國立高雄大學圖資館 |

Language: English

Back

Robust environmental perception and reliability control for intelligent vehicles

Record Type:	Electronic resources : Monograph/item
Title/Author:	Robust environmental perception and reliability control for intelligent vehiclesby Huihui Pan ... [et al.].
other author:	Pan, Huihui.
Published:	Singapore :Springer Nature Singapore :2024.
Description:	xi, 301 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Intelligent transportation systems.
Online resource:	https://doi.org/10.1007/978-981-99-7790-1
ISBN:	9789819977901$q(electronic bk.)

Robust environmental perception and reliability control for intelligent vehicles
Robust environmental perception and reliability control for intelligent vehicles[electronic resource] /by Huihui Pan ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xi, 301 p. :ill. (some col.), digital ;24 cm. - Recent advancements in connected autonomous vehicle technologies,v. 42731-0035 ;. - Recent advancements in connected autonomous vehicle technologies ;v. 1..

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 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.

ISBN: 9789819977901$q(electronic bk.)

Standard No.: 10.1007/978-981-99-7790-1doiSubjects--Topical Terms:

403795
Intelligent transportation systems.

LC Class. No.: TE228.3

Dewey Class. No.: 388.312

Robust environmental perception and reliability control for intelligent vehicles
LDR:03252nmm a2200337 a 4500 001 660904
003 DE-He213
005 20231125180931.0
006 m d
007 cr nn 008maaau
008 241106s2024 si s 0 eng d
020 $a 9789819977901$q(electronic bk.)
020 $a 9789819977895$q(paper)
024 7 $a 10.1007/978-981-99-7790-1 $2 doi
035 $a 978-981-99-7790-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TE228.3
072 7 $a TRC $2 bicssc
072 7 $a TEC009090 $2 bisacsh
072 7 $a TRC $2 thema
082 0 4 $a 388.312 $2 23
090 $a TE228.3 $b .R667 2024
245 0 0 $a Robust environmental perception and reliability control for intelligent vehicles $h [electronic resource] / $c by Huihui Pan ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xi, 301 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Recent advancements in connected autonomous vehicle technologies, $x 2731-0035 ; $v v. 4
505 0 $a 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.
520 $a 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 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.
650 0 $a Intelligent transportation systems. $3 403795
650 0 $a Vehicular ad hoc networks (Computer networks) $3 383232
650 1 4 $a Automotive Engineering. $3 308930
650 2 4 $a Control and Systems Theory. $3 825946
650 2 4 $a Computational Intelligence. $3 338479
700 1 $a Pan, Huihui. $3 972222
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Recent advancements in connected autonomous vehicle technologies ; $v v. 1. $3 917485
856 4 0 $u https://doi.org/10.1007/978-981-99-7790-1
950 $a Engineering (SpringerNature-11647)