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Robust environmental perception and reliability control for intelligent vehicles

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Robust environmental perception and reliability control for intelligent vehicles/ by Huihui Pan ... [et al.].
其他作者:	Pan, Huihui.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xi, 301 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Intelligent transportation systems. -
電子資源:	https://doi.org/10.1007/978-981-99-7790-1
ISBN:	9789819977901

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. 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 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

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

1066364
Intelligent transportation systems.

LC Class. No.: TE228.3

Dewey Class. No.: 388.312

Robust environmental perception and reliability control for intelligent vehicles
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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.
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