潍坊科技学院图书馆书目检索系统

Online Public Access Catalogue

题名/责任者:

Robust environmental perception and reliability control for intelligent vehicles / Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao.

出版发行项:

Singapore : Springer, [2024]

出版发行项:

2024

ISBN:

9789819977895

ISBN:

9819977894

ISBN:

9789819977925

ISBN:

9819977924

载体形态项:

xi, 301 pages : illustrations (chiefly color) ; 24 cm.

丛编说明:

Recent advancements in connected autonomous vehicle technologies, 2731-0027 ; volume 4

丛编统一题名:

Recent advancements in connected autonomous vehicle technologies ; v. 4.

个人责任者:

Pan, Huihui (Of Haerbin gong ye da xue), author.

附加个人名称:

Wang, Jue, active 2024, author.

附加个人名称:

Yu, Xinghu, author.

附加个人名称:

Sun, Weichao, author.

附加个人名称:

Gao, Huijun, author.

论题主题:

Intelligent transportation systems.

论题主题:

Vehicular ad hoc networks (Computer networks)

论题主题:

Syste mes de transport intelligents.

论题主题:

Re seaux ad hoc de ve hicules.

中图法分类号:

U491

书目附注:

Includes bibliographical references.

摘要附注:

"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."--

豆瓣简介：