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008			230828s2023 si | o |||| 0|eng d
020			_a9789819932801
024	7		_a10.1007/978-981-99-3280-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTJ211 _b2023 EB
100	1		_aZhang, Xinyu _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689628
245	1	0	_aMulti-sensor Fusion for Autonomous Driving _cby Xinyu Zhang, Jun Li, Zhiwei Li, Huaping Liu, Mo Zhou, Li Wang, Zhenhong Zou
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
505	0		_aPart 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.
520			_aAlthough 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9160676 _aRobótica
700	1		_9689633 _aLi, Jun _eautor
700	1		_9689629 _aLi, Zhiwei _eautor
700	1		_9689630 _aLiu, Huaping _d1976- _eautor
700	1		_9689631 _aZhou, Mo _eautor
700	1		_931226 _aWang, Li _eautor
700	1		_9689632 _aZou, Zhenhong _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-3280-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI