04594nam a22005895i 4500 978-981-99-7790-1 DE-He213 20251030110839.0 cr nn 008mamaa 231125s2024 si | s |||| 0|eng d 9789819977901 978-981-99-7790-1 10.1007/978-981-99-7790-1 doi TL1-483 TRC bicssc TEC009090 bisacsh TRC thema Pan, Huihui. author. aut http://id.loc.gov/vocabulary/relators/aut 67556 Robust Environmental Perception and Reliability Control for Intelligent Vehicles [electronic resource] / by Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XI, 301 p. 140 illus., 137 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Recent Advancements in Connected Autonomous Vehicle Technologies, 2731-0035 ; 4 Chapter 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. This 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. Automotive engineering. Control engineering. Computational intelligence. 64052 Automotive Engineering. Control and Systems Theory. 65868 Computational Intelligence. 64052 Wang, Jue. author. aut http://id.loc.gov/vocabulary/relators/aut 67557 Yu, Xinghu. author. aut http://id.loc.gov/vocabulary/relators/aut 67558 Sun, Weichao. author. aut http://id.loc.gov/vocabulary/relators/aut 67559 Gao, Huijun. author. aut http://id.loc.gov/vocabulary/relators/aut 67560 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819977895 Printed edition: 9789819977918 Printed edition: 9789819977925 Recent Advancements in Connected Autonomous Vehicle Technologies, 2731-0035 ; 4 67561 https://doi.org/10.1007/978-981-99-7790-1 ZDB-2-ENG ZDB-2-SXE 56635 56635