Map updating method, computer device and storage medium

Abstract

This application relates to the field of map technology, specifically providing a map updating method, computer device, and storage medium, aiming to solve the problem that existing maps do not update for road surface bumps. To this end, this application provides a map updating method, in which the map is set with a bump event point of interest layer, the bump event point of interest layer including bump event point of interest information; the method includes: acquiring a set of bump events detected by vehicles and predicted by the cloud for the current day; acquiring a daily increase in bump event activity index based on the set of bump events detected by vehicles and predicted by the cloud for the current day; acquiring a historical bump event point of interest layer of the initial map at least one day prior to the current day; determining bump events to be added based on the historical bump event point of interest layer and the daily increase in bump event activity index for the current day; and adding the determined bump events to be added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

Description

Technical Field

[0001] This application relates to the field of map technology, specifically providing a map updating method, computer equipment, and storage medium. Background Technology

[0002] Among numerous urban infrastructure projects, road traffic is undoubtedly the most critical. Due to daily human activities and continuous environmental damage, road conditions are deteriorating, facing varying degrees of wear and tear. As map applications expand, people can use electronic map navigation technology to obtain information such as hotels, shopping malls, restaurants, schools, and gas stations. The point data within these maps are called POIs (Points of Interest). The quantity, accuracy, and update speed of POIs in a navigation map significantly impact its usability and user experience. Currently, while map navigation can provide basic navigation routes based on lane lines, it cannot effectively assess road surface roughness, leading to a poor driving experience.

[0003] Accordingly, a new map update scheme is needed in this field to solve the above problems. Summary of the Invention

[0004] In order to overcome the above-mentioned deficiencies, this application is made to provide a solution or at least a partial solution to the above-mentioned technical problems.

[0005] In a first aspect, this application provides a map updating method, wherein the map is configured with a bump event point of interest layer, the bump event point of interest layer including bump event point of interest information; the method includes:

[0006] Obtain the set of bump events detected by the vehicle and predicted by the cloud on the same day;

[0007] The daily increase in bump event activity index is obtained based on the set of bump events detected by the vehicle and predicted by the cloud on the same day.

[0008] Retrieve the historical turbulence event point of interest layer of the initial map at least one day prior to the current day;

[0009] The bump events to be added are determined based on the historical bump event point of interest layer and the daily increase in bump event activity index.

[0010] The identified bump events to be added are added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

[0011] In one technical solution of the above map update method, obtaining the daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud on the same day includes:

[0012] Based on the set of bump events detected by the vehicle on the same day and predicted by the cloud, obtain the event information of all bump events in the set of bump events;

[0013] Based on the event information of the bumpy events, the bumpy events are aggregated in the first stage to determine the daily increase in the activity index of bumpy events.

[0014] In one technical solution of the above map update method, the event information includes at least the bump type; the first aggregation process of the bump events based on the event information to determine the daily increase in bump event activity index includes:

[0015] The bump events are classified based on their bump type to identify bump events of the same type.

[0016] For each type of bump event, the bump events are clustered based on a spatial clustering algorithm to obtain at least one cluster of bump events under each type of bump.

[0017] Based on the event information, assign corresponding weights to all turbulence events within each turbulence event cluster;

[0018] The daily increase in turbulence event activity index for each turbulence event cluster is determined based on the corresponding weights of all turbulence events within each turbulence event cluster.

[0019] In one technical solution of the above map update method, the step of clustering bump events for each type of bump based on a spatial clustering algorithm includes:

[0020] Obtain the density matrix of turbulence events of the same type based on event information;

[0021] For each type of bump event, bump events are clustered based on the density matrix to obtain at least one cluster of bump events under each type of bump event.

[0022] In one technical solution of the above map update method, the event information further includes event ID, latitude and longitude, heading angle, event status, map type and road type identifier, and the event status includes addition and deletion;

[0023] The process of obtaining the density matrix for turbulence events of the same type based on event information includes:

[0024] Determine the event distance between two bump events based on their latitude and longitude under the same bump type;

[0025] The density of turbulence events under the same turbulence type is determined based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance.

[0026] Based on the density between various bump events of the same bump type, determine the density matrix of bump events of the same bump type.

[0027] In one technical solution of the above map update method, determining the density between turbulence events of the same turbulence type based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance includes:

[0028] If two bump events of the same type have the same set index in the bump event set, then the density between the two bump events is determined to be zero.

[0029] If two bump events of the same bump type have different set indices in the bump event set, but the map type, event ID, and road type identifier of the two bump events are the same, and the heading angle difference between the two bump events is less than or equal to a preset heading angle difference threshold, and the event distance is less than or equal to a preset distance threshold, then the density of the two bump events is the event distance between the two bump events.

[0030] If the two bump events have different set indices in the bump event set, or at least one of the map type, event ID, or road type identifier of the two bump events is different, or the heading angle difference between the two bump events is greater than a preset heading angle difference threshold, or the event distance between the two bump events is greater than a preset distance threshold, then the density of the two bump events is a preset maximum value.

[0031] In one technical solution of the above map update method, the step of clustering bump events for each bump type based on the density matrix to obtain at least one bump event cluster under each bump type includes:

[0032] For each type of bump event, the density between bump events under each bump type is obtained based on the density matrix;

[0033] Clustering is performed based on the density among the bump events, and bump events with the same density range are identified as a bump event cluster.

[0034] In one technical solution of the above map update method, the event information also includes the vehicle's actual geographical location and the corresponding geographical location of the vehicle on the navigation map;

[0035] Assigning corresponding weights to all turbulence events within each turbulence event cluster based on the event information includes:

[0036] Based on the offset distance between the vehicle's actual geographical location and its corresponding geographical location on the navigation map, the event status, the type of bump, and the frequency of occurrence of events of the same type of bump, all bump events within each bump event cluster are assigned corresponding weights.

[0037] In one technical solution of the above map update method, determining the daily increase in turbulence event activity index for each turbulence event cluster based on the corresponding weights of all turbulence events within each turbulence event cluster includes:

[0038] The daily increase in turbulence event activity index for each turbulence event cluster is determined based on the sum of the corresponding weights of all turbulence events within each turbulence event cluster.

[0039] In one technical solution of the above map update method, determining the turbulence event to be added based on the historical turbulence event point of interest layer and the daily increase in turbulence event activity index includes:

[0040] Historical aggregated data is obtained based on the historical bump event point of interest layer, and the historical aggregated data is the historical activity index of each bump type at least one day prior to the current day.

[0041] Based on the event information, the daily increase in turbulence event activity index and historical aggregated data are subjected to secondary aggregation processing to determine the full aggregated data;

[0042] Based on the full aggregated data, determine the bump events to be added.

[0043] In one technical solution of the above map update method, the event information also includes scene information;

[0044] Based on the event information, a second aggregation process is performed on the daily increase in turbulence event activity index and historical aggregated data to determine the full aggregated data, including:

[0045] Based on scenario information, weights are assigned to the daily activity index of different turbulence types in the daily increase turbulence event activity index and the historical activity index of different turbulence types in the historical aggregated data.

[0046] The total activity index for each bump type is determined by a weighted sum of the daily and historical activity indices for each bump type, to obtain the total aggregated data, which includes the total activity index for each bump type.

[0047] In one technical solution of the above map update method, determining the bump events to be added based on the full aggregated data includes:

[0048] The type of bump with the highest full-volume activity index in the full aggregated data is selected as the bump event to be added.

[0049] In a second aspect, a computer device is provided, comprising a processor and a memory, the memory being adapted to store a plurality of program codes, the program codes being adapted to be loaded and executed by the processor to perform the map updating method described in any of the above-described technical solutions.

[0050] In a third aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the map update method described in any of the above-described map update method technical solutions.

[0051] Solution 1. A map updating method, characterized in that the map is provided with a bump event point of interest layer, the bump event point of interest layer including bump event point of interest information; the method includes:

[0052] Obtain the set of bump events detected by the vehicle and predicted by the cloud on the same day;

[0053] The daily increase in bump event activity index is obtained based on the set of bump events detected by the vehicle and predicted by the cloud on the same day.

[0054] Retrieve the historical turbulence event point of interest layer of the initial map at least one day prior to the current day;

[0055] The bump events to be added are determined based on the historical bump event point of interest layer and the daily increase in bump event activity index.

[0056] The identified bump events to be added are added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

[0057] Solution 2. The map update method according to Solution 1, characterized in that, the step of obtaining the daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud on the same day includes:

[0058] Based on the set of bump events detected by the vehicle on the same day and predicted by the cloud, obtain the event information of all bump events in the set of bump events;

[0059] Based on the event information of the bumpy events, the bumpy events are aggregated in the first stage to determine the daily increase in the activity index of bumpy events.

[0060] Option 3. The map update method according to Option 2, characterized in that the event information includes at least the bump type; the first aggregation processing of the bump events based on the event information of the bump events to determine the daily increase in bump event activity index includes:

[0061] The bump events are classified based on their bump type to identify bump events of the same type.

[0062] For each type of bump event, the bump events are clustered based on a spatial clustering algorithm to obtain at least one cluster of bump events under each type of bump.

[0063] Based on the event information, assign corresponding weights to all turbulence events within each turbulence event cluster;

[0064] The daily increase in turbulence event activity index for each turbulence event cluster is determined based on the corresponding weights of all turbulence events within each turbulence event cluster.

[0065] Solution 4. The map update method according to Solution 3, characterized in that, the step of clustering bump events for each type of bump based on a spatial clustering algorithm includes:

[0066] Obtain the density matrix of turbulence events of the same type based on event information;

[0067] For each type of bump event, bump events are clustered based on the density matrix to obtain at least one cluster of bump events under each type of bump event.

[0068] Option 5. The map update method according to Option 4, characterized in that the event information further includes event ID, latitude and longitude, heading angle, event status, map type and road type identifier, and the event status includes addition and deletion;

[0069] The process of obtaining the density matrix for turbulence events of the same type based on event information includes:

[0070] Determine the event distance between two bump events based on their latitude and longitude under the same bump type;

[0071] The density of turbulence events under the same turbulence type is determined based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance.

[0072] Based on the density between various bump events of the same bump type, determine the density matrix of bump events of the same bump type.

[0073] Solution 6. The map update method according to Solution 5, characterized in that, determining the density between turbulence events of the same turbulence type based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance includes:

[0074] If two bump events of the same type have the same set index in the bump event set, then the density between the two bump events is determined to be zero.

[0075] If two bump events of the same bump type have different set indices in the bump event set, but the map type, event ID, and road type identifier of the two bump events are the same, and the heading angle difference between the two bump events is less than or equal to a preset heading angle difference threshold, and the event distance is less than or equal to a preset distance threshold, then the density of the two bump events is the event distance between the two bump events.

[0076] If the two bump events have different set indices in the bump event set, or at least one of the map type, event ID, or road type identifier of the two bump events is different, or the heading angle difference between the two bump events is greater than a preset heading angle difference threshold, or the event distance between the two bump events is greater than a preset distance threshold, then the density of the two bump events is a preset maximum value.

[0077] Scheme 7. The map update method according to any one of Schemes 4 to 6, characterized in that, the step of clustering bump events for each bump type based on the density matrix to obtain at least one bump event cluster under each bump type includes:

[0078] For each type of bump event, the density between bump events under each bump type is obtained based on the density matrix;

[0079] Clustering is performed based on the density among the bump events, and bump events with the same density range are identified as a bump event cluster.

[0080] Solution 8. The map update method according to any one of Solutions 4 to 6, characterized in that the event information further includes the actual geographical location of the vehicle and the corresponding geographical location of the vehicle on the navigation map;

[0081] Assigning corresponding weights to all turbulence events within each turbulence event cluster based on the event information includes:

[0082] Based on the offset distance between the vehicle's actual geographical location and its corresponding geographical location on the navigation map, the event status, the type of bump, and the frequency of occurrence of events of the same type of bump, all bump events within each bump event cluster are assigned corresponding weights.

[0083] Solution 9. The map update method according to Solution 8, characterized in that, determining the daily increase in turbulence event activity index for each turbulence event cluster based on the corresponding weights of all turbulence events within each turbulence event cluster includes:

[0084] The daily increase in turbulence event activity index for each turbulence event cluster is determined based on the sum of the corresponding weights of all turbulence events within each turbulence event cluster.

[0085] Solution 10. The map update method according to Solution 1, characterized in that, determining the turbulence event to be added based on the historical turbulence event point of interest layer and the daily increase in turbulence event activity index includes:

[0086] Historical aggregated data is obtained based on the historical bump event point of interest layer, and the historical aggregated data is the historical activity index of each bump type at least one day prior to the current day.

[0087] Based on the event information, the daily increase in turbulence event activity index and historical aggregated data are subjected to secondary aggregation processing to determine the full aggregated data;

[0088] Based on the full aggregated data, determine the bump events to be added.

[0089] Solution 11. The map update method according to Solution 10, wherein the event information further includes scene information;

[0090] Based on the event information, a second aggregation process is performed on the daily increase in turbulence event activity index and historical aggregated data to determine the full aggregated data, including:

[0091] Based on scenario information, weights are assigned to the daily activity index of different turbulence types in the daily increase turbulence event activity index and the historical activity index of different turbulence types in the historical aggregated data.

[0092] The total activity index for each bump type is determined by a weighted sum of the daily and historical activity indices for each bump type, to obtain the total aggregated data, which includes the total activity index for each bump type.

[0093] Solution 12. The map update method according to Solution 11, characterized in that, determining the bump events to be added based on the full aggregated data includes:

[0094] The type of bump with the highest full-volume activity index in the full aggregated data is selected as the bump event to be added.

[0095] Option 13. A computer device comprising a processor and a memory, the memory being adapted to store a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by the processor to perform the map updating method as described in any one of Options 1 to 12.

[0096] Scheme 14. A computer-readable storage medium storing a plurality of program codes, characterized in that the program codes are adapted to be loaded and run by a processor to perform the map updating method described in any one of Schemes 1 to 12.

[0097] The above-described technical solutions of this application have at least one or more of the following beneficial effects:

[0098] The map update method of this application includes a bump event point of interest layer on the map, which includes bump event point of interest information. The method includes: acquiring a set of bump events detected by the vehicle and predicted by the cloud for the current day; acquiring a daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud for the current day; acquiring a historical bump event point of interest layer of the initial map for at least one day prior to the current day; determining bump events to be added based on the historical bump event point of interest layer and the daily increase in bump event activity index for the current day; and adding the determined bump events to be added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated. This application first collects a set of bump events, including those detected by vehicles and predicted by the cloud, to provide rich data support for constructing a map containing a bump event point of interest layer. Based on the set of bump events detected by vehicles and predicted by the cloud, the daily increase in bump event activity index is obtained, which can determine the daily activity of bump events. Then, based on the historical bump event point of interest layer and the daily increase in bump event activity index, bump events to be added can be determined. Finally, the bump events to be added are added to the bump event point of interest layer of the map to obtain a map with an updated bump event point of interest layer. This improves the accuracy of existing solutions in identifying bumpy road sections, enhances the functionality and reliability of the map, and improves the user's driving experience. Attached Figure Description

[0099] The disclosure of this application will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this application. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0100] Figure 1 This is a schematic flowchart of the main steps of a map updating method according to an embodiment of this application;

[0101] Figure 2This is a schematic diagram illustrating the steps of obtaining full aggregated data according to an embodiment of this application;

[0102] Figure 3 This is a schematic diagram of the main structure of a computer device according to an embodiment of this application. Detailed Implementation

[0103] Some embodiments of this application are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of this application and are not intended to limit the scope of protection of this application.

[0104] In the description of this application, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and can also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0105] Currently, when using map navigation technology to obtain driving reference routes, although map navigation can provide basic navigation paths based on lane lines, it cannot effectively determine the bumps and jolts of the road surface, resulting in a poor user experience during driving.

[0106] The map update method of this application includes a bump event point of interest layer on the map, which includes bump event point of interest information. The method includes: acquiring a set of bump events detected by the vehicle and predicted by the cloud for the current day; acquiring a daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud for the current day; acquiring a historical bump event point of interest layer of the initial map for at least one day prior to the current day; determining bump events to be added based on the historical bump event point of interest layer and the daily increase in bump event activity index for the current day; and adding the determined bump events to be added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

[0107] This application first collects a set of bump events, including those detected by vehicles and predicted by the cloud, to provide rich data support for constructing a map containing a bump event point of interest layer. Based on the set of bump events detected by vehicles and predicted by the cloud, the daily increase in bump event activity index is obtained, which can determine the daily increase in bump event activity. Then, based on the historical bump event point of interest layer and the daily increase in bump event activity index, bump events to be added can be determined. Finally, the bump events to be added are added to the bump event point of interest layer of the map to obtain a map with an updated bump event point of interest layer. This improves the accuracy of existing solutions in identifying bumpy road sections, enhances the functionality and reliability of the map, and improves the user's driving experience.

[0108] See appendix Figure 1 , Figure 1 This is a schematic flowchart of the main steps of a map updating method according to an embodiment of this application.

[0109] like Figure 1 As shown in the embodiment of this application, the map update method includes a bump event point of interest layer on the map, which includes bump event point of interest information. The method mainly includes the following steps S101-S105.

[0110] Step S101: Obtain the set of bump events detected by the vehicle and predicted by the cloud on the same day.

[0111] In this embodiment, a bump event refers to an abstract representation of a bumpy location on the road. The set of bump events detected by the vehicle and predicted by the cloud includes bump events detected by the vehicle on the same day and bump events predicted by the cloud based on sensor data uploaded by the vehicle.

[0112] Step S102: Obtain the daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud on the same day.

[0113] Step S103: Obtain the historical turbulence event point of interest layer of the initial map at least one day prior to the current day.

[0114] In this embodiment, the point of interest (POI) refers to the bump point in the map bump event layer.

[0115] Step S104: Determine the bump events to be added based on the historical bump event point of interest layer and the daily increase in bump event activity index.

[0116] Step S105: Add the determined bump events to be added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

[0117] Based on steps S101-S105 above, this application first collects a set of bump events, including those detected by the vehicle and predicted by the cloud, to provide rich data support for constructing a map containing a bump event point of interest layer. Based on the set of bump events detected by the vehicle and predicted by the cloud, the daily increase in bump event activity index is obtained, which determines the daily increase in bump event activity. Then, based on the historical bump event point of interest layer and the daily increase in bump event activity index, the bump events to be added can be determined. Finally, the bump events to be added are added to the bump event point of interest layer of the map to obtain a map with an updated bump event point of interest layer. This improves the accuracy of existing solutions in identifying bumpy road sections, enhances the functionality and reliability of the map, and improves the user's driving experience.

[0118] The following provides further explanation of steps S101-S105.

[0119] For step S101, obtain the set of bump events detected by the vehicle and predicted by the cloud for the day.

[0120] Specifically, a bump event refers to an abstract representation of the location of bumps in the road. The set of bump events detected by the vehicle and predicted by the cloud includes bump events detected by the vehicle on the same day and bump events predicted by the cloud based on sensor data uploaded by the vehicle.

[0121] Regarding step S102, in some embodiments, obtaining the daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud on the same day includes: obtaining event information of all bump events in the set of bump events based on the set of bump events detected by the vehicle and predicted by the cloud on the same day; performing a first aggregation process on the bump events based on the event information of the bump events to determine the daily increase in bump event activity index.

[0122] Specifically, the system first obtains the bump information of all bump events in the set of bump events detected by the vehicle and predicted by the cloud on the same day, and then performs the first aggregation processing on the bump events based on the event information. The aggregation can adopt conventional data aggregation methods, as long as the daily increase in the activity index of bump events can be determined.

[0123] In some implementations, the event information includes at least a bump type; the first aggregation process of the bump events based on the event information to determine the daily increase in bump event activity index includes: classifying the bump events based on the bump type to identify bump events of the same bump type; clustering the bump events of each bump type using a spatial clustering algorithm to obtain at least one bump event cluster under each bump type; assigning corresponding weights to all bump events within each bump event cluster based on the event information; and determining the daily increase in bump event activity index for each bump event cluster based on the corresponding weights of all bump events within each bump event cluster.

[0124] Specifically, bump types are determined by analyzing sensor signals at the vehicle end. These sensor signals can include wheel speed, vehicle height, and acceleration. Bump types can include uphill connecting roads, downhill connecting roads, speed bumps, large potholes, small undulations, medium undulations, large undulations, continuous bumps, and continuous undulations.

[0125] All bump events in the bump event set are classified by bump type to identify those belonging to the same bump type. Then, bump events of the same type are clustered using a spatial clustering algorithm to obtain at least one bump event cluster under each bump type, with each cluster containing at least one bump event. All bump events within each cluster are assigned corresponding weights based on event information. Finally, the daily increase in bump event activity index for each cluster is determined based on the sum of the weights of all bump events within each cluster.

[0126] In some implementations, the step of clustering bump events for each type of bump based on a spatial clustering algorithm includes: obtaining a density matrix for bump events of the same type based on event information; and clustering bump events for each type of bump based on the density matrix to obtain at least one cluster of bump events under each type of bump.

[0127] Specifically, based on the event information of bump events of the same type, the density between each bump event is determined, thereby determining the density matrix of bump events of the same type. Then, based on a clustering algorithm, bump events of each type are clustered according to the density matrix to obtain at least one bump event cluster under each bump type. A bump event cluster contains at least one bump event, and the density between the bump events in a bump event cluster is contained within a certain density range.

[0128] In some implementations, the event information further includes event ID, latitude and longitude, heading angle, event status, map type, and road type identifier, wherein the event status includes addition and deletion; obtaining the density matrix of turbulence events of the same turbulence type based on the event information includes: determining the event distance between two turbulence events based on the latitude and longitude of two turbulence events of the same turbulence type; determining the density between each turbulence event of the same turbulence type based on the map type, event ID, road type identifier, heading angle of the turbulence event, and event distance; and determining the density matrix of turbulence events of the same turbulence type based on the density between each turbulence event of the same turbulence type.

[0129] Specifically, the event ID is a unique identifier for each bump event; the latitude and longitude are the vehicle's latitude and longitude when the bump event is collected; the heading angle is the vehicle's driving angle; the event status is the state of each event, including addition and deletion. For example, if bump event A is collected on the same day, its event status is "added"; furthermore, the event status can also include "existence unchanged" and "existence changed," that is, if bump event B was detected before the current day and has not changed on the current day, its event status is "existence unchanged," otherwise it is "existence changed"; the map type is the navigation map provider used by the vehicle when collecting bump events, such as Gaode Maps, Baidu Maps, etc.; the road type identifier is the road identifier of the navigation map, which can be road level, form way, road name, lane, etc.

[0130] First, based on the latitude and longitude of two turbulence events of the same turbulence type, the Haversine algorithm is used to calculate the event distance between the two turbulence events. Then, based on the map type, event ID, road type identifier, heading angle, and event distance of each turbulence event, the density between each turbulence event of the same turbulence type is determined. These conditions for determining the density between each turbulence event can be selected according to actual needs and are not limited here. Finally, based on the density between each turbulence event of the same turbulence type, the density matrix of turbulence events of the same turbulence type is determined.

[0131] In some implementations, determining the density between turbulence events of the same turbulence type based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance includes: if two turbulence events of the same turbulence type have the same set index in the turbulence event set, then the density between the two turbulence events is determined to be zero; if two turbulence events of the same turbulence type have different set indices in the turbulence event set, but the map type, event ID, and road type identifier of the two turbulence events are the same, and the heading angle difference between the two turbulence events is less than or equal to a preset heading angle difference threshold, and the event distance is less than or equal to a preset distance threshold, then the density of the two turbulence events is the event distance between the two turbulence events; if the set indexes of the two turbulence events in the turbulence event set are different, at least one of the map type, event ID, and road type identifier of the two turbulence events is different, or the heading angle difference between the two turbulence events is greater than a preset heading angle difference threshold, or the event distance between the two turbulence events is greater than a preset distance threshold, then the density of the two turbulence events is a preset maximum value.

[0132] Specifically, the density of turbulence events of the same turbulence type is determined based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance, using the following formula:

[0133]

[0134] Where i and j are the set indices of the two bump events in the set of bump events. It is the density of two turbulence events. It is the distance between two turbulence events. For map type, For event ID, For road type identification, The difference in heading angle between the two turbulence events. The heading angle difference threshold, The range of values ​​is [0,360); The event distance threshold, The range of values ​​is ≥0, Indicates the range of real numbers.

[0135] In some implementations, the step of clustering bump events for each bump type based on the density matrix to obtain at least one bump event cluster under each bump type includes: obtaining the density between bump events under each bump type based on the density matrix; and clustering based on the density between the bump events to determine bump events within the same density range as a bump event cluster.

[0136] Specifically, the density of each turbulence event under each turbulence type is determined based on the density matrix. Then, a density clustering algorithm is used to cluster the turbulence events. The density clustering algorithm determines the cluster structure based on the density of the sample distribution. It uses the number of points in a certain neighborhood as the standard of connectivity and continuously expands the clusters based on this connectivity to obtain the final clustering result. A category label is also defined for each cluster. In this embodiment, the density clustering algorithm determines turbulence events belonging to the same density range. These turbulence events in the same density range are identified as a turbulence event cluster. Each turbulence event cluster has a category label. For example, the category label can be -1 and 0, 1, 2, etc.

[0137] In some implementations, the event information also includes the vehicle's actual geographical location and the corresponding geographical location of the vehicle on the navigation map; assigning corresponding weights to all bump events within each bump event cluster based on the event information includes: assigning corresponding weights to all bump events within each bump event cluster based on the offset distance between the vehicle's actual geographical location and the corresponding geographical location of the vehicle on the navigation map, the event status, the bump type, and the frequency of occurrence of events of the same bump type.

[0138] Specifically, the actual geographical location of the vehicle is the actual geographical location of the vehicle's GPS positioning, and the geographical location of the vehicle on the navigation map is the location of the vehicle identified by the navigation map; the frequency of occurrence of the same type of bump event is determined based on the number of events of the same type of bump in the set of bump events detected by the vehicle and predicted by the cloud on the same day.

[0139] In this embodiment, based on the offset distance between the actual geographical location of the vehicle and its corresponding geographical location on the navigation map, the event status, the type of bump, and the frequency of occurrence of events of the same type of bump, all bump events within each bump event cluster are assigned corresponding weights.

[0140] Specifically, for example, if the offset distance between the actual geographical location of the vehicle and its corresponding geographical location on the navigation map for bump event C is 0.5 meters, the event status is newly added, the bump type is large undulation, and the occurrence frequency of large undulation type events is 71, then it is considered that the offset distance of bump event C is small, and the newly added event status should be given attention. The bump type is also relatively complex, so bump event C should be given a higher weight, such as 80.

[0141] For example, if the actual geographical location of the vehicle on the navigation map for bump event D is 10 meters away from its corresponding geographical location, the event status is "removal," the bump type is "speed bump," and the frequency of speed bump events is 10, then bump event C has a larger offset distance. The event status of "removal" can be considered according to actual needs. Since speed bump events are less common, bump event D should be assigned a lower weight, such as 40.

[0142] In some implementations, determining the daily increase in turbulence event activity index for each turbulence event cluster based on the corresponding weights of all turbulence events within each turbulence event cluster includes: determining the daily increase in turbulence event activity index for each turbulence event cluster based on the sum of the corresponding weights of all turbulence events within each turbulence event cluster.

[0143] Specifically, the daily increase in turbulence event activity index for a turbulence event cluster is calculated by summing the weights of all turbulence events within that cluster.

[0144] For step S103, obtain the historical turbulence event point of interest layer of the initial map for at least one day prior to the current day.

[0145] In this embodiment, the map is set with a bump event point of interest layer. The initial map is the map before the current day that includes the historical bump event point of interest layer. If the obtained historical bump event point of interest layer is blank, the bump event point of interest layer is determined based on the daily increase in bump event activity index. The bump event point of interest layer is obtained from the bump event point of interest information. Point of interest is POI (Point of Interest), which in this application refers to bump points in the map event layer.

[0146] Regarding step S104, in some embodiments, determining the turbulence event to be added based on the historical turbulence event point of interest layer and the daily increase in turbulence event activity index includes: obtaining historical aggregated data based on the historical turbulence event point of interest layer, wherein the historical aggregated data is the historical activity index of each turbulence type for at least one day prior to the current day; performing secondary aggregation processing on the daily increase in turbulence event activity index and the historical aggregated data based on the event information to determine the full aggregated data; and determining the turbulence event to be added based on the full aggregated data.

[0147] Specifically, the historical turbulence event point of interest layer is constructed based on historical aggregated data for each turbulence type. Therefore, historical aggregated data can be obtained based on the historical turbulence event point of interest layer. The historical aggregated data includes the historical activity index of each turbulence type for at least one day prior to the current day. The historical activity index of each turbulence type is based on the cumulative daily increase in turbulence event activity index for each turbulence type for at least one day prior to the current day, calculated as follows: Figure 2 As shown.

[0148] In some implementations, the event information further includes scene information; the step of performing secondary aggregation processing on the daily increase in turbulence event activity index and historical aggregated data based on the event information to determine the full aggregated data includes: assigning weights to the daily activity index of different turbulence types in the daily increase in turbulence event activity index and the historical activity index of different turbulence types in the historical aggregated data based on the scene information; determining the full activity index of each turbulence type based on the weighted sum of the daily activity index and the historical activity index of each turbulence type to obtain the full aggregated data, wherein the full aggregated data includes the full activity index of each turbulence type.

[0149] Specifically, in this embodiment, the scene information includes internal roads and non-internal roads. A set of weighting rules is set based on internal roads, and another set of weighting rules is set based on non-internal roads. In addition, other information can be used as scene information according to actual needs, and then corresponding weighting rules are set based on the defined scene information respectively. This is not limited here.

[0150] According to the preset weighting rules, the daily activity index of different turbulence types in the daily increase turbulence event activity index and the historical activity index of different turbulence types in the historical aggregated data are assigned weights. Then, the weighted sum of the daily activity index and the historical activity index of each turbulence type is calculated to obtain the full activity index of each turbulence type. Finally, the full aggregated data is obtained from the full activity index of each turbulence type.

[0151] In some implementations, determining the bump event to be added based on the full aggregated data includes: selecting the bump type with the highest full activity index in the full aggregated data as the bump event to be added.

[0152] Specifically, the type of bump with the highest full-activity index in the full aggregated data is considered as a bump event to be added to the map bump event point of interest layer.

[0153] For step S105, the determined bump events to be added are added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated.

[0154] Specifically, the event information for the bump events to be added is added to the bump event point of interest layer of the map, and the historical bump event point of interest layer of the initial map is updated to obtain the updated map. In this embodiment, Elasticsearch can be used to add or manage the event information for bump events.

[0155] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effect of this application, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of this application.

[0156] Furthermore, this application also provides a map updating device.

[0157] The map update device in this embodiment mainly includes a bump event set acquisition module 10, a daily increasing bump event activity index acquisition module 20, a historical bump event point of interest layer acquisition module 30, a determination module 40, and a map update module 50. In some embodiments, one or more of the bump event set acquisition module 10, the daily increasing bump event activity index acquisition module 20, the historical bump event point of interest layer acquisition module 30, the determination module 40, and the map update module 50 can be combined into a single module. In some embodiments, the bump event set acquisition module 10 can be configured to acquire a set of bump events detected by the vehicle and predicted by the cloud for the current day. The daily increasing bump event activity index acquisition module 20 can be configured to acquire a daily increasing bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud for the current day. The historical bump event point of interest layer acquisition module 30 can be configured to acquire a historical bump event point of interest layer of the initial map from at least one day prior to the current day. The determining module 40 can be configured to determine the turbulence events to be added based on the historical turbulence event point of interest layer and the daily increase in turbulence event activity index. The map updating module 50 can be configured to add the determined turbulence events to be added to the turbulence event point of interest layer of the map to obtain a map with the turbulence event point of interest layer updated. In one embodiment, a description of the specific implementation functions can be found in steps 101-S105.

[0158] The aforementioned map update device is used to perform Figure 1 The map update method embodiments shown are similar in technical principle, the technical problems they solve, and the technical effects they produce. Those skilled in the art can clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the map update device can be found in the embodiments of the map update method, and will not be repeated here.

[0159] Those skilled in the art will understand that all or part of the processes in the method of the above-described embodiment can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0160] Furthermore, this application also provides a computer device.

[0161] See appendix Figure 3 , Figure 3 This is a schematic diagram of the main structure of a computer device according to an embodiment of this application.

[0162] like Figure 3 As shown, the computer device in this embodiment of the invention mainly includes a memory 11 and a processor 12. The memory 11 can be configured to store a program for executing the map update method of the above method embodiments, and the processor 12 can be configured to execute the program in the memory. This program includes, but is not limited to, a program for executing the map update method of the above method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown. For specific technical details not disclosed, please refer to the method section of the embodiments of the present invention.

[0163] In embodiments of the present invention, the computer device may be a computer device comprising various electronic devices. In some possible implementations, the computer device may include multiple memories 11 and multiple processors 12. The program executing the map update method of the above method embodiments may be divided into multiple subroutines, each subroutine may be loaded and run by a processor to execute different steps of the map update method of the above method embodiments. Specifically, each subroutine may be stored in different memories 11, and each processor 12 may be configured to execute programs in one or more memories 11 to jointly implement the map update method of the above method embodiments, that is, each processor 12 executes different steps of the map update method of the above method embodiments to jointly implement the map update method of the above method embodiments.

[0164] The aforementioned multiple processors 12 can be processors deployed on the same device. For example, the aforementioned computer device can be a high-performance device composed of multiple processors, and the aforementioned multiple processors 12 can be processors configured on that high-performance device. Alternatively, the aforementioned multiple processors 12 can also be processors deployed on different devices. For example, the aforementioned computer device can be a server cluster, and the aforementioned multiple processors 12 can be processors on different servers within the server cluster.

[0165] Furthermore, this application also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to this application, the computer-readable storage medium can be configured to store a program that performs the map update method of the above-described method embodiments. This program can be loaded and run by a processor to implement the above-described map update method. For ease of explanation, only the parts related to the embodiments of this application are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of this application. The computer-readable storage medium can be a storage device device including various electronic devices. Optionally, in the embodiments of this application, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0166] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device described in this application, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of both. Therefore, the number of modules shown in the figures is merely illustrative.

[0167] The relevant user personal information that may be involved in the various embodiments of this application is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and includes personal information that users actively provide or that is generated as a result of using the product / service, as well as personal information obtained with user authorization.

[0168] The personal information processed in this application will vary depending on the specific product / service scenario and will be based on the specific scenario in which the user uses the product / service. This may involve the user's account information, device information, driving information, vehicle information, or other related information. The applicant will treat the user's personal information and its processing with the utmost diligence.

[0169] This application attaches great importance to the security of users' personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect users' information and prevent unauthorized access, disclosure, use, modification, damage or loss of personal information.

[0170] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of this application; therefore, the technical solutions after splitting or combining will fall within the protection scope of this application.

[0171] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. Without departing from the principles of this application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of this application.

Claims

1. A map updating method, characterized in that, The map is configured with a bump event point of interest layer, which includes bump event point of interest information; the method includes: Obtain the set of bump events detected by the vehicle and predicted by the cloud on the same day; The daily increase in bump event activity index is obtained based on the set of bump events detected by the vehicle and predicted by the cloud on the same day. Retrieve the historical turbulence event point of interest layer of the initial map at least one day prior to the current day; The bump events to be added are determined based on the historical bump event point of interest layer and the daily increase in bump event activity index. The identified bump events to be added are added to the bump event point of interest layer of the map to obtain a map with the bump event point of interest layer updated. The method for obtaining the daily increase in bump event activity index based on the set of bump events detected by the vehicle and predicted by the cloud on the same day includes: Based on the set of bump events detected by the vehicle on the same day and predicted by the cloud, obtain the event information of all bump events in the set of bump events; Based on the event information of the aforementioned bump events, the bump events are subjected to a first aggregation process to determine the daily increase in the bump event activity index. The event information includes at least the type of bumps; the first aggregation process of the bump events based on the event information to determine the daily increase in bump event activity index includes: The bump events are classified according to their bump type to identify bump events of the same type. For each bump type, the bump events are clustered using a spatial clustering algorithm to obtain at least one bump event cluster under each bump type. Based on the event information, all bump events in each bump event cluster are assigned corresponding weights. Based on the corresponding weights of all bump events in each bump event cluster, the daily increase in bump event activity index for each bump event cluster is determined.

2. The map updating method according to claim 1, characterized in that, The process of clustering bump events for each type of bump event based on a spatial clustering algorithm includes: Obtain the density matrix of turbulence events of the same type based on event information; For each type of bump event, bump events are clustered based on the density matrix to obtain at least one cluster of bump events under each type of bump event.

3. The map updating method according to claim 2, characterized in that, The event information also includes event ID, latitude and longitude, heading angle, event status, map type, and road type identifier. The event status includes addition and deletion. The process of obtaining the density matrix for turbulence events of the same type based on event information includes: Determine the event distance between two bump events based on their latitude and longitude under the same bump type; The density of turbulence events under the same turbulence type is determined based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance. Based on the density between various bump events of the same bump type, determine the density matrix of bump events of the same bump type.

4. The map updating method according to claim 3, characterized in that, The method of determining the density of turbulence events of the same turbulence type based on map type, event ID, road type identifier, heading angle of the turbulence event, and event distance includes: If two bump events of the same type have the same set index in the bump event set, then the density between the two bump events is determined to be zero. If two bump events of the same bump type have different set indices in the bump event set, but the map type, event ID, and road type identifier of the two bump events are the same, and the heading angle difference between the two bump events is less than or equal to a preset heading angle difference threshold, and the event distance is less than or equal to a preset distance threshold, then the density of the two bump events is the event distance between the two bump events. If the two bump events have different set indices in the bump event set, or at least one of the map type, event ID, or road type identifier of the two bump events is different, or the heading angle difference between the two bump events is greater than a preset heading angle difference threshold, or the event distance between the two bump events is greater than a preset distance threshold, then the density of the two bump events is a preset maximum value.

5. The map updating method according to any one of claims 2 to 4, characterized in that, The step of clustering turbulence events for each turbulence type based on the density matrix to obtain at least one turbulence event cluster under each turbulence type includes: For each type of bump event, the density between bump events under each bump type is obtained based on the density matrix; Clustering is performed based on the density among the bump events, and bump events with the same density range are identified as a bump event cluster.

6. The map updating method according to any one of claims 2 to 4, characterized in that, The event information also includes the vehicle's actual geographical location and the corresponding geographical location of the vehicle on the navigation map; Assigning corresponding weights to all turbulence events within each turbulence event cluster based on the event information includes: Based on the offset distance between the vehicle's actual geographical location and its corresponding geographical location on the navigation map, the event status, the type of bump, and the frequency of occurrence of events of the same type of bump, all bump events within each bump event cluster are assigned corresponding weights.

7. The map updating method according to claim 6, characterized in that, The determination of the daily increase in turbulence event activity index for each turbulence event cluster based on the corresponding weights of all turbulence events within each turbulence event cluster includes: The daily increase in turbulence event activity index for each turbulence event cluster is determined based on the sum of the corresponding weights of all turbulence events within each turbulence event cluster.

8. A computer device comprising a processor and a memory, said memory being adapted to store a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by the processor to perform the map update method according to any one of claims 1 to 7.

9. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform the map update method according to any one of claims 1 to 7.

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