High-precision map road boundary updating method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a high-precision map road boundary updating method and device, electronic equipment and storage medium, relates to the technical field of computers, and particularly relates to the fields of autonomous driving, intelligent transportation and big data. The specific implementation scheme is as follows: determining at least one matching pair that satisfies a preset condition from at least one first vector point corresponding to collected road boundary data and at least one second vector point corresponding to historical road boundary data, each matching pair including one first vector point and one second vector point; calculating the normal distance between the first vector point and the second vector point in each matching pair; determining a matching pair set according to the normal distance; and updating the historical road boundary data according to the first vector point of each matching pair in the matching pair set.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of computer, in particular to the fields of automatic driving, intelligent transportation and big data, and more particularly to a high-definition map road boundary updating method and device, an electronic device and a storage medium. BACKGROUND

[0002] As a scarce resource and a necessity in the field of unmanned driving, the high-definition map plays a core role in the entire field and can help the unmanned vehicle to pre-knowledge the complex information on the road surface, such as slope, curvature, heading, etc., in combination with intelligent path planning, so that the unmanned vehicle makes correct decisions, which is an indispensable data source for unmanned vehicle driving. In order to ensure the safe driving of the unmanned vehicle to the destination, the unmanned driving needs to compare the information collected by the sensor with the stored high-definition map to determine the position and direction. Therefore, the accuracy of high-definition map data collection is very important for unmanned driving. SUMMARY

[0003] The present disclosure provides a high-definition map road boundary updating method and device, an electronic device and a storage medium.

[0004] According to an aspect of the present disclosure, a high-definition map road boundary updating method is provided, comprising: determining at least one matching pair that satisfies a preset condition from at least one first vector point corresponding to collected road boundary data and at least one second vector point corresponding to historical road boundary data, each of the matching pairs comprising one of the first vector points and one of the second vector points; calculating, for each of the matching pairs, a normal distance between the first vector point and the second vector point in the matching pair; determining a matching pair set according to the normal distance; and updating the historical road boundary data according to the first vector point of each of the matching pairs in the matching pair set.

[0005] According to another aspect of the present disclosure, a high-definition map road boundary updating device is provided, comprising: a first determining module configured to determine at least one matching pair that satisfies a preset condition from at least one first vector point corresponding to collected road boundary data and at least one second vector point corresponding to historical road boundary data, each of the matching pairs comprising one of the first vector points and one of the second vector points; a calculating module configured to calculate, for each of the matching pairs, a normal distance between the first vector point and the second vector point in the matching pair; a second determining module configured to determine a matching pair set according to the normal distance; and a first updating module configured to update the historical road boundary data according to the first vector point of each of the matching pairs in the matching pair set.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the high-definition map road boundary updating method as described above.

[0007] According to another aspect of the present disclosure, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to perform the high-definition map road boundary updating method as described above.

[0008] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the high-definition map road boundary updating method as described above.

[0009] It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become apparent through the following description. BRIEF DESCRIPTION OF DRAWINGS

[0010] The accompanying drawings are used to better understand the present scheme, and do not limit the present disclosure. Among them:

[0011] Figure 1 An exemplary system architecture to which the high-definition map road boundary updating method and device according to embodiments of the present disclosure can be applied is schematically shown;

[0012] Figure 2 A flowchart of the high-definition map road boundary updating method according to embodiments of the present disclosure is schematically shown;

[0013] Figure 3 A flowchart of the high-definition map road boundary updating method according to embodiments of the present disclosure is schematically shown;

[0014] Figure 4 A block diagram of the high-definition map road boundary updating device according to embodiments of the present disclosure is schematically shown; and

[0015] Figure 5 A schematic block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0016] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0017] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and there is no violation of public order and good morals.

[0018] High-definition maps play a crucial role in autonomous vehicles. In particular, the timeliness of data within high-definition maps is of paramount importance to autonomous driving. The production and updating of high-definition map data primarily involves specialized data collection vehicles periodically gathering various sensor data on roads. Then, automated algorithms combined with manual annotation are used to identify and update data changes. Sensor data mainly includes IMU (Inertial Measurement Unit) trajectory data, laser point cloud data, and high-speed camera image data. By applying raw data-based annotation methods on a large scale to the production line, and by developing automated annotation algorithms, it is possible to significantly reduce the time spent by annotation personnel, improve annotation efficiency, and lower production costs.

[0019] In realizing this disclosed concept, the inventors discovered that raw data, after certain data processing, generates visualized labeled data, including 2D / 3D point cloud views, image views, trajectory data, etc. Labellers use customized labeling tools to refer to these data and compare them with existing master database data to discover and update road boundary change points. The labeled road boundaries mainly include guardrails and curbs. The main pain points are as follows: Automated algorithms face numerous scenarios, easily leading to recognition errors, such as road wear, road occlusion resulting in unrecovered data, unstable data collection quality due to various factors, and misidentification due to environmental and system reasons. The efficiency of discovering road boundary change points is low; labellers need to re-examine the entire map data to find change points, resulting in high costs. Labeling efficiency is low; labellers need to manually perform operations such as deleting, updating, and adding change point data. High skill requirements are placed on labellers; high-precision maps require a certain level of data accuracy, thus requiring labellers to undergo extensive training to be competent in this work, increasing labor costs.

[0020] In view of this, this disclosure provides a method for updating road boundaries in high-precision maps, comprising: determining at least one matching pair satisfying preset conditions from at least one first vector point corresponding to collected road boundary data and at least one second vector point corresponding to historical road boundary data, each matching pair including a first vector point and a second vector point; calculating the normal distance between the first vector point and the second vector point in each matching pair for each matching pair; determining a set of matching pairs based on the normal distance; and updating the historical road boundary data based on the first vector point of each matching pair in the set of matching pairs.

[0021] Figure 1 The illustration schematically shows an exemplary system architecture for applying a high-precision map road boundary update method and apparatus according to embodiments of the present disclosure.

[0022] It is important to note that Figure 1 The examples shown are merely examples of system architectures applicable to embodiments of this disclosure, intended to help those skilled in the art understand the technical content of this disclosure. However, they do not imply that embodiments of this disclosure cannot be used in other devices, systems, environments, or scenarios. For instance, in another embodiment, an exemplary system architecture applicable to the high-precision map road boundary update method and apparatus may include a terminal device. However, the terminal device can implement the high-precision map road boundary update method and apparatus provided by embodiments of this disclosure without interacting with a server.

[0023] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the terminal devices 101, 102, and 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0024] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (for example only).

[0025] Terminal devices 101, 102, and 103 can be various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0026] Server 105 can be a server providing various services, such as a backend management server supporting the content browsed by users using terminal devices 101, 102, and 103 (for example only). The backend management server can analyze and process received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated based on user requests) to the terminal devices. The server can be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service system, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") in terms of management difficulty and weak business scalability. The server can also be a server for a distributed system or a server integrated with blockchain technology.

[0027] It should be noted that the high-precision map road boundary update method provided in this embodiment can generally be executed by terminal devices 101, 102, or 103. Accordingly, the high-precision map road boundary update device provided in this embodiment can also be disposed in terminal devices 101, 102, or 103.

[0028] Alternatively, the high-precision map road boundary update method provided in this embodiment can generally be executed by server 105. Correspondingly, the high-precision map road boundary update device provided in this embodiment can generally be located in server 105. The high-precision map road boundary update method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105. Correspondingly, the high-precision map road boundary update device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with terminal devices 101, 102, 103 and / or server 105.

[0029] For example, when road boundary data needs to be updated, terminal devices 101, 102, and 103 can acquire collected road boundary data and historical road boundary data, and then send the acquired road boundary data and historical road boundary data to server 105. Server 105 determines at least one matching pair that meets preset conditions from at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data. Each matching pair includes a first vector point and a second vector point. For each matching pair, the normal distance between the first vector point and the second vector point in the matching pair is calculated. Based on the normal distance, a set of matching pairs is determined, and the historical road boundary data is updated based on the first vector point of each matching pair in the set of matching pairs. Alternatively, a server or server cluster capable of communicating with terminal devices 101, 102, 103 and / or server 105 can analyze the collected road boundary data and historical road boundary data and update the historical road boundary data.

[0030] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0031] Figure 2 A flowchart illustrating a high-precision map road boundary update method according to an embodiment of the present disclosure is shown.

[0032] like Figure 2 As shown, the method includes operations S210 to S240.

[0033] In operation S210, at least one matching pair that meets preset conditions is determined from at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data. Each matching pair includes a first vector point and a second vector point.

[0034] In operation S220, for each matching pair, the normal distance between the first vector point and the second vector point in the matching pair is calculated.

[0035] In operation S230, the set of matching pairs is determined based on the normal distance.

[0036] In operation S240, the historical road boundary data is updated based on the first vector point of each matching pair in the matching pair set.

[0037] According to embodiments of this disclosure, the collected road boundary data may include bounding box data containing road boundary features newly collected at the current time or within a preset time period. The bounding box data may include road boundary features within a preset three-dimensional spatial range centered on the current collection point. Road boundary features may include at least one of the following: location data of guardrails, curbs, etc., image data, point cloud data, etc. By performing vector modeling on the newly collected bounding box data of guardrails, curbs, etc., the road boundary features represented by the bounding box data can be converted into a vector point representation. Historical road boundary data may include road network data stored in a road network database that represents the road boundary features of historical road boundaries. Road network data may be stored in the road network database in the form of vector points. The point information of a vector point may represent the location information corresponding to that point, and the direction information of the vector point may represent the extension or expansion direction of the road boundary. It should be noted that the collected road boundary data may also include road boundary features collected for road boundaries in various regions without a limited scope. The current time and preset time period for collecting the road boundary data are both later than the storage time of the historical road boundary data.

[0038] According to embodiments of this disclosure, by uniformly sampling bounding box data and road network data represented in vector point form with a predetermined sampling step size, first vector points and second vector points can be obtained respectively. During sampling, spatial relationship information such as predecessor and successor, left and right relationships between sampling points can also be determined by using a kdtree (K-dimension tree, a balanced binary tree) based on the physical spatial relationship between each sampling point.

[0039] According to embodiments of this disclosure, the preset conditions may include: two vector points being contained within the same predefined area, and the result of matching and differencing the two vector points satisfying at least one of the preset matching formulas. If any one of the aforementioned conditions is satisfied, the corresponding first and second vector points can be determined as a matching pair.

[0040] According to embodiments of this disclosure, normal distance can represent the perpendicular distance from a point to a vector. For example, in this embodiment, the normal distance between a first vector point and a second vector point may include at least one of a first perpendicular distance from the point represented by the first vector point to the vector represented by the second vector point, and a second perpendicular distance from the point represented by the second vector point to the vector represented by the first vector point.

[0041] According to embodiments of this disclosure, the matching pair set may include one matching pair or multiple matching pairs. Based on the matching pair set determined by the normal distance, new road boundary data with effective changes compared to historical road boundary data can be further obtained, i.e., the first vector point of each matching pair in the matching pair set. The method of determining the matching pair set based on the normal distance may include: predefining a preset range and constructing the matching pair set based on the matching pairs corresponding to the normal distances within that preset range; or sorting the values ​​of the normal distances to obtain a sorting result, and then constructing the matching pair set based on the matching pairs corresponding to each of the preset number of normal distances with larger values ​​in the sorting result. It should be noted that the method of determining the matching pair set is not limited here, and other feasible determination methods may be included in other embodiments of this disclosure.

[0042] According to embodiments of this disclosure, after determining the set of matching pairs, since the first vector point of each matching pair in the set is a valid change point compared to the historical road boundary data, the road boundary data to be added can be determined based on the first vector points in the set of matching pairs. Then, the road boundary data to be added can be added to the historical road boundary data, completing the update of the historical road boundary data.

[0043] Through the above embodiments of this disclosure, a set of matching pairs is determined based on preset conditions and normal distance. From the first and second vector points that have been matched as matching pairs, a first vector point that has a valid change compared to the historical road boundary data can be further determined. Then, automatic updates are performed, which improves the precision and recall rate of automatic annotation, effectively reduces the input of manual annotation, and improves the accuracy and completeness of the discovery of valid change points, thereby improving the update efficiency of high-precision maps.

[0044] The following describes specific embodiments. Figure 2 The method shown will be further explained.

[0045] According to embodiments of this disclosure, determining at least one matching pair that satisfies preset conditions from at least one first vector point corresponding to collected road boundary data and at least one second vector point corresponding to historical road boundary data may include: for each first vector point, calculating the distance and direction angle between the first vector point and each second vector point respectively. Matching pairs that satisfy the preset conditions are determined based on first and second vector points whose distance is less than or equal to a third preset threshold and whose direction angle is less than or equal to a fourth preset threshold.

[0046] According to embodiments of this disclosure, the third preset threshold may be less than or equal to the above-mentioned sampling step size. The preset conditions may include the following preset matching formula (1):

[0047] d∈[0, wd ]&&θ∈[0,w θ (1)

[0048] Where d can represent the distance between the first vector point and the second vector point, such as Euclidean distance, Manhattan distance, Chebyshev distance, etc. d This can represent the maximum value for a predefined distance, i.e., the third preset threshold mentioned above. θ can represent the directional angle between the first vector point and the second vector point. θ It can represent the maximum value of the predefined directional angle, namely the fourth preset threshold mentioned above.

[0049] According to an embodiment of this disclosure, based on the aforementioned preset matching formula (1), for example, the Euclidean distance d1 and the direction angle θ1 between the first vector point and the second vector point can be calculated first. Then, according to the preset matching formula (1), the points satisfying d1∈[0, w d ]&&θ1∈[0,w θ The first and second vector points are determined as a matching pair.

[0050] Through the above embodiments of this disclosure, at least one first vector point and at least one second vector point can be converted into a matching pair. By performing subsequent calculations based on the matching pairs, the amount of computation is effectively reduced, and the update efficiency of high-precision maps is improved.

[0051] According to embodiments of this disclosure, determining a set of matching pairs based on the normal distance may include: determining a target matching pair corresponding to the normal distance if the normal distance is greater than or equal to a first preset threshold; and determining a set of matching pairs based on the target matching pairs.

[0052] According to embodiments of this disclosure, based on the foregoing definition of normal distance, a normal distance greater than or equal to a first preset threshold may include at least one of a first vertical distance greater than or equal to the first preset threshold, a second vertical distance greater than or equal to the first preset threshold, etc.

[0053] According to embodiments of this disclosure, for matching pairs that meet preset conditions, a matching pair set can be constructed only based on matching pairs where the normal distance between the first vector point and the second vector point in the matching pair is greater than or equal to the first preset threshold, or the second vertical distance is greater than or equal to the first preset threshold, or both the first vertical distance and the second vertical distance are greater than or equal to the first preset threshold. For matching pairs where both the first vertical distance and the second vertical distance are less than the first preset threshold, the first vector point and the second vector point can be determined as invalid change points.

[0054] Through the above embodiments of this disclosure, the calculation of the normal distance between the first vector point and the second vector point in the matching pair is introduced, and the pair is filtered according to the relationship between the normal distance and the first preset threshold. This allows for the determination of a set of matching pairs with less data and a higher proportion of effective change points from the original matching pairs for subsequent calculations, effectively reducing the amount of calculation and improving the update efficiency of high-precision maps.

[0055] According to embodiments of this disclosure, the matching pair set includes multiple target matching pairs. Determining the matching pair set based on the target matching pairs may include: determining at least two first vector points from a plurality of first vector points corresponding to the plurality of target matching pairs, wherein the continuous length of a road boundary is greater than or equal to a second preset threshold. The matching pair set is determined based on the target matching pairs corresponding to each of the at least two first vector points.

[0056] According to embodiments of this disclosure, the first vector point obtained by sampling from the collected road boundary data may include the location information and direction information of the road boundary. Based on the differences in direction information between different vector points, it can be determined whether different vector points originate from the same road boundary. For example, an angle threshold can be predefined; for two vector points with an angle greater than or equal to this angle threshold, it can be determined that the two vector points originate from different road boundaries. When it is determined that multiple vector points originate from the same road boundary, the continuous length of the road boundary represented by the multiple vector points can be determined based on the location information of each of the multiple vector points. For example, when it is determined that multiple vector points originate from the same road boundary, the multiple vector points can be sequentially connected based on their respective location information, and the length of this connection can represent the continuous length of the road boundary corresponding to the multiple vector points.

[0057] According to embodiments of this disclosure, for a plurality of first vector points determined from a set of matching pairs, first vector points originating from the same road boundary can be determined firstly based on the direction information of each first vector point. If only one first vector point originating from the same road boundary is determined, that first vector point can be identified as an invalid change point. If at least two first vector points originating from the same road boundary are determined, the continuous length of the road boundary represented by the at least two first vector points can be determined based on their respective position information. If the continuous length is less than a second preset threshold, the at least two first vector points can be identified as invalid change points. If the continuous length is greater than or equal to the second preset threshold, the at least two first vector points can be identified as valid change points, and a set of matching pairs can be determined based on the target matching pairs corresponding to each of the at least two first vector points.

[0058] Through the above embodiments of this disclosure, when multiple target matching pairs are determined, the calculation of the continuous length of the road boundary represented by multiple first vector points corresponding to the multiple target matching pairs is introduced, and the selection is performed according to the relationship between the continuous length and the second preset threshold. This allows for the determination of a set of matching pairs with less data and a higher proportion of effective change points from the target matching pairs for subsequent calculations, thereby reducing the amount of calculation and improving the update efficiency and accuracy of high-precision maps.

[0059] According to embodiments of this disclosure, first and second vector points outside of a matching pair can be used as valid change points for updating road boundaries in a high-precision map. In this case, the high-precision map road boundary update method may further include: determining at least one target first vector point and at least one target second vector point included in at least one matching pair; determining other first vector points besides the target first vector point from the at least one first vector point; determining other second vector points besides the target second vector point from the at least one second vector point; and updating historical road boundary data based on the other first vector points and the other second vector points.

[0060] According to embodiments of this disclosure, when matching a first vector point obtained from bounding box data sampling and a second vector point obtained from road network data sampling, there may be situations where they cannot be matched. For example, there may be at least one of the following: no second vector point matches the first vector point (i.e., other first vector points exist), or no first vector point matches the second vector point (i.e., other second vector points exist). In either case where no matching first or second vector point exists, the isolated other first and second vector points, since they do not match the historical road boundary data and the collected road boundary data respectively, can be identified as valid change points, and the historical road boundary data can be updated based on these other first and second vector points.

[0061] Through the above embodiments of this disclosure, road boundary data can be updated based on the first vector point and the second vector point other than the matching pair, which can further improve the integrity of road boundary updates and enhance the accuracy of high-precision maps.

[0062] According to embodiments of this disclosure, updating historical road boundary data based on other first vector points and other second vector points includes: determining road boundary data to be added based on the other first vector points; adding data related to the road boundary data to be added to the historical road boundary data; determining road boundary data to be deleted based on the other second vector points; and deleting data related to the road boundary data to be deleted from the historical road boundary data.

[0063] According to embodiments of this disclosure, changes to road boundaries (such as guardrails, curbs, etc.) may include methods such as road widening and reconstruction. These methods all involve adding or removing lanes at the road level. All lane addition or removal operations can be uniformly configured as additions or deletions of road boundaries when updating historical road boundary data.

[0064] For example, when adding guardrails, curbs, or other features to historical road boundary data, the collected road boundary data may include relevant data for these new guardrails, curbs, etc. At least one first vector point determined based on the collected road boundary data may include a first vector point representing the relevant data for the new guardrails, curbs, etc. During the matching of the first and second vector points, other first vector points may appear that do not match the historical road boundary data. In this case, these other first vector points are considered new points relative to the points in the historical road boundary data. Therefore, the road boundary data corresponding to these other first vector points can be identified as the road boundary data to be added. During the updating of historical road boundary data, the update can be achieved by adding this road boundary data to be added.

[0065] For example, if operations such as deleting guardrails and curbs have been performed on historical road boundary data, the collected road boundary data will not include data related to the deleted guardrails and curbs. When matching the first and second vector points, other second vector points may appear that do not match the collected road boundary data. In this case, these other second vector points are redundant relative to the points in the collected road boundary data. Therefore, the road boundary data corresponding to these other second vector points can be identified as lane line data to be deleted. During the updating of historical road boundary data, this data can be updated by deleting the road boundary data to be deleted.

[0066] According to embodiments of this disclosure, modification operations on historical road boundary data may include at least one of adding new road boundaries or deleting existing road boundaries. For the corresponding modification method, implementation schemes corresponding to the above-described addition and deletion operations can be adopted to update the historical road boundary data.

[0067] Through the above embodiments of this disclosure, the update operation for road boundaries can be simplified to operations such as adding and deleting historical road boundary data, which simplifies the scenario complexity and improves the update efficiency.

[0068] Figure 3 The diagram illustrates the overall flowchart of a high-precision map road boundary update method according to an embodiment of the present disclosure.

[0069] like Figure 3 As shown, the method includes operations S301 to S312.

[0070] In operation S301, the bounding box data is sampled to obtain at least one first vector point.

[0071] In operation S302, the road network data is sampled to obtain at least one second vector point.

[0072] In operation S303, a one-to-one matching is performed between the first vector point and the second vector point, and the distance d between the first vector point and the second vector point, the direction angle θ, and the normal distance d are calculated. ⊥ And define a first preset threshold value1, a second preset threshold value2, a third preset threshold value3 and a fourth preset threshold value4.

[0073] In operation S304, determine whether d is less than or equal to value3 and θ is less than or equal to value4 simultaneously. If yes, then execute operations S305 to S306; otherwise, execute operations S311 to S312.

[0074] In operation S305, the first vector point and the second vector point are determined as a matching pair.

[0075] In operation S306, determine d ⊥ Is it greater than or equal to value1? If yes, execute operations S307 to S308; otherwise, execute operation S310.

[0076] In operation S307, calculate the condition that satisfies d. ⊥ The continuous length L of the road boundary represented by the first vector point corresponding to the matching pair that is greater than or equal to value1.

[0077] In operation S308, determine whether L is greater than or equal to value2. If yes, execute operation S309; ​​otherwise, execute operation S310.

[0078] In operation S309, the road network data is updated based on the first vector point.

[0079] In operation S310, it is determined that both the first vector point and the second vector point are invalid change points.

[0080] In operation S311, it is determined that both the first vector point and the second vector point are valid change points.

[0081] In operation S312, add road boundary data related to the first vector point to the road network data, and delete road boundary data related to the second vector point from the road network data.

[0082] Through the embodiments described above, a set of matching pairs is determined based on preset conditions and normal distance. This allows for the further identification of valid change points from the first and second vector points that have already been matched, improving the accuracy and completeness of valid change point discovery. Combined with automated update operations, the precision and recall of automated annotation are improved, and manual annotation input is effectively reduced, enhancing the update efficiency of high-precision maps. Furthermore, unifying the update operations for road boundaries into an add / delete mode simplifies the complexity of the scene.

[0083] Figure 4 A block diagram of a high-precision map road boundary update apparatus according to an embodiment of the present disclosure is shown schematically.

[0084] like Figure 4 As shown, the high-precision map road boundary update device 400 includes a first determining module 410, a calculation module 420, a second determining module 430, and a first updating module 440.

[0085] The first determining module 410 is used to determine at least one matching pair that satisfies preset conditions from at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data. Each matching pair includes a first vector point and a second vector point.

[0086] The calculation module 420 is used to calculate the normal distance between the first vector point and the second vector point in each matching pair.

[0087] The second determining module 430 is used to determine the set of matching pairs based on the normal distance.

[0088] The first update module 440 is used to update the historical road boundary data based on the first vector point of each matching pair in the matching pair set.

[0089] According to embodiments of this disclosure, the second determining module includes a first determining unit and a second determining unit.

[0090] The first determining unit is used to determine the target matching pair corresponding to the normal distance when the normal distance is greater than or equal to a first preset threshold.

[0091] The second determining unit is used to determine the set of matching pairs based on the target matching pairs.

[0092] According to embodiments of this disclosure, the matching pair set includes multiple target matching pairs. The second determining unit includes a first determining subunit and a second determining subunit.

[0093] The first determining subunit is used to determine, from a plurality of first vector points corresponding to a plurality of target matching pairs, at least two first vector points whose continuous length for characterizing the road boundary is greater than or equal to a second preset threshold.

[0094] The second determining subunit is used to determine a set of matching pairs based on the target matching pairs corresponding to at least two first vector points.

[0095] According to embodiments of this disclosure, the first determining module includes a calculation unit and a third determining unit.

[0096] The calculation unit is used to calculate the distance and direction angle between the first vector point and each second vector point for each first vector point.

[0097] The third determining unit is used to determine matching pairs that meet preset conditions based on the first vector point and the second vector point whose distance is less than or equal to the third preset threshold and whose directional angle is less than or equal to the fourth preset threshold.

[0098] According to embodiments of this disclosure, the high-precision map road boundary update device further includes a third determining module, a fourth determining module, a fifth determining module, and a second updating module.

[0099] The third determining module is used to determine at least one target first vector point and at least one target second vector point included in at least one matching pair.

[0100] The fourth determining module is used to determine other first vector points besides the target first vector point from at least one first vector point.

[0101] The fifth determining module is used to determine other second vector points besides the target second vector point from at least one second vector point.

[0102] The second update module is used to update historical road boundary data based on other first vector points and other second vector points.

[0103] According to embodiments of this disclosure, the second update module includes a fourth determining unit, an adding unit, a fifth determining unit, and a deleting unit.

[0104] The fourth determining unit is used to determine the road boundary data to be added based on other first vector points.

[0105] Add a unit to add data related to the road boundary data to be added to the historical road boundary data.

[0106] The fifth determining unit is used to determine the road boundary data to be deleted based on other second vector points.

[0107] The deletion unit is used to delete data related to the road boundary data to be deleted from the historical road boundary data.

[0108] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0109] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0110] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause a computer to perform the method described above.

[0111] According to an embodiment of this disclosure, a computer program product includes a computer program that, when executed by a processor, implements the method described above.

[0112] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0113] like Figure 5 As shown, device 500 includes a computing unit 501, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 502 or a computer program loaded from storage unit 508 into random access memory (RAM) 503. RAM 503 may also store various programs and data required for the operation of device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. Input / output (I / O) interface 505 is also connected to bus 504.

[0114] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0115] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the high-precision map road boundary update method. For example, in some embodiments, the high-precision map road boundary update method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the high-precision map road boundary update method described above can be performed. Alternatively, in other embodiments, computing unit 501 may be configured to perform a high-precision map road boundary update method by any other suitable means (e.g., by means of firmware).

[0116] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0117] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0118] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, 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 machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0119] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0120] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0121] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, distributed system servers, or servers incorporating blockchain technology.

[0122] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0123] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for updating road boundaries in a high-precision map, comprising: From at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data, at least one matching pair that meets preset conditions is determined, and each matching pair includes one first vector point and one second vector point; For each matching pair, the normal distance between a first vector point and a second vector point in the matching pair is calculated. The normal distance between the first vector point and the second vector point includes at least one of the following: a first perpendicular distance from the point represented by the first vector point to the vector represented by the second vector point, and a second perpendicular distance from the point represented by the second vector point to the vector represented by the first vector point. Based on the normal distance, determine the set of matching pairs; as well as The historical road boundary data is updated based on the first vector point of each matching pair in the set of matching pairs. The step of determining the set of matching pairs based on the normal distance includes: If the normal distance is greater than or equal to a first preset threshold, a target matching pair corresponding to the normal distance is determined; and The set of matching pairs is determined based on the target matching pairs.

2. The method according to claim 1, wherein, The matching pair set includes multiple target matching pairs; Determining the set of matching pairs based on the target matching pairs includes: From a plurality of first vector points corresponding to a plurality of target matching pairs, at least two first vector points whose continuous length for characterizing the road boundary is greater than or equal to a second preset threshold are determined; and The matching pair set is determined based on the target matching pairs corresponding to each of the at least two first vector points.

3. The method according to claim 1, wherein, Determining at least one matching pair that satisfies preset conditions from at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data includes: For each of the first vector points, calculate the distance and the angle of orientation between the first vector point and each of the second vector points; and Based on the first vector point and the second vector point whose distance is less than or equal to a third preset threshold and whose directional angle is less than or equal to a fourth preset threshold, a matching pair that satisfies the preset conditions is determined.

4. The method according to any one of claims 1 to 3, further comprising: Determine at least one target first vector point and at least one target second vector point included in the at least one matching pair; From the at least one first vector point, determine other first vector points besides the target first vector point; From the at least one second vector point, determine other second vector points besides the target second vector point; and The historical road boundary data is updated based on the other first vector points and the other second vector points.

5. The method according to claim 4, wherein, The step of updating the historical road boundary data based on the other first vector points and the other second vector points includes: Based on the other first vector points, determine the road boundary data to be added; Add data related to the road boundary data to be added to the historical road boundary data; Based on the other second vector points, determine the road boundary data to be deleted; and Delete the data related to the road boundary data to be deleted from the historical road boundary data.

6. A high-precision map road boundary update device, comprising: The first determining module is used to determine at least one matching pair that meets preset conditions from at least one first vector point corresponding to the collected road boundary data and at least one second vector point corresponding to the historical road boundary data, wherein each matching pair includes a first vector point and a second vector point; The calculation module is configured to calculate, for each matching pair, the normal distance between a first vector point and a second vector point in the matching pair, wherein the normal distance between the first vector point and the second vector point includes at least one of a first perpendicular distance from the point represented by the first vector point to the vector represented by the second vector point, and a second perpendicular distance from the point represented by the second vector point to the vector represented by the first vector point. The second determining module is used to determine the set of matching pairs based on the normal distance; as well as The first update module is used to update the historical road boundary data based on the first vector point of each matching pair in the matching pair set; The second determining module includes: The first determining unit is configured to determine a target matching pair corresponding to the normal distance when the normal distance is greater than or equal to a first preset threshold; and The second determining unit is used to determine the set of matching pairs based on the target matching pairs.

7. The apparatus according to claim 6, wherein, The matching pair set includes multiple target matching pairs; The second determining unit includes: The first determining subunit is used to determine, from a plurality of first vector points corresponding to a plurality of target matching pairs, at least two first vector points whose continuous length for characterizing the road boundary is greater than or equal to a second preset threshold. as well as The second determining subunit is used to determine the set of matching pairs based on the target matching pairs corresponding to each of the at least two first vector points.

8. The apparatus according to claim 6, wherein, The first determining module includes: The calculation unit is configured to calculate, for each of the first vector points, the distance and the included angle of direction between the first vector point and each of the second vector points; and The third determining unit is used to determine a matching pair that satisfies the preset conditions based on the first vector point and the second vector point whose distance is less than or equal to a third preset threshold and whose directional angle is less than or equal to a fourth preset threshold.

9. The apparatus according to any one of claims 6 to 8, further comprising: The third determining module is used to determine at least one target first vector point and at least one target second vector point included in the at least one matching pair; The fourth determining module is used to determine other first vector points besides the target first vector point from the at least one first vector point; The fifth determining module is used to determine other second vector points besides the target second vector point from the at least one second vector point; as well as The second update module is used to update the historical road boundary data based on the other first vector points and the other second vector points.

10. The apparatus according to claim 9, wherein, The second update module includes: The fourth determining unit is used to determine the road boundary data to be added based on the other first vector points; An adding unit is used to add data related to the road boundary data to be added to the historical road boundary data; The fifth determining unit is used to determine the road boundary data to be deleted based on the other second vector points; and The deletion unit is used to delete data related to the road boundary data to be deleted from the historical road boundary data.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

13. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

Images