Map data processing method, device, equipment and medium

Abstract

The present disclosure relates to a method, apparatus, device, and medium for map data processing. The method includes: obtaining trajectory point data to be processed; performing segmentation processing on the trajectory point data to be processed based on a preset length to obtain a plurality of sub-trajectory point sets, each sub-trajectory point set including a plurality of trajectory points; for each sub-trajectory point set, generating a target trajectory block according to the plurality of trajectory points, the target trajectory block being used to define an area for obtaining map vector data and map acquisition data; obtaining map vector data corresponding to each target trajectory block based on a first rule, and / or obtaining map acquisition data corresponding to each target trajectory block based on a second rule, the map acquisition data including laser point cloud data and / or photo trajectory data. In this way, downstream processing links can organize data granularity according to their processing capabilities, achieving rapid construction of production lines and rapid horizontal expansion of processing capabilities, and meeting the requirements of different production lines for accuracy and currency.

Description

Technical Field

[0001] The present disclosure relates to the technical field of map data processing, and particularly to a method, apparatus, device, and medium for map data processing. Background Art

[0002] In the related art, during the production of high-precision maps, it is necessary to segment the collected data for subsequent map production. However, due to different production line precisions, different precisions of collection devices, and various road types, etc., the segmentation models and segmentation methods used during data segmentation are different, resulting in detailed and complex segmentation rules, increasing the difficulty of data maintenance, and being unfavorable for the rapid construction and horizontal expansion of production lines. Summary of the Invention

[0003] To solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a method, apparatus, device, and medium for map data processing.

[0004] In a first aspect, the present disclosure provides a method for map data processing, the method comprising:

[0005] Obtaining data of trajectory points to be processed;

[0006] Performing segmentation processing on the data of the trajectory points to be processed based on a preset length to obtain a plurality of sub-trajectory point sets, wherein each sub-trajectory point set includes a plurality of trajectory points;

[0007] For each sub-trajectory point set, generating a target trajectory block according to the plurality of trajectory points, the target trajectory block being used to define an area for obtaining map vector data and map collection data;

[0008] Based on a first rule, obtaining map vector data corresponding to each target trajectory block, and / or, based on a second rule, obtaining map collection data corresponding to each target trajectory block, the map collection data including laser point cloud data and / or photo trajectory data.

[0009] In a second aspect, the present disclosure further provides a map data processing apparatus, the apparatus comprising:

[0010] A first data acquisition module, configured to obtain data of trajectory points to be processed;

[0011] A segmentation processing module, configured to perform segmentation processing on the data of the trajectory points to be processed based on a preset length to obtain a plurality of sub-trajectory point sets, wherein each sub-trajectory point set includes a plurality of trajectory points;

[0012] A trajectory block generation module, configured to generate a target trajectory block according to the plurality of trajectory points for each sub-trajectory point set, the target trajectory block being used to define an area for obtaining map vector data and map collection data;

[0013] A second data acquisition module, configured to acquire map vector data corresponding to each target trajectory segment based on a first rule, and / or acquire map acquisition data corresponding to each target trajectory segment based on a second rule, where the map acquisition data includes lidar point cloud data and / or photo trajectory data.

[0014] In a third aspect, the present disclosure also provides an electronic device, which includes: a memory and a processor;

[0015] The memory is used to store executable instructions executable by the processor;

[0016] The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement any of the above map data processing methods.

[0017] In a fourth aspect, the present disclosure also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement any of the above map data processing methods.

[0018] The technical solutions provided in the embodiments of the present disclosure have at least the following advantages compared with the prior art: In the embodiments of the present disclosure, the acquired trajectory point data to be processed is uniformly segmented according to a preset length to obtain a plurality of sub-trajectory point sets; for each sub-trajectory point set, a target trajectory block is generated according to a plurality of trajectory points; the target trajectory segment is used as the smallest unit for high-precision map data processing, and based on a preset rule, map data corresponding to the target trajectory segment is acquired, and the map data includes map vector data and map acquisition data, and the map acquisition data may include lidar point cloud data and / or photo trajectory data, so that the map data corresponding to each target trajectory segment is complete and complete, and the downstream processing links can organize the granularity of the map data by themselves to adapt to the processing capabilities of the current link, realizing the rapid construction of the production line and the rapid horizontal expansion of the processing capabilities, meeting the requirements of different production lines for accuracy and realism. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In combination with the drawings and referring to the following specific embodiments, the above and other features, advantages and aspects of the embodiments of the present disclosure will become more obvious. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the original and elements are not necessarily drawn to scale.

[0020] Figure 1 It is a schematic flowchart of a map data processing method provided by an embodiment of the present disclosure;

[0021] Figure 2Schematic flowchart of another map data processing method provided by an embodiment of the present disclosure;

[0022] Figure 3 For Figure 1 In the shown map data processing method, a refined flowchart of S130;

[0023] Figure 4 In the map data processing method shown in 3, a refined flowchart of S330;

[0024] Figure 5 Schematic diagram of the principle for obtaining target trajectory blocks provided by an embodiment of the present disclosure;

[0025] Figure 6 For Figure 1 In the shown map data processing method, a refined flowchart of "obtaining map vector data corresponding to each target trajectory block based on the first rule";

[0026] Figure 7 Schematic diagram of the principle for obtaining lane line data provided by an embodiment of the present disclosure;

[0027] Figure 8 For Figure 1 In the shown map data processing method, another refined flowchart of "obtaining map vector data corresponding to each target trajectory block based on the first rule";

[0028] Figure 9 Schematic diagram of the principle for obtaining marker data provided by an embodiment of the present disclosure;

[0029] Figure 10 For Figure 1 In the shown map data processing method, a flowchart of "obtaining map acquisition data corresponding to each target trajectory block based on the second rule";

[0030] Figure 11 For Figure 10 In the shown map data processing method, a refined flowchart of "determining the visible extension range corresponding to each target trajectory block";

[0031] Figure 12 Schematic diagram of the visible extension ranges of two types of acquisition vehicles provided by an embodiment of the present disclosure;

[0032] Figure 13 Schematic diagram of the structure of a map data processing device provided by an embodiment of the present disclosure;

[0033] Figure 14 Schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0034] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Instead, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not used to limit the protection scope of the present disclosure.

[0035] It should be understood that the various steps recited in the method embodiments of the present disclosure can be executed in a different order and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this regard.

[0036] As used herein, the term "comprising" and its variations are open-ended, i.e., "including but not limited to". The term "based on" is "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0037] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0038] It should be noted that the modifications of "one" and "plural" mentioned in the present disclosure are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise clearly specified in the context, it should be understood as "one or more".

[0039] In the prior art, the segmentation models and segmentation methods adopted for different production line precisions, acquisition devices or road types are also different. The segmentation rules are detailed and complex, which is not conducive to the maintenance of segmentation rules and data organization capabilities and the rapid iteration of production lines. In view of the above problems, the embodiments of the present disclosure provide a solution. By uniformly segmenting the obtained trajectory point data to be processed according to a preset length, a plurality of sub-trajectory point sets are obtained. For each sub-trajectory point set, a target trajectory block is generated based on a plurality of trajectory points. The target trajectory block is used as the smallest unit for high-precision map data processing. Based on the preset rules, map data corresponding to the target trajectory block is obtained. The map data includes map vector data and map acquisition data, and the map acquisition data may include laser point cloud data and / or photo trajectory data, so that the map data corresponding to each target trajectory block is complete and perfect. The downstream processing links can organize the granularity of the map data by themselves to adapt to the processing capabilities of the current link, realizing the rapid construction of the production line and the horizontal expansion of the processing capabilities, meeting the requirements of different production lines for precision and currency. Furthermore, during the above process of standardizing the collected trajectory data, by thinning the trajectory points in the sub-trajectory point set, the problem of trajectory point accumulation in the sub-trajectory point set in the case of traffic jams or sharp-turn U-turn scenarios is solved.

[0040] Figure 1 FIG. is a schematic flowchart of a map data processing method provided by an embodiment of the present disclosure, which is applicable to the standardized segmentation processing of trajectory data. The map data processing method can be executed by a map data processing device, which can be implemented by software and / or hardware and can be integrated on any server or terminal device with computing capabilities.

[0041] As Figure 1 shown, the map data processing method includes:

[0042] S110. Obtain the trajectory point data to be processed.

[0043] Specifically, in this step, the trajectory point data on the set section is acquired by the acquisition device, and the trajectory point data is parsed so that the data format of the parsed trajectory point data is consistent. This step performs a standardization process on the trajectory point data. Since the acquisition devices are different, the formats of the acquired trajectory point data are also different. Different data parsing modes are adopted for the trajectory point data from different acquisition device sources (with different formats), but the data formats of the parsed trajectory point data are consistent. In this way, in the traditional production line processing process, due to the use of different processing methods for the trajectory data from different device sources, the monitoring systems act independently, and it is difficult to make the production scheduling review transparent. After the standardization process in this step, the format of the trajectory point data is decoupled from the production line, and there is no need to pay attention to the format differences of the trajectory point data brought by different production lines, so unified monitoring and unified production scheduling review can be carried out.

[0044] S120. Perform a segmentation process on the trajectory point data to be processed based on a preset length, and obtain multiple sub-trajectory point sets.

[0045] In this step, considering that the distance of the above-mentioned set section is relatively long and the memory of the corresponding trajectory point data is also large, the trajectory point data is segmented according to the preset length, and the trajectory point data of the set section is segmented into multiple sub-trajectory point sets; each sub-trajectory point set includes multiple trajectory points.

[0046] The preset length can be flexibly set according to the requirements of the map data processing method. For example, the preset length is set to 50 meters, and this is not limited here.

[0047] S130. For each sub-trajectory point set, generate a target trajectory block based on multiple trajectory points.

[0048] Among them, the target trajectory block is used to define the area for obtaining map vector data and map acquisition data.

[0049] Specifically, each sub-trajectory point set includes multiple trajectory points. By connecting multiple trajectory points in the same sub-trajectory point set, the trajectory line corresponding to the sub-trajectory point set can be obtained; the JTS algorithm is used to generate the expansion polygon corresponding to each sub-trajectory point set; and then the target trajectory block is generated based on the expansion polygon corresponding to each sub-trajectory point set. The specific expansion method can include horizontal expansion and vertical expansion; the horizontal direction is perpendicular to the trajectory line, and the horizontal expansion is to expand to the left and right sides of the trajectory line of each sub-trajectory point set (i.e., the width direction of the road); the horizontal expansion range covers the width range of the entire road, that is, to obtain all useful data within the width range of the entire road, so that the completeness of the data is guaranteed; the vertical direction is the same as the direction of travel of the trajectory line, and the vertical expansion is to expand outward from the two end points of the trajectory line of each sub-trajectory point set (i.e., the length direction of the road), so that there will be no holes between the target trajectory blocks corresponding to adjacent sub-trajectory point sets, avoiding the loss of original trajectory data. Among them, the JTS algorithm used for the above expansion can be implemented by calling the Application Program Interface (API) for processing geographic data in Java.

[0050] It should be noted that, in this embodiment, only the JTS algorithm is used to generate the expansion polygon corresponding to each sub-track point set, which does not constitute a limitation on the map data processing method provided by the embodiment of the present disclosure. In other embodiments, other applications known to those skilled in the art may be used to generate the expansion polygon corresponding to each sub-track point set, which is not limited here.

[0051] S140: Based on the first rule, obtain map vector data corresponding to each target trajectory block, and / or, based on the second rule, obtain map acquisition data corresponding to each target trajectory block.

[0052] Among them, the map collection data includes laser point cloud data and / or photo trajectory data, wherein the laser point cloud data is collected by laser radar; and the photo trajectory data includes photos taken by a camera, as well as the time and trajectory point location information of taking the photo, so that the photo has a time stamp and trajectory attributes.

[0053] The map vector data includes at least one of lane line data and markers; the markers include ground markers (such as vehicle guide lines) and non-ground markers (such as signboards and sign poles).

[0054] Specifically, the target trajectory blocks after the above processing are used as the smallest units for map data processing, and the map data corresponding to each target trajectory block is stored as a storage unit; the map data corresponding to each target trajectory block includes map acquisition data and / or map vector data. In this way, the downstream links of the production line can freely organize the data. The production line can perform data merging processing or individual processing according to its own processing capabilities, without caring about the data source, production line identification, and data usage. It only needs to appropriately increase the number of merged data according to the processing capabilities of the current node for processing nodes with fast processing speeds, that is, merge on demand.

[0055] The embodiments of the present disclosure provide a method for processing map data. The method uniformly segments the acquired trajectory point data to be processed according to a preset length to obtain multiple sub-trajectory point sets; for each sub-trajectory point set, a target trajectory block is generated based on multiple trajectory points; the target trajectory block is used as the smallest unit for high-precision map data processing. Based on a preset rule, map data corresponding to the target trajectory block is obtained. The map data includes map vector data and map acquisition data, and the map acquisition data may include laser point cloud data and / or photo trajectory data, so that the map data corresponding to each target trajectory block is complete and complete. The downstream processing links can organize the granularity of the map data by themselves to adapt to the processing capabilities of the current link, realizing the rapid construction of the production line and the horizontal expansion of the processing capabilities, meeting the requirements of different production lines for accuracy and realism.

[0056] In some embodiments, as Figure 2 shown, it is a schematic flowchart of another method for processing map data provided by the embodiments of the present disclosure. Referring to Figure 2 ,"After performing the segmentation processing on the trajectory point data to be processed to obtain multiple sub-trajectory point sets", the method further includes:

[0057] S230. Obtain the trajectory point density in each sub-trajectory point set.

[0058] Specifically, according to the number of trajectory points in each sub-trajectory point set and the mileage corresponding to the sub-trajectory point set (i.e., the preset length), the trajectory point density in each sub-trajectory point set is calculated. Further, the trajectory point density in the sub-trajectory point set is compared with a preset threshold. If the trajectory point density in the sub-trajectory point set is greater than the preset threshold, it indicates that there are redundant trajectory points in the sub-trajectory point set, and then the trajectory points in the sub-trajectory point set are processed according to a preset thinning ratio; if the trajectory point density in the sub-trajectory point set is equal to or less than the preset threshold, it indicates that there are no redundant trajectory points in the sub-trajectory point set, and there is no need to thin the trajectory point data.

[0059] Among them, the preset threshold can be set to the average trajectory point density collected by the acquisition device in each sub-trajectory point set in a normal driving scenario, or set to the upper limit value of the acceptable trajectory point density in each sub-trajectory point set, which is not limited here.

[0060] In traffic jam scenarios and U-turn scenarios with large curvatures, the acquisition device collects a large number of trajectory points within a short mileage, resulting in a trajectory point accumulation effect; at this time, the trajectory point density in the sub-trajectory point set is greater than the preset threshold, and it is necessary to thin out the trajectory points in the sub-trajectory point set so that the trajectory point density in each sub-trajectory point set after thinning is equal to or less than the preset threshold. The trajectory point accumulation effect in the sub-trajectory point set in traffic jam or U-turn scenarios with large curvatures is solved through thinning processing.

[0061] Exemplarily, as Figure 2 shown, the map data processing method includes:

[0062] S210. Obtain the trajectory point data to be processed.

[0063] Among them, this step is the same as S110, and for details, please refer to the explanation at S110, which will not be elaborated here.

[0064] S220. Based on a preset length, perform segmentation processing on the trajectory point data to be processed to obtain multiple sub-trajectory point sets.

[0065] Among them, each sub-trajectory point set includes multiple trajectory points. This step is the same as S120, and for details, please refer to the explanation at S120, which will not be elaborated here.

[0066] S230. Obtain the trajectory point density in each sub-trajectory point set.

[0067] Specifically, according to the number of trajectory points in each sub-trajectory point set and the mileage corresponding to the sub-trajectory point set (i.e., the preset length), the trajectory point density in each sub-trajectory point set is calculated.

[0068] S240. Determine whether the trajectory point density in the sub-trajectory point set is greater than the preset threshold.

[0069] Specifically, compare the trajectory point density in the sub-trajectory point set with the preset threshold. If the trajectory point density in the sub-trajectory point set is greater than the preset threshold, the determination result is yes, and it is necessary to thin out the trajectory points in the sub-trajectory point set and execute S250; if the trajectory point density in the sub-trajectory point set is less than or equal to the preset threshold, the determination result is no, and it is not necessary to thin out the trajectory points in the sub-trajectory point set and execute S260.

[0070] S250. If the trajectory point density in the sub-trajectory point set is greater than the preset threshold, thin out the trajectory points in the sub-trajectory point set according to the preset thinning ratio.

[0071] Among them, the preset thinning ratio is an empirical value, and its value range is between 10% and 20%.

[0072] Specifically, thin out the trajectory data according to the preset thinning ratio, so that the trajectory point density in each sub-trajectory point set after thinning is equal to or less than the preset threshold. Exemplarily, the preset length of each sub-trajectory point set is 50 meters. When the trajectory point density in the sub-trajectory point set is greater than the preset threshold, the thinning ratio is set to 15%.

[0073] S260. For each sub-trajectory point set, generate a target trajectory block according to multiple trajectory points.

[0074] Among them, the target trajectory block is used to define the area for obtaining map vector data and map acquisition data. Among them, this step is the same as S130. For details, see the explanation at S130 and will not be repeated here.

[0075] S270. Based on the first rule, obtain the map vector data corresponding to each target trajectory block, and / or, based on the second rule, obtain the map acquisition data corresponding to each target trajectory block.

[0076] Among them, the map acquisition data includes laser point cloud data and / or photo trajectory data. Among them, this step is the same as S140. For details, see the explanation at S140 and will not be repeated here.

[0077] In some embodiments, as Figure 3 shown, for Figure 1 the map data processing method shown, a refined flow schematic diagram of S130. Refer to Figure 3 , "For each sub-trajectory point set, generate a target trajectory block according to multiple trajectory points", including:

[0078] S310. Based on the trajectory points in the sub-trajectory point set, obtain the trajectory line corresponding to each sub-trajectory point set for connecting each trajectory point.

[0079] Among them, the acquisition device obtains trajectory point data according to the set acquisition frequency and acquisition time. There is a time interval between two adjacent trajectory points. Therefore, the trajectory points in each sub-trajectory point set are scattered points, rather than continuous line segments; each sub-trajectory point set includes multiple scattered trajectory points. Connecting the multiple trajectory points in the same sub-trajectory point set can obtain the trajectory line corresponding to the sub-trajectory point set.

[0080] S320. Horizontally and vertically expand based on the trajectory line corresponding to each set of sub-trajectory points to obtain the expanded polygon corresponding to each set of sub-trajectory points.

[0081] Among them, the above-mentioned expanded polygon is located on the plane where the road surface is located. Horizontally is the direction perpendicular to the trajectory line, and horizontal expansion is to expand to the left and right sides (i.e., the width direction of the road) of the trajectory line corresponding to each set of sub-trajectory points; vertically is the same as the traveling direction of the trajectory line, and vertical expansion is to expand outward from the two end points of the trajectory line of each set of sub-trajectory points (i.e., the length direction of the road), that is, the first trajectory point in the set of sub-trajectory points expands along the direction opposite to the traveling direction, and the last trajectory point expands along the direction same as the traveling direction; in this way, the expanded polygons corresponding to adjacent sets of sub-trajectory points are continuous, and even there is a certain overlapping area, ensuring that there will be no holes. The expansion range can be determined according to the road width and the working ability of the acquisition device. For example, the horizontal expansion range is set to 30 meters, covering the width range of the entire road; the vertical expansion range is set to 6 meters.

[0082] S330. Segment based on the expanded polygon corresponding to each set of sub-trajectory points to obtain the target trajectory blocks corresponding to each set of sub-trajectory points.

[0083] Specifically, after the processing of S320, there may be a certain overlap between the expanded polygons corresponding to adjacent sets of sub-trajectory points. In order to clearly distinguish the target trajectory blocks corresponding to different sets of sub-trajectory points, so as to obtain the corresponding map data in the subsequent steps, in this step, the expanded polygon corresponding to each set of sub-trajectory points is segmented, and after segmentation, the target trajectory blocks corresponding to each set of sub-trajectory points are obtained, and each target trajectory block is continuous.

[0084] In some embodiments, as Figure 4 shown, FIG. 3 shows a schematic detailed flowchart of S330 in the map data processing method. Referring to Figure 4 , "Segment based on the expanded polygon corresponding to each set of sub-trajectory points to obtain the target trajectory points blocks corresponding to each set of sub-trajectory points" includes:

[0085] S410. Determine the last trajectory point on the trajectory line of each set of sub-trajectory points, and the first trajectory point on the trajectory line of the next set of sub-trajectory points.

[0086] In this step, taking two adjacent sets of sub-trajectory points as a group, obtain the last trajectory point on the trajectory line of the previous set of sub-trajectory points among the two, and the first trajectory point on the trajectory line of the next set of sub-trajectory points.

[0087] S420. Obtain a perpendicular line perpendicular to the line connecting the last trajectory point and the first trajectory point.

[0088] In this step, a perpendicular line is drawn to the line connecting the last trajectory point of the previous sub-trajectory point set and the first trajectory point of the next sub-trajectory set; the foot of the perpendicular of this perpendicular line is located on the line connecting the last trajectory point and the first trajectory point. For example, the foot of the perpendicular is the midpoint of the line segment, so that each obtained target trajectory segment is evenly divided.

[0089] S430. Use the perpendicular line to divide the externally expanded polygon corresponding to each sub-trajectory point set to obtain the target trajectory segment of each sub-trajectory point set.

[0090] Specifically, after the S320 process, there is a certain overlap between the externally expanded polygons corresponding to adjacent sub-trajectory point sets, and it is necessary to divide them; the specific method is: obtain the last trajectory point of the trajectory line of each sub-trajectory point set and the last trajectory point on the trajectory line of the next adjacent sub-trajectory point set, draw a perpendicular line to the line connecting the last trajectory point and the first trajectory point, and the length of this perpendicular line is greater than the width of the lateral external expansion of the sub-trajectory point set; the externally expanded polygons corresponding to adjacent sub-trajectory point sets are divided using the common perpendicular line, so as to ensure that the target trajectory segments corresponding to each sub-trajectory point set are continuous and there is no hole phenomenon, avoiding the loss of the original trajectory data.

[0091] Exemplarily, as Figure 5 shown, it is a schematic diagram of the working principle in a map data processing method provided by an embodiment of the present disclosure. Among them, the horizontal X represents the width direction of the road, and the vertical Y represents the length direction of the road. Referring to Figure 5 , each sub-trajectory point set includes multiple trajectory points. Connect the trajectory points in each trajectory point set to obtain the trajectory line of each trajectory point set; expand the trajectory line of each trajectory point set in two directions of horizontal X and vertical Y to obtain the externally expanded polygon corresponding to each trajectory point set, and there is an overlapping area between the externally expanded polygons corresponding to adjacent sub-trajectory point sets; draw a perpendicular line to the line connecting the last trajectory point of the previous sub-trajectory point set and the first trajectory point of the next sub-trajectory set, and the foot of the perpendicular of this perpendicular line is located on the line connecting the last trajectory point and the first trajectory point; thus, the externally expanded polygons corresponding to two adjacent sub-trajectory point sets are divided by the perpendicular line; and so on, the externally expanded polygons corresponding to each sub-trajectory point set are divided to obtain the target trajectory segments of each sub-trajectory point set. The target trajectory segments corresponding to each sub-trajectory point set are continuous and there is no hole phenomenon, avoiding the loss of the original trajectory data.

[0092] In some embodiments, as Figure 6 shown, it is Figure 1In the shown method for processing map data, a schematic diagram of a refined process of "acquiring map vector data corresponding to each target trajectory block based on the first rule". Refer to Figure 6 , the map vector data includes lane line data. "Acquiring map vector data corresponding to each target trajectory block based on the first rule" includes:

[0093] S610. Based on the trajectory points in the sub-trajectory point set, acquire a trajectory line corresponding to each sub-trajectory point set for connecting the trajectory points.

[0094] In this step, the trajectory line is used to connect the trajectory points in the sub-trajectory point set, and the trajectory line can be determined according to the trajectory points in the sub-trajectory point set.

[0095] S620. Determine the last trajectory point on the trajectory line corresponding to each sub-trajectory point set, and the first trajectory point on the trajectory line of the next sub-trajectory set.

[0096] Among them, this step is the same as S410. For specific explanations, refer to the explanations at S410. To avoid repeated descriptions, it is not limited here.

[0097] S630. Acquire a perpendicular line perpendicular to the connection line of the last trajectory point and the first trajectory point.

[0098] Among them, this step is the same as S420. For specific explanations, refer to the explanations at S420. To avoid repeated descriptions, it is not limited here.

[0099] S640. Divide each lane line according to the perpendicular line of the trajectory line to obtain at least two lane line segments.

[0100] Among them, for the map data of the actual scenario, the types and lengths of lane lines are complex and diverse, including solid lines and dashed lines of different lengths; and usually the trajectory mileage of each target trajectory block is 50 meters. Therefore, it is necessary to perform standardized division of the corresponding lane lines according to the target trajectory block; if a lane line spans multiple target trajectory blocks, the lane line needs to be divided. The division method of the lane line is similar to the division method of the outer-expanded polygon. As Figure 7 shown, it is a schematic diagram of the working principle for acquiring lane line data provided by an embodiment of the present disclosure. Refer to Figure 7 , use the connection line of the last trajectory point of the previous sub-trajectory point set and the first trajectory point of the next sub-trajectory set to make a perpendicular line. The perpendicular line intersects the lane line, and the lane line is divided into at least two lane line segments. The number of divided lane line segments is equal to the number of target trajectory blocks.

[0101] S650. Determine the lane line segments corresponding to each target trajectory block according to the positions of the lane line segments.

[0102] Among them, according to the position of the lane line segment, the corresponding relationship between the lane line segment and the target trajectory block is determined, and the lane line data belonging to the current target trajectory block is stored in the data storage unit of the current target trajectory block; the remaining lane line data after segmentation needs to be processed again by subsequent target trajectory blocks, and this process is a recursive process.

[0103] In some embodiments, as Figure 8 shown, for Figure 1 the map data processing method shown, another detailed flowchart of "acquiring the map vector data corresponding to each target trajectory block based on the first rule". Refer to Figure 8 , the vector data includes marker data, and "acquiring the map vector data corresponding to each target trajectory block based on the first rule" includes:

[0104] S810. Determine the center point position of the marker.

[0105] Among them, the markers include ground markers (such as lane guiding lines) and non-ground markers (such as signboards and indicator poles).

[0106] S820. Determine the marker corresponding to each target trajectory block according to the center point position.

[0107] Specifically, as Figure 9 shown, according to the actual type of the marker (such as point, line or surface), calculate the center point position of the marker, and then determine the corresponding relationship between the marker and each target trajectory block, and determine the marker whose center point position is within each target trajectory block as the marker corresponding to each target trajectory block. For example, the markers corresponding to the rightmost target trajectory block include a U-turn lane guiding line and a traffic indicator light.

[0108] In some embodiments, considering that for map acquisition data, such as lidar point cloud data and photo trajectory data, the acquisition methods of the lidar and camera used during acquisition are different from the acquisition method of trajectory data. Among the trajectory points in each of the above-set sub-trajectory point sets, the original acquisition data collected by the lidar and camera often has a certain visible extension range, thus exceeding the distance range of the corresponding target trajectory block. At this time, in order to ensure the completeness of the map acquisition data on each target trajectory block, a certain visible extension range can be preset based on the type, installation quantity, and installation angle of the acquisition device, and when acquiring the map acquisition data corresponding to each target trajectory block, also acquire the map acquisition data limited by the above visible extension range. As Figure 10 shown, for Figure 1 the map data processing method shown, a flowchart of "acquiring the map acquisition data corresponding to each target trajectory block based on the second rule". Refer toFigure 10 , based on the second rule, obtain the map acquisition data corresponding to each target trajectory segment, including:

[0109] S1010. Determine the visible extension range corresponding to each target trajectory segment.

[0110] Among them, the visible extension range includes the forward visible extension range and the backward visible extension range.

[0111] Among them, the visible extension range is related to the configuration of the acquisition device. The configuration of the acquisition device includes but is not limited to device type, installation quantity, and installation angle. According to the pre-set corresponding relationship between the acquisition device configuration and its visible extension range, after obtaining the configuration of the acquisition device, the corresponding forward visible extension range and backward visible extension range can be obtained.

[0112] S1020. Obtain the map acquisition data corresponding to each target trajectory segment and the visible extension range.

[0113] Specifically, according to the obtained forward visible extension range and backward visible extension range, traverse forward and backtrack backward for the two endpoints of each target trajectory segment. The forward traversal range is the forward visible extension range, and the backward backtracking range is the backward visible extension range. By expanding bidirectionally in the forward direction (forward) and backward direction (backward) of each target trajectory segment, it is ensured that the map acquisition data corresponding to each target trajectory segment and the visible extension range is completely covered. Even if there is some duplicate data between adjacent target trajectory segments, the duplicate data can be fused and deduplicated in the subsequent algorithm steps used. Through the technical solution provided in this embodiment, the loss of map acquisition data can be avoided.

[0114] In some embodiments, as Figure 10 shown, the method further includes:

[0115] S1030. Determine and record the correspondence between the map acquisition data corresponding to each target trajectory segment and the visible extension range and the trajectory points within the target trajectory segment.

[0116] Among them, in this step, a correspondence needs to be established between the map acquisition data corresponding to each target trajectory segment and the visible extension range and the trajectory points within the target trajectory segment, that is, the map acquisition data within the visible extension range corresponds to the trajectory points within the target trajectory segment; both the trajectory points within the target trajectory segment and the map acquisition data (including laser point cloud data and / or photo trajectory data) carry time stamps. Based on the acquisition time, establish the correspondence between the original acquisition data and the trajectory points in the sub-trajectory point set.

[0117] In some embodiments, as Figure 11 shown, for Figure 10In the shown method for processing map data, a schematic flow diagram of a refinement process of "determining the visible extension range corresponding to each target trajectory block" is shown. Refer to Figure 11 ,"determining the visible extension range corresponding to each target trajectory block" includes:

[0118] S1110. Determine the visible extension range corresponding to the lidar point cloud data of each target trajectory block.

[0119] And / or,

[0120] S1120. Determine the visible extension range corresponding to the photo trajectory data of each target trajectory block.

[0121] It should be noted that S1110 and S1120 are two parallel steps, and both of these steps can be executed or either one can be executed; when the acquisition device includes a lidar and a camera, both S1110 and S1120 are executed; when the acquisition device only includes a lidar, S1110 is executed; when the acquisition device only includes a camera, S1120 is executed.

[0122] S130. Obtain the map acquisition data corresponding to each target trajectory block and the visible extension range.

[0123] Among them, this step is the same as S1020. For details, refer to the explanation at S1020 and will not be elaborated here.

[0124] Exemplarily, as Figure 12 shown, it is a schematic diagram of the visible extension range of two types of acquisition vehicles provided by an embodiment of the present disclosure; among them, the acquisition vehicle is a vehicle used to acquire raw trajectory point data, and various sensors are installed on it, such as a lidar, an Inertial Navigation System (INS), and a camera, etc.; different types of sensors are installed according to specific acquisition scenarios. Refer to Figure 12Both the first - type collection vehicle and the second - type collection vehicle are equipped with two collection devices, namely lidar and camera. The data collected by the lidar is lidar point - cloud data, and the data collected by the camera is photo - trajectory data. There are three lidars and four cameras installed on the first - type collection vehicle. The forward visible extension range and the backward visible extension range of the lidar point - cloud data it collects are both 60 meters. The forward visible extension range of the photo - trajectory data is 10 meters, and the backward visible extension range is 30 meters. While on the second - type collection vehicle, there is one lidar and one camera. The forward visible extension range of the lidar point - cloud data it collects is - 3 meters, and the backward visible extension range is 60 meters. The forward visible extension range of the photo - trajectory data is 10 meters, and the backward visible extension range is 30 meters. Since the visible direction of the camera is the ray direction with the lens as the endpoint, and the cameras are set on the front hood and the rear hood of the vehicle, the visible extension ranges corresponding to the photo - trajectory data of the two vehicle types are the same. While the lidar achieves 360° circumferential visibility through circumferential scanning, that is, the visible direction of the lidar is the ray direction that continuously expands outward with the installation position of the lidar as the center. Due to the differences in the installation positions and quantities of the lidars on the two vehicle types, the visible ranges corresponding to the lidar point - cloud data of the two types of collection vehicles are different.

[0125] Based on the same inventive concept, the embodiments of the present disclosure also provide a map data processing device. This device can execute the steps of any map data processing method provided by the embodiments of the present disclosure, and has the corresponding functional modules and beneficial effects for executing the method. To avoid repeated description, it will not be elaborated here. This device can be implemented by software and / or hardware, and can be integrated on any terminal device such as a server or a computer with computing capabilities.

[0126] Figure 13 is a schematic structural diagram of another map data processing device provided by the embodiments of the present disclosure. Referring to Figure 13 , the map data processing device 1300 includes: a first acquisition module 1301, configured to acquire trajectory point data to be processed; a segmentation processing module 1302, configured to perform segmentation processing on the trajectory point data to be processed based on a preset length to obtain a plurality of sub - trajectory point sets, where each sub - trajectory point set includes a plurality of trajectory points; a trajectory block generation module 1303, configured to generate a target trajectory block for each sub - trajectory point set according to the plurality of trajectory points, and the target trajectory block is used to define the area for acquiring map vector data and map acquisition data; a second data acquisition module 1304, configured to acquire the map vector data corresponding to each target trajectory block based on a first rule, and / or acquire the map acquisition data corresponding to each target trajectory block based on a second rule, where the map acquisition data includes lidar point - cloud data and / or photo - trajectory data.

[0127] In some embodiments, after the segmentation processing module is used to segment the to-be-processed trajectory point data to obtain multiple sub-trajectory point sets, it is further used to: obtain the trajectory point density in each sub-trajectory point set; if the trajectory point density in the sub-trajectory point set is greater than a preset threshold, thin the trajectory points in the sub-trajectory point set according to a preset thinning ratio.

[0128] In some embodiments, the trajectory block generation module is used to generate a target trajectory block for each sub-trajectory point set according to multiple trajectory points, including: based on the trajectory points in the sub-trajectory point set, obtain the trajectory line corresponding to each sub-trajectory point set for connecting each trajectory point; perform horizontal expansion and vertical expansion based on the trajectory line corresponding to each sub-trajectory point set to obtain the expanded polygon corresponding to each sub-trajectory point set; segment the expanded polygon corresponding to each sub-trajectory point set to obtain the target trajectory block corresponding to each sub-trajectory point set.

[0129] In some embodiments, the trajectory block generation module is used to segment the expanded polygon corresponding to each sub-trajectory point set to obtain the target trajectory block corresponding to each sub-trajectory point set, including: determine the last trajectory point on the trajectory line of each sub-trajectory point set; obtain the perpendicular line passing through the last trajectory point and perpendicular to the trajectory line corresponding to the sub-trajectory point set; use the perpendicular line to segment the expanded polygon corresponding to each sub-trajectory point set to obtain the target trajectory block corresponding to each sub-trajectory point set.

[0130] In some embodiments, the map vector data includes lane line data, and the second data acquisition module is used to obtain the map vector data corresponding to each target trajectory block based on the first rule, including: based on the trajectory points in the sub-trajectory point set, obtain the trajectory line corresponding to each sub-trajectory point set for connecting each trajectory point; determine the last trajectory point on the trajectory line corresponding to each sub-trajectory point set; obtain the perpendicular line passing through the last trajectory point and perpendicular to the trajectory line corresponding to the sub-trajectory point set; segment each lane line according to the perpendicular line to obtain at least two lane line segments; determine the lane line segments corresponding to each target trajectory block according to the positions of the lane line segments.

[0131] In some embodiments, the map vector data includes marker data, and the second data acquisition module is used to obtain the map vector data corresponding to each target trajectory block based on the first rule, including: determine the center point position of the marker; determine the marker corresponding to each target trajectory block according to the center point position.

[0132] In some embodiments, the second data acquisition module is configured to acquire map acquisition data corresponding to each target trajectory block based on a second rule, including: determining a visible extension range corresponding to each target trajectory block, where the visible extension range includes a forward visible extension range and a backward visible extension range; and acquiring map acquisition data corresponding to each target trajectory block and within the visible extension range.

[0133] In some embodiments, the apparatus further includes: a recording module, configured to determine and record the correspondence between the map acquisition data corresponding to each target trajectory block and within the visible extension range and the trajectory points within the target trajectory block.

[0134] In some embodiments, the second data acquisition module is configured to determine the visible extension range corresponding to each target trajectory block, including: determining the visible extension range corresponding to the laser point cloud data of each target trajectory block; and / or determining the visible extension range corresponding to the photo trajectory data of each target trajectory block.

[0135] The content not described in detail in the apparatus embodiments of the present disclosure may refer to the description in any method embodiment of the present disclosure.

[0136] Figure 14 FIG. is a schematic structural diagram of an electronic device provided for an embodiment of the present disclosure, which is used to exemplarily illustrate the electronic device for implementing any map data processing method in the embodiments of the present disclosure, and should not be construed as a specific limitation to the embodiments of the present disclosure.

[0137] As Figure 14 shown, the electronic device 1400 may include a processor (such as a central processing unit, a graphics processing unit, etc.) 1401, which may perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1402 or a program loaded from a storage device 1408 into a random access memory (RAM) 1403. In the RAM 1403, various programs and data required for the operation of the electronic device 1400 are also stored. The processor 1401, the ROM 1402, and the RAM 1403 are connected to each other through a bus 1404. An input / output (I / O) interface 1405 is also connected to the bus 1404.

[0138] Typically, the following devices can be connected to the I / O interface 1405: an input device 1406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1408 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1409. The communication device 1409 can allow the electronic device 1400 to communicate with other devices wirelessly or wiredly to exchange data. Although the electronic device 1400 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices can be implemented or had alternatively.

[0139] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device 1409, or installed from the storage device 1408, or installed from the ROM 1402. When the computer program is executed by the processor 1401, the functions defined in any of the map data processing methods provided by the embodiments of the present disclosure can be executed.

[0140] It should be noted that the above-mentioned computer-readable medium in the present disclosure may be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0141] In some embodiments, the client and the server can communicate using any currently known or future-developed network protocol such as HTTP (HyperText Transfer Protocol), and can be interconnected with digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), the Internet (e.g., the Internet), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed network.

[0142] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist separately and not be assembled into the electronic device.

[0143] The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device is caused to:

[0144] Obtain the trajectory point data to be processed; based on a preset length, perform segmentation processing on the trajectory point data to be processed to obtain a plurality of sub-trajectory point sets, where each sub-trajectory point set includes a plurality of trajectory points; for each sub-trajectory point set, generate a target trajectory block according to the plurality of trajectory points, and the target trajectory block is used to define the area for obtaining map vector data and map acquisition data; based on the first rule, obtain the map vector data corresponding to each target trajectory block, and / or, based on the second rule, obtain the map acquisition data corresponding to each target trajectory block, and the map acquisition data includes laser point cloud data and / or photo trajectory data.

[0145] In the embodiments of the present disclosure, computer program code for performing the operations of the present disclosure can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on a computer, partially on a computer, executed as an independent software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0147] The units involved in the embodiments of the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not constitute a limitation to the unit itself in some cases.

[0148] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), Systems on Chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

[0149] In the context of this disclosure, a computer-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable medium can be either a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or Flash memory), an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0150] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present disclosure.

[0151] In addition, although the operations are depicted in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although a number of specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of separate embodiments can also be implemented combinatorially in a single embodiment. Conversely, the various features described in the context of a single embodiment can also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0152] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A method for processing map data, including: Obtaining trajectory point data to be processed; Based on a preset length, performing segmentation processing on the trajectory point data to be processed to obtain a plurality of sub-trajectory point sets, where each sub-trajectory point set includes a plurality of trajectory points; Based on the trajectory points within the sub-trajectory point set, obtaining a trajectory line corresponding to each sub-trajectory point set for connecting each trajectory point; Performing horizontal expansion and vertical expansion based on the trajectory line corresponding to each sub-trajectory point set to obtain an expanded polygon corresponding to each sub-trajectory point set; Determining the last trajectory point on the trajectory line of each sub-trajectory point set and the first trajectory point on the trajectory line of the next sub-trajectory set; Obtaining a perpendicular line perpendicular to the line connecting the last trajectory point and the first trajectory point; Using the perpendicular line to divide the expanded polygon corresponding to each sub-trajectory point set to obtain a target trajectory block corresponding to each sub-trajectory point set, where the target trajectory block is used to define the area for obtaining map vector data and map acquisition data; Based on a first rule, obtaining map vector data corresponding to each target trajectory block, and / or based on a second rule, obtaining map acquisition data corresponding to each target trajectory block, where the map acquisition data includes laser point cloud data and / or photo trajectory data.

2. The method according to claim 1, wherein, after performing the segmentation processing on the trajectory point data to be processed to obtain a plurality of sub-trajectory point sets, further including: Obtaining the trajectory point density in each sub-trajectory point set; If the trajectory point density in the sub-trajectory point set is greater than a preset threshold, thinning the trajectory points in the sub-trajectory point set according to a preset thinning ratio.

3. The method according to claim 1, wherein, the map vector data includes lane line data, and based on the first rule, obtaining map vector data corresponding to each target trajectory block includes: Based on the trajectory points within the sub-trajectory point set, obtaining a trajectory line corresponding to each sub-trajectory point set for connecting each trajectory point; Determining the last trajectory point on the trajectory line corresponding to each sub-trajectory point set and the first trajectory point on the trajectory line of the next sub-trajectory set; Obtaining a perpendicular line perpendicular to the line connecting the last trajectory point and the first trajectory point; Dividing each lane line according to the perpendicular line to obtain at least two lane line segments; Determining the lane line segments corresponding to each target trajectory block according to the positions of the lane line segments.

4. The method according to claim 1, wherein, the map vector data includes marker data, and based on the first rule, obtaining map vector data corresponding to each target trajectory block includes: Determining the center point position of the marker; Determining the marker corresponding to each target trajectory block according to the center point position.

5. The method according to claim 1, wherein, based on the second rule, obtaining map acquisition data corresponding to each target trajectory block includes: Determining the visible extension range corresponding to each target trajectory block, where the visible extension range includes a forward visible extension range and a backward visible extension range; Obtain each of the target trajectory segments and the corresponding map acquisition data within the visible extension range.

6. The method according to claim 5, wherein, further comprising: Determine and record the correspondence between each of the target trajectory segments and the corresponding map acquisition data within the visible extension range and the trajectory points within the target trajectory segment.

7. The method according to claim 5, wherein, The determination of the visible extension range corresponding to each of the target trajectory segments includes: Determine the visible extension range corresponding to the laser point cloud data of each target trajectory segment; and / or, Determine the visible extension range corresponding to the photo trajectory data of each target trajectory segment.

8. A map data processing device, comprising: A first data acquisition module, configured to acquire trajectory point data to be processed; A segmentation processing module, configured to perform segmentation processing on the trajectory point data to be processed based on a preset length to obtain a plurality of sub-trajectory point sets, where each sub-trajectory point set includes a plurality of trajectory points; A trajectory segment generation module, configured to obtain, based on the trajectory points within the sub-trajectory point set, a trajectory line corresponding to each sub-trajectory point set for connecting the trajectory points; perform lateral expansion and longitudinal expansion based on the trajectory line corresponding to each sub-trajectory point set to obtain an expanded polygon corresponding to each sub-trajectory point set; determine the last trajectory point on the trajectory line of each sub-trajectory point set and the first trajectory point on the trajectory line of the next sub-trajectory set; obtain a perpendicular line perpendicular to the connection line of the last trajectory point and the first trajectory point; use the perpendicular line to divide the expanded polygon corresponding to each sub-trajectory point set to obtain a target trajectory segment corresponding to each sub-trajectory point set, where the target trajectory segment is used to define the area for obtaining map vector data and map acquisition data; A second data acquisition module, configured to acquire, based on a first rule, the map vector data corresponding to each target trajectory segment, and / or, based on a second rule, acquire the map acquisition data corresponding to each target trajectory segment, where the map acquisition data includes laser point cloud data and / or photo trajectory data.

9. An electronic device, comprising: A memory and a processor, The memory is used to store executable instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the map data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the map data processing method according to any one of claims 1 to 7.

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