CN115966095A - Traffic data fusion processing method, device, equipment and medium based on vehicle - Google Patents
Traffic data fusion processing method, device, equipment and medium based on vehicle Download PDFInfo
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Abstract
The embodiment of the specification discloses a traffic data fusion processing method, a device, equipment and a medium based on vehicles, wherein the method comprises the following steps: acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of the vehicle; determining grids corresponding to each traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment; and performing vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid. In the embodiment, the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment, then the grids corresponding to each traffic data are determined, and then the non-homologous data between each grid and the grid adjacent to the grid are subjected to fusion processing, so that the calculation amount of data processing is reduced, and the data processing efficiency is improved.
Description
Technical Field
The application relates to the technical field of internet automatic driving, in particular to a traffic data fusion processing method, device, equipment and medium based on vehicles.
Background
In the internet automatic driving field, a Roadside Computing Unit (RCU) can send vehicle data to a cloud end, specifically, the Roadside Computing Unit (RCU) senses the vehicle data of vehicles on the road through a camera, a laser radar, a millimeter wave radar and other devices, uploads the vehicle data to the cloud end, and the cloud end generates an instruction for assisting the vehicle to complete automatic driving according to the vehicle data.
In real life, a plurality of roadside computing units of two adjacent roadside computing units or road intersections can have overlapped perception areas, a plurality of roadside computing units can perceive a vehicle, and then a plurality of vehicle data of the vehicle are uploaded to the cloud, and the plurality of vehicle data can not be completely consistent due to differences of positioning accuracy, information uploading time and the like, so that the cloud needs to process the vehicle data to judge which vehicle data belong to the vehicle data of the same vehicle.
The conventional vehicle data processing method is suitable for the condition of less vehicle data, and when the vehicle data is more, the calculation amount of the conventional data processing method is multiplied, and the data processing efficiency is seriously reduced.
Disclosure of Invention
The embodiment of the specification provides a traffic data fusion processing method, a traffic data fusion processing device, traffic data fusion processing equipment and a traffic data fusion processing medium based on vehicles, and aims to solve the problem of low processing efficiency of existing traffic data.
In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:
an embodiment of the present specification provides a traffic data fusion processing method based on a vehicle, including:
acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of a vehicle;
determining grids corresponding to the traffic data according to the position information of the vehicles; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
Optionally, the data source acquisition device is located in a preset geographic area; the grid is divided in the following way:
making a rectangle containing the preset geographic area based on the position information of the preset geographic area;
and dividing the rectangle into a plurality of grids according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment.
Optionally, the determining the grids corresponding to the traffic data according to the position information of the vehicle specifically includes:
determining the number of rows and the number of columns of the grids in the preset geographic area according to the size of the grids;
calculating the grid line number and the grid column number of each piece of traffic data in the grids by using an algorithm with time complexity O (1) according to the position information of the vehicle, the position information of the preset geographic area and the line number and column number of the grids in the preset geographic area;
and determining grids corresponding to the traffic data based on the grid line number and the grid column number of the traffic data in the grids.
Optionally, before the step of performing the vehicle-based fusion process on the non-homologous traffic data between the traffic data corresponding to each of the grids and the traffic data corresponding to the grids adjacent to the grid, the method further includes:
and performing vehicle-based fusion processing on the non-homologous traffic data in the traffic data corresponding to each grid.
Optionally, the data source collecting device specifically includes: a roadside calculation unit;
or, the data source collecting device specifically includes: a road side calculation unit and a road side unit;
or, the data source collecting device specifically includes: the system comprises a road side computing unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: a road side unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: the system comprises a road side computing unit, a road side unit and an on-board terminal.
Optionally, the data source collecting device collects the position information of the vehicle with a collecting error of 5 meters.
Optionally, the traffic data further comprises at least one of heading and speed of the vehicle.
An embodiment of the present specification provides a traffic data fusion processing apparatus based on a vehicle, including:
the data acquisition module is used for acquiring a plurality of traffic data acquired by the data source acquisition equipment; the traffic data includes location information of a vehicle;
the grid determining module is used for determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and the fusion processing module is used for carrying out vehicle-based fusion processing on the non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
An embodiment of the present specification provides a traffic data fusion processing device based on a vehicle, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of a vehicle;
determining grids corresponding to the traffic data according to the position information of the vehicles; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
Embodiments of the present specification provide a computer-readable medium, on which computer-readable instructions are stored, where the computer-readable instructions are executable by a processor to implement a traffic data fusion processing method described above.
One embodiment of the present description achieves the following advantageous effects: the grids are divided according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment in advance, then the grids corresponding to each traffic data are determined, and then the non-homologous data between each grid and the grid adjacent to the grid are subjected to fusion processing, so that a plurality of traffic data acquired by the data source acquisition equipment can be fused, the calculation amount of data processing is reduced, and the data processing efficiency is improved.
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In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a traffic data fusion processing method based on vehicles according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of a traffic data fusion processing device based on a vehicle according to an embodiment of the present disclosure;
fig. 3 is an architecture diagram of a vehicle-based traffic data fusion processing device according to an embodiment of the present disclosure;
fig. 4 is a structural diagram of a vehicle-based traffic data fusion processing device according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of one or more embodiments of the present disclosure more apparent, the technical solutions of one or more embodiments of the present disclosure will be clearly and completely described below with reference to specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of one or more embodiments in the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
In the prior art, a plurality of roadside computing units of two adjacent roadside computing units or road intersections have overlapped perception regions, a plurality of roadside computing units all perceive a certain vehicle, and then upload a plurality of vehicle data of the vehicle to the condition at high in the clouds, and these a plurality of vehicle data can be because difference such as positioning accuracy, information upload time, can not be completely consistent, consequently, the high in the clouds need be handled vehicle data to judge which vehicle data belong to the vehicle data of same vehicle.
When a single Roadside Computing Unit (RCU) performs data fusion, the collection time of traffic data collected by the Roadside Computing Unit is predicted to an appointed timestamp, for example, the collection time of the traffic data collected by a camera and a laser radar on the single Roadside Computing Unit is predicted, then the distance between vehicles under different collection sources (the Roadside Computing Unit generally comprises the camera, the laser radar, a millimeter wave radar and other devices) is calculated pairwise, and the vehicle deduplication is performed by using Hungary matching calculation cost to obtain the traffic data after deduplication processing.
Assuming that the vehicles corresponding to the traffic data collected by the camera are a, b and c, the vehicles corresponding to the traffic data collected by the laser radar are o, p and q, and the distances between every two vehicles are as follows:
| a | b | c | |
| o | 1.0 | 5.2 | 8.1 |
| p | 5.1 | 0.9 | 4.2 |
| q | 8.2 | 4.3 | 1.2 |
through the Hungarian algorithm, the minimum matching cost of the vehicle a and the vehicle o, the vehicle b and the vehicle p and the vehicle c and the vehicle q can be calculated to be 1.0, 0.9 and 1.2 respectively, so that the vehicle a and the vehicle o are the same vehicle, the vehicle b and the vehicle p are the same vehicle, and the vehicle c and the vehicle q are the same vehicle.
If the data fusion method is used, when a plurality of data collected by a plurality of road side calculation units are fused, the data processing efficiency is seriously reduced due to the increase of the data quantity. For example, 10000 vehicles travel on the road if there are 100 roadside computing units, and each roadside computing unit acquires traffic data of 100 vehicles. According to the previous algorithm, the traffic data of 100 vehicles collected by each roadside computing unit needs to be subjected to distance calculation with the traffic data of 9900 vehicles collected by the other roadside computing units, namely 99 roadside computing units, wherein the calculation amount of each roadside computing unit is 100 × 9900=990000, and the calculation amount of 100 roadside computing units is 990000 100=99000000. It can be seen that the data processing efficiency is extremely low.
In order to solve the defects in the prior art, the scheme provides the following embodiments:
in the embodiment of the invention, from the viewpoint of a program, an execution main body of the flow may be a program loaded on a server or a cloud. Fig. 1 is a flowchart of a traffic data fusion processing method based on a vehicle according to an embodiment of the present disclosure, and as shown in fig. 1, the traffic data fusion processing method based on a vehicle may include the following steps:
step 110: acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of the vehicle.
In step 110, the data source collecting device may be a road side computing unit, and the road side computing unit sends the collected multiple pieces of traffic data to the cloud, so that the cloud obtains the multiple pieces of traffic data. Wherein the traffic data further comprises at least one of heading and speed of the vehicle.
Step 120: determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment.
In step 120, the preset geographical area may be previously gridded. The preset geographic area is an area where the method is expected to be implemented, such as a county, a city, and the like, and can also be understood as a service range of the method, and the data source acquisition device is located in the preset geographic area.
Further, the preset geographical area may be gridded in the following manner:
making a rectangle containing the preset geographic area based on the position information of the preset geographic area;
and dividing the rectangle into a plurality of grids according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment.
For example, a rectangular coordinate system can be made according to a plan view of a preset geographic area, a minimum abscissa minX, a maximum abscissa maxX, a minimum ordinate minY, and a maximum ordinate minY of the plan view of the preset geographic area in the rectangular coordinate system are obtained, a rectangle containing the preset geographic area is made according to a point (minX, minY), a point (minX, maxY), a point (maxX, minY), and a point (maxX, maxY), and coordinates of four vertexes of the rectangle are (minX, minY), (minX, maxY), (maxX, minY), and (maxX, maxY), respectively.
In an embodiment, the minimum bounding rectangle of the preset geographic area may also be made based on the position information of the preset geographic area, so as to obtain a rectangle containing the preset geographic area.
After a rectangle containing a preset geographic area is made, the rectangle is divided into a plurality of grids according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment.
In the present embodiment, the acquisition error is 5 meters, and each grid may be a square with a side length of 5 meters.
After the grids are divided, the vehicles corresponding to each traffic data need to be positioned in each grid, that is, the grids corresponding to each traffic data are determined according to the position information of the vehicles, which specifically includes:
and determining the number of rows and the number of columns of the grids in the preset geographic area according to the size of the grids.
And calculating the grid line number and the grid column number of each traffic data in the grids by using an algorithm with time complexity O (1) according to the position information of the vehicle, the position information of the preset geographic area and the line number and column number of the grids in the preset geographic area.
And determining grids corresponding to the traffic data based on the grid line number and the grid column number of the traffic data in the grids.
The number of rows and columns of a plurality of grids in a preset geographic area is first determined. The side length of the grid, for example, 5 m, is converted into longitude and latitude to obtain longitude difference and latitude difference expressed by 5 m. And calculating the row number and the column number of the grids in the preset geographic area according to the longitude difference and the latitude difference represented by the side length of each grid and the longitude and the latitude of the preset geographic area.
And then positioning the vehicle corresponding to each traffic data into each grid. The present embodiment employs an algorithm with a time complexity of O (1), which is a function that qualitatively describes the running time of the algorithm, which is a function of the length of the string representing the input value of the algorithm. The time complexity O (1) is the lowest space-time complexity, i.e. the consumption of time resources or space resources is independent of the size of the input data, and is not changed no matter how many times the input data is increased. The hash algorithm is typical of O (1) time complexity, and no matter how large the data size is, the target can be found after one calculation (no consideration is given to collision). The calculation steps may be:
calculating the number of lines of the traffic data: row = (vehicle longitude-X)/longitude difference
Calculating the number of columns of the traffic data: col = (vehicle latitude-Y)/latitude difference
And calculating the grid where the traffic data is located: grid = row colNums + col
row is the row number of the grid where the vehicle corresponding to the traffic data is located, the vehicle longitude is the longitude in the position information of the vehicle, X is the longitude represented by the minimum horizontal coordinate of the plan view of the preset geographic area in the rectangular coordinate system, the longitude difference is the longitude difference represented by the length of the grid, col is the column number of the grid where the vehicle corresponding to the traffic data is located, the vehicle latitude is the latitude in the position information of the vehicle, Y is the latitude represented by the minimum vertical coordinate of the plan view of the preset geographic area in the rectangular coordinate system, the latitude difference is the latitude difference represented by the width of the grid, grid is the grid where the vehicle corresponding to the traffic data is located, and colNums is the column number of the multiple grids in the preset geographic area.
Step 130: and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
In step 130, for each grid where the vehicle corresponding to the traffic data is located, the traffic data corresponding to the grid and the traffic data corresponding to the grid adjacent to the grid, and the non-homologous traffic data between the two traffic data, are subjected to vehicle-based fusion processing.
The non-homologous traffic data in this embodiment represents traffic data acquired by different data source acquisition devices. When the data source acquisition device specifically comprises the roadside computing units, the traffic data acquired by different roadside computing units are non-homogeneous traffic data.
When the fusion processing based on the vehicles is carried out on the non-homologous traffic data, the fusion processing can be carried out according to the position information, the course or the speed of the vehicles in the traffic data. For example, fusion processing is performed according to the position information of the vehicle, the position information of the vehicle in the grid is matched with the position information of the vehicle in the grid adjacent to the grid aiming at a certain grid where the vehicle corresponding to the traffic data is located, if the matching is successful, the vehicle in the grid and the vehicle in the grid adjacent to the grid are the same vehicle, the traffic data corresponding to the vehicle in the two grids are fused, and if the matching is unsuccessful, the vehicle in the grid and the vehicle in the grid adjacent to the grid are not the same vehicle, and the data fusion is not performed.
In one embodiment, to ensure the accuracy of the traffic data fusion process, after step 120 and before step 130, the vehicle-based traffic data fusion processing method further comprises:
and performing vehicle-based fusion processing on the non-homologous traffic data in the traffic data corresponding to each grid.
This is because non-homologous traffic data may also exist within a certain mesh. For example, a plurality of roadside computing units are set at a certain intersection, vehicles corresponding to traffic data acquired by the roadside computing units may be in a grid, or an overlapping area exists between two roadside computing units, and if a vehicle exists in the overlapping area, two pieces of traffic data of the vehicle exist in the grid where the vehicle is located. In order to further improve the accuracy of the traffic data fusion process, a vehicle-based fusion process is performed on the non-homologous traffic data in the traffic data corresponding to each grid.
It is understood that, in order to improve the accuracy of the traffic data fusion process, the non-homologous traffic data in each mesh may also be subjected to the fusion process after step 130, that is, after step 130, the vehicle-based traffic data fusion process further includes:
and performing vehicle-based fusion processing on the non-homologous traffic data in the traffic data corresponding to each grid.
In addition, in real activities, the road side unit and the vehicle-mounted terminal can also upload traffic data of the vehicle to the cloud. The Road Side Unit (RSU) receives traffic data sent by a vehicle connected to the internet with the V2X function through a V2X (vehicle to outside information exchange) function, and uploads the received traffic data to the cloud. And the vehicle-mounted terminal (OnBoardUnit, OBU) uploads the traffic data of the vehicle to the cloud terminal through 4G/5G communication.
It can be seen that, for the same vehicle, the traffic data may also be uploaded to the cloud by other data source collecting devices. However, the existing data processing method generally processes a small amount of data uploaded by the roadside computing unit, and cannot process data uploaded by two or three data source acquisition devices among the roadside computing unit, the roadside unit and the vehicle-mounted terminal.
The traffic data fusion processing method based on the vehicle provided in this embodiment includes grid division of a preset geographic area where data source acquisition equipment is located, then determining grids corresponding to each traffic data according to position information of the vehicle, and finally performing vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to grids adjacent to the grids, where the traffic data acquired by different data source acquisition equipment, that is, traffic data uploaded by two or three data source acquisition equipment, that is, the traffic data fusion processing method provided in this embodiment, the data source acquisition equipment specifically includes: a roadside calculation unit;
or, the data source collecting device specifically includes: a road side calculation unit and a road side unit;
or, the data source collecting device specifically includes: the system comprises a road side computing unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: a road side unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: the system comprises a road side calculation unit, a road side unit and a vehicle-mounted terminal.
Based on the same idea, the embodiment can also process pedestrian data collected by different roadside computing units, namely, pedestrian data is included in a plurality of traffic data collected by the roadside computing units. Based on the method provided in this embodiment, the fusion processing based on the pedestrian data can be performed according to at least one of the position information, the speed information, and the direction information in the pedestrian data, which is not described herein again.
In this embodiment, the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition device (if the acquisition error is 5 meters, each grid may be a square with a side length of 5 meters), so that the grid where the vehicle corresponding to the acquired traffic data is located and the grid where the actual vehicle is located may differ by at most one grid, and for each grid where the vehicle corresponding to the traffic data is located, the fusion processing based on the vehicle is performed on the non-homologous traffic data between the grid and the traffic data corresponding to the grid adjacent to the grid, so that the acquisition error of the data source acquisition device may be ignored, and the traffic data based on the vehicle may be processed more accurately.
In addition, in the embodiment, for each grid where the vehicle corresponding to the traffic data is located, the fusion processing based on the vehicle is performed on the non-homologous traffic data between the grid and the traffic data corresponding to the grid adjacent to the grid.
In addition, the embodiment can also process the traffic data collected by at least two data source collecting devices in the road side calculating unit, the road side unit and the vehicle-mounted terminal, thereby realizing the technical effect of rapid and accurate processing of the traffic data and having wider application.
Based on the same idea, this embodiment further provides a traffic data fusion processing device based on vehicle, as shown in fig. 2, the traffic data fusion processing device based on vehicle includes:
a data obtaining module 210, configured to obtain a plurality of traffic data collected by a data source collecting device; the traffic data includes location information of the vehicle.
The grid determining module 220 is configured to determine a grid corresponding to each piece of traffic data according to the position information of the vehicle; the grids are divided in advance according to acquisition errors of the position information of the vehicle acquired by the data source acquisition equipment.
A fusion processing module 230, configured to perform vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to a grid adjacent to the grid.
Further, as shown in fig. 3, the fusion processing module 230 in this embodiment may include a plurality of fusion units, and the fusion units may process non-homologous traffic data between a certain mesh and traffic data corresponding to meshes adjacent to the mesh.
Specifically, if there is non-homogeneous traffic data between the traffic data corresponding to the mesh and the traffic data corresponding to the mesh adjacent to the mesh, the fusion processing module 230 may add the traffic data corresponding to the mesh adjacent to the mesh to the traffic data corresponding to the mesh as a fusion data packet. The fusion processing module 230 traverses each mesh containing traffic data to obtain a plurality of fusion data packets, and the fusion unit processes data in the plurality of fusion data packets.
To reduce data processing delay, multiple fusion units may process data in parallel. In addition, when the non-homologous traffic data needing to be processed is more, the plurality of fusion units can be deployed in a distributed processing process among a plurality of servers, so that the more non-homologous traffic data can be processed quickly. When the non-homologous traffic data needing to be processed is less, the multiple fusion units can also be deployed in multiple processing threads in one process, so that the effect of rapidly processing more non-homologous traffic data is realized while a small memory is occupied.
It is also understood that by expanding the number of fusion units, the amount of computation processing per fusion unit can be reduced, as well as the processing delay of the fusion unit.
And after the fusion processing is carried out on the data, the data is sent to a fusion data application service. The data application service is an application applying the data after the fusion processing, such as a scene calculation application and a traffic analysis application. The scene calculation application can enable the vehicle to complete an automatic driving function based on the data after the fusion processing, and the traffic analysis application can complete a cloud traffic management function based on the data after the fusion processing.
In one embodiment, as shown in fig. 3, the fusion processing module 230 further sends the traffic data that is not subjected to the fusion processing in the traffic data corresponding to each mesh to the fusion data application service, that is, sends the traffic data that does not require the fusion processing to the fusion data application service. The sending process may be asynchronous, i.e., does not affect the vehicle-based fusion process performed by the fusion processing module 230 on the traffic data.
In this embodiment, the frequency of the vehicle-based fusion processing performed by the fusion processing module 230 may also be set according to the frequency of the fused data application service usage data, if the frequency of the fused data application service usage data is higher, a higher data fusion frequency may be set to the fusion processing module 230, and if the frequency of the fused data application service usage data is lower, a lower data fusion frequency may be set to the fusion processing module 230. This frequency may be set to 10 hz in this embodiment, i.e., the fusion processing module 230 may perform the vehicle-based fusion processing every 100 ms.
Based on the same idea, the embodiment of the present specification further provides a device corresponding to the above method. Fig. 4 is a block diagram of a vehicle-based traffic data fusion processing device provided in an embodiment of the present disclosure, and as shown in fig. 4, the device 400 may include:
at least one processor 410; and the number of the first and second groups,
a memory 430 communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory 430 stores instructions 420 executable by the at least one processor 410 to enable the at least one processor 410 to:
acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of a vehicle;
determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
Based on the same idea, the embodiment of the present specification further provides a computer-readable medium corresponding to the above method. A computer readable medium has stored thereon computer readable instructions which, when executed by a processor, implement a method as in any one of the embodiments of the application.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the traffic data fusion processing device for vehicles shown in fig. 4, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the partial description of the method embodiment.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain a corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical blocks. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital symbol system is "integrated" onto a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually manufacturing an integrated Circuit chip, such programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abll (advanced desktop Expression Language), AHDL (alternate Hardware Description Language), traffic, CUPL (computer unified programming Language), HDCal, jhddl (java Description Language), lang, lola, HDL, las, software, rhyd (Hardware Description Language), and the like, which are currently used in the field-Hardware Language. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer readable medium that stores computer readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and embedded microcontrollers, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, atmel at91SAM, microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic of the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be regarded as a hardware component and the means for performing the various functions included therein may also be regarded as structures within the hardware component. Or even means for performing the functions may be conceived to be both a software module implementing the method and a structure within a hardware component.
The systems, apparatuses, modules or units described in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, respectively. Of course, the functionality of the various elements may be implemented in the same one or more pieces of software and/or hardware in the practice of the present application.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information and/or data which can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement or the like made within the spirit and principle of the present application shall be included in the scope of the claims of the present application.
Claims (10)
1. A traffic data fusion processing method based on vehicles is characterized by comprising the following steps:
acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of a vehicle;
determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
2. The traffic data fusion processing method according to claim 1, wherein the data source acquisition device is located within a preset geographic area; the grid is divided in the following way:
making a rectangle containing the preset geographic area based on the position information of the preset geographic area;
and dividing the rectangle into a plurality of grids according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment.
3. The traffic data fusion processing method according to claim 2, wherein the determining the grid corresponding to each piece of traffic data according to the position information of the vehicle specifically includes:
determining the number of rows and the number of columns of the grids in the preset geographic area according to the size of the grids;
calculating the grid line number and the grid column number of each piece of traffic data in the grids by using an algorithm with time complexity O (1) according to the position information of the vehicle, the position information of the preset geographic area and the line number and column number of the grids in the preset geographic area;
and determining grids corresponding to the traffic data based on the grid line number and the grid column number of the traffic data in the grids.
4. The traffic data fusion processing method of claim 1, wherein prior to the step of performing vehicle-based fusion processing for non-homologous traffic data between the traffic data corresponding to each of the meshes and traffic data corresponding to meshes adjacent to the meshes, the method further comprises:
and performing vehicle-based fusion processing on the non-homologous traffic data in the traffic data corresponding to each grid.
5. The traffic data fusion processing method according to claim 1, wherein the data source acquisition device specifically comprises: a roadside calculation unit;
or, the data source collecting device specifically includes: a roadside calculation unit and a roadside unit;
or, the data source collecting device specifically includes: the system comprises a road side calculation unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: a road side unit and a vehicle-mounted terminal;
or, the data source collecting device specifically includes: the system comprises a road side calculation unit, a road side unit and a vehicle-mounted terminal.
6. The traffic data fusion processing method according to claim 1, wherein an acquisition error of the data source acquisition device for acquiring the position information of the vehicle is 5 meters.
7. The traffic data fusion processing method of claim 1, wherein the traffic data further comprises at least one of a heading and a speed of a vehicle.
8. A vehicle-based traffic data fusion processing apparatus, comprising:
the data acquisition module is used for acquiring a plurality of traffic data acquired by the data source acquisition equipment; the traffic data includes location information of a vehicle;
the grid determining module is used for determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and the fusion processing module is used for carrying out vehicle-based fusion processing on the non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
9. A vehicle-based traffic data fusion processing device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory stores instructions executable by the at least one processor to cause the at least one processor to:
acquiring a plurality of traffic data acquired by data source acquisition equipment; the traffic data includes location information of a vehicle;
determining grids corresponding to the traffic data according to the position information of the vehicle; the grids are divided in advance according to the acquisition error of the position information of the vehicle acquired by the data source acquisition equipment;
and carrying out vehicle-based fusion processing on non-homologous traffic data between the traffic data corresponding to each grid and the traffic data corresponding to the grid adjacent to the grid.
10. A computer readable medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to implement the traffic data fusion processing method of any one of claims 1 to 7.
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