CN112800153A - Method, device and equipment for mining isolation zone information and computer storage medium - Google Patents

Method, device and equipment for mining isolation zone information and computer storage medium Download PDF

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Publication number
CN112800153A
CN112800153A CN201911112083.2A CN201911112083A CN112800153A CN 112800153 A CN112800153 A CN 112800153A CN 201911112083 A CN201911112083 A CN 201911112083A CN 112800153 A CN112800153 A CN 112800153A
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road
travel track
isolation zone
image
physical isolation
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CN112800153B (en
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丁世强
李成洲
朱重黎
黄际洲
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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
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Abstract

The application discloses a method, a device and equipment for mining isolation zone information and a computer storage medium, and relates to the field of big data. The specific implementation scheme is as follows: the method comprises the steps of obtaining travel track data of a user on a road to be detected within set time, wherein the travel track comprises a first type of travel track and a second type of travel track; determining an overlapping area of the first type of travel track and the second type of travel track; determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track; determining whether a physical isolation zone is present on the road based on the ratio. This application utilizes user's trip track data to carry out automatic excavation to the road whether contains the median on the road, compares the mode of artifical collection, has reduced human cost and time cost.

Description

Method, device and equipment for mining isolation zone information and computer storage medium
Technical Field
The application relates to the technical field of computer application, in particular to a method, a device, equipment and a computer storage medium for mining isolation zone information in the field of big data.
Background
The user of riding is the vulnerable group compared driving user in the road trip, and when the accident appears between general motor vehicle and the bicycle, often the bicycle is impaired seriously, and the user of riding can be injured with high probability. Relevant researches show that the physical isolation belt on the road can greatly reduce the accident rate and improve the safety of riding users. On similar grounds, a bidirectional lane isolation strip in a motorway can also improve the safety of driving users.
Therefore, if the information of the isolation zone can be incorporated in the route planning process, the safety of the user in trip can be greatly improved. This is achieved on the premise that it is necessary to know whether the road contains a median. In the prior art, the acquisition mode of road median information mainly relies on manual collection, and this kind of mode can consume a large amount of manpowers and time cost, and the timeliness is poor.
Disclosure of Invention
In view of the above, the present application provides a method, an apparatus, a device, and a computer storage medium for mining information of an isolation zone, so as to reduce the labor and time costs for acquiring the information of the isolation zone.
In a first aspect, the present application provides a method for mining information of an isolated zone, including:
the method comprises the steps of obtaining travel track data of a user on a road to be detected within set time, wherein the travel track comprises a first type of travel track and a second type of travel track;
determining an overlapping area of the first type of travel track and the second type of travel track;
determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track;
determining whether a physical isolation zone is present on the road based on the ratio.
According to a preferred embodiment of the present application, the method further comprises:
and storing the information of whether the physical isolation zone exists on the road in a map database.
According to a preferred embodiment of the present application, the determining an overlapping area of the first travel trajectory and the second travel trajectory includes:
determining the distance range of the first travel track sampling point with a preset proportion from the center of a road;
determining the distance range of the second travel track sampling point with the preset proportion from the center of the road;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
According to a preferred embodiment of the present application, determining whether a physical separation zone exists on the road based on the ratio comprises:
if the ratio is larger than or equal to a preset first ratio threshold, determining that no physical isolation zone exists on the road;
if the ratio is smaller than or equal to a preset second ratio threshold, determining that a physical isolation zone exists on the road;
wherein the second duty ratio threshold is less than the first duty ratio threshold.
According to a preferred embodiment of the present application, the method further comprises:
and if the ratio is larger than a second ratio threshold and smaller than a first ratio threshold, determining whether a physical isolation zone exists on the road by using the image data of the road to be detected.
According to a preferred embodiment of the present application, the method further comprises:
and if the total number of the sampling points of the first type of travel track or the total number of the sampling points of the second type of travel track is smaller than a preset number threshold, determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected.
According to a preferred embodiment of the present application, determining whether a physical isolation zone exists on the road by using the image data of the road to be detected includes:
inputting the image of the road to be detected into an image classification model obtained by pre-training;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
According to a preferred embodiment of the present application, the image classification model is obtained by pre-training in the following manner:
acquiring a training sample, wherein the training sample comprises a road image and an artificial labeling result of whether the road image contains a physical isolation zone;
and taking the road image as the input of the depth residual error network, taking the artificial labeling result corresponding to the road image as the output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
According to a preferred embodiment of the present application, determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected includes:
obtaining the classification result of the image classification model on a plurality of images of the road to be detected;
and if the classification result is that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result is that the ratio of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset ratio threshold, determining that the road to be detected has the physical isolation zones.
According to a preferred embodiment of the application, the first-type travel track and the second-type travel track are riding tracks and driving tracks of users in the same direction respectively; or,
the first type of travel track and the second type of travel track are driving tracks in opposite directions respectively.
In a second aspect, the present application further provides an excavating device for isolation zone information, the device including:
the system comprises a track acquisition unit, a data acquisition unit and a data processing unit, wherein the track acquisition unit is used for acquiring distribution data of user travel tracks on a road to be detected within set time, and the user travel tracks comprise a first type of travel track and a second type of travel track;
the area determining unit is used for determining an overlapping area of the first type of travel track and the second type of travel track;
the first mining unit is used for determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track; determining whether a physical isolation zone is present on the road based on the ratio.
According to a preferred embodiment of the present application, the apparatus further comprises:
and the information storage unit is used for storing the information of whether the physical isolation strip exists on the road in a map database.
According to a preferred embodiment of the present application, the region determining unit specifically performs:
determining the distance range of the first travel track sampling point with a preset proportion from the center of a road;
determining the distance range of the second travel track sampling point with the preset proportion from the center of the road;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
According to a preferred embodiment of the present application, the first excavation unit specifically performs:
if the ratio is larger than or equal to a preset first ratio threshold, determining that no physical isolation zone exists on the road;
if the ratio is smaller than or equal to a preset second ratio threshold, determining that a physical isolation zone exists on the road;
wherein the second duty ratio threshold is less than the first duty ratio threshold.
According to a preferred embodiment of the present application, the apparatus further comprises:
and the second mining unit is used for determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected if the ratio is greater than a second ratio threshold and smaller than a first ratio threshold.
According to a preferred embodiment of the present application, the apparatus further comprises:
and the second mining unit is used for determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected if the total number of the sampling points of the first type of travel track or the total number of the sampling points of the second type of travel track is smaller than a preset number threshold.
According to a preferred embodiment of the present application, when determining whether a physical isolation zone exists on the road by using the image data of the road to be detected, the second mining unit specifically performs:
inputting the image of the road to be detected into an image classification model obtained by pre-training;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
According to a preferred embodiment of the present application, the apparatus further comprises:
the model training unit is used for acquiring a training sample, wherein the training sample comprises a road image and an artificial labeling result of whether the road image contains a physical isolation zone; and taking the road image as the input of the depth residual error network, taking the artificial labeling result corresponding to the road image as the output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
In a third aspect, the present application further provides an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.
In a fourth aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.
According to the technical scheme, the technical scheme provided by the application has the following advantages:
1) this application utilizes user's trip track data to carry out automatic excavation to the road whether contains the median on the road, compares the mode of artifical collection, has reduced human cost and time cost.
2) According to the method and the device, the trip track data in the set time can be utilized to mine the isolation zone information at intervals, so that the timeliness of obtaining the road isolation zone information is effectively guaranteed.
3) By using the method, the information about whether the physical isolation zone exists on the road is stored in the map database, so that a data basis can be provided for map services, and the method can be used for path planning to improve the traveling safety of a user.
4) Under the condition that the mode of mining through travel track data cannot be accurately determined, the mode of identifying and classifying road images can be combined to assist in determining whether a physical isolation zone exists in the road.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a diagram illustrating an exemplary system architecture of a method or apparatus for mining information in an isolated zone according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a method provided in an embodiment of the present application;
FIG. 3 is a flowchart of a method provided in the second embodiment of the present application;
fig. 4 is a schematic diagram of an excavating device for information of an isolation zone according to an embodiment of the present disclosure;
fig. 5 is a block diagram of an electronic device for implementing a method of route planning according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 shows an exemplary system architecture to which the mining method or apparatus for isolation zone information according to the embodiment of the present application may be applied.
As shown in fig. 1, the system architecture may include terminal devices 101 and 102, a network 103, and a server 104. The network 103 serves as a medium for providing communication links between the terminal devices 101, 102 and the server 104. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may interact with server 104 through network 103 using terminal devices 101 and 102. Various applications, such as a map-like application, a voice interaction application, a web browser application, a communication-like application, etc., may be installed on the terminal devices 101 and 102.
The terminal apparatuses 101 and 102 may be various electronic apparatuses. Including but not limited to smart phones, tablets, smart speakers, smart televisions, and the like. The route planning apparatus provided by the present application may be configured and operated in the terminal device 101 or 102, or configured and operated in the server 104. It may be implemented as a plurality of software or software modules (for example, for providing distributed services), or as a single software or software module, which is not specifically limited herein.
The server 104 may be a single server or a server group including a plurality of servers. The method for mining the information of the isolation zone in the embodiment of the application can be executed by the server 104, and the mining device for the information of the isolation zone in the server 104 can mine the information of the isolation zone of the road by using the travel track data of the user and store the information of the isolation zone of the road into the map database. The server 104 may provide services to the terminal device 101 or 102 based on data in the map database. It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The method has the core idea that whether the road comprises the isolation zone or not is mined by utilizing the travel track data of the users on the road. The method provided by the application is described in detail below with reference to examples.
The first embodiment,
Fig. 2 is a flowchart of a method provided in an embodiment of the present application, and as shown in fig. 2, the method may include the following steps:
in 201, travel track data of a user on a road to be detected within a set time is acquired.
In the application, the user behavior log can be obtained from the database, and the travel track data of the user can be obtained from the user behavior log. Since each sampling point of the travel track is related to a specific position, for example, there is a longitude and latitude of each sampling point, the travel track data of the user can be mapped onto each road.
The travel tracks used in the embodiment of the present application may include a first type of travel track and a second type of travel track, and may also include other types of travel tracks. In the application, two different types of travel tracks are distinguished by a first type of travel track and a second type of travel track, wherein the first type and the second type are only distinguished by names and do not contain limitation meanings such as quantity and sequence. The travel trajectory used may include, but is not limited to, the following two cases:
in the first case: the first-class travel track and the second-class travel track are respectively the travel track of the travel category corresponding to the adjacent lanes in the same direction.
For example, the driving trajectory and the riding trajectory correspond to a motorway and a non-motorway in the same direction, respectively. This may determine whether a physical separation exists between the motorway and the non-motorway.
The physical isolation belt referred to in the application refers to an isolation belt used for isolating different lanes on a road, and can be in a green belt form, a guardrail form and the like. The name "isolation belt" may also refer to isolation fence, isolation guardrail, etc.
In the second case: the first-class travel track and the second-class travel track are respectively travel tracks of travel classes corresponding to adjacent lanes in opposite directions.
For example, the driving paths of the opposite motor vehicle lanes correspond to the driving paths respectively. This allows a determination of whether a physical separation exists between two oppositely directed vehicle lanes.
For another example, if a road does not allow a vehicle to travel, there may be riding tracks corresponding to opposite non-motor lanes. This may determine whether a physical separation exists between opposing non-motorized lanes.
In this step, each road to be detected with the median information may be used as the road to be detected in the map database to execute the process provided by the present application.
At 202, an overlapping area of the first type of travel trajectory and the second type of travel trajectory is determined.
In the step, the distance range of the first travel track sampling point with the preset proportion from the center of the road is determined. The road center can determine a road center line as the road center according to the road shape, and also can take a median, a mean value and the like of all travel tracks on the road width as the road center according to the symmetry of the travel tracks on the road.
And then determining the distance range of the second travel track sampling point with the preset proportion from the road center.
And taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
For example, if 90% of driving track sampling points are located between 3 and 5 meters away from the center of the road and 90% of riding track sampling points are located between 4 and 8 meters away from the center of the road on the right side of the center of the road, the range of the right side of the road from the center of the road is determined as the overlapping area.
In 203, the ratio of the number of the sampling points of the first row trace in the overlapping region to the total number of the sampling points of the first row trace is determined.
Continuing the previous example, determining the total number of riding track sampling points within the range of 4-5 meters from the center of the road on the right side of the road and the total number of sampling points sample of the riding track on the right side of the roadtotalThen determining the ratio sample of the tworatio。
In 204, it is determined whether a physical isolation zone is present on the road to be detected based on the ratio.
Specifically, if the ratio determined in step 203 is greater than or equal to the preset first ratio threshold, that is, if sampleratio≥thresholdno-existDetermining that no physical isolation zone exists on the road to be detected; if the ratio is less than or equal to the second predetermined ratio threshold, that is, if sampleratio≤thresholdexistAnd determining that the physical isolation zone exists on the road to be detected.
Wherein the second duty ratio threshold isexistLess than a first thresholdno-exist. Second duty ratio thresholdexistAnd a first occupancy thresholdno-existEmpirical values or experimental values can be taken, and control adjustment can be carried out according to the requirement of accuracy. For example, the second duty threshold value thresholdno-existCan be 60%, and the second ratio threshold isexistCan be 40 percent.
In addition to this, there are some cases, such as thresholdexist<sampleratio<thresholdno-existOr, sampletotal<thresholdtotalThe situation of (2) in which whether the corresponding physical isolation strip is included is still in doubt can be further determined in combination with other ways. For example, further combined with a road image-based recognition technique, which will be described in detail in embodiment two.
In 205, information on whether a physical median exists on the road is stored in a map database.
After the physical isolation zones of roads are excavated, a map database can be stored. The map data maintained at the server side includes road information. The road information may include road location information, name, length, road grade, and the like, and in this embodiment, the road information further includes a condition including a physical isolation zone. The condition in which the physical isolation zone is included may be, for example, whether the physical isolation zone is included, and may further include the type of the physical isolation zone. The type of the physical isolation belt can be a physical isolation belt between motor vehicle lanes, a physical isolation belt between a motor vehicle lane and a non-motor vehicle lane, and the like.
Example II,
Fig. 3 is a flowchart of a method provided in the second embodiment of the present application, and as shown in fig. 3, the method may include the following steps:
step 301 is the same as step 201 in the first embodiment, and will not be described herein.
In 302, judging whether the total number of sampling points of the first type of travel track or the total number of sampling points of the second type of travel track on the road to be detected is smaller than a preset number threshold, if not, executing 303; if so, 308 is performed.
In this step, if the total sampling number of a certain travel track is small, the detection result of the median obtained based on the track may be considered to be inaccurate, and a mode based on road image recognition may be further combined, that is, 308 is executed.
In 303, an overlapping area of the first travel track and the second travel track on the road to be detected is determined.
In 304, a ratio of the number of sampling points of the first row trace in the overlapping region to the total number of sampling points of the first row trace is determined.
Steps 303-304 are the same as steps 202-203 in the first embodiment, and are not described herein.
At 305, comparing the determined ratio with a first ratio threshold and a second ratio threshold, and if the determined ratio is greater than or equal to the first ratio threshold, executing 306 to determine that no physical isolation zone exists on the road; if the ratio is less than or equal to the second ratio threshold, 307 is executed, and a physical isolation zone exists on the road; if the ratio is between the second and first duty thresholds, 308 is performed.
At 308, an image of the road to be detected is acquired.
And acquiring an image of the road to be detected from the map database. The source of the image of the road to be detected in the map database is not limited, and the image may be captured by a camera of a traffic system, captured and uploaded by a user, or acquired from equipment such as a vehicle data recorder.
In 309, the image of the road to be detected is input into a prestrained Resnet (deep Residual Network), and whether a physical isolation zone exists on the road to be detected is determined according to a classification result of the image of the road to be detected by the Resnet.
In the embodiment of the present application, Resnet actually classifies the road image, and the classification result is the existence of the physical isolation zone or the absence of the physical isolation zone. And calculating a probability value of whether a physical isolation zone exists or not for the road image by Resnet, and if the probability value exceeds a preset probability threshold, considering that the isolation zone exists, otherwise, considering that the isolation zone does not exist.
The Resnet network can be pre-trained in the following way:
and acquiring a training sample, wherein the training sample comprises a road image and an artificial labeling result of whether the road image contains a physical isolation zone. For example, some road images containing physical isolation zones are acquired as positive samples, and road images not containing physical isolation zones are acquired as negative samples.
And taking the road image as the input of the depth residual error network, taking the artificial labeling result corresponding to the road image as the output target of the depth residual error network, and training the depth residual error network.
When determining whether the physical isolation zone exists on the road to be detected according to the classification result of the Resnet network on the image of the road to be detected, if only one image of the road to be detected exists, directly determining whether the physical isolation zone exists on the road to be detected according to the classification result of the Resnet network on the image.
If a plurality of images of the road to be detected exist, obtaining a classification result of the Resnet network for the plurality of images, and if the classification result indicates that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result indicates that the ratio of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset ratio threshold, determining that the physical isolation zones exist on the road to be detected. For example, if 5 images of a road are classified, and the classification results of 3 images all indicate that a physical isolation zone exists, it is determined that the physical isolation zone exists on the road.
In this embodiment, the image classification model obtained by training the Resnet Network is described as an example, but in addition to the Resnet Network, other types of convolution application networks such as VGG (Visual Geometry Group Network) and Alexnet may be used to obtain the image classification model.
Since the excavation method based on the track overlapping degree is low in cost and highly efficient, this method is preferably adopted. However, the method based on the road image recognition has high accuracy, but has high cost and low timeliness, so that as the auxiliary excavation method, the image recognition is performed only on the road which cannot be accurately recognized by the excavation method based on the track overlapping degree.
The method provided by the embodiment of the present application is described in detail above, and the device provided by the embodiment of the present application is described in detail below.
Example III,
Fig. 4 is a schematic view of an excavating device for information of an isolation zone provided in an embodiment of the present application, and as shown in fig. 4, the device may include: the trajectory acquisition unit 01, the area determination unit 02, and the first mining unit 03 may further include: an information storage unit 04, a second mining unit 05 and a model training unit 06. The main functions of each component unit are as follows:
the track obtaining unit 01 is configured to obtain distribution data of user travel tracks on a road to be detected within a set time, where the user travel tracks include a first type of travel track and a second type of travel track.
The travel trajectory used may include, but is not limited to, the following two cases:
in the first case: the first-class travel track and the second-class travel track are respectively the travel track of the travel category corresponding to the adjacent lanes in the same direction.
For example, the driving trajectory and the riding trajectory correspond to a motorway and a non-motorway in the same direction, respectively. This may determine whether a physical separation exists between the motorway and the non-motorway.
The physical isolation belt referred to in the application refers to an isolation belt used for isolating different lanes on a road, and can be in a green belt form, a guardrail form and the like. The name "isolation belt" may also refer to isolation fence, isolation guardrail, etc.
In the second case: the first-class travel track and the second-class travel track are respectively travel tracks of travel classes corresponding to adjacent lanes in opposite directions.
For example, the driving paths of the opposite motor vehicle lanes correspond to the driving paths respectively. This allows a determination of whether a physical separation exists between two oppositely directed vehicle lanes.
For another example, if a road does not allow a vehicle to travel, there may be riding tracks corresponding to opposite non-motor lanes. This may determine whether a physical separation exists between opposing non-motorized lanes.
The region determining unit 02 is configured to determine an overlapping region of the first-type travel trajectory and the second-type travel trajectory.
Specifically, the area determining unit 02 may determine a distance range from a preset proportion of the first travel track sampling point to the road center; determining the distance range of the second travel track sampling point with the preset proportion from the center of the road; and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as an overlapping area.
The road center can determine a road center line as the road center according to the road shape, and also can take a median, a mean value and the like of all travel tracks on the road width as the road center according to the symmetry of the travel tracks on the road.
The first mining unit 03 is used for determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track; it is determined whether a physical isolation zone exists on the road based on the ratio.
And an information storage unit 04 for storing information on whether a physical isolation zone exists on the road in the map database.
Specifically, if the ratio is greater than or equal to a preset first ratio threshold, the first excavation unit 03 determines that no physical isolation zone exists on the road; if the ratio is smaller than or equal to a preset second ratio threshold, the first digging unit 03 determines that a physical isolation zone exists on the road; wherein the second duty ratio threshold is less than the first duty ratio threshold.
If the ratio is greater than the second ratio threshold and smaller than the first ratio threshold, the second mining unit 05 determines whether a physical isolation zone exists on the road by using the image data of the road to be detected.
If the total number of the sampling points of the first type of travel track or the total number of the sampling points of the second type of travel track is smaller than a preset number threshold, the second mining unit 05 determines whether a physical isolation zone exists on the road or not by using the image data of the road to be detected.
When determining whether a physical isolation zone exists on a road by using image data of the road to be detected, the second mining unit 05 can input the image of the road to be detected into an image classification model obtained by pre-training; and determining whether a physical isolation zone exists on the road according to the classification result of the image of the road to be detected by the image classification model.
The model training unit 06 acquires training samples, wherein the training samples comprise road images and manual labeling results of whether the road images contain physical isolation zones; and taking the road image as the input of the Resnet network, taking the manual labeling result corresponding to the road image as the output target of the Resnet network, and training the Resnet network to obtain an image classification model.
Resnet is actually a classification of road images with or without physical isolation zones. And calculating a probability value of whether a physical isolation zone exists or not for the road image by Resnet, and if the probability value exceeds a preset probability threshold, considering that the isolation zone exists, otherwise, considering that the isolation zone does not exist.
When determining whether the physical isolation zone exists on the road to be detected according to the classification result of the Resnet network on the image of the road to be detected, if only one image of the road to be detected exists, directly determining whether the physical isolation zone exists on the road to be detected according to the classification result of the Resnet network on the image.
If a plurality of images of the road to be detected exist, obtaining a classification result of the Resnet network for the plurality of images, and if the classification result indicates that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result indicates that the ratio of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset ratio threshold, determining that the physical isolation zones exist on the road to be detected. For example, if 5 images of a road are classified, and the classification results of 3 images all indicate that a physical isolation zone exists, it is determined that the physical isolation zone exists on the road.
In this embodiment, an image classification model obtained by training the Resnet network is described as an example, but in addition to the Resnet network, other types of convolution application networks such as a VGG network and an Alexnet network may be used to train the image classification model.
After the median information of each road is obtained by mining and stored in the image database by adopting each embodiment, the median information of each road can be applied to various application scenes, such as the following two:
application scenarios 1,
The method includes the steps of integrating the condition that a road includes a physical isolation zone into a path planning, for example, if a user wants to improve safety factors in the path planning process, preferentially selecting the road including the physical isolation zone from a path planned from a starting point to a destination by the user, or integrating whether the factor includes the physical isolation zone in the planned path sorting process, so as to improve the recommended sorting of the path including the physical isolation zone, and the like.
Application scenarios 2,
When the map interface is displayed, the information whether the road contains the isolation zone can be marked on the map interface. For example, when the user locates to the granularity of road level, the path containing the median may be displayed with a special color or logo to help the user make a trip decision. For another example, when a route planned for the user is displayed on a map, a road including an isolation zone in the displayed route may be displayed in a special color or a special sign.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 5, is a block diagram of an electronic device according to an embodiment of the application. 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 present application that are described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 501, memory 502, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 5, one processor 501 is taken as an example.
Memory 502 is a non-transitory computer readable storage medium as provided herein. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform the methods provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the methods provided herein.
Memory 502, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present application. The processor 501 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 502, that is, implements the isolation zone mining method in the above method embodiment.
The memory 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device for route planning, and the like. Further, the memory 502 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 502 optionally includes memory located remotely from processor 501, which may be connected to an electronic device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device may further include: an input device 503 and an output device 504. The processor 501, the memory 502, the input device 503 and the output device 504 may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The input device 503 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 504 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), 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.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
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: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
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.
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 application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. 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 application shall be included in the protection scope of the present application.

Claims (20)

1. A mining method for isolation zone information is characterized by comprising the following steps:
the method comprises the steps of obtaining travel track data of a user on a road to be detected within set time, wherein the travel track comprises a first type of travel track and a second type of travel track;
determining an overlapping area of the first type of travel track and the second type of travel track;
determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track;
determining whether a physical isolation zone is present on the road based on the ratio.
2. The method of claim 1, further comprising:
and storing the information of whether the physical isolation zone exists on the road in a map database.
3. The method of claim 1, wherein determining an overlap region of the first type of travel trajectory and the second type of travel trajectory comprises:
determining the distance range of the first travel track sampling point with a preset proportion from the center of a road;
determining the distance range of the second travel track sampling point with the preset proportion from the center of the road;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
4. The method of claim 1, wherein determining whether a physical median exists on the roadway based on the ratio comprises:
if the ratio is larger than or equal to a preset first ratio threshold, determining that no physical isolation zone exists on the road;
if the ratio is smaller than or equal to a preset second ratio threshold, determining that a physical isolation zone exists on the road;
wherein the second duty ratio threshold is less than the first duty ratio threshold.
5. The method of claim 4, further comprising:
and if the ratio is larger than a second ratio threshold and smaller than a first ratio threshold, determining whether a physical isolation zone exists on the road by using the image data of the road to be detected.
6. The method of claim 1, further comprising:
and if the total number of the sampling points of the first type of travel track or the total number of the sampling points of the second type of travel track is smaller than a preset number threshold, determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected.
7. The method according to claim 5 or 6, wherein determining whether a physical isolation zone exists on the road by using the image data of the road to be detected comprises:
inputting the image of the road to be detected into an image classification model obtained by pre-training;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
8. The method of claim 7, wherein the image classification model is pre-trained by:
acquiring a training sample, wherein the training sample comprises a road image and an artificial labeling result of whether the road image contains a physical isolation zone;
and taking the road image as the input of the depth residual error network, taking the artificial labeling result corresponding to the road image as the output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
9. The method according to claim 7, wherein determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected comprises:
obtaining the classification result of the image classification model on a plurality of images of the road to be detected;
and if the classification result is that the number of the images with the physical isolation zones reaches a preset number threshold, or if the classification result is that the ratio of the number of the images with the physical isolation zones to the number of the images of the road to be detected exceeds a preset ratio threshold, determining that the road to be detected has the physical isolation zones.
10. The method according to claim 1, wherein the first type of travel trajectory and the second type of travel trajectory are respectively a riding trajectory and a driving trajectory of users in the same direction; or,
the first type of travel track and the second type of travel track are driving tracks in opposite directions respectively.
11. An excavation device for isolation zone information, the device comprising:
the system comprises a track acquisition unit, a data acquisition unit and a data processing unit, wherein the track acquisition unit is used for acquiring distribution data of user travel tracks on a road to be detected within set time, and the user travel tracks comprise a first type of travel track and a second type of travel track;
the area determining unit is used for determining an overlapping area of the first type of travel track and the second type of travel track;
the first mining unit is used for determining the ratio of the number of sampling points of the first-class travel track in the overlapping area to the total number of sampling points of the first-class travel track; determining whether a physical isolation zone is present on the road based on the ratio.
12. The apparatus of claim 11, further comprising:
and the information storage unit is used for storing the information of whether the physical isolation strip exists on the road in a map database.
13. The apparatus according to claim 11, wherein the area determination unit specifically performs:
determining the distance range of the first travel track sampling point with a preset proportion from the center of a road;
determining the distance range of the second travel track sampling point with the preset proportion from the center of the road;
and taking the corresponding area of the intersection of the two determined distance ranges on the road to be detected as the overlapping area.
14. The apparatus according to claim 11, characterized in that said first excavation unit, in particular performs:
if the ratio is larger than or equal to a preset first ratio threshold, determining that no physical isolation zone exists on the road;
if the ratio is smaller than or equal to a preset second ratio threshold, determining that a physical isolation zone exists on the road;
wherein the second duty ratio threshold is less than the first duty ratio threshold.
15. The apparatus of claim 14, further comprising:
and the second mining unit is used for determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected if the ratio is greater than a second ratio threshold and smaller than a first ratio threshold.
16. The apparatus of claim 11, further comprising:
and the second mining unit is used for determining whether a physical isolation zone exists on the road or not by using the image data of the road to be detected if the total number of the sampling points of the first type of travel track or the total number of the sampling points of the second type of travel track is smaller than a preset number threshold.
17. The apparatus according to claim 15 or 16, wherein the second mining unit, when determining whether a physical isolation zone exists on the road by using the image data of the road to be detected, specifically performs:
inputting the image of the road to be detected into an image classification model obtained by pre-training;
and determining whether a physical isolation zone exists on the road according to the classification result of the image classification model on the image of the road to be detected.
18. The apparatus of claim 17, further comprising:
the model training unit is used for acquiring a training sample, wherein the training sample comprises a road image and an artificial labeling result of whether the road image contains a physical isolation zone; and taking the road image as the input of the depth residual error network, taking the artificial labeling result corresponding to the road image as the output target of the depth residual error network, and training the depth residual error network to obtain an image classification model.
19. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.
20. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.
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