CN113256984A - Multi-data-source data fusion processing method and device based on vehicle-road cooperation - Google Patents
Multi-data-source data fusion processing method and device based on vehicle-road cooperation Download PDFInfo
- Publication number
- CN113256984A CN113256984A CN202110709339.9A CN202110709339A CN113256984A CN 113256984 A CN113256984 A CN 113256984A CN 202110709339 A CN202110709339 A CN 202110709339A CN 113256984 A CN113256984 A CN 113256984A
- Authority
- CN
- China
- Prior art keywords
- data
- vehicle
- position data
- characteristic information
- collected
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
Abstract
The application relates to a data fusion processing method and device based on vehicle-road cooperation and used for multiple data sources. The method comprises the following steps: receiving vehicle data collected from multiple data sources; extracting position data in each vehicle data, and dividing a plurality of position data into a plurality of initial position data sets; aiming at any initial position data set, obtaining vehicle characteristic information corresponding to each position data in the initial position data set; comparing the vehicle characteristic information, and fusing position data corresponding to the same vehicle characteristic information according to a comparison result to obtain a first target position data set; and comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in the second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained. The scheme can effectively, comprehensively and accurately fuse the vehicle information of the same vehicle reported by a plurality of data sources.
Description
Technical Field
The application relates to the technical field of vehicle networking, in particular to a data fusion processing method and device based on vehicle-road cooperation and used for multiple data sources.
Background
The cloud control basic platform consists of an urban cloud and a central cloud to form a logically-collaborative and physically-dispersed cloud computing center. The cloud control basic platform takes real-time dynamic data such as self-vehicle collection, road information, road infrastructure collection and environment as a core, combines the data of existing traffic related systems and facilities supporting cloud control application, and provides standardized common basic service for the intelligent vehicle road cloud.
In a scenario of vehicle-road coordination, the cloud server receives vehicle data from a plurality of data sources, for example, for any vehicle a, the data received by the cloud server about the vehicle a includes: the data of the vehicle a reported by the vehicle itself, the data of the vehicle a collected by the vehicle through the ADAS system (such as a camera, a laser radar, etc.), and the data of the vehicle a collected by the road side equipment (such as multiple cameras on the road side) (if the vehicle a is in a range visible by all the multiple cameras, there are multiple data of the vehicle a). Therefore, the cloud server receives the vehicle information from the multiple data sources, and how to effectively fuse the vehicle information of the same vehicle reported by the multiple data sources is a technical problem to be solved in the field.
Disclosure of Invention
In order to solve the problems in the related art, the application provides a data fusion processing method and device of multiple data sources based on vehicle-road cooperation.
The first aspect of the present application provides a data fusion processing method for multiple data sources based on vehicle-road coordination, where the method includes:
receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle;
extracting position data in each vehicle data, and dividing the extracted plurality of position data into a plurality of initial position data sets;
aiming at any initial position data set, obtaining vehicle characteristic information corresponding to each position data in the initial position data set;
comparing the vehicle characteristic information, and fusing position data corresponding to the same vehicle characteristic information according to a comparison result to obtain a first target position data set; discarding position data corresponding to vehicle characteristic information different from the vehicle characteristic information in the first target position data set;
comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in any second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained;
wherein the vehicle corresponding to the position data in the second set of target position data is not the same vehicle as the vehicle corresponding to the position data in the first set of target position data.
The second aspect of the present application provides a data fusion processing apparatus based on multiple data sources in vehicle-road cooperation, the apparatus including:
the receiving module is used for receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle;
the extraction module is used for extracting position data in each piece of vehicle data;
a grouping module for dividing the extracted plurality of position data into a plurality of initial position data sets;
the system comprises an acquisition module, a comparison module and a comparison module, wherein the acquisition module is used for acquiring vehicle characteristic information corresponding to each position data in an initial position data set and comparing the vehicle characteristic information aiming at any initial position data set;
the first fusion module is used for fusing position data corresponding to the same vehicle characteristic information according to the comparison result to obtain a first target position data set; and discarding the corresponding location data for vehicle characteristic information different from the vehicle characteristic information in the first set of target location data;
the second fusion module is used for comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in any second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained; wherein the vehicle corresponding to the position data in the second set of target position data is not the same vehicle as the vehicle corresponding to the position data in the first set of target position data.
A third aspect of the present application provides an electronic device comprising:
a processor; and
a memory having executable code stored thereon, which when executed by the processor, causes the processor to perform the method as described above.
A fourth aspect of the present application provides a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform a method as described above.
The technical scheme provided by the application can comprise the following beneficial effects: according to the scheme, vehicle position data are divided into a plurality of initial position data sets according to a certain mode, and then further screening and fusion are carried out on any initial position data set through vehicle characteristic information identification to obtain a first target position data set. The method can effectively solve the problem that the positioning of multiple data sources on the same vehicle has relative deviation, thereby accurately identifying which vehicle position data uploaded by the multiple data sources are of the same vehicle, and further fusing the data of the same vehicle uploaded by the multiple data sources together. The purpose of effectively fusing the vehicle information of the same vehicle reported by a plurality of data sources is achieved.
The method and the device can also compare the discarded position data in the fusion process with other second target position data sets, and fuse the discarded position data into the corresponding second target position data sets after the consistent vehicle characteristic information is obtained, so that the condition that the discarded data cannot be utilized in the data fusion process can be further avoided, and the vehicle data of each vehicle can be comprehensively and accurately fused.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The foregoing and other objects, features and advantages of the application will be apparent from the following more particular descriptions of exemplary embodiments of the application, as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the application.
Fig. 1 is a schematic flowchart of a data fusion processing method based on multiple data sources of vehicle-road coordination according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data fusion processing device based on multiple data sources of vehicle-road coordination according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.
Detailed Description
Preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another.
In view of the above problems, embodiments of the present application provide a data fusion processing method for multiple data sources based on vehicle-road coordination, which can effectively fuse vehicle information of the same vehicle reported by multiple data sources. Specifically, as mentioned in the background art, in a scenario of vehicle-road cooperation, the cloud server receives vehicle data of multiple data sources, including vehicle data reported by a vehicle, ADAS data uploaded by the vehicle via an ADAS (Advanced Driving Assistance System), and road configuration data reported by road-side devices. For any current vehicle, the vehicle data is vehicle data uploaded by the current vehicle, the ADAS data is vehicle data uploaded by other vehicles and the road setting data is vehicle data uploaded by road side equipment. For example, the vehicle reports the position data of the vehicle's GPS/RTK (Real-time kinematic); position data of the vehicle predicted by the roadside monitoring device; the vehicle can calculate the position information of other vehicles by itself and upload the position information to the cloud end, and can also send the obtained data of other vehicles to the cloud end, so that the position data of the vehicle can be calculated by the cloud end. Therefore, the cloud end can receive the position data uploaded by the plurality of data sources, and therefore, which position information represents the same vehicle in the position data needs to be identified, and the purpose of effectively fusing the vehicle information of the same vehicle reported by the plurality of data sources is achieved.
The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a data fusion processing method based on multiple data sources of vehicle-road coordination according to an embodiment of the present application.
Referring to fig. 1, the method provided by the present application includes:
s110: receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle.
In this step, the multiple data sources mainly include own vehicle data acquired by the vehicle, ADAS data acquired by the vehicle through ADAS, and road layout data acquired by the road side device. It can be understood that, in a scenario of vehicle-road coordination, the cloud may receive vehicle data for each source, for example, for any vehicle road, if there is no roadside device currently, the cloud may only receive own vehicle data of the vehicle and ADAS data of the vehicle acquired by another vehicle through ADAS; if no other vehicle exists, the cloud end can only receive the own vehicle data of the vehicle and the road setting data of the vehicle, which is acquired by the road side equipment. If there are data sources of other terminals besides the data from the three terminals, the cloud terminal may also receive the data from the other terminals, which do not limit the present application. For convenience of explanation, the multi-source data in this embodiment is exemplified by data from three sources, i.e., the vehicle data, the ADAS data, and the road layout data.
In this step, the vehicle data includes, but is not limited to, operation data of the vehicle (such as data of vehicle acceleration, brake, gear, steering wheel angle, steering wheel speed, and the like), vehicle usage data (such as data of driving mileage, driving time, driving road section, and the like), vehicle performance data (such as data of remaining oil/electricity, oil/electricity consumption, and the like), and environmental data of the current vehicle (such as data of weather, road type, road condition, congestion condition, relative position with other vehicles, relative distance, relative speed, and the like). Of these vehicle data, the position data of the vehicle may be represented as latitude and longitude data of the vehicle.
It will be appreciated by those skilled in the art that the collection of data regarding the vehicle may utilize various types of vehicle mounted sensors, such as ultrasonic sensors, millimeter wave radar, lidar, vision sensors, etc., as well as various types of road side mounted sensors. The embodiment does not limit the acquisition mode of the multi-source data.
In a specific example, for example, when vehicle data uploaded by a vehicle end (vehicle data acquired by various sensors installed on the vehicle and vehicle data of other vehicles) and a road side (vehicle data acquired by various sensors installed on the road side) are received at a cloud end, a delay check operation is usually performed first, and when the vehicle data meet a specified real-time uploading requirement (three-end uploading rule definition can be made before uploading the vehicle data, and the vehicle data are periodically and synchronously pushed to the vehicle end and the road side by a cloud rule service, for example, data are uploaded every 200ms, and the delay time of data received by the cloud end does not exceed 100 ms), a Redis Geo algorithm storage operation is performed on the current vehicle data, such as: geo name is "14-bit prefix identification _ type (vehicle type) _ time of longitude and latitude morton code", and then vehicle detail data is set by using name as name, such as list < name, vehicle detail list > for storage.
S120: position data in each vehicle data is extracted, and the extracted plurality of position data is divided into a plurality of initial position data sets.
In this step, the position data is latitude and longitude data of the vehicle. In this way, the distances between the longitude and latitude data are compared for the plurality of extracted position data, and the position data having a distance smaller than the set distance is divided into the same initial position data set. In a specific embodiment, as in the above example, the vehicle data is stored in the first 14 bits of the longitude and latitude morton code, that is, after the vehicle data is received, the method of the present application further includes converting the longitude and latitude data in the vehicle data into the morton code. When the position data is divided into a plurality of initial position data sets, the morton codes of the vehicle position data can be extracted, and the morton codes with different set distances are divided into the same initial position data set, so that the distance between the morton codes contained in each initial position data set is smaller than the set distance. As an example, the set distance may be 1.5-1.7 meters, for example, 1.6 meters, so that the first 14 bits of each morton code may be compared, and then the first 14 bits of the morton codes with a difference of 1.6 meters are regarded as vehicle position data of the same vehicle, that is, the morton codes with a difference of 1.6 meters may be divided into an initial position data set.
S130: and acquiring vehicle characteristic information corresponding to each position data in any initial position data set.
In order to further make the vehicle position data in the obtained position data sets more accurate, i.e. to ensure that the vehicle position data in any one position data set represents the same vehicle, further screening may be performed on the basis of the initial position data set. That is, in the initial position data sets obtained by dividing the Morton code, although it is considered that the data in the sets represent the same vehicle, in order to avoid the error existing in the Morton code division, for example, because two vehicles on a road are close to each other, the difference between the front 14 bits of the Morton code of the two vehicles is 1.6 meters, the position data of the two vehicles are divided into the same set, therefore, after the initial position data sets are obtained, the vehicle feature information corresponding to each position data in the set can be obtained for any initial position data set, and the vehicle feature information includes one or more of the license plate number, the body color, the vehicle type and the vehicle sign of the vehicle.
S140: comparing the vehicle characteristic information, and fusing position data corresponding to the same vehicle characteristic information according to a comparison result to obtain a first target position data set; and discarding the position data corresponding to the vehicle characteristic information different from the vehicle characteristic information in the first target position data set.
Taking a license plate number as an example, in any initial position data set, if there are 5 data sources (for example, one data source uploaded by a self vehicle, two data sources uploaded by two other vehicles, and two data sources uploaded by two roadside devices), respectively obtaining license plate numbers corresponding to position data of the 5 data sources (for example, an image video of the position data can be shot by a camera, and then obtaining the license plate numbers based on the image video), then comparing the license plate numbers of the 5 data sources, if the first 4 license plate numbers are consistent, and the 5 th license plate number is different from the first 4 license plate numbers, then considering that the 5 th license plate number and the first 4 license plate numbers are not the position data of the same vehicle, at this time, fusing the position data with the first 4 license plate numbers to obtain a first target data set, and the 5 th license plate number is different from the license plate numbers in the first target data set, therefore, the position data corresponding to the 5 th license plate number is discarded.
In the above embodiment, if the license plate number cannot be identified when the vehicle feature information is acquired, different vehicles may be distinguished by identifying the vehicle body color, the vehicle type, the vehicle sign, and the like, so as to ensure that the vehicle position data in the finally obtained first target position data set all represent the same vehicle, and the discarded position data do not represent the vehicle. By the method, the problem that the positioning of multiple data sources on the same vehicle has relative deviation can be effectively solved, so that the vehicle position data uploaded by the multiple data sources are accurately identified to be of the same vehicle, and the data of the same vehicle uploaded by the multiple data sources can be fused together.
S150: comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in any second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained; and the vehicle corresponding to the position data in the second target position data set is not the same vehicle as the vehicle corresponding to the position data in the first target position data set.
For clarity, a plurality of target position data sets may be obtained according to the above steps S110 to S140, and the target position data set obtained for the current initial position data set is used as the first target position data set, and the target position data sets obtained for the other initial position data sets are unified into the second target position data set. That is, the vehicle corresponding to the position data in the second set of target position data is not the same vehicle as the vehicle corresponding to the position data in the first set of target position data. Thus, in this step, although the discarded position data does not belong to the vehicle represented by the position data in the first set of target position data, it may be the same vehicle as that represented by the position data in some second set of target position data. Continuing with the above example, after discarding the location data corresponding to the 5 th license plate number, the location data of the data source may be discarded to a common storage area, then the 5 th license plate number is compared with license plates in other second target location data sets, and if the 5 th license plate number is consistent with a license plate number in a certain second target location data set, it is described that the location data and the location data in the second target location data set represent the same vehicle, that is, the location data and the second target location data set may be fused. The situation that discarded data cannot be fused in the data fusion process can be further avoided through the step, and therefore vehicle data of each vehicle can be fused comprehensively and accurately. In this embodiment, the used vehicle features are consistent, that is, the license plate number or the body color, the vehicle type, and the like can be used.
It should be noted that, by fusing the position data corresponding to the same vehicle feature information, it can be understood that: since the initial position data set is divided according to the distance between the morton codes, therefore, most (or even all) of the vehicle characteristic information in the initial position data set should be consistent, for example, if there are 7 data sources in the initial position data set, the distance between morton codes of the 7 data sources is less than 1.6 meters, there is a large probability that the vehicles are the same, and a small probability that there is some error (i.e., there is data that is not the same vehicle), that small probability has a small number of data sources, that is, at most, the vehicle characteristic information of only one or two of the 7 data sources is inconsistent with the other vehicle characteristic information, and at this time, it can be judged that the two data sources and the other data sources are not the same vehicle, so that the two data sources can be excluded, and the remaining data sources can be fused. In other words, when the position data corresponding to the same vehicle feature information is merged, a plurality of data sources corresponding to the vehicle feature information in the initial position data set are merged, and a small number of data sources not corresponding to the vehicle feature information are discarded.
As a specific implementation manner, in the above steps S110 to S140, when vehicle data collected from multiple data sources is received, position data of the vehicle is extracted, and in this case, when data storage is performed, two data tables may be provided, that is, position data extracted from each vehicle data is stored in the first data table, vehicle data is stored in the second data table, and any position data in the first data table is associated with corresponding vehicle data in the second data table. In this way, only the position data in the first data table need be fused to obtain a first set of target position data, the position data in the first set of target position data representing the same vehicle. That is to say, the position data from multiple data sources for any vehicle are divided into the same first target data set, and when the vehicle data needs to be sent to the vehicle, only a certain position data in the target data set needs to be determined, the vehicle data corresponding to the position data in the second data table can be obtained through the position data, and then the obtained vehicle data can be sent to the vehicle. Meanwhile, the discarded position data can be compared with other second target data sets, and comprehensive and accurate fusion of the data is achieved.
To meet the above-mentioned, to arbitrary vehicle A, because the vehicle data that comes from different data sources can be received to the high in the clouds, also a plurality of vehicle data that vehicle A can be received to the high in the clouds, then issue this vehicle A with which vehicle data, the design of this application is as follows: after vehicle data collected from multiple data sources is received, sequencing the vehicle data into the following sequence: the vehicle data collected by high-precision equipment in the self-vehicle data, the vehicle data collected by high-precision equipment in the ADAS data, the vehicle data collected by high-precision equipment in the road setting data, the vehicle data collected by common equipment in the self-vehicle data, the vehicle data collected by common equipment in the ADAS data and the vehicle data collected by common equipment in the road setting data. In this way, when data fusion is performed, the vehicle data and the position data extracted from the vehicle data are sorted as described above, and the position data in the finally obtained first target position data set is also sorted as described above. At this time, when the vehicle data needs to be issued to the vehicle, the position data with the top ranking can be selected according to the ranking mode, and then the vehicle data corresponding to the position data is issued to the vehicle, so that the vehicle can obtain the optimal vehicle information.
Corresponding to the embodiment of the application function implementation method, the application also provides a data fusion processing device and electronic equipment based on multiple data sources of vehicle-road cooperation and a corresponding embodiment.
Fig. 2 is a schematic structural diagram of a data fusion processing device based on multiple data sources of vehicle-road coordination according to an embodiment of the present application.
Referring to fig. 2, the present application provides an apparatus comprising:
the receiving module 201, the receiving module 201 is used for receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle;
the extraction module 202, the extraction module 202 is used for extracting the position data in each vehicle data;
the grouping module 203, the grouping module 203 is used for dividing the extracted position data into a plurality of initial position data sets;
the obtaining module 204 is configured to, for any initial position data set, obtain vehicle characteristic information corresponding to each position data in the initial position data set, and compare the vehicle characteristic information;
the first fusion module 205 is configured to fuse, according to the comparison result, the position data corresponding to the same vehicle feature information to obtain a first target position data set; and discarding location data corresponding to vehicle characteristic information different from the vehicle characteristic information in the first set of target location data;
the second fusion module 206, the second fusion module 206 is configured to compare the vehicle feature information corresponding to the discarded position data with the vehicle feature information in any second target position data set, and after obtaining the vehicle feature information that is consistent in comparison, fuse the discarded position data into the corresponding second target position data set; and the vehicle corresponding to the position data in the second target position data set is not the same vehicle as the vehicle corresponding to the position data in the first target position data set.
Further, the position data is longitude and latitude data of the vehicle; the grouping module is specifically configured to: and comparing the distances among the longitude and latitude data of the extracted position data, and dividing the position data with the distance smaller than the set distance into the same initial position data set.
Further, the device further comprises a conversion module 207, wherein the conversion module 207 is used for converting the plurality of longitude and latitude data into corresponding morton codes respectively; and the grouping module 203 is specifically configured to: and comparing the distances among the morton codes, and dividing the morton codes with the set distance difference into the same initial position data set.
Further, the apparatus further includes a data storing unit 208, the data storing unit 208 includes a first data table 2081 and a second data table 2082; position data extracted from each vehicle data is stored in the first data table 2081, and vehicle data is stored in the second data table 2082; any position data in the first data table 2081 is associated with corresponding vehicle data in the second data table 2082.
Further, the multiple data sources comprise self-vehicle data acquired by the vehicle and ADAS data acquired by the vehicle through ADAS and/or road setting data acquired by road side equipment; the vehicle characteristic information comprises one or more of the license plate number, the body color, the vehicle type and the vehicle mark of the vehicle.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 3 is a schematic structural diagram of an electronic device shown in an embodiment of the present application.
Referring to fig. 3, the electronic device 1000 includes a memory 1010 and a processor 1020.
The Processor 1020 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 1010 may include various types of storage units, such as system memory, Read Only Memory (ROM), and permanent storage. Wherein the ROM may store static data or instructions that are needed by the processor 1020 or other modules of the computer. The persistent storage device may be a read-write storage device. The persistent storage may be a non-volatile storage device that does not lose stored instructions and data even after the computer is powered off. In some embodiments, the persistent storage device employs a mass storage device (e.g., magnetic or optical disk, flash memory) as the persistent storage device. In other embodiments, the permanent storage may be a removable storage device (e.g., floppy disk, optical drive). The system memory may be a read-write memory device or a volatile read-write memory device, such as a dynamic random access memory. The system memory may store instructions and data that some or all of the processors require at runtime. Further, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), magnetic and/or optical disks, among others. In some embodiments, memory 1010 may include a removable storage device that is readable and/or writable, such as a Compact Disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, dual layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, min SD card, Micro-SD card, etc.), a magnetic floppy disc, or the like. Computer-readable storage media do not contain carrier waves or transitory electronic signals transmitted by wireless or wired means.
The memory 1010 has stored thereon executable code that, when processed by the processor 1020, may cause the processor 1020 to perform some or all of the methods described above.
The present embodiments also provide a non-transitory machine-readable storage medium having stored thereon executable code, which when executed by a processor of an electronic device, causes the processor to perform the method of the above.
The aspects of the present application have been described in detail hereinabove with reference to the accompanying drawings. In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments. Those skilled in the art should also appreciate that the acts and modules referred to in the specification are not necessarily required in the present application. In addition, it can be understood that the steps in the method of the embodiment of the present application may be sequentially adjusted, combined, and deleted according to actual needs, and the modules in the device of the embodiment of the present application may be combined, divided, and deleted according to actual needs.
Having described embodiments of the present application, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
1. A data fusion processing method of multiple data sources based on vehicle-road cooperation is characterized in that: the method comprises the following steps:
receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle;
extracting position data in each vehicle data, and dividing the extracted plurality of position data into a plurality of initial position data sets;
aiming at any initial position data set, obtaining vehicle characteristic information corresponding to each position data in the initial position data set;
comparing the vehicle characteristic information, and fusing position data corresponding to the same vehicle characteristic information according to a comparison result to obtain a first target position data set; discarding position data corresponding to vehicle characteristic information different from the vehicle characteristic information in the first target position data set;
comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in any second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained;
wherein the vehicle corresponding to the position data in the second set of target position data is not the same vehicle as the vehicle corresponding to the position data in the first set of target position data.
2. The data fusion processing method according to claim 1, characterized in that: the location data includes longitude and latitude data of the vehicle;
dividing the extracted plurality of position data into a plurality of initial position data sets, including:
and comparing the distances among the longitude and latitude data of the extracted position data, and dividing the position data with the distance smaller than the set distance into the same initial position data set.
3. The data fusion processing method according to claim 2, characterized in that: after receiving the vehicle data, the method further comprises: converting longitude and latitude data in the vehicle data into morton codes; and
comparing the distance between the longitude and latitude data, and dividing the position data with the distance less than the set distance into the same initial position data set, specifically comprising:
and comparing the distances among the morton codes, and dividing the morton codes with the set distance difference into the same initial position data set.
4. The data fusion processing method according to claim 3, characterized in that: the set distance is 1.5-1.7 meters;
dividing the morton codes with the difference set distance into the same initial position data set, wherein the morton codes comprise:
and dividing the Morton code with the difference of the first 14 bits of the Morton code between 1.5 and 1.7 meters into the same initial position data set.
5. The data fusion processing method according to claim 1, characterized in that: position data extracted from the respective pieces of vehicle data is stored in a first data table, and the pieces of vehicle data are stored in a second data table;
wherein any position data in the first data table is associated with corresponding vehicle data in the second data table.
6. The data fusion processing method according to any one of claims 1 to 5, characterized in that: the multiple data sources include at least two of the following data: the method comprises the following steps that (1) vehicle data collected by a vehicle, ADAS data collected by the vehicle through ADAS, and road setting data collected by road side equipment are obtained; for any current vehicle, the vehicle data is vehicle data uploaded by the current vehicle, the ADAS data is vehicle data of the current vehicle uploaded by other vehicles, and the road setting data is vehicle data of the current vehicle uploaded by road side equipment;
and/or the vehicle characteristic information comprises one or more of the license plate number, the body color, the vehicle type and the vehicle mark of the vehicle.
7. The data fusion processing method according to claim 6, characterized in that: after vehicle data collected from multiple data sources are received, the vehicle data are sequenced in sequence as follows:
the vehicle data collected by high-precision equipment in the self-vehicle data, the vehicle data collected by high-precision equipment in the ADAS data, the vehicle data collected by high-precision equipment in the road setting data, the vehicle data collected by common equipment in the self-vehicle data, the vehicle data collected by common equipment in the ADAS data and the vehicle data collected by common equipment in the road setting data.
8. The utility model provides a many data sources's data fusion processing apparatus based on vehicle road is cooperative which characterized in that: the device includes:
the receiving module is used for receiving vehicle data collected from multiple data sources; the vehicle data collected by each data source comprises the position data of the vehicle;
the extraction module is used for extracting position data in each piece of vehicle data;
a grouping module for dividing the extracted plurality of position data into a plurality of initial position data sets;
the system comprises an acquisition module, a comparison module and a comparison module, wherein the acquisition module is used for acquiring vehicle characteristic information corresponding to each position data in an initial position data set and comparing the vehicle characteristic information aiming at any initial position data set;
the first fusion module is used for fusing position data corresponding to the same vehicle characteristic information according to the comparison result to obtain a first target position data set; and discarding the corresponding location data for vehicle characteristic information different from the vehicle characteristic information in the first set of target location data;
the second fusion module is used for comparing the vehicle characteristic information corresponding to the discarded position data with the vehicle characteristic information in any second target position data set, and fusing the discarded position data into the corresponding second target position data set after the vehicle characteristic information which is consistent in comparison is obtained; wherein the vehicle corresponding to the position data in the second set of target position data is not the same vehicle as the vehicle corresponding to the position data in the first set of target position data.
9. The data fusion processing apparatus according to claim 8, characterized in that: the position data is longitude and latitude data of the vehicle;
the grouping module is specifically configured to: and comparing the distances among the longitude and latitude data of the extracted position data, and dividing the position data with the distance value smaller than the set distance into the same initial position data set.
10. The data fusion processing apparatus according to claim 9, characterized in that: the device also comprises a conversion module, wherein the conversion module is used for respectively converting the longitude and latitude data into corresponding morton codes; and
the grouping module is specifically configured to: and comparing the distances among the morton codes, and dividing the morton codes with the set distance difference into the same initial position data set.
11. The data fusion processing apparatus according to claim 8, characterized in that: the device also comprises a data storage unit, wherein the data storage unit comprises a first data table and a second data table;
position data extracted from the respective pieces of vehicle data is stored in a first data table, and the pieces of vehicle data are stored in a second data table;
wherein any position data in the first data table is associated with corresponding vehicle data in the second data table.
12. The data fusion processing apparatus according to any one of claims 8 to 11, characterized in that: the multiple data sources include at least two of the following data: the method comprises the following steps that (1) vehicle data collected by a vehicle, ADAS data collected by the vehicle through ADAS, and road setting data collected by road side equipment are obtained; for any current vehicle, the vehicle data is vehicle data uploaded by the current vehicle, the ADAS data is vehicle data of the current vehicle uploaded by other vehicles, and the road setting data is vehicle data of the current vehicle uploaded by road side equipment;
and/or the vehicle characteristic information comprises one or more of the license plate number, the body color, the vehicle type and the vehicle mark of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110709339.9A CN113256984B (en) | 2021-06-25 | 2021-06-25 | Multi-data-source data fusion processing method and device based on vehicle-road cooperation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110709339.9A CN113256984B (en) | 2021-06-25 | 2021-06-25 | Multi-data-source data fusion processing method and device based on vehicle-road cooperation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113256984A true CN113256984A (en) | 2021-08-13 |
| CN113256984B CN113256984B (en) | 2021-11-05 |
Family
ID=77189586
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110709339.9A Active CN113256984B (en) | 2021-06-25 | 2021-06-25 | Multi-data-source data fusion processing method and device based on vehicle-road cooperation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113256984B (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026278A1 (en) * | 2000-08-28 | 2002-02-28 | Estimotion Inc. | Method and system for modeling and processing vehicular traffic data and information and applying thereof |
| CN101034406A (en) * | 2007-04-04 | 2007-09-12 | 同济大学 | Multicomponent data source traffic information fusion method based on distributed architecture |
| CN101571997A (en) * | 2009-05-31 | 2009-11-04 | 上海宝康电子控制工程有限公司 | Method and device for fusion processing of multi-source traffic information |
| CN102779410A (en) * | 2012-07-19 | 2012-11-14 | 杭州师范大学 | Parallel implementation method of multi-source heterogeneous traffic data fusion |
| CN103499350A (en) * | 2013-09-28 | 2014-01-08 | 长安大学 | High-precision vehicle positioning method for fusing multi-source information under GPS (global positioning system) blind area and device |
| CN103838772A (en) * | 2012-11-26 | 2014-06-04 | 香港生产力促进局 | Multi-source traffic data fusion method |
| CN105426372A (en) * | 2014-09-17 | 2016-03-23 | 高德软件有限公司 | Electronic map data manufacturing and updating method and apparatus |
| CN106203467A (en) * | 2016-06-27 | 2016-12-07 | 深圳大学 | The consistency check of a kind of multi-source position data and fusion method and system |
| CN109410576A (en) * | 2018-11-08 | 2019-03-01 | 深圳北斗应用技术研究院有限公司 | Road condition analyzing method, apparatus, storage medium and the system of multisource data fusion |
| CN110766936A (en) * | 2018-07-25 | 2020-02-07 | 高德软件有限公司 | Traffic running state sensing method and system based on multi-source data fusion |
| CN111524357A (en) * | 2020-05-19 | 2020-08-11 | 河北德冠隆电子科技有限公司 | Method for fusing multiple data required for safe driving of vehicle |
| CN111524362A (en) * | 2020-05-19 | 2020-08-11 | 河北德冠隆电子科技有限公司 | Vehicle safety driving guarantee system and method based on multi-data fusion |
| CN112262420A (en) * | 2018-06-20 | 2021-01-22 | 歌乐株式会社 | Parking assist system and parking assist apparatus |
| CN112461246A (en) * | 2020-12-01 | 2021-03-09 | 上海交通大学 | Method and system for fusing multi-source heterogeneous positioning path data |
-
2021
- 2021-06-25 CN CN202110709339.9A patent/CN113256984B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026278A1 (en) * | 2000-08-28 | 2002-02-28 | Estimotion Inc. | Method and system for modeling and processing vehicular traffic data and information and applying thereof |
| CN101034406A (en) * | 2007-04-04 | 2007-09-12 | 同济大学 | Multicomponent data source traffic information fusion method based on distributed architecture |
| CN101571997A (en) * | 2009-05-31 | 2009-11-04 | 上海宝康电子控制工程有限公司 | Method and device for fusion processing of multi-source traffic information |
| CN102779410A (en) * | 2012-07-19 | 2012-11-14 | 杭州师范大学 | Parallel implementation method of multi-source heterogeneous traffic data fusion |
| CN103838772A (en) * | 2012-11-26 | 2014-06-04 | 香港生产力促进局 | Multi-source traffic data fusion method |
| CN103499350A (en) * | 2013-09-28 | 2014-01-08 | 长安大学 | High-precision vehicle positioning method for fusing multi-source information under GPS (global positioning system) blind area and device |
| CN105426372A (en) * | 2014-09-17 | 2016-03-23 | 高德软件有限公司 | Electronic map data manufacturing and updating method and apparatus |
| CN106203467A (en) * | 2016-06-27 | 2016-12-07 | 深圳大学 | The consistency check of a kind of multi-source position data and fusion method and system |
| CN112262420A (en) * | 2018-06-20 | 2021-01-22 | 歌乐株式会社 | Parking assist system and parking assist apparatus |
| CN110766936A (en) * | 2018-07-25 | 2020-02-07 | 高德软件有限公司 | Traffic running state sensing method and system based on multi-source data fusion |
| CN109410576A (en) * | 2018-11-08 | 2019-03-01 | 深圳北斗应用技术研究院有限公司 | Road condition analyzing method, apparatus, storage medium and the system of multisource data fusion |
| CN111524357A (en) * | 2020-05-19 | 2020-08-11 | 河北德冠隆电子科技有限公司 | Method for fusing multiple data required for safe driving of vehicle |
| CN111524362A (en) * | 2020-05-19 | 2020-08-11 | 河北德冠隆电子科技有限公司 | Vehicle safety driving guarantee system and method based on multi-data fusion |
| CN112461246A (en) * | 2020-12-01 | 2021-03-09 | 上海交通大学 | Method and system for fusing multi-source heterogeneous positioning path data |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113256984B (en) | 2021-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113160571B (en) | Multi-data-source data fusion processing method and device based on vehicle-road cooperation | |
| US20210325207A1 (en) | Map updating system and method for autonomous driving | |
| US9983022B2 (en) | Vehicle position estimation system, device, method, and camera device | |
| Koukoumidis et al. | Leveraging smartphone cameras for collaborative road advisories | |
| US10393852B2 (en) | Method and system of location estimation and navigation of autonomous vehicles | |
| US11686582B2 (en) | Sensor plausibility using GPS road information | |
| US11710074B2 (en) | System for providing corridor metrics for a corridor of a road network | |
| US11408739B2 (en) | Location correction utilizing vehicle communication networks | |
| CN113127590B (en) | Map updating method and device | |
| US20210003419A1 (en) | System for Generating Confidence Values in Digital Road Maps | |
| CN111380544A (en) | Method and device for generating map data of lane line | |
| CN115470884A (en) | Platform for perception system development of an autopilot system | |
| CN111212375B (en) | Positioning position adjusting method and device | |
| CN113256984B (en) | Multi-data-source data fusion processing method and device based on vehicle-road cooperation | |
| WO2022067295A1 (en) | Architecture for distributed system simulation timing alignment | |
| US11809790B2 (en) | Architecture for distributed system simulation timing alignment | |
| CN113611120A (en) | Vehicle position information prediction method, device and equipment | |
| CN111356074A (en) | Method and device for positioning bus station, server and medium | |
| US20200225671A1 (en) | Remove Objects From a Digital Road Map | |
| JP2019096084A (en) | Information processing device | |
| CN115129796A (en) | Positioning data analysis method, apparatus, device and medium | |
| CN106781470B (en) | Method and device for processing running speed of urban road | |
| CN112829757A (en) | Automatic driving control system, server device, and storage medium storing program | |
| CN112116828A (en) | Method and system capable of positioning parking space | |
| CN111352964A (en) | Method, device and equipment for acquiring interest point information and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |