CN110800032A - Traffic data processing method and vehicle-mounted client - Google Patents
Traffic data processing method and vehicle-mounted client Download PDFInfo
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- CN110800032A CN110800032A CN201780092637.XA CN201780092637A CN110800032A CN 110800032 A CN110800032 A CN 110800032A CN 201780092637 A CN201780092637 A CN 201780092637A CN 110800032 A CN110800032 A CN 110800032A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/0969—Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
Abstract
A traffic data processing method and a vehicle-mounted client are used for solving the problem of the accuracy of traffic data cached from a cloud server to a local area in a vehicle-mounted system. The method comprises the following steps: determining a first traffic node (101); determining from the first traffic node at least two second traffic nodes (102) connected to the first traffic node; determining at least two adjacent road segments (103) from the first traffic node and the at least two second traffic nodes; obtaining at least two first traffic data (104) from a cloud server; acquiring second traffic data (105); determining a probability (106) of the target vehicle in each neighbouring road segment from the at least two first traffic data and the second traffic data; and acquiring traffic data (107) of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
Description
The invention relates to the field of communication, in particular to a traffic data processing method and a vehicle-mounted client.
With the popularity of automobiles, more and more automobiles are beginning to be equipped with on-board systems. When the automobile runs, the vehicle-mounted system needs to acquire traffic data from the cloud server to help the vehicle-mounted system provide accurate maps, geographic information and clear running routes, the cloud server is a database optimized or deployed in a virtual computing environment, and all traffic data of the current running road of the automobile are stored in the cloud server. However, when the vehicle-mounted system acquires traffic data through the cloud, there are two major problems:
1. when the positioning is not accurate enough, the data provided by the cloud server cannot be used as data for reference;
2. data transmission delays, resulting in the inability to effectively use cloud data even if an accurate location is obtained.
To solve the two problems, the prior art uses a buffering mode to cache the cloud data to the local database of the vehicle-mounted system, and then the cloud data is provided for the vehicle, and the vehicle runs by using the data buffered by the cloud.
However, in the prior art, the data of the cloud server is cached in the local database of the vehicle-mounted system by using a buffering manner, and it is possible that the cloud data cached in the local database of the vehicle-mounted system is incorrect due to sudden jump-off of the positioning position, but at this time, the vehicle-mounted system cannot know whether the currently buffered data is correct or not.
Disclosure of Invention
The embodiment of the invention provides a traffic data processing method and a vehicle-mounted client, which are used for solving the problem of the accuracy of data cached to the local from a cloud server by a vehicle-mounted system.
A first aspect of an embodiment of the present invention provides a traffic data processing method, including:
determining a first traffic node, which is a nearest traffic node traveled by a target vehicle;
determining at least two second traffic nodes connected with the first traffic node according to the first traffic node;
determining at least two adjacent road segments according to the first traffic node and the at least two second traffic nodes, wherein the at least two adjacent road segments are road segments between the first traffic node and each second traffic node;
acquiring at least two pieces of first traffic data from a cloud server, wherein each piece of first traffic data is traffic data of a first road section on each corresponding adjacent road section, and the first road section is a road section which is away from the first traffic node by a preset distance in each adjacent road section;
acquiring second traffic data, wherein the second traffic data is acquired from the first traffic node driven by the target vehicle to the road section from the current position of the target vehicle;
determining a probability of the target vehicle in each adjacent road segment according to the at least two first traffic data and the second traffic data;
and acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
With reference to the first aspect, in a first possible implementation manner of the first aspect, when the vehicle-mounted client needs to determine at least two traffic nodes, at least two road segments connected to a first traffic node may be determined first, where the first traffic node is located on one side of the at least two road segments, and a traffic node on the other side of each of the at least two road segments is determined as at least two second traffic nodes.
With reference to the first aspect, in a second possible implementation manner of the first aspect, when the vehicle-mounted client needs to determine at least two traffic nodes, all road segments connected to the first node may be determined, where the all road segments include at least two road segments, all traffic nodes on the other side of all road segments connected to the first traffic node are obtained, driving direction data or destination data of the target vehicle is obtained, and the at least two traffic nodes are screened out from all traffic nodes according to a preset rule according to the driving direction data or the destination data.
With reference to the first aspect, in a third possible implementation manner of the first aspect, when the vehicle-mounted client obtains the traffic data of the corresponding adjacent road section from the cloud server according to the probability of the target vehicle in each adjacent road section, the probability of the target vehicle in each adjacent road section may be determined first, the traffic data of the adjacent road section with the highest probability is obtained from the cloud server, and at this time, the traffic data of the adjacent road section with the highest probability is determined as the traffic data of the corresponding adjacent road section.
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, when the vehicle-mounted client acquires traffic data of a corresponding adjacent road section from the cloud server according to the probability of the target vehicle in each adjacent road section, it may further determine an adjacent road section where the probability of the target vehicle in each adjacent road section reaches a preset value, acquire traffic data of the adjacent road section where the probability reaches the preset value from the cloud server, and determine visual data of the adjacent road section where the probability reaches the preset value as traffic data of the corresponding adjacent road section.
With reference to the first aspect and any one implementation manner of the first possible implementation manner of the first aspect to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, after the vehicle-mounted client acquires the traffic data of the corresponding adjacent road section from the cloud server according to the probability of the target vehicle in each adjacent road section, the vehicle-mounted client may use the traffic data of the corresponding adjacent road section as the traffic data used by the target vehicle.
A second aspect of an embodiment of the present invention provides a vehicle-mounted client, including:
a central processing unit, a storage medium and an input/output interface;
the storage medium is stored with a program code, and the central processing unit is used for calling the program code to execute the following steps:
determining a first traffic node, which is a nearest traffic node traveled by a target vehicle;
determining at least two second traffic nodes connected with the first traffic node according to the first traffic node;
determining at least two adjacent road segments according to the first traffic node and the at least two second traffic nodes, wherein the at least two adjacent road segments are road segments between the first traffic node and each second traffic node;
acquiring at least two pieces of first traffic data from a cloud server, wherein each piece of first traffic data is traffic data of a first road section on each corresponding adjacent road section, and the first road section is a road section which is away from a first traffic node by a preset distance in each adjacent road section;
acquiring second traffic data, wherein the second traffic data is acquired from the first traffic node driven by the target vehicle to the road section from the current position of the target vehicle;
determining a probability of the target vehicle in each adjacent road segment according to the at least two first traffic data and the second traffic data;
and acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
Optionally, the central processing unit is further configured to call the program code to perform the following steps:
determining at least two road segments connected with the first traffic node, the first traffic node being located at one side of the at least two road segments;
determining a traffic node on the other side of each of the at least two road segments as the at least two second traffic nodes.
Optionally, the central processing unit is further configured to call the program code to perform the following steps:
determining all road segments connected with the first traffic node, wherein the all road segments comprise at least two road segments;
acquiring all traffic nodes on the other side of all road sections connected with the first traffic node;
acquiring driving direction data or destination data of the target vehicle;
and screening out the at least two traffic nodes from all traffic nodes according to the driving direction data or the destination data and preset rules.
Optionally, the central processing unit is further configured to call the program code to perform the following steps:
determining the adjacent road section with the highest probability of the target vehicle in the adjacent road sections;
acquiring traffic data of the adjacent road section with the highest probability from the cloud server;
determining the visual data of the adjacent road section with the highest probability as the traffic data of the corresponding adjacent road section.
Optionally, the central processing unit is further configured to call the program code to perform the following steps:
determining adjacent road sections of which the probability of the target vehicle in each adjacent road section reaches a preset value;
acquiring traffic data of an adjacent road section of which the probability reaches a preset value from the cloud server;
and determining the visual data of the adjacent road sections with the probability reaching the preset value as the traffic data of the corresponding adjacent road sections.
Optionally, the central processing unit is further configured to call the program code to perform the following steps:
and taking the traffic data of the corresponding adjacent road section as the traffic data used by the target vehicle.
Therefore, compared with the prior art that corresponding traffic data are directly used as the traffic data of the target vehicle according to the positioning position, the vehicle-mounted client side determines the probability of the target vehicle in each adjacent road section by using the acquired traffic data and the collected traffic data, and acquires the corresponding traffic data of the adjacent road section from the cloud server according to the probability of the target vehicle in the adjacent road section, so that the accuracy of the traffic data used as the target vehicle is improved, namely, the accuracy of the traffic data used as the target vehicle is improved when the positioning position suddenly jumps away.
FIG. 1 is a schematic diagram of an embodiment of a traffic data processing method according to an embodiment of the present invention;
FIG. 2 is a diagram of an embodiment of an in-vehicle client in an embodiment of the present invention;
FIG. 3 is a schematic diagram of a first traffic point, a second traffic node, second traffic data, and a location of a target vehicle according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of an in-vehicle client in the embodiment of the present invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of the invention and the above-described figures are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, an embodiment of a traffic data processing method according to an embodiment of the present invention includes:
101. a first traffic node is determined, the first traffic node being the closest traffic node traveled by the target vehicle.
In this embodiment, when the target vehicle needs to obtain traffic data to travel during traveling, the vehicle-mounted client determines a first traffic node, where the first traffic node is a traffic node that the target vehicle has traveled, and the traffic node is connected to at least three road segments, that is, a road segment that has traveled and two road segments that have not traveled.
102. At least two second traffic nodes connected to the first traffic node are determined according to the first traffic node.
In this embodiment, after determining the first traffic node, the in-vehicle client may determine at least two second traffic nodes connected to the first traffic node according to the first traffic node.
103. At least two adjacent road segments are determined according to the first traffic node and the at least two second traffic nodes. The adjacent road segment refers to a road segment between the first traffic node and each second traffic node.
104. At least two pieces of first traffic data are obtained from a cloud server.
In this embodiment, after the vehicle-mounted client determines the at least two adjacent road sections, the traffic data of the first road section on each adjacent road section can be acquired from the cloud server, the first road section is a road section with a preset distance from the first traffic node in the adjacent road sections, the visual data of the first road section on each adjacent road section is used as the first traffic data of each adjacent road section, the cloud server includes a cloud database, and the cloud server is connected with the target vehicle in a wireless manner.
It should be noted that the traffic data includes visual data and other data, such as weather data, driving direction data, driving speed data, and altitude data of the current road segment.
105. Second traffic data is acquired.
In this embodiment, the target vehicle may acquire and store traffic data in real time during the driving process. The second traffic data refers to traffic data collected when the target vehicle travels a road segment from the first traffic node to the current position of the target vehicle.
106. The probability of the target vehicle in each adjacent road section is determined according to the at least two first traffic data and the second traffic data.
In this embodiment, after determining at least two pieces of first traffic data and second traffic data, the vehicle-mounted client performs similarity calculation on the second traffic data and each piece of first traffic data respectively to obtain similarity between the second traffic data and each piece of first traffic data, further obtains matching degree between the second traffic data and each piece of first traffic data, and then calculates probability that the target vehicle is located in each adjacent road section according to the matching degree between the second traffic data and each piece of first traffic data.
It should be noted that the similarity between the second traffic data and the first traffic data may be obtained by performing similarity calculation on the sub-data in the second traffic data and the corresponding sub-data in the first traffic data. That is, the second traffic data and some key data in each first traffic data may be extracted, the key data in the second traffic data may be compared with the key data in each first traffic data, and the probability of the target vehicle in each adjacent road section may be determined according to the comparison result, for example, the similarity between the picture at each position stored in the database of the cloud server and the picture acquired by the target vehicle at the corresponding distance may be determined, and then the probability of the target vehicle in each adjacent road section may be determined by integrating according to the similarities.
107. And acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
In this embodiment, after determining the probability of the target vehicle in each adjacent road segment, the vehicle-mounted client may obtain the probability of the corresponding adjacent road segment from the cloud server according to the probability of the target vehicle in each adjacent road segment, may obtain traffic data corresponding to a road segment with the highest probability of the target vehicle in each adjacent road segment, and may also obtain traffic data corresponding to a road segment with the probability of the target vehicle in each adjacent road segment reaching a preset value.
In summary, when the target vehicle needs to cache the visual data from the cloud server, the vehicle-mounted client may determine the first traffic node, and determine at least two second traffic nodes connected to the first traffic node according to the first traffic node; determining at least two adjacent road sections according to the first traffic node and the at least two second traffic nodes; acquiring at least two first traffic data; acquiring second traffic data; determining the probability of the target vehicle in each adjacent road section according to the at least two pieces of first traffic data and the second traffic data; and acquiring the visual data of the corresponding adjacent road sections according to the probability of the target vehicle in each adjacent road section. Therefore, compared with the prior art that corresponding traffic data are directly used as the traffic data of the target vehicle according to the positioning position, the vehicle-mounted client side determines the probability of the target vehicle in each adjacent road section by using the acquired traffic data and the collected traffic data, and acquires the corresponding traffic data of the adjacent road section from the cloud server according to the probability of the target vehicle in the adjacent road section, so that the accuracy of the traffic data used as the target vehicle is improved, namely, the accuracy of the traffic data used as the target vehicle is improved when the positioning position suddenly jumps away.
Referring to fig. 2, another embodiment of the traffic data processing method according to the embodiment of the present invention includes:
201. a first traffic node is determined, the first traffic node being a closest traffic node traveled by a target vehicle.
In this embodiment, when the target vehicle needs to obtain traffic data to travel during traveling, the vehicle-mounted client may determine a first traffic node, where the first traffic node is a traffic node that the target vehicle has traveled, and the traffic node is connected to at least three road segments, that is, a road segment that has traveled and two road segments that have not traveled.
202. At least two second traffic nodes connected to the first traffic node are determined according to the first traffic node.
In this embodiment, the vehicle-mounted client may determine all road segments (at least two) connected to the first traffic node, and determine the traffic node on the other side of all road segments connected to the first traffic node as the second traffic node, where the second traffic node connects at least three road segments.
In another embodiment, the vehicle-mounted client may determine all road segments (at least two) connected to the first traffic node, and obtain a traffic node on the other side of all road segments connected to the first traffic node; the vehicle-mounted client further acquires the driving direction data or destination data of the target vehicle, and selects at least two traffic nodes from all traffic nodes connected with the first traffic node as second traffic nodes according to the driving direction data or destination data of the target vehicle. For example, at least two traffic nodes that are within a sector area that is a sector area having the traveling direction of the target vehicle as an axis are determined as the second traffic node among all the traffic nodes. Or determining at least two traffic nodes in a sector area as second traffic nodes, wherein the sector area is a sector area with the direction of the current position of the target vehicle pointing to the destination as an axis.
It should be noted that, the above two ways of determining at least two traffic nodes connected to the first traffic node according to the first traffic node are only used, and there may be other ways, and the details are not limited herein.
203. At least two adjacent road segments are determined according to the first traffic node and the at least two second traffic nodes. The adjacent road segment refers to a road segment between the first traffic node and each second traffic node.
204. At least two pieces of first traffic data are obtained from a cloud server.
In this embodiment, after the vehicle-mounted client determines at least two adjacent road segments, the traffic data of the first road segment on each adjacent road segment may be acquired from the cloud server, where the first road segment is a road segment of the adjacent road segments, where a distance from the first traffic node is a preset distance, and the visual data of the first road segment on each adjacent road segment is used as the first traffic data of each adjacent road segment.
It should be noted that the traffic data includes visual data and other data, such as weather data, driving direction data, driving speed data, and altitude data of the current road segment.
205. Second traffic data is acquired.
In this embodiment, the target vehicle may acquire and store traffic data in real time during the driving process. The second traffic data refers to visual data collected from a road section from the first traffic node traveled by the target vehicle to the current position of the target vehicle.
For convenience of understanding, please refer to fig. 3, in which fig. 3 is a schematic diagram of a first traffic point, a second traffic node, second traffic data, and a location of a target vehicle, where the target vehicle may be any location on any road segment between the first traffic node and the second traffic node, the first traffic node is a closest traffic node that the vehicle travels through, the second traffic node is a traffic node connected to the first traffic node, the second traffic data is traffic data of a road segment between the first traffic node and a current location of the target vehicle, and the first traffic data is traffic data of a road segment between the first traffic node and the second traffic node, which is a preset distance away from the first traffic node.
206. The probability of the target vehicle in each adjacent road section is determined according to the at least two first traffic data and the second traffic data.
In this embodiment, after determining at least two pieces of first traffic data and second traffic data, the vehicle-mounted client performs similarity calculation on the second traffic data and each piece of first traffic data respectively to obtain similarity between the second traffic data and each piece of first traffic data, further obtains matching degree between the second traffic data and each piece of first traffic data, and then calculates probability that the target vehicle is located in each adjacent road section according to the matching degree between the second traffic data and each piece of first traffic data.
It should be noted that the similarity between the second traffic data and the first traffic data may be obtained by performing similarity calculation on the sub-data in the second traffic data and the corresponding sub-data in the first traffic data. That is, the second traffic data and some key data in each first traffic data may be extracted, the key data in the second traffic data may be compared with the key data in each first traffic data, and the probability of the target vehicle in each adjacent road section may be determined according to the comparison result, for example, the similarity between the picture at each position stored in the database of the cloud server and the picture acquired by the target vehicle at the corresponding distance may be determined, and then the probability of the target vehicle in each adjacent road section may be determined by integrating according to the similarities.
207. And acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
In this embodiment, after determining the probability of the target vehicle in each neighboring road segment, the vehicle-mounted client may determine the neighboring road segment with the highest probability of the target vehicle in each neighboring road segment, acquire the visual data corresponding to the neighboring road segment with the highest probability from the cloud server, and use the visual data of the neighboring road segment with the highest probability as the target data of the target vehicle.
It should be noted that, the vehicle-mounted client may also obtain, after determining the probability of the target vehicle in each adjacent road section, an adjacent road section where the probability of the target vehicle in each adjacent road section reaches a preset value, obtain, from the cloud server, the visual data of the adjacent road section where the probability reaches the preset value, and use the visual data of the adjacent road section where the probability reaches the preset value as the target data of the target vehicle.
208. And taking the acquired traffic data of the corresponding adjacent road sections as the traffic data used by the target vehicle.
In this embodiment, after obtaining the traffic data of the corresponding adjacent road segment, the vehicle-mounted client may use the traffic data of the corresponding adjacent road segment as the traffic data used by the target vehicle.
In summary, when the target vehicle needs to cache the visual data from the cloud server, the vehicle-mounted client may determine the first traffic node, and determine at least two second traffic nodes connected to the first traffic node according to the first traffic node; determining at least two adjacent road sections according to the first traffic node and the at least two second traffic nodes; acquiring at least two first traffic data; acquiring second traffic data; determining the probability of the target vehicle in each adjacent road section according to the at least two pieces of first traffic data and the second traffic data; and acquiring visual data of the corresponding adjacent road sections according to the probability of the target vehicle in each adjacent road section, and taking the traffic data of the corresponding adjacent road sections as the traffic data used by the target vehicle. Therefore, compared with the prior art that corresponding traffic data are directly used as the traffic data of the target vehicle according to the positioning position, the vehicle-mounted client side determines the probability of the target vehicle in each adjacent road section by using the acquired traffic data and the collected traffic data, and acquires the corresponding traffic data of the adjacent road section from the cloud server according to the probability of the target vehicle in the adjacent road section, so that the accuracy of the traffic data used as the target vehicle is improved, namely, the accuracy of the traffic data used as the target vehicle is improved when the positioning position suddenly jumps away.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an in-vehicle client according to an embodiment of the present invention, the in-vehicle client 400 may generate a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 422 (e.g., one or more processors), one or more storage media 430 (the storage media may be one or more mass storage devices, or may be a temporary storage device such as one or more memories, or may be one or more hard disks, or may be used by one or more memories and hard disks, which is not limited herein) for storing an application 442 or data 444. Storage medium 430 may be, among other things, transient or persistent storage. The program stored in the storage medium 430 may include a series of instruction operations for the in-vehicle client. Still further, central processor 422 may be configured to communicate with storage medium 430 to execute a series of instructional operations on storage medium 430 at in-vehicle client 400.
In-vehicle client 400 may also include one or more input/output interfaces 458 (which may be one or more wired or wireless network interfaces, or other input/output interfaces, without limitation) and/or one or more operating systems 441, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.
The steps performed by the in-vehicle client in the above embodiment may be based on the in-vehicle client structure shown in fig. 4.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (12)
- A traffic data processing method, comprising:determining a first traffic node, which is a nearest traffic node traveled by a target vehicle;determining at least two second traffic nodes connected with the first traffic node according to the first traffic node;determining at least two adjacent road segments according to the first traffic node and the at least two second traffic nodes, wherein the at least two adjacent road segments are road segments between the first traffic node and each second traffic node;acquiring at least two pieces of first traffic data from a cloud server, wherein each piece of first traffic data is traffic data of a first road section on each corresponding adjacent road section, and the first road section is a road section which is away from a first traffic node by a preset distance in each adjacent road section;acquiring second traffic data, wherein the second traffic data is acquired from the first traffic node driven by the target vehicle to the road section from the current position of the target vehicle;determining a probability of the target vehicle in each adjacent road segment according to the at least two first traffic data and the second traffic data;and acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
- The traffic data processing method of claim 1, wherein the determining, from the first traffic node, at least two second traffic nodes connected to the first traffic node comprises:determining at least two road segments connected with the first traffic node, the first traffic node being located at one side of the at least two road segments;determining a traffic node on the other side of each of the at least two road segments as the at least two second traffic nodes.
- The traffic data processing method of claim 1, wherein the determining, from the first traffic node, at least two second traffic nodes connected to the first traffic node comprises:determining all road segments connected with the first traffic node, wherein the all road segments comprise at least two road segments;acquiring all traffic nodes on the other side of all road sections connected with the first traffic node;acquiring driving direction data or destination data of the target vehicle;and screening out the at least two traffic nodes from all traffic nodes according to the driving direction data or the destination data and preset rules.
- The traffic data processing method according to claim 1, wherein the obtaining traffic data of the corresponding neighboring segment from the cloud server according to the probability of the target vehicle in each neighboring segment comprises:determining the adjacent road section with the highest probability of the target vehicle in the adjacent road sections;acquiring traffic data of the adjacent road section with the highest probability from the cloud server;determining the visual data of the adjacent road section with the highest probability as the traffic data of the corresponding adjacent road section.
- The traffic data processing method according to claim 1, wherein the obtaining traffic data of the corresponding neighboring segment from the cloud server according to the probability of the target vehicle in each neighboring segment comprises:determining adjacent road sections of which the probability of the target vehicle in each adjacent road section reaches a preset value;acquiring traffic data of an adjacent road section of which the probability reaches a preset value from the cloud server;and determining the visual data of the adjacent road sections with the probability reaching the preset value as the traffic data of the corresponding adjacent road sections.
- The traffic data processing method according to any one of claims 1 to 5, wherein after the obtaining of the traffic data of the corresponding adjacent road segment from the cloud server according to the probability of the target vehicle in each adjacent road segment, the method further comprises:and taking the traffic data of the corresponding adjacent road section as the traffic data used by the target vehicle.
- An in-vehicle client, comprising:a central processing unit, a storage medium and an input/output interface;the storage medium is stored with a program code, and the central processing unit is used for calling the program code to execute the following steps:determining a first traffic node, which is a nearest traffic node traveled by a target vehicle;determining at least two second traffic nodes connected with the first traffic node according to the first traffic node;determining at least two adjacent road segments according to the first traffic node and the at least two second traffic nodes, wherein the at least two adjacent road segments are road segments between the first traffic node and each second traffic node;acquiring at least two pieces of first traffic data from a cloud server, wherein each piece of first traffic data is traffic data of a first road section on each corresponding adjacent road section, and the first road section is a road section which is away from a first traffic node by a preset distance in each adjacent road section;acquiring second traffic data, wherein the second traffic data is acquired from the first traffic node driven by the target vehicle to the road section from the current position of the target vehicle;determining a probability of the target vehicle in each adjacent road segment according to the at least two first traffic data and the second traffic data;and acquiring the traffic data of the corresponding adjacent road sections from the cloud server according to the probability of the target vehicle in each adjacent road section.
- The in-vehicle client of claim 7, wherein the central processor is further configured to call the program code to perform the following steps:determining at least two road segments connected with the first traffic node, the first traffic node being located at one side of the at least two road segments;determining a traffic node on the other side of each of the at least two road segments as the at least two second traffic nodes.
- The in-vehicle client of claim 7, wherein the central processor is further configured to call the program code to perform the following steps:determining all road segments connected with the first traffic node, wherein the all road segments comprise at least two road segments;acquiring all traffic nodes on the other side of all road sections connected with the first traffic node;acquiring driving direction data or destination data of the target vehicle;and screening out the at least two traffic nodes from all traffic nodes according to the driving direction data or the destination data and preset rules.
- The in-vehicle client of claim 7, wherein the central processor is further configured to call the program code to perform the following steps:determining the adjacent road section with the highest probability of the target vehicle in the adjacent road sections;acquiring traffic data of the adjacent road section with the highest probability from the cloud server;determining the visual data of the adjacent road section with the highest probability as the traffic data of the corresponding adjacent road section.
- The in-vehicle client of claim 7, wherein the central processor is further configured to call the program code to perform the following steps:determining adjacent road sections of which the probability of the target vehicle in each adjacent road section reaches a preset value;acquiring traffic data of an adjacent road section of which the probability reaches a preset value from the cloud server;and determining the visual data of the adjacent road sections with the probability reaching the preset value as the traffic data of the corresponding adjacent road sections.
- The in-vehicle client of any one of claims 7 to 11, wherein the central processor is further configured to call the program code to perform the following steps:and taking the traffic data of the corresponding adjacent road section as the traffic data used by the target vehicle.
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| Application Number | Priority Date | Filing Date | Title |
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| PCT/CN2017/097019 WO2019028809A1 (en) | 2017-08-11 | 2017-08-11 | Traffic data processing method and vehicle-mounted client |
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| CN110800032A true CN110800032A (en) | 2020-02-14 |
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| CN101109639A (en) * | 2006-07-18 | 2008-01-23 | 株式会社电装 | Navigation system |
| KR20150052915A (en) * | 2013-11-06 | 2015-05-15 | 현대자동차주식회사 | Method for Offering the Meeting Spot Between Two Differential Guidance Systems |
| CN104823184A (en) * | 2013-09-29 | 2015-08-05 | 华为技术有限公司 | Data processing method, system and client |
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| JP5783093B2 (en) * | 2012-03-13 | 2015-09-24 | アイシン・エィ・ダブリュ株式会社 | Moving body position detection system, moving body position detection apparatus, moving body position detection method, and computer program |
| CN109286893B (en) * | 2015-01-20 | 2022-03-08 | 北京嘀嘀无限科技发展有限公司 | Positioning method and device |
| CN106205161B (en) * | 2016-07-09 | 2018-09-18 | 腾讯科技（深圳）有限公司 | traffic information transmission method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101109639A (en) * | 2006-07-18 | 2008-01-23 | 株式会社电装 | Navigation system |
| CN104823184A (en) * | 2013-09-29 | 2015-08-05 | 华为技术有限公司 | Data processing method, system and client |
| KR20150052915A (en) * | 2013-11-06 | 2015-05-15 | 현대자동차주식회사 | Method for Offering the Meeting Spot Between Two Differential Guidance Systems |
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