Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception

Language: English

Published by Springer Vieweg Jan 2026, 2026

3658507896 / 9783658507893

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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

Title
Scene Data Augmentation with Real and Virtual Data for Enhanced AI-Driven Automated Driving Perception
Author
Kun Gao
Publisher
Springer Vieweg Jan 2026
Publication year
2026
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3658507896
ISBN 13
9783658507893
Item weight
236 grams
Dimensions
210x148x10 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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AbeBooks seller since January 23, 2017

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