WO2021082663A1 - 一种基于链路可靠性和稳定性的车辆分簇方法及系统 - Google Patents

一种基于链路可靠性和稳定性的车辆分簇方法及系统 Download PDF

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WO2021082663A1
WO2021082663A1 PCT/CN2020/110738 CN2020110738W WO2021082663A1 WO 2021082663 A1 WO2021082663 A1 WO 2021082663A1 CN 2020110738 W CN2020110738 W CN 2020110738W WO 2021082663 A1 WO2021082663 A1 WO 2021082663A1
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vehicle
cluster
clustering
vehicles
speed
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PCT/CN2020/110738
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English (en)
French (fr)
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张登银
张敏
丁飞
李永军
张念启
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南京邮电大学
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Priority to US17/165,887 priority Critical patent/US11356816B2/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
    • H04W4/08—User group management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/46—Cluster building
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/091—Traffic information broadcasting
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096791—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is another vehicle
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • H04W84/20—Master-slave selection or change arrangements

Definitions

  • the invention relates to the technical field of vehicle networking information transmission, in particular to a vehicle clustering method and system based on link reliability and stability.
  • DSRC Dedicated Short Range Communication
  • the object of the present invention is to provide a vehicle clustering method and system based on link reliability and stability, so as to improve the stability and reliability of determining cluster heads.
  • the present invention provides a vehicle clustering method based on link reliability and stability, the method includes:
  • the vehicle dynamic information includes vehicle position and vehicle speed;
  • the cluster head is extracted and the vehicle in the cluster with the largest priority index is selected as the cluster head.
  • performing vehicle clustering based on the vehicle dynamic information includes:
  • the specific formula for calculating the link reliability of the vehicles in the cluster is as follows:
  • r i the i link reliability of the vehicle u
  • c i the number of neighbors i u vehicle vehicles, D i, j as u i and u j vehicle
  • the physical distance between vehicles, R is the D2D communication distance between vehicles
  • (x i , y i ) is the vehicle position of the vehicle u i
  • (x j , y j ) is the vehicle position of the vehicle u j
  • x i is the vehicle
  • y i the coordinates of the vehicle u i perpendicular to its driving direction
  • x j is the coordinates of the vehicle u j along its driving direction
  • y j is the coordinate of the vehicle u j perpendicular to its driving direction
  • the specific formula for calculating the link stability of the vehicles in the cluster is as follows:
  • c i count ⁇ D i,j ⁇ R ⁇
  • s i is the i link stability of the vehicle u
  • v ar_i u i is the relative velocity of the vehicle in the cluster
  • c i is the number of neighbors i u vehicle vehicle
  • k m is the total number of the vehicle
  • v i is the vehicle
  • the vehicle speed of u i , v j is the vehicle speed of vehicle u j
  • D i,j is the physical distance between vehicles u i and u j
  • R is the D2D communication distance between vehicles
  • (x i ,y i ) Is the vehicle position of the vehicle u i
  • (x j ,y j ) is the vehicle position of the vehicle u j
  • x i is the coordinate of the vehicle u i along its traveling direction
  • y i is the coordinate of the vehicle u i perpendicular to its traveling direction
  • x j is the coordinate
  • the specific formula for calculating the cluster head selection priority index is as follows:
  • ⁇ i ⁇ r i + ⁇ s i ;
  • ⁇ i vehicle selected as a cluster head priority index i
  • ⁇ and ⁇ are weighting factors
  • ⁇ link reliability ⁇ link stability of the vehicle ⁇ .
  • the method further includes:
  • the cluster head is extracted and the vehicle in the cluster with the second highest priority index is selected as the backup cluster head.
  • the method further includes:
  • the cluster head receives the traffic safety information sent by the base station and broadcasts it to other vehicles in the cluster;
  • the cluster head receives vehicle driving information sent by other vehicles in the cluster and sends it to the base station;
  • the traffic safety information includes at least one of forward road condition information, forward safety information, and emergency situations, and the vehicle driving information includes current speed, At least one of motion track and current position.
  • the present invention also provides a vehicle clustering system based on link reliability and stability, and the system includes:
  • An acquisition module for acquiring vehicle dynamic information includes vehicle position and vehicle speed;
  • the vehicle clustering module performs vehicle clustering based on the vehicle dynamic information
  • the link reliability and link stability determination module calculates the link reliability and link stability of the vehicles in the cluster based on the vehicle dynamic information
  • the cluster head selection priority index determination module establishes a cluster head selection priority index based on the link reliability and link stability
  • the cluster head determination module is used to extract the cluster head and select the vehicle in the cluster with the largest priority index as the cluster head.
  • the vehicle clustering module includes:
  • the initial clustering unit performs initial clustering based on the vehicle position to obtain an initial cluster
  • a clustering factor determining unit which determines the clustering factor of the vehicles in the initial cluster based on the vehicle speed
  • the specific formula for calculating the link reliability of the vehicles in the cluster is as follows:
  • r i the i link reliability of the vehicle u
  • c i the number of neighbors i u vehicle vehicles, D i, j as u i and u j vehicle
  • the physical distance between vehicles, R is the D2D communication distance between vehicles
  • (x i , y i ) is the vehicle position of the vehicle u i
  • (x j , y j ) is the vehicle position of the vehicle u j
  • x i is the vehicle
  • y i the coordinates of the vehicle u i perpendicular to its driving direction
  • x j is the coordinates of the vehicle u j along its driving direction
  • y j is the coordinate of the vehicle u j perpendicular to its driving direction
  • the present invention discloses the following technical effects:
  • the invention discloses a vehicle clustering method and system based on link reliability and stability.
  • the method includes: acquiring vehicle dynamic information; performing vehicle clustering based on the vehicle dynamic information; and calculating the vehicles in the cluster based on the vehicle dynamic information
  • the cluster head selection priority index is established; the cluster head is extracted and the vehicle in the cluster with the largest priority index is selected as the cluster head.
  • the present invention not only considers the link stability between the cluster head and other vehicles in the cluster, but also considers the link reliability between the cluster head and other vehicles in the cluster. Therefore, when the cluster head is selected, the stability of the cluster head is guaranteed. It also guarantees the reliability of the cluster head.
  • FIG. 1 is a flowchart of a method for vehicle clustering according to an embodiment of the present invention
  • Fig. 2 is a schematic diagram of a car networking architecture according to an embodiment of the present invention.
  • the purpose of the present invention is to provide a vehicle clustering method and system based on link reliability and stability, so as to improve the stability and reliability of determining the cluster head.
  • Fig. 1 is a flowchart of a vehicle clustering method according to an embodiment of the present invention. As shown in Fig. 1, the present invention discloses a vehicle clustering method based on link reliability and stability, which includes the following steps:
  • Step 1 u i vehicle periodically acquires its own dynamic information during traveling, the vehicle including a position (x i, y i), vehicle speed v i, the driving lane (R 1, R 2 &) , where x i Is the coordinate of the vehicle u i along its traveling direction, and yi is the coordinate of the vehicle u i perpendicular to its traveling direction.
  • Step 2 Assuming that the vehicle speed conforms to the Gaussian distribution, that is, the vehicle u i obeys
  • ⁇ i is the average value of the vehicle speed of the vehicle u i
  • Calculate the average velocity v avg_m of the initial cluster, where m 1, 2,...,M.
  • Step 3 Determine the clustering factor, the specific formula is:
  • Step 4 Calculate the link reliability of the vehicles in the cluster, the specific sequence is as follows:
  • ⁇ i,j
  • Step 5 Calculate the link stability of the vehicles in the cluster, the specific sequence is as follows:
  • Step 6 Establish the cluster head selection priority index ⁇ i , the specific formula is:
  • ⁇ i ⁇ r i + ⁇ s i ;
  • ⁇ and ⁇ are weighting coefficients, which can be set according to the actual traffic scene, and are usually set to 0.5.
  • Step 7 Sort the cluster head selection priority index of each vehicle from small to large, and select the vehicle with the largest cluster head selection priority index as the cluster head. Considering the dynamics of vehicle driving, the cluster head selection priority index is the second largest The vehicle of is selected as the spare cluster head.
  • Step 8 After the cluster head receives the traffic safety information sent by the base station, it broadcasts it to the rest of the vehicles in the cluster. After the other members receive the broadcast packet, they automatically define themselves as member nodes.
  • Step 9 D2D communication is used between the member nodes in the cluster and the cluster head.
  • the member nodes periodically send their driving information to the cluster head, including current speed, trajectory, current position, etc.; the cluster head will receive the driving information Send to the base station.
  • CH is the cluster head
  • CH2 is the backup cluster head
  • the remaining vehicles are member nodes.
  • Cellular communication is adopted between the cluster head and the base station
  • D2D communication is adopted between the cluster head and each member node.
  • the member nodes periodically send driving information such as current speed, motion trajectory, and current position to the cluster head, and the cluster head collects and combines Maintain this information and report it to the base station periodically; at the same time, based on the information received from the base station, the cluster head will also broadcast data to each member node, including information about the road conditions ahead, safety information, and unexpected emergencies, so as to assist the vehicle Drive safely and reduce the incidence of traffic accidents.
  • the load of the operator's cellular network can be well optimized.
  • the spare cluster head is immediately put into use, thereby ensuring the reliability of real-time communication in the Internet of Vehicles.
  • the present invention also provides a vehicle clustering system based on link reliability and stability, and the system includes:
  • An acquisition module for acquiring vehicle dynamic information includes vehicle position and vehicle speed;
  • the vehicle clustering module performs vehicle clustering based on the vehicle dynamic information
  • the link reliability and link stability determination module calculates the link reliability and link stability of the vehicles in the cluster based on the vehicle dynamic information
  • the cluster head selection priority index determination module establishes a cluster head selection priority index based on the link reliability and link stability
  • the cluster head determination module is used to extract the cluster head and select the vehicle in the cluster with the largest priority index as the cluster head.
  • the vehicle clustering module of the present invention includes:
  • the initial clustering unit performs initial clustering based on the vehicle position to obtain an initial cluster
  • a clustering factor determining unit which determines the clustering factor of the vehicles in the initial cluster based on the vehicle speed
  • r i the i link reliability of the vehicle u
  • c i the number of neighbors i u vehicle vehicles, D i, j as u i and u j vehicle
  • the physical distance between vehicles, R is the D2D communication distance between vehicles
  • (x i , y i ) is the vehicle position of the vehicle u i
  • (x j , y j ) is the vehicle position of the vehicle u j
  • x i is the vehicle
  • y i the coordinates of the vehicle u i perpendicular to its driving direction
  • x j is the coordinates of the vehicle u j along its driving direction
  • y j is the coordinate of the vehicle u j perpendicular to its driving direction
  • c i count ⁇ D i,j ⁇ R ⁇
  • s i is the i link stability of the vehicle u
  • v ar_i u i is the relative velocity of the vehicle in the cluster
  • c i is the number of neighbors i u vehicle vehicle
  • k m is the total number of the vehicle
  • v i is the vehicle
  • the vehicle speed of u i , v j is the vehicle speed of vehicle u j
  • D i,j is the physical distance between vehicles u i and u j
  • R is the D2D communication distance between vehicles
  • (x i ,y i ) Is the vehicle position of the vehicle u i
  • (x j ,y j ) is the vehicle position of the vehicle u j
  • x i is the coordinate of the vehicle u i along its traveling direction
  • y i is the coordinate of the vehicle u i perpendicular to its traveling direction
  • x j is the coordinate
  • ⁇ i ⁇ r i + ⁇ s i ;
  • ⁇ i vehicle selected as a cluster head priority index i
  • ⁇ and ⁇ are weighting factors
  • ⁇ link reliability ⁇ link stability of the vehicle ⁇ .
  • system of the present invention further includes:
  • the spare cluster head determination module is used to extract the cluster head and select the vehicle in the cluster with the second highest priority index as the spare cluster head.
  • system of the present invention further includes:
  • the broadcast module is used for the cluster head to receive the traffic safety information sent by the base station and broadcast it to the rest of the vehicles in the cluster;
  • the information sending module is used for the cluster head to receive the vehicle driving information sent by the other vehicles in the cluster and send it to the base station;
  • the traffic safety information includes at least one of forward road condition information, forward safety information, and sudden emergencies, the vehicle
  • the driving information includes at least one of current speed, motion trajectory, and current position.
  • the present invention clusters vehicles within a certain distance according to location.
  • a clustering factor is defined, and the vehicle nodes with a relatively high average relative speed are deleted from the cluster, avoiding the fact that the vehicle and the cluster The inconsistent movement of other vehicles causes overall instability, thus improving the stability of the clustered communication link.
  • the present invention considers the communication time between the cluster head and other vehicles in the cluster, the number of neighboring vehicles of the cluster head, the link stability between the cluster head and other vehicles in the cluster, and the cluster head and other vehicles in the cluster.
  • the relative speed of the vehicle not only ensures the stability of the cluster head, but also ensures the reliability of the cluster head;
  • the present invention considers that the selected cluster head may fail, and therefore also selects a spare cluster head. Once the cluster head fails, the spare cluster head can be put into use immediately, ensuring the high requirements of the Internet of Vehicles for real-time performance .
  • the present invention is based on the D2D technology-based intra-cluster message transmission, instead of the traditional DSRC technology in the Internet of Vehicles. Since the data traffic of the D2D communication method does not pass through the base station and core network, it can optimize the operator's cellular The network load has the advantages of reliability and high performance.

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Abstract

本发明公开一种基于链路可靠性和稳定性的车辆分簇方法及系统，方法包括：获取车辆动态信息；基于所述车辆动态信息进行车辆分簇；基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；提取簇头选择优先指数最大的簇内车辆作为簇头。本发明既考虑了簇头与簇内其余车辆的的链路稳定性，还考虑了簇头与簇内其余车辆的的链路可靠性，因此在选择簇头时，既保证了簇头的稳定性，又保证了簇头的可靠性。

Description

一种基于链路可靠性和稳定性的车辆分簇方法及系统
本申请要求于2019年10月29日提交中国专利局、申请号为201911036660.4、发明名称为“一种基于链路可靠性和稳定性的车辆分簇方法”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及车联网信息传输技术领域，特别是涉及一种基于链路可靠性和稳定性的车辆分簇方法及系统。
背景技术
目前，在车联网中，通常采用专用短程通信(Dedicated Short Range Communication，DSRC)技术进行信息交互，车辆需要周期性地把自己的行驶信息报告给基站，数据量小，但交互频率高，会在一定程度上造成网络拥塞，进而对车联网的连通性和信息的实时性造成不利影响。
发明内容
基于此，本发明的目的是提供一种基于链路可靠性和稳定性的车辆分簇方法及系统，以提高确定簇头的稳定性和可靠性。
为实现上述目的，本发明提供了一种基于链路可靠性和稳定性的车辆分簇方法，所述方法包括：
获取车辆动态信息；所述车辆动态信息包括车辆位置和车辆速度；
基于所述车辆动态信息进行车辆分簇；
基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；
基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；
提取簇头选择优先指数最大的簇内车辆作为簇头。
可选地，基于所述车辆动态信息进行车辆分簇，包括：
基于所述车辆位置进行初始分簇，获得初始簇；
基于所述车辆速度确定初始簇内车辆的入簇因子；
判断初始簇内各所述入簇因子是否大于设定阈值；如果初始簇内各所述入簇因子大于设定阈值，则剔除初始簇内所述入簇因子大于预设阈值的车辆。
可选地，计算簇内车辆的链路链路可靠性的具体公式如下：
Figure PCTCN2020110738-appb-000001
其中，c i＝count{D i,j≤R}，
Figure PCTCN2020110738-appb-000002
Figure PCTCN2020110738-appb-000003
Figure PCTCN2020110738-appb-000004
μ i,j＝|μ i-μ j|；式中，r i为车辆u i的链路可靠性，c i为车辆u i的邻居车辆数，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标，r i,j为车辆u i相对于车辆u j的链路可靠性，T i,j为车辆u i与车辆u j之间的连通时间，f(t)为车辆u i与车辆u j连通时间的概率密度函数，t为时间，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，
Figure PCTCN2020110738-appb-000005
为车辆u i相对于u j的相对速度的方差，
Figure PCTCN2020110738-appb-000006
为车辆u i的车辆速度的方差，
Figure PCTCN2020110738-appb-000007
为车辆u j的车辆速度的方差，μ i,j为车辆u i相对于u j的相对速度的平均值，μ i为车辆u i的车辆速度的平均值，μ j为车辆u j的车辆速度的平均值。
可选地，计算簇内车辆的链路稳定性的具体公式如下：
Figure PCTCN2020110738-appb-000008
其中，
Figure PCTCN2020110738-appb-000009
c i＝count{D i,j≤R}，
Figure PCTCN2020110738-appb-000010
式中，s i为车辆u i的链路稳定性，v ar_i为车辆u i在簇内的相对速度，c i为车辆u i的邻居车辆数，k m为车辆的总数，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标。
可选地，计算簇头选择优先指数的具体公式如下：
λ i＝αr i+βs i；
式中，λ i为车辆μ i的簇头选择优先指数，α和β均为加权系数，r i为车辆μ i的链路可靠性，s i为车辆μ i的链路稳定性。
可选地，所述方法还包括：
提取簇头选择优先指数次大的簇内车辆作为备用簇头。
可选地，所述方法还包括：
簇头接收基站发送的交通安全信息，并向簇内其余车辆广播；
簇头接收簇内其余车辆发送的车辆行驶信息，并向基站发送；所述交通安全信息包括前方路况信息、前方安全信息和突发紧急情况中至少一项，所述车辆行驶信息包括当前速度、运动轨迹和当前位置中至少一项。
本发明还提供一种基于链路可靠性和稳定性的车辆分簇系统，所述系统包括：
获取模块，用于获取车辆动态信息；所述车辆动态信息包括车辆位置和车辆速度；
车辆分簇模块，基于所述车辆动态信息进行车辆分簇；
链路可靠性和链路稳定性确定模块，基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；
簇头选择优先指数确定模块，基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；
簇头确定模块，用于提取簇头选择优先指数最大的簇内车辆作为簇头。
可选地，所述车辆分簇模块包括：
初始分簇单元，基于所述车辆位置进行初始分簇，获得初始簇；
入簇因子确定单元，基于所述车辆速度确定初始簇内车辆的入簇因子；
判断单元，用于判断初始簇内各所述入簇因子是否大于设定阈值；如果初始簇内各所述入簇因子大于设定阈值，则剔除初始簇内所述入簇因子大于预设阈值的车辆。
可选地，计算簇内车辆的链路链路可靠性的具体公式如下：
Figure PCTCN2020110738-appb-000011
其中，c i＝count{D i,j≤R}，
Figure PCTCN2020110738-appb-000012
Figure PCTCN2020110738-appb-000013
Figure PCTCN2020110738-appb-000014
μ i,j＝|μ i-μ j|；式中，r i为车辆u i的链路可靠性，c i为车辆u i的邻居车辆数，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标，r i,j为车辆u i相对于车辆u j的链路可靠性，T i,j为车辆u i与车辆u j之间的连通时间，f(t)为车辆u i与车辆u j连通时间的概率密度函数，t为时间，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，
Figure PCTCN2020110738-appb-000015
为车辆u i相 对于u j的相对速度的方差，
Figure PCTCN2020110738-appb-000016
为车辆u i的车辆速度的方差，
Figure PCTCN2020110738-appb-000017
为车辆u j的车辆速度的方差，μ i,j为车辆u i相对于u j的相对速度的平均值，μ i为车辆u i的车辆速度的平均值，μ j为车辆u j的车辆速度的平均值。
根据本发明提供的具体实施例，本发明公开了以下技术效果：
本发明公开一种基于链路可靠性和稳定性的车辆分簇方法及系统，方法包括：获取车辆动态信息；基于所述车辆动态信息进行车辆分簇；基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；提取簇头选择优先指数最大的簇内车辆作为簇头。本发明既考虑了簇头与簇内其余车辆的的链路稳定性，还考虑了簇头与簇内其余车辆的的链路可靠性，因此在选择簇头时，既保证了簇头的稳定性，又保证了簇头的可靠性。
说明书附图
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例车辆分簇方法流程图；
图2为本发明实施例车联网架构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的目的是提供一种基于链路可靠性和稳定性的车辆分簇方法及系统，以提高确定簇头的稳定性和可靠性。
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
图1为本发明实施例车辆分簇方法流程图，如图1所示，本发明公开 一种基于链路可靠性和稳定性的车辆分簇方法，包括以下步骤：
步骤1：车辆u i在行驶过程中周期性地获取自己的动态信息，包括车辆位置(x i,y i)、车辆速度v i、行驶车道(R 1、R 2……)，其中x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标。根据车辆位置，对行驶车辆进行初始分簇，获取初始簇。假设共存在N辆车，分别为u i,i＝1,2,......,N，该N辆车共分为M个初始簇。
步骤2：假设车辆速度符合高斯分布，即车辆u i服从
Figure PCTCN2020110738-appb-000018
式中，μ i为车辆u i的车辆速度的平均值，
Figure PCTCN2020110738-appb-000019
为车辆u i的车辆速度的方差。计算初始簇的平均速度v avg_m，其中m＝1,2,......,M。
步骤3：确定入簇因子，具体公式为：
Figure PCTCN2020110738-appb-000020
其中，
Figure PCTCN2020110738-appb-000021
为车辆u i的入簇因子。
如果
Figure PCTCN2020110738-appb-000022
将该车辆u i从初始簇内剔除，否则将该车辆u i继续保留在其所属簇内。
步骤4：计算簇内车辆的链路可靠性，具体顺序如下：
(1)假设车辆之间设备到设备(Device-to-Device，D2D)的通信距离为R，簇内另一车辆u j，服从于
Figure PCTCN2020110738-appb-000023
其沿道路方向的位置、垂直于道路方向的位置、车辆速度以及平均速度分别为x j、y j、v j和μ j，
Figure PCTCN2020110738-appb-000024
为车辆u j的车辆速度的方差。
(2)计算车辆u i与车辆u j之间的物理距离D i,j，具体公式为：
Figure PCTCN2020110738-appb-000025
(3)计算车辆u i与u j之间的连通时间T i,j，具体公式为：
Figure PCTCN2020110738-appb-000026
(4)计算车辆u i与u j连通时间的概率密度函数f(t)，定义如下：
Figure PCTCN2020110738-appb-000027
其中，
Figure PCTCN2020110738-appb-000028
μ i,j＝|μ i-μ j|，
Figure PCTCN2020110738-appb-000029
为车辆u i相对于u j的相对速度的方差，e为自然常数，t为时间，μ i,j为车辆u i相对于u j的相对速度的平均值。
(5)计算车辆u i相对于车辆u j的链路可靠性r i,j，具体公式为：
Figure PCTCN2020110738-appb-000030
(6)计算车辆u i的邻居车辆数c i，具体公式为：
c i＝count{D i,j≤R}。
(7)计算车辆u i的链路可靠性r i，具体公式为：
Figure PCTCN2020110738-appb-000031
步骤5：计算簇内车辆的链路稳定性，具体顺序如下：
(1)假设分簇后初始簇内车辆的总数为k m，其中m＝1,2,......,M，定义车辆u i在簇内的相对速度v ar_i，具体公式为：
Figure PCTCN2020110738-appb-000032
(2)计算车辆u i在簇内的链路稳定性s i，具体公式为：
Figure PCTCN2020110738-appb-000033
步骤6：建立簇头选择优先指数λ i，具体公式为：
λ i＝αr i+βs i；
式中，α和β为加权系数，可以根据实际的交通场景进行设置，通常情况下均设为0.5。
步骤7：对每台车辆的簇头选择优先指数按由小到大排序，将簇头选择优先指数最大的车辆选为簇头，考虑到车辆行驶的动态性，把簇头选择 优先指数次大的车辆选为备用簇头。
步骤8：簇头接收基站发送的交通安全信息后，将其向簇内其余车辆广播，其余成员收到广播包后，自动定义自己为成员节点。
步骤9：簇内成员节点与簇头之间使用D2D通信，成员节点周期性地发送自己的行驶信息给簇头，包括当前速度、运动轨迹、当前位置等；簇头将所接收到的行驶信息向基站发送。
如图2所示，CH为簇头，CH2为备用簇头，其余车辆为成员节点。簇头与基站之间采用蜂窝通信方式，簇头与各成员节点之间采用D2D通信方式，成员节点周期性地将当前速度、运动轨迹、当前位置等行驶信息发送给簇头，簇头收集并维护这些信息，周期性地汇报给基站；同时，簇头基于从基站接收到的信息，也会向各成员节点广播数据，包括前方路况信息、安全信息以及突发的紧急情况等，从而辅助车辆安全行驶，降低交通事故的发生率。由于D2D通信方式的数据流量不经过基站和核心网，因而可以很好地优化运营商蜂窝网络负载。当簇头出现故障时，备用簇头立即投入使用，从而保证了车联网实时通信的可靠性。
本发明还提供一种基于链路可靠性和稳定性的车辆分簇系统，所述系统包括：
获取模块，用于获取车辆动态信息；所述车辆动态信息包括车辆位置和车辆速度；
车辆分簇模块，基于所述车辆动态信息进行车辆分簇；
链路可靠性和链路稳定性确定模块，基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；
簇头选择优先指数确定模块，基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；
簇头确定模块，用于提取簇头选择优先指数最大的簇内车辆作为簇头。
作为一种实施方式，本发明所述车辆分簇模块，包括：
初始分簇单元，基于所述车辆位置进行初始分簇，获得初始簇；
入簇因子确定单元，基于所述车辆速度确定初始簇内车辆的入簇因子；
判断单元，用于判断初始簇内各所述入簇因子是否大于设定阈值；如果初始簇内各所述入簇因子大于设定阈值，则剔除初始簇内所述入簇因子大于预设阈值的车辆。
作为一种实施方式，本发明计算簇内车辆的链路链路可靠性的具体公式如下：
Figure PCTCN2020110738-appb-000034
其中，c i＝count{D i,j≤R}，
Figure PCTCN2020110738-appb-000035
Figure PCTCN2020110738-appb-000036
Figure PCTCN2020110738-appb-000037
μ i,j＝|μ i-μ j|；式中，r i为车辆u i的链路可靠性，c i为车辆u i的邻居车辆数，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标，r i,j为车辆u i相对于车辆u j的链路可靠性，T i,j为车辆u i与车辆u j之间的连通时间，f(t)为车辆u i与车辆u j连通时间的概率密度函数，t为时间，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，
Figure PCTCN2020110738-appb-000038
为车辆u i相对于u j的相对速度的方差，
Figure PCTCN2020110738-appb-000039
为车辆u i的车辆速度的方差，
Figure PCTCN2020110738-appb-000040
为车辆u j的车辆速度的方差，μ i,j为车辆u i相对于u j的相对速度的平均值，μ i为车辆u i 的车辆速度的平均值，μ j为车辆u j的车辆速度的平均值。
作为一种实施方式，本发明计算簇内车辆的链路稳定性的具体公式如下：
Figure PCTCN2020110738-appb-000041
其中，
Figure PCTCN2020110738-appb-000042
c i＝count{D i,j≤R}，
Figure PCTCN2020110738-appb-000043
式中，s i为车辆u i的链路稳定性，v ar_i为车辆u i在簇内的相对速度，c i为车辆u i的邻居车辆数，k m为车辆的总数，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标。
作为一种实施方式，本发明计算簇头选择优先指数的具体公式如下：
λ i＝αr i+βs i；
式中，λ i为车辆μ i的簇头选择优先指数，α和β均为加权系数，r i为车辆μ i的链路可靠性，s i为车辆μ i的链路稳定性。
作为一种实施方式，本发明所述系统还包括：
备用簇头确定模块，用于提取簇头选择优先指数次大的簇内车辆作为备用簇头。
作为一种实施方式，本发明所述系统还包括：
广播模块，用于簇头接收基站发送的交通安全信息，并向簇内其余车辆广播；
信息发送模块，用于簇头接收簇内其余车辆发送的车辆行驶信息，并向基站发送；所述交通安全信息包括前方路况信息、前方安全信息和突发紧急情况中至少一项，所述车辆行驶信息包括当前速度、运动轨迹和当前 位置中至少一项。
本发明公开方案具有以下优点：
(1)本发明按位置对一定距离内的车辆进行分簇，在进行分簇时，定义了入簇因子，把平均相对速度比较大的车辆节点从簇内删除，避免由于该车辆与簇内其余车辆运动不一致而造成整体不稳定，因而提高了分簇通信链路的稳定性。
(2)本发明在选择簇头时，考虑了簇头与簇内其余车辆的通信时间、簇头的邻居车辆数、簇头与簇内其余车辆的链路稳定性以及簇头与簇内其余车辆的相对速度，不仅保证了簇头的稳定性，还保证了簇头的可靠性；
(3)本发明考虑到所选择的簇头可能会出现故障，因而还选取了一个备用簇头，一旦簇头出现故障，备用簇头可以立即投入使用，保证了车联网对于实时性的高要求。
(4)本发明在车联网中基于D2D技术的簇内消息传输，代替车联网中传统的DSRC技术，由于D2D通信方式的数据流量不经过基站和核心网，因而可以很好地优化运营商蜂窝网络负载，更具有可靠性和高性能的优势。
本说明书中各个实施例采用递进的方式描述，每个实施例重点说明的都是与其他实施例的不同之处，各个实施例之间相同相似部分互相参见即可。
本文中应用了具体个例对本发明的原理及实施方式进行了阐述，以上实施例的说明只是用于帮助理解本发明的方法及其核心思想；同时，对于本领域的一般技术人员，依据本发明的思想，在具体实施方式及应用范围上均会有改变之处。综上所述，本说明书内容不应理解为对本发明的限制。

Claims (10)

  1. 一种基于链路可靠性和稳定性的车辆分簇方法，其特征在于，所述方法包括：
    获取车辆动态信息；所述车辆动态信息包括车辆位置和车辆速度；
    基于所述车辆动态信息进行车辆分簇；
    基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；
    基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；
    提取簇头选择优先指数最大的簇内车辆作为簇头。
  2. 根据权利要求1所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，基于所述车辆动态信息进行车辆分簇，包括：
    基于所述车辆位置进行初始分簇，获得初始簇；
    基于所述车辆速度确定初始簇内车辆的入簇因子；
    判断初始簇内各所述入簇因子是否大于设定阈值；如果初始簇内各所述入簇因子大于设定阈值，则剔除初始簇内所述入簇因子大于预设阈值的车辆。
  3. 根据权利要求1所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，计算簇内车辆的链路链路可靠性的具体公式如下：
    Figure PCTCN2020110738-appb-100001
    其中，c i＝count{D i,j≤R}，
    Figure PCTCN2020110738-appb-100002
    Figure PCTCN2020110738-appb-100003
    Figure PCTCN2020110738-appb-100004
    μ i,j＝|μ i-μ j|；式中，r i为车辆u i的链路可靠性，c i为车辆u i的邻居车辆数，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车 辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标，r i,j为车辆u i相对于车辆u j的链路可靠性，T i,j为车辆u i与车辆u j之间的连通时间，f(t)为车辆u i与车辆u j连通时间的概率密度函数，t为时间，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，
    Figure PCTCN2020110738-appb-100005
    为车辆u i相对于u j的相对速度的方差，
    Figure PCTCN2020110738-appb-100006
    为车辆u i的车辆速度的方差，
    Figure PCTCN2020110738-appb-100007
    为车辆u j的车辆速度的方差，μ i,j为车辆u i相对于u j的相对速度的平均值，μ i为车辆u i的车辆速度的平均值，μ j为车辆u j的车辆速度的平均值。
  4. 根据权利要求1所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，计算簇内车辆的链路稳定性的具体公式如下：
    Figure PCTCN2020110738-appb-100008
    其中，
    Figure PCTCN2020110738-appb-100009
    c i＝count{D i,j≤R}，
    Figure PCTCN2020110738-appb-100010
    式中，s i为车辆u i的链路稳定性，v ar_i为车辆u i在簇内的相对速度，c i为车辆u i的邻居车辆数，k m为车辆的总数，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标。
  5. 根据权利要求1所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，计算簇头选择优先指数的具体公式如下：
    λ i＝αr i+βs i；
    式中，λ i为车辆μ i的簇头选择优先指数，α和β均为加权系数，r i为 车辆μ i的链路可靠性，s i为车辆μ i的链路稳定性。
  6. 根据权利要求1至5中任一项所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，所述方法还包括：
    提取簇头选择优先指数次大的簇内车辆作为备用簇头。
  7. 根据权利要求1至5中任一项所述的基于链路可靠性和稳定性的车辆分簇方法，其特征在于，所述方法还包括：
    簇头接收基站发送的交通安全信息，并向簇内其余车辆广播；
    簇头接收簇内其余车辆发送的车辆行驶信息，并向基站发送；所述交通安全信息包括前方路况信息、前方安全信息和突发紧急情况中至少一项，所述车辆行驶信息包括当前速度、运动轨迹和当前位置中至少一项。
  8. 一种基于链路可靠性和稳定性的车辆分簇系统，其特征在于，所述系统包括：
    获取模块，用于获取车辆动态信息；所述车辆动态信息包括车辆位置和车辆速度；
    车辆分簇模块，基于所述车辆动态信息进行车辆分簇；
    链路可靠性和链路稳定性确定模块，基于所述车辆动态信息计算簇内车辆的链路可靠性和链路稳定性；
    簇头选择优先指数确定模块，基于所述链路可靠性和链路稳定性，建立簇头选择优先指数；
    簇头确定模块，用于提取簇头选择优先指数最大的簇内车辆作为簇头。
  9. 根据权利要求8所述的基于链路可靠性和稳定性的车辆分簇系统，其特征在于，所述车辆分簇模块，包括：
    初始分簇单元，基于所述车辆位置进行初始分簇，获得初始簇；
    入簇因子确定单元，基于所述车辆速度确定初始簇内车辆的入簇因子；
    判断单元，用于判断初始簇内各所述入簇因子是否大于设定阈值；如果初始簇内各所述入簇因子大于设定阈值，则剔除初始簇内所述入簇因子 大于预设阈值的车辆。
  10. 根据权利要求8所述的基于链路可靠性和稳定性的车辆分簇系统，其特征在于，计算簇内车辆的链路链路可靠性的具体公式如下：
    Figure PCTCN2020110738-appb-100011
    其中，c i＝count{D i,j≤R}，
    Figure PCTCN2020110738-appb-100012
    Figure PCTCN2020110738-appb-100013
    Figure PCTCN2020110738-appb-100014
    μ i,j＝|μ i-μ j|；式中，r i为车辆u i的链路可靠性，c i为车辆u i的邻居车辆数，D i,j为车辆u i与u j之间的物理距离，R为车辆之间D2D的通信距离，(x i,y i)为车辆u i的车辆位置，(x j,y j)为车辆u j的车辆位置，x i为车辆u i沿其行驶方向的坐标，y i为车辆u i垂直于其行驶方向的坐标，x j为车辆u j沿其行驶方向的坐标，y j为车辆u j垂直于其行驶方向的坐标，r i,j为车辆u i相对于车辆u j的链路可靠性，T i,j为车辆u i与车辆u j之间的连通时间，f(t)为车辆u i与车辆u j连通时间的概率密度函数，t为时间，v i为车辆u i的车辆速度，v j为车辆u j的车辆速度，
    Figure PCTCN2020110738-appb-100015
    为车辆u i相对于u j的相对速度的方差，
    Figure PCTCN2020110738-appb-100016
    为车辆u i的车辆速度的方差，
    Figure PCTCN2020110738-appb-100017
    为车辆u j的车辆速度的方差，μ i,j为车辆u i相对于u j的相对速度的平均值，μ i为车辆u i的车辆速度的平均值，μ j为车辆u j的车辆速度的平均值。
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