04409nam a22005415i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001200172072001600184072002300200072001500223100008100238245017200319250001800491264007200509300006600581336002600647337002600673338003600699347002400735490008600759505046400845520168601309650002802995650002503023650003903048650002803087650003903115650003903154700007903193700008003272700008203352700008103434710003403515773002603549776003603575776003603611776003603647830009303683856004603776912001403822912001403836999001703850978-981-99-7790-1DE-He21320251030110839.0cr nn 008mamaa231125s2024    si |    s    |||| 0|eng d  a97898199779019978-981-99-7790-17 a10.1007/978-981-99-7790-12doi 4aTL1-483 7aTRC2bicssc 7aTEC0090902bisacsh 7aTRC2thema1 aPan, Huihui.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96755610aRobust Environmental Perception and Reliability Control for Intelligent Vehiclesh[electronic resource] /cby Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXI, 301 p. 140 illus., 137 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aRecent Advancements in Connected Autonomous Vehicle Technologies,x2731-0035 ;v40 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.  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. 0aAutomotive engineering. 0aControl engineering. 0aComputational intelligence.96405214aAutomotive Engineering.24aControl and Systems Theory.96586824aComputational Intelligence.9640521 aWang, Jue.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9675571 aYu, Xinghu.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9675581 aSun, Weichao.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9675591 aGao, Huijun.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9675602 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981997789508iPrinted edition:z978981997791808iPrinted edition:z9789819977925 0aRecent Advancements in Connected Autonomous Vehicle Technologies,x2731-0035 ;v496756140uhttps://doi.org/10.1007/978-981-99-7790-1  aZDB-2-ENG  aZDB-2-SXE  c56635d56635