CN118865694B - Vehicle cooperative control method and control system based on vehicle road cloud - Google Patents
Vehicle cooperative control method and control system based on vehicle road cloud Download PDFInfo
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- 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]
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- 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
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- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
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- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
本发明公开了基于车路云的车辆协同控制方法及控制系统，属于车路协同技术领域，具体包括：获取道路网中各个车辆的第一导航路径并获取车辆实时上报的行驶信息；依次选取任意车辆的第一导航路径并获取该导航路径的全部道路交汇点；获取计算任一道路段的拥堵系数，若存在任一道路段的拥堵系数大于等于预设阈值，则将该道路段标定为目标道路段，并按大到小的顺序进行排序生成优先序列，依次识别第一导航路径中包含对应目标道路段的所有车辆，依次选取任一车辆并对该车辆第一导航路径进行再规划得到第二导航路径，将所述第二导航路径推送至该车辆，本发明实现了路网统筹调度，缓解了道路交通堵塞。
The present invention discloses a vehicle cooperative control method and control system based on vehicle-road cloud, which belongs to the field of vehicle-road cooperative technology, and specifically includes: obtaining a first navigation path of each vehicle in a road network and obtaining driving information reported by the vehicle in real time; selecting the first navigation path of any vehicle in turn and obtaining all road intersections of the navigation path; obtaining and calculating the congestion coefficient of any road segment, if there is any road segment whose congestion coefficient is greater than or equal to a preset threshold, marking the road segment as a target road segment, and sorting them in descending order to generate a priority sequence, identifying all vehicles in the first navigation path that contain the corresponding target road segment in turn, selecting any vehicle in turn and replanning the first navigation path of the vehicle to obtain a second navigation path, and pushing the second navigation path to the vehicle. The present invention realizes the coordinated dispatching of the road network and alleviates road traffic congestion.
Description
Technical Field
The invention relates to the technical field of vehicle-road coordination, in particular to a vehicle coordination control method and a vehicle coordination control system based on a vehicle-road cloud.
Background
The concept of the internet of things is derived from the internet of things, namely the internet of things of vehicles, and the running vehicles are used as information sensing objects, and network connection between the vehicles and X (namely the vehicles, the people, the roads and the service platforms) is realized by means of a new generation of information communication technology, so that the overall intelligent driving level of the vehicles is improved, safe, comfortable, intelligent and efficient driving feeling and traffic service are provided for users, meanwhile, the traffic running efficiency is improved, and the intelligent level of social traffic service is improved. The internet of vehicles realizes the omnibearing network links of vehicles and cloud platforms, vehicles and vehicles, vehicles and roads, vehicles and people, in-vehicle and the like through a new generation information communication technology, and mainly realizes 'three-network integration', namely, the integration of in-vehicle networks, inter-vehicle networks and vehicle mobile Internet. The internet of vehicles is used for sensing the state information of vehicles, and realizing intelligent management of traffic, intelligent decision of traffic information service and intelligent control of vehicles by means of a wireless communication network and a modern intelligent information processing technology.
The urban traffic dynamic guidance is mainly based on a macroscopic traffic guidance system, typically comprises a variable information board, traffic broadcasting and an electronic map, the road congestion condition is judged by collecting the information of a detector, and the information is prompted to a nearby driver, so that the driver can select an appropriate route according to the real-time traffic condition.
At present, data used in the research of a path planning system is mainly obtained indirectly based on sources such as a moving track, a mobile phone signaling, a detector and the like, and has a certain deviation from a real scene, so that the calculation accuracy and the real-time performance are still slightly insufficient, and the characteristic that the time spent by different steering of vehicles under the influence of a lamp control signal is different is not considered in the method for calculating a path resistance weight value by each road section in the traditional method, so that the path resistance change caused by signal control is very obvious in urban roads.
Disclosure of Invention
The invention aims to provide a vehicle cooperative control method and a control system based on a vehicle road cloud, which solve the following technical problems:
At present, data used in the research of a path planning system is mainly obtained indirectly based on sources such as a moving track, a mobile phone signaling, a detector and the like, and has a certain deviation from a real scene, so that the calculation accuracy and the instantaneity are still slightly insufficient. In the conventional method, the characteristic that the time spent by the vehicle for different steering under the influence of the light control signal is different is not considered in the method for calculating the road resistance weight value of each road section, so that the road resistance change caused by the signal control is obvious in the urban road.
The aim of the invention can be achieved by the following technical scheme:
A vehicle cooperative control method based on a vehicle road cloud comprises the following steps:
s1, acquiring a first navigation path of each vehicle in a road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
s2, sequentially selecting a first navigation path of any vehicle, acquiring all road junction points of the navigation path, and dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points to obtain a plurality of road segments corresponding to the first navigation paths of all vehicles;
S3, obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is marked as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small, so as to generate a priority sequence D1, D2.
And S4, sequentially identifying all vehicles containing the corresponding target road section in the first navigation path according to the priority sequence, selecting any vehicle, re-planning the first navigation path of the vehicle, obtaining a second navigation path, and pushing the second navigation path to the vehicle.
As a further scheme of the invention: in the step S1, the first navigation path is any navigation path selected from a shortest distance route, a shortest travel time route, a least congestion route and a constant travel route of the vehicle.
As a further scheme of the invention: in the step S3, the specific calculation process of the congestion coefficient is as follows:
s11, acquiring road information of the target road, setting a safe vehicle distance between vehicles as l, acquiring the average length of the vehicle bodies and marking the average length as l1, determining a buffer road section in the target road and acquiring the buffer road section length S; calculating the maximum total number of vehicles which can be simultaneously accommodated in the non-buffered road section, and marking the maximum total number as the vehicle capacity and the maximum total number as Nmax, wherein:
；
S12, obtaining all vehicles which exit a buffer road section in the time of green light passing of a signal lamp in a red-green period on a road junction, obtaining the total time T1 of a target road for passing of any vehicle and the time T2 of the vehicles which stay in the buffer road section, and calculating the running time (T1-T2) of the vehicles on a conventional road section, thereby obtaining the average value T of the running time of a plurality of vehicles on the conventional road section; then, according to the average value T of time, the length S of the buffer road section and the total length L of the target road, the vehicle flow speed V is calculated, wherein:
V=（L-S）/T；
S13, calculating a congestion coefficient TCI according to the obtained target road traffic flow, the obtained vehicle capacity and the obtained traffic flow speed, wherein:
；
；
wherein N is the vehicle flow of the target road, beta is a preset correction coefficient, D is the road density, R is the curvature of the target road, and m is the number of lanes.
As a further scheme of the invention: in S11, the specific process of determining the buffer section is:
And obtaining the maximum speed limit V of a target road, obtaining the time t consumed when the vehicle with the speed V decelerates to 0 at the preset acceleration a, calculating the distance S travelled in the process of decelerating the vehicle with the speed V to the stop, and selecting a road section with the length S from the target road along the opposite direction of the navigation path by taking the target road as a starting point and marking the road section as a buffer road section.
As a further scheme of the invention: in the step S4, the specific process of re-planning is as follows:
S21, calculating and generating all alternative routes which bypass a target road section according to the current position and the target position of the vehicle, marking the alternative routes as d1, d2, & gt, dm, selecting any alternative route, acquiring all road intersection points of the alternative route, dividing the navigation path into a plurality of road sections by taking the adjacent intersection points as dividing points, sequentially acquiring road information of any road section, and obtaining the average speed V of the vehicle of each road section according to a calculation formula V= (L-S)/T, wherein L is the length of any road section, S is the buffer road section length of the road section, and T is the average value T of the running time of the vehicle in the non-buffer road section;
S22, acquiring the current state of the traffic signal lamp at each intersection point on the alternative road and a preset change sequence thereof, calculating a specific time point when the vehicle reaches the next road section based on the average speed V of the vehicle on the road section, and determining the state of the traffic signal lamp at the intersection point when the vehicle reaches each road section by combining the time point when the vehicle reaches each road section intersection point and the state change sequence of the corresponding traffic signal lamp; the traffic signal lamp state is divided into a red light state and a green light state;
s23, calculating the evaluation score of each alternative route at the current moment ; Mu is a preset correction coefficient, qi is a road congestion coefficient of any alternative route, ei is the number of intersection points which the vehicles of any alternative route need to pass through, ei is the number of intersection points which the traffic signal lamps are in green light states when the vehicles of any alternative route pass through the intersection points, and the alternative route with the smallest evaluation score is selected as a second navigation path of the vehicle.
As a further scheme of the invention: the method also comprises the step of modifying the first navigation path of the target vehicle to be a second navigation path, wherein the second navigation planning routes of different target vehicles are different.
As a further scheme of the invention: the method further comprises the steps of obtaining feedback information of all target vehicles, and selecting a set first navigation planning route or updating the set first navigation planning route into a second navigation planning route; and if the vehicle selects the updated second navigation path, acquiring the running information reported by the vehicle again, and updating the road information according to the running information.
Vehicle cooperative control system based on car way cloud includes:
The data acquisition module is used for acquiring a first navigation path of each vehicle in the road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
The data analysis module is used for sequentially selecting a first navigation path of any vehicle and acquiring all road junction points of the navigation path, dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points, and obtaining a plurality of road segments corresponding to the first navigation paths of all vehicles;
obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is calibrated as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small to generate priority sequences D1, D2, and Dn;
And the result generation module is used for sequentially identifying all vehicles containing the corresponding target road section in the first navigation path according to the priority sequence, selecting any vehicle, re-planning the first navigation path of the vehicle, obtaining a second navigation path and pushing the second navigation path to the vehicle.
The invention has the beneficial effects that:
The invention firstly obtains the first navigation path of each vehicle in the road network and obtains the running information reported by any vehicle in real time, sequentially selects the first navigation path of any vehicle and obtains all road junction points of the navigation path, divides the navigation path into a plurality of road segments by taking adjacent junction points as dividing points, obtains a plurality of road segments corresponding to the first navigation paths of all vehicles, obtains the road information of any road segment and calculates the congestion coefficient of the road segment, marks the road segment as a target road segment if the congestion coefficient of any road segment is greater than or equal to a preset threshold value, and can understand that part of vehicles adopt the navigation paths to run and part of vehicles are free to drive because the running behaviors of the vehicles on the road are different, by calculating the congestion coefficient of the road section and calibrating the target road section, it can be understood that the road traffic is influenced when the congestion coefficient reaches a certain threshold value, so that the vehicles which are determined to pass through the road section are controlled to relieve the traffic pressure of the road section, and meanwhile, the running efficiency of the vehicles is improved, all the target road sections are sequenced according to the congestion coefficient from large to small to generate a priority sequence, all the vehicles which correspond to the target road section in the first navigation path are sequentially identified according to the priority sequence, any vehicle is sequentially selected, the first navigation path of the vehicle is re-planned to obtain a second navigation path, and the second navigation path is pushed to the vehicle.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a vehicle cooperative control method based on a road cloud.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses a vehicle cooperative control method based on a vehicle road cloud, which comprises the following steps:
s1, acquiring a first navigation path of each vehicle in a road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
s2, sequentially selecting a first navigation path of any vehicle, acquiring all road junction points of the navigation path, and dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points to obtain a plurality of road segments corresponding to the first navigation paths of all vehicles;
S3, obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is marked as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small, so as to generate a priority sequence D1, D2.
And S4, sequentially identifying all vehicles containing the corresponding target road section in the first navigation path according to the priority sequence, selecting any vehicle, re-planning the first navigation path of the vehicle, obtaining a second navigation path, and pushing the second navigation path to the vehicle.
The invention firstly obtains the first navigation path of each vehicle in the road network and obtains the running information reported by any vehicle in real time, sequentially selects the first navigation path of any vehicle and obtains all road junction points of the navigation path, divides the navigation path into a plurality of road segments by taking adjacent junction points as dividing points, obtains a plurality of road segments corresponding to the first navigation paths of all vehicles, obtains the road information of any road segment and calculates the congestion coefficient of the road segment, marks the road segment as a target road segment if the congestion coefficient of any road segment is greater than or equal to a preset threshold value, and can understand that part of vehicles adopt the navigation paths to run and part of vehicles are free to drive because the running behaviors of the vehicles on the road are different, by calculating the congestion coefficient of the road section and calibrating the target road section, it can be understood that the road traffic is influenced when the congestion coefficient reaches a certain threshold value, so that the vehicles which are determined to pass through the road section are controlled to relieve the traffic pressure of the road section, and meanwhile, the running efficiency of the vehicles is improved, all the target road sections are sequenced according to the congestion coefficient from large to small to generate a priority sequence, all the vehicles which correspond to the target road section in the first navigation path are sequentially identified according to the priority sequence, any vehicle is sequentially selected, the first navigation path of the vehicle is re-planned to obtain a second navigation path, and the second navigation path is pushed to the vehicle.
In a preferred case of the present embodiment, in S1, the first navigation path is any one of a shortest distance route, a shortest travel time route, a least congestion route, or a vehicle constant travel route.
In another preferable case of this embodiment, in S3, a specific calculation process of the congestion coefficient is:
s11, acquiring road information of the target road, setting a safe vehicle distance between vehicles as l, acquiring the average length of the vehicle bodies and marking the average length as l1, determining a buffer road section in the target road and acquiring the buffer road section length S; calculating the maximum total number of vehicles which can be simultaneously accommodated in the non-buffered road section, and marking the maximum total number as the vehicle capacity and the maximum total number as Nmax, wherein:
；
S12, obtaining all vehicles which exit a buffer road section in the time of green light passing of a signal lamp in a red-green period on a road junction, obtaining the total time T1 of a target road for passing of any vehicle and the time T2 of the vehicles which stay in the buffer road section, and calculating the running time (T1-T2) of the vehicles on a conventional road section, thereby obtaining the average value T of the running time of a plurality of vehicles on the conventional road section; then, according to the average value T of time, the length S of the buffer road section and the total length L of the target road, the vehicle flow speed V is calculated, wherein:
V=（L-S）/T；
S13, calculating a congestion coefficient TCI according to the obtained target road traffic flow, the obtained vehicle capacity and the obtained traffic flow speed, wherein:
；
；
wherein N is the vehicle flow of the target road, beta is a preset correction coefficient, D is the road density, R is the curvature of the target road, and m is the number of lanes.
The invention evaluates the congestion degree by combining the traffic capacity and the traffic flow speed, wherein the traffic capacity refers to the maximum number of vehicles which can be borne by a road in a time period, the traffic flow speed refers to the average speed of vehicles passing through the road, when the traffic volume exceeds the traffic capacity, the traffic flow speed is reduced, and the congestion phenomenon is caused, and it can be understood that a signal lamp on the road can influence the speed of the vehicles on the road, so that a buffer road section is set, the total time T1 of any vehicle passing through the target road and the time T2 of the vehicle staying in the buffer road section are acquired, the running time of the vehicles on the conventional road section is calculated, the running time of a plurality of vehicles on the conventional road section is obtained, the actual running speed of the vehicles on the current road is calculated according to the average time, and the congestion coefficient TCI of the actual target road is calculated according to the obtained target road traffic flow, the vehicle capacity and the traffic flow speed.
In another preferable case of the present embodiment, in S11, the specific process of determining the buffer segment is:
And obtaining the maximum speed limit v of a target road, obtaining the time t consumed when the vehicle with the speed v decelerates to 0 at the preset acceleration a, calculating the distance S travelled in the process of decelerating the vehicle with the speed v to the stop, and selecting a road section with the length S from the target road along the opposite direction of the navigation path by taking the target road as a starting point and marking the road section as a buffer road section.
In another preferable case of this embodiment, in S4, the specific process of re-planning is:
S21, calculating and generating all alternative routes which bypass a target road section according to the current position and the target position of the vehicle, marking the alternative routes as d1, d2, & gt, dm, selecting any alternative route, acquiring all road intersection points of the alternative route, dividing the navigation path into a plurality of road sections by taking the adjacent intersection points as dividing points, sequentially acquiring road information of any road section, and obtaining the average speed V of the vehicle of each road section according to a calculation formula V= (L-S)/T, wherein L is the length of any road section, S is the buffer road section length of the road section, and T is the average value T of the running time of the vehicle in the non-buffer road section;
S22, acquiring the current state of the traffic signal lamp at each intersection point on the alternative road and a preset change sequence thereof, calculating a specific time point when the vehicle reaches the next road section based on the average speed V of the vehicle on the road section, and determining the state of the traffic signal lamp at the intersection point when the vehicle reaches each road section by combining the time point when the vehicle reaches each road section intersection point and the state change sequence of the corresponding traffic signal lamp; the traffic signal lamp state is divided into a red light state and a green light state;
s23, calculating the evaluation score of each alternative route at the current moment ; Mu is a preset correction coefficient, qi is a road congestion coefficient of any alternative route, ei is the number of intersection points which the vehicles of any alternative route need to pass through, ei is the number of intersection points which the traffic signal lamps are in green light states when the vehicles of any alternative route pass through the intersection points, and the alternative route with the smallest evaluation score is selected as a second navigation path of the vehicle.
The method is characterized in that all alternative routes bypassing the target road section are generated through calculation, so that the current road is helped to avoid road congestion, the average speed of the vehicle on each road section is calculated through the formula V= (L-S)/T, the road length, the buffer road section length and the average running time of the non-buffer road section are considered, speed calculation is more accurate, running time is predicted more accurately, the current state and the change sequence of traffic signal lamps are acquired, the signal lamp state when the vehicle reaches each intersection point can be predicted dynamically by combining the average speed and the arrival time of the vehicle, the running route is planned better, the road congestion degree, the number of intersection points and the green light state are comprehensively considered through calculation of evaluation scores, the optimal alternative route under the current traffic condition is selected, the navigation route is adjusted dynamically according to the traffic information acquired in real time, the real-time performance and the response of the navigation system are improved, the running efficiency of the vehicle is improved, and the traffic congestion is reduced.
In another preferable case of the embodiment, the method further includes modifying the first navigation path of the target vehicle to be a second navigation path, and the second navigation planned route of the different target vehicles is different.
It can be understood that the second navigation paths of different vehicles are different, traffic flow can be effectively dispersed, congestion conditions of specific road sections are reduced, the utilization rate of the whole road network can be improved by planning different paths for different vehicles, meanwhile, the situation that the road network is re-congested due to secondary navigation planning is avoided, traffic jam is reduced, and the running efficiency of the vehicles is improved.
In another preferable case of the embodiment, the method further includes obtaining feedback information of all target vehicles, and selecting the first navigation planning route or updating the first navigation planning route to a second navigation planning route; and if the vehicle selects the updated second navigation path, acquiring the running information reported by the vehicle again, and updating the road information according to the running information.
It can be understood that when the vehicle selects the updated second navigation path, the road information of each road section can be updated based on the running information reported by the vehicle, so as to provide the latest navigation information for other vehicles, reduce misguidance caused by outdated information, thereby optimizing traffic flow, reducing road traffic jam and improving road use efficiency.
Vehicle cooperative control system based on car way cloud includes:
The data acquisition module is used for acquiring a first navigation path of each vehicle in the road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
The data analysis module is used for sequentially selecting a first navigation path of any vehicle and acquiring all road junction points of the navigation path, dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points, and obtaining a plurality of road segments corresponding to the first navigation paths of all vehicles;
obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is calibrated as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small to generate priority sequences D1, D2, and Dn;
And the result generation module is used for sequentially identifying all vehicles containing the corresponding target road section in the first navigation path according to the priority sequence, selecting any vehicle, re-planning the first navigation path of the vehicle, obtaining a second navigation path and pushing the second navigation path to the vehicle.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (7)
1. The vehicle cooperative control method based on the vehicle road cloud is characterized by comprising the following steps of:
s1, acquiring a first navigation path of each vehicle in a road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
s2, sequentially selecting a first navigation path of any vehicle, acquiring all road junction points of the navigation path, and dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points to obtain a plurality of road segments corresponding to the first navigation paths of all vehicles;
S3, obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is marked as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small, so as to generate a priority sequence D1, D2.
S4, sequentially identifying all vehicles containing corresponding target road sections in the first navigation path according to the priority sequence, sequentially selecting any vehicle, re-planning the first navigation path of the vehicle to obtain a second navigation path, and pushing the second navigation path to the vehicle;
the specific calculation process of the congestion coefficient is as follows:
s11, acquiring road information of the target road, setting a safe vehicle distance between vehicles as l, acquiring the average length of the vehicle bodies and marking the average length as l1, determining a buffer road section in the target road and acquiring the buffer road section length S; calculating the maximum total number of vehicles which can be simultaneously accommodated in the non-buffered road section, and marking the maximum total number as the vehicle capacity and the maximum total number as Nmax, wherein:
；
S12, obtaining all vehicles which exit a buffer road section in the time of green light passing of a signal lamp in a red-green period on a road junction, obtaining the total time T1 of a target road for passing of any vehicle and the time T2 of the vehicles which stay in the buffer road section, and calculating the running time (T1-T2) of the vehicles on a conventional road section, thereby obtaining the average value T of the running time of a plurality of vehicles on the conventional road section; then, according to the average value T of time, the length S of the buffer road section and the total length L of the target road, the vehicle flow speed V is calculated, wherein:
V=（L-S）/T；
S13, calculating a congestion coefficient TCI according to the obtained target road traffic flow, the obtained vehicle capacity and the obtained traffic flow speed, wherein:
；
；
wherein N is the vehicle flow of the target road, beta is a preset correction coefficient, D is the road density, R is the curvature of the target road, and m is the number of lanes.
2. The vehicle cooperative control method based on the vehicle road cloud according to claim 1, wherein in S1, the first navigation path is any one of a shortest distance route, a shortest travel time route, a least congestion route, or a vehicle constant travel route.
3. The vehicle cooperative control method based on the vehicle road cloud as set forth in claim 1, wherein in S11, the specific process of determining the buffer road section is:
the method comprises the steps of obtaining the maximum speed limit v of a target road, obtaining time t consumed when a vehicle with the speed v decelerates to 0 at a preset acceleration a, calculating the distance S travelled by the vehicle with the speed v in the process of decelerating to stop, selecting a first target road intersection along the opposite direction of a navigation path, selecting a road section with the length S from the target road by taking the road intersection as a starting point, and marking the road section as a buffer road section.
4. The vehicle cooperative control method based on the vehicle road cloud according to claim 1, wherein in S4, the specific process of re-planning is as follows:
S21, calculating and generating all alternative routes which bypass a target road section according to the current position and the target position of the vehicle, marking the alternative routes as d1, d2, & gt, dm, selecting any alternative route, acquiring all road intersection points of the alternative route, dividing the navigation path into a plurality of road sections by taking the adjacent intersection points as dividing points, sequentially acquiring road information of any road section, and obtaining the average speed V of the vehicle of each road section according to a calculation formula V= (L-S)/T, wherein L is the length of any road section, S is the buffer road section length of the road section, and T is the average value T of the running time of the vehicle in the non-buffer road section;
S22, acquiring the current state of the traffic signal lamp at each intersection point on the alternative road and a preset change sequence thereof, calculating a specific time point when the vehicle reaches the next road section based on the average speed V of the vehicle on the road section, and determining the state of the traffic signal lamp at the intersection point when the vehicle reaches each road section by combining the time point when the vehicle reaches each road section intersection point and the state change sequence of the corresponding traffic signal lamp; the traffic signal lamp state is divided into a red light state and a green light state;
S23, calculating an evaluation score c=μ of each alternative route at the current time QiEi/Ei; mu is a preset correction coefficient, qi is a road congestion coefficient of any alternative route, ei is the number of intersection points which the vehicles of any alternative route need to pass through, ei is the number of intersection points which the traffic signal lamps are in green light states when the vehicles of any alternative route pass through the intersection points, and the alternative route with the smallest evaluation score is selected as a second navigation path of the vehicle.
5. The vehicle cooperative control method based on the road cloud as claimed in claim 4, further comprising modifying the first navigation path of the vehicle to be a second navigation path, wherein the second navigation paths of different vehicles are different.
6. The vehicle cooperative control method based on the vehicle road cloud according to claim 1, further comprising acquiring feedback information of all vehicles, and selecting a given first navigation path or updating the given first navigation path to a second navigation path; and if the vehicle selects the updated second navigation path, acquiring the running information reported by the vehicle again, and updating the road information according to the running information.
7. Vehicle cooperative control system based on car way cloud, its characterized in that includes:
The data acquisition module is used for acquiring a first navigation path of each vehicle in the road network and acquiring running information reported by any vehicle in real time, wherein the running information comprises a running track, a current speed and a current position;
The data analysis module is used for sequentially selecting a first navigation path of any vehicle and acquiring all road junction points of the navigation path, dividing the navigation path into a plurality of road segments by taking adjacent junction points as dividing points, and obtaining a plurality of road segments corresponding to the first navigation paths of all vehicles;
obtaining road information of any road section and calculating a congestion coefficient of the road section, wherein the road information comprises the maximum speed limit of a road, the number of lanes, the total length of the road, the curvature of the road and the number of vehicles, if the congestion coefficient of any road section is greater than or equal to a preset threshold value, the road section is calibrated as a target road section, all the target road sections are obtained, and the road sections are ordered according to the order of the congestion coefficients from large to small to generate priority sequences D1, D2, and Dn;
the result generation module is used for sequentially identifying all vehicles containing corresponding target road sections in the first navigation path according to the priority sequence, selecting any vehicle, re-planning the first navigation path of the vehicle, obtaining a second navigation path and pushing the second navigation path to the vehicle;
the specific calculation process of the congestion coefficient is as follows:
Acquiring road information of the target road, setting a safe vehicle distance between vehicles as l, acquiring the average length of the vehicle body and marking as l1, determining a buffer road section in the target road and acquiring the buffer road section length S; calculating the maximum total number of vehicles which can be simultaneously accommodated in the non-buffered road section, and marking the maximum total number as the vehicle capacity and the maximum total number as Nmax, wherein:
Nmax=（L-S）/（l1+l）m；
Obtaining all vehicles which exit a buffer road section in the time of green light passing of a signal lamp in a red-green period on a road junction, obtaining the total time T1 of a target road for passing of any vehicle and the time T2 of the vehicles which stay in the buffer road section, and calculating the running time (T1-T2) of the vehicles on a conventional road section, thereby obtaining the average value T of the running time of a plurality of vehicles on the conventional road section; then, according to the average value T of time, the length S of the buffer road section and the total length L of the target road, the vehicle flow speed V is calculated, wherein:
V=（L-S）/T；
And calculating a congestion coefficient TCI according to the obtained target road traffic flow, the obtained vehicle capacity and the obtained traffic flow speed, wherein:
TCI=NV/Nmax（D+Rβ）；
D=N/m（L-S）；
wherein N is the vehicle flow of the target road, beta is a preset correction coefficient, D is the road density, R is the curvature of the target road, and m is the number of lanes.
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