Intelligent Vehicular(智能车)研究综述
Intelligent Vehicular 智能车 - Vehicle speed prediction is quite essential for many intelligent vehicular and transportation applications. [1]车速预测对于许多智能车辆和运输应用来说非常重要。 [1]
Secure Intelligent Vehicular
The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). [1] 11p, using VFC for a secure intelligent vehicular network. [2]所提出的方案被称为“使用雾计算的安全智能车载网络”（SIVNFC）。 [1] 11p，使用 VFC 实现安全的智能车载网络。 [2]
intelligent vehicular network 智能车联网
In intelligent vehicular networks, vehicles have enhanced sensing capabilities and carry computing and communication platforms to enable new versatile systems known as Vehicular Communication (VC) systems. [1] Towards future intelligent vehicular network, the machine learning as the promising artificial intelligence tool is widely researched to intelligentize communication and networking functions. [2] Software-defined vehicular networks (SDVNs) have been a vital addition to the design of intelligent vehicular networks. [3] In this paper, we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks, which aims at collecting the vehicles' locations, trajectories and other key driving parameters for the time-critical autonomous driving's requirement. [4] Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. [5] Intelligent vehicular networks converged with software-defined networking provides several flow-based surveillance services to mobile applications on vehicular nodes. [6] The proposed scheme is termed “Secure Intelligent Vehicular Network using fog computing” (SIVNFC). [7] In this article, we propose an intelligent vehicular network framework for smart cities that enables route selection based on real-time data received from neighboring vehicles in an ad hoc fashion. [8] How to manage these data stream will play a very important role in the development of next-generation intelligent vehicular networks. [9] Intelligent Vehicular Networks goal is to provide high-quality services that can learn and forecast clients' needs and intentions. [10] 11p, using VFC for a secure intelligent vehicular network. [11] In this paper, we analyse the dynamic resource management for intelligent vehicular networks based on Multi-access Edge Computing architecture services. [12] The advent of IOT has changed the Traditional vehicular networks in to intelligent vehicular networks called Internet of vehicles. [13] This paper investigates the problem of persistent traffic measurement, which was not adequately studied in the prior art, particularly in the context of intelligent vehicular networks. [14] Therefore, content distribution is one of the critical challenges in connected cars and intelligent vehicular networks. [15] With the increasing demand for real-time road safety services and infotainment applications on vehicles, the development of an efficient wireless mobile communication became crucial for the content delivery of such services in Intelligent Vehicular Networks (IVN). [16] Understanding the mobility of vehicles plays a fundamental role in the design of solutions for intelligent vehicular networks. [17] In this paper, we propose the location-based and information-centric (LoICen) architecture to improve the content request procedure and reduce the broadcast storm problem in intelligent vehicular networks. [18]在智能车载网络中，车辆具有增强的传感能力并携带计算和通信平台，以启用称为车载通信 (VC) 系统的新型多功能系统。 [1] 面向未来的智能车联网，机器学习作为有前途的人工智能工具被广泛研究，以实现通信和网络功能的智能化。 [2] 软件定义的车载网络 (SDVN) 已成为智能车载网络设计的重要补充。 [3] 在本文中，我们为智能车辆网络中的智能交通运输提供了一种新方法，旨在收集车辆的位置、轨迹和其他关键驾驶参数，以满足时间紧迫的自动驾驶需求。 [4] 智能车辆网络作为一种有前途的技术出现，可以在交通系统和智能城市中提供高效的数据通信。 [5] 与软件定义网络融合的智能车载网络为车载节点上的移动应用程序提供了多种基于流的监控服务。 [6] 所提出的方案被称为“使用雾计算的安全智能车载网络”（SIVNFC）。 [7] 在本文中，我们提出了一种用于智能城市的智能车辆网络框架，该框架能够根据从相邻车辆接收的实时数据以特别的方式进行路线选择。 [8] 如何管理这些数据流将对下一代智能车联网的发展起到非常重要的作用。 [9] 智能车载网络的目标是提供可以学习和预测客户需求和意图的高质量服务。 [10] 11p，使用 VFC 实现安全的智能车载网络。 [11] 在本文中，我们分析了基于多接入边缘计算架构服务的智能车载网络的动态资源管理。 [12] 物联网的出现将传统的车载网络转变为智能车载网络，称为车联网。 [13] 本文研究了持续交通测量的问题，该问题在现有技术中没有得到充分研究，特别是在智能车辆网络的背景下。 [14] 因此，内容分发是联网汽车和智能车载网络的关键挑战之一。 [15] 随着对车辆上实时道路安全服务和信息娱乐应用的需求不断增加，开发高效的无线移动通信对于智能车载网络 (IVN) 中此类服务的内容交付变得至关重要。 [16] 了解车辆的移动性在智能车辆网络解决方案的设计中发挥着重要作用。 [17] 在本文中，我们提出了基于位置和以信息为中心（LoICen）的架构来改进内容请求过程并减少智能车载网络中的广播风暴问题。 [18]
intelligent vehicular application 智能车载应用
The rapid development of internet of vehicles (IoV) has recently led to the emergence of diverse intelligent vehicular applications such as automatic driving, auto navigation, and advanced driver assistance, etc. [1] The adjusting of autonomous vehicles (AVs) steering angle represents a challenging issue in the intelligent vehicular applications. [2] To operate intelligent vehicular applications such as automated driving, mechanisms including machine learning (ML), artificial intelligence (AI), and others are used to abstract knowledge from information. [3] A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS-cGAN) is proposed for intelligent vehicular applications. [4]近年来，随着车联网（IoV）的快速发展，自动驾驶、自动导航、高级驾驶辅助等多种智能车辆应用层出不穷。 [1] 自动驾驶汽车 (AV) 转向角的调整是智能汽车应用中的一个具有挑战性的问题。 [2] 为了操作自动驾驶等智能车辆应用，机器学习 (ML)、人工智能 (AI) 等机制用于从信息中提取知识。 [3] 提出了一种基于条件生成对抗网络（IPS-cGAN）的新型热红外行人分割算法，用于智能车辆应用。 [4]
intelligent vehicular communication 智能车载通讯
This paper presents a machine-learning-based scenario identification model for intelligent vehicular communications. [1] Intelligent vehicular communication is fundamental to manage vehicle-to-grid (V2G) interaction, where electric vehicles (EVs) provide energy to balance demand of critical loads (CLs). [2] These issues hinder the promotion and implementation of multi-carrier relay selection for intelligent vehicular communications. [3] In this paper, a review of the state of the art of the technologies and architectures generated around the Internet of the vehicles (IoV) is carried out and with this, an implementation of the simulation of intelligent vehicular communications through the IEEE 802. [4]本文提出了一种基于机器学习的智能车载通信场景识别模型。 [1] 智能车辆通信是管理车辆与电网 (V2G) 交互的基础，其中电动汽车 (EV) 提供能量以平衡关键负载 (CL) 的需求。 [2] 这些问题阻碍了智能车载通信多载波中继选择的推广和实施。 [3] 在本文中，对围绕车联网 (IoV) 产生的最新技术和架构进行了回顾，并以此实现了通过 IEEE 802 对智能车辆通信的仿真。 [4]
intelligent vehicular transport
This study is to implement an intelligent vehicular transport design to improve the road safety, navigation and comfort. [1] In order to realize intelligent vehicular transport networks and self driving cars, connected autonomous vehicles (CAVs) are required to be able to estimate their position to the nearest centimeter. [2]本研究旨在实施智能车辆运输设计，以提高道路安全、导航和舒适度。 [1] 为了实现智能车辆运输网络和自动驾驶汽车，联网自动驾驶汽车 (CAV) 需要能够将其位置估计到最近的厘米。 [2]
intelligent vehicular system 智能车载系统
Motivated by these issues, this paper addresses a drone-enabled intelligent vehicular system, which is secure, easy to deploy and reliable in quality. [1] Intelligent vehicular systems and smart city applications are the fastest growing Internet of things (IoT) implementations at a compound annual growth rate of 30%. [2]受这些问题的启发，本文提出了一种支持无人机的智能车辆系统，该系统安全、易于部署且质量可靠。 [1] 智能车辆系统和智慧城市应用是增长最快的物联网 (IoT) 实施，年复合增长率为 30%。 [2]