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1. |
Record Nr. |
UNINA9910742498103321 |
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Autore |
Zhang Xinyu |
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Titolo |
Multi-sensor Fusion for Autonomous Driving [[electronic resource] /] / by Xinyu Zhang, Jun Li, Zhiwei Li, Huaping Liu, Mo Zhou, Li Wang, Zhenhong Zou |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (237 pages) |
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Altri autori (Persone) |
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LiJun |
LiZhiwei |
LiuHuaping |
ZhouMo |
WangLi |
ZouZhenhong |
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Disciplina |
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Soggetti |
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Robotics |
Computer vision |
Data mining |
Computer Vision |
Data Mining and Knowledge Discovery |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Part I: Basic -- Chapter 1. Introduction -- Chapter 2. Overview of Data Fusion in Autonomous Driving Perception -- Part II: Method -- Chapter 3. Multi-sensor Calibration -- Chapter 4. Multi-sensor Object Detection -- Chapter 5. Multi-sensor Scene Segmentation -- Chapter 6. Multi-sensor Fusion Localization -- Part III: Advance -- Chapter 7. OpenMPD: An Open Multimodal Perception Dataset -- Chapter 8. Vehicle-Road Multi-view Interactive Data Fusion -- Chapter 9. Information Quality in Data Fusion -- Chapter 10. Conclusions. |
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Sommario/riassunto |
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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, |
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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. |
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