXVII, 145 p. Textbook for German language market. |
| Verlagsort | Wiesbaden |
| Sprache | englisch |
| Maße | 148 x 210 mm |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik ► Fahrzeugbau / Schiffbau | |
| Technik ► Maschinenbau | |
| Schlagworte | AI-based object detection • Challenging scene generation • Real and virtual data domain adaptation • Risk factor analysis for perception systems • Scene data augmentation • Scene parameter-space exploration • Simulation-augmented training data |
| ISBN-10 | 3-658-50789-6 / 3658507896 |
| ISBN-13 | 978-3-658-50789-3 / 9783658507893 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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