A method, device and equipment for processing linear ground object data on a road
By dynamically segmenting linear feature data based on the curvature of reference lines according to road geometry parameters, the problem of setting data segmentation scales is solved, achieving efficient and accurate data segmentation and aggregation, adapting to different data sources and precision levels.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 合肥四维图新科技有限公司
- Filing Date
- 2023-02-03
- Publication Date
- 2026-07-10
Smart Images
Figure CN116067362B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, equipment, medium and product for processing linear feature data on roads. Background Technology
[0002] High-precision maps require high activity, meaning that changes in traffic scenarios need to be updated in the high-precision map in a timely manner and then distributed to vehicles using the map. This necessitates a large number of vehicles covering the traffic scenarios, and high-frequency updates to the high-precision map are achieved through crowdsourcing.
[0003] In existing crowdsourced mapping technologies, the point data collected by the crowdsourced data collection vehicle needs to be segmented. However, fixed-scale segmentation is used in this process, which presents a challenge in setting the segmentation scale. For example, setting the segmentation scale too large will result in the loss of some important linear information, while setting it too small will not only amplify the impact of errors and cause linear distortion, but also increase the amount of data computation and storage. Summary of the Invention
[0004] This specification provides a method, apparatus, equipment, medium, and product for processing linear feature data on roads, in order to solve the problem of difficulty in setting data segmentation scales in existing data processing methods.
[0005] To solve the above-mentioned technical problems, the embodiments in this specification are implemented as follows:
[0006] This specification provides an embodiment of a method for processing linear feature data on roads, the method comprising:
[0007] Obtain a reference line characterizing the road's geometric parameters;
[0008] For each shape value point of the reference line, the reference line between two adjacent shape value points is taken as a reference line segment; the length of a specific reference line segment between any two adjacent shape value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment;
[0009] Based on each of the reference line segments, the data collection points of linear features on the road are segmented.
[0010] Optionally, after defining a reference line as a reference segment between two adjacent reference points for each reference point of the reference line, the method further includes:
[0011] For a given reference line segment, the length of the reference line segment is adjusted based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
[0012] Optionally, the step of segmenting the data collection points of linear features on the road based on each of the reference line segments specifically includes:
[0013] Based on the latitude and longitude information in the collection point string data of the linear feature and the latitude and longitude information of each reference line segment, the collection point string data of the linear feature is projected onto each reference line segment to obtain the segmented collection point string data.
[0014] Optionally, after segmenting the data collection points of linear features on the road based on each of the reference line segments, the method further includes:
[0015] The segmented data collection points are aggregated to obtain multiple shape points of the linear feature's linear data.
[0016] Based on the positional relationship of each reference line segment in the reference line, multiple shape value points of the linear feature's linear data are connected to obtain the linear data of the linear feature on the road.
[0017] Optionally, the aggregation of the segmented collection point string data to obtain multiple shape value points of the linear feature's linear data specifically includes:
[0018] Establish a northeast coordinate system with the starting point of the reference line as the origin;
[0019] Obtain the offset of the data collection point string after each segment relative to the origin of the coordinate system;
[0020] Based on the offset of the data collection point string after each segment relative to the origin of the coordinate system, multiple shape value points of the linear feature's linear data are determined.
[0021] Optionally, the linear features specifically include at least one of lane markings, guardrails, and curbs.
[0022] This specification provides an embodiment of a device for processing linear feature data on roads, the device comprising:
[0023] A reference line acquisition module is used to acquire reference lines that characterize the road's geometric parameters;
[0024] The reference line segmentation module is used to take the reference line between two adjacent model value points as a reference line segment for each model value point of the reference line; the length of a specific reference line segment between any two adjacent model value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment;
[0025] The data segmentation module is used to segment the collection point data of linear features on the road based on each of the reference line segments.
[0026] Optionally, the device further includes:
[0027] The correction module is used to correct the length of a reference line segment based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
[0028] Optionally, the data segmentation module specifically includes:
[0029] The data segmentation unit is used to project the data collection point string of the linear feature onto each of the reference line segments based on the latitude and longitude information in the data collection point string of the linear feature and the latitude and longitude information of each of the reference line segments, so as to obtain the segmented data collection point string.
[0030] Optionally, the device further includes:
[0031] The aggregation module is used to aggregate the segmented collection point string data to obtain multiple shape value points of the linear feature's linear data;
[0032] The linear data acquisition module is used to connect multiple shape value points of the linear feature's linear data based on the positional relationship of each reference line segment in the reference line, so as to obtain the linear feature's linear data on the road.
[0033] Optionally, the aggregation module specifically includes:
[0034] The coordinate system establishment unit is used to establish a northeast coordinate system with the starting point of the reference line as the origin.
[0035] Offset acquisition unit, used to acquire the offset of the data string of each segmented acquisition point relative to the origin of the coordinate system;
[0036] The shape point determination unit is used to determine multiple shape points of the linear feature's linear data based on the offset of the data string of collected points after each segment relative to the coordinate origin.
[0037] Optionally, the linear features specifically include at least one of lane markings, guardrails, and curbs.
[0038] This specification provides an embodiment of a computer device, the device comprising:
[0039] A memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.
[0040] This specification provides an embodiment of a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the method described above.
[0041] This specification provides an embodiment of a computer program product, including computer instructions that, when executed by a processor, implement the steps of the method described above.
[0042] One embodiment of this specification achieves the following beneficial effects: based on the curvature of each part of the reference line, the reference line is segmented. The length of a specific reference line segment between any two adjacent shape value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment. Then, based on each reference line segment, the data of the collection points of linear features on the road is segmented. This solves the problems in the prior art where the segmentation scale is too large, resulting in the loss of important linear information, and the segmentation scale is too small, which amplifies the error and causes linear distortion, and increases the amount of data calculation and storage. Attached Figure Description
[0043] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0044] Figure 1 A flowchart illustrating a method for processing linear feature data on roads, provided as an embodiment of this specification;
[0045] Figure 2 This is a schematic diagram illustrating the determination of a reference line segment as provided in an embodiment of this specification.
[0046] Figure 3 A schematic diagram of a segmented reference line provided in an embodiment of this specification;
[0047] Figure 4 This is a schematic diagram illustrating the projection of a collection point string of data onto a reference line segment, as provided in an embodiment of this specification.
[0048] Figure 5 This is a schematic diagram illustrating the aggregation of data from a series of collection points, provided in an embodiment of this specification.
[0049] Figure 6 This is a schematic diagram illustrating a method for processing linear feature data on roads, provided in an embodiment of this specification.
[0050] Figure 7A schematic diagram of a device for processing linear feature data on a road, provided in an embodiment of this specification;
[0051] Figure 8 This is a schematic diagram of a computer device provided as an embodiment of this specification. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of them. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of one or more embodiments of this specification.
[0053] The technical solutions provided in the various embodiments of this specification are described in detail below with reference to the accompanying drawings.
[0054] In existing crowdsourced mapping technologies, fixed-scale segmentation is used when segmenting data from collection points of linear features, making the setting of the data segmentation scale a challenge. If the segmentation scale is too large, important linear information will be lost, such as straightening bends and turning arcs into broken lines. If the segmentation scale is too small, it will not only increase the amount of data computation and storage but also amplify errors, causing linear distortion, such as lines that should be straight appearing wavy due to shape recognition at too small a scale. Furthermore, different segmentation scales need to be set for different data collection sources, further increasing the difficulty of setting the data segmentation scale.
[0055] To address the shortcomings of existing technologies, this solution provides the following embodiments:
[0056] Figure 1 This document provides a flowchart of a method for processing linear feature data on roads, as illustrated in an embodiment of the specification. From a programming perspective, the execution entity of the process can be a program hosted on an application server or an application client. Figure 1 As shown, the method may include the following steps:
[0057] Step 110: Obtain a reference line that characterizes the road geometry parameters.
[0058] In this embodiment, a road refers to a road with continuously distributed linear features, specifically including at least one of lane markings, guardrails, and curbs. This embodiment segments the data collection points of the linear features on the road using reference lines that characterize the road's geometric parameters.
[0059] Among them, the reference line is the basic line shape that runs through the entire road, such as the representative line shape that represents the road in an open source map, such as the RoadLink (road representation line) in OSM (OpenStreetMap).
[0060] In one specific embodiment, the reference line can also be a road marking line in the crowdsourced data collection vehicle.
[0061] Step 120: For each shape value point of the reference line, take the reference line between two adjacent shape value points as a reference line segment; the length of a specific reference line segment between any two adjacent shape value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment.
[0062] In step 120, the reference line is segmented based on the curvature of each part of the reference line to obtain multiple reference line segments, wherein the length of the reference line segment is inversely proportional to the curvature of the corresponding reference line segment.
[0063] The following further explains the segmentation of the reference line and the determination of the length of the reference line segment in the embodiments of this specification.
[0064] Figure 2 This is a schematic diagram illustrating the determination of a reference line segment as provided in an embodiment of this specification, such as... Figure 2 As shown, Figure 2 As shown, determining the reference line segment may include:
[0065] Step 210: Obtain the various value points of the reference line.
[0066] Model points refer to a small number of data points on a curve or surface that describe its geometry, obtained through measurement or calculation. After obtaining the model points of a reference line, the curvature of the reference line can be determined based on these points, and then the reference line can be segmented.
[0067] Step 220: Calculate the length of the reference line segment.
[0068] For each shape point of the reference line, the reference line between two adjacent shape points is considered as a reference line segment. The length of a specific reference line segment between any two adjacent shape points is equal to a preset ratio divided by the curvature corresponding to that specific reference line segment.
[0069] The curvature of the reference line segment is determined based on the shape points at both ends of the reference line segment. Specifically, the curvature of the reference line segment is calculated based on the circle formed by the shape points at both ends of the reference line segment and the midpoint of the reference line segment.
[0070] In one embodiment, the reference line between the current model value point and the second model value point following the current model value point can also be used as a reference line segment. The curvature of the corresponding reference line segment is calculated using the circle formed by the current model value point, the first model value point following the current model value point, and the second model value point following the current model value point.
[0071] After the curvature of the reference line segment is calculated, the length of the reference line segment is determined based on the relationship that its length equals a preset ratio divided by the curvature of the reference line segment. In this embodiment, the preset ratio is 0.006, and under this preset ratio, the calculated length of the reference line segment is in meters.
[0072] Figure 3 This is a schematic diagram of a segmented reference line provided in an embodiment of this specification, such as... Figure 3 As shown, for example, if the curvature of a certain reference line segment is 0.002, then the corresponding length of the reference line segment is 0.006 / 0.002 = 3 meters.
[0073] Step 230: Determine whether the length of the reference line segment is within the preset range.
[0074] To avoid the reference line segment being too long or too short, this embodiment limits the length of the reference line segment to be greater than or equal to 1 meter and less than or equal to 10 meters.
[0075] If the length of the reference line segment is not within the preset range, proceed to step 240;
[0076] If the length of the reference line segment is within the preset range, proceed to step 250.
[0077] Step 240: Ensure the length of the reference line segment is within the preset range.
[0078] If the calculated length of the reference line segment is greater than 10 meters, the reference line segment is divided into segments, such that the length of each segment is less than or equal to 10 meters. If the calculated length of the reference line segment is less than 1 meter, the length of the reference line segment is set to 1 meter.
[0079] To further optimize the length of the reference line segment and avoid errors and redundancy caused by the reference line segment being too large or too small, this embodiment also modifies the length of the reference line segment, specifically including:
[0080] For a given reference line segment, the length of the reference line segment is adjusted based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
[0081] For example, if the calculated length of the current reference line segment is 1 meter, while the calculated lengths of the previous and next reference line segments are both 10 meters, then the length of the current reference line segment is determined to be 10 meters.
[0082] Step 250: Determine the reference line segment.
[0083] The reference line segment whose length is within the preset range is determined as the final reference line segment.
[0084] Step 130: Based on each of the reference line segments, segment the data of the collection points of linear features on the road.
[0085] In this embodiment, the data collection points of linear features are the data collection points of linear features collected by the crowdsourced data collection vehicle on the road.
[0086] In this embodiment, step 130 may specifically include:
[0087] Based on the latitude and longitude information in the collection point string data of the linear feature and the latitude and longitude information of each reference line segment, the collection point string data of the linear feature is projected onto each reference line segment to obtain the segmented collection point string data.
[0088] Figure 4 This is a schematic diagram illustrating the projection of a collection point string of data onto a reference line segment, as provided in an embodiment of this specification. Figure 4 As shown, there can be multiple sets of data collection points, with each data collection source collecting a set of data collection points. Based on the latitude and longitude of the data collection points, the data collection points within a certain latitude and longitude range of a reference line segment can be projected onto that reference line segment, thus completing the segmentation processing of the data collection points.
[0089] This embodiment dynamically and adaptively segments the data based on the curvature of each part of the reference line representing the road's geometric parameters. When the road curvature is small, meaning the road conditions are relatively simple, there is no important linear information on the road, so a larger scale can be used to segment the data, resulting in fewer segments and less subsequent data computation and storage. When the road curvature is large, meaning the road conditions are relatively complex, a smaller scale can be used to segment the data, preserving the detailed information of linear features on the road and ensuring the accuracy of data segmentation. This solves the problem of difficulty in setting the segmentation scale under existing fixed-scale segmentation.
[0090] Furthermore, existing technologies require setting different fixed scales for segmentation when dealing with data from different data sources and with different data precision. However, the data segmentation method in this embodiment is independent of the data source and data precision, and can segment the data simply based on the curvature of each part of the reference line.
[0091] In one specific embodiment, in order to obtain the linear data of linear features on the road, after step 130, the method may further include:
[0092] The segmented data collection points are aggregated to obtain multiple shape points of the linear feature's linear data.
[0093] Based on the positional relationship of each reference line segment in the reference line, multiple shape value points of the linear feature's linear data are connected to obtain the linear data of the linear feature on the road.
[0094] Furthermore, the aggregation of the segmented collection point string data to obtain multiple shape value points of the linear feature's linear data specifically includes:
[0095] Establish a northeast coordinate system with the starting point of the reference line as the origin;
[0096] Obtain the offset of the data collection point string after each segment relative to the origin of the coordinate system;
[0097] Based on the offset of the data collection point string after each segment relative to the origin of the coordinate system, multiple shape value points of the linear feature's linear data are determined.
[0098] The NorthEastDown (NED) coordinate system, also known as the navigation coordinate system, is a reference coordinate system selected for navigation calculations based on the needs of the navigation system. In this embodiment, the origin of the reference line is used as the origin to establish the NorthEastDown coordinate system.
[0099] Then, obtain the offset of the data from each segment of the collection point string relative to the origin, and in the N-axis, E-axis, and D-axis directions of the northeast-northeast coordinate system.
[0100] The offsets are then Gaussian aggregated to obtain the coordinates of each shape point in the linear data. Based on the coordinates of each shape point, multiple shape points in the linear data are then determined.
[0101] Figure 5 This is a schematic diagram illustrating the aggregation of data from a series of collection points, as provided in an embodiment of this specification. Figure 5 As shown, the segmented collection point data is aggregated to obtain the shape value points of the linear data. Based on the positional relationship of each reference line segment in the reference line, multiple shape value points of the linear data are connected to obtain the linear data of the linear feature.
[0102] Figure 6 This diagram illustrates a method for processing linear feature data on roads, as provided in an embodiment of this specification. Figure 6 As shown, the method may include:
[0103] Step 610: Calculate the curvature of each part of the reference line.
[0104] Step 620: Segment the reference line based on the curvature of each part of the reference line.
[0105] Step 630: Project the data from the collection points of the linear features onto each reference line segment.
[0106] Step 640: Aggregate the segmented collection point data to obtain multiple shape points of the linear feature's linear data.
[0107] Step 650: Connect multiple shape points of the linear feature data to obtain the linear feature data on the road.
[0108] Based on the same idea, embodiments of this specification also provide apparatus corresponding to the above methods.
[0109] Figure 7 This is a schematic diagram of a device for processing linear feature data on roads, provided as an embodiment of this specification. Figure 7 As shown, the device may include:
[0110] Reference line acquisition module 710 is used to acquire reference lines that characterize the road geometric parameters;
[0111] The reference line segmentation module 720 is used to take the reference line between two adjacent model value points as a reference line segment for each model value point of the reference line; the length of a specific reference line segment between any two adjacent model value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment;
[0112] The data segmentation module 730 is used to segment the collection point data of linear features on the road based on each of the reference line segments.
[0113] In this embodiment, the device further includes:
[0114] The correction module is used to correct the length of a reference line segment based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
[0115] Furthermore, the data segmentation module specifically includes:
[0116] The data segmentation unit is used to project the data collection point string of the linear feature onto each of the reference line segments based on the latitude and longitude information in the data collection point string of the linear feature and the latitude and longitude information of each of the reference line segments, so as to obtain the segmented data collection point string.
[0117] In this embodiment, the device further includes:
[0118] The aggregation module is used to aggregate the segmented collection point string data to obtain multiple shape value points of the linear feature's linear data;
[0119] The linear data acquisition module is used to connect multiple shape value points of the linear feature's linear data based on the positional relationship of each reference line segment in the reference line, so as to obtain the linear data of the linear feature on the road.
[0120] Furthermore, the aggregation module specifically includes:
[0121] The coordinate system establishment unit is used to establish a northeast coordinate system with the starting point of the reference line as the origin.
[0122] Offset acquisition unit, used to acquire the offset of the data string of each segmented acquisition point relative to the origin of the coordinate system;
[0123] The shape point determination unit is used to determine multiple shape points of the linear feature's linear data based on the offset of the data string of collected points after each segment relative to the coordinate origin.
[0124] In this embodiment, the linear features specifically include at least one of lane markings, guardrails, and curbs.
[0125] Based on the same idea, this specification also provides devices corresponding to the above methods in its embodiments.
[0126] Figure 8 This is a schematic diagram of a computer device provided as an embodiment of this specification. Figure 8 As shown, device 800 may include memory 810, processor 820, and computer program 830 stored in memory, wherein processor 820 executes computer program 830 to implement the steps of the method described above.
[0127] Based on the same idea, embodiments of this specification also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the steps of the above-described method.
[0128] Based on the same idea, embodiments of this specification also provide a computer program product, including computer instructions that, when executed by a processor, implement the steps of the above-described method.
[0129] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, for... Figure 8As the computer device shown is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.
[0130] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Moreover, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compiler used when writing program development code. The original code before compilation must also be written in a specific programming language, which is called a Hardware Description Language (HDL). There is not just one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using the aforementioned hardware description languages and programming it into an integrated circuit, the hardware circuit that implements the logic method flow can be easily obtained.
[0131] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.
[0132] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.
[0133] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.
[0134] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0135] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0136] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0137] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0138] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0139] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0140] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0141] It should also be noted that 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 limitation, 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.
[0142] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0143] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0144] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A method for processing linear feature data on roads, characterized in that, The method includes: Obtain a reference line characterizing the road's geometric parameters; For each shape value point of the reference line, the reference line between two adjacent shape value points is taken as a reference line segment; the length of a specific reference line segment between any two adjacent shape value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment; Determine whether the length of the reference line segment is within a preset range; If the length of the reference line segment is not within the preset range, the length of the reference line segment is corrected to bring it within the preset range. If the length of the reference line segment is within a preset range, then the reference line segment whose length is within the preset range is determined as the target reference line segment; Based on each of the target reference line segments, the data collection point string of linear features on the road is segmented; The correction of the length of the reference line segment includes: For the reference line segment, the length of the reference line segment is corrected based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
2. The method according to claim 1, characterized in that, The segmentation of the data collection points for linear features on the road, based on each of the target reference line segments, specifically includes: Based on the latitude and longitude information in the collection point string data of the linear feature and the latitude and longitude information of each target reference line segment, the collection point string data of the linear feature is projected onto each target reference line segment to obtain the segmented collection point string data.
3. The method according to claim 1, characterized in that, After segmenting the data collection points of linear features on the road based on each of the target reference line segments, the method further includes: The segmented data collection points are aggregated to obtain multiple shape points of the linear feature's linear data. Based on the positional relationship of each reference line segment in the reference line, multiple shape value points of the linear feature's linear data are connected to obtain the linear data of the linear feature on the road.
4. The method according to claim 3, characterized in that, The aggregation of the segmented data collection points to obtain multiple shape points of the linear feature's linear data specifically includes: Establish a northeast coordinate system with the starting point of the reference line as the origin; Obtain the offset of the data collection point string after each segment relative to the origin of the coordinate system; Based on the offset of the data collection point string after each segment relative to the origin of the coordinate system, multiple shape value points of the linear feature's linear data are determined.
5. The method according to claim 1, characterized in that, The linear features specifically include at least one of lane markings, guardrails, and curbs.
6. A device for processing linear feature data on roads, characterized in that, The device includes: A reference line acquisition module is used to acquire reference lines that characterize the road's geometric parameters; The reference line segmentation module is used to, for each shape value point of the reference line, take the reference line between two adjacent shape value points as a reference line segment; the length of a specific reference line segment between any two adjacent shape value points is equal to a preset ratio divided by the curvature corresponding to the specific reference line segment; determine whether the length of the reference line segment is within a preset range; if the length of the reference line segment is not within the preset range, then correct the length of the reference line segment to make the length of the reference line segment within the preset range; if the length of the reference line segment is within the preset range, then determine the reference line segment whose length is within the preset range as the target reference line segment; The data segmentation module is used to segment the collection point data of linear features on the road based on each of the target reference line segments; The correction module is used to correct the length of the reference line segment based on the length of the preceding reference line segment adjacent to the reference line segment and the length of the following reference line segment adjacent to the reference line segment.
7. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to claims 1 to 5.
8. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method described in claims 1 to 5.
9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the method described in claims 1 to 5.