CN117351714A - Intelligent traffic flow optimization method and system based on Internet of vehicles - Google Patents
Intelligent traffic flow optimization method and system based on Internet of vehicles 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
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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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- 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
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- G—PHYSICS
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- 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
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Abstract
The invention provides an intelligent traffic flow optimizing method and system based on the internet of vehicles, wherein the method comprises the following steps: acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters, and determining traffic running characteristics in the target road and driving behavior characteristics of different vehicles; constructing a vehicle coordination control model based on vehicle operation requirements, analyzing road topology structures, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free operation track; dynamic guide graphs of different vehicles are generated based on the collision-free running tracks of multiple vehicles, and the dynamic guide graphs are issued to corresponding vehicle terminals based on the Internet of vehicles to remind running routes. The accuracy and the reliability of optimizing the traffic flow on the target road are guaranteed, the running efficiency of the vehicle on the target road is improved, and the traffic pressure is relieved.
Description
Technical Field
The invention relates to the technical field of road monitoring and data processing, in particular to an intelligent traffic flow optimization method and system based on the Internet of vehicles.
Background
The vehicle networking is that the vehicle-mounted equipment on the vehicle effectively utilizes all vehicle dynamic information in the information network platform through a wireless communication technology, different functional services are provided in the running process of the vehicle, more and more people select self-driving travel along with the continuous improvement of living standard, and great convenience is provided for the travel of users due to the occurrence of the vehicle networking;
however, the internet of vehicles used on the current vehicles mostly focuses on research and development of functions of the internet of vehicles, such as anti-collision, short-distance vehicle following early warning and the like, the internet of vehicles is not applied to the field of traffic flow optimization at present, and the current traffic flow optimization methods mostly adopt means of traffic broadcasting or artificial dredging and the like, and can be found after traffic congestion occurs, so that the traffic flow optimization effect is greatly reduced, meanwhile, clear travelling route guidance cannot be sent to different vehicles, the effect of optimizing traffic flow is greatly reduced, and the travelling efficiency of the vehicles is also reduced;
therefore, in order to overcome the defects, the invention provides an intelligent traffic flow optimization method and system based on the Internet of vehicles.
Disclosure of Invention
The invention provides an intelligent traffic flow optimizing method and system based on the internet of vehicles, which are used for analyzing road topological structures of a target road and running state parameters of different vehicles on the target road through the internet of vehicles, realizing accurate and effective determination of traffic flow running characteristics on the target road and driving behavior characteristics of different vehicles, providing convenience and guarantee for traffic flow optimization, analyzing the obtained road topological structures, the traffic flow running characteristics and the driving behavior characteristics of each vehicle through a constructed vehicle coordination control model, realizing determination of collision-free running tracks of multiple vehicles, ensuring that the multiple vehicles can orderly advance on the target road, finally, generating a dynamic guiding graph according to the collision-free running tracks of the multiple vehicles, transmitting the dynamic guiding graph to corresponding vehicle terminals, guaranteeing the accuracy and reliability of traffic flow optimization on the target road, improving the running efficiency of the vehicles on the target road, and relieving the traffic pressure.
The invention provides an intelligent traffic flow optimization method based on the Internet of vehicles, which comprises the following steps:
step 1: acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters, and determining traffic running characteristics in the target road and driving behavior characteristics of different vehicles;
step 2: constructing a vehicle coordination control model based on vehicle operation requirements, analyzing road topology structures, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free operation track;
step 3: dynamic guide graphs of different vehicles are generated based on the collision-free running tracks of multiple vehicles, and the dynamic guide graphs are issued to corresponding vehicle terminals based on the Internet of vehicles to remind running routes.
Preferably, in step 1, a road topology structure of a target road is obtained based on the internet of vehicles, which includes:
acquiring road driving videos of different vehicles on a target road according to the Internet of vehicles based on the data management platform, and dividing the road driving videos of different vehicles to obtain a static frame image set of the road driving videos;
Extracting image characteristics in each static frame image in the static frame image set, and de-duplicating the static frame image based on the image characteristics to obtain a standard static frame image set;
determining adjacent standard static frame images based on the image features, determining overlapping areas of the adjacent standard static frame images based on the image features, and splicing the adjacent standard static frame images by taking the overlapping areas as image splicing boundaries;
and obtaining a complete road image of the target road based on the splicing result, and determining the road topology structure of the target road based on the complete road image.
Preferably, the intelligent traffic flow optimizing method based on the internet of vehicles determines a road topology structure of a target road based on a complete road image, and comprises the following steps:
acquiring an obtained complete road image of a target road, extracting a target road main body in the complete road image, performing characteristic traversal on the target road main body, and determining a main road and a branch corresponding to the target road;
marking bifurcation points of a main road and a branch road in a target road in a complete road image based on the traversing result, and obtaining a first road topological structure of the target road based on the marking result;
meanwhile, obtaining road basic parameters of a main road and a branch road in a target road based on a standard result, obtaining the number of lanes and the number of curves corresponding to the main road and the branch road in the target road based on the road basic parameters, and obtaining a second road topology structure of the target road based on the number of lanes and the number of curves;
And summarizing the first road topological structure and the second road topological structure, and obtaining the final road topological structure of the target road based on the summarizing result.
Preferably, in step 1, obtaining a road topology structure of a target road and a vehicle running state parameter in the target road based on the internet of vehicles, the method comprises the following steps:
acquiring configuration information of a data management platform, and distributing a communication port to a vehicle-mounted terminal in a vehicle in the data management platform based on the configuration information;
constructing a wireless communication link between a vehicle-mounted terminal of a vehicle in a target road and a data management platform based on a communication port, transmitting vehicle networking data of the vehicle to the data management platform based on the wireless communication link, monitoring data communication characteristics of the communication port in unit time in real time, determining the uploading amount of the vehicle networking data in unit time based on the data communication characteristics, expanding the communication port when the uploading amount of the vehicle networking data in unit time is greater than a preset threshold value, and receiving the vehicle networking data of the vehicle in real time based on an expansion result;
analyzing the acquired internet of vehicles data based on the data management platform to obtain a data source corresponding to the internet of vehicles data, marking the internet of vehicles data based on an identity tag of the data source, and obtaining vehicle running state parameters in a target road based on a marking result.
Preferably, in step 1, vehicle running state parameters are analyzed to determine traffic flow running characteristics in a target road and driving behavior characteristics of different vehicles, including:
acquiring a preset reference point in a target road, analyzing the acquired vehicle running state parameters, and carrying out feature screening on analysis results of the vehicle running state parameters based on attribute information of the preset reference point to acquire target analysis results related to the preset reference point;
determining the number of vehicles passing through a preset reference point in unit time and the running speed of each vehicle based on the target analysis result, and determining the average speed of the vehicles on a target road based on the number of vehicles and the running speed of each vehicle;
the method comprises the steps of obtaining the influence coefficient of road conditions on traffic flow, respectively determining the number of vehicles, the average speed and the influence weight of the influence coefficient of the road conditions on the traffic flow operation characteristics, and comprehensively analyzing the number of vehicles, the average speed and the influence coefficient of the road conditions on the traffic flow based on the influence weight to obtain the traffic flow operation characteristics in a target road.
Preferably, in step 1, vehicle running state parameters are analyzed to determine traffic flow running characteristics in a target road and driving behavior characteristics of different vehicles, including:
the method comprises the steps of obtaining vehicle running state parameters, analyzing the vehicle running state parameters, determining global running data of different vehicles on a target road, and determining the speed increasing times, speed increasing points, lane changing frequency and speed change characteristics of the different vehicles on the target road based on the global running data;
and obtaining driving behavior characteristics of different vehicles based on the speed increasing times, the speed increasing points, the lane changing frequency and the speed change characteristics.
Preferably, in step 2, a vehicle coordination control model is constructed based on vehicle running requirements, and road topology structures, traffic flow running characteristics and driving behavior characteristics of each vehicle are analyzed based on the vehicle coordination control model, so as to determine a collision-free running track of multiple vehicles, which comprises the following steps:
acquiring a vehicle running requirement, determining a safety index of the vehicle when the vehicle runs on a road based on the vehicle running requirement, and searching a preset database in a server based on the safety index to obtain historical vehicle running data of the vehicle on the road;
Analyzing historical vehicle driving data, determining a driving event of a vehicle on a road, determining a corresponding reference driving parameter when the vehicle safely runs on the road based on the driving event, and training a preset convolutional neural network based on the reference driving parameter and a reference road traffic rule to obtain a vehicle coordination control model;
analyzing a road topology structure, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on a vehicle coordination control model to obtain a speed-position-time relation of each vehicle on a target road, and performing simulation on driving states of different vehicles based on a preset simulation model according to the speed-position-time relation of each vehicle to obtain driving states corresponding to the different vehicles at the current moment, wherein the driving states comprise a driving speed, a driving position, a following distance with a preceding vehicle, a lane changing position and a lane changing time point;
dynamically tracking the driving states of the corresponding vehicles after the target time period based on the driving states of the different vehicles corresponding to the current moment, and obtaining the target driving states of the different vehicles corresponding to the different moments based on dynamic tracking results;
and summarizing the corresponding target driving states of different vehicles at different moments to obtain the collision-free running tracks of multiple vehicles of each vehicle on the target road.
Preferably, in step 3, a dynamic guiding graph for different vehicles is generated based on collision-free running tracks of multiple vehicles, which comprises the following steps:
the method comprises the steps of obtaining a plurality of conflict-free running tracks of vehicles, dividing the obtained conflict-free running tracks of the vehicles to obtain target running tracks of different vehicles on a target road, and obtaining a basic guide map based on the target running tracks;
determining the running direction of the vehicle on the target running track, calling a direction guide icon from a guide icon library based on the running direction, and carrying out first dynamic display on the direction guide icon on a basic guide map;
determining driving state change points of the vehicle on the target running track and driving characteristics corresponding to each driving state change point, retrieving driving state change guide icons from a guide icon library based on the driving characteristics, and carrying out second dynamic display on the driving state change points of the driving state change guide icons on the basic guide diagram;
and summarizing the first dynamic display and the second dynamic display on the basic guide graph, and obtaining the dynamic guide graphs of different vehicles based on the summarizing result.
Preferably, in step 3, the method for optimizing the intelligent traffic flow based on the internet of vehicles issues the dynamic guiding graph to the corresponding vehicle terminal for reminding the running route based on the internet of vehicles, which comprises the following steps:
acquiring the obtained dynamic guide graph, and adding a terminal identity label to the corresponding dynamic guide graph based on the guide characteristics;
distributing the dynamic guide graph to the corresponding vehicle terminal according to the terminal identity tag and the Internet of vehicles based on the adding result, and performing format conversion on the received dynamic guide graph based on the vehicle terminal;
displaying the dynamic guide map after format conversion on a vehicle terminal, sending a running route prompt to a vehicle driver based on a display result, and updating the working state of the dynamic guide map on the vehicle terminal in real time based on the prompt result and the running state of the vehicle.
The invention provides an intelligent traffic flow optimizing system based on the internet of vehicles, which comprises:
the feature determining module is used for acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters and determining traffic running features in the target road and driving behavior features of different vehicles;
The track determining module is used for constructing a vehicle coordination control model based on vehicle running requirements, analyzing road topological structures, traffic flow running characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free running track;
the route guiding module is used for generating dynamic guiding diagrams of different vehicles based on the collision-free running tracks of the multiple vehicles, and issuing the dynamic guiding diagrams to corresponding vehicle terminals for carrying out running route reminding based on the Internet of vehicles.
Compared with the prior art, the invention has the following beneficial effects:
1. the road topology structure of the target road and the running state parameters of different vehicles on the target road are analyzed through the Internet of vehicles, the accurate and effective determination of the traffic flow running characteristics on the target road and the driving behavior characteristics of different vehicles is realized, convenience and guarantee are provided for traffic flow optimization, secondly, the obtained road topology structure, the traffic flow running characteristics and the driving behavior characteristics of each vehicle are analyzed through the constructed vehicle coordination control model, the determination of the collision-free running tracks of multiple vehicles is realized, the orderly progress of the multiple vehicles on the target road is ensured, finally, a dynamic guiding graph is generated according to the collision-free running tracks of the multiple vehicles, the dynamic guiding graph is issued to corresponding vehicle terminals, the accuracy and reliability of traffic flow optimization on the target road are ensured, the running efficiency of the vehicles on the target road is also improved, and the traffic pressure is relieved.
2. The vehicle running requirement is analyzed, the safety index of the vehicle running on the road is effectively determined, the historical vehicle running data of the vehicle on the road is obtained from the server according to the safety index, the obtained historical vehicle running data is analyzed, the corresponding reference driving parameters of the vehicle running safely on the road are accurately obtained, the preset convolutional neural network is trained according to the obtained reference driving parameters and the reference road traffic rules, the vehicle coordination control model is accurately and effectively constructed, and finally, the road topology structure, the traffic flow running characteristics and the driving behavior characteristics of each vehicle are analyzed through the constructed vehicle coordination control model, so that the multi-vehicle collision-free running track on the target road is accurately and effectively formulated, reliable support is provided for traffic flow optimization, the efficiency and the accuracy of traffic flow optimization are ensured, and the vehicle running efficiency on the road is also improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an intelligent traffic flow optimizing method based on the Internet of vehicles in an embodiment of the invention;
FIG. 2 is a flowchart of step 1 in an intelligent traffic flow optimization method based on Internet of vehicles according to an embodiment of the present invention;
fig. 3 is a block diagram of an intelligent traffic flow optimizing system based on internet of vehicles in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment provides an intelligent traffic flow optimization method based on the internet of vehicles, as shown in fig. 1, comprising the following steps:
step 1: acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters, and determining traffic running characteristics in the target road and driving behavior characteristics of different vehicles;
Step 2: constructing a vehicle coordination control model based on vehicle operation requirements, analyzing road topology structures, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free operation track;
step 3: dynamic guide graphs of different vehicles are generated based on the collision-free running tracks of multiple vehicles, and the dynamic guide graphs are issued to corresponding vehicle terminals based on the Internet of vehicles to remind running routes.
In this embodiment, the target road refers to a road on which traffic flow optimization is currently required, and is at least one road.
In this embodiment, the road topology refers to a main road and a branch road included in the target road, an intersection between the main road and the road, a shape of the target road, a road size parameter, and the like.
In this embodiment, the vehicle running state parameters refer to the running speed of the vehicle on the target road, the running position, the distance from the surrounding vehicle, and the like.
In this embodiment, the traffic flow operation feature refers to the location of the traffic flow on the target road, the congestion condition of a certain section of traffic flow, and the like.
In this embodiment, the driving behavior feature refers to the guidance frequency, the speed increase condition, and the like of the vehicle on the target road.
In this embodiment, the vehicle running requirement is determined based on historical driving data, including following distance, lane changing conditions, and the like.
In this embodiment, the vehicle coordination control model coordinates the running situation of the vehicle on the target road, so as to ensure that the congestion situation on the target road is not caused by excessive vehicles.
In this embodiment, the collision-free running track of the plurality of vehicles refers to a running route in which the plurality of vehicles run on the target road without collision (i.e., without traffic congestion).
In this embodiment, the dynamic guidance map is a guidance map for conveying the driving speed, the driving position, and the lane change position corresponding to different times for different vehicles, and the guidance mark on the image is updated in real time.
In this embodiment of the present invention, the process is performed,
the beneficial effects of the technical scheme are as follows: the road topology structure of the target road and the running state parameters of different vehicles on the target road are analyzed through the Internet of vehicles, the accurate and effective determination of the traffic flow running characteristics on the target road and the driving behavior characteristics of different vehicles is realized, convenience and guarantee are provided for traffic flow optimization, secondly, the obtained road topology structure, the traffic flow running characteristics and the driving behavior characteristics of each vehicle are analyzed through the constructed vehicle coordination control model, the determination of the collision-free running tracks of multiple vehicles is realized, the orderly progress of the multiple vehicles on the target road is ensured, finally, a dynamic guiding graph is generated according to the collision-free running tracks of the multiple vehicles, the dynamic guiding graph is issued to corresponding vehicle terminals, the accuracy and reliability of traffic flow optimization on the target road are ensured, the running efficiency of the vehicles on the target road is also improved, and the traffic pressure is relieved.
Example 2:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, as shown in fig. 2, in step 1, obtaining a road topology structure of a target road based on the internet of vehicles, including:
step 101: acquiring road driving videos of different vehicles on a target road according to the Internet of vehicles based on the data management platform, and dividing the road driving videos of different vehicles to obtain a static frame image set of the road driving videos;
step 102: extracting image characteristics in each static frame image in the static frame image set, and de-duplicating the static frame image based on the image characteristics to obtain a standard static frame image set;
step 103: determining adjacent standard static frame images based on the image features, determining overlapping areas of the adjacent standard static frame images based on the image features, and splicing the adjacent standard static frame images by taking the overlapping areas as image splicing boundaries;
step 104: and obtaining a complete road image of the target road based on the splicing result, and determining the road topology structure of the target road based on the complete road image.
In this embodiment, the road running video refers to video images acquired when different vehicles run on the target road.
In this embodiment, the still frame image set refers to each frame image obtained by discretizing the obtained road running image.
In this embodiment, the image feature refers to the road condition contained in each still frame image, and may be, for example, a road width, a road shape, or the like.
In this embodiment, the standard still frame image set refers to a final image set obtained after image deduplication is performed on the obtained still frame image set, where an individual included in the standard still frame image set is a standard still frame image.
The beneficial effects of the technical scheme are as follows: by processing the road driving videos of different vehicles on the target road, accurate and effective determination of the road topology result of the target road according to the road driving videos of different vehicles is realized, and convenience and guarantee are provided for traffic flow optimization.
Example 3:
on the basis of embodiment 2, the embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, which determines a road topology structure of a target road based on a complete road image, and comprises the following steps:
acquiring an obtained complete road image of a target road, extracting a target road main body in the complete road image, performing characteristic traversal on the target road main body, and determining a main road and a branch corresponding to the target road;
Marking bifurcation points of a main road and a branch road in a target road in a complete road image based on the traversing result, and obtaining a first road topological structure of the target road based on the marking result;
meanwhile, obtaining road basic parameters of a main road and a branch road in a target road based on a standard result, obtaining the number of lanes and the number of curves corresponding to the main road and the branch road in the target road based on the road basic parameters, and obtaining a second road topology structure of the target road based on the number of lanes and the number of curves;
and summarizing the first road topological structure and the second road topological structure, and obtaining the final road topological structure of the target road based on the summarizing result.
In this embodiment, the target road body is the target road area recorded in the complete road image.
In this embodiment, feature traversal refers to a comprehensive analysis of road features of a target road body, so as to facilitate determination of main roads and branches on the target road.
In this embodiment, the first road topology refers to the relative positional relationship between the road and the branch in the target road.
In this embodiment, the road basic parameters refer to the widths, lengths, the number of lanes included, and the like of the main road and the branch road in the target road.
In this embodiment, the second road topology refers to the number of lanes contained in the target road and the number of plays.
The beneficial effects of the technical scheme are as follows: by analyzing the obtained complete road image, the bifurcation point of the main road and the branch road on the target road and the road basic parameters are accurately and effectively determined, the road topology structure of the target road is accurately and effectively determined according to the bifurcation point and the road basic parameters, traversal and guarantee are provided for effective traffic flow optimization, and the running efficiency of the vehicle on the target road is also improved.
Example 4:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, in step 1, obtaining a road topology structure of a target road and a vehicle running state parameter in the target road based on the internet of vehicles, including:
acquiring configuration information of a data management platform, and distributing a communication port to a vehicle-mounted terminal in a vehicle in the data management platform based on the configuration information;
constructing a wireless communication link between a vehicle-mounted terminal of a vehicle in a target road and a data management platform based on a communication port, transmitting vehicle networking data of the vehicle to the data management platform based on the wireless communication link, monitoring data communication characteristics of the communication port in unit time in real time, determining the uploading amount of the vehicle networking data in unit time based on the data communication characteristics, expanding the communication port when the uploading amount of the vehicle networking data in unit time is greater than a preset threshold value, and receiving the vehicle networking data of the vehicle in real time based on an expansion result;
Analyzing the acquired internet of vehicles data based on the data management platform to obtain a data source corresponding to the internet of vehicles data, marking the internet of vehicles data based on an identity tag of the data source, and obtaining vehicle running state parameters in a target road based on a marking result.
In this embodiment, the configuration information refers to requirements of the data management platform on the running environment and running conditions in the running process.
In this embodiment, the internet of vehicles data refers to road conditions collected by vehicles during driving, and includes data such as the number of surrounding vehicles, the relative distance between the surrounding vehicles, and the road conditions.
In this embodiment, the data communication feature refers to the amount of data that the communication port passes through per unit time, the data format of the data passing through the communication port, and the like.
In this embodiment, the preset threshold is set in advance, and is used to characterize the maximum amount of data that the communication port can pass through in a unit time.
In this embodiment, the data source refers to the end corresponding to the different internet of vehicles data, that is, the target vehicle corresponding to the different internet of vehicles data. Wherein the identity tag is a marking symbol that characterizes different vehicles.
The beneficial effects of the technical scheme are as follows: the communication ports are distributed to the vehicles on the target road through the data management platform, the internet of vehicles data of different vehicles are accurately and effectively received through the communication ports, and the received internet of vehicles data are marked, so that the accurate and effective determination of the vehicle running state parameters of different vehicles is realized, the accuracy of determining the traffic flow running characteristics in the target road and the driving behavior characteristics of different vehicles is ensured, and the accuracy and the reliability of traffic flow optimization are conveniently improved.
Example 5:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on internet of vehicles, in step 1, analyzing vehicle running state parameters, determining traffic flow running characteristics in a target road and driving behavior characteristics of different vehicles, including:
acquiring a preset reference point in a target road, analyzing the acquired vehicle running state parameters, and carrying out feature screening on analysis results of the vehicle running state parameters based on attribute information of the preset reference point to acquire target analysis results related to the preset reference point;
determining the number of vehicles passing through a preset reference point in unit time and the running speed of each vehicle based on the target analysis result, and determining the average speed of the vehicles on a target road based on the number of vehicles and the running speed of each vehicle;
the method comprises the steps of obtaining the influence coefficient of road conditions on traffic flow, respectively determining the number of vehicles, the average speed and the influence weight of the influence coefficient of the road conditions on the traffic flow operation characteristics, and comprehensively analyzing the number of vehicles, the average speed and the influence coefficient of the road conditions on the traffic flow based on the influence weight to obtain the traffic flow operation characteristics in a target road.
In this embodiment, the preset reference points are set on the target road in advance, and are reference bases for determining the traffic flow running characteristics in the target road, and are at least two.
In this embodiment, the attribute information refers to the position of the preset reference point on the target road and the magnitude of the distance between adjacent preset reference points.
In this embodiment, the target analysis result related to the preset reference point is a result corresponding to the vehicle running state parameter corresponding to the preset reference point included in the vehicle running state parameter.
In this embodiment, the influence coefficient refers to the degree to which the road condition affects the running speed and the following distance of the vehicle, and the influence coefficient corresponding to each road condition is different.
The beneficial effects of the technical scheme are as follows: the method comprises the steps of determining the predicted reference point on the target road, analyzing the analysis result of the vehicle running state parameters according to the preset reference point, extracting the analysis result containing the preset reference point, determining the average speed on the target road, the number of vehicles and the influence coefficient of road conditions on traffic flow according to the extracted target analysis result, and reliably analyzing the running characteristics of the traffic flow on the target road according to the average speed, the number of vehicles and the influence coefficient of the road conditions on traffic flow, thereby providing convenience and basis for traffic flow optimization and guaranteeing the accuracy and reliability of traffic flow optimization.
Example 6:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on internet of vehicles, in step 1, analyzing vehicle running state parameters, determining traffic flow running characteristics in a target road and driving behavior characteristics of different vehicles, including:
the method comprises the steps of obtaining vehicle running state parameters, analyzing the vehicle running state parameters, determining global running data of different vehicles on a target road, and determining the speed increasing times, speed increasing points, lane changing frequency and speed change characteristics of the different vehicles on the target road based on the global running data;
and obtaining driving behavior characteristics of different vehicles based on the speed increasing times, the speed increasing points, the lane changing frequency and the speed change characteristics.
In this embodiment, the global travel data refers to travel data corresponding to the vehicle throughout the target road.
In this embodiment, the speed change characteristic refers to the speed increase speed of the vehicle, and the like.
The beneficial effects of the technical scheme are as follows: by analyzing the vehicle running state parameters, the speed increasing times, the speed increasing points, the lane changing frequency and the speed change characteristics of different vehicles on the target road are accurately determined, the driving behavior characteristics of different vehicles are locked according to the speed increasing times, the speed increasing points, the lane changing frequency and the speed change characteristics, convenience is provided for traffic flow optimization, and the reliability of traffic flow optimization is ensured.
Example 7:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, in step 2, a vehicle coordination control model is constructed based on vehicle operation requirements, and road topology structures, traffic flow operation characteristics and driving behavior characteristics of each vehicle are analyzed based on the vehicle coordination control model, so as to determine a collision-free operation track of multiple vehicles, including:
acquiring a vehicle running requirement, determining a safety index of the vehicle when the vehicle runs on a road based on the vehicle running requirement, and searching a preset database in a server based on the safety index to obtain historical vehicle running data of the vehicle on the road;
analyzing historical vehicle driving data, determining a driving event of a vehicle on a road, determining a corresponding reference driving parameter when the vehicle safely runs on the road based on the driving event, and training a preset convolutional neural network based on the reference driving parameter and a reference road traffic rule to obtain a vehicle coordination control model;
analyzing a road topology structure, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on a vehicle coordination control model to obtain a speed-position-time relation of each vehicle on a target road, and performing simulation on driving states of different vehicles based on a preset simulation model according to the speed-position-time relation of each vehicle to obtain driving states corresponding to the different vehicles at the current moment, wherein the driving states comprise a driving speed, a driving position, a following distance with a preceding vehicle, a lane changing position and a lane changing time point;
Dynamically tracking the driving states of the corresponding vehicles after the target time period based on the driving states of the different vehicles corresponding to the current moment, and obtaining the target driving states of the different vehicles corresponding to the different moments based on dynamic tracking results;
and summarizing the corresponding target driving states of different vehicles at different moments to obtain the collision-free running tracks of multiple vehicles of each vehicle on the target road.
In this embodiment, the safety index refers to the minimum driving requirement standard that needs to be maintained when the vehicle is driving on the target road, and may be, for example, the following distance, the safe driving speed, and the like.
In this embodiment, the preset database is known in advance in the server for storing different types of data.
In this embodiment, the historical vehicle travel data refers to travel data generated during travel of different vehicles on the target road.
In this embodiment, the driving event refers to a traffic accident, a lane change situation, and the like that occur when the vehicle is driving on the target road.
In this embodiment, the reference driving parameters refer to the safe following distance and the safe running speed and the safe lane change speed, the relative distance to other vehicles at the time of the safe lane change, and the like.
In this embodiment, the reference road traffic rules are known in advance and are characterized by traffic rules that the vehicle needs to follow when traveling on the target road.
In this embodiment, the preset convolution network is known in advance and is a model base for constructing the vehicle coordination control model.
In this embodiment, the preset simulation model is a known program in the computer, and is used to perform a simulation model on the current running situation of the vehicle on the target road according to the relationship between the vehicle speed and the position and the time, so as to determine the corresponding driving states of different vehicles when no conflict occurs on the target road.
In this embodiment, the target driving state refers to driving states of different vehicles corresponding to different times in the future.
The beneficial effects of the technical scheme are as follows: the vehicle running requirement is analyzed, the safety index of the vehicle running on the road is effectively determined, the historical vehicle running data of the vehicle on the road is obtained from the server according to the safety index, the obtained historical vehicle running data is analyzed, the corresponding reference driving parameters of the vehicle running safely on the road are accurately obtained, the preset convolutional neural network is trained according to the obtained reference driving parameters and the reference road traffic rules, the vehicle coordination control model is accurately and effectively constructed, and finally, the road topology structure, the traffic flow running characteristics and the driving behavior characteristics of each vehicle are analyzed through the constructed vehicle coordination control model, so that the multi-vehicle collision-free running track on the target road is accurately and effectively formulated, reliable support is provided for traffic flow optimization, the efficiency and the accuracy of traffic flow optimization are ensured, and the vehicle running efficiency on the road is also improved.
Example 8:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, in step 3, a dynamic guiding graph for different vehicles is generated based on a collision-free running track of multiple vehicles, including:
the method comprises the steps of obtaining a plurality of conflict-free running tracks of vehicles, dividing the obtained conflict-free running tracks of the vehicles to obtain target running tracks of different vehicles on a target road, and obtaining a basic guide map based on the target running tracks;
determining the running direction of the vehicle on the target running track, calling a direction guide icon from a guide icon library based on the running direction, and carrying out first dynamic display on the direction guide icon on a basic guide map;
determining driving state change points of the vehicle on the target running track and driving characteristics corresponding to each driving state change point, retrieving driving state change guide icons from a guide icon library based on the driving characteristics, and carrying out second dynamic display on the driving state change points of the driving state change guide icons on the basic guide diagram;
and summarizing the first dynamic display and the second dynamic display on the basic guide graph, and obtaining the dynamic guide graphs of different vehicles based on the summarizing result.
In this embodiment, the target moving track refers to a result obtained by dividing moving tracks of different vehicles from collision-free moving tracks of multiple vehicles, that is, independent moving tracks corresponding to each vehicle.
In this embodiment, the basic guidance map is a travel route map corresponding to the target travel tracks of different vehicles.
In this embodiment, the guide icon library is set in advance, and is used to store different guide icons.
In this embodiment, the first dynamic display refers to displaying the direction guide icon on the base guide map in order to characterize the traveling direction of the vehicle.
In this embodiment, the driving state change point refers to a change road position and a speed change point of the vehicle on the target road.
In this embodiment, the driving characteristics refer to driving behaviors of the vehicle corresponding to different driving state change points, and may be lane change, deceleration, acceleration, or the like, for example.
In this embodiment, the driving state change guide icon refers to a guide icon corresponding to a driving feature of the vehicle at a driving state change point, and is used to characterize a change situation of the driving feature.
In this embodiment, the second dynamic display means that the driving state change guide icon is displayed on the basic guide map, so that the driver is conveniently reminded of the need of making driving state change at the current moment and the current position, and the purpose of optimizing the traffic flow is achieved.
The beneficial effects of the technical scheme are as follows: the method comprises the steps of analyzing the obtained conflict-free running tracks of multiple vehicles, determining target running tracks of different vehicles, generating basic guide diagrams of different vehicles according to the target running tracks, finally, respectively determining direction guide icons and driving state change guide icons of the vehicles on a target road, dynamically displaying the direction guide icons and the driving state change guide icons on the obtained basic guide diagrams, accurately and effectively formulating the dynamic guide diagrams of different vehicles, providing reference basis for optimizing traffic flow of different vehicles, and guaranteeing accuracy and efficiency of traffic flow optimization.
Example 9:
on the basis of embodiment 1, the present embodiment provides an intelligent traffic flow optimizing method based on the internet of vehicles, in step 3, the dynamic guiding graph is issued to a corresponding vehicle terminal based on the internet of vehicles to carry out a running route reminding, which includes:
acquiring the obtained dynamic guide graph, and adding a terminal identity label to the corresponding dynamic guide graph based on the guide characteristics;
distributing the dynamic guide graph to the corresponding vehicle terminal according to the terminal identity tag and the Internet of vehicles based on the adding result, and performing format conversion on the received dynamic guide graph based on the vehicle terminal;
Displaying the dynamic guide map after format conversion on a vehicle terminal, sending a running route prompt to a vehicle driver based on a display result, and updating the working state of the dynamic guide map on the vehicle terminal in real time based on the prompt result and the running state of the vehicle.
In this embodiment, the guidance feature refers to the type of guidance contained in the different dynamic guidance graphs.
In this embodiment, the terminal identity tag refers to a tag for characterizing a vehicle terminal that does not pass through the dynamic boot graph.
The beneficial effects of the technical scheme are as follows: the terminal identity label is added to the obtained dynamic guide map, and vehicle terminals corresponding to different dynamic guide maps are determined according to the addition result, so that the dynamic guide map is accurately issued to the corresponding vehicle terminal, route reminding is carried out, and the accuracy and reliability of traffic flow optimization are ensured.
Example 10:
the embodiment provides an intelligent traffic flow optimizing system based on the internet of vehicles, as shown in fig. 3, including:
the feature determining module is used for acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters and determining traffic running features in the target road and driving behavior features of different vehicles;
The track determining module is used for constructing a vehicle coordination control model based on vehicle running requirements, analyzing road topological structures, traffic flow running characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free running track;
the route guiding module is used for generating dynamic guiding diagrams of different vehicles based on the collision-free running tracks of the multiple vehicles, and issuing the dynamic guiding diagrams to corresponding vehicle terminals for carrying out running route reminding based on the Internet of vehicles.
The beneficial effects of the technical scheme are as follows: the road topology structure of the target road and the running state parameters of different vehicles on the target road are analyzed through the Internet of vehicles, the accurate and effective determination of the traffic flow running characteristics on the target road and the driving behavior characteristics of different vehicles is realized, convenience and guarantee are provided for traffic flow optimization, secondly, the obtained road topology structure, the traffic flow running characteristics and the driving behavior characteristics of each vehicle are analyzed through the constructed vehicle coordination control model, the determination of the collision-free running tracks of multiple vehicles is realized, the orderly progress of the multiple vehicles on the target road is ensured, finally, a dynamic guiding graph is generated according to the collision-free running tracks of the multiple vehicles, the dynamic guiding graph is issued to corresponding vehicle terminals, the accuracy and reliability of traffic flow optimization on the target road are ensured, the running efficiency of the vehicles on the target road is also improved, and the traffic pressure is relieved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. An intelligent traffic flow optimizing method based on the internet of vehicles is characterized by comprising the following steps:
step 1: acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters, and determining traffic running characteristics in the target road and driving behavior characteristics of different vehicles;
step 2: constructing a vehicle coordination control model based on vehicle operation requirements, analyzing road topology structures, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free operation track;
step 3: dynamic guide graphs of different vehicles are generated based on the collision-free running tracks of multiple vehicles, and the dynamic guide graphs are issued to corresponding vehicle terminals based on the Internet of vehicles to remind running routes.
2. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 1, obtaining a road topology of a target road based on the internet of vehicles comprises:
acquiring road driving videos of different vehicles on a target road according to the Internet of vehicles based on the data management platform, and dividing the road driving videos of different vehicles to obtain a static frame image set of the road driving videos;
extracting image characteristics in each static frame image in the static frame image set, and de-duplicating the static frame image based on the image characteristics to obtain a standard static frame image set;
determining adjacent standard static frame images based on the image features, determining overlapping areas of the adjacent standard static frame images based on the image features, and splicing the adjacent standard static frame images by taking the overlapping areas as image splicing boundaries;
and obtaining a complete road image of the target road based on the splicing result, and determining the road topology structure of the target road based on the complete road image.
3. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 2, wherein determining the road topology of the target road based on the complete road image comprises:
Acquiring an obtained complete road image of a target road, extracting a target road main body in the complete road image, performing characteristic traversal on the target road main body, and determining a main road and a branch corresponding to the target road;
marking bifurcation points of a main road and a branch road in a target road in a complete road image based on the traversing result, and obtaining a first road topological structure of the target road based on the marking result;
meanwhile, obtaining road basic parameters of a main road and a branch road in a target road based on a standard result, obtaining the number of lanes and the number of curves corresponding to the main road and the branch road in the target road based on the road basic parameters, and obtaining a second road topology structure of the target road based on the number of lanes and the number of curves;
and summarizing the first road topological structure and the second road topological structure, and obtaining the final road topological structure of the target road based on the summarizing result.
4. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 1, obtaining the road topology of the target road and the vehicle running state parameters in the target road based on the internet of vehicles comprises:
Acquiring configuration information of a data management platform, and distributing a communication port to a vehicle-mounted terminal in a vehicle in the data management platform based on the configuration information;
constructing a wireless communication link between a vehicle-mounted terminal of a vehicle in a target road and a data management platform based on a communication port, transmitting vehicle networking data of the vehicle to the data management platform based on the wireless communication link, monitoring data communication characteristics of the communication port in unit time in real time, determining the uploading amount of the vehicle networking data in unit time based on the data communication characteristics, expanding the communication port when the uploading amount of the vehicle networking data in unit time is greater than a preset threshold value, and receiving the vehicle networking data of the vehicle in real time based on an expansion result;
analyzing the acquired internet of vehicles data based on the data management platform to obtain a data source corresponding to the internet of vehicles data, marking the internet of vehicles data based on an identity tag of the data source, and obtaining vehicle running state parameters in a target road based on a marking result.
5. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 1, the vehicle running state parameters are analyzed to determine the running characteristics of the traffic flow in the target road and the driving behavior characteristics of different vehicles, and the method comprises the following steps:
Acquiring a preset reference point in a target road, analyzing the acquired vehicle running state parameters, and carrying out feature screening on analysis results of the vehicle running state parameters based on attribute information of the preset reference point to acquire target analysis results related to the preset reference point;
determining the number of vehicles passing through a preset reference point in unit time and the running speed of each vehicle based on the target analysis result, and determining the average speed of the vehicles on a target road based on the number of vehicles and the running speed of each vehicle;
the method comprises the steps of obtaining the influence coefficient of road conditions on traffic flow, respectively determining the number of vehicles, the average speed and the influence weight of the influence coefficient of the road conditions on the traffic flow operation characteristics, and comprehensively analyzing the number of vehicles, the average speed and the influence coefficient of the road conditions on the traffic flow based on the influence weight to obtain the traffic flow operation characteristics in a target road.
6. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 1, the vehicle running state parameters are analyzed to determine the running characteristics of the traffic flow in the target road and the driving behavior characteristics of different vehicles, and the method comprises the following steps:
The method comprises the steps of obtaining vehicle running state parameters, analyzing the vehicle running state parameters, determining global running data of different vehicles on a target road, and determining the speed increasing times, speed increasing points, lane changing frequency and speed change characteristics of the different vehicles on the target road based on the global running data;
and obtaining driving behavior characteristics of different vehicles based on the speed increasing times, the speed increasing points, the lane changing frequency and the speed change characteristics.
7. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 2, a vehicle coordination control model is constructed based on vehicle operation requirements, and road topology, traffic flow operation characteristics and driving behavior characteristics of each vehicle are analyzed based on the vehicle coordination control model, and a multi-vehicle collision-free operation track is determined, which comprises:
acquiring a vehicle running requirement, determining a safety index of the vehicle when the vehicle runs on a road based on the vehicle running requirement, and searching a preset database in a server based on the safety index to obtain historical vehicle running data of the vehicle on the road;
analyzing historical vehicle driving data, determining a driving event of a vehicle on a road, determining a corresponding reference driving parameter when the vehicle safely runs on the road based on the driving event, and training a preset convolutional neural network based on the reference driving parameter and a reference road traffic rule to obtain a vehicle coordination control model;
Analyzing a road topology structure, traffic flow operation characteristics and driving behavior characteristics of each vehicle based on a vehicle coordination control model to obtain a speed-position-time relation of each vehicle on a target road, and performing simulation on driving states of different vehicles based on a preset simulation model according to the speed-position-time relation of each vehicle to obtain driving states corresponding to the different vehicles at the current moment, wherein the driving states comprise a driving speed, a driving position, a following distance with a preceding vehicle, a lane changing position and a lane changing time point;
dynamically tracking the driving states of the corresponding vehicles after the target time period based on the driving states of the different vehicles corresponding to the current moment, and obtaining the target driving states of the different vehicles corresponding to the different moments based on dynamic tracking results;
and summarizing the corresponding target driving states of different vehicles at different moments to obtain the collision-free running tracks of multiple vehicles of each vehicle on the target road.
8. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 3, a dynamic guidance map for different vehicles is generated based on a collision-free running track of a plurality of vehicles, comprising:
The method comprises the steps of obtaining a plurality of conflict-free running tracks of vehicles, dividing the obtained conflict-free running tracks of the vehicles to obtain target running tracks of different vehicles on a target road, and obtaining a basic guide map based on the target running tracks;
determining the running direction of the vehicle on the target running track, calling a direction guide icon from a guide icon library based on the running direction, and carrying out first dynamic display on the direction guide icon on a basic guide map;
determining driving state change points of the vehicle on the target running track and driving characteristics corresponding to each driving state change point, retrieving driving state change guide icons from a guide icon library based on the driving characteristics, and carrying out second dynamic display on the driving state change points of the driving state change guide icons on the basic guide diagram;
and summarizing the first dynamic display and the second dynamic display on the basic guide graph, and obtaining the dynamic guide graphs of different vehicles based on the summarizing result.
9. The intelligent traffic flow optimizing method based on the internet of vehicles according to claim 1, wherein in step 3, the dynamic guiding graph is issued to the corresponding vehicle terminal for operation route reminding based on the internet of vehicles, and the method comprises the following steps:
Acquiring the obtained dynamic guide graph, and adding a terminal identity label to the corresponding dynamic guide graph based on the guide characteristics;
distributing the dynamic guide graph to the corresponding vehicle terminal according to the terminal identity tag and the Internet of vehicles based on the adding result, and performing format conversion on the received dynamic guide graph based on the vehicle terminal;
displaying the dynamic guide map after format conversion on a vehicle terminal, sending a running route prompt to a vehicle driver based on a display result, and updating the working state of the dynamic guide map on the vehicle terminal in real time based on the prompt result and the running state of the vehicle.
10. An intelligent traffic flow optimizing system based on the internet of vehicles is characterized by comprising:
the feature determining module is used for acquiring a road topology structure of a target road and vehicle running state parameters in the target road based on the Internet of vehicles, analyzing the vehicle running state parameters and determining traffic running features in the target road and driving behavior features of different vehicles;
the track determining module is used for constructing a vehicle coordination control model based on vehicle running requirements, analyzing road topological structures, traffic flow running characteristics and driving behavior characteristics of each vehicle based on the vehicle coordination control model, and determining a multi-vehicle collision-free running track;
The route guiding module is used for generating dynamic guiding diagrams of different vehicles based on the collision-free running tracks of the multiple vehicles, and issuing the dynamic guiding diagrams to corresponding vehicle terminals for carrying out running route reminding based on the Internet of vehicles.
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