Map data retrieval methods and devices, storage media

Abstract

The embodiment of the application provides a kind of map data retrieval method and device, storage medium, the method comprises obtaining event report data and vehicle operation data;Based on event report data, determine event grid in grid map data;Based on vehicle operation data, determine vehicle grid in the grid map data;Based on the event grid and the vehicle grid, send event notification to the affected vehicle.By this scheme, the map can be divided into multiple small range areas by grid, and a spatial index is constructed based on event information and vehicle location information, so that the query in a small range can be realized, the calculation complexity and the retrieval time can be reduced, and the query efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of spatial data computing technology, and in particular to a method, apparatus and storage medium for retrieving map data. Background Technology

[0002] In the intelligent connected vehicle industry, information exchange between vehicles, roadside equipment, and the cloud is very frequent. However, this information is high-flow-rate and massive for cloud information systems, and the geographical information carried by the messages describes that they come from various locations in space. The vehicles affected by the messages should be precise rather than covering the entire spatial range. Efficient retrieval and calculation methods are needed to identify these affected vehicles and provide basic retrieval capabilities for subsequent business operations (such as message notifications). The general approach is to extract the latitude and longitude of the event, calculate the current location of all vehicles in the entire spatial range, and then filter them. The spatial calculation efficiency of this method is directly proportional to the spatial range of the retrieval, resulting in a large amount of computation, high computational complexity, and long retrieval time. Summary of the Invention

[0003] This application provides a map data retrieval method, apparatus, and storage medium. The map is divided into multiple small areas using a grid, and a spatial index is constructed based on event information and vehicle location information, thereby enabling queries within a small area. This reduces computational complexity and retrieval time, thus improving query efficiency.

[0004] In a first aspect, embodiments of this application provide a method for retrieving map data, including acquiring event reporting data and vehicle operation data; determining event grids in gridded map data based on event reporting data; determining vehicle grids in gridded map data based on vehicle operation data; and sending event notifications to affected vehicles based on the event grids and the vehicle grids.

[0005] In one possible implementation, the acquisition of event reporting data and vehicle operation data includes: acquiring event reporting data sent by the vehicle and / or roadside equipment, wherein the event reporting data includes the location and time of the event; and acquiring vehicle operation data sent by the vehicle, wherein the vehicle operation data includes vehicle identity information and vehicle location.

[0006] In one possible implementation, determining the event grid in the gridded map data based on the event reporting data includes: determining the set of grid IDs of the grid where the event occurs based on the location of the event in the event reporting data, and storing the association between the set of grid IDs and the event in a cache database.

[0007] In one possible implementation, determining the vehicle grid in the gridded map data based on vehicle operation data includes: determining the grid ID of the grid where the vehicle is located based on the vehicle location in the vehicle operation data, and storing the association between the grid ID and vehicle identity information in a cache database; wherein, based on the vehicle operation data, whenever it is determined that the grid where the vehicle is located has changed, the grid ID of the association between the grid ID and vehicle identity information in the cache database is updated.

[0008] In one possible implementation, sending an event notification to the affected vehicle based on the event grid and the vehicle grid includes: when the vehicle is in navigation mode, generating a point string according to the vehicle's navigation planning path, wherein the point string includes a set of latitude and longitude coordinates of multiple points on the navigation planning path; comparing the point string with the grid ID set of the event grid, and when it is determined that the vehicle is about to pass through the event grid, sending an event notification to the affected vehicle.

[0009] In one possible implementation, sending an event notification to the affected vehicle based on the event grid and the vehicle grid includes: determining whether the vehicle has entered a first event range based on the grid where the vehicle is located; when it is determined that the vehicle has entered the first event range, a notification message is sent to the target vehicle; wherein the first event range includes eight grids adjacent to the event grid.

[0010] In one possible implementation, the vehicle operation data also includes the vehicle heading angle. When it is determined that the vehicle is about to pass through the event grid or that the vehicle is entering the first event range, the vehicle heading angle is used to determine whether the vehicle and the event are in the same lane. When it is determined that they are in the same lane, additional information that the vehicle and the event are in the same lane is added to the event notification, and the event notification containing the additional information is sent to the vehicle.

[0011] In one possible implementation, determining whether a vehicle and an event are in the same lane based on the vehicle's heading angle includes: obtaining the absolute value of the angle between the line connecting the vehicle's position and the event's position and the vehicle's heading angle; determining whether the absolute value of the angle is less than a preset angle, wherein if the absolute value of the angle is less than the preset angle, the event's position is determined to be in front of the vehicle's position, and if the absolute value of the angle is not less than the preset angle, the event's position is determined to be behind the vehicle's position; after determining that the event's position is in front of the vehicle's position, based on the direction of the vehicle's heading angle, obtaining the nearest first lane line to the right of the vehicle, and based on the direction of the vehicle's heading angle, obtaining the nearest second lane line to the right of the event's position; determining whether the first lane line and the second lane line are the same lane line based on the lane line information; if they are the same lane line, the vehicle and the event are determined to be in the same lane; if they are not the same lane line, the vehicle and the event are determined not to be in the same lane.

[0012] Secondly, embodiments of this application also provide a map data retrieval device, including: a processor and a memory, wherein the memory is used to store at least one instruction, and the instruction is loaded and executed by the processor to implement the map data retrieval method provided in the first aspect.

[0013] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the map data retrieval method provided in the first aspect.

[0014] The above technical solution quickly locates the location of an event by dividing the area into grids. After selecting the corresponding grid, the large-scale spatial calculation is reduced to a small grid. By reducing the scope, the factors that are positively correlated with the calculation efficiency are reduced, thereby improving the calculation efficiency. By focusing on vehicles within the grid and combining high-precision map element information, the vehicles affected by the event can be accurately identified, which greatly improves the screening accuracy and saves computing resources. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a map data retrieval method provided in one embodiment of this application;

[0017] Figure 2This is a schematic diagram of an application scenario architecture provided in one embodiment of this application;

[0018] Figure 3 This is a schematic diagram of map grid division provided in one embodiment of this application;

[0019] Figure 4 This is a schematic diagram of navigation planning path matching grid information provided in one embodiment of this application;

[0020] Figure 5 A schematic diagram of a first event range grid provided for one embodiment of this application;

[0021] Figure 6 This is a schematic diagram illustrating event location determination according to an embodiment of this application;

[0022] Figure 7 This is a schematic diagram illustrating the determination of event location according to another embodiment of this application;

[0023] Figure 8 This is a schematic diagram of lane determination provided in one embodiment of this application;

[0024] Figure 9 A schematic diagram of lane determination provided for another embodiment of this application;

[0025] Figure 10 This is a schematic diagram of event lane determination in navigation state provided in one embodiment of this application;

[0026] Figure 11 This is a schematic diagram of the structure of a map data retrieval device provided in one embodiment of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] Figure 1 This is a flowchart of a map data retrieval method provided in one embodiment of this application.

[0029] Reference Figure 1 As shown, the method for retrieving this map data may include the following steps:

[0030] S101: Obtain event reporting data and vehicle operation data.

[0031] S102: Determine the event grid based on event reporting data.

[0032] S103: Determine the vehicle grid based on vehicle operation data.

[0033] S104: Based on the event grid and vehicle grid, send event notifications to vehicles within the event-affected grid.

[0034] Regarding S101

[0035] In some real-world scenarios, unforeseen events may occur in the vehicle driving environment that affect vehicle movement. Examples include vehicle collisions and natural disasters. Vehicle collisions can include accidents caused by collisions between vehicles or between pedestrians and vehicles. Natural disasters can include events such as typhoons uprooting trees or causing signs and other objects to fall onto the road, obstructing traffic. These unforeseen events can disrupt normal traffic flow on the affected roads, leading to traffic congestion. Therefore, it is necessary to obtain information about unforeseen events in the vehicle driving environment and the vehicles that may be affected, in order to promptly notify the affected vehicles.

[0036] Figure 2 This is a schematic diagram of an application scenario architecture provided in one embodiment of this application.

[0037] Reference Figure 2 As shown, in one embodiment of this application, the application scenario may include: vehicle 10, roadside equipment 20, and cloud server 30.

[0038] Reference Figure 2As shown, in some embodiments, the cloud server 30 can receive event reporting data and vehicle operation data sent by the vehicle terminal 10 and the roadside equipment 20 through the data receiving and processing layer. In one embodiment, the event reporting data can be data collected by either the vehicle 10 or the roadside equipment 20. The vehicle operation data is the data uploaded by each vehicle 10 to the cloud server 30. The event reporting data can be directly uploaded to the cloud server 30 by the collecting end (vehicle 10 or roadside equipment 20). If the event reporting data is collected by the vehicle terminal 10, the vehicle terminal 10 can first transmit the event reporting data to the roadside equipment 20 with which it has a current communication connection. The roadside equipment 20 will then filter the data and upload the highest quality data from multiple event reporting information corresponding to the same event sent by multiple vehicles to the cloud server 30. This avoids different vehicle terminals 10 sending duplicate event reporting data to the cloud server 30 and ensures the validity and high quality of the uploaded data. In another implementation, the cloud server 30 can also fuse the event reporting data uploaded by the vehicle terminal 10 and the event reporting data uploaded by the roadside device 20 to ensure the comprehensiveness and high quality of the uploaded data (for example, due to limitations such as the performance and installation location of the vehicle sensors, the performance and installation location of the roadside sensing devices, and the obstruction of the surrounding environment, the vehicle terminal can only obtain some event information through its own sensors, and the roadside device can only obtain some event information through its own sensing devices, and neither may be able to cover all event information).

[0039] In some embodiments, event reporting data and vehicle operation data can be sent to the cloud server via messages. Specifically, for the same event, the vehicle or roadside device may send only one message to the cloud server to transmit event reporting data. Regarding vehicle operation data, the vehicle can periodically send messages containing vehicle operation data to the cloud server, allowing the cloud server to periodically obtain the vehicle operation data. The period for sending vehicle operation data can be preset based on requirements; this application does not impose any restrictions on this sending period.

[0040] In some embodiments, the event reporting data may include the location of the event and the time of the event. In one embodiment, the location of the event may be the latitude and longitude information of the event's location, and the event information may indicate the start / end time of the event. In another embodiment, the event reporting data may further include event image information, which may be photos or videos taken of the event.

[0041] In some embodiments, vehicle operation data may include vehicle identification information and vehicle location information. In one embodiment, the vehicle identification information may be a vehicle ID, and the vehicle location information may be the latitude and longitude of the vehicle's current location.

[0042] In some embodiments, after receiving data (event reporting data and / or vehicle operation data) sent by the vehicle and roadside equipment, the data receiving and processing layer can further parse and process the received data and store snapshots. In one embodiment, messages sent by the vehicle and roadside equipment can be reported to the cloud server via MQTT, HTTP, TCP, etc., and the cloud server parses the data according to different protocols. The parsed content needs to be stored as snapshots according to different subjects (events, vehicles) to ensure that detailed information of the subject can be read based on the subject's unique identifier. Data storage can be performed using Redis, and the stored content can include vehicle snapshots, with a key and a value, where the key is the vehicle ID and the value is the message containing vehicle operation information, such as vehicle location data. In other embodiments, the stored content can also include event snapshots, which include a unique event code (event ID), event start / end time, event latitude and longitude, etc. The unique event code can be generated by the event reporting end (vehicle or roadside equipment) or by the cloud server.

[0043] Regarding S102

[0044] In some embodiments, after receiving event reporting data, the cloud server can determine the event grid based on the event reporting data through the grid computing layer.

[0045] In some embodiments, the cloud server can pre-grid the high-precision map data. Specifically, the high-precision map can be divided into multiple grids. The following is a detailed description of the gridding implementation of high-precision map data.

[0046] Methods of grid division

[0047] In some embodiments, GeoHash can be used as a spatial index. When dividing the map into grids, the grid levels can be set, and the side lengths of different grid levels can vary, ranging from several kilometers to a few centimeters. The basic principle is to understand the Earth as a two-dimensional plane, with reference to... Figure 3As shown, by encoding two-dimensional spatial latitude and longitude data into a string, a plane can be recursively decomposed into smaller sub-blocks, each with the same encoding within a certain latitude and longitude range. Building a spatial index using GeoHash can improve the efficiency of latitude and longitude retrieval of spatial POI (Point of Interest) data. For example, grid division methods can include map polygon segmentation using GeoHash, Google S2, Uber H3, etc.

[0048] Regarding grid data cutting and storage

[0049] In some embodiments, the segmentation and storage of grid data can be achieved through the following multiple steps:

[0050] S1: Determine the spatial dataset to be divided (spatial data exists as datasets, one map corresponds to one dataset), calculate the maximum and minimum latitude and longitude of each dataset range, and calculate the minimum geohash that can contain this dataset.

[0051] S2: Calculate the geohash value that needs to be divided into levels based on the minimum geohash and the level.

[0052] S3: Each geohash value for each level corresponds to a spatial rectangle. Then, the intersection operation is performed between the geohash spatial rectangle and the dataset to obtain the dataset within the range of each geohash rectangle. Finally, these datasets are stored in the database.

[0053] S4: If you only need to divide the spatial dataset within a custom range, first perform an intersection operation between the custom range and the dataset to obtain the dataset within the divided range. Then, perform an intersection operation between the geohash spatial rectangle and the divided dataset to obtain the dataset within each geohash rectangle range. Finally, store these datasets in the database.

[0054] It should be noted that the grid data imported into the database can eventually be pieced together to form the original map, and the grid data contains the corresponding map features.

[0055] 1: Obtain spatial data from the dataset, such as points, lines, surfaces, and their corresponding attributes.

[0056] 2: According to the required spatial range, each spatial data is cut into segments to obtain the spatial dataset within the segmented range.

[0057] 3. Divide each spatial dataset into grids to obtain spatial data under each grid. At the same time, perform geoHash encoding on the divided data so that the corresponding spatial data can be obtained through geohash encoding in the future.

[0058] It should also be noted that, taking Geohash encoding as an example, within the same layer, the Geohash encoding for different grids is fixed. The grid division and encoding techniques used in Google S2 and Uber H3 are similar. Grid encoding is used as a spatial index to narrow down the search range. The divided spatial data is then stored in a database, completing the grid division of the dataset, thus obtaining the gridded high-precision map data.

[0059] The above describes the process of gridding high-precision map data.

[0060] Based on the acquired gridded high-precision map data, in some embodiments, the grid computing layer of the cloud server can determine the grid information of the event on the gridded high-precision map data based on the event location (latitude and longitude information) in the received event reporting data. Specifically, it can generate a set of grid IDs and an association relationship between the event (specifically, an event ID) and store it in a cache database. After determining each event reporting data, the grid computing layer of the cloud server can generate a set of grid IDs and an association relationship between the event and store it in the cache database, so that subsequent applications can be made to determine the impact of the event on vehicles based on the set of grid IDs and the association relationship between the event and the cache database.

[0061] In some embodiments, when storing the association between the grid ID set and the event, the location (latitude and longitude information) of the event can be further stored so that in the subsequent process of determining the impact of the event on the vehicle, a more refined determination can be made based on the precise location (latitude and longitude) of the event, thereby enabling a more refined division of vehicles passing through the event grid into affected vehicles and unaffected vehicles.

[0062] In some embodiments, when storing the association between the grid ID set and the event, the event occurrence time can be further stored so that the event end time can be calculated subsequently based on the event occurrence time. In some application scenarios, the duration of some events is fixed, such as road control events. Before implementing road control, the start and end times of the control are usually notified in advance. Therefore, the grid computing layer can establish and store the association between the grid ID set and the event based on the start and end times of the road control event, and perform event cancellation at the end time calculated based on the road control duration or the pre-announced end time, that is, the association between the stored grid ID set and the event can be deleted at the end time.

[0063] Regarding S103

[0064] Based on the acquired gridded high-precision map data, in some embodiments, the grid computing layer of the cloud server can determine the vehicle grid on the gridded high-precision map data based on the received vehicle operation data. Specifically, the vehicle's current location can be converted into a grid ID, and then the association between the grid ID corresponding to the vehicle's current location and the vehicle's identity information (such as vehicle ID) can be stored in a cache database. In one embodiment, after the grid computing layer determines that the vehicle has entered a new grid based on the vehicle operation information reported by the vehicle, the association between the grid ID corresponding to the vehicle's current location and the vehicle's identity information (such as vehicle ID) in the cache database is updated.

[0065] Regarding S104

[0066] In some embodiments, the vehicle's driving state can be divided into navigation state and cruise state. Navigation state refers to the vehicle driving according to a planned route, while cruise state refers to the driver driving the vehicle freely.

[0067] In some embodiments, after the spatial computing layer of the cloud server determines the vehicles affected by the event based on the event grid and the vehicle grid, the spatial computing layer can send the affected vehicle information (such as vehicle ID) and event information (such as event ID) to the business processing layer of the cloud server. Then, the business processing layer can send event notifications to the vehicles within the event-affected grid. The specific implementation can distinguish different execution processes according to the different driving states of the vehicles (navigation state or cruise state). The following describes in detail the different execution methods of sending event notifications to vehicles based on the division of different driving states.

[0068] In some embodiments, a cloud server can provide navigation services for a vehicle, wherein navigation data (navigation planning route) can be configured for the vehicle based on the vehicle's current location and the destination entered by the user on the vehicle, and the navigation data can be sent to the vehicle to realize the vehicle navigation function.

[0069] In some embodiments, when the vehicle is in navigation mode, the grid computing layer can generate a string of points based on the navigation planning path for the corresponding vehicle. This string of points may include a set of latitude and longitude coordinates of multiple points on the navigation planning path. Furthermore, the grid computing layer can also convert the calculated string of points into a corresponding set of grid IDs and store it in a cache database.

[0070] Figure 4 This is a schematic diagram of navigation planning path matching grid information provided in one embodiment of this application.

[0071] Reference Figure 4As shown, when a vehicle needs to travel from point A to point B, the cloud server provides navigation services to the vehicle. The cloud server can plan a navigation path based on the vehicle's origin (point A) and destination (point B) and send this navigation data containing the planned path to the vehicle. On the other hand, the cloud server can also convert this navigation path into a point string, which can include the latitude and longitude sets of multiple points along the navigation path from point A to point B, where the latitude and longitude information of the i-th point is (Xi, Yi). Further, the grid computing layer can determine the grid through which the navigation path from point A to point B passes based on the generated point string, referring to... Figure 4 As shown, the navigation path from point A to point B traverses multiple grid cells corresponding to the shaded areas in the diagram. Furthermore, the association between the vehicle's navigation information from point A to point B and the grid cells traversed can be stored in a cache database. Specifically, this navigation information can be the navigation ID contained in the navigation data; that is, the association between the navigation ID and the corresponding set of grid IDs derived from the point string conversion can be stored in the cache database.

[0072] In some embodiments, after the association between vehicle navigation information and grid ID set is stored in the cache database, the spatial computing layer of the cloud server can determine the vehicles affected by the event based on the event grid and vehicle grid, and the business processing layer can send event notifications to vehicles within the event-affected grid. In one embodiment, the spatial computing layer can calculate whether the currently processed navigation route passes through the grid where the event occurred based on the association between the grid ID set and the event in the cache database and the association between the grid ID set and the navigation information. Specifically, the grid ID set corresponding to the event ID can be compared with the grid ID set corresponding to the navigation ID. If the grid ID set corresponding to the event ID falls within the grid ID set corresponding to the navigation ID, it is determined that the navigation route passes through the grid where the event occurred; otherwise, it is determined that the navigation route does not pass through the grid where the event occurred.

[0073] In some embodiments, when the spatial computing layer determines that the navigation route passes through the grid where the event occurred, the service processing layer can send a notification message to the target vehicle based on the vehicle ID corresponding to the navigation ID. For example, the notification message can be sent to the target vehicle in the form of a message to inform the vehicle that the current navigation planning route will pass through the grid where the event occurred, which may affect the vehicle's driving. The driver of the vehicle can then decide whether to switch the navigation route based on the notification message.

[0074] In some embodiments, when the vehicle is cruising, it can detect whether the vehicle has entered the first event range. When it is determined that the vehicle has entered the first event range, a notification message is sent to the target vehicle to inform the vehicle that it has entered the first event range.

[0075] In some embodiments, the eight adjacent grids around the grid where the event occurred can be set as the first event range.

[0076] Figure 5 This is a schematic diagram of a first event range grid provided for one embodiment of this application.

[0077] Reference Figure 5 As shown in the figure, the grid with the pentagram is the event occurrence grid. The eight green grids around the event occurrence grid can be set as the first event range. In other embodiments, the event occurrence grid and the eight surrounding green grids can also be set as the first event range together.

[0078] In some embodiments, when the grid computing layer stores the set of grid IDs corresponding to a new event and the association relationship of the event, it can also trigger the spatial computing layer to filter out all vehicles within the first event range of the new event in the cache database, and trigger the business processing layer to send notification information to all the filtered vehicles. The notification information may include emergency event information, specifically including event image information received and processed by the cloud server, and event description information, such as traffic accidents, traffic control, etc. The method of sending the emergency event information may also include event description, such as broadcasting the event on the vehicle or displaying a reminder on the navigation map.

[0079] In some embodiments, the grid computing layer can update the association between grid IDs and vehicle identity information (such as vehicle IDs) in the cached database based on vehicle operation data uploaded by the vehicle. Specifically, when the grid computing layer determines that the vehicle's current grid has changed (i.e., the vehicle has entered a new grid) based on the vehicle location information in the vehicle operation data, it updates the grid ID in the association between grid IDs and vehicle identity information. For example, if the vehicle location information in the vehicle operation data uploaded by the vehicle at the first moment is still in grid A1, and the vehicle location information in the vehicle operation data uploaded by the vehicle at the second moment changes to grid A2, the grid computing layer can update the grid ID in the association between grid IDs and vehicle identity information in the cached database from grid A1 to grid A2.

[0080] In some embodiments, when a vehicle is cruising, after the grid computing layer detects a change in the vehicle's grid, it triggers the sending of vehicle grid change information to the spatial computing layer. This grid change information may include the vehicle ID and the updated grid ID. Upon receiving this grid change information, the spatial computing layer triggers an event impact detection for the target vehicle. Specifically, it checks whether the updated grid ID in the grid change information falls within the first event range. If the spatial computing layer confirms that the updated grid ID falls within the first event range, the service processing layer can send communication information to the target vehicle based on the vehicle ID in the grid change information to inform the vehicle that it has entered the first event range and may be affected by an event. The driver can then decide whether to change their route based on this notification.

[0081] The above-mentioned retrieval method for determining whether a vehicle will be affected by an event based on the comparison between the vehicle grid ID and the first event range is the first-level retrieval method provided in this application embodiment. This retrieval method triggers a detection only when the grid where the vehicle is located changes, which can greatly reduce the retrieval frequency, improve retrieval efficiency and system response speed.

[0082] To improve retrieval accuracy, this application also provides a secondary retrieval method, which can achieve lane-level detection. The secondary retrieval method will be described in detail below with reference to the accompanying drawings.

[0083] In some embodiments, the vehicle operation data uploaded by the vehicle may also include the vehicle's heading angle. The grid computing layer can then associate the vehicle's heading angle with the vehicle ID and store the heading angle in a cache database. Furthermore, when the spatial computing layer identifies vehicles affected by an event, it can further determine whether the vehicle and the event are in the same lane based on the vehicle's heading angle, provided that the grid where the vehicle is located falls within the first event range.

[0084] In some embodiments, the spatial computing layer may determine whether a vehicle and an event are in the same lane based on the vehicle's heading angle, including:

[0085] Obtain the absolute value of the angle between the line connecting the vehicle's location (vehicle latitude and longitude) and the event's location (event latitude and longitude) and the vehicle's heading angle.

[0086] Determine whether the absolute value of the included angle is less than a preset angle. For example, the preset angle can be 90 degrees. When the absolute value of the included angle is less than the preset angle, the event location is determined to be in front of the vehicle. When the absolute value of the included angle is greater than the preset angle, the event location is determined to be behind the vehicle.

[0087] Figure 6 This is a schematic diagram illustrating the determination of event location according to an embodiment of this application.

[0088] Reference Figure 6 As shown, the pentagram represents the location of the event. The vehicle's heading angle points forward. By connecting the vehicle's position (e.g., the front of the vehicle) with the event location, an angle θ is formed between this line and the vehicle's heading angle. It can be determined that the absolute value of the angle corresponding to the current vehicle position is less than a preset angle (90 degrees). Therefore, it can be determined that the event location is in front of the vehicle's position.

[0089] Figure 7 This is a schematic diagram illustrating the determination of event location according to another embodiment of this application.

[0090] Reference Figure 7 As shown, the pentagram represents the location of the event. The vehicle's heading angle points forward. Connecting the vehicle's position (e.g., the front of the vehicle) to the event location creates an angle θ between this line and the vehicle's heading angle. If the absolute value of this angle is greater than a preset angle (90 degrees), the event location can be determined to be behind the vehicle. When the event location is determined to be behind the vehicle, it can be concluded that the event has no impact on the vehicle's movement, and no notification information needs to be sent to the vehicle.

[0091] In some embodiments, after determining that the event location is in front of the vehicle, it can be further determined whether the event location is in the same lane as the vehicle. Specifically, the detection method for determining whether the event location is in the same lane as the vehicle includes:

[0092] Based on the vehicle's heading angle, the nearest lane line to the right of the vehicle is obtained. Similarly, based on the vehicle's heading angle, the nearest lane line to the right of the event location is obtained. Furthermore, based on the pre-stored lane line information, it can be determined whether the nearest lane line to the right of the vehicle and the nearest lane line to the right of the event location are the same lane line. If they are the same lane line, it is determined that the event location and the vehicle are in the same lane; if they are not the same lane line, it is determined that the event location and the vehicle are not in the same lane.

[0093] Figure 8 This is a schematic diagram of lane determination provided for one embodiment of this application.

[0094] Reference Figure 8 As shown, based on the vehicle's heading angle, the nearest lane line L1 to the right of the vehicle can be obtained. Similarly, based on the vehicle's heading angle, the nearest lane line L2 to the right of the event location can be obtained. Based on pre-stored lane line information, it can be determined whether lane line L1 and the lane line are the same lane line. Figure 8 The lane line information shown indicates that lane line L1 and lane line L2 are not the same lane line, thus confirming that the event location (the location shown by the pentagram) and the vehicle are not in the same lane.

[0095] Figure 9 This is a schematic diagram of lane determination provided for another embodiment of this application.

[0096] Reference Figure 9 As shown, based on the vehicle's heading angle, the nearest lane line L1 to the right of the vehicle can be obtained. Similarly, based on the vehicle's heading angle, the nearest lane line L2 to the right of the event location can be obtained. Based on the pre-stored lane line information, it can be determined whether lane line L1 and lane line L2 are the same lane line. By determining that lane line L1 and lane line L2 are the same lane line through the lane line information, it can be confirmed that the event location (the location shown by the pentagram) and the vehicle are in the same lane.

[0097] In some embodiments, after the spatial computing layer determines that the location of the event is in front of the vehicle, the business processing layer can send a notification message to the vehicle to inform the driver that an event has occurred in front of the vehicle.

[0098] In other embodiments, after the spatial computing layer determines that the location of the event is in front of the vehicle, the business processing layer can send a notification message to the vehicle. This notification message can inform the driver that an event has occurred in front of the vehicle. Furthermore, the notification message also includes precise information that the event location is in the same lane as the vehicle, or information about the lanes that the event may affect. As a result, the driver can change lanes in time (in advance) based on the notification message to reduce the impact of the event on the vehicle's driving.

[0099] It should be noted that the lane determination provided in this application can be applied not only to the vehicle's navigation status but also to its cruise status. For example, refer to... Figure 10 As shown, the vehicle travels from point A to point B according to the navigation plan. The spatial computing layer identifies that the navigation route passes through the grid where the event occurred (e.g., Figure 10 When using the grid E4 shown, it is possible to further identify whether the event location and the vehicle's expected driving lane are in the same lane. Specifically, if the lane determination method above determines that the event location and the vehicle's expected driving lane are not in the same lane, for example, by combining... Figure 8 and Figure 10 As shown, based on the vehicle's heading angle, the nearest lane line L1 to the right of the vehicle can be obtained. Similarly, based on the vehicle's heading angle, the nearest lane line L2 to the right of the event location can be obtained. Based on pre-stored lane line information, it can be determined whether lane line L1 and the lane line are the same lane line. Figure 8The lane lines L1 and L2 shown are not the same lane. Even if lane lines L1 and L2 are not the same lane, it's possible to determine whether they are adjacent lane lines based on lane information (specifically, lane spacing) in the high-precision map data. If they are adjacent, it's possible to determine whether the lane line L2 adjacent to the right of the event location is a yellow lane. If L2 is a yellow lane, the event location is in the opposite lane of the vehicle's expected driving lane. Therefore, when the vehicle approaches the event location, the event may not affect its current driving. Thus, the business processing layer may not send a notification to the vehicle. Alternatively, the business processing layer can send a notification to the vehicle, which may include information that the vehicle's navigation route passes through the event grid and that the event location is in the opposite lane. If the lane determination method described above determines that the event location is in the same lane as the vehicle's expected driving lane, the service processing layer can send a notification message to the vehicle. This notification message may include information such as the vehicle's navigation route passing through the event grid and the event location being within the vehicle's expected driving lane. In one implementation, the service processing layer can be triggered to send the notification message to the corresponding vehicle immediately upon determining that the event location is in the same lane as the vehicle's expected driving lane. In another implementation, the notification can be sent after determining that the event location is in the same lane as the vehicle's expected driving lane, and after the vehicle enters the first event area (e.g., the vehicle enters...). Figure 10 When the vehicle is within the grid D5 shown, the business processing layer is triggered to send a notification message to the corresponding vehicle.

[0100] Figure 11 This is a schematic diagram of the structure of a map data retrieval device provided in one embodiment of this application.

[0101] Reference Figure 11 As shown, the map data retrieval device may include a processor 1101 and a memory 1102. The memory 1102 is used to store at least one instruction. When the instruction is loaded and executed by the processor 1101, it implements the map data retrieval method provided in any embodiment of this application.

[0102] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the map data retrieval method provided in any embodiment of this application.

[0103] It should be noted that the terminals involved in the embodiments of this application may include, but are not limited to, personal computers (PCs), personal digital assistants (PDAs), wireless handheld devices, tablet computers, mobile phones, MP3 players, MP4 players, etc.

[0104] It is understood that the application may be a native application installed on the terminal, or it may be a web application of a browser on the terminal. This application embodiment does not limit this.

[0105] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0107] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0108] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0109] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A retrieval method of map data, characterized by, The method comprises: obtaining event reporting data and vehicle operation data; determining an event grid in grid map data based on the event reporting data; determining a vehicle grid in the grid map data based on the vehicle operation data; sending an event notification to an affected vehicle based on the event grid and the vehicle grid; The vehicle operation data further comprises a vehicle heading angle, when it is determined that the vehicle will pass through the event grid or that the vehicle travels into a first event range, the vehicle and the event are determined to be in the same lane based on the vehicle heading angle, when it is determined that they are in the same lane, additional information that the vehicle and the event are in the same lane is added to the event notification, and the event notification containing the additional information is sent to the vehicle end; The determination of whether the vehicle and the event are in the same lane based on the vehicle heading angle comprises: obtaining the absolute value of the included angle between the line connecting the vehicle position and the event position and the vehicle heading angle; determining whether the absolute value of the included angle is less than a preset angle, wherein when the absolute value of the included angle is less than the preset angle, it is determined that the event position is in front of the vehicle position, and when the absolute value of the included angle is not less than the preset angle, it is determined that the event position is behind the vehicle position; After it is determined that the event position is in front of the vehicle position, the nearest first lane line on the right side of the vehicle is obtained based on the direction of the vehicle heading angle, and the nearest second lane line on the right side of the event position is obtained based on the direction of the vehicle heading angle, the first lane line and the second lane line are determined to be the same lane line based on the lane line information, if they are the same lane line, it is determined that the vehicle and the event are in the same lane, and if they are not the same lane line, it is determined that the vehicle and the event are not in the same lane.

2. The method of claim 1, wherein, The obtaining of the event reporting data and the vehicle operation data comprises: obtaining event reporting data sent by a vehicle end and / or a roadside device, wherein the event reporting data comprises an event occurrence position and an event occurrence time; obtaining vehicle operation data sent by the vehicle end, wherein the vehicle operation data comprises vehicle identity information and a vehicle position.

3. The method of claim 2, wherein, The determination of an event grid in grid map data based on event reporting data comprises: determining a set of grid IDs of the grid in which the event is located based on the event occurrence position of the event reporting data, and storing the association between the set of grid IDs and the event in a cache database.

4. The method of claim 2, wherein, The determination of a vehicle grid in the grid map data based on the vehicle operation data comprises: determining the grid ID of the grid in which the vehicle is located based on the vehicle position in the vehicle operation data, and storing the association between the grid ID and the vehicle identity information in a cache database; wherein based on the vehicle operation data, whenever it is determined that the grid in which the vehicle is located changes, the grid ID of the association between the grid ID and the vehicle identity information in the cache database is updated.

5. The method of claim 3, wherein, The sending of an event notification to an affected vehicle based on the event grid and the vehicle grid comprises: When the vehicle is in a navigation state, a point string is generated according to a navigation planning path of the vehicle, wherein the point string comprises a set of longitude and latitude of a plurality of point positions on the navigation planning path; The point string is compared with a set of grid IDs of the grid where the event is located, and when it is determined that the vehicle will pass through the event grid, an event notification is sent to the affected vehicle.

6. The method of claim 4, wherein, The event notification sent to the affected vehicle based on the event grid and the vehicle grid comprises: Based on the grid where the vehicle is located, it is determined whether the vehicle enters a first event range, and when it is determined that the vehicle travels into the first event range, a notification information is sent to the target vehicle; The first event range comprises 8 grids adjacent to the event grid.

7. A map data search device characterized by comprising: The device comprises: A processor and a memory, the memory is used to store at least one instruction, the instruction is loaded and executed by the processor, and the method in any one of claims 1-6 is realized.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the method in any one of claims 1-6.

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