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024 7 _a10.1007/978-981-99-7790-1
_2doi
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072 7 _aTRC
_2bicssc
072 7 _aTEC009090
_2bisacsh
072 7 _aTRC
_2thema
100 1 _aPan, Huihui.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967556
245 1 0 _aRobust Environmental Perception and Reliability Control for Intelligent Vehicles
_h[electronic resource] /
_cby Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXI, 301 p. 140 illus., 137 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 deepdual-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.
650 0 _aAutomotive engineering.
650 0 _aControl engineering.
650 0 _aComputational intelligence.
_964052
650 1 4 _aAutomotive Engineering.
650 2 4 _aControl and Systems Theory.
_965868
650 2 4 _aComputational Intelligence.
_964052
700 1 _aWang, Jue.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967557
700 1 _aYu, Xinghu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967558
700 1 _aSun, Weichao.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967559
700 1 _aGao, Huijun.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_967560
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819977895
776 0 8 _iPrinted edition:
_z9789819977918
776 0 8 _iPrinted edition:
_z9789819977925
830 0 _aRecent Advancements in Connected Autonomous Vehicle Technologies,
_x2731-0035 ;
_v4
_967561
856 4 0 _uhttps://doi.org/10.1007/978-981-99-7790-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
999 _c56635
_d56635