Road Terrain Classification Technology for Autonomous Vehicle / by Shifeng Wang.
By: Wang, Shifeng., autor
Contributor(s): SpringerLink (Online service)
Series: (Engineering (Springer-11647)); (Unmanned System Technologies, 2523-3734).Publisher: Singapore : Springer Singapore : Imprint: Springer, 2019Description: 1 recurso en línea (XVI, 97 páginas) : 43 ilustraciones, 32 ilustraciones a color.ISBN: 9789811361555.Subject: Vehículos de motor -- Control automáticoOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | TL152.8 2019 EB (Browse shelf(Opens below)) | Acceso electrónico | eBooks24062722 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| TL152.8 2018 EB Design and Advanced Robust Chassis Dynamics Control for X-by-Wire Unmanned Ground Vehicle | TL152.8 2018 EB Creating Autonomous Vehicle Systems | TL152.8 2019 EB Safe, Autonomous and Intelligent Vehicles | TL152.8 2019 EB Road Terrain Classification Technology for Autonomous Vehicle | TL152.8 2019 EB Road Vehicle Automation 5 | TL152.8 2019 EB Autonomy and Unmanned Vehicles : Augmented Reactive Mission and Motion Planning Architecture | TL152.8 2019 EB Predictive cruise control for road vehicles using road and traffic information |
Introduction -- Review of Related Work -- Acceleration Based Road Terrain Classification -- Image Based Road Terrain Classification -- LRF Based Road Terrain Classification -- Multiple-Sensor Based Road Terrain Classification -- Conclusion and Future Direction.
This book provides cutting-edge insights into autonomous vehicles and road terrain classification, and introduces a more rational and practical method for identifying road terrain. It presents the MRF algorithm, which combines the various sensors' classification results to improve the forward LRF for predicting upcoming road terrain types. The comparison between the predicting LRF and its corresponding MRF show that the MRF multiple-sensor fusion method is extremely robust and effective in terms of classifying road terrain. The book also demonstrates numerous applications of road terrain classification for various environments and types of autonomous vehicle, and includes abundant illustrations and models to make the comparison tables and figures more accessible. .
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