机读格式显示(MARC)
- 000 03448cam a2200457 i 4500
- 008 230928t20242024si a b 000 0 eng d
- 020 __ |a 9789819977895 |q (hardcover)
- 020 __ |a 9819977894 |q (hardcover)
- 020 __ |a 9789819977925 |q (paperback)
- 020 __ |a 9819977924 |q (paperback)
- 020 __ |z 9789819977901 |q (eBook)
- 020 __ |z 9819977908 |q (eBook)
- 035 __ |a (OCoLC)1399562818
- 040 __ |a YDX |b eng |e rda |c YDX |d INU |d OCLCO |d BDX |d OCLCQ |d OCLCL
- 050 _4 |a TE228.3 |b .P36 2024
- 082 04 |a 388.3/12 |q OCoLC |2 23/eng/20231205
- 100 1_ |a Pan, Huihui |c (Of Haerbin gong ye da xue), |e author.
- 245 10 |a Robust environmental perception and reliability control for intelligent vehicles / |c Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao.
- 260 __ |a Singapore : |b Springer, |c [2024]
- 300 __ |a xi, 301 pages : |b illustrations (chiefly color) ; |c 24 cm.
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 490 1_ |a Recent advancements in connected autonomous vehicle technologies, |x 2731-0027 ; |v volume 4
- 504 __ |a Includes bibliographical 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."-- |c Back cover.
- 650 _0 |a Intelligent transportation systems.
- 650 _0 |a Vehicular ad hoc networks (Computer networks)
- 650 _6 |a Syste mes de transport intelligents.
- 650 _6 |a Re seaux ad hoc de ve hicules.
- 700 1_ |a Wang, Jue, |d active 2024, |e author.
- 700 1_ |a Yu, Xinghu, |e author.
- 700 1_ |a Sun, Weichao, |e author.
- 700 1_ |a Gao, Huijun, |e author.
- 830 _0 |a Recent advancements in connected autonomous vehicle technologies ; |v v. 4.