CN113742437A - Map updating method and device, electronic equipment and storage medium - Google Patents

Map updating method and device, electronic equipment and storage medium Download PDF

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CN113742437A
CN113742437A CN202110951027.9A CN202110951027A CN113742437A CN 113742437 A CN113742437 A CN 113742437A CN 202110951027 A CN202110951027 A CN 202110951027A CN 113742437 A CN113742437 A CN 113742437A
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track
target
road
point
coordinate position
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CN113742437B (en
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龚文兵
李曼
谷艳蕾
杨建忠
卢振
白红霞
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30241—Trajectory

Abstract

The disclosure discloses a map updating method, a map updating device, electronic equipment and a storage medium, and relates to the fields of intelligent transportation, automatic driving and navigation. The specific implementation scheme is as follows: the method comprises the steps of obtaining target driving data of a plurality of moving objects on a target road, generating a track central line representing the form of the target road according to the target driving data, and updating the electronic map according to the difference between a first coordinate position of each position point in the target road in the electronic map and a second coordinate position of a corresponding track point in the track central line. Therefore, the electronic map can be updated according to the driving data of the moving object without acquiring road form data by a special acquisition vehicle, so that the updating cost of the electronic map can be reduced, and the updating efficiency can be improved.

Description

Map updating method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of internet, and in particular, to the fields of intelligent transportation, automatic driving, and navigation, and in particular, to a map updating method, apparatus, electronic device, and storage medium.
Background
The map producer can collect road form data through the collection vehicle, and objectively present the collected road form data in the electronic map so as to act on daily travel navigation of people. The road form data mainly comprises information such as road length, road width, road trend, road curvature degree, road grade, whether the road is a split road and the like.
The method is limited by the collection cost of a collection vehicle, and is generally difficult to collect road form data of all regions in the country within one year, even a plurality of remote regions can complete one-time collection for many years, but the road change of the real world is different day by day, so that the road form data in an electronic map cannot be updated in time, and the condition of presenting errors is caused, and further when planning travel navigation for a user, the condition of voice broadcast errors is caused, even the condition of leading the user to turn around in a violation manner can occur when the user drives a vehicle, and the travel experience of the user is seriously reduced.
Therefore, it is very important how to update the road form data in the electronic map in time to improve the user travel experience.
Disclosure of Invention
The disclosure provides a map updating method, a map updating device, an electronic device and a storage medium.
According to an aspect of the present disclosure, there is provided a map updating method, including:
acquiring target driving data of a plurality of moving objects on a target road;
generating a track central line representing the target road form according to the plurality of target driving data;
acquiring a first coordinate position of each position point in the target road in an electronic map;
and updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
According to another aspect of the present disclosure, there is provided a map updating apparatus including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring target driving data of a plurality of moving objects on a target road;
the generating module is used for generating a track central line representing the target road form according to the plurality of target driving data;
the second acquisition module is used for acquiring a first coordinate position of each position point in the target road in the electronic map;
and the updating module is used for updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
According to still another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a map updating method according to the above aspect of the disclosure.
According to yet another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium of computer instructions for causing a computer to perform the map updating method set forth in the above-described aspect of the present disclosure.
According to yet another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the map updating method set forth in the above-mentioned aspect of the present disclosure.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
fig. 1 is a schematic flowchart of a map updating method according to a first embodiment of the disclosure;
fig. 2 is a schematic flowchart of a map updating method according to a second embodiment of the disclosure;
fig. 3 is a schematic flowchart of a map updating method according to a third embodiment of the disclosure;
FIG. 4 is a schematic cross-sectional view of a roadway in an embodiment of the present disclosure;
FIG. 5 is a schematic diagram illustrating the calculation of a center point of a trajectory in an embodiment of the present disclosure;
fig. 6 is a flowchart illustrating a map updating method according to a fourth embodiment of the disclosure;
FIG. 7 is a schematic diagram illustrating a trajectory center point fit in an embodiment of the present disclosure;
FIG. 8 is a first schematic diagram illustrating a first exemplary variation of a road profile based on a trajectory centerline;
fig. 9 is a schematic flowchart of a map updating method according to a fifth embodiment of the disclosure;
fig. 10 is a flowchart illustrating a map updating method according to a sixth embodiment of the disclosure;
FIG. 11 is a schematic diagram of a second embodiment of the present disclosure illustrating a morphological change of a road excavated based on a trajectory centerline;
fig. 12 is a schematic structural diagram of a map updating apparatus according to a seventh embodiment of the disclosure;
FIG. 13 shows a schematic block diagram of an example electronic device that may be used to implement embodiments of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
In the related art, road form data are collected through a collection vehicle, specifically, a corresponding regional collection plan is formulated every year, and the collection and the update of the road form data are performed on the total amount of roads in each region according to the regional collection plan.
However, the above method has the disadvantages of high acquisition cost and long acquisition period, and also has the disadvantages of not wide coverage, which results in that the electronic map cannot be updated in time after the road shape changes in the real world.
Therefore, in view of the above existing problems, the present disclosure provides a map updating method, apparatus, electronic device, and storage medium.
A map updating method, apparatus, electronic device, and storage medium of the embodiments of the present disclosure are described below with reference to the accompanying drawings.
Fig. 1 is a flowchart illustrating a map updating method according to a first embodiment of the disclosure.
The map updating method is exemplified by being configured in a map updating apparatus, which can be applied to any electronic device, so that the electronic device can execute a map updating function.
The electronic device may be any device with computing capability, for example, a personal computer, a mobile terminal, a server, and the like, and the mobile terminal may be a hardware device with various operating systems, touch screens, and/or display screens, such as an in-vehicle device, a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and the like.
As shown in fig. 1, the map updating method may include the steps of:
step 101, acquiring target driving data of a plurality of moving objects on a target road.
In the embodiment of the present disclosure, the target road may be any one road or line or trunk line in the electronic map.
In the embodiment of the present disclosure, the moving object may be a movable object such as a person or a vehicle.
In the disclosed embodiment, the target travel data may include at least one of a coordinate position, an instantaneous speed, and a time stamp of a track point on the target road to which the mobile object travels.
In the embodiment of the present disclosure, the target driving data may be collected by a relevant sensor in the vehicle or the mobile terminal, for example, taking a moving object as the vehicle, the coordinate Position of each track point on the target road where the vehicle drives may be collected by a positioning device (such as a GPS (Global positioning System)), and the instantaneous speed of each track point on the target road where the vehicle drives is determined according to data collected by a speed sensor and a displacement sensor on the vehicle.
In the embodiment of the present disclosure, target driving data collected by a vehicle or a mobile terminal may be integrated into a map end, so that in the present disclosure, target driving data of a plurality of moving objects on a target road may be acquired from the map end. Alternatively, the target driving data of the plurality of moving objects on the target road may also be obtained from a third-party data source, which is not limited by the present disclosure.
And 102, generating a track central line representing the target road form according to the plurality of target driving data.
In the embodiment of the present disclosure, a trajectory center line for representing a target road shape may be generated from target travel data of a plurality of moving objects.
And 103, acquiring a first coordinate position of each position point in the target road in the electronic map.
And 104, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
In the embodiment of the present disclosure, the coordinate position of each position point in the target road in the electronic map may be marked as a first coordinate position, and the coordinate position of each track point in the track center line may be marked as a second coordinate position.
In the embodiment of the present disclosure, the first coordinate position of each position point in the target road may be obtained from the electronic map, the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track center line is determined, and the electronic map is updated according to the difference.
For example, assuming that the driving direction of the road corresponds to the longitudinal axis (y-axis) of the coordinate system, the value of the first coordinate position of the position point in the target road is equal to the value of the y-axis of the second coordinate position of the corresponding track point in the track center line, and the difference between the values of the x-axis in the first coordinate position and the second coordinate position can be calculated.
It is understood that the smaller the difference is, the smaller the target road form change is, and the larger the difference is, the larger the target road form change is, and therefore, in a possible implementation manner of the embodiment of the present disclosure, when the difference is smaller than the set threshold, the electronic map may not need to be updated, and when the difference is larger than the set threshold, the electronic map may be updated.
According to the map updating method, the target driving data of the plurality of moving objects on the target road are obtained, the track central line representing the form of the target road is generated according to the plurality of target driving data, and the electronic map is updated according to the difference between the first coordinate position of each position point in the target road in the electronic map and the second coordinate position of the corresponding track point in the track central line. Therefore, the electronic map can be updated according to the driving data of the moving object without acquiring road form data by a special acquisition vehicle, so that the updating cost of the electronic map can be reduced, and the updating efficiency can be improved.
In the technical scheme of the present disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information (such as driving data) of the user are all performed under the premise of obtaining the consent of the user, and all meet the regulations of the relevant laws and regulations, and do not violate the public customs.
In one possible implementation of the disclosed embodiments, the target driving data of each moving object on the target road may be determined based on a hidden markov model. The above process is described in detail with reference to example two.
Fig. 2 is a flowchart illustrating a map updating method according to a second embodiment of the disclosure.
As shown in fig. 2, the map updating method may include the steps of:
in step 201, driving data of a plurality of moving objects is acquired.
In the disclosed embodiments, the travel data of a plurality of moving objects may be obtained from a navigation source (such as an electronic map) or a third-party data source.
Step 202, aiming at each moving object, inputting the driving data of the moving object into a hidden Markov model to obtain a road matched with each first track point in the driving data output by the hidden Markov model.
In the embodiment of the present disclosure, each locus point in the travel data of the moving object may be locus-matched with the target road using an HMM (Hidden Markov Model). That is, for each moving object, the travel data of the moving object may be input to the hidden markov model to obtain a road on which the first track points in the travel data output by the hidden markov model match.
As an example, the hidden markov model may be input as sequence information corresponding to each first track point in the driving data, such as the following: x is the number of1,y1,t1；x2,y2,t2；…；xn,yn,tnAnd outputting a matching result corresponding to each track point and the road: x is the number of1,y1,t1,link1,dist1,angle1,ep1；x2,y2,t2,link2,dist2,angle2,ep2；…；xn,yn,tn,linkn,distn,anglen,epn. Wherein, n is the number of first track point in the data of traveling, and x is the longitude coordinate of first track point, and y is the latitude coordinate of first track point, and t is the timestamp that the moving object traveled to first track point, and link is the road that first track point matches, and dist is the projection distance on first track point to the road link that matches, and angle is the contained angle between first track point and the road link that matches, and ep is the radiation probability that first track point matches. It should be noted that the above listed outputs are only a part of the actual matching results, and the disclosure does not describe in detail other information output by the hidden markov model.
And step 203, determining a second track point matched with the target road from the first track points of the mobile objects according to the road matched with the first track points of the mobile objects.
In the embodiment of the present disclosure, for each moving object, a second track point that matches the target road may be determined from the first track points of the moving object.
And step 204, determining target driving data from the driving data according to the second track points of the moving objects.
In the embodiment of the present disclosure, the target travel data including only the second track point may be filtered from the first track points of the travel data.
Step 205, generating a track central line representing the target road form according to the plurality of target driving data.
And step 206, acquiring a first coordinate position of each position point in the target road in the electronic map.
And step 207, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
The execution process of steps 205 to 207 may refer to the execution process of any embodiment, which is not described herein.
The map updating method of the embodiment of the disclosure includes acquiring driving data of a plurality of moving objects, inputting the driving data of the moving objects into a hidden Markov model for each moving object to obtain a road matched with each first track point in the driving data output by the hidden Markov model; determining second track points matched with the target road from the first track points of the moving objects according to the road matched with the first track points of the moving objects; and determining target driving data from the driving data according to the second track point of each moving object. Therefore, the track matching is carried out by adopting the hidden Markov model, and the accuracy and the reliability of the target driving data matching result can be improved.
In order to clearly illustrate how the trajectory center line representing the target road shape is generated in any of the above embodiments, the present disclosure proposes another map updating method.
Fig. 3 is a flowchart illustrating a map updating method according to a third embodiment of the disclosure.
As shown in fig. 3, the map updating method may include the steps of:
in step 301, target driving data of a plurality of moving objects on a target road is acquired.
The execution process of step 301 may refer to any of the above embodiments, which are not described herein.
Step 302, segmenting the target road at preset intervals according to the driving direction of the target road to obtain a plurality of sections.
For example, the mark preset interval is M.
It should be noted that the value of the preset interval M affects the number of the cross sections, for example, when M is larger, the number of the cross sections is smaller, and when M is smaller, the number of the cross sections is larger. When the number of the sections is large, the accuracy of the result generated by the track center line of the target road can be improved, but the calculation complexity is obviously improved by reducing the value of M to obtain a large number of sections.
Therefore, in a possible implementation manner of the embodiment of the present disclosure, in order to consider both the complexity of the calculation and the accuracy of the result generated by the trajectory center line, different M values may be set for roads in different scenes, for example, for closed roads such as expressways, the number of sections may be reduced by increasing the M value due to a straight road trend, and while the calculation amount is significantly reduced, the accuracy of the result generated by the trajectory center line may not be affected, and for urban roads, especially roads in a construction area, the number of sections may be increased by decreasing the M value due to a frequent road trend change.
As an example, as shown in fig. 4, M is 10 meters for illustration, and if the length of the target road is 50 meters, the target road may have 6 cross sections in total, starting from the start point of the target road, and taking one cross section every 10 meters.
Step 303, generating a corresponding target track according to the coordinate position and the timestamp of each track point in the target driving data of each moving object.
In the embodiment of the present disclosure, for the target travel data of each moving object, a corresponding target track may be generated according to the coordinate position and the timestamp of each track point (i.e., the second track point in fig. 2) in the target travel data.
As an example, in order to reduce the complexity of the calculation, for each track point in the target driving data, the track points corresponding to the coordinate position may be sequentially connected from small to large according to the value of the timestamp, so as to generate the target track corresponding to the target driving data.
As another example, in order to improve the accuracy of curve fitting, curve fitting may be performed on each trace point according to a timestamp of each trace point in the target driving data, so as to generate a target track corresponding to the target driving data.
And step 304, determining a third track point intersected with each section according to the target track of each moving object.
As an example, taking the driving direction of the target road as the arrow direction in fig. 5 for illustration, 9 track points are in total in the target driving data matched to the target road, the target track corresponding to the target driving data may be as shown by a curve 1 in fig. 5, and the third track point where the target track intersects with the first cross section is an intersection point of a connecting line of the track point 2 and the track point 3 and the cross section.
And 305, determining a track center point corresponding to each section according to the intersected third track points.
In the embodiment of the present disclosure, for each cross section, since the target trajectory of each moving object intersects with the cross section, a plurality of third trajectory points intersecting with each cross section are provided.
And step 306, generating a track central line of the target road according to the track central points corresponding to the plurality of sections.
In the embodiment of the present disclosure, a trajectory center line of the target road may be generated according to the trajectory center point corresponding to the plurality of cross sections.
As an example, in order to reduce the complexity of the calculation, the track center lines may be obtained by sequentially connecting the track center points according to the driving direction of the road.
As another example, in order to improve the accuracy of curve fitting, curve fitting may be performed on the central points of the respective tracks to obtain track central lines.
And 307, acquiring a first coordinate position of each position point in the target road in the electronic map.
And 308, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
The execution process of steps 307 to 308 may refer to the execution process of any of the above embodiments, which is not described herein again.
According to the map updating method, the track central line representing the target road form is obtained through fitting according to the actually measured target driving data, and the accuracy and reliability of the track central line generation result can be improved.
In a possible implementation manner of the embodiment of the present disclosure, the trajectory center point corresponding to each cross section may be determined based on a gaussian mixture model. The above process is described in detail with reference to example four.
Fig. 6 is a flowchart illustrating a map updating method according to a fourth embodiment of the disclosure.
As shown in fig. 6, the map updating method may include the steps of:
step 601, acquiring target driving data of a plurality of moving objects on a target road.
Step 602, according to the driving direction of the target road, the target road is segmented at preset intervals to obtain a plurality of sections.
Step 603, generating a corresponding target track according to the coordinate position and the timestamp of each track point in the target driving data of each moving object.
And step 604, determining a third track point intersected with each section according to the target track of each moving object.
The execution process of steps 601 to 604 may refer to any of the above embodiments, which are not described herein.
And step 605, inputting the coordinate positions of the intersected third track points into a Gaussian mixture model for each section.
In the disclosed embodiment, the GMM (Gaussian Mixture Model) may include at least one sub-Gaussian Model, and the number of labeled sub-Gaussian models is N. For example, N may be 1 when the target road has only a straight road, 2 when the target road includes a straight road and a right road, and 3 when the target road includes a straight road, a right road, and a left road.
For example, the probability distribution form of GMM can be as shown in equation (1):
Figure BDA0003218626970000101
wherein alpha isiWeight coefficient, μ, for sub-Gaussian model iiThe mean value of the one-dimensional coordinates corresponding to a plurality of third track points intersected with the cross section is obtained; deltaiIs one corresponding to a plurality of third track points with cross sections intersectingMean square error of dimensional coordinates, θ, refers to the sum of all parameters, i.e. represents α in the probability distributioni、μiAnd deltai。
As an example, as shown in fig. 7, the driving direction of the target road is taken as the positive direction of the vertical axis, and for the first cross section in the target road, since the y-axis value in the coordinate position of each third track point intersecting with the cross section is the same, the μ can be determined only according to the one-dimensional coordinate (i.e., the x-axis value) corresponding to each third track pointiAnd deltai。
And 606, determining the coordinate position corresponding to the peak of the probability distribution of the Gaussian mixture model.
And step 607, taking the coordinate position corresponding to the wave crest as the track central point corresponding to the cross section.
As shown in fig. 7, for the first cross section in the target road, the coordinate position corresponding to the peak of the probability distribution of the gaussian mixture model may be used as the center point of the track corresponding to the cross section.
Step 608, generating a track center line of the target road according to the track center points corresponding to the plurality of cross sections.
It should be noted that when N is 1, one trajectory center line is generated, and when N > 1, multiple trajectory center lines are generated, that is, each section has multiple trajectory center points, and multiple trajectory center lines can be obtained by fitting according to the multiple trajectory center points corresponding to each section, for example, when N is 2, two trajectory center lines may be generated, one being a main trajectory center line (for example, a trajectory center line representing a straight road form in the target road), and the other being an auxiliary trajectory center line (for example, a trajectory center line representing a right-turn road or a left-turn road form in the target road).
And step 609, acquiring a first coordinate position of each position point in the target road in the electronic map.
And step 610, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
The execution process of steps 608 to 610 may refer to the execution process of any of the above embodiments, and is not described herein again.
In any embodiment of the present disclosure, when N is equal to 1, a difference between a first coordinate position of each position point and a second coordinate position of a corresponding track point in the track center line may be determined, and whether the difference is greater than a first set threshold value or not may be determined, if yes, the degree of curvature of the target road in the electronic map may be updated, and if not, the degree of curvature of the target road in the electronic map may not be updated.
In any embodiment of the present disclosure, when N is greater than 1, a difference between a first coordinate position of each location point and a second coordinate position of a corresponding track point in each track center line may be determined, and if there is a difference corresponding to at least one track center line that is greater than a first set threshold, the degree of curvature of the target road in the electronic map is updated.
As an example, as shown in fig. 8, the arrow direction is a driving direction of the target road, and a DTW (Dynamic Time Warping) distance (i.e., an average distance between two curves) between each position point in the target road and a corresponding track point in the track center line may be calculated. The DWT distance is used for measuring the difference between the road form in the electronic map and the road form in the real world. When the DTW distance is greater than a certain threshold (for example, 15 meters), it indicates that the trend or the degree of curvature of the target road changes, and if the degree of curvature of the target road in the electronic device is not updated, it may happen that the user is prompted to keep going straight when navigating through the target road, but actually needs to be prompted to drive left and front.
Therefore, in the present disclosure, when the DTW distance is greater than a certain threshold, or when the difference is greater than a first set threshold, the degree of curve of the target road in the electronic device may be updated. For example, the form of the target road in the electronic map may be updated according to the track center line, and for example, if the track center line is S-shaped, the form of the target road may be updated to S-shaped. Or the coordinate position of the corresponding position point of the target road in the electronic map can be updated according to the coordinate position of each track point in the track center line. Therefore, the target road form can be updated, so that the target road in the electronic map is more practical and more reasonable when the navigation planning is performed for the user.
The map updating method of the embodiment of the disclosure determines the track central point corresponding to each section based on the peak of the probability distribution of the gaussian mixture model, so that the determined track central point can represent the commonality of each third track point, thereby generating the track central line of the target road based on the track central points corresponding to each section, and improving the rationality and reliability of the track central line generation result.
It should be noted that the target road may be a bidirectional driving road, and correspondingly, the generated track center line may be multiple, that is, each driving direction has a corresponding track center line. The above process is described in detail with reference to example four.
Fig. 9 is a flowchart illustrating a map updating method according to a fifth embodiment of the disclosure.
As shown in fig. 9, the map updating method may include the steps of:
step 901, obtaining target driving data of a plurality of moving objects on a target road, wherein the target road is a bidirectional driving road and comprises a first driving direction and a second driving direction.
For example, the first travel direction may be from east to west and the second travel direction may be from west to east, or the first travel direction may be from south to north and the second travel direction may be from north to south. Of course, the first and second directions of travel may be other directions as well, and the present disclosure is not limited thereto.
Step 902 is generating trajectory center lines corresponding to the first driving direction and the second driving direction respectively according to the plurality of target driving data.
In the embodiment of the present disclosure, the target road may be segmented at preset intervals according to the first driving direction or the second driving direction to obtain a plurality of sections, and the driving direction of each target driving data may be determined, and then, the first driving data whose driving direction matches the first driving direction and the second driving data whose driving direction matches the second driving direction may be determined from the plurality of target driving data, respectively.
Generating corresponding first target tracks according to the coordinate positions and the timestamps of the track points in the first running data, determining fourth track points intersected with the sections aiming at each first target track, and determining a first track central point corresponding to each section according to a plurality of intersected fourth track points aiming at each section; therefore, the track central line corresponding to the first traveling direction can be generated according to the first track central points corresponding to the plurality of cross sections.
Similarly, a corresponding second target track can be generated according to the coordinate position and the timestamp of each track point in each second driving data, a fifth track point intersecting each section can be determined for each second target track, and a second track center point corresponding to each section can be determined for each section according to a plurality of intersecting fifth track points; therefore, the track central line corresponding to the second driving direction can be generated according to the second track central points corresponding to the plurality of cross sections.
It should be noted that the first track center point and the second track point corresponding to each cross section may be determined based on a gaussian mixture model, and a specific implementation process may refer to the fourth embodiment, which has similar implementation principles and is not described herein.
Step 903, acquiring a first coordinate position of each position point in the target road in the electronic map.
And 904, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
The execution process of steps 903 to 904 can refer to any of the above embodiments, and is not described herein.
In any of the embodiments of the present disclosure, a first difference between a first coordinate position of each position point and a second coordinate position of a corresponding track point in a first track center line may be determined, a second difference between the first coordinate position of each position point and a second coordinate position of the corresponding track point in a second track center line may be determined, and whether the first difference and the second difference are greater than a first set threshold may be determined.
For example, when the first difference is larger than the first set threshold, the form of the target road in the electronic map may be updated according to a track center line corresponding to the first driving direction, and for example, when the track center line is S-shaped, the form of the target road may be updated to S-shaped. Or updating the coordinate position of the corresponding position point of the target road in the electronic map according to the coordinate position of each track point in the track center line corresponding to the first driving direction. Therefore, the target road form can be updated, so that the target road in the electronic map is more practical and more reasonable when navigating a user.
According to the map updating method disclosed by the embodiment of the disclosure, the track center line corresponding to each driving direction is respectively generated, so that the form of the target road is excavated based on the track center line corresponding to each driving direction, the electronic map is updated based on the excavation result, and the reliability and the accuracy of the updating result can be improved.
It should be noted that, for an undivided road, a user can turn around freely when driving a vehicle on the road, but for a separated road, the user can only turn around at a designated intersection, and in practical application, a target road may also be changed from the undivided road to the separated road, and if the type of the target road in the electronic map is not updated in time, the user may be prompted to turn around freely during navigation, which is very likely to cause a situation that the user is penalized due to illegal turning around when driving the vehicle. Therefore, in any embodiment of the disclosure, in order to improve the use experience of the user, whether the type of the target road changes or not may also be identified, and if so, the electronic map is updated in time. The above process is described in detail with reference to example six.
Fig. 10 is a flowchart illustrating a map updating method according to a sixth embodiment of the disclosure.
As shown in fig. 10, the map updating method may include the steps of:
step 1001, acquiring target driving data of a plurality of moving objects on a target road, wherein the target road is a bidirectional driving road and comprises a first driving direction and a second driving direction, and the type of the target road marked in the electronic map is an unseparated type.
Step 1002, generating trajectory center lines corresponding to the first driving direction and the second driving direction respectively according to the plurality of target driving data.
Step 1003, acquiring a first coordinate position of each position point in the target road in the electronic map.
And 1004, updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
The execution process of steps 1001 to 1004 may refer to any of the above embodiments, which are not described herein.
Step 1005, determining the distance between the track center line corresponding to the first driving direction and the track center line corresponding to the second driving direction.
In the embodiment of the present disclosure, the average distance between two track center lines may be determined according to the coordinate position of each track point in the track center line corresponding to the first driving direction and the coordinate position of each track point in the track center line corresponding to the second driving direction.
And step 1006, in response to the distance being greater than a second set threshold, updating the type of the target road in the electronic map to a split type.
It is understood that when the distance between the track center line corresponding to the first traveling direction and the track center line corresponding to the second traveling direction is small, for example, less than 10 meters, the type of the target road may not be changed, and when the distance between the track center line corresponding to the first traveling direction and the track center line corresponding to the second traveling direction is large, for example, greater than 40 meters, at this time, the target road is wide, and a situation where the target road becomes a split road may occur.
Therefore, in the embodiment of the present disclosure, in order to update the electronic map in time, it may be determined whether a distance between a track center line corresponding to the first driving direction and a track center line corresponding to the second driving direction is greater than a second set threshold (for example, 40 meters), and if so, it is determined that the target road is probably changed to the split road, and therefore, the type of the target road in the electronic map may be updated to the split road.
As an example, as shown in fig. 11, assuming that the type of the target road is an undivided type, the user may turn around freely when driving a vehicle on the target road, and if the average distance between the trajectory center lines corresponding to the forward direction and the reverse direction of the target road is greater than a certain threshold (for example, 40 meters), it indicates that the target road is likely to have become a divided type road, and when navigating through the target road, the user may be prompted to turn around at a specified intersection, so as to avoid a situation that the user is penalized due to illegal turning around.
Certainly, in order to improve the reliability and accuracy of the update result of the electronic map, other technical means may be combined to assist in verifying whether the target road is changed into a separate road, or, after verification, lane information, electronic eyes, intersections and other element information may be updated.
In summary, the track central points of all roads can be calculated based on massive driving data, whether the road form changes or not is determined according to the track central lines of all roads, automatic identification of road form changes can be achieved, updating timeliness of road form data can be shortened to the day level, and therefore user travel experience is greatly improved.
Specifically, the method can acquire the driving data of a large number of users or vehicles generated in the real world, and bind the driving data with each road in the electronic map; then, calculating a track central line corresponding to each driving direction of each road according to the total driving data corresponding to each road; and finally, extracting relevant features based on the track center line of each road, judging whether the road form changes or not through a certain judgment criterion, and updating and validating the changed road form data in time. The method mainly comprises the following steps:
first, computation of the center line of the road track.
First, the roads are sectioned at intervals of M meters, with the smaller M, the more accurate the trajectory centerline calculation, but the higher the computational complexity. Taking M as 10, if the road length is 50 meters, 6 sections will be produced including the head and the tail. And secondly, acquiring all the user driving data, and performing track binding based on the latest road network to obtain the road bound by each track point of each piece of driving data. And then, taking the road as a main body, and acquiring all the driving data bound to the road in the forward direction or the reverse direction, namely, each piece of driving data has at least one track point on the road. And then, calculating the intersection points of the sections and the tracks corresponding to each piece of driving data to obtain the intersection point distribution of the sections and the full tracks corresponding to the roads. And finally, fitting the intersection data of each section through GMM, taking the position of the peak as a track central point corresponding to the section, and sequentially connecting the track central points corresponding to the sections according to the road driving direction to obtain the track central line of the road in the road driving direction.
Second, excavation with road morphology change.
Generally, the larger the amount of driving data passing through a road is, the more accurate the calculated track center line is, and the more the road shape information of the road in the real world can be reflected. The track center line based on the road can be used for digging various road form changes, such as the trend or the bending degree of the road, whether the road is a split road or not, and the like.
For the excavation of the change of the trend or the bending degree of the road, the DTW distance between the road coordinate sequence in the basic road network and the track central line coordinate sequence corresponding to a certain driving direction can be calculated, and the DWT distance measures the difference between the road form in the electronic map and the road form in the real world. If the calculated DTW distance is greater than a certain threshold (e.g., 15 meters), the road shape such as the road direction or the curve degree in the electronic map can be corrected according to the position of the track center line.
For the excavation of whether the road is a separated road, the non-separated bidirectional driving road is assumed to be arranged in front of a certain road in the electronic map, the middle of the road is separated by a yellow dotted line or has no marked line, the road becomes a separated road due to the widening of the road construction, and the middle of the road is separated by an isolation guardrail or a yellow solid line. Such mining for road morphology change usually calculates the average distance of the track center lines in the two driving directions of the road on each section, and if the average distance exceeds a certain threshold (e.g. 40 meters), it may be considered that the road has become a split road, and may provide traction for subsequent updating of lane information, electronic eyes, intersections, and other elements on the road.
Therefore, the driving data of a user or a vehicle and the road network data in the electronic map are used as input, the track central line of each road is calculated, and the road form change information is mined by extracting the characteristics between the track central line and the road, so that the defects of high acquisition cost, long acquisition period, low coverage and the like of the traditional vehicle acquisition mode can be well overcome, the updating timeliness of road form change can be shortened to the day level, and better navigation travel experience is brought to the user. In addition, the track center line of a single road can be calculated, and the track center lines of a plurality of roads can be calculated, for example, the track center lines of the roads in multiple directions such as straight running, left turning, right turning, turning around and the like are generated, so that whether the road form changes or not is determined based on the plurality of track center lines, and the accuracy and the reliability of the determination result can be improved.
The map updating method of the embodiment of the disclosure determines the distance between the track center line corresponding to the first driving direction and the track center line corresponding to the second driving direction; and updating the type of the target road in the electronic map into a separation type in response to the distance being larger than a second set threshold value. Therefore, the type of the target road can be updated in time, and the condition that the user is penalized due to illegal turning when driving the vehicle is avoided.
Corresponding to the map updating method provided in the embodiments of fig. 1 to 10, the present disclosure also provides a map updating apparatus, and since the map updating apparatus provided in the embodiments of the present disclosure corresponds to the map updating method provided in the embodiments of fig. 1 to 10, the implementation manner of the map updating method is also applicable to the map updating apparatus provided in the embodiments of the present disclosure, and will not be described in detail in the embodiments of the present disclosure.
Fig. 12 is a schematic structural diagram of a map updating apparatus according to a seventh embodiment of the disclosure.
As shown in fig. 12, the map updating apparatus 1200 may include: a first obtaining module 1201, a generating module 1202, a second obtaining module 1203, and an updating module 1204.
The first acquiring module 1201 is configured to acquire target driving data of a plurality of moving objects on a target road.
The generating module 1202 is configured to generate a track centerline representing a target road form according to a plurality of target driving data.
The second obtaining module 1203 is configured to obtain a first coordinate position of each position point in the target road in the electronic map.
And the updating module 1204 is configured to update the electronic map according to a difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track center line.
In a possible implementation manner of the embodiment of the present disclosure, the first obtaining module 1201 is specifically configured to: acquiring driving data of a plurality of moving objects; aiming at each moving object, inputting the driving data of the moving object into a hidden Markov model to obtain a road matched with each first track point in the driving data output by the hidden Markov model; determining second track points matched with the target road from the first track points of the moving objects according to the road matched with the first track points of the moving objects; and determining target driving data from the driving data according to the second track point of each moving object.
In a possible implementation manner of the embodiment of the present disclosure, the generating module 1202 is specifically configured to: segmenting the target road at preset intervals according to the driving direction of the target road to obtain a plurality of sections; generating a corresponding target track according to the coordinate position and the timestamp of each track point in the target driving data of each moving object; determining a third track point intersected with each section according to the target track of each moving object; determining a track central point corresponding to each section according to a plurality of intersected third track points aiming at each section; and generating a track central line of the target road according to the track central points corresponding to the plurality of sections.
In a possible implementation manner of the embodiment of the present disclosure, the generating module 1202 is specifically configured to: inputting the coordinate positions of a plurality of intersected third track points into a Gaussian mixture model aiming at each section; determining a coordinate position corresponding to a peak of probability distribution of the Gaussian mixture model; and taking the coordinate position corresponding to the wave crest as the central point of the track corresponding to the cross section.
In a possible implementation manner of the embodiment of the present disclosure, the updating module 1204 is specifically configured to: and when the difference is larger than a first set threshold value, updating the bending degree of the target road in the electronic map.
In a possible implementation manner of the embodiment of the present disclosure, the target road is a bidirectional driving road, which includes a first driving direction and a second driving direction, and the generating module 1204 is specifically configured to: and respectively generating track center lines corresponding to the first driving direction and the second driving direction according to the plurality of target driving data.
In a possible implementation manner of the embodiment of the present disclosure, the updating module 1204 is specifically configured to: determining a first difference between a first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the first traveling direction; determining a second difference between the first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the second driving direction; and when the first difference or the second difference is larger than a first set threshold value, updating the bending degree of the target road in the electronic map.
In a possible implementation manner of the embodiment of the present disclosure, the type of the target road in the electronic map is an unseparated type, and the map updating apparatus 1200 may further include:
and the determining module is used for determining the distance between the track central line corresponding to the first driving direction and the track central line corresponding to the second driving direction.
The updating module 1204 is further configured to update the type of the target road in the electronic map to a separate type in response to the distance being greater than a second set threshold.
The map updating device of the embodiment of the disclosure updates the electronic map by acquiring target driving data of a plurality of moving objects on a target road and generating a track center line representing a target road form according to the plurality of target driving data, so as to update the electronic map according to a difference between a first coordinate position of each position point in the target road in the electronic map and a second coordinate position of a corresponding track point in the track center line. Therefore, the electronic map can be updated according to the driving data of the moving object without acquiring road form data by a special acquisition vehicle, so that the updating cost of the electronic map can be reduced, and the updating efficiency can be improved.
In order to implement the foregoing embodiments, the present disclosure also provides an electronic device, where the electronic device may include the anchor client or the server in the foregoing embodiments, and the electronic device may include at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the map updating method according to any one of the above embodiments of the disclosure.
In order to implement the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the map updating method proposed by any of the above embodiments of the present disclosure.
In order to implement the above embodiments, the present disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the map updating method proposed by any of the above embodiments of the present disclosure.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
FIG. 13 shows a schematic block diagram of an example electronic device that may be used to implement embodiments of the present disclosure. The electronic device may include the server and the client in the above embodiments. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 13, the device 1300 includes a computing unit 1301 that can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 1302 or a computer program loaded from a storage unit 1307 into a RAM (Random Access Memory) 1303. In the RAM 1303, various programs and data necessary for the operation of the device 1300 can also be stored. The calculation unit 1301, the ROM 1302, and the RAM 1303 are connected to each other via a bus 1304. An I/O (Input/Output) interface 1305 is also connected to the bus 1304.
A number of components in the device 1300 connect to the I/O interface 1305, including: an input unit 1306 such as a keyboard, a mouse, or the like; an output unit 1307 such as various types of displays, speakers, and the like; storage unit 1308, such as a magnetic disk, optical disk, or the like; and a communication unit 1309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 1309 allows the device 1300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
Computing unit 1301 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing Unit 1301 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The calculation unit 1301 performs the respective methods and processes described above, such as the above-described map update method. For example, in some embodiments, the map updating methods described above may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 1308. In some embodiments, some or all of the computer program may be loaded onto and/or installed onto device 1300 via ROM 1302 and/or communications unit 1309. When the computer program is loaded into the RAM 1303 and executed by the computing unit 1301, one or more steps of the map updating method described above may be performed. Alternatively, in other embodiments, the computing unit 1301 may be configured to perform the above-described map update method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, System On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in a conventional physical host and a VPS (Virtual Private Server). The server may also be a server of a distributed system, or a server incorporating a blockchain.
It should be noted that artificial intelligence is a subject for studying a computer to simulate some human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware and software technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing, and the like; the artificial intelligence software technology mainly comprises a computer vision technology, a voice recognition technology, a natural language processing technology, machine learning/deep learning, a big data processing technology, a knowledge map technology and the like.
According to the technical scheme of the embodiment of the disclosure, the electronic map is updated according to the difference between the first coordinate position of each position point in the target road in the electronic map and the second coordinate position of the corresponding track point in the track center line by acquiring the target driving data of a plurality of moving objects on the target road and generating the track center line representing the form of the target road according to the plurality of target driving data. Therefore, the electronic map can be updated according to the driving data of the moving object without acquiring road form data by a special acquisition vehicle, so that the updating cost of the electronic map can be reduced, and the updating efficiency can be improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel or sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims (19)

1. A map updating method, comprising the steps of:
acquiring target driving data of a plurality of moving objects on a target road;
generating a track central line representing the target road form according to the plurality of target driving data;
acquiring a first coordinate position of each position point in the target road in an electronic map;
and updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
2. The method of claim 1, wherein the obtaining target driving data of a plurality of moving objects on a target road comprises:
acquiring driving data of a plurality of moving objects;
inputting the driving data of the moving object into a hidden Markov model aiming at each moving object to obtain a road matched with each first track point in the driving data output by the hidden Markov model;
determining a second track point matched with the target road from the first track points of the moving objects according to the road matched with the first track points of the moving objects;
and determining the target driving data from the driving data according to the second track point of each moving object.
3. The method of claim 1, wherein said generating a trajectory centerline characterizing said target road morphology from said plurality of target driving data comprises:
segmenting the target road at preset intervals according to the driving direction of the target road to obtain a plurality of sections;
generating a corresponding target track according to the coordinate position and the timestamp of each track point in the target driving data of each moving object;
determining a third track point intersecting each section according to the target track of each moving object;
for each section, determining a track central point corresponding to the section according to the intersected third track points;
and generating a track central line of the target road according to the track central points corresponding to the plurality of sections.
4. The method according to claim 3, wherein the determining, for each of the cross sections, a center point of a track corresponding to the cross section from the plurality of third track points that intersect comprises:
inputting coordinate positions of a plurality of intersected third track points into a Gaussian mixture model aiming at each section;
determining a coordinate position corresponding to a peak of the probability distribution of the Gaussian mixture model;
and taking the coordinate position corresponding to the wave crest as the track central point corresponding to the cross section.
5. The method according to any one of claims 1-4, wherein said updating the electronic map according to the difference between the first coordinate position of each of the location points and the second coordinate position of the corresponding track point in the track centerline comprises:
and when the difference is larger than a first set threshold value, updating the degree of curvature of the target road in the electronic map.
6. The method of claim 1, wherein the target road is a bi-directional driving road including a first driving direction and a second driving direction,
generating a track center line representing the target road form according to the plurality of target driving data, wherein the method comprises the following steps:
and respectively generating track center lines corresponding to the first driving direction and the second driving direction according to the plurality of target driving data.
7. The method of claim 6, wherein the updating the electronic map according to the difference between the first coordinate position of each of the location points and the second coordinate position of the corresponding track point in the track centerline comprises:
determining a first difference between a first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the first traveling direction;
determining a second difference between the first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the second driving direction;
when the first difference or the second difference is larger than a first set threshold value, the degree of curvature of the target road in the electronic map is updated.
8. The method of claim 6, wherein the type of the target road in the electronic map is unseparated, the method further comprising:
determining the distance between the track center line corresponding to the first driving direction and the track center line corresponding to the second driving direction;
and in response to the distance being larger than a second set threshold, updating the type of the target road in the electronic map to a split type.
9. A map updating apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring target driving data of a plurality of moving objects on a target road;
the generating module is used for generating a track central line representing the target road form according to the plurality of target driving data;
the second acquisition module is used for acquiring a first coordinate position of each position point in the target road in the electronic map;
and the updating module is used for updating the electronic map according to the difference between the first coordinate position of each position point and the second coordinate position of the corresponding track point in the track central line.
10. The apparatus according to claim 9, wherein the first obtaining module is specifically configured to:
acquiring driving data of a plurality of moving objects;
inputting the driving data of the moving object into a hidden Markov model aiming at each moving object to obtain a road matched with each first track point in the driving data output by the hidden Markov model;
determining a second track point matched with the target road from the first track points of the moving objects according to the road matched with the first track points of the moving objects;
and determining the target driving data from the driving data according to the second track point of each moving object.
11. The apparatus according to claim 9, wherein the generating module is specifically configured to:
segmenting the target road at preset intervals according to the driving direction of the target road to obtain a plurality of sections;
generating a corresponding target track according to the coordinate position and the timestamp of each track point in the target driving data of each moving object;
determining a third track point intersecting each section according to the target track of each moving object;
for each section, determining a track central point corresponding to the section according to the intersected third track points;
and generating a track central line of the target road according to the track central points corresponding to the plurality of sections.
12. The apparatus of claim 11, wherein the generating module is specifically configured to:
inputting coordinate positions of a plurality of intersected third track points into a Gaussian mixture model aiming at each section;
determining a coordinate position corresponding to a peak of the probability distribution of the Gaussian mixture model;
and taking the coordinate position corresponding to the wave crest as the track central point corresponding to the cross section.
13. The apparatus according to any one of claims 9 to 12, wherein the update module is specifically configured to:
and when the difference is larger than a first set threshold value, updating the degree of curvature of the target road in the electronic map.
14. The apparatus of claim 9, wherein the target road is a bi-directional travel road including a first direction of travel and a second direction of travel,
the generation module is specifically configured to:
and respectively generating track center lines corresponding to the first driving direction and the second driving direction according to the plurality of target driving data.
15. The apparatus according to claim 14, wherein the update module is specifically configured to:
determining a first difference between a first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the first traveling direction;
determining a second difference between the first coordinate position of each position point and a second coordinate position of a corresponding track point in a track center line corresponding to the second driving direction;
when the first difference or the second difference is larger than a first set threshold value, the degree of curvature of the target road in the electronic map is updated.
16. The apparatus of claim 14, wherein the type of the target road in the electronic map is unseparated, the apparatus further comprising:
the determining module is used for determining the distance between the track center line corresponding to the first driving direction and the track center line corresponding to the second driving direction;
the updating module is further used for responding to the fact that the distance is larger than a second set threshold value, and updating the type of the target road in the electronic map to be a separated type.
17. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the map updating method of any one of claims 1-8.
18. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the map updating method according to any one of claims 1 to 8.
19. A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the map updating method of any one of claims 1-8.
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