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Vehicular Ad Hoc Networks (VANETs) represent an emerging topic in wireless communication and networking. VANETs are currently receiving a lot of attention due to their promise of considerably increasing safety and comfort while being on the road. High mobility, adverse channel conditions, and market introduction are some of the key challenges one has to deal with when designing these spontaneous networks. Inter-Vehicle-Communications Based on Ad Hoc Networking Principles covers results and achievements of the FleetNet project, a project devoted to the design of a vehicular ad hoc network.
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This book provides an insight on both the challenges and the technological solutions of several approaches, which allow connecting vehicles between each other and with the network. It underlines the trends on networking capabilities and their issues, further focusing on the MAC and Physical layer challenges. Ranging from the advances on radio access technologies to intelligent mechanisms deployed to enhance cooperative communications, cognitive radio and multiple antenna systems have been given particular highlight.
Vehicular ad hoc networks (Computer networks) --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems --- Automotive technology & trades
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This book provides an insight for students, researchers and practitioners on the area of vehicular communications explaining and presenting solutions for some of the most critical issues in this field and, hopefully, inspiring new research directions. The book is organized in Sections, which respond to different layers and aspects of the vehicular technology: infrastructures, cells deployment and its integration with the V2V part, access procedures, advanced services and applications as localization, spectrum sensing, relay-based cooperative networks.
Vehicular ad hoc networks (Computer networks) --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems --- Automotive technology & trades
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Vehicular ad hoc networks (Computer networks) --- Security measures. --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems
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Drone aircraft --- Vehicular ad hoc networks (Computer networks) --- Control systems. --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems --- Flight control
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Vehicular ad hoc networks (Computer networks) --- Safety measures. --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems
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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 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.
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Mobile communication systems. --- Vehicular ad hoc networks (Computer networks) --- Radiocommunications mobiles. --- Intelligent vehicular ad hoc networks (Computer networks) --- Inter-vehicle ad-hoc networks --- VANETs --- Vehicluar networks (Computer networks) --- Ad hoc networks (Computer networks) --- Intelligent transportation systems --- Vehicles --- Vehicular communication systems --- Radio --- Wireless communication systems --- Communication systems
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