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Titlebook: Robust Environmental Perception and Reliability Control for Intelligent Vehicles; Huihui Pan,Jue Wang,Huijun Gao Book 2024 The Editor(s) (

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發(fā)表于 2025-3-21 19:31:16 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Robust Environmental Perception and Reliability Control for Intelligent Vehicles
編輯Huihui Pan,Jue Wang,Huijun Gao
視頻videohttp://file.papertrans.cn/832/831309/831309.mp4
概述Captures recent advances of control theory, technological aspects, and applications for intelligent vehicles.Provides a series of latest results in sensors, artificial intelligence, and other fields i
叢書名稱Recent Advancements in Connected Autonomous Vehicle Technologies
圖書封面Titlebook: Robust Environmental Perception and Reliability Control for Intelligent Vehicles;  Huihui Pan,Jue Wang,Huijun Gao Book 2024 The Editor(s) (
描述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 tak
出版日期Book 2024
關(guān)鍵詞Imaging processing; Monocular 3D object detection; Semantic segmentation; Artificial intelligence; Relia
版次1
doihttps://doi.org/10.1007/978-981-99-7790-1
isbn_softcover978-981-99-7792-5
isbn_ebook978-981-99-7790-1Series ISSN 2731-0027 Series E-ISSN 2731-0035
issn_series 2731-0027
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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,Robust Environmental Perception of?Multi-sensor Data Fusion,usion can solve the problem of perception reliability when some sensors fail by using data redundancy and improve the robustness of the perception algorithms. In Sect.?2.1, a 2D-3D pose estimation network based on keypoints is proposed, which can be applied to calibrate the camera and LiDAR in real-
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,Robust Environmental Perception of?Monocular 3D Object Detection, the category of object, location and orientation. Utilizing monocular cameras for this purpose has several advantages, such as being cost-effective and easily accessible compared to other types of sensors. The realization of 3D object detection through monocular vision alone has considerable potent
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