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008			231125s2024 si | o |||| 0|eng d
020			_a9789819977901
024	7		_a10.1007/978-981-99-7790-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTE228.3 _b2024 EB
100	1		_aPan, Huihui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9690403
245	1	0	_aRobust Environmental Perception and Reliability Control for Intelligent Vehicles _cby Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao
250			_a1st ed. 2024
264		1	_aSingapore _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aRecent Advancements in Connected Autonomous Vehicle Technologies _x2731-0035 _v4
505	0		_aChapter 1. Background -- Chapter 2. Robust Environmental Perception of Multi-Sensor Data Fusion -- Chapter 3. Robust Environmental Perception of Monocular 3D Object Detection -- Chapter 4. Robust Environmental Perception of Semantic Segmentation -- Chapter 5. Robust Environmental Perception of Trajectory Prediction -- Chapter 6 Robust Environmental Perception of Multi-object Tracking -- Chapter 7. Reliability Control of Intelligent Vehicles -- References.
520			_aThis book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.
988			_aSpringer_Engineering_2024
650		7	_2embne _9405815 _aSistemas inteligentes de transporte
700	1		_9678457 _aWang, Jun _eautor
700	1		_9690407 _aYu, Xinghu _eautor
700	1		_9690408 _aSun, Weichao _eautor
700	1		_9690409 _aGao, Huijun _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-7790-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b06/2024 _dz _eIG _zSI