Name: Multi-sensor Fusion for Autonomous Driving
Price: 5366.00 UYU
Availability: InStock
Author: Huaping Liu;Zhiwei Li;Zhenhong Zou;Xinyu Zhang;Mo Zhou;Li Wang;Jun Li
ISBN: 9789819932825

portada Multi-sensor Fusion for Autonomous Driving

Multi-sensor Fusion for Autonomous Driving

Formato

Libro Físico

Editorial

Springer Verlag, Singapore

Autor

Huaping Liu;Zhiwei Li;Zhenhong Zou;Xinyu Zhang;Mo Zhou;Li Wang;Jun Li

Año

2024

N° páginas

232

Encuadernación

Tapa Blanda

Dimensiones

23.50 x 15.50 cm

ISBN13

9789819932825

Multi-sensor Fusion for Autonomous Driving

Huaping Liu;Zhiwei Li;Zhenhong Zou;Xinyu Zhang;Mo Zhou;Li Wang;Jun Li (Autor) · Springer Verlag, Singapore · Tapa Blanda

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Reseña del libro "Multi-sensor Fusion for Autonomous Driving"

Although sensor fusion is an essential prerequisite for autonomous driving, it entails a number of challenges and potential risks. For example, the commonly used deep fusion networks are lacking in interpretability and robustness. To address these fundamental issues, this book introduces the mechanism of deep fusion models from the perspective of uncertainty and models the initial risks in order to create a robust fusion architecture. This book reviews the multi-sensor data fusion methods applied in autonomous driving, and the main body is divided into three parts: Basic, Method, and Advance. Starting from the mechanism of data fusion, it comprehensively reviews the development of automatic perception technology and data fusion technology, and gives a comprehensive overview of various perception tasks based on multimodal data fusion. The book then proposes a series of innovative algorithms for various autonomous driving perception tasks, to effectively improve the accuracy and robustness of autonomous driving-related tasks, and provide ideas for solving the challenges in multi-sensor fusion methods. Furthermore, to transition from technical research to intelligent connected collaboration applications, it proposes a series of exploratory contents such as practical fusion datasets, vehicle-road collaboration, and fusion mechanisms. In contrast to the existing literature on data fusion and autonomous driving, this book focuses more on the deep fusion method for perception-related tasks, emphasizes the theoretical explanation of the fusion method, and fully considers the relevant scenarios in engineering practice. Helping readers acquire an in-depth understanding of fusion methods and theories in autonomous driving, it can be used as a textbook for graduate students and scholars in related fields or as a reference guide for engineers who wish to apply deep fusion methods.