CN116009046B - Vehicle positioning method and device - Google Patents
Vehicle positioning method and deviceInfo
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- CN116009046B CN116009046B CN202310156512.6A CN202310156512A CN116009046B CN 116009046 B CN116009046 B CN 116009046B CN 202310156512 A CN202310156512 A CN 202310156512A CN 116009046 B CN116009046 B CN 116009046B
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Abstract
本申请实施例提供一种车辆定位方法及装置，该方法包括：根据目标车辆的定位单元所确定的自车定位信息，确定目标车辆的自车定位轨迹。接收路侧计算设备发送的至少一条感知车辆轨迹，感知车辆轨迹为路侧计算设备根据采集的路侧信息确定的。在至少一条感知车辆轨迹中，确定与自车定位轨迹相匹配的目标车辆轨迹。针对第一时段内的任一个第一时刻，若目标车辆的自车定位信息满足第一预设条件，则根据目标车辆轨迹，预测目标车辆在第一时刻的车辆位置。本申请的技术方案在基站覆盖不足或者无法接收到基站信号的情况下，可以有效提升车辆定位准确性。
This application provides a vehicle positioning method and apparatus. The method includes: determining the vehicle positioning trajectory of a target vehicle based on the vehicle positioning information determined by the positioning unit of the target vehicle; receiving at least one perceived vehicle trajectory sent by a roadside computing device, the perceived vehicle trajectory being determined by the roadside computing device based on collected roadside information; determining the target vehicle trajectory that matches the self-positioning trajectory among the at least one perceived vehicle trajectory; and predicting the vehicle position of the target vehicle at any first moment within a first time period if the self-positioning information of the target vehicle meets a first preset condition based on the target vehicle trajectory. The technical solution of this application can effectively improve the accuracy of vehicle positioning when base station coverage is insufficient or base station signals cannot be received.
Description
Technical Field
The embodiment of the application relates to a computer technology, in particular to a vehicle positioning method and device.
Background
The high-precision positioning technology can realize high-precision positioning by using RTK (Real-TIME KINEMATIC, real-time dynamic differential) positioning, self-vehicle inertial navigation and high-precision road network.
Wherein the high-precision positioning technology can be generally enhanced by using a ground base station, thereby improving the traditional positioning effect. However, under the condition of insufficient coverage of the ground base station, high-precision positioning drift or failure can be caused, positioning can be corrected in a short time by means of inertial navigation fusion and map matching technology, and under the influence of long-term positioning deviation, positioning results corrected by using inertial navigation fusion and map matching also generate accumulated errors.
Therefore, the existing high-precision positioning technology has the problem of poor vehicle positioning accuracy under the condition that the ground base station is insufficient in coverage or cannot receive the base station signals.
Disclosure of Invention
The embodiment of the application provides a vehicle positioning method and device, which are used for solving the problem of poor vehicle positioning accuracy.
In a first aspect, an embodiment of the present application provides a vehicle positioning method, including:
Determining a self-vehicle positioning track of the target vehicle according to the self-vehicle positioning information determined by the positioning unit of the target vehicle;
receiving at least one perceived vehicle track sent by a road side computing device, wherein the perceived vehicle track is determined by the road side computing device according to the acquired road side information;
Determining a target vehicle track matched with the self-vehicle positioning track in the at least one perceived vehicle track;
and predicting the vehicle position of the target vehicle at any first moment in the first time period according to the target vehicle track if the self-vehicle positioning information of the target vehicle meets a first preset condition.
In a second aspect, an embodiment of the present application provides a vehicle positioning device, including:
the determining module is used for determining the self-vehicle positioning track of the target vehicle according to the self-vehicle positioning information determined by the positioning unit of the target vehicle;
the receiving module is used for receiving at least one perceived vehicle track sent by the road side computing equipment, wherein the perceived vehicle track is determined by the road side computing equipment according to the acquired road side information;
The determining module is further configured to determine, in the at least one perceived vehicle trajectory, a target vehicle trajectory that matches the self-vehicle positioning trajectory;
the processing module is used for predicting the vehicle position of the target vehicle at any first moment in the first time period according to the target vehicle track if the self-vehicle positioning information of the target vehicle meets a first preset condition.
In a third aspect, an embodiment of the present application provides an electronic device, including:
A memory for storing a program;
A processor for executing the program stored by the memory, the processor being adapted to perform the method of the first aspect and any of the various possible designs of the first aspect as described above when the program is executed.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect above and any of the various possible designs of the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program which, when executed by a processor, implements a method as described in the first aspect and any of the various possible designs of the first aspect.
The embodiment of the application provides a vehicle positioning method and device, wherein the method comprises the following steps: and determining the self-vehicle positioning track of the target vehicle according to the self-vehicle positioning information determined by the positioning unit of the target vehicle. At least one perceived vehicle track sent by the road side computing device is received, wherein the perceived vehicle track is determined by the road side computing device according to the acquired road side information. In at least one perceived vehicle trajectory, a target vehicle trajectory is determined that matches the vehicle localization trajectory. And for any first moment in the first time period, if the self-vehicle positioning information of the target vehicle meets a first preset condition, predicting the vehicle position of the target vehicle at the first moment according to the track of the target vehicle. The method comprises the steps of determining a target vehicle track matched with the self-vehicle positioning track of the target vehicle in a plurality of perceived vehicle tracks sent by the road side computing equipment, so that the vehicle track determined by the road side computing equipment aiming at the target vehicle can be obtained, and then under the condition that the accuracy of the self-vehicle positioning information of the target vehicle cannot be ensured, predicting the vehicle position of the target vehicle according to the target vehicle track, because the vehicle track determined by the road side computing equipment is not influenced by the deployment of the base station, the vehicle positioning accuracy is effectively improved under the condition that the coverage of the base station is insufficient or a base station signal cannot be received.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 illustrates an application scenario of a vehicle positioning method according to an embodiment of the present application;
FIG. 2 is a flow chart of a vehicle positioning method according to an embodiment of the present application;
FIG. 3 is a second flowchart of a vehicle positioning method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of an implementation of determining a target vehicle track according to an embodiment of the present application;
FIG. 5 is a third flowchart of a vehicle positioning method according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a trace data acquisition frequency provided by an embodiment of the present application;
FIG. 7 is a schematic diagram of an implementation of the track point time alignment according to an embodiment of the present application;
FIG. 8 is a schematic diagram of an interface for displaying vehicle information according to an embodiment of the present application;
FIG. 9 is a schematic diagram illustrating an implementation of a smoothing process for perceived vehicle trajectories according to an embodiment of the present application;
FIG. 10 is a schematic diagram of a vehicle positioning method according to an embodiment of the present application;
FIG. 11 is a schematic structural view of a vehicle positioning device according to an embodiment of the present application;
fig. 12 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to better understand the technical scheme of the present application, the related concepts related to the present application will be described first.
Lane-level navigation-the ability to provide lane-level positioning and lane-level guidance for vehicles using high-precision road networks and high-precision positioning techniques.
High-precision road network, which is a road network topological structure formed by lanes, road sections, intersections and upstream and downstream relations thereof.
High-precision positioning, namely the capacity of realizing centimeter-level high-precision positioning by using RTK positioning, self-vehicle inertial navigation and high-precision road network.
And the perception fusion is a method for obtaining vehicle longitude and latitude data by mapping vehicles identified by the perception devices such as millimeter wave radars, laser radars, high-definition cameras and the like with a high-definition road network and obtaining a full-path lane-level track by utilizing the perception data to carry out cross-point-location track fusion.
Track prediction, namely predicting tracks in m time slices in the future by utilizing track information of n points in front of the vehicle in combination with historical track data.
On the basis of the above description, the related art to which the present application relates will be described in further detail below.
The purpose of lane-level navigation techniques is to provide lane-level positioning and lane-level guidance for a vehicle, which can promote yaw analysis from road level to lane level and re-route a current lane as compared to conventional navigation techniques.
To achieve accurate lane-level navigation, it is important to accurately determine positioning information of a vehicle in a road, and current lane-level navigation technologies typically achieve high-precision positioning by means of RTK positioning, self-vehicle inertial navigation and high-precision road network technologies.
Because the RTK technology itself needs to rely on the ground base station, in order to further improve the positioning effect, the current high-precision positioning technology can generally strengthen the ground base station, so as to improve the traditional high-precision positioning effect, and enable the positioning effect to reach the decimeter level or even the centimeter level.
However, under the condition that the ground base station is insufficient in coverage or cannot receive the base station signals, high-precision positioning drift or failure is caused, at the moment, positioning results can be corrected in a short time by means of inertial navigation fusion and map matching technology, but long-term positioning deviation can occur in the scenes such as mountain shielding or tunnels, and more serious accumulated errors can occur in the positioning results corrected by the inertial navigation fusion and map matching. Therefore, the existing high-precision positioning technology has the problem of poor positioning accuracy under the condition that the ground base station is insufficient in coverage or cannot receive the base station signals.
Aiming at the technical problems, the application provides the following technical conception that a vehicle-road cooperation technology can be adopted, and the road-side sensing result sent by sensing equipment in a road is received and fused with the positioning result of a self vehicle so as to determine the positioning of the vehicle, so that the vehicle position can be effectively determined under the condition that the ground base station is not covered or cannot receive a base station signal, and the accuracy of the vehicle positioning is improved.
The following describes an application scenario of the vehicle positioning method provided by the present application with reference to fig. 1, and fig. 1 illustrates an application scenario of the vehicle positioning method provided by the embodiment of the present application.
As shown in fig. 1, there are road side devices in the road, which may include, for example, a road side perception device 101, a road side computing device 102, and a road side communication device 103.
The road side sensing device 101 is used for collecting road side information in a road, wherein the road side sensing device 101 can be a camera, a millimeter wave radar, a laser radar or the like, and the specific implementation of the road side sensing device can be determined according to actual situations. The road side information collected by the road side sensing device may include, for example, a vehicle image, collected vehicle information, and a road image, etc.
The roadside computing device 102 may be deployed along a road for performing high-performance operations and decisions on roadside information and/or vehicle-side information, so that the roadside computing device has a data processing function. For example, the roadside computing device 102 may receive the roadside information transmitted by the roadside sensing device 101 and determine the travel track of the vehicle in the road according to the roadside information. As shown in fig. 1, the roadside computing device may be, for example, an MEC (Mobile Edge Computing ) device, or may also be the remaining data processing unit.
The Road Side communication device 103 may communicate with the vehicle terminal wirelessly, and the Road Side device may be, for example, an RSU (Road Side Unit), or may also be a communication base station or the like.
As will be understood with reference to fig. 1, for example, the road side sensing device 101 may collect road side information and report the road side information to the road side computing device 102, where the road side computing device 102 may determine a driving track of a vehicle in a road according to the road side information, for example. The roadside computing device 102 may then broadcast the travel track of the vehicle to each vehicle in the road, for example, via the roadside communication device 103.
It will be appreciated that the roadside computing device 102 determines the vehicle track according to the roadside information reported by the roadside sensing device 101, so that the roadside computing device 102 may calculate the vehicle tracks of a plurality of vehicles located within the collection range of the roadside sensing device 101, for example. And when the vehicle travel track is transmitted through the roadside communication device 103 in the form of broadcasting, the vehicle within the communication range of the roadside communication device 103 can receive the vehicle travel track.
In the road, the road side sensing device, the road side computing device and the road side communication device are generally configured correspondingly, and may be independent multiple devices or integrated devices. It will thus be appreciated that in practice the road side device may determine the travel track of a vehicle within a certain range and broadcast the determined vehicle travel track to vehicles within that range.
In one possible implementation, an OBU (On board Unit) may be present in the in-vehicle terminal, for example, so that when the road side computing device sends the vehicle track to the in-vehicle terminal, it may be sent over a link, for example, MEC-RSU-OBU. Correspondingly, when the vehicle-mounted terminal needs to report information to the road side computing equipment, vehicle-end data can be converged to the MEC through the OBU-RSU-MEC link.
On the basis of the above description, the vehicle positioning method provided by the present application is described below with reference to specific embodiments, where the method provided by each embodiment of the present application is applied to a target vehicle, and in one possible implementation, for example, each step may be executed by a processor or a chip in the target vehicle, so as to achieve a corresponding effect.
First, referring to fig. 2, fig. 2 is a flowchart of a vehicle positioning method according to an embodiment of the present application.
As shown in fig. 2, the method includes:
s201, determining the self-vehicle positioning track of the target vehicle according to the self-vehicle positioning information determined by the positioning unit of the target vehicle.
In the target vehicle, there is a positioning unit, which can determine positioning information of the target vehicle itself, for example, by means of the above-described RTK positioning and inertial navigation of the own vehicle, which is referred to as the own vehicle positioning information in this embodiment.
The target vehicle can determine a vehicle positioning track of the target vehicle according to the vehicle positioning information determined by the positioning unit at a plurality of moments, wherein the vehicle positioning track is a vehicle running track determined by the target vehicle.
S202, receiving at least one perceived vehicle track sent by the road side computing equipment, wherein the perceived vehicle track is determined by the road side computing equipment according to the acquired road side information.
The target device in this embodiment may further receive at least one perceived vehicle track sent by the roadside computing device, and based on the above description, it may be determined that the roadside computing device may determine a vehicle track of a vehicle in a road according to the roadside information collected by the roadside perceiving device, where the vehicle track calculated by the roadside computing device is referred to as a perceived vehicle track. The roadside computing device may then send the vehicle trajectory to the target terminal via the roadside communication device.
In one possible implementation manner, the road side computing device may detect, track and position the vehicle in the road by using a high-precision road network and a radar fusion technology, and perform cross-point track fusion by using space-time constraint, characteristics such as lanes, speeds, vehicle attributes, front-back relations and the like, so as to determine lane-level track restoration results of the whole path within the coverage area of the device, and realize tracking relay of the vehicle, thereby effectively determining the perceived vehicle track of the vehicle in the road.
S203, determining a target vehicle track matched with the self-vehicle positioning track in at least one perceived vehicle track.
As can be determined based on the above description, the perceived vehicle trajectory in the present embodiment is a vehicle trajectory determined by the roadside computing device through fusion calculation, and there may be a plurality of pieces, the specific number depending on the vehicle conditions in the road. The vehicle positioning track is determined by the target vehicle according to the vehicle positioning information acquired by the target vehicle. Because the target vehicle is also traveling in the road, there may be a vehicle track corresponding to the target vehicle in at least one perceived vehicle track.
In one possible implementation, a target vehicle trajectory that matches the vehicle localization trajectory may be determined from at least one perceived vehicle trajectory, where the target vehicle trajectory may be considered a vehicle trajectory of the target vehicle determined by the roadside computing device.
S204, for any first moment in the first period, if the self-vehicle positioning information of the target vehicle meets a first preset condition, predicting the vehicle position of the target vehicle at the first moment according to the track of the target vehicle.
It will be appreciated that, because the vehicles in the road will move, when the road side computing unit sends the perceived vehicle trajectory to the vehicles in the road, for example, the latest perceived vehicle trajectory may be sent regularly, and then the target vehicle will receive the perceived vehicle trajectory sent by the road side computing unit multiple times at different times.
The period between each time the target vehicle receives the perceived vehicle trajectory and the next time the perceived vehicle trajectory is received, for example, may be the first period in the present embodiment. In the first period, the target vehicle performs corresponding positioning processing according to the latest received perceived vehicle track. When a new perceived vehicle trajectory is received, a new first period of time is entered, and then corresponding positioning processing is performed according to the new perceived vehicle trajectory. Or the first period may be any period in which the vehicle position of the target vehicle needs to be determined, and may be selected and set according to actual requirements.
There may be a plurality of times within the first period, for each of which the target vehicle needs to determine its vehicle position, and the manner of processing for each of the times within the first period is similar, and therefore is described below for any of the first times within the first period.
In this embodiment, a first preset condition is set for the vehicle positioning information, where the first preset condition may be that the positioning accuracy of the vehicle positioning information is less than or equal to a first threshold, for example, the positioning accuracy may be determined according to the matching condition of the vehicle positioning information and the road, and the accuracy may be considered to be poor when the vehicle positioning information is obviously deviated from the road. Or the accuracy of the positioning can be analyzed according to a plurality of pieces of self-vehicle positioning information at historical moments, for example, abnormal fluctuation of the self-vehicle positioning information at a plurality of adjacent moments can be considered to be poor in accuracy.
Or the first preset condition may be that the vehicle positioning information is acquired when the target vehicle is located in a preset type of road section, for example, a road section where the base station is less covered, or a road section of a preset type may be a tunnel road section, a mountain road section, or the like.
It can be appreciated that when the vehicle positioning information satisfies the first preset condition, it indicates that the vehicle positioning information is significantly abnormal or lost. When the positioning accuracy of the vehicle positioning information of the target vehicle meets a first preset condition, the position of the target vehicle can be determined, which cannot be determined accurately according to the vehicle positioning information.
In one possible implementation, because the vehicle trajectory determined by the roadside computing device is not affected by the base station deployment, the vehicle position of the target vehicle at the first time may be predicted from the target vehicle trajectory, thereby determining the vehicle position of the target vehicle at the first time.
In another possible implementation manner, if the vehicle positioning information of the target vehicle does not meet the first preset condition, the vehicle positioning information of the target vehicle may be considered to be relatively accurate, so in this case, the vehicle position of the target vehicle at the first moment may be determined according to the vehicle positioning information of the target vehicle at the first moment.
The vehicle positioning method provided by the embodiment of the application comprises the steps of determining the vehicle positioning track of the target vehicle according to the vehicle positioning information determined by the positioning unit of the target vehicle. At least one perceived vehicle track sent by the road side computing device is received, wherein the perceived vehicle track is determined by the road side computing device according to the acquired road side information. In at least one perceived vehicle trajectory, a target vehicle trajectory is determined that matches the vehicle localization trajectory. And for any first moment in the first time period, if the self-vehicle positioning information of the target vehicle meets a first preset condition, predicting the vehicle position of the target vehicle at the first moment according to the track of the target vehicle. The method comprises the steps of determining a target vehicle track matched with the self-vehicle positioning track of the target vehicle in a plurality of perceived vehicle tracks sent by the road side computing equipment, so that the vehicle track determined by the road side computing equipment aiming at the target vehicle can be obtained, and then under the condition that the accuracy of the self-vehicle positioning information of the target vehicle cannot be ensured, predicting the vehicle position of the target vehicle according to the target vehicle track, because the vehicle track determined by the road side computing equipment is not influenced by the deployment of the base station, the vehicle positioning accuracy is effectively improved under the condition that the coverage of the base station is insufficient or a base station signal cannot be received.
Based on the above description, after predicting the vehicle position of the target vehicle at the first time according to the target vehicle track, for example, the sub-track may be determined according to the vehicle positions at a plurality of times in the first time period, and then the sub-track is spliced to the vehicle positioning track of the target vehicle, so as to obtain the updated vehicle positioning track. Therefore, the abnormal track points are replaced by the normal track points in the self-vehicle positioning track determined by the target vehicle, and the accuracy of the self-vehicle positioning track is ensured, so that a correct data basis is provided for subsequent vehicle positioning data.
Therefore, the technical scheme of the application can integrate the self-vehicle positioning result and the road side sensing result to improve the positioning accuracy of the target vehicle, and can remove the abnormal result of self-vehicle positioning and correct the position. On the other hand, under the condition that the positioning of the vehicle is lost, the road side sensing result is utilized to complete the track, so that the high-precision positioning result of the vehicle under special road sections such as mountain shielding or in a tunnel is ensured.
Based on the above description, it may be determined that, in the present embodiment, a target vehicle track that matches a self-vehicle positioning track of a target vehicle needs to be determined in multiple perceived vehicle tracks, and a specific implementation manner of determining the target vehicle track is described in further detail below with reference to fig. 3 to 4, where fig. 3 is a second flowchart of a vehicle positioning method provided by an embodiment of the present application, and fig. 4 is a schematic implementation diagram of determining the target vehicle track provided by an embodiment of the present application.
As shown in fig. 3, the method includes:
s301, acquiring track identifiers of the perceived vehicle tracks.
The road side sensing device determines the track identification corresponding to each determined sensing vehicle track so as to distinguish a plurality of different tracks, and can continuously maintain each track according to the track identification. Therefore, the target vehicle in this embodiment may acquire the track identifiers corresponding to the perceived vehicle tracks.
S302, judging whether preset track identifiers exist in the track identifiers, if yes, executing S303, and if not, executing S304.
Based on the above description, it may be determined that the roadside computing device may send the perceived vehicle trajectory to the target vehicle multiple times at regular intervals, and that the roadside computing device may continuously maintain one perceived vehicle trajectory using the same trajectory identification. Then upon receiving the perceived vehicle trajectory, for example, it may first be determined whether there is a previously matched perceived vehicle trajectory.
In one possible implementation, for example, it may be determined whether a preset track identifier exists among a plurality of track identifiers, where the preset track identifier is a track identifier of a perceived vehicle track that matches a vehicle positioning track of the target vehicle during a history period.
S303, determining the perceived vehicle track corresponding to the preset track mark as the target vehicle track.
In one possible implementation manner, if a preset track identifier exists in the plurality of track identifiers, the perceived vehicle track corresponding to the preset track identifier can be determined to be matched before, so that the perceived vehicle track corresponding to the preset track identifier can be directly determined to be the target vehicle track matched with the vehicle positioning track of the target vehicle.
For example, as can be understood in conjunction with fig. 4, as shown in fig. 4, the track 401 is assumed to be a self-vehicle positioning track of the target vehicle, and the target vehicle is assumed to receive 3 perceived vehicle tracks sent by the roadside computing device, namely, track 1, track 2 and track 3 shown in fig. 4.
Assuming that track 2 is a perceived vehicle track that matches the vehicle positioning track 401 over a historical period of time, then the track identity of track 2 is the preset track identity, and thus track 2 can be determined to be the currently determined target vehicle track.
In one possible implementation manner, in order to ensure that the perceived vehicle track corresponding to the preset track identifier is actually the vehicle track corresponding to the target vehicle track, the similarity between the perceived vehicle track corresponding to the preset track identifier and the vehicle positioning track may be further determined. If the similarity is greater than or equal to a second preset threshold, the perceived vehicle track corresponding to the preset track identifier can be determined to be the target vehicle track, so that the accuracy of the determined target vehicle track can be further improved.
For example, in the example of fig. 4, the similarity between the track 401 and the track 2 is determined, and if the similarity is greater than or equal to the second preset threshold, it may be determined that the track 2 is the target vehicle track to which the track 401 matches.
S304, determining the similarity of the perceived vehicle track and the self-vehicle positioning track according to the track point information of each track point in the perceived vehicle track and the track point information of each track point in the self-vehicle positioning track.
In another possible implementation manner, if the preset track identifier does not exist in the track identifiers, the track identifier indicates that the previously matched vehicle track does not exist in the plurality of perceived vehicle tracks sent by the current road side computing device, so that the similarity of the perceived vehicle track and the self-vehicle positioning track can be determined according to the track point information of each track point in the perceived vehicle track and the track point information of each track point in the self-vehicle positioning track.
Or if the similarity between the perceived vehicle track corresponding to the preset track mark and the self-vehicle positioning track is smaller than the second preset threshold value, the perceived vehicle track corresponding to the current preset track mark is not possibly matched with the self-vehicle positioning track, and the similarity between the perceived vehicle track and the self-vehicle positioning track can be determined according to the track point information of each track point in the perceived vehicle track and the track point information of each track point in the self-vehicle positioning track.
It may be appreciated that in this embodiment, in the case where there are previously matched perceived vehicle trajectories of the target vehicle among the plurality of perceived vehicle identifications, the target vehicle trajectory may be determined directly, or only the similarity between the single perceived vehicle trajectory and the vehicle positioning trajectory may be determined.
Under the condition that the previously matched perceived vehicle track of the target vehicle does not exist or under the condition that the similarity between the current perceived vehicle track and the self-vehicle positioning track of the previously matched perceived vehicle track is not good, the similarity needs to be determined for a plurality of perceived vehicle tracks, so that the calculation resources can be effectively saved, and the vehicle positioning efficiency is improved.
The track point information in this embodiment may include, for example, longitude and latitude position, position altitude, travel speed, acceleration, direction angle, and the like of the track point. For example, the lateral distance, the longitudinal distance, the position height difference, the running speed difference, the acceleration difference and the direction angle difference between two corresponding track points in the perceived vehicle track and the self-vehicle positioning track can be determined according to the track point information, then the track point similarity between the two corresponding track points is determined, and then the similarity between the two tracks is determined according to the track point similarities. In determining the similarity, for example, the covariance of the multidimensional data described above may be determined, and then the joint gaussian probability distribution corresponding to a plurality of tracks is compared, where the perceived vehicle track with the highest probability corresponds to the perceived vehicle track with the highest similarity.
In the actual implementation process, the specific implementation of determining the similarity between the tracks can also be selected and set according to the actual requirements, which is not limited in this embodiment.
And S305, determining the perceived vehicle trajectory with the highest similarity and the similarity larger than a first preset threshold as the target vehicle trajectory.
After determining the similarity between each perceived vehicle trajectory and the own-vehicle positioning trajectory, for example, a perceived vehicle trajectory having the highest similarity and a similarity greater than a first preset threshold may be determined as the target vehicle trajectory.
The first preset threshold and the second preset threshold in this embodiment may be selected and set according to actual requirements, and may be the same or different, which is not limited in this embodiment.
According to the vehicle positioning method provided by the embodiment of the application, whether the preset track mark exists or not is determined in the track marks of the plurality of perceived vehicle tracks. Under the condition that the preset track mark exists, the perceived vehicle track corresponding to the preset track mark is directly determined to be the target vehicle track. Under the condition that the preset track mark does not exist, the similarity of each perceived vehicle track and the self-vehicle positioning track is determined, so that the data calculation amount can be effectively reduced, and the vehicle positioning efficiency is improved. And after the similarity of each perceived vehicle track and the vehicle positioning track is determined, the perceived vehicle track with the highest similarity and the similarity larger than or equal to the first preset threshold value can be determined as the target vehicle track, so that the target vehicle track corresponding to the target vehicle can be effectively determined.
On the basis of the above description, since the frequency of data acquisition performed by the road side sensing device and the frequency of data acquisition performed by the target vehicle may not be the same, the time of sensing each track point of the vehicle track and the vehicle positioning track may not correspond, and in order to correctly perform subsequent data processing, in this embodiment, before performing data processing according to the sensed vehicle track, the track points in the sensed vehicle track and the track points in the vehicle positioning track may be aligned in time.
The following describes in further detail a specific implementation manner of time alignment with reference to fig. 5 to 7, fig. 5 is a flowchart III of a vehicle positioning method provided by an embodiment of the present application, fig. 6 is a schematic diagram of track data acquisition frequency provided by an embodiment of the present application, and fig. 7 is a schematic diagram of implementation manner of track point time alignment provided by an embodiment of the present application.
As shown in fig. 5, the method includes:
And S501, performing delay compensation on the acquisition time of each track point in the perceived vehicle track according to the delay time length to obtain the perceived vehicle track after delay compensation.
For example, the frequency of the track points of the perceived vehicle track and the vehicle positioning track can be understood first in connection with fig. 6. As shown in fig. 6, the acquired data that we desire is high frequency and equally spaced as shown at 601. In practical implementations, however, high frequency acquisition of data may not be possible, with possible acquisition frequencies shown as 602 and 603 in fig. 6.
Referring to fig. 6, the data acquisition frequency of the target vehicle may be, for example, one frame acquired every 100ms, and then the acquisition time between two adjacent track points in the vehicle positioning track is 100ms apart as shown by 602 in fig. 6.
And referring to fig. 6, assuming that the data acquisition frequency of the road side sensing device is acquired once every 160ms, as shown by 603 in fig. 6, the acquisition time between two adjacent track points in the sensing vehicle track is spaced 160ms.
Therefore, each track point in the perceived vehicle track and each track point in the self-vehicle positioning track are not aligned in time, and in order to ensure the accuracy of subsequent data processing, the track points in the two tracks can be aligned in time.
In one possible implementation manner, since the road side computing device passes through a certain data transmission link when sending the perceived vehicle trajectory to the target vehicle, the data transmission link may cause data delay, for example, delay compensation may be performed on the acquisition time of each track point in the perceived vehicle trajectory according to the delay duration, so as to obtain the perceived vehicle trajectory after delay compensation.
S502, determining at least one track point pair according to the corresponding relation between each track point in the perceived vehicle track after delay compensation and each track point in the self-vehicle positioning track.
After determining the delay-compensated perceived vehicle trajectory, at least one pair of trajectory points may be determined, for example, based on the correspondence between each of the trajectory points in the delay-compensated perceived vehicle trajectory and each of the trajectory points in the vehicle positioning trajectory. In each pair of track points, one first track point in the perceived vehicle track after delay compensation and one second track point in the self-vehicle positioning track are included. The correspondence relationship may be determined according to the similarity of the acquisition moments of the track points, for example, for any track point a in the perceived vehicle track after delay compensation, one track point b closest to the acquisition moment is determined among the track points of the vehicle positioning track, and then the track point a and the track point b are determined as the corresponding track points.
For example, as can be understood with reference to fig. 7, as shown in fig. 7, assuming that the interval between adjacent track points of the vehicle positioning track is 100ms, fig. 7 illustrates that the vehicle positioning track includes track points 1 to 8. And assuming that the interval between adjacent track points of the self-vehicle positioning track is 160ms, fig. 7 illustrates that the self-vehicle positioning track includes track points a to e.
For example, the track point pairs are determined according to the similarity of the acquisition time, and as shown in fig. 7, 5 track point pairs are determined, namely, a track point pair of track point 2 and track point a, a track point pair of track point 3 and track point b, a track point pair of track point 5 and track point c, a track point pair of track point 6 and track point d, and a track point pair of track point 8 and track point e. It will be appreciated that the number of pairs of trace points is dependent on the number of trace points in the trace where the acquisition frequency is low.
S503, determining a target moment according to a first acquisition moment corresponding to the first track point and a second acquisition moment corresponding to the second track point aiming at any track point pair.
In this embodiment, after determining the track point pair, time alignment may be performed on two track points in the track point pair, and to perform time alignment, it is necessary to determine an alignment time of a reference, so that, for example, the target time is determined according to a first acquisition time corresponding to a first track point in the track point pair and a second acquisition time corresponding to a second track point, where the target time is the time when the two track points need to be aligned.
In one possible implementation, for example, the later of the first acquisition time and the second acquisition time may be determined as the target time. Alternatively, the earlier one of the first acquisition time and the second acquisition time may be determined as the acquisition time. Or the target time may be determined between the first acquisition time and the second acquisition time, for example, the target time is an intermediate time between the first acquisition time and the second acquisition time, or the target time may be any time between the first acquisition time and the second acquisition time, which is not limited in the specific implementation manner of the target time, so long as the same reference time for alignment is determined for the first track point and the second track point.
S504, a third track point with the acquisition time being the target time in the delay-compensated perceived vehicle track and a fourth track point with the acquisition time being the target time are determined in the self-vehicle positioning track.
After determining the target time, the third track point with the acquisition time as the target time can be determined according to the perceived vehicle track after delay compensation. And determining a fourth track point with the acquisition time as the target time according to the self-vehicle positioning track.
The third track point may also be understood as the first track point after time alignment, and for example, the frame insertion process may be performed at the target time in the perceived vehicle track after delay compensation, so as to determine the third track point. And the fourth track point can also be understood as the second track point after time alignment, for example, the frame inserting process can be performed at the target time of the vehicle positioning track, so as to determine the fourth track point.
For example, as can be understood in conjunction with fig. 7, the target time is determined by determining the later one of the first acquisition time and the second acquisition time as the target time in the example of fig. 7. For example, for the track point pair of track point 2 and track point a, the later acquisition time is 200ms, and then the target time can be determined to be 200ms.
Then, for example, the track point 2 'with the acquisition time of 200ms in the self-vehicle positioning track can be determined (which is actually equivalent to the track point 2) according to the self-vehicle positioning track, and the track point a' with the acquisition time of 200ms in the perceived vehicle track can be determined according to the perceived vehicle track after delay compensation (which can be predicted by performing frame interpolation processing). The locus point 2 'can be understood as the fourth locus point described above and the locus point a' can be understood as the third locus point described above.
Similar processing may also be performed for the remaining pairs of trajectory points illustrated in fig. 7, and will not be described again.
S505, determining the track point pair after time alignment by the third track point and the fourth track point.
After determining the third and fourth track points, the third and fourth track points may be determined as pairs of time-aligned track points.
S506, determining a perceived vehicle track after time alignment and a self-vehicle positioning track after time alignment according to the plurality of track point pairs after time alignment.
And then, according to the plurality of track point pairs after time alignment, a plurality of third track points are sequentially connected to form a perceived vehicle track after time alignment, and a plurality of fourth track points are sequentially connected to form a self-vehicle positioning track after time alignment. The acquisition time of each track point in the perceived vehicle track after time alignment and the self-vehicle positioning track after time alignment corresponds.
According to the vehicle positioning method provided by the embodiment of the application, through aligning each track point in the perceived vehicle track and the self-vehicle positioning track to the same target time, the data error caused by different data acquisition frequencies of the road side perception equipment and the target vehicle can be effectively avoided, and the self-vehicle positioning track and the track point of the perceived vehicle track are ensured to be at the same time point, so that high-quality track data can be provided for subsequent data processing, and the accuracy of vehicle positioning can be further improved.
On the basis of the above description, it should also be noted that, in addition to the positioning information of the target vehicle, the vehicle positioning method provided by the present application may also provide positioning information of other vehicles in the road.
It can be appreciated that in the related art, if a vehicle needs to know information of other vehicles around a road, it is generally necessary to rely on an automatic driving sensing technology, and a camera of the vehicle and a laser radar are used to perform multi-sensor fusion, so as to realize sensing of surrounding vehicles. However, the sensing technology of automatic driving requires the vehicle to be provided with expensive software and hardware sensing equipment, and many finished vehicles do not have the capabilities, so that the information of the rest vehicles in the road cannot be acquired by the current vehicles.
After the target vehicle track matched with the self-vehicle positioning track is determined, the vehicle positions of the rest vehicles except the target vehicle in the road at a plurality of moments in the first moment can be determined according to the rest perceived vehicle tracks except the target vehicle track, so that the non-self-driving vehicle has the capability of perceiving surrounding vehicles.
In one possible implementation, the locations of the remaining vehicles may be rendered in the graphical user interface of the target vehicle at various times within the first period, for example, based on the vehicle locations of the remaining vehicles. And rendering the position information of the target vehicle in the graphical user interface of the target vehicle at each moment of the first moment according to the vehicle position of the target vehicle, so that a user can quickly and effectively acquire the vehicle condition in the road.
For example, it can be understood with reference to fig. 8, and fig. 8 is a schematic diagram of an interface for displaying vehicle information according to an embodiment of the present application.
As shown in fig. 8, for example, location information of a target vehicle may be rendered in a graphical user interface based on the vehicle location of the target vehicle, such as shown with reference to 801. And may also render location information of the remaining vehicles in the graphical user interface based on the vehicle locations of the remaining vehicles, such as shown with reference to 802-805 in fig. 8.
In one possible implementation, the roadside computing device may also detect events in the road, for example, using video recognition capabilities, and determine a classification of the events. The event may be, for example, a vehicle collision event, a traffic jam event, an obstacle event, etc., and the embodiment does not limit the specific implementation of the event in the road, and then the road side computing device may send event information to the target vehicle, for example, where the event information may include, for example, event classification, event location, event image, etc., and the embodiment does not limit the specific implementation of the event information.
The target vehicle can render the event identifier corresponding to the event in the graphical user interface according to the event information, so as to remind the user of a certain event in the corresponding position.
In one possible implementation, when rendering the vehicle information of the target vehicle, the vehicle information of the other vehicles, and the event information, for example, the position of the other vehicles and the position of the event information may be converted into a position relative to the target vehicle, and then rendered. Meanwhile, risk early warning services such as collision early warning of surrounding vehicles, vehicles beyond the range of vision, early warning of vehicles and the like can be provided.
In summary, the technical scheme of the application can rely on a road-side sensing data vehicle road cooperation technology, and the road-side sensing equipment such as a camera or a millimeter wave radar erected on a road intelligent pole is utilized to acquire road-side information, and the road-side computing equipment is used for determining the vehicle track and the event information, and then the vehicle track and the event information are sent to the target equipment. The target equipment can combine the self-vehicle positioning information and the road side perception information, so that on one hand, the self-vehicle positioning accuracy is improved, particularly, the self-vehicle positioning effect can be greatly improved in places with poor signals such as tunnels, and on the other hand, the information such as other vehicles and events in roads can be obtained, so that a large number of non-automatic driving vehicles have the capability of obtaining the information such as surrounding perception vehicles and events, and further, early warning of risks, perception of beyond-sight risks and capability of improving the vehicle passing efficiency are realized.
In one possible implementation, the perceived vehicle trajectory issued by the roadside computing device may have some noise, disorder, frame loss, and the like, and may be smoothed first after the perceived vehicle trajectory is received. For example, it can be understood with reference to fig. 9, and fig. 9 is a schematic diagram of implementation of a smoothing process for sensing a vehicle track according to an embodiment of the present application.
As shown in fig. 9, the received perceived vehicle track may have a situation where data is repeatedly issued, for example, referring to the first situation in fig. 9, track points that are repeated at time t2 and time t3, and track points that are repeated at time t4 and time t5 exist in the received track data, and for these situations, for example, duplicate processing may be performed on the duplicate track points, for example, duplicate track points are removed, and only one of them remains.
And referring to the second case in fig. 9, the received perceived vehicle trajectory may have a data delay issue, for example, a certain trajectory point itself should correspond to the time t1, but actually corresponds to the time t 1'. And say that a certain track point itself should correspond to the instant t2, but in fact to the instant t 2'. Delay compensation may be performed when this occurs.
And referring to the third case in fig. 9, the received perceived vehicle trajectory may have a data missing situation, for example, there should be two trajectory points between time t1 and time t2, but because of the data missing, these two trajectory points are not present. For this case, for example, missing track points may be padded according to the received track points.
And referring to the fourth situation in fig. 9, the received perceived vehicle track may have track points out of order, and for the out of order situation, the track points may be correctly ordered according to the acquisition time of the track points, so as to implement the track smoothing process.
And then, carrying out subsequent processing based on the perceived vehicle track after the smoothing processing, so that the accuracy and the effectiveness of data processing can be effectively ensured.
On the basis of the above-described embodiments, a specific implementation of the vehicle positioning method provided by the present application will be described in further detail with reference to fig. 10, and fig. 10 is a schematic system diagram of the vehicle positioning method provided by the embodiment of the present application.
As shown in fig. 10, there are three sides of a cloud server, a road side device, and a target vehicle, where the cloud server may provide control services for the road side device, for example, control how the road side computing unit performs the computing process, and the cloud server may also provide map services for the target vehicle.
The road side computing device, such as the MEC shown in fig. 10, may perform event sensing and track fusion to determine event information and sense a vehicle track, and send related information to the target vehicle through the link of the MEC-RSU-OBU.
Among them, for example, there may be a map service application program of lane-level navigation in the target vehicle, in which map services, such as navigation processing, may be performed based on map services provided by the cloud through a map SDK (Software Development Kit ). And track fusion can be carried out according to the positioning result of the own vehicle and the perceived vehicle track, so that the driving track of the vehicle can be determined, the positioning result of the own vehicle, the positioning result of the other vehicle and the event in the road can be determined, and rendering is carried out in the application program, so that the corresponding vehicle and the event result are displayed.
Based on the above description, it can be determined that, according to the vehicle positioning method provided by the embodiment of the application, the perceived vehicle track issued by the road side computing device is received, and the positioning result of the own vehicle and the positioning result of the other vehicle are determined according to the perceived vehicle track, so that lane-level navigation service can be provided for the vehicle, and event information issued by the road side computing device can be received, and early warning of risk such as vehicle collision, accident event and the like can be performed in advance according to the event information, without depending on the perceived devices such as laser radars, cameras and the like on the vehicle side. Meanwhile, when the vehicle positioning is determined, the vehicle positioning is comprehensively determined according to the perceived vehicle track and the vehicle positioning track, so that the vehicle position can be accurately and effectively determined under the condition that the base station signal is not good or the base station signal cannot be received, and the positioning precision of the vehicle under special road sections such as a tunnel or mountain shielding is improved.
Fig. 11 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present application. As shown in fig. 11, the apparatus 110 includes a determining module 1101, a receiving module 1102, and a processing module 1103.
A determining module 1101, configured to determine a vehicle positioning track of the target vehicle according to the vehicle positioning information determined by the positioning unit of the target vehicle;
A receiving module 1102, configured to receive at least one perceived vehicle track sent by a roadside computing device, where the perceived vehicle track is determined by the roadside computing device according to collected roadside information;
the determining module 1101 is further configured to determine, among the at least one perceived vehicle trajectory, a target vehicle trajectory that matches the vehicle positioning trajectory;
The processing module 1103 is configured to predict, for any first moment in a first period, a vehicle position of the target vehicle at the first moment according to the target vehicle track if the vehicle positioning information of the target vehicle meets a first preset condition.
In one possible design, the processing module 1103 is further configured to:
for any first moment in the first period, if the vehicle positioning information of the target vehicle does not meet the first preset condition, determining the vehicle position of the target vehicle at the first moment according to the vehicle positioning information of the target vehicle at the first moment;
The first period is a period between a moment when the perceived vehicle track sent by the road side computing device is currently received and a moment when the perceived vehicle track sent by the road side computing device is received next.
In one possible design, the processing module 1103 is further configured to:
determining a sub-track according to the vehicle positions at a plurality of moments in the first period after predicting the vehicle position of the target vehicle at the first moment according to the target vehicle track;
and splicing the sub-track to the self-vehicle positioning track of the target vehicle to obtain an updated self-vehicle positioning track.
In one possible design, the determining module 1101 is specifically configured to:
Acquiring respective track identifiers of the perceived vehicle tracks;
If a preset track identifier exists in the track identifiers, determining a perceived vehicle track corresponding to the preset track identifier as the target vehicle track, wherein the preset track identifier is a track identifier of the perceived vehicle track matched with the self-vehicle positioning track of the target vehicle in a history period;
If the preset track mark does not exist in the track marks, determining the target vehicle track in the at least one perceived vehicle track according to the track points in the perceived vehicle track and the track points in the self-vehicle positioning track.
In one possible design, the determining module 1101 is specifically configured to:
Determining the similarity of the perceived vehicle track and the self-vehicle positioning track according to the track point information of each track point in the perceived vehicle track and the track point information of each track point in the self-vehicle positioning track;
And determining the perceived vehicle track with the highest similarity and the similarity larger than a first preset threshold value as the target vehicle track.
In one possible design, the determining module 1101 is specifically configured to:
determining the similarity between the perceived vehicle track corresponding to the preset track mark and the self-vehicle positioning track;
and if the similarity is greater than or equal to a second preset threshold, determining the perceived vehicle track corresponding to the preset track mark as the target vehicle track.
In one possible design, the processing module 1103 is further configured to:
before the target vehicle track matched with the self-vehicle positioning track is determined in the at least one perceived vehicle track, carrying out delay compensation on the acquisition time of each track point in the perceived vehicle track according to delay time length to obtain a perceived vehicle track after delay compensation;
determining at least one track point pair according to the corresponding relation between each track point in the perceived vehicle track after delay compensation and each track point in the self-vehicle positioning track;
Aiming at any track point pair, performing time alignment treatment on two track points in the track point pair to obtain a track point pair after time alignment;
and determining the perceived vehicle track after time alignment and the self-vehicle positioning track after time alignment according to the track point pairs after time alignment.
In one possible design, the pair of track points includes a first track point in the delay compensated perceived vehicle track and a second track point in the self-propelled vehicle positioning track;
the processing module 1103 is specifically configured to:
determining a target moment according to a first acquisition moment corresponding to the first track point and a second acquisition moment corresponding to the second track point;
The third track point with the acquisition time being the target time in the delay-compensated perceived vehicle track and the fourth track point with the acquisition time being the target time are determined in the self-vehicle positioning track;
and determining the track point pair after time alignment by the third track point and the fourth track point.
In one possible design, the determining module 1101 is further configured to:
After the target vehicle track matched with the self-vehicle positioning track is determined in the at least one perceived vehicle track, vehicle positions of other vehicles at a plurality of moments in a first moment are determined according to the rest perceived vehicle tracks except the target vehicle track, wherein the rest vehicles are vehicles except the target vehicle in a road.
In one possible design, the processing module 1103 is further configured to:
Rendering position information of the remaining vehicles in a graphical user interface of the target vehicle at each time within the first period according to vehicle positions of the remaining vehicles, and
And rendering the position information of the target vehicle in a graphical user interface of the target vehicle at each moment in the first period according to the vehicle position of the target vehicle.
The device provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and its implementation principle and technical effects are similar, and this embodiment will not be described herein again.
Fig. 12 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application, and as shown in fig. 12, the electronic device 120 of this embodiment includes a processor 1201 and a memory 1202, where
A memory 1202 for storing computer-executable instructions;
A processor 1201 for executing computer-executable instructions stored in a memory to perform the steps performed by the vehicle positioning method of the above embodiment. Reference may be made in particular to the relevant description of the embodiments of the method described above.
Alternatively, the memory 1202 may be separate or integrated with the processor 1201.
When the memory 1202 is provided separately, the electronic device further comprises a bus 1203 for connecting said memory 1202 and the processor 1201.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, the vehicle positioning method executed by the electronic equipment is realized.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The integrated modules, which are implemented in the form of software functional modules, may be stored in a computer readable storage medium. The software functional module is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (english: processor) to perform some of the steps of the methods according to the embodiments of the application.
It should be understood that the above Processor may be a central processing unit (english: central Processing Unit, abbreviated as CPU), or may be other general purpose processors, a digital signal Processor (english: DIGITAL SIGNAL Processor, abbreviated as DSP), an Application-specific integrated Circuit (english: application SPECIFIC INTEGRATED Circuit, abbreviated as ASIC), or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor for execution, or in a combination of hardware and software modules in a processor for execution.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile memory NVM, such as at least one magnetic disk memory, and may also be a U-disk, a removable hard disk, a read-only memory, a magnetic disk or optical disk, etc.
The bus may be an industry standard architecture (Industry Standard Architecture, ISA) bus, an external device interconnect (PERIPHERAL COMPONENT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of the present application are not limited to only one bus or to one type of bus.
The storage medium may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
Those of ordinary skill in the art will appreciate that all or a portion of the steps of implementing the various method embodiments described above may be implemented by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs the steps comprising the method embodiments described above, and the storage medium described above includes various media capable of storing program code, such as ROM, RAM, magnetic or optical disk.
It should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solution described in the above embodiments may be modified or some or all of the technical features may be equivalently replaced, and these modifications or substitutions do not make the essence of the corresponding technical solution deviate from the scope of the technical solution of the embodiments of the present application.
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