基于C-V2X的车路协同自动驾驶关键技术与应用

随着车联网技术的演进,自动驾驶在单车智能的基础上,又有了新的发展形态——车路协同自动驾驶。通过“人-车-路-云”深度融合形成的一体化复杂信息物理系统（cyber physical system,CPS）,可以与自动驾驶车辆实现协同感知、协同决策规划甚至协同控制,提升自动驾驶安全性,帮助克服各类复杂交通环境。首先介绍了车路协同的复杂信息物理系统的概念内涵和总体架构,并提出了车路协同自动驾驶的一系列典型应用场景、技术原理、C-V2X(cellular vehicle-to-everything)性能要求、车路协同系统功能与性能要求,可以为下一阶段智能网联汽车与智能交通的深度融合发展提供参考和解决思路。     

5G车路协同自动驾驶应用研究

本文提出了一种5G车路协同自动驾驶解决方案，该方案主要依托5G移动通信、高精度定位技术、五维时空融合技术、边缘计算、边云协同等技术，实现边缘平台算力部署，构建“端-边缘-云”分层架构，建立智能可靠车联网通信、车辆的实时高精度定位、交通态势感知、交通管控等技术体系，实现5G车路协同，全面提升车辆感知决策控制能力。当前，该方案已在武汉经开区智能网联汽车与智慧道路自动驾驶示范区应用落地。     

计算机技术在智能交通系统中的应用研究

借助智能交通系统,优化公共交通运行效率,为人们提供更大的便利。文章对智能交通系统内涵进行了简要分析,并从数据处理系统、交通监控系统、车辆导航系统以及车辆辅助驾驶系统4个方面对计算机技术在智能交通系统中的应用展开了讨论,旨在为技术研究人员提供有价值的技术建议。     

一种基于双目视觉的安全车距测量方法

为了加强智能交通系统建设,提出了车载视频在辅助驾驶系统中的应用.首先提出了视频终端的4项基本功能,即车内视频监控、辅助驾驶、可视倒车系统、视频娱乐系统,其中车内视频监控、可视倒车、辅助驾驶是智能交通建设车辆端的重要功能.接着采用双目视觉方式实现汽车的辅助驾驶,详细分析了如何对安全车距进行预警和制动.最后进行测距步骤及测距结果分析.实验证明,该方法适合安全车距测量,具有广阔的应用前景.     

智能汽车ADAS电磁辐射抗扰度测试方法研究

针对智能汽车先进驾驶辅助系统(ADAS)电磁辐射抗扰度测试的难点,通过分析ADAS的工作原理,总结出在电磁兼容试验室内激活和控制车辆ADAS的有效方法。并分别对配备单雷达、双雷达、图形识别系统的具有自适应巡航、前碰撞预警、自动紧急制动的智能汽车进行电磁辐射抗扰度测试。     

基于车联网的酒后驾驶智能呼叫系统研究

文章介绍了一种基于车联网的酒后驾驶智能呼叫系统,可以对驾驶员酒后预驾驶车辆和酒后驾驶车辆等行为进行有效判断,结合判断结果,利用人工智能技术确定驾驶员身份,通过车联网技术、移动通信技术联系车主、驾驶员家人及交通警察部门。该系统的设计与安装能形成对驾驶员行为的实时监控,大大减少驾驶员酒后驾驶车辆带来的社会危害,有效保障驾驶员、车上乘员的人身财产安全和社会公共安全。     

车联网在无人驾驶技术中的运用

随着大众交通工具的不断更新换代,二十一世纪以来汽车作为人们的重要代步工具也不断地进步,在满足基本动力,外观和其他功能的条件下,人们将更多高新科技与汽车技术越来越多地结合,而无人驾驶技术技术就是现在乃至未来汽车发展的重要方向。车联网作为无人驾驶技术的重要组成部分,是横跨多领域多学科的课题,与智能交通、辅助驾驶、主动安全和智能车辆息息相关。本文将对无人驾驶技术发展概况进行简单的总结,对车联网的特点和应用做出一定的分析,并介绍其在无人驾驶领域中的应用,最后对车联网和无人驾驶技术融合及后续发展做出展望。     

新型多功能汽车异常驾驶检测报警系统的设计与实现

为有效降低交通安全事故,本文设计了新型多功能汽车异常驾驶检测报警系统。首先系统分析驾驶员状态、驾驶行为和车辆行驶状态关系;然后构建了新型多功能汽车异常驾驶检测报警系统模型,设计了基于双核DSP控制单元硬件平台;其次利用PERCLOS算法等判断驾驶人疲劳驾驶状态,并运用数据融合技术将车辆状态信息与驾驶人状态信息进行融合处理与异常驾驶判断,最终实现汽车异常驾驶报警,实践表明该系统实用可靠。     

基于自组织映射神经网络的VANET组网算法

研究了应用于汽车辅助驾驶、无人驾驶等智能交通领域的车辆组网方法,提出一种将自组织映射神经网络算法应用于车辆自组织网络进行车辆组网的算法,该算法根据车辆定时发出的消息中位置、行驶方向等信息对车辆按目的地、行驶方向的相似性进行组网,组网后的车辆主要接收并处理与之在同一个网络中的车辆的信息。理论分析和仿真结果表明,组网后的系统传输时延远低于未组网通信情况,吞吐量有显著提高。     

物联网环境下的智能汽车技术研究

提出一种在物联网环境下实现智能化汽车的解决方案。在汽车智能化趋势不可阻挡的潮流下,多因素、复杂交通环境成为阻碍智能驾驶技术发展的瓶颈。由人—车、车-车、车-环境组成的,在大交通控制系统背景下的,以物联网技术支撑的智能驾驶成为可能。     

Traffimatics — Intelligent Co-Operative Vehicle Highway Systems

This paper describes the vision of the Traffimatics project — the vision of the connected car. Traffimatics aims to develop the concept of intelligent co-operative vehicle highway systems, supporting a wide range of applications ranging from real-time traffic information and safety warnings to in-car entertainment and personal telematics. The project also investigates the possibilities of using vehicles to create a large-scale self-organising sensor and communications network enabling the intelligent co-operative vehicle highway systems vision. This paper looks into the state-of-the-art technologies that could enable the Traffimatics vision and also discusses market barriers and opportunities.     

智能车载车联网服务平台

智能车载服务平台又被称为车联网服务中心,是车联网的核心部分,主要可以实现车辆和信息的交互,是一个基于物联网、云计算等技术的智能车载平台。智能车载服务平台可实现车辆的远程监控、紧急报警、远程升级等功能。车联网将汽车与互联网相结合,利用先进的传感技术、通信技术和信息技术等,实现了对汽车的全面监测与管理。物联网技术与信息通信技术的深度融合,是未来车联网发展的必然趋势。     

An efficient adaptive intelligent routing system for multi‐intersections

With the rapid development of wireless technologies and the growing emphasis on vehicle safety, many vehicular ad hoc network applications have been extensively used. This study attempts to use vehicular ad hoc network technologies for autonomous driving to improve and reduce traffic congestion and vehicle waiting time. Therefore, this study proposes an adaptively intelligent routing system, which uses V2V communications to increase vehicle speed, allows vehicles to communicate with traffic control systems, arranges appropriate vehicle routes based on queuing theory, and uses traffic signals for information exchange. The timing of traffic signals is decided according to road traffic density. To decrease vehicle waiting time at intersections, every vehicle's speed is adjusted based on the distance between the vehicle and the traffic signals. In the simulation, automated vehicles and a more realistic car‐following model are taken into consideration and vehicle speeds are regulated based on speed limits and safe following distance on most roads. The simulation result reveals that our proposed adaptively intelligent routing system outperforms periodic system in average vehicle speed and average waiting time at both single and double cross intersections. Copyright © 2016 John Wiley & Sons, Ltd.     

一种基于视频技术的车辆跟踪方法

在智能交通系统中,基于视频技术的车辆跟踪是交通参数和交通事件检测的关键技术之一。本文首先研究了现有的基于视频技术的车辆跟踪技术,然后提出了一种使用Kalman滤波器预测车辆位置,并用距离和颜色信息辅助识别车辆的跟踪方法。实验结果表明,该方法能够有效地实现车辆的跟踪。     

基于图像处理的交通路口智能播报系统的研究

从智能交通系统的基本理论着手,介绍了将图像处理技术应用到智能交通系统的基本思路,并详细阐述了整个系统的算法设计。主要优势是对帧间差分法进行了改进优化,即改进的基于边缘信息的邻域帧间差分法,使得改进后的算法可以实时进行运动车辆检测,然后通过设置虚拟线来对车辆的参数进行间接的检测,最后通过LED提示行人和车辆。经实验验证改进后的算法简单方便且性能良好,能够准确的检测出运动车辆,增强了系统的可靠性。     

A Global Optimal Path Planning and Controller Design Algorithm for Intelligent Vehicles

Autonomous vehicle guidance and trajectory planning is one of the key technologies in the autonomous control system for intelligent vehicles. Firstly, the target pursuit model for intelligent vehicles was established and described in this text. Then, the research work for global motion planning was carried out based on Stackelberg Differential Game Theory, and the global optimal solution was obtained by using the survival type differential game. Finally, to overcome errors, we use a polynomial method to achieve the smooth motion planning. So, based on Terminal Sliding Mode method, the Active Front Steering controller design was used to calculate the desired active wheel angle for intelligent vehicle path tracking. The simulation and experiment results demonstrate the feasibility and effectiveness of this method for intelligent vehicles’ path planning and tracking.     

基于远距离射频卡的机动车物联网技术

针对机动车信息识别和监控课题,提出在机动车上安装远距离射频信息卡,在道口安装机动车信息固定采集器,为执法人员配置移动或手持采集器,采集器将机动车信息汇集到监控中心,从而构建低成本的机动车物联网和智能交通系统,有效地解决了机动车流量监控、可疑车辆追踪、套牌假牌识别等问题。     

In‐Vehicle AR‐HUD System to Provide Driving‐Safety Information

Augmented reality (AR) is currently being applied actively to commercial products, and various types of intelligent AR systems combining both the Global Positioning System and computer‐vision technologies are being developed and commercialized. This paper suggests an in‐vehicle head‐up display (HUD) system that is combined with AR technology. The proposed system recognizes driving‐safety information and offers it to the driver. Unlike existing HUD systems, the system displays information registered to the driver's view and is developed for the robust recognition of obstacles under bad weather conditions. The system is composed of four modules: a ground obstacle detection module, an object decision module, an object recognition module, and a display module. The recognition ratio of the driving‐safety information obtained by the proposed AR‐HUD system is about 73%, and the system has a recognition speed of about 15 fps for both vehicles and pedestrians.     

Network condition and application‐based data adaptive intelligent message routing in vehicular network

Recent advances in intelligent transportation systems enable a broad range of potential applications that significantly improve the vehicle and road safety and facilitate the efficient dissemination of information among the vehicles. To assist the vehicle traffic, message broadcasting is a widely adopted technique for road safety. But efficient message broadcasting is a significant issue, especially in a high network density due to the broadcast storm problem. To solve this issue, several methods are proposed to eliminate the redundant transmission of safety packets. However, they lack in restricting the broadcasting region of safety messages, and the transmissions of safety packets outside the dangerous region, and force the vehicles to unnecessary detours. This paper proposes an adaptive multimode routing protocol, network condition, and application‐based data adaptive intelligent message routing in vehicular network (NetCLEVER) that supports 2 modes of operation such as message broadcasting and intelligent routing. In message broadcasting mode, the NetCLEVER decides the dangerous region of the network by considering the changes of neighbor vehicles velocity, instead of current vehicle velocity, because a vehicle decision in velocity is interdependent with the preceding vehicles. In intelligent routing mode, the NetCLEVER exploits the cuckoo search optimization in routing by taking into account multiple routing factors such as the road topology of intersections and traffic signals and their impact on link stability, which improves the reliability of routing packets significantly. The performance evaluation illustrates that the proposed NetCLEVER improves reliable wireless communication as well as road safety in vehicular ad hoc networks.