Elevation confidence calculation method, device and equipment of road network data, medium and vehicle

Abstract

The present disclosure relates to a method and device for calculating the confidence of the height of road network data, equipment, medium and vehicle, the method comprising: obtaining standard map road network data in a preset area and corresponding matching vehicle driving trajectory data, the standard map road network data comprising a plurality of road network segments, and the vehicle driving trajectory data comprising a plurality of trajectory segments; calculating a normalized proportion value of the trajectory points contained in the trajectory segment matched by the road network segment, the normalized proportion value being used to represent the data amount of the trajectory points matched by the road network segment; calculating a distance proportion value of the total length of the target trajectory segment and the road network segment; calculating the product of the normalized proportion value and the distance proportion value to obtain the confidence of the height of the road network segment, and obtaining the total confidence of the standard map road network data in the preset area according to the confidence of the height of the road network segment. The present disclosure realizes the evaluation of the confidence of the height of the road network data, and makes the obtained map height more accurate and reliable.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, equipment, medium, and vehicle for calculating the elevation confidence of road network data. Background Technology

[0002] With the development of technology in the field of intelligent driving, navigation maps have been widely used. The process of creating these maps usually requires the use of specialized data collection vehicles equipped with LiDAR, satellite-inertial navigation combined positioning systems, cameras, and other equipment to collect data on areas of interest. The data is then labeled, manually verified, and put into use.

[0003] In existing technologies, the method for assigning map elevation values ​​involves using surveying vehicles to collect GPS elevation data and then manually marking lane elevation values ​​based on the elevation data to obtain high-precision map data. However, this method cannot assess the confidence level of the elevation values. Summary of the Invention

[0004] To address the aforementioned technical issues, this disclosure provides a method, apparatus, equipment, medium, and vehicle for calculating the elevation confidence level of road network data, thereby improving the efficiency and accuracy of elevation confidence level calculation for road network data.

[0005] In a first aspect, embodiments of this disclosure provide a method for calculating the confidence level of road network data, including:

[0006] Obtain standard map road network data and corresponding matching vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments.

[0007] Calculate the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment, whereby the normalized percentage is used to characterize the amount of data of trajectory points matched by the road network segment.

[0008] Calculate the distance ratio between the target trajectory segment and the total length of the road network segment;

[0009] Calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment.

[0010] In some embodiments, calculating the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment includes:

[0011] Calculate the average number of trajectory points contained in the trajectory segment;

[0012] The ratio of the number of trajectory points contained in the trajectory segment to the average value is normalized to obtain the normalized proportion of the road network.

[0013] In some embodiments, before calculating the distance ratio between the target trajectory segment and the total length of the road network segment, the method further includes:

[0014] Determine the length of each trajectory segment matched by the road network segment, and take the longest trajectory segment as the target trajectory segment.

[0015] In some embodiments, after acquiring standard map road network data and corresponding matching vehicle trajectory data within a preset area, the method further includes:

[0016] Based on the information collected by the trajectory acquisition device, noise data in the vehicle driving trajectory data is removed.

[0017] In some embodiments, obtaining the total elevation confidence of the standard map road network data within the preset area based on the elevation confidence of the road network segments includes:

[0018] A smoothing operation is performed on the elevation confidence of multiple road network segments within the preset area to obtain the total elevation confidence of the standard map road network data within the preset area.

[0019] In some embodiments, smoothing the elevation confidence of multiple road network segments within the preset area to obtain the total elevation confidence of the standard map road network data within the preset area includes:

[0020] For each road network segment, obtain the elevation confidence of the preceding road network segment and the elevation confidence of the subsequent road network segment.

[0021] The elevation confidence scores of the road network segments, the preceding road network segments, and the subsequent road network segments are averaged to obtain the total elevation confidence score of the standard map road network data.

[0022] Secondly, embodiments of this disclosure provide an apparatus for calculating the elevation confidence level of road network data, comprising:

[0023] The acquisition module is used to acquire standard map road network data and corresponding matching vehicle driving trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle driving trajectory data includes several trajectory segments.

[0024] The first calculation module is used to calculate the normalized proportion of trajectory points contained in the trajectory segment matched by the road network segment, and the normalized proportion is used to characterize the amount of data of trajectory points matched by the road network segment.

[0025] The second calculation module is used to calculate the distance ratio between the target trajectory segment and the total length of the road network segment;

[0026] The third calculation module is used to calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and to obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment.

[0027] Thirdly, embodiments of this disclosure provide an electronic device, including:

[0028] Memory;

[0029] Processor; and

[0030] Computer programs;

[0031] The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in the first aspect.

[0032] Fourthly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the method described in the first aspect.

[0033] Fifthly, embodiments of this disclosure also provide a vehicle that includes the elevation confidence calculation device for road network data as described above; or the electronic device as described above; or the computer-readable storage medium as described above.

[0034] The elevation confidence calculation method, apparatus, equipment, medium, and vehicle for road network data provided in this disclosure determine the confidence of road network data by quantifying the confidence of the road network data based on the normalized proportion of trajectory points contained in the trajectory and the distance proportion of the trajectory, thereby achieving the evaluation of the confidence of the elevation value of the road network data and making the obtained map elevation more accurate and reliable. Attached Figure Description

[0035] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0036] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 Flowchart of the method for calculating the elevation confidence of road network data provided in this embodiment of the disclosure;

[0038] Figure 2 Flowchart of a method for calculating the elevation confidence level of road network data provided in another embodiment of this disclosure;

[0039] Figure 3 Flowchart of a method for calculating the elevation confidence level of road network data provided in another embodiment of this disclosure;

[0040] Figure 4 A schematic diagram of the structure of the elevation confidence calculation device for road network data provided in this embodiment of the disclosure;

[0041] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0042] To better understand the above-mentioned objectives, features, and advantages of this disclosure, the solutions disclosed herein will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0043] Numerous specific details are set forth in the following description in order to provide a full understanding of this disclosure, but this disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some, and not all, of the embodiments of this disclosure.

[0044] This disclosure provides a method for calculating the elevation confidence level of road network data. The method will be described below with reference to specific embodiments.

[0045] Figure 1 This is a flowchart illustrating the method for calculating the elevation confidence level of road network data provided in this embodiment. This method can be applied to terminal devices, such as in-vehicle systems, smartphones, PDAs, tablets, laptops, all-in-one computers, and autonomous driving devices. It is understood that the method for calculating the elevation confidence level of road network data provided in this embodiment can also be applied to other scenarios.

[0046] The following is about Figure 1 The method for calculating the elevation confidence level of the road network data shown is introduced, and the specific steps of the method are as follows:

[0047] S101. Obtain standard map road network data and corresponding matching vehicle driving trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle driving trajectory data includes several trajectory segments.

[0048] The road network data may include the elevation information of the road network, that is, the absolute or relative elevation information of each road network point.

[0049] The preset region is the area where the road network data for which confidence scores need to be calculated, also known as the region of interest. The road network data within the region of interest is divided into multiple segments. For each segment, multiple matching vehicle trajectory data are obtained. Trajectory data is a collection of location data and timestamps. Each trajectory can be collected during vehicle operation using sensors installed on the vehicle, such as by collecting vehicle operating status information. Vehicle operating status information includes, but is not limited to, vehicle positioning information and vehicle kinematic information. Vehicle positioning information can be obtained using the Global Positioning System (GPS) or other positioning systems; vehicle kinematic information includes, but is not limited to, vehicle speed, acceleration, and turning angle, which can be collected by components such as speed sensors, acceleration sensors, and inertial measurement sensors within the vehicle.

[0050] The collected trajectory data are matched with road network data within the region of interest based on their location information. If the location similarity between a trajectory and the road network data is greater than a preset threshold, the trajectory is considered a match for the road network data. Optionally, for a given trajectory, if the ratio of the number of trajectory points falling within the road network to the total number of trajectory points in the trajectory is greater than a preset ratio, the trajectory is considered a match for the road network data.

[0051] S102. Calculate the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment. The normalized percentage is used to characterize the amount of data of trajectory points matched by the road network segment.

[0052] The normalized percentage value is used to characterize the amount of data contained in a trajectory, and the amount of data contained in a trajectory represents the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data. The larger the amount of data contained in a trajectory, the higher the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data; the smaller the amount of data contained in a trajectory, the lower the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data.

[0053] Among the trajectory data collected by the trajectory acquisition device, there may be some shorter trajectories containing fewer trajectory points. Although these trajectory data match the road network trajectory, their reference value for the confidence level of the road network data is small. Alternatively, some trajectories may be longer, but due to malfunctions in the trajectory acquisition equipment, they may contain fewer trajectory points. This type of trajectory data also has little reference value for the confidence level of the road network data.

[0054] S103. Calculate the distance ratio between the target trajectory segment and the total length of the road network segment.

[0055] The distance percentage is used to characterize the distribution of trajectory points in a trajectory. The more evenly the trajectory points are distributed in a trajectory, the higher the reliability of the trajectory data for the elevation of its corresponding road network data; conversely, if the trajectory points are too concentrated or too scattered, the reliability of the trajectory data for the elevation of its corresponding road network data is lower.

[0056] Typically, the trajectory data collected by a trajectory acquisition device should have a roughly uniform distribution of trajectory points within each trajectory. If the trajectory points within a trajectory are too concentrated or too dispersed, an error may have occurred during the trajectory acquisition process. Alternatively, multiple trajectory data points matched for a road network may be concentrated at the same location within that road network, covering only a small area or a short section. In this case, the trajectory data has a low reliability in terms of elevation confidence relative to the corresponding road network data.

[0057] Optionally, the ratio of road network points matching the trajectory points to all road network points in the segment of road network data can be calculated. If the ratio is lower than a preset threshold, the trajectory data is considered to have low coverage of the corresponding road network, poor distribution of trajectory points, and low reference reliability of the elevation confidence of the trajectory data for its corresponding road network data.

[0058] S104. Calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment.

[0059] This embodiment of the disclosure acquires standard map road network data and corresponding matching vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments. It calculates the normalized percentage of trajectory points contained in the trajectory segments matched by the road network segments, where the normalized percentage represents the amount of data for the trajectory points matched by the road network segments. It also calculates the distance percentage between the target trajectory segment and the total length of the road network segments. The product of the normalized percentage and the distance percentage is calculated to obtain the elevation confidence of the road network segments. Based on the elevation confidence of the road network segments, the total elevation confidence of the standard map road network data within the preset area is obtained. Finally, the elevation confidence of the road network data is quantified and determined based on the normalized percentage of trajectory points contained in the trajectory and the distance percentage of the trajectory. This achieves the evaluation of the elevation confidence of the road network data, making the acquired map elevation more accurate and reliable.

[0060] Figure 2A flowchart illustrating a method for calculating the elevation confidence level of road network data, provided in another embodiment of this disclosure. Figure 2 As shown, the method includes the following steps:

[0061] S201. Obtain standard map road network data and corresponding matching vehicle driving trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle driving trajectory data includes several trajectory segments.

[0062] Specifically, the implementation process and principle of S201 and S101 are the same, and will not be repeated here.

[0063] S202. Calculate the average number of trajectory points contained in the trajectory segment.

[0064] In multiple trajectory datasets matching a road network segment, the number of trajectory points contained in each trajectory may differ, and some trajectories may also have different numbers of trajectory points. For multiple trajectory datasets matching a road network segment, the average number of trajectory points contained in each trajectory is calculated.

[0065] S203. Normalize the ratio of the number of trajectory points contained in the trajectory segment to the average value to obtain the normalized proportion of the road network.

[0066] The normalized percentage value is used to characterize the amount of data contained in a trajectory, and the amount of data contained in a trajectory represents the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data. The larger the amount of data contained in a trajectory, the higher the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data; the smaller the amount of data contained in a trajectory, the lower the degree of reference reliability of the trajectory data to the elevation confidence of its corresponding road network data.

[0067] Normalization involves processing data to limit it to a certain range. It involves obtaining the average number of trajectory points across multiple trajectories from the previous steps, then comparing the number of trajectory points in each trajectory to the average number of trajectory points across all trajectories, and finally normalizing this ratio for each trajectory. Specifically, the ratio for each trajectory can be normalized using the following formula:

[0068]

[0069] Where h is the normalized proportion of the road network, and n is the ratio of the number of trajectory points in each trajectory to the average number of trajectory points in multiple trajectories. min() is the number of trajectory points contained in the trajectory with the fewest trajectory points among the multiple trajectory data matched for a certain road network segment, and max(n) is the number of trajectory points contained in the trajectory with the most trajectory points among the multiple trajectory data matched for a certain road network segment.

[0070] S204. Determine the length of each trajectory segment matched by the road network segment, and take the longest trajectory segment as the target trajectory segment.

[0071] S205. Calculate the distance ratio between the target trajectory segment and the total length of the road network segment.

[0072] The distance percentage is used to characterize the distribution of trajectory points in a trajectory. The more evenly the trajectory points are distributed in a trajectory, the higher the reliability of the trajectory data for the elevation of its corresponding road network data; conversely, if the trajectory points are too concentrated or too scattered, the reliability of the trajectory data for the elevation of its corresponding road network data is lower.

[0073] Based on road network data, the length of a specific road network segment is determined. Then, based on trajectory data from multiple tracks matching that road network segment, the length of each track segment can be determined. The length of the longest track matching that segment is taken, and its ratio to the total length of the road network segment is calculated to obtain the distance percentage of that segment. Specifically, the distance percentage of a given road network segment can be calculated using the following formula:

[0074]

[0075] Where k is the distance percentage of a certain road network segment, j is the length of the longest trajectory among the multiple trajectories matched with that road network segment, and i is the length of that road network segment.

[0076] S206. Calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment.

[0077] Specifically, the elevation confidence level of road network data can be calculated using the following formula:

[0078] o=hk

[0079] Where o is the elevation confidence level of the road network data segment, h is the normalized proportion of the road network, and k is the distance proportion of the road network.

[0080] This embodiment of the disclosure obtains standard map road network data and corresponding matching vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments. The method involves: calculating the average number of trajectory points contained in each trajectory segment; normalizing the ratio of the number of trajectory points in each trajectory segment to the average value to obtain the normalized proportion of the road network; determining the length of each trajectory segment matched by the road network segments, and selecting the longest trajectory segment as the target trajectory segment; calculating the distance proportion between the target trajectory segment and the total length of the road network segments; calculating the product of the normalized proportion and the distance proportion to obtain the elevation confidence of the road network segments; and obtaining the total elevation confidence of the standard map road network data within the preset area based on the elevation confidence of the road network segments. This proposes a specific method for calculating the confidence of road network data, which can be applied to the calculation of confidence of large-scale city-level road network data. It enables the evaluation of the confidence of elevation values ​​in road network data, making the obtained map elevation more accurate and reliable.

[0081] Figure 3 A flowchart illustrating a method for calculating the elevation confidence level of road network data, provided in another embodiment of this disclosure. Figure 3 As shown, the method includes the following steps:

[0082] S301. Obtain standard map road network data and corresponding matching vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments.

[0083] S302. Based on the information collected by the trajectory acquisition device, remove noise data from the vehicle driving trajectory data.

[0084] Due to positioning drift, some trajectory points may contain anomalies in their positioning data. These are considered noise points and need to be processed before using the trajectory data. A trajectory consists of a series of trajectory points, each typically containing three attributes: longitude, latitude, and a timestamp. During trajectory acquisition, instability in hardware signals can easily lead to the loss of some trajectory points, resulting in a trajectory that does not accurately represent the vehicle's original driving route. In some cases, the trajectory acquisition device can identify abnormal operating conditions during trajectory acquisition. These abnormalities are recorded in the device's log. Based on this information, the data acquired by the trajectory acquisition device during these abnormal operating conditions is identified as noise data and removed.

[0085] S303. Calculate the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment. The normalized percentage is used to characterize the amount of data of trajectory points matched by the road network segment.

[0086] Optionally, the average number of trajectory points contained in the trajectory segment is calculated; the ratio of the number of trajectory points contained in the trajectory segment to the average value is normalized to obtain the normalized proportion of the road network.

[0087] Specifically, the ratio corresponding to each trajectory can be normalized using the following formula:

[0088]

[0089] Where h is the normalized proportion of the road network, and n is the ratio of the number of trajectory points in each trajectory to the average number of trajectory points in multiple trajectories. min() is the number of trajectory points contained in the trajectory with the fewest trajectory points among the multiple trajectory data matched for a certain road network segment, and max(n) is the number of trajectory points contained in the trajectory with the most trajectory points among the multiple trajectory data matched for a certain road network segment.

[0090] S304. Calculate the distance ratio between the target trajectory segment and the total length of the road network segment.

[0091] Optionally, the length of each trajectory segment matched by the road network segment is determined, and the longest trajectory segment is taken as the target trajectory segment; the distance ratio between the target trajectory segment and the total length of the road network segment is calculated.

[0092] Specifically, the distance proportion of a certain segment of the road network can be calculated using the following formula:

[0093]

[0094] Where k is the distance percentage of a certain road network segment, j is the length of the longest trajectory among the multiple trajectories matched with that road network segment, and i is the length of that road network segment.

[0095] S305. Calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence level of the road network segment.

[0096] Specifically, the elevation confidence level of road network data can be calculated using the following formula:

[0097] o=hk

[0098] Where o is the elevation confidence level of the road network data segment, h is the normalized proportion of the road network, and k is the distance proportion of the road network.

[0099] S306. Perform a smoothing operation on the elevation confidence of multiple road network segments within the preset area to obtain the total elevation confidence of the standard map road network data within the preset area.

[0100] Following the steps described above, the elevation confidence score of each road network segment within the region of interest can be obtained. Further, a smoothing operation is performed on the elevation confidence scores of multiple road network segments within the region of interest. Specifically, for the elevation confidence score of each road network segment, the elevation confidence scores of the preceding and subsequent road network segments are obtained; the average of the elevation confidence scores of the road network segment, the preceding road network segment, and the subsequent road network segment is calculated to obtain the total elevation confidence score of the standard map road network data.

[0101] To avoid an excessive amount of low-confidence road network data within the region of interest (ROI), leading to significant data gaps, a smoothing operation is performed on the elevation confidence of the road network data. The elevation confidence of the preceding and following road network data segments to be processed is taken, and the average of these three confidence scores is calculated as the overall confidence score of the road network data to be processed. For example, with a confidence threshold of 90, if the confidence scores of three adjacent road network data segments are 95, 88, and 95 respectively (meaning the elevation confidence of the road network data to be processed is 88, and the confidence scores of its preceding and following road network data are 95), after smoothing the elevation confidence of the road network data, the elevation confidence of the road network data to be processed is greater than the confidence threshold and can be considered reliable data. This reduces the data gaps in road network data with confidence scores near the confidence threshold.

[0102] This embodiment of the disclosure acquires standard map road network data and corresponding matched vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments. Based on the information collected by the trajectory acquisition device, noise data in the vehicle trajectory data is removed. A normalized percentage value is calculated for the trajectory points contained in the trajectory segments matched by the road network segments. This normalized percentage value represents the amount of data of the trajectory points matched by the road network segments. A distance percentage value is calculated between the target trajectory segment and the total length of the road network segments. The product of the normalized percentage value and the distance percentage value is calculated to obtain the road network segment. Elevation confidence: The elevation confidence of multiple road network segments within the preset area is smoothed to obtain the total elevation confidence of the standard map road network data within the preset area. On the one hand, the elevation confidence of the road network data is quantitatively determined based on the normalized proportion of trajectory points contained in the trajectory and the distance proportion of the trajectory, realizing the evaluation of the elevation confidence of the road network data and making the obtained map elevation more accurate and reliable. On the other hand, by cleaning the trajectory data, removing noise data, and smoothing the elevation confidence of the road network data within the region of interest, the accuracy of the elevation confidence calculation method of the road network data is further improved, and the possibility of missing data within the region of interest is reduced.

[0103] Figure 4 This is a schematic diagram of the structure of the elevation confidence calculation device for road network data provided in this embodiment of the disclosure. The elevation confidence calculation device for road network data can be the terminal device described in the above embodiment, or it can be a component or assembly within the terminal device. The elevation confidence calculation device for road network data provided in this embodiment of the disclosure can execute the processing flow provided in the embodiment of the elevation confidence calculation method for road network data, such as... Figure 4 As shown, the road network data confidence calculation device 40 includes: an acquisition module 41, a first calculation module 42, a second calculation module 43, and a third calculation module 44. The acquisition module 41 acquires standard map road network data and corresponding matched vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments. The first calculation module 42 calculates the normalized percentage value of the trajectory points contained in the trajectory segments matched by the road network segments. The normalized percentage value represents the amount of data of the trajectory points matched by the road network segments. The second calculation module 43 calculates the distance percentage value between the target trajectory segment and the total length of the road network segment. The third calculation module 44 calculates the product of the normalized percentage value and the distance percentage value to obtain the elevation confidence of the road network segment, and obtains the total elevation confidence of the standard map road network data within the preset area based on the elevation confidence of the road network segment.

[0104] Optionally, the first calculation module 42 is specifically used to calculate the average number of trajectory points contained in the trajectory segment; and to normalize the ratio of the number of trajectory points contained in the trajectory segment to the average value to obtain the normalized proportion of the road network.

[0105] Optionally, the second calculation module 43 is also used to determine the length of each trajectory segment matched by the road network segment, and to take the longest trajectory segment as the target trajectory segment.

[0106] Optionally, the road network data confidence calculation device 40 further includes a removal module 45, which is used to remove noise data from the vehicle driving trajectory data based on the collection information of the trajectory acquisition device after acquiring standard map road network data and corresponding matching vehicle driving trajectory data within a preset area.

[0107] Optionally, the road network data confidence calculation device 40 further includes a smoothing module 46, which is used to smooth the elevation confidence of multiple road network segments within the preset area to obtain the total elevation confidence of the standard map road network data within the preset area.

[0108] Optionally, the smoothing module 46 is specifically used to obtain the elevation confidence of the preceding road network segment and the elevation confidence of the subsequent road network segment for each road network segment; calculate the average of the elevation confidence of the road network segment, the elevation confidence of the preceding road network segment, and the elevation confidence of the subsequent road network segment to obtain the total elevation confidence of the standard map road network data.

[0109] Figure 4 The elevation confidence calculation device for road network data in the illustrated embodiment can be used to execute the technical solution of the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0110] Furthermore, this disclosure also provides a vehicle that includes an elevation confidence calculation device for road network data as described in the above embodiments.

[0111] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device can be a terminal device as described in the above embodiments. The electronic device provided in this disclosure can execute the processing flow provided in the embodiment of the method for calculating the elevation confidence of road network data, such as... Figure 5 As shown, the electronic device 50 includes: a memory 51, a processor 52, a computer program, and a communication interface 53; wherein, the computer program is stored in the memory 51 and is configured to be executed by the processor 52 to perform the elevation confidence calculation method for road network data as described above.

[0112] In addition, this disclosure also provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the elevation confidence calculation method for road network data described in the above embodiments.

[0113] Furthermore, this disclosure also provides a computer program product, which includes a computer program or instructions that, when executed by a processor, implement the elevation confidence calculation method for road network data as described above.

[0114] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0116] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0117] The above description is merely a specific embodiment of this disclosure, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for calculating the elevation confidence level of road network data, characterized in that, The method includes: Obtain standard map road network data and corresponding matching vehicle trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle trajectory data includes several trajectory segments. Calculate the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment, whereby the normalized percentage is used to characterize the amount of data of trajectory points matched by the road network segment. Calculate the distance ratio between the target trajectory segment and the total length of the road network segments; Calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment; The calculation of the normalized percentage of trajectory points contained in the trajectory segment matched by the road network segment includes: Calculate the average number of trajectory points contained in the trajectory segment; The ratio of the number of trajectory points contained in the trajectory segment to the average value is normalized to obtain the normalized proportion of the road network.

2. The method according to claim 1, characterized in that, Before calculating the distance ratio between the target trajectory segment and the total length of the road network segments, the method further includes: Determine the length of each trajectory segment matched by the road network segment, and take the longest trajectory segment as the target trajectory segment.

3. The method according to claim 1, characterized in that, After acquiring standard map road network data and corresponding matching vehicle trajectory data within the preset area, the method further includes: Based on the information collected by the trajectory acquisition device, noise data in the vehicle driving trajectory data is removed.

4. The method according to claim 1, characterized in that, The step of obtaining the total elevation confidence of the standard map road network data within the preset area based on the elevation confidence of the road network segments includes: A smoothing operation is performed on the elevation confidence of multiple road network segments within the preset area to obtain the total elevation confidence of the standard map road network data within the preset area.

5. The method according to claim 4, characterized in that, The step of smoothing the elevation confidence scores of multiple road network segments within the preset area to obtain the total elevation confidence score of the standard map road network data within the preset area includes: For each road network segment, obtain the elevation confidence of the preceding road network segment and the elevation confidence of the subsequent road network segment. The elevation confidence scores of the road network segments, the preceding road network segments, and the subsequent road network segments are calculated to obtain the total elevation confidence score of the standard map road network data.

6. A device for calculating the elevation confidence level of road network data, characterized in that, The device includes: The acquisition module is used to acquire standard map road network data and corresponding matching vehicle driving trajectory data within a preset area. The standard map road network data includes several road network segments, and the vehicle driving trajectory data includes several trajectory segments. The first calculation module is used to calculate the normalized proportion of trajectory points contained in the trajectory segment matched by the road network segment, and the normalized proportion is used to characterize the amount of data of trajectory points matched by the road network segment. The second calculation module is used to calculate the distance ratio between the target trajectory segment and the total length of the road network segments; The third calculation module is used to calculate the product of the normalized proportion value and the distance proportion value to obtain the elevation confidence of the road network segment, and to obtain the total elevation confidence of the standard map road network data in the preset area based on the elevation confidence of the road network segment. The first calculation module is specifically used to calculate the average number of trajectory points contained in the trajectory segment; and to normalize the ratio of the number of trajectory points contained in the trajectory segment to the average value to obtain the normalized proportion of the road network.

7. An electronic device, characterized in that, include: Memory; processor; as well as Computer programs; The computer program is stored in the memory and configured to be executed by the processor to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-5.

9. A vehicle, characterized in that, The vehicle includes the elevation confidence calculation device for road network data as described in claim 6; or the electronic device as described in claim 7; or the computer-readable storage medium as described in claim 8.

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