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1. |
Record Nr. |
UNINA990009108110403321 |
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Autore |
Dannecker, Gerhard |
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Titolo |
Parteispendenproblematik : Strafrecht und Gesellschaft / Gerhard Dannecker[et al.] |
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Pubbl/distr/stampa |
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koln : Dr. Peter Deubner Verlag GmbH Koln, 1986 |
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ISBN |
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Descrizione fisica |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNISA996215443803316 |
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Titolo |
CPA practice management forum |
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Pubbl/distr/stampa |
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Chicago, : CCH Inc., ©2005- |
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Disciplina |
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Soggetti |
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Accounting firms - Management |
Periodicals. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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3. |
Record Nr. |
UNINA9910766894303321 |
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Autore |
Pan Huihui |
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Titolo |
Robust Environmental Perception and Reliability Control for Intelligent Vehicles / / by Huihui Pan, Jue Wang, Xinghu Yu, Weichao Sun, Huijun Gao |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (308 pages) |
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Collana |
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Recent Advancements in Connected Autonomous Vehicle Technologies, , 2731-0035 ; ; 4 |
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Disciplina |
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Soggetti |
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Automotive engineering |
Automatic control |
Computational intelligence |
Automotive Engineering |
Control and Systems Theory |
Computational Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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 deepdual-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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