A map road network updating method and device, computer equipment and storage medium

Abstract

The application discloses a map road network updating method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring driving track data of a vehicle in a parking lot; acquiring a first matching rate for representing a probability that a track point in the track data matches a node in a target map road network, and a second matching rate for representing a probability that a target node exists in the node matching the track point in the target map road network; determining whether the driving track data satisfies a preset updating condition based on the first matching rate and the second matching rate; and updating the target map road network based on the driving track data if the preset updating condition is satisfied. The method can screen driving tracks of multiple vehicles in the parking lot, and update the map road network of the parking lot based on the screened driving tracks.

Description

Technical Field

[0001] This application relates to the field of map processing technology, and more specifically, to a method, apparatus, computer equipment, and storage medium for updating a map road network. Background Technology

[0002] In the process of realizing automotive intelligence, to broaden its application, vehicles can acquire driving trajectory data in parking lots to obtain the parking lot's road network map, and then use this map network for navigation and positioning. However, in practical applications, the driving trajectory data acquired by vehicles is often complex and contains a lot of redundant data. Furthermore, due to user behavior habits, a large amount of trajectory data overlaps and has no reference value, resulting in a lot of ineffective processing during the acquisition of the road network map. Summary of the Invention

[0003] In view of the above problems, this application proposes a method, apparatus, computer equipment and storage medium for updating a map road network.

[0004] In a first aspect, embodiments of this application provide a method for updating a map road network. The method includes: acquiring driving trajectory data of a vehicle in a parking lot; acquiring a first matching rate and a second matching rate between the driving trajectory data and a target map road network, wherein the first matching rate is used to characterize the probability that a trajectory point in the trajectory data matches a node in the target map road network, and the second matching rate is used to characterize the probability that a target node exists among the nodes in the target map road network that match the trajectory point, wherein the target node is a node whose maturity is greater than a preset maturity among the matched nodes, and the maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot; determining whether the driving trajectory data meets a preset update condition based on the first matching rate and the second matching rate; and updating the target map road network based on the driving trajectory data if the preset update condition is met.

[0005] Secondly, embodiments of this application provide a map road network updating device, the device comprising: a trajectory acquisition module, a matching rate determination module, a trajectory determination module, and a road network updating module, wherein the trajectory acquisition module is used to acquire driving trajectory data of a vehicle in a parking lot; the matching rate determination module is used to acquire a first matching rate and a second matching rate between the driving trajectory data and the target map road network, the first matching rate being used to characterize the probability that a trajectory point in the trajectory data matches a node in the target map road network, the second matching rate being used to characterize the probability that a target node exists among the nodes in the target map road network that match the trajectory point, the target node being a node whose maturity is greater than a preset maturity among the matched nodes, the maturity corresponding to the node being used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot; the trajectory determination module is used to determine whether the driving trajectory data meets preset update conditions based on the first matching rate and the second matching rate; the road network updating module is used to update the target map road network based on the driving trajectory data if the preset update conditions are met.

[0006] Thirdly, embodiments of this application provide a computer device, including: one or more processors; a memory; and one or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more application programs are configured to perform the map road network update method provided in the first aspect above.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing program code that can be called by a processor to execute the map road network update method provided in the first aspect above.

[0008] The map road network update method provided in this application obtains vehicle driving trajectory data in a parking lot, acquires a first matching rate (representing the probability that trajectory points in the trajectory data match nodes in the target map road network), and a second matching rate (representing the probability that a target node exists among the nodes in the target map road network that match the trajectory points). The target node is a node among the matched nodes whose maturity level is greater than a preset maturity level. The maturity level of the node represents the number of trajectories that have passed through the node in the historical trajectory of the parking lot. Based on the first and second matching rates, it is determined whether the driving trajectory data meets preset update conditions. If the preset update conditions are met, the target map road network is updated based on the driving trajectory data. Therefore, by filtering the driving trajectories of multiple vehicles in the parking lot before updating the map road network based on the driving trajectory data, invalid processing during map road network updates can be avoided. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A schematic diagram of the hardware environment for the map road network update method provided in this application embodiment is shown.

[0011] Figure 2 A schematic flowchart of a map road network update method provided in one embodiment of this application is shown.

[0012] Figure 3 A schematic flowchart of a map road network updating method provided in another embodiment of this application is shown.

[0013] Figure 4 A schematic flowchart of a map road network updating method provided in another embodiment of this application is shown.

[0014] Figure 5 A schematic flowchart of a map road network updating method provided in another embodiment of this application is shown.

[0015] Figure 6 This diagram illustrates a structural block diagram of a map road network updating device provided in an embodiment of this application.

[0016] Figure 7 A structural block diagram of a computer device provided in an embodiment of this application is shown.

[0017] Figure 8 A structural block diagram of a computer-readable storage medium provided in an embodiment of this application is shown. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0019] Currently, automotive intelligence is developing rapidly, and cars have numerous usage scenarios and functions. However, most of these applications focus on extending the vehicle's capabilities while it is driving on the road. There are still some challenges in achieving intelligent driving in low-speed and complex environments such as parking lots. For example, parking lots are mostly located underground, where GPS signals are weak, vehicle positioning capabilities are poor, and navigation within the parking lot is difficult. Furthermore, obtaining navigation maps for parking lots is challenging, leaving autonomous driving in parking lots without the support of location and map data.

[0020] In some technical solutions, vehicles acquire different driving trajectory data from multiple vehicles in the same parking lot, and then stitch and merge these trajectory data to form a complete parking lot map road network. This not only guides vehicles without navigation signals but also provides a relatively accurate parking lot map road network. However, in practical use, this method often suffers from ineffective processing during the acquisition of the map road network because the driving trajectory data acquired by vehicles is often complex and contains a lot of redundant data. Furthermore, user behavior habits lead to a large amount of overlapping trajectory data, rendering it unreliable.

[0021] To address the aforementioned problems, the inventors have proposed a method, apparatus, computer device, and storage medium for updating a map road network, as provided in the embodiments of this application. These methods enable the filtering of vehicle trajectories in parking lots and the updating of the parking lot map road network. The specific map road network updating method will be described in detail in subsequent embodiments.

[0022] The hardware environment of the map road network update method provided in the embodiments of this application is described below.

[0023] Please see Figure 1 It illustrates that the hardware environment involved in the map road network update method provided in this application embodiment includes an update system 10, which includes multiple vehicles 300 ( Figure 1 (Only one is shown in the image) User terminal 400 and computer device 100, and vehicle 300 and computer device 100 can be wirelessly connected. Vehicle 300 may include sensors for acquiring vehicle trajectory data. Vehicle 300 can upload the trajectory data to computer device 100 so that computer device 100 can update the map road network based on the uploaded trajectory data. Of course, the user terminal 400 and the display terminal in vehicle 300 can receive the map road network sent by computer device 100 for display. Computer device 100 can also output prompts to user terminal 400 or display terminal in vehicle 300 for manual judgment when computer device 100 cannot determine whether to update the map road network based on the uploaded trajectory data.

[0024] The method for updating the map road network provided in the embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0025] Please see Figure 2 , Figure 2 A schematic flowchart of a map road network updating method according to an embodiment of this application is shown. The following will focus on... Figure 2 The process is described in detail below. The method for updating the map road network may specifically include the following steps:

[0026] Step S110: Obtain the vehicle's driving trajectory data in the parking lot.

[0027] In this embodiment, a computer device can acquire vehicle trajectory data in a parking lot, and then match this trajectory data with an existing parking lot map network to ultimately form a complete parking lot map network. Specifically, the vehicle trajectory data acquired by the computer device can be obtained by recording the vehicle's coordinates at fixed time intervals during the vehicle's journey, and connecting all the acquired trajectory points to form the vehicle's trajectory in the parking lot. It is understood that the shorter the interval between acquiring trajectory points, the closer the vehicle's trajectory data acquired by the computer device is to the actual trajectory in the parking lot.

[0028] In some implementations, the driving trajectory data may include trajectory points at different times during the vehicle's journey through the parking lot. Optionally, the computer device may collect the coordinates of the vehicle's location as a trajectory point after each fixed distance traveled during the vehicle's journey, thereby obtaining the vehicle's driving trajectory data in the parking lot. Alternatively, it may collect the coordinates of the vehicle's location as a trajectory point after each fixed time interval.

[0029] In some implementations, the computer equipment can acquire the vehicle's driving trajectory data in the parking lot through sensors installed in the vehicle. That is, the sensors can acquire the coordinates of the vehicle's current location in real time, and the computer equipment can acquire multiple trajectory points during the vehicle's driving process according to a pre-set standard and form the corresponding driving trajectory data of the vehicle. This data is used to update the map road network corresponding to the parking lot. The sensors can also acquire the vehicle's pose data or the gear position data of the vehicle, which can be used to preprocess the driving trajectory data based on the pose data or gear data.

[0030] In some implementations, to obtain map road networks for more parking lots, multiple computer devices can simultaneously acquire driving trajectory data transmitted from different parking lots to update the map road networks corresponding to different parking lots. Specifically, the computer devices can group the massive amounts of driving trajectory data uploaded by vehicles, dividing it into multiple sets of driving trajectory data according to the processing capacity of each computer. Each set of driving trajectory data can be processed serially by one computer device, while driving trajectory data can be processed in parallel between different computer devices.

[0031] Step S120: Obtain a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that the trajectory point in the trajectory data matches the node in the target map road network. The second matching rate is used to characterize the probability that there is a target node among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot.

[0032] In this embodiment, after acquiring the driving trajectory data of a vehicle in a parking lot, the computer device can obtain the number of all trajectory points included in the driving trajectory data. Then, by matching the trajectory points with nodes in the map road network, it can obtain a first matching rate and a second matching rate between the driving trajectory data and the target map road network. Specifically, it obtains a first matching rate to characterize the probability that a trajectory point in the driving trajectory data matches a node in the target map road network, and a second matching rate to characterize the probability that a target node exists among the nodes in the target map road network that match the driving trajectory data. It is understood that the magnitude of the first matching rate is positively correlated with the number of trajectory points in the driving trajectory data that match nodes in the target map road network; that is, the more matching trajectory points, the higher the first matching rate, which also means a higher degree of overlap between the driving trajectory data and the target map road network. The computer device can determine whether the corresponding driving trajectory data has value in updating the target map road network based on the first matching rate. The magnitude of the second matching rate is positively correlated with the number of target nodes in the target map road network; that is, the more target nodes, the higher the second matching rate, and the better the overall quality of the target map road network. The preset maturity level can be a pre-set maturity value, which indicates that when the maturity level of a node is greater than the preset maturity level, the node has strong robustness and is less prone to errors.

[0033] Specifically, among all nodes in the target map road network that match the driving trajectory data, the higher the maturity of the node, the more historical trajectory data passing through that node in the target map road network. When the maturity of a node is greater than a preset maturity, the node can be considered a mature node, meaning that the target map road network no longer needs more driving trajectory data passing through that node for confirmation. The more mature nodes in the target map road network, the higher the second matching rate, which means that a larger number of trajectory points in the driving trajectory data match mature nodes in the target map road network. In this case, the benefit effect of the driving trajectory data on the target map road network is less significant.

[0034] In some implementations, when a target map road network with parking lots has not been generated, the computer device can use the first driving trajectory data obtained from the vehicle as the target map road network, and the trajectory points in the first driving trajectory data as nodes in the target map road network. After obtaining the second and subsequent driving trajectory data, these driving trajectory data are matched with the target map road network, thereby updating the target map road network.

[0035] In some implementations, the method by which a computer device determines the magnitude of a first matching rate based on the number of matched trajectory points may be by obtaining the ratio of the number of matched trajectory points to the total number of trajectory points in the driving trajectory data and using it as the first matching rate; or it may be by using the ratio of the number of matched trajectory points to a fixed value as the first matching rate, wherein the fixed value may be the average of the number of trajectory points included in multiple driving trajectory data.

[0036] In some implementations, determining whether any target trajectory point in the driving trajectory data matches any target node in the target map road network can be achieved by obtaining the distance between the coordinates of the target trajectory point and the coordinates of the target node. If the distance between the two points is less than a preset distance, the computer device can consider the target trajectory point and the target node as a match. The preset distance can be any pre-defined distance of any length; it is understood that the shorter the preset distance, the higher the matching accuracy.

[0037] In some implementations, the computer device can obtain the maturity of matching nodes in the target map road network, which can include not only the connection between various passages in the parking lot, but also the nodes in the parking lot and the maturity of the nodes.

[0038] In some implementations, the method by which the computer device determines the second matching rate between the driving trajectory data and the target map road network based on the number of matched nodes and the number of mature nodes can be to use the ratio of the number of mature nodes to the number of matched nodes as the second matching rate. Since the number of nodes obtained by the computer device may have errors, the computer device can also take a representative data of the interval in which the number of matched nodes and the number of mature nodes are located as the number of matched nodes and the number of mature nodes, respectively, and then use the ratio of these two representative data as the second matching rate. Alternatively, the number of matched nodes and the number of mature nodes can be added to or subtracted by a specified value, which can be any value in the interval [0, 100], or the exponent of these two values ​​can be taken, and then the ratio of the two calculated values ​​can be used as the second matching rate, thus reducing errors.

[0039] Step S130: Based on the first matching rate and the second matching rate, determine whether the driving trajectory data meets the preset update conditions.

[0040] In this embodiment, after acquiring the first matching rate and the second matching rate between the driving trajectory data and the target map road network, the computer device can determine whether the driving trajectory data meets the preset update conditions, i.e., whether it is necessary to update the target map road network based on the driving trajectory data. The preset update conditions serve as the basis for determining whether the driving trajectory data can be used to update the target map road network. Specifically, if the driving trajectory data meets the preset update conditions, it indicates that updating the target map road network based on the driving trajectory data can expand the display range of the target map road network or increase the robustness of each node in the target map road network, thereby giving the target map road network an effective gain.

[0041] The first matching rate refers to the degree of overlap between the driving trajectory data and the existing target map road network. A higher first matching rate indicates a greater degree of overlap. In this case, updating the target map road network using the driving trajectory data will not provide any effective benefit; on the contrary, repeated updates may lead to the accumulation of errors. Therefore, if the first matching rate is high and does not increase the robustness of each node in the target map road network, the computer can consider the corresponding driving trajectory data as useless. The second matching rate refers to the maturity of each node in the target map road network. A higher second matching rate indicates a more mature node in the target map road network that matches the driving trajectory data. If the maturity of the node corresponding to the driving trajectory data has reached the expected maturity, the computer does not need to update the target map road network based on the driving trajectory data. Therefore, to ensure the final quality of the target map road network, the computer can comprehensively determine whether to update the target map road network based on the driving trajectory data, considering both the first and second matching rates.

[0042] Step S140: If the preset update conditions are met, the target map road network is updated based on the driving trajectory data.

[0043] In this embodiment of the application, after determining that the driving trajectory data meets the preset update conditions, the computer device can update the target map road network based on the driving trajectory data. Specifically, updating the target map road network can involve merging trajectory points from the driving trajectory data into the target map road network. That is, for trajectory points that have appeared in the driving trajectory data but have not appeared in the target map road network, these trajectory points can be added to the target map road network, and the maturity level of the nodes can be marked.

[0044] The map road network update method provided in this application embodiment obtains vehicle driving trajectory data in a parking lot, and obtains a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that a trajectory point in the trajectory data matches a node in the target map road network, and the second matching rate is used to characterize the probability that a target node exists among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot. Based on the first matching rate and the second matching rate, it is determined whether the driving trajectory data meets the preset update conditions. If the preset update conditions are met, the target map road network is updated based on the driving trajectory data. Therefore, since the driving trajectories of multiple vehicles in the parking lot are filtered before updating the map road network based on the driving trajectory data, and then the parking lot map road network is updated based on the filtered driving trajectories, invalid processing during map road network updates can be avoided.

[0045] Please see Figure 3 This illustrates a flowchart of a map road network update method provided in another embodiment of this application. The following will focus on... Figure 3 The process is described in detail below. The method for updating the map road network may specifically include the following steps:

[0046] Step S210: Obtain the vehicle's driving trajectory data in the parking lot.

[0047] Step S220: Obtain the number of trajectory points that match the driving trajectory data and the target map road network, as the first number.

[0048] In this embodiment, after acquiring the vehicle's driving trajectory data in the parking lot, the computer device can match all trajectory points included in the driving trajectory data with nodes in the target map road network to obtain the number of matching trajectory points between the driving trajectory data and the target map road network. Specifically, a trajectory point matching a node can be considered as the distance between a trajectory point in the driving trajectory data and a node in the target map road network being less than a preset distance. The number of matching trajectory points is used as a first quantity to judge the driving trajectory data based on the first quantity.

[0049] Step S230: Obtain the number of trajectory points in the driving trajectory data as the second quantity.

[0050] In this embodiment of the application, the computer device can use the number of all trajectory points included in the driving trajectory data as the second quantity. The size of the second quantity is related to the selection interval of the trajectory points in the driving trajectory data and also to the driving length of the driving trajectory data. The computer device can make a judgment on the driving trajectory data based on the first quantity and the second quantity.

[0051] Step S240: Obtain the ratio of the first quantity to the second quantity as the first matching rate between the driving trajectory data and the target map road network.

[0052] In this embodiment, after acquiring the first and second quantities corresponding to the driving trajectory data, the computer device can calculate the ratio of the first quantity to the second quantity, that is, the ratio of the number of trajectory points in the driving trajectory data that match the target map road network to the total number of trajectory points included in the driving trajectory data. The computer device can use this ratio as the first matching rate between the driving trajectory data and the target map road network to characterize the degree of overlap between them. Specifically, given a fixed total number of trajectory points in the driving trajectory data, the more trajectory points that match the target map road network, the higher the first matching rate, meaning a greater overlap between the driving trajectory data and the target map road network. It is understood that if the first matching rate is large, updating the target map road network based on the driving trajectory data has a smaller gain effect on the target map road network; that is, the driving trajectory data cannot expand the representation range of the target map road network.

[0053] Step S250: Obtain the number of nodes that match the driving trajectory data and the target map road network, as the third quantity.

[0054] In this embodiment, after acquiring driving trajectory data, the computer device can obtain the number of nodes that match the driving trajectory data with the target map road network, and use this number as a third quantity. Specifically, when the distance between any trajectory point in the driving trajectory data and any node in the target map road network is less than a preset distance, the computer device can consider the trajectory point and the node as a match, and thus use the number of nodes that match the target map road network in the acquired driving trajectory data as the third quantity. Generally, the third quantity is the same as the first quantity mentioned above. The computer device can make judgments on the driving trajectory data based on the third quantity.

[0055] Step S260: Based on the maturity of each node in the matched nodes, obtain the number of target nodes in the matched nodes as the fourth quantity. The target node is a node in the matched nodes whose maturity is greater than a preset maturity. The maturity of the node is used to characterize the number of trajectories that pass through the nodes in the historical trajectory of the parking lot.

[0056] In this embodiment, the computer device can obtain the maturity level of nodes in the target map road network that match the driving trajectory data, i.e., the number of historical trajectories of the matched nodes in the target map road network. It then obtains the number of nodes with a maturity level greater than a preset maturity level and uses this as the fourth quantity. It can be understood that nodes with a maturity level greater than the preset maturity level have all undergone confirmation through multiple historical trajectories. These nodes can be considered as already drawn mature nodes. For example, if nodes with a number of historical trajectories greater than or equal to 3 are considered mature nodes, then these mature nodes in the target map road network do not require further confirmation through more driving trajectory data. Therefore, the computer device can determine the number of mature nodes in the driving trajectory data based on the magnitude of the fourth quantity, and thus determine whether it is necessary to update the maturity level of the matched nodes in the map road network based on the driving trajectory data.

[0057] Step S270: Obtain the ratio of the fourth quantity to the third quantity, as the second matching rate between the driving trajectory data and the target map road network.

[0058] In this embodiment of the application, the computer device can use the ratio of the acquired fourth data to the third data, that is, the ratio between the number of nodes with a maturity greater than a preset maturity and the number of matched nodes, as the second matching rate between the driving trajectory data and the target map road network, so as to determine whether to retain the driving trajectory data to update the maturity of the nodes in the target map road network.

[0059] Step S280: Based on the first matching rate and the second matching rate, determine whether the driving trajectory data meets the preset update conditions.

[0060] Step S290: If the preset update conditions are met, the target map road network is updated based on the driving trajectory data.

[0061] In the embodiments of this application, steps S210, S280 and S290 can be referred to the contents of other embodiments, and will not be repeated here.

[0062] The map road network update method provided in this application provides a way to obtain a first matching rate and a second matching rate. Based on the number of trajectory points that match between the driving trajectory data and the target map road network, and the number of nodes with a maturity greater than a preset maturity among the matched nodes, it determines whether to update the target map road network based on the driving trajectory data. This achieves the screening of driving trajectory data before updating the target map road network based on the driving trajectory data, avoids invalid processing, and improves the efficiency and accuracy of the target map road network update.

[0063] Please see Figure 4 This illustrates a flowchart of a map road network update method provided in another embodiment of this application. The following will focus on... Figure 4 The process is described in detail below. The method for updating the map road network may specifically include the following steps:

[0064] Step S310: Obtain the vehicle's driving trajectory data in the parking lot.

[0065] Step S320: Obtain a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that the trajectory point in the trajectory data matches the node in the target map road network. The second matching rate is used to characterize the probability that there is a target node among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot.

[0066] In the embodiments of this application, steps S310 and S320 can be referred to the contents of other embodiments, and will not be repeated here.

[0067] Step S330: If the first matching rate is greater than the first threshold and the second matching rate is less than the second threshold, then the driving trajectory data is determined to meet the preset update conditions.

[0068] In this embodiment, after acquiring a first matching rate and a second matching rate between driving trajectory data and a target map road network, the computer device can determine whether to update the target map road network based on the range of the first and second matching rates. If the first matching rate is greater than a first threshold and the second matching rate is less than a second threshold, it can be determined that the driving trajectory data meets a preset update condition, meaning the driving trajectory data has a beneficial effect on the target map road network. Specifically, if the first matching rate is greater than the first threshold, it indicates a high degree of overlap between the driving trajectory data and the target map road network. In this case, updating the target map road network based on the driving trajectory data will not significantly increase the number of nodes in the target map road network. However, since the second matching rate is less than the second threshold, it indicates that the maturity of the nodes in the target map road network is low, meaning these nodes are more likely to malfunction. Therefore, the computer device needs to update the target map road network based on the driving trajectory data. The purpose of this update is primarily to increase the maturity of the nodes in the target map road network, thereby determining that the driving trajectory data meets the preset update condition, i.e., the driving trajectory data has a beneficial effect on the target map road network.

[0069] Step S340: If the first matching rate is greater than the first threshold and the second matching rate is greater than or equal to the second threshold, then it is determined that the driving trajectory data does not meet the preset update conditions.

[0070] In this embodiment, if the first matching rate is greater than the first threshold, based on the analysis in the above steps, it can be seen that the overlap between the driving trajectory data and the target map road network is high. In this case, updating the target map road network based on the driving trajectory data will not significantly update the number of nodes in the target map road network; that is, the driving trajectory data does not have the function of expanding the representation range of the target map road network. Simultaneously, if the second matching rate is greater than or equal to the second threshold, it means that each node in the target map road network also has a high maturity level, i.e., the probability of these nodes being incorrect is low. In this case, updating the map road network based on the driving trajectory data will not increase the maturity level of each node in the target map road network. Therefore, the computer device can determine that the driving trajectory data does not meet the preset update conditions, i.e., it cannot produce a beneficial effect on the target map road network.

[0071] In some implementations, for driving trajectory data that does not meet the preset update conditions, the computer device can directly delete the driving trajectory data or store it on a cloud server for later use.

[0072] Step S350: If the first matching rate is less than or equal to the first threshold and the first matching rate is greater than the third threshold, then it is determined that the driving trajectory data meets the preset update conditions, and the third threshold is less than the first threshold.

[0073] In this embodiment, if the first matching rate is less than or equal to the first threshold and greater than the third threshold, where the third threshold is less than the first threshold (i.e., the first matching rate is between the third and first thresholds), it indicates that the driving trajectory data and the target map road network have some overlapping trajectories and some non-overlapping trajectories. Regardless of the relationship between the second matching rate and the second threshold, the computer device can update the target map road network based on the driving trajectory data. Updating the target map road network at this time not only expands its representation range but also increases the maturity of each node in the target map road network. In other words, the driving trajectory data has a beneficial effect on the target map road network, and the computer device can determine that the driving trajectory data meets the preset update conditions. Specifically, when the computer device updates the target map road network based on the driving trajectory data, the target map road network can acquire some new node data, and the maturity of each node in the target map road network will also increase accordingly.

[0074] Step S360: If the first matching rate is less than the third threshold, then it is determined that the driving trajectory data does not meet the preset update conditions.

[0075] In this embodiment, if the first matching rate is less than the third threshold, it indicates a significant difference between the driving trajectory data and the target map road network, with few overlapping trajectory points. In this case, the driving trajectory data may not actually be a driving trajectory within the target parking lot, or it may have encountered an error during transmission. If the target map road network is updated based on this driving trajectory data, the probability of the target map road network being incorrect is high. Therefore, to ensure the correctness of the target map road network, the computer can confirm that the driving trajectory data does not meet the preset update conditions, i.e., discard this driving trajectory data.

[0076] In some implementations, if the first matching rate is less than the third threshold, and it is determined that the driving trajectory data does not meet the preset update conditions, a specified prompt message can be output. The specified prompt message is used to prompt the user to determine whether to update the target map road network based on the driving trajectory data.

[0077] Optionally, the computer device can update the target map road network based on the driving trajectory data after receiving the user's confirmation information, wherein the user's confirmation information is used to indicate that the computer device's driving trajectory data is error-free and can update the target map road network.

[0078] Step S370: If the preset update conditions are met, the target map road network is updated based on the driving trajectory data.

[0079] In this embodiment, if the first matching rate is less than or equal to the first threshold and the first matching rate is greater than the third threshold, after determining that the driving trajectory data meets the preset update conditions, the nodes in the target map road network can be updated based on the driving trajectory data. Optionally, trajectory points in the driving trajectory data that do not match the nodes in the target map road network can be obtained, and the mismatched trajectory points can be added to the target map road network as nodes.

[0080] In this embodiment, if the first matching rate is greater than a first threshold and the second matching rate is less than a second threshold, after determining that the driving trajectory data meets the preset update conditions, the maturity of the nodes corresponding to the nodes in the target map road network can be updated based on the driving trajectory data. Specifically, if the first matching rate is greater than the first threshold and the second matching rate is less than the second threshold, as analyzed above, the driving trajectory data increases the maturity of each node in the target map road network. Therefore, the computer device can update the maturity of the nodes corresponding to the nodes in the target map road network based on the driving trajectory data. Specifically, since the maturity of a node is used to characterize the number of historical trajectories passing through that node in the target map road network, the computer device can increase the maturity of the nodes in the driving trajectory data that match those nodes in the target map road network.

[0081] In some implementations, the method for updating the maturity of nodes in a target map road network may include: obtaining nodes that match the driving trajectory data with the target map road network; increasing the maturity of the matching nodes in the target map road network; and storing the driving trajectory data, which is used to verify the target map road network.

[0082] In the method of updating the maturity of nodes in the target map road network based on driving trajectory data, the computer equipment can obtain the nodes that match the driving trajectory data and the target map road network, and update the maturity based on the maturity of the matched nodes.

[0083] After acquiring matching nodes between driving trajectory data and the target map road network, the computer device can increase the maturity level of the matching nodes, thereby updating the target map road network based on the driving trajectory data. The magnitude by which the computer device increases the maturity level of the matching nodes can be predefined and is not limited, but the same magnitude should be applied each time the maturity level of a node in the target map road network is increased based on the driving trajectory data. Simultaneously, the computer device can store the driving trajectory data for quality verification after the complete parking lot map road network is formed. Specifically, the computer device can generate corresponding indexes for all driving trajectory data that have a beneficial effect on the target map road network and store all indexed data in a dataset to quickly find valid driving trajectory data when verifying the parking lot map road network.

[0084] In this embodiment, the specific values ​​of the first threshold, the second threshold, and the third threshold are not limited. Optionally, the first threshold may be between 95% and 99%, for example, 98%; the second threshold may be between 15% and 30%, for example, 20%; and the third threshold may be between 50% and 70%, for example, 60%. The map road network update method provided in this embodiment, when determining whether driving trajectory data meets preset update conditions based on the first matching rate and the second matching rate, uses the physical meaning of the first and second matching rates and their corresponding thresholds to determine whether the driving trajectory data meets the preset update conditions. This achieves accurate filtering of driving trajectory data and updates of the target map road network under different conditions, avoiding invalid processing when updating the map road network.

[0085] Please see Figure 5 This illustrates a flowchart of a map road network update method provided in another embodiment of this application. The following will focus on... Figure 5 The process is described in detail below. The method for updating the map road network may specifically include the following steps:

[0086] Step S410: Obtain the vehicle's driving trajectory data in the parking lot.

[0087] Step S420: Based on the speed information corresponding to each trajectory point in the driving trajectory data, delete the trajectory points corresponding to the reversing action in the driving trajectory data. The speed direction of the trajectory points corresponding to the reversing action is opposite to the reference direction corresponding to the trajectory points.

[0088] In this embodiment, after acquiring the vehicle's driving trajectory data in the parking lot, the computer device can preprocess the data. That is, in actual vehicle driving, the situation is complex, and the computer device can abstract or filter out useless data from the driving trajectory data. Specifically, the computer device can delete trajectory points corresponding to reversing actions from the driving trajectory data based on the speed information corresponding to each trajectory point. The computer device can determine the trajectory points corresponding to reversing actions in the driving trajectory data based on the fact that the speed direction of the trajectory point corresponding to the reversing action is opposite to the reference direction corresponding to the trajectory point.

[0089] In some implementations, the computer device can acquire the vehicle's position and orientation data based on sensors installed in the vehicle, and then determine whether the vehicle is in reversing motion based on the position and orientation data; the computer device can also determine whether the vehicle is in reversing motion based on the vehicle's current gear position data.

[0090] Step S430: Obtain a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that the trajectory point in the trajectory data matches the node in the target map road network. The second matching rate is used to characterize the probability that there is a target node among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot.

[0091] Step S440: Based on the first matching rate and the second matching rate, determine whether the driving trajectory data meets the preset update conditions.

[0092] Step S450: If the preset update conditions are met, the target map road network is updated based on the driving trajectory data.

[0093] In this embodiment, steps S410, S440 to S450 can be found in other embodiments and will not be repeated here.

[0094] The map road network update method provided in this application preprocesses the driving trajectory data, that is, deletes the reversing data in the driving trajectory data, so as to reduce the computer equipment's judgment of the driving trajectory data, thereby realizing the rapid filtering of driving trajectory data and the updating of the target map road network based on the driving trajectory data.

[0095] Please see Figure 6This document illustrates a map road network updating device 200 provided in an embodiment of this application. The device includes: a trajectory acquisition module 210, a matching rate determination module 220, a trajectory determination module 240, and a road network updating module 250. The trajectory acquisition module 210 acquires driving trajectory data of a vehicle in a parking lot. The matching rate determination module 220 acquires a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate represents the probability that a trajectory point in the trajectory data matches a node in the target map road network. The second matching rate represents the probability that a target node exists among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node represents the number of trajectories that have passed through the node in the historical trajectory of the parking lot. The trajectory determination module 240 determines whether the driving trajectory data meets preset update conditions based on the first matching rate and the second matching rate. The road network updating module 250 updates the target map road network based on the driving trajectory data if the preset update conditions are met.

[0096] In some implementations, the matching rate determination module 220 can be used to obtain the number of trajectory points that match between the driving trajectory data and the target map road network as a first quantity; obtain the number of trajectory points in the driving trajectory data as a second quantity; and obtain the ratio of the first quantity to the second quantity as a first matching rate between the driving trajectory data and the target map road network.

[0097] In some implementations, the matching rate determination module 220 can also be used to obtain the number of nodes that match between the driving trajectory data and the target map road network as a third quantity; based on the maturity of each node in the matching nodes, obtain the number of target nodes in the matching nodes as a fourth quantity, where the target node is a node in the matching nodes whose maturity is greater than a preset maturity, and the maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot; and obtain the ratio of the fourth quantity to the third quantity as a second matching rate between the driving trajectory data and the target map road network.

[0098] In some embodiments, the trajectory determination module 240 includes a first determination unit, a second determination unit, a third determination unit, and a fourth determination unit. The first determination unit is configured to determine that the driving trajectory data meets a preset update condition if a first matching rate is greater than a first threshold and a second matching rate is less than a second threshold; the second determination unit is configured to determine that the driving trajectory data does not meet the preset update condition if the first matching rate is greater than the first threshold and the second matching rate is greater than or equal to the second threshold; the third determination unit is configured to determine that the driving trajectory data meets the preset update condition if the first matching rate is less than or equal to the first threshold and the first matching rate is greater than a third threshold, wherein the third threshold is less than the first threshold; and the fourth determination unit is configured to determine that the driving trajectory data does not meet the preset update condition if the first matching rate is less than the third threshold.

[0099] In some embodiments, the map road network updating device 200 may further include a prompting module. The prompting module can be used to output a specified prompt message after determining that the driving trajectory data does not meet preset update conditions if the first matching rate is less than the third threshold. The specified prompt message is used to prompt the user to determine whether to update the target map road network based on the driving trajectory data.

[0100] In some implementations, the road network update module 250 can be used to update the maturity of nodes in the target map road network based on driving trajectory data.

[0101] In some implementations, the road network update module 250 may be specifically used to: obtain nodes that match the driving trajectory data and the target map road network; increase the maturity of the matching nodes in the target map road network and store the driving trajectory data, which is used to verify the target map road network.

[0102] In some embodiments, the map road network updating device 200 may further include a trajectory point filtering module. The trajectory point filtering module can be used to delete trajectory points corresponding to reversing actions in the driving trajectory data based on the speed information corresponding to each trajectory point in the driving trajectory data before determining a first matching rate between the driving trajectory data and the target map road network, wherein the speed direction of the trajectory points corresponding to the reversing action is opposite to the reference direction corresponding to the trajectory point.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0104] In the several embodiments provided in this application, the coupling between modules can be electrical, mechanical, or other forms of coupling.

[0105] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0106] In summary, the solution provided in this application obtains vehicle trajectory data in a parking lot, acquires a first matching rate (representing the probability of a trajectory point in the trajectory data matching a node in the target map road network), and a second matching rate (representing the probability that a target node exists among the nodes matching the trajectory point in the target map road network). The target node is a node among the matched nodes whose maturity level is greater than a preset maturity level. The maturity level of the node represents the number of trajectories that have passed through the node in the historical trajectory of the parking lot. Based on the first and second matching rates, it is determined whether the trajectory data meets preset update conditions. If the preset update conditions are met, the target map road network is updated based on the trajectory data. Therefore, by filtering the trajectories of multiple vehicles in the parking lot before updating the map road network based on the trajectory data, and then updating the parking lot map road network based on the filtered trajectories, invalid processing during map road network updates can be avoided.

[0107] Please refer to Figure 7 This illustration shows a structural block diagram of a computer device provided in an embodiment of this application. The computer device 100 can be a physical server, cloud server, PC, laptop, or other computer device capable of running applications. The server 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more applications, wherein the one or more applications can be stored in the memory 120 and configured to be executed by the one or more processors 110, and the one or more applications are configured to perform the methods described in the foregoing method embodiments.

[0108] Processor 110 may include one or more processing cores. Processor 110 connects to various parts of the computer device using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 120, and by calling data stored in memory 120. Optionally, processor 110 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 110 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the displayed content; and the modem handles wireless communication. It is understood that the modem may also not be integrated into processor 110 and may be implemented separately using a communication chip.

[0109] The memory 120 may include random access memory (RAM) or read-only memory (ROM). The memory 120 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 120 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the various method embodiments described below. The data storage area may also store data created during the use of the computer device (such as phonebook data, audio and video data, chat log data, etc.).

[0110] Please refer to Figure 8 This diagram illustrates a structural block diagram of a computer-readable storage medium provided in an embodiment of this application. The computer-readable storage medium 800 stores program code that can be called by a processor to execute the methods described in the above method embodiments.

[0111] The computer-readable storage medium 800 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 800 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 800 has storage space for program code 810 that performs any of the method steps described above. This program code can be read from or written to one or more computer program products. The program code 810 may be compressed, for example, in a suitable form.

[0112] 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for updating a map road network, characterized in that, The method includes: Obtain vehicle trajectory data in the parking lot; A first matching rate and a second matching rate are obtained between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that the trajectory point in the trajectory data matches the node in the target map road network. The second matching rate is used to characterize the probability that there is a target node among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot. Based on the first matching rate and the second matching rate, determine whether the driving trajectory data meets the preset update conditions; If the preset update conditions are met, the target map road network is updated based on the driving trajectory data.

2. The method according to claim 1, characterized in that, The step of obtaining the first matching rate between the driving trajectory data and the target map road network includes: The number of trajectory points that match the driving trajectory data and the target map road network is obtained as the first quantity; The number of trajectory points in the driving trajectory data is used as the second quantity; The ratio of the first quantity to the second quantity is obtained as the first matching rate between the driving trajectory data and the target map road network.

3. The method according to claim 1, characterized in that, The step of obtaining the second matching rate between the driving trajectory data and the target map road network includes: The number of nodes that match the driving trajectory data and the target map road network is obtained as a third quantity; Based on the maturity of each node in the matched nodes, the number of target nodes in the matched nodes is obtained as a fourth quantity. The target node is a node in the matched nodes whose maturity is greater than a preset maturity. The maturity of the node is used to characterize the number of trajectories that pass through the nodes in the historical trajectory of the parking lot. The ratio of the fourth quantity to the third quantity is obtained as the second matching rate between the driving trajectory data and the target map road network.

4. The method according to claim 1, characterized in that, The step of determining whether the driving trajectory data meets the preset update conditions based on the first matching rate and the second matching rate includes: If the first matching rate is greater than the first threshold and the second matching rate is less than the second threshold, then the driving trajectory data is determined to meet the preset update conditions. If the first matching rate is greater than the first threshold and the second matching rate is greater than or equal to the second threshold, then it is determined that the driving trajectory data does not meet the preset update conditions. If the first matching rate is less than or equal to the first threshold and the first matching rate is greater than the third threshold, then the driving trajectory data is determined to meet the preset update conditions, wherein the third threshold is less than the first threshold. If the first matching rate is less than the third threshold, then it is determined that the driving trajectory data does not meet the preset update conditions.

5. The method according to claim 4, characterized in that, If the preset update conditions are met, the target map road network is updated based on the driving trajectory data, including: If the first matching rate is greater than the first threshold and the second matching rate is less than the second threshold, the maturity of the nodes in the target map road network is updated based on the driving trajectory data.

6. The method according to claim 5, characterized in that, The step of updating the maturity of nodes in the target map road network based on the driving trajectory data includes: Obtain the nodes that match the driving trajectory data with the target map road network; Increase the maturity level of the matching nodes in the target map road network and store the driving trajectory data, which is used to verify the target map road network.

7. The method according to any one of claims 1-6, characterized in that, Before acquiring the first matching rate and the second matching rate between the driving trajectory data and the target map road network, the method further includes: Based on the speed information corresponding to each trajectory point in the driving trajectory data, the trajectory points corresponding to the reversing action in the driving trajectory data are deleted, and the speed direction of the trajectory points corresponding to the reversing action is opposite to the reference direction corresponding to the trajectory points.

8. A map road network updating device, characterized in that, The device includes: a trajectory acquisition module, a matching rate determination module, a trajectory determination module, and a road network update module, wherein, The trajectory acquisition module is used to acquire the vehicle's driving trajectory data in the parking lot; The matching rate determination module is used to obtain a first matching rate and a second matching rate between the driving trajectory data and the target map road network. The first matching rate is used to characterize the probability that the trajectory point in the trajectory data matches the node in the target map road network. The second matching rate is used to characterize the probability that there is a target node among the nodes in the target map road network that match the trajectory point. The target node is a node whose maturity is greater than a preset maturity among the matched nodes. The maturity of the node is used to characterize the number of trajectories that pass through the node in the historical trajectory of the parking lot. The trajectory determination module is used to determine whether the driving trajectory data meets the preset update conditions based on the first matching rate and the second matching rate. The road network update module is used to update the target map road network based on the driving trajectory data if preset update conditions are met.

9. A computer device, characterized in that, include: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program code that can be invoked by a processor to execute the method as described in any one of claims 1-7.

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