LEADER 03794nam 2200457 450 001 9910766894303321 005 20231208161545.0 010 $a981-9977-90-8 024 7 $a10.1007/978-981-99-7790-1 035 $a(MiAaPQ)EBC30971115 035 $a(Au-PeEL)EBL30971115 035 $a(DE-He213)978-981-99-7790-1 035 $a(EXLCZ)9929038516700041 100 $a20231208d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRobust Environmental Perception and Reliability Control for Intelligent Vehicles /$fHuihui Pan [and four others] 205 $aFirst edition. 210 1$aSingapore :$cSpringer,$d[2024] 210 4$dİ2024 215 $a1 online resource (308 pages) 225 1 $aRecent Advancements in Connected Autonomous Vehicle Technologies Series ;$vVolume 4 311 08$aPrint version: Pan, Huihui Robust Environmental Perception and Reliability Control for Intelligent Vehicles Singapore : Springer,c2024 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults. 410 0$aRecent advancements in connected autonomous vehicle technologies ;$vVolume 4. 606 $aVehicular ad hoc networks (Computer networks) 615 0$aVehicular ad hoc networks (Computer networks) 676 $a359.8205 700 $aPan$b Huihui$01449941 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910766894303321 996 $aRobust Environmental Perception and Reliability Control for Intelligent Vehicles$93659984 997 $aUNINA