Name: Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (en Inglés)
SKU: 66556100
Price: 87.97 USD
Availability: InStock
Author: Gao, Kun

portada Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (en Inglés)

Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (en Inglés)

Formato

Libro Físico

Editorial

Springer Vieweg

Autor

Gao, Kun

Idioma

Inglés

N° páginas

145

Encuadernación

Tapa Blanda

Dimensiones

21.2x15.2x1 cm

ISBN13

9783658507893

Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (en Inglés)

Gao, Kun (Autor) · Springer Vieweg · Tapa Blanda

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Reseña del libro "Scene Data Augmentation with Real and Virtual Data for Enhanced Ai-Driven Automated Driving Perception (en Inglés)"

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.