CN116304830A - Self-adaptive yaw hot spot road section identification method and system - Google Patents

Abstract

The invention discloses a self-adaptive yaw hot spot road section identification method and a self-adaptive yaw hot spot road section identification system, wherein the method comprises the following steps: acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route; extracting yaw data from the database and performing data preprocessing based on a preset time interval; performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio; carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters; calculating the density of cluster yaw points in the cluster outsourcing polygon; intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections; and counting the data of the yaw hot spot road sections to output the yaw heat index of the yaw hot spot road sections.

Description

Self-adaptive yaw hot spot road section identification method and system

Technical Field

The invention relates to the technical field of urban road yaw hot spot recognition technology, in particular to a self-adaptive yaw hot spot road section recognition method and system.

Background

With the application and popularization of Location Based Services (LBS) technology, electronic map navigation is increasingly widely applied in the travel industry. Yaw is a phenomenon that the running track of a vehicle deviates from a preset planning route, and is influenced by road complexity, accessibility, road identification, road conditions and the like and is also related to driving behaviors of a driver. The yawing phenomenon has the characteristic of local aggregation in space and is obvious on urban roads. Through big data analysis mining, the yawing hot spot road sections in the urban roads are identified, on one hand, whether the yawing behavior is subjective detouring or not is facilitated, and decision basis is provided for network taxi drivers to judge responsibilities. On the other hand, the navigation broadcasting guide and the road enlarged graph can be provided in advance when a driver passes near the yaw hot spot road section, and the method has practical value for improving the service level of the navigation electronic map.

Chinese patent publication No. CN112815948A discloses a method, apparatus, computer device and storage medium for identifying yaw pattern. The invention is used to distinguish between active yaw caused by subjective user selection and passive yaw caused by route attributes. The method mainly comprises the following steps: determining a yaw point corresponding to the yaw behavior on the navigation route through comparison of the track data and the navigation data; acquiring yaw statistics information obtained by statistics aiming at the yaw points and respectively acquiring route attribute information of a yaw route and a navigation route, and identifying whether the yaw behavior which occurs is active yaw caused by subjective selection of a user or passive yaw caused by route attributes based on the route attribute information combined with the yaw statistics information, the yaw route and the navigation route.

The Chinese patent publication No. CN103955596A discloses an accident hot spot comprehensive judging method based on a traffic accident collecting technology. The method comprises the following steps: basic attribute data of each road section of each road of the road network are obtained; merging and classifying the similar road sections; acquiring information data of accidents occurring in each road section of each road in a certain history internal road network; calculating the equivalent value of the accident of the road section to be tested in the historical year; calculating the equivalent index of the accident of the road section to be tested in the historical year; calculating average equivalent index critical values of accidents of similar road sections of the road sections to be tested in the historical years; and judging whether the road section to be tested belongs to the accident hot spot or not according to the critical value of the equivalent index and the average equivalent index.

The Chinese patent publication No. CN112365595A discloses an analysis method for identifying multiple points of traffic accidents based on alarm data point elements. The implementation steps are as follows: collecting alarm traffic accident data, generating WGS84 coordinates, importing a GIS platform, modeling accident point elements, fusing the collected temperature data, and identifying regional accident multiple points by adopting Getis-Ord Gi hot spot analysis; clustering analysis is carried out on the accidents, and partial multiple point identification is carried out on the abnormal values of the periphery of the multiple points of the accidents through abnormal value analysis; and identifying accident multiple points by using a cluster analysis method of Geographic Information System (GIS) software, thiessen polygons, nuclear density analysis, hot spot analysis and outlier analysis.

As can be seen from the above prior art, in the prior art, the method for identifying the traffic hot spot area mainly uses sub-road segments as a unit to count the number or adopts a hot spot analysis method, a spatial clustering method and the like to identify the hot spot. The former recognition effect is easily influenced by the sub-road section segmentation parameters; the latter can not be positioned to the road carrier, is mainly used for regional characteristic analysis, and can not directly support business applications such as bypass judgment, safety early warning and the like.

Disclosure of Invention

In order to overcome the technical defects, the invention provides a self-adaptive yaw hot spot road section identification method and a self-adaptive yaw hot spot road section identification system.

In order to solve the problems, the invention is realized according to the following technical scheme:

in a first aspect, the invention discloses a self-adaptive yaw hot spot road section identification method, which comprises the following steps:

step S1: acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route;

step S2: extracting yaw data from the database and performing data preprocessing based on a preset time interval;

step S3: performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio;

Step S4: carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of polygons wrapped by the clustering clusters;

step S5: calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, of which the density of the cluster yaw points is n percent before the sorting, as a hot spot area;

step S6: intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections;

step S7: and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section to output the yaw heat index of the yaw hot spot road section.

As a preferred implementation manner of the first aspect of the present invention, the calculating of the overlap ratio of the route planning before and after yaw is performed on the preprocessed yaw data, and the yaw data belonging to the pseudo yaw is removed based on the overlap ratio, which specifically includes the following substeps:

analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude (x) of re-planning guide point sequence when re-planning route after yaw _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with which the line overlap ratio is larger than a preset value as yaw data of pseudo yaw;

and eliminating all yaw data of the pseudo yaw.

As a preferred embodiment of the first aspect of the present invention, the data clustering is performed on yaw coordinate points in all yaw data to obtain a yaw point cluster, so as to obtain the number of yaw points in the yaw point cluster and the area of an outer polygon of the cluster, and specifically includes the following substeps:

Acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

the yaw point cluster comprises a yaw point sequence and a center point coordinate;

counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring coordinates (x) of all yaw points in the yaw point cluster _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

As a preferred embodiment of the first aspect of the present invention, the calculating the density of cluster yaw points in the cluster outsourcing polygon takes the cluster outsourcing polygon with n% of the cluster yaw points before the density ordering as the hot spot area, and specifically includes the following sub-steps:

calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

Wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

As a preferred embodiment of the first aspect of the present invention, the specific calculation formula of the yaw heat index is:

wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

And (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

In a second aspect, the present invention also discloses a system for adaptively identifying a yaw hot spot road section, including:

the yaw monitoring module M1 is used for acquiring yaw information of the vehicle and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planning route and a re-planning route;

a data preprocessing module M2 for extracting yaw data from the database and performing data preprocessing based on a preset time interval;

the interference rejection module M3 is used for performing yaw front-rear route planning coincidence degree calculation on the preprocessed yaw data and rejecting yaw data belonging to pseudo yaw based on the coincidence degree;

the data clustering module M4 is used for carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of the polygon outsourced by the clustering clusters;

the density calculation module M5 is used for calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, n% before the density ordering of the cluster yaw points, as a hot spot area;

The hot spot marking module M6 is used for intercepting road network data based on each vertex of the cluster outsourcing polygon and marking road segments within the cluster outsourcing polygon as yaw hot spot road segments;

and the heat output module M7 is used for counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section so as to output the yaw heat index of the yaw hot spot road section.

As a preferred embodiment of the second aspect of the present invention, the interference rejection module M3, at runtime, specifically performs the following sub-steps:

analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude (x) of re-planning guide point sequence when re-planning route after yaw _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

Statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with which the line overlap ratio is larger than a preset value as yaw data of pseudo yaw;

and eliminating all yaw data of the pseudo yaw.

As a preferred embodiment of the second aspect of the present invention, the data clustering module M4, at runtime, specifically performs the following sub-steps:

acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

the yaw point cluster comprises a yaw point sequence and a center point coordinate;

Counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring coordinates (x) of all yaw points in the yaw point cluster _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

As a preferred embodiment of the second aspect of the present invention, the density calculation module M5, at run-time, specifically performs the following sub-steps:

calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

Acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

As a preferred embodiment of the second aspect of the present invention, the specific calculation formula of the yaw heat index is:

wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

and (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

Compared with the prior art, the invention has the beneficial effects that:

the invention creatively excavates the yaw hot spot road section through the historical data analysis by a data driving method, does not need to divide the sub road section in advance, and has the self-adaptive characteristic. The identification effect of the self-adaptive yaw hot spot road section identification method disclosed by the invention is not easily influenced by the sub road section segmentation parameters, can be positioned to a road carrier, and can directly support business applications such as detour judgment, safety early warning and the like, so that the yaw early warning is higher in speed and more accurate. The method can timely remind a driver to concentrate attention based on the yaw hot spot index and the yaw hot spot area, and early warn intentional detour, so as to optimize yaw display during navigation.

Drawings

The invention is described in further detail below with reference to the attached drawing figures, wherein:

FIG. 1 is a flow chart of an adaptive yaw hot spot segment identification method of the present invention;

fig. 2 is a schematic structural diagram of the adaptive yaw hot spot segment identification system of the present invention.

Detailed Description

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are illustrated in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

The term "comprising" and variations thereof as used herein means open ended, i.e., "including but not limited to. The term "or" means "and/or" unless specifically stated otherwise. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment. The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.

The access device and the server may be connected directly or indirectly by wired or wireless communication. The access device may be a terminal or a server. The access device has a target application running thereon. The target application is an application program capable of initiating a data request to a server, such as a social application, a payment application, a gaming application, and the like. The server may be an application server for providing a service to the target application, or may be a proxy server for distinguishing the application server from the application server corresponding to the target application. The server is used for identifying whether each access device belongs to a malicious device or not, and intercepting data messages from the malicious device. When the server is a proxy server, the proxy server forwards data messages not belonging to the malicious device to the application server. The terminal may be a desktop terminal or a mobile terminal, and the mobile terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, or the like, but is not limited thereto. The server and the server can be independent physical servers, can be a server cluster or a distributed system formed by a plurality of physical servers, and can also be cloud servers for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms.

Example 1

As shown in fig. 1, in a first aspect, the present invention discloses a self-adaptive yaw hot spot road segment identification method, which includes the following steps:

step S1: acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route;

specifically, the driver side monitors the yaw event of the map navigation module. After yaw occurs, the driver side reports yaw records to the data server. The data server extracts data from the yaw record including at least: yaw point longitude, yaw point latitude, yaw time, order number, driver number, original planned route guidance point longitude and latitude sequence (x) _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an And (x) re-planning the guiding point longitude and latitude sequence _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ,)。

In this example, the yaw technique data is preferably stored using a pgsql database. The data server stores the planned route guidance point data by adopting a geometric body format, so that the space processing and analysis are facilitated.

Step S2: extracting yaw data from the database and performing data preprocessing based on a preset time interval;

in particular, the specified time interval may be 1 week, 1 month, 1 quarter or 1 year. And the data server extracts the yaw record in the time range according to the time stamp of the yaw record and stores the yaw record as a temporary table.

Preferably, the data preprocessing at least includes: abnormal data rejection and data standardization processing.

The abnormal data rejection at least comprises the following processing contents: firstly, eliminating abnormal longitude and latitude data of a yaw point, and eliminating data with longitude values of minus 180, 180 and latitude values of minus 90, 90. And secondly, eliminating yaw records of which the linear distance between the yaw point and the starting point of the re-planned route exceeds a threshold value.

The data normalization processing at least comprises the following processing contents: one is to use a statistical coordinate system, which is selectable: 2000 national geodetic coordinate system, GCJ-02 coordinate system, wgs84 coordinate system, etc. And secondly, storing longitude and latitude coordinate data into a double-precision format, and reserving 6-bit decimal.

Step S3: performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio; the method specifically comprises the following substeps:

analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude (x) of re-planning guide point sequence when re-planning route after yaw _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with the line overlap ratio larger than a preset value as yaw data of the pseudo yaw, and marking the planned line with the line overlap ratio larger than 95% as the pseudo yaw;

and eliminating all yaw data of the pseudo yaw.

The geometry, buffer (double distance) function is preferably called to generate a buffer of line segments using the GeoTools open source package. The distance is a buffer distance and is double-precision. Specifically, the buffer distance was set to 20 meters.

Step S4: carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of polygons wrapped by the clustering clusters; the method specifically comprises the following substeps:

acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

the yaw point cluster comprises a yaw point sequence and a center point coordinate;

counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring coordinates (x) of all yaw points in the yaw point cluster _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

Step S5: calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, of which the density of the cluster yaw points is n percent before the sorting, as a hot spot area; the method specifically comprises the following substeps:

In this embodiment, preferably, a clustering algorithm, such as BIRCH and DBSCAN, is used to calculate the yaw point cluster without specifying the number of clusters in advance. Specifically, a scikit-learn open source machine learning library is used to realize BIRCH and DBSCAN clustering algorithms.

The external polygon is a polygon obtained by connecting the outermost points in the point set. The present example preferably uses the GeoTools open source package to call the geometry.

Calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

Acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

In particular, the embodiment takes a cluster of yaw points with the point density of the first 20% as a hot spot area.

Step S6: intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections;

and the circumscribing polygon of the hot spot area is a circumscribing polygon of a yaw point cluster with the point density of the first n%. And intercepting the road network data, namely performing superposition operation on the polygon and the road network, and extracting road sections contained in the polygon.

The example preferably uses a GeoTools open source package and uses the clippetfeatureresolution class to calculate the clipped road network data. Specifically, after the road network clipping is completed, the topological relation of the road network should be rebuilt.

Step S7: and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section to output the yaw heat index of the yaw hot spot road section.

As a preferred embodiment of the first aspect of the present invention, the specific calculation formula of the yaw heat index is:

Wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

and (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

The invention creatively excavates the yaw hot spot road section through the historical data analysis by a data driving method, does not need to divide the sub road section in advance, and has the self-adaptive characteristic. The identification effect of the self-adaptive yaw hot spot road section identification method disclosed by the invention is not easily influenced by the sub road section segmentation parameters, can be positioned to a road carrier, and can directly support business applications such as detour judgment, safety early warning and the like, so that the yaw early warning is higher in speed and more accurate. The method can timely remind a driver to concentrate attention based on the yaw hot spot index and the yaw hot spot area, and early warn intentional detour, so as to optimize yaw display during navigation.

Taking a network taxi travel scene as an example, a monitoring map navigation module records yaw coordinate points, an original planning route and a re-planning route when the yaw is triggered, and stores the yaw coordinate points, the original planning route and the re-planning route in a database; extracting yaw records from a database according to a specified time interval to execute data preprocessing; calculating the coincidence ratio of route planning before and after yaw, and eliminating pseudo yaw data; clustering yaw point data, calculating the area of an outsourcing polygon, counting the density of points, and extracting the first n% as a hot spot area; intercepting road network data by using polygon vertices wrapped by the clusters, and marking road sections in the boundary polygon range as yaw hot spot road sections; and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section, and calculating the yaw heat index of the road section.

The invention uses a data driving method to analyze and mine the yaw hot spot road section through historical data, and the sub road section does not need to be divided in advance, thereby having the self-adaptive characteristic. The beneficial effects include: (1) When a driver passes through the road section, navigation provides yaw early warning voice broadcasting, and the attention of the driver is improved. (2) In the network taxi safety early warning application scene, a data basis is provided for judging whether a driver deliberately bypasses, and if the driver triggers navigation on a non-yaw road section, the early warning of deliberately bypassing is triggered with a larger opportunity. (3) The electronic map navigation page is optimally displayed at the yaw position, and an enlarged graph appears so as to reduce the probability of a driver to walk and yaw.

The other steps of the adaptive yaw hot spot segment identification method described in this embodiment are referred to in the prior art.

Example 2

As shown in fig. 2, in a second aspect, the present invention further discloses an adaptive yaw hotspot road segment identification system, which includes:

the yaw monitoring module M1 is used for acquiring yaw information of the vehicle and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planning route and a re-planning route;

a data preprocessing module M2 for extracting yaw data from the database and performing data preprocessing based on a preset time interval;

The interference rejection module M3 is used for performing yaw front-rear route planning coincidence degree calculation on the preprocessed yaw data and rejecting yaw data belonging to pseudo yaw based on the coincidence degree;

the data clustering module M4 is used for carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of the polygon outsourced by the clustering clusters;

the density calculation module M5 is used for calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, n% before the density ordering of the cluster yaw points, as a hot spot area;

the hot spot marking module M6 is used for intercepting road network data based on each vertex of the cluster outsourcing polygon and marking road segments within the cluster outsourcing polygon as yaw hot spot road segments;

and the heat output module M7 is used for counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section so as to output the yaw heat index of the yaw hot spot road section.

As a preferred embodiment of the second aspect of the present invention, the interference rejection module M3, at runtime, specifically performs the following sub-steps:

Analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude (x) of re-planning guide point sequence when re-planning route after yaw _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with which the line overlap ratio is larger than a preset value as yaw data of pseudo yaw;

And eliminating all yaw data of the pseudo yaw.

As a preferred embodiment of the second aspect of the present invention, the data clustering module M4, at runtime, specifically performs the following sub-steps:

acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

the yaw point cluster comprises a yaw point sequence and a center point coordinate;

counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring coordinates (x) of all yaw points in the yaw point cluster _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

As a preferred embodiment of the second aspect of the present invention, the density calculation module M5, at run-time, specifically performs the following sub-steps:

calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

Wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

As a preferred embodiment of the second aspect of the present invention, the specific calculation formula of the yaw heat index is:

wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

And (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

In summary, the adaptive yaw hot spot segment identification system according to the embodiment of the present invention can execute all the steps of the adaptive yaw hot spot segment identification method according to embodiment 1 and achieve the same technical effects.

Other structures of the adaptive yaw hot spot segment identification system described in this embodiment are referred to in the prior art.

Example 3

The invention also discloses an electronic device, at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, and the at least one processor executes the instructions, specifically realizes the following steps: acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route; extracting yaw data from the database and performing data preprocessing based on a preset time interval; performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio; carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of polygons wrapped by the clustering clusters; calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, of which the density of the cluster yaw points is n percent before the sorting, as a hot spot area; intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections; and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section to output the yaw heat index of the yaw hot spot road section.

Example 4

The invention also discloses a storage medium storing a computer program which, when executed by a processor, realizes the following steps: acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route; extracting yaw data from the database and performing data preprocessing based on a preset time interval; performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio; carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of polygons wrapped by the clustering clusters; calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, of which the density of the cluster yaw points is n percent before the sorting, as a hot spot area; intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections; and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section to output the yaw heat index of the yaw hot spot road section.

The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing aspects of the present disclosure.

The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.

The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.

Computer program instructions for performing the operations of the present disclosure can be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, c++, java, and the like, as well as conventional procedural programming languages, such as the "C" language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.

Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.

These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The embodiments of the present disclosure have been described above, the foregoing description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.

Claims (10)

1. The self-adaptive yaw hot spot road section identification method is characterized by comprising the following steps of:

acquiring vehicle yaw information, and storing the acquired yaw data into a database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route;

extracting yaw data from the database and performing data preprocessing based on a preset time interval;

performing yaw front-back route planning coincidence ratio calculation on the preprocessed yaw data, and removing yaw data belonging to pseudo yaw based on the coincidence ratio;

carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of polygons wrapped by the clustering clusters;

Calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking the cluster outsourcing polygons, of which the density of the cluster yaw points is n percent before the sorting, as a hot spot area;

intercepting road network data based on each vertex of the cluster outsourcing polygon, and marking road sections in the cluster outsourcing polygon range as yaw hot spot road sections;

and counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section to output the yaw heat index of the yaw hot spot road section.

2. The adaptive yaw hot spot segment identification method according to claim 1, wherein the yaw data pre-processing method performs the yaw front-rear route planning overlap ratio calculation, and eliminates the yaw data belonging to the pseudo yaw based on the overlap ratio, and specifically comprises the following sub-steps:

analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude of re-planning guide point sequence when re-planning route after yaw

(x _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with which the line overlap ratio is larger than a preset value as yaw data of pseudo yaw;

and eliminating all yaw data of the pseudo yaw.

3. The adaptive yaw hot spot road segment identification method according to claim 2, wherein the data clustering is performed on yaw coordinate points in all yaw data to obtain yaw point clusters, and further obtain the number of yaw points in the yaw point clusters and the area of the polygon wrapped by the clusters, and specifically comprises the following sub-steps:

Acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

the yaw point cluster comprises a yaw point sequence and a center point coordinate;

counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring yaw pointsCoordinates of all yaw points in the cluster (x _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

4. The adaptive yaw hot spot segment identification method according to claim 3, wherein the calculating the density of cluster yaw points in the cluster outsourcing polygon takes a cluster outsourcing polygon with n% of the density of cluster yaw points before ordering as a hot spot area, specifically comprising the following sub-steps:

calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

Wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

5. The adaptive yaw hotspot segment identification method of claim 4, wherein:

the specific calculation formula of the yaw heat index is as follows:

wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

And (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

6. An adaptive yaw hot spot segment identification system, comprising:

the yaw monitoring module is used for acquiring vehicle yaw information and storing the acquired yaw data into the database, wherein the yaw data comprises yaw coordinate points, an original planned route and a re-planned route;

a data preprocessing module for extracting yaw data from the database and performing data preprocessing based on a preset time interval;

the interference rejection module is used for performing yaw front-back route planning coincidence degree calculation on the preprocessed yaw data and rejecting yaw data belonging to pseudo yaw based on the coincidence degree;

the data clustering module is used for carrying out data clustering on yaw coordinate points in all yaw data to obtain yaw point clustering clusters, and further obtaining the number of yaw points in the yaw point clustering clusters and the area of the polygon outsourced by the clustering clusters;

the density calculation module is used for calculating the density of cluster yaw points in the cluster outsourcing polygons, and taking n% of cluster outsourcing polygons before the density ordering of the cluster yaw points as hot spot areas;

The hot spot marking module is used for intercepting road network data based on each vertex of the cluster outsourcing polygon and marking road segments within the cluster outsourcing polygon as yaw hot spot road segments;

and the heat output module is used for counting the number of yaw points and the number of passing tracks contained in the yaw hot spot road section so as to output the yaw heat index of the yaw hot spot road section.

7. The adaptive yaw hotspot segment identification system of claim 6, wherein the interference rejection module, when running, performs the sub-steps of:

analyzing the yaw data to extract yaw coordinate points, original planned routes and re-planned routes respectively;

acquiring longitude and latitude (x) of track point sequence in original planning route _a1 ,y _a1 ,x _a2 ,y _a2 ,x _a3 ,y _a3 ....x _an ,y _an )；

Acquiring yaw point (x) _p ，y _p ) Coordinates (x) of the nearest track point nearest to the track point sequence in the original planned route _ap ,y _ap )；

In terms of the coordinates (x) _ap ,y _ap ) As a starting point, the original planned route is intercepted to obtain a track guidance point sequence (x _ap ,y _ap ,x _ap+1 ,y _ap+2 ,x _ap+3 ,y _ap+3 ....x _an ,y _an ) To form a baseline planned route;

acquiring longitude and latitude of re-planning guide point sequence when re-planning route after yaw

(x _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) To form a planned route to be compared;

acquiring a designated threshold value, and acquiring a buffer area A of a baseline planning route according to the designated threshold value;

Statistics of planned route guidance points (x) _b1 ,y _b1 ,x _b2 ,y _b2 ,x _b3 ,y _b3 ....x _bn ,y _bn ) Number num in buffer area _x ；

Calculating the coincidence ratio between the planned route to be compared and the baseline planned route, wherein the specific calculation formula is as follows:

wherein the num is _x Planning a number of route guidance points for comparison to fall into the buffer area a of the baseline route;

the num is _cnt The total number of the guide points of the planned route to be compared is calculated;

the Ratio is _overlap The value range of the line overlap ratio is [0,1]；

After the line overlap ratio is obtained, marking the planned line to be compared with which the line overlap ratio is larger than a preset value as yaw data of pseudo yaw;

and eliminating all yaw data of the pseudo yaw.

8. The adaptive yaw hotspot segment identification system of claim 7, wherein the data clustering module, when run, performs the following sub-steps:

acquiring longitude and latitude (x) of all yaw point coordinates within a specified time interval ₁ ,y ₁ )，(x ₂ ,y ₂ ),...,(x _n ,y _n )；

Setting a distance calculation mode of yaw point clustering;

constructing a similarity model by considering a road network structure, and adopting the shortest driving distance between two points;

a clustering algorithm without the need of specifying the number of clusters in advance is used, wherein the clustering algorithm comprises a BIRCH algorithm and a DBSCAN algorithm, and yaw point clusters are obtained after calculation;

The yaw point cluster comprises a yaw point sequence and a center point coordinate;

counting the number Pt of yaw points in yaw point cluster _cnt ；

Acquiring coordinates (x) of all yaw points in the yaw point cluster _a1 ,y _a1 ,x _a2 ,y _a2 ,...,x _an ,y _an ) Calculating a cluster outsourcing polygon Poly (x) corresponding to the yaw point cluster through a minimum circumscribed polygon generation algorithm _b1 ,y _b1 ,x _b2 ,y _b2 ,...,x _bn ,y _bn ) Further calculating and obtaining the area A of the polygon Poly wrapped by the cluster _poly 。

9. The adaptive yaw hotspot segment identification system of claim 8, wherein the density calculation module, when run, performs the following sub-steps:

calculating the density of yaw points in the cluster outsourcing polygon corresponding to the yaw point cluster, and specifically calculating according to the following formula:

wherein the Pt is _cnt For the number of yaw points in the cluster, the A _poly Wrapping the area of the polygon for the cluster;

the z fraction normalization processing method is adopted to normalize the point density data, and the method is realized by the following formula:

wherein the said

Wrapping the cluster with the average value of the cluster yaw point density in the polygon, wherein the sigma _A Wrapping the cluster with the standard deviation of the cluster yaw point density in the polygon;

after the cluster yaw point density in each cluster outsourcing polygon is obtained, sorting the cluster outsourcing polygons based on the density of the corresponding cluster yaw points;

Acquiring a preset hot spot area value range;

taking the cluster outsourcing polygon of which the cluster yaw points are n% before the density ordering as a hot spot area.

10. The adaptive yaw hot spot segment identification system of claim 9, wherein:

the specific calculation formula of the yaw heat index is as follows:

wherein the Pt is _cnt Data for yaw points contained in a yaw path segment obtained by creating a buffer of the path segment and summarizing the number of yaw points contained in the buffer;

the Trip _cnt For the number of driving tracks passing through the road section in a specified time, the number of driving tracks is obtained by creating a buffer zone of the road section and counting the number of tracks intersected with the buffer zone;

and (5) carrying out standardization processing on the YawIndex by adopting a z-fraction planning processing method.

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