Deep Learning and Its Applications for Vehicle Networks

图书简介

Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security.(II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and

PART I Deep learning for vehicle safety and security 1 Deep learning for vehicle safety Raiyan Talkhani, Tao Huang, Shushi Gu, Zhaoxia Guo, Guanglin Zhang and Wei Xiang 2 Deep learning for driver drowsiness classification for a safe vehicle application Sadegh Arefnezhad and Arno Eichberger 3 A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence Manoj Basnet and Mohd Hasan Ali PART II Deep learning for vehicle communications 4 Deep learning for UAV network optimization Jian Wang, Yongxin Liu, Shuteng Niu and Houbing Song 5 State-of-the-art in PHY layer deep learning for future wireless communication systems and networks Konstantinos Koufos, Karim El Haloui, Cong Zhou, Valerio Frascolla and Mehrdad Dianati 6 Deep learning-based index modulation systems for vehicle communications Junfeng Wang, Yue Cui, Zeyad A. H. Qasem, Haixin Sun, Guangjie Han and Mohsen Guizani 7 Deep reinforcement learning applications in connected-automated transportation systems H. M. Abdul Aziz and Sanjoy Das PART III Deep learning for vehicle control 8 Vehicle emission control on road with temporal traffic information using deep reinforcement learning Zhenyi Xu, Yang Cao, Yu Kang and Zhenyi Zhao 9 Load prediction of an electric vehicle charging pile Peng Shurong, Peng Jiayi, Yang Yunhao and Li Bin 10 Deep learning for autonomous vehicles: a vision-based approach to selfadapted robust control Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, José Nuno A. D. Bueno, Marco Henrique Terra and Valdir Grassi Junior PART IV DL for informatio

Trade Policy 买家须知

关于产品：
● 正版保障：本网站隶属于中国国际图书贸易集团公司，确保所有图书都是100%正版。
● 环保纸张：进口图书大多使用的都是环保轻型张，颜色偏黄，重量比较轻。
● 毛边版：即书翻页的地方，故意做成了参差不齐的样子，一般为精装版，更具收藏价值。
关于退换货：
由于预订产品的特殊性，采购订单正式发订后，买方不得无故取消全部或部分产品的订购。
由于进口图书的特殊性，发生以下情况的，请直接拒收货物，由快递返回：
● 外包装破损/发错货/少发货/图书外观破损/图书配件不全（例如：光盘等）
并请在工作日通过电话400-008-1110联系我们。
签收后，如发生以下情况，请在签收后的5个工作日内联系客服办理退换货：
● 缺页/错页/错印/脱线
关于发货时间：
一般情况下：
●【现货】下单后48小时内由北京（库房）发出快递。
●【预订】【预售】下单后国外发货，到货时间预计5-8周左右，店铺默认中通快递，如需顺丰快递邮费到付。
● 需要开具发票的客户，发货时间可能在上述基础上再延后1-2个工作日（紧急发票需求，请联系010-68433105/3213）；
● 如遇其他特殊原因，对发货时间有影响的，我们会第一时间在网站公告，敬请留意。
关于到货时间：
由于进口图书入境入库后，都是委托第三方快递发货，所以我们只能保证在规定时间内发出，但无法为您保证确切的到货时间。
● 主要城市一般2-4天
● 偏远地区一般4-7天
关于接听咨询电话的时间：
010-68433105/3213正常接听咨询电话的时间为：周一至周五上午8：30～下午5：00，周六、日及法定节假日休息，将无法接听来电，敬请谅解。
其它时间您也可以通过邮件联系我们：customer@readgo.cn,工作日会优先处理。
关于快递：
● 已付款订单：主要由中通、宅急送负责派送，订单进度查询请拨打010-68433105/3213。

Deep Learning and Its Applications for Vehicle Networks 深度学习及其在车辆网络中的应用 计算机科学技术基础学科

Deep Learning and Its Applications for Vehicle Networks

深度学习及其在车辆网络中的应用
计算机科学技术基础学科