Lane line correction method and device, electronic equipment and storage medium

Abstract

The present disclosure provides a lane line correction method and device, electronic equipment and storage medium, relates to the technical field of computers, and particularly relates to the technical field of automatic driving, high-precision maps and the like. The implementation scheme is as follows: a reflectivity base map of a road surface is acquired, the reflectivity base map is generated by splicing a plurality of sets of point cloud data of the road surface; at least one first lane line in the reflectivity base map is identified; a second lane line in single set of collection data is identified, the single set of collection data includes at least one of point cloud data and an image obtained by scanning the road surface once; a reference lane line matched with the second lane line is determined from the at least one first lane line; and the second lane line is corrected based on the reference lane line.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the fields of autonomous driving and high-precision maps, specifically to a lane line correction method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] High-precision maps, also known as high-resolution maps, are used by autonomous vehicles. They possess accurate vehicle location information and rich road element data, helping vehicles anticipate complex road conditions such as slope, curvature, and heading, thus enabling them to better avoid potential risks. Lane lines, as one of the most critical elements in high-precision maps, play a crucial role in the localization and driving strategy planning of autonomous vehicles.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] This disclosure provides a lane line correction method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0005] According to one aspect of this disclosure, a lane line correction method is provided, comprising: acquiring a reflectance base map of a road surface, wherein the reflectance base map includes reflectance information of multiple sampling points on the road surface, the reflectance base map being generated by stitching together multiple sets of point cloud data of the road surface, each set of point cloud data being obtained by scanning the road surface once; identifying at least one first lane line in the reflectance base map; identifying a second lane line in a single set of acquired data, wherein the single set of acquired data includes at least one of point cloud data obtained by scanning the road surface once and an image; determining a reference lane line matching the second lane line from the at least one first lane line; and correcting the second lane line based on the reference lane line.

[0006] According to another aspect of this disclosure, a lane line correction device is provided, comprising: an acquisition module configured to acquire a reflectance base map of a road surface, wherein the reflectance base map includes reflectance information of multiple sampling points on the road surface, the reflectance base map being generated by stitching together multiple sets of point cloud data of the road surface, each set of point cloud data being obtained by scanning the road surface once; a first identification module configured to identify at least one first lane line in the reflectance base map; a second identification module configured to identify a second lane line in a single set of acquired data, wherein the single set of acquired data includes at least one of point cloud data obtained by scanning the road surface once and an image; a determination module configured to determine a reference lane line matching the second lane line from the at least one first lane line; and a correction module configured to correct the second lane line based on the reference lane line.

[0007] According to one aspect of this disclosure, an electronic device is provided, comprising: 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 lane line correction method described above.

[0008] According to one aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform the lane line correction method described above.

[0009] According to one aspect of this disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the lane line correction method described above.

[0010] According to one or more embodiments of this disclosure, the accuracy of lane line information can be improved.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0013] Figure 1A schematic diagram of an exemplary system in which the various methods described herein may be implemented according to embodiments of the present disclosure is shown;

[0014] Figure 2 A flowchart of a lane line correction method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 , Figure 4 Comparison images of point cloud lane lines before and after location information correction according to embodiments of the present disclosure are shown respectively;

[0016] Figure 5 , Figure 6 Comparison images of lane lines before and after location information correction according to embodiments of the present disclosure are shown respectively;

[0017] Figure 7 A comparison diagram of lane lines before and after attribute information correction according to an embodiment of the present disclosure is shown;

[0018] Figure 8 A structural block diagram of a lane line correction device according to an embodiment of the present disclosure is shown; and

[0019] Figure 9 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0020] 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 of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0021] In this disclosure, unless otherwise stated, the use of terms such as "first," "second," etc., to describe various elements is not intended to limit the positional, temporal, or importance relationships of these elements; such terms are merely used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of that element, while in other cases, based on the context, they may refer to different instances.

[0022] The terminology used in the description of the various examples described in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context explicitly indicates otherwise, an element may be one or more unless the number of elements is specifically limited. Furthermore, the term "and / or" as used in this disclosure covers any one of the listed items and all possible combinations thereof.

[0023] Lane line recognition technology is an important component of the field of autonomous driving technology. Functions such as Lane Departure Warning (LDW) and Lane Keeping Assist (LKA) in autonomous driving technology all rely on the high-precision lane line information that is identified.

[0024] In related technologies, lane lines are extracted from single-frame point clouds or single-frame images of the road surface. However, both LiDAR and cameras have very limited acquisition ranges, making it difficult to cover the entire road surface. Furthermore, due to camera lens distortion, there are discrepancies in the position of lane lines in single-frame point clouds and single-frame images, resulting in low accuracy of lane lines and failing to meet the accuracy requirements of autonomous driving technology.

[0025] To address the aforementioned issues, this disclosure provides a lane line correction method that can improve the accuracy of lane line information.

[0026] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0027] Figure 1 A schematic diagram of an exemplary system 100 in which the various methods and apparatus described herein can be implemented according to embodiments of this disclosure is shown. Reference Figure 1 The system 100 includes a motor vehicle 110, a server 120, and one or more communication networks 130 that couple the motor vehicle 110 to the server 120.

[0028] In embodiments of this disclosure, the motor vehicle 110 may include a computing device according to embodiments of this disclosure and / or be configured to perform a method according to embodiments of this disclosure.

[0029] Server 120 may run one or more services or software applications that enable the execution of lane correction methods. In some embodiments, server 120 may also provide other services or software applications, which may include non-virtual environments and virtual environments. Figure 1 In the configuration shown, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or combinations thereof that can be executed by one or more processors. A user of motor vehicle 110 may sequentially interact with server 120 using one or more client applications to utilize the services provided by these components. It should be understood that various different system configurations are possible and may differ from system 100. Therefore, Figure 1 This is an example of a system used to implement the various methods described herein, and is not intended to be limiting.

[0030] Server 120 may include one or more general-purpose computers, special-purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running a virtual operating system, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that can be virtualized to maintain virtual storage devices for servers). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0031] The computing unit in server 120 can run one or more operating systems, including any of the aforementioned operating systems and any commercially available server operating system. Server 120 can also run any of a variety of additional server applications and / or middleware applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0032] In some implementations, server 120 may include one or more applications to analyze and merge data feeds and / or event updates received from vehicle 110. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of vehicle 110.

[0033] Network 130 can be any type of network well known to those skilled in the art, and can support data communication using any of a variety of available protocols (including, but not limited to, TCP / IP, SNA, IPX, etc.). By way of example only, one or more networks 130 can be satellite communication networks, local area networks (LANs), Ethernet-based networks, token ring networks, wide area networks (WANs), the Internet, virtual networks, virtual private networks (VPNs), intranets, extranets, blockchain networks, public switched telephone networks (PSTNs), infrared networks, wireless networks (including, for example, Bluetooth, Wi-Fi), and / or any combination of these with other networks.

[0034] System 100 may also include one or more databases 150. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 150 may be used to store information such as audio files and video files. Databases 150 may reside in various locations. For example, a database used by server 120 may be local to server 120, or it may be located away from server 120 and may communicate with server 120 via a network-based or dedicated connection. Databases 150 may be of different types. In some embodiments, databases 150 used by server 120 may be relational databases. One or more of these databases may store, update, and retrieve data from and from the databases in response to commands.

[0035] In some embodiments, one or more of the databases 150 may also be used by an application to store application data. The databases used by the application may be of different types, such as key-value stores, object stores, or regular stores supported by a file system.

[0036] Motor vehicle 110 may include sensors 111 for sensing the surrounding environment. Sensors 111 may include one or more of the following sensors: a visual camera, an infrared camera, an ultrasonic sensor, a millimeter-wave radar, and a lidar (LiDAR). Different sensors can provide different detection accuracy and range. Cameras may be mounted in front of, behind, or at other locations on the vehicle. Visual cameras can capture the situation inside and outside the vehicle in real time and present it to the driver and / or passengers. In addition, by analyzing the images captured by the visual cameras, information such as traffic light indications, intersection conditions, and the operating status of other vehicles can be obtained. Infrared cameras can capture objects in night vision conditions. Ultrasonic sensors may be mounted around the vehicle to measure the distance of objects outside the vehicle using the strong directionality of ultrasound. Millimeter-wave radar may be mounted in front of, behind, or at other locations on the vehicle to measure the distance of objects outside the vehicle using the characteristics of electromagnetic waves. LiDAR may be mounted in front of, behind, or at other locations on the vehicle to detect the edges and shape information of objects, thereby performing object recognition and tracking. Due to the Doppler effect, the radar device can also measure the speed changes of the vehicle and moving objects.

[0037] The motor vehicle 110 may also include a communication device 112. The communication device 112 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., BeiDou, GPS, GLONASS, and GALILEO) from satellite 141 and generating coordinates based on these signals. The communication device 112 may also include a module for communicating with a mobile communication base station 142. The mobile communication network can implement any suitable communication technology, such as current or emerging wireless communication technologies (e.g., 5G technology) like GSM / GPRS, CDMA, and LTE. The communication device 112 may also have a vehicle-to-everything (V2X) module, configured to enable vehicle-to-the-world communication, for example, vehicle-to-vehicle (V2V) communication with other vehicles 143 and vehicle-to-infrastructure (V2I) communication with infrastructure 144. Furthermore, the communication device 112 may also have a module configured to communicate with a user terminal 145 (including but not limited to smartphones, tablets, or wearable devices such as watches) via, for example, a wireless local area network conforming to the IEEE 802.11 standard or Bluetooth. Using the communication device 112, the motor vehicle 110 can also access the server 120 via the network 130.

[0038] The motor vehicle 110 may also include a control unit 113. The control unit 113 may include a processor, such as a central processing unit (CPU) or a graphics processing unit (GPU), or other dedicated processors, that communicates with various types of computer-readable storage devices or media. The control unit 113 may include an autonomous driving system for automatically controlling various actuators in the vehicle. The autonomous driving system is configured to control the powertrain, steering system, and braking system of the motor vehicle 110 (not shown) via multiple actuators in response to inputs from multiple sensors 111 or other input devices to control acceleration, steering, and braking respectively, without human intervention or with limited human intervention. Some processing functions of the control unit 113 can be implemented via cloud computing. For example, some processing can be performed using an onboard processor while other processing can be performed using cloud computing resources. The control unit 113 may be configured to perform methods according to this disclosure. Furthermore, the control unit 113 may be implemented as an example of a computing device on the motor vehicle side (client) according to this disclosure.

[0039] Figure 1 The system 100 can be configured and operated in various ways to enable the application of the various methods and apparatus described in this disclosure.

[0040] According to some embodiments, the lane line correction method of this disclosure can be executed by server 120, or by other servers ( Figure 1 (Not shown in the image) or performed by a motor vehicle 110. The road surface point cloud data and / or road surface image data involved in the lane line correction method of this disclosure can be acquired by the sensor 111 of the motor vehicle 110, or by other means, which are not limited here.

[0041] According to embodiments of this disclosure, a lane line correction method is provided. Figure 2 A flowchart of a lane line correction method 200 according to an embodiment of the present disclosure is shown. The execution entity of each step of method 200 is typically a server (e.g., Figure 1 The server 120 shown Figure 1 Other servers not shown in the diagram can also be clients (e.g., vehicle 110).

[0042] like Figure 2 As shown, method 200 includes steps S210-S250.

[0043] In step S210, a reflectance base map of the road surface is obtained. The reflectance base map includes reflectance information of multiple sampling points on the road surface. The reflectance base map is generated by stitching together multiple sets of point cloud data of the road surface. Each set of point cloud data is obtained by scanning the road surface once.

[0044] In step S220, at least one first lane line in the reflectivity base map is identified.

[0045] In step S230, the second lane line in a single set of acquired data is identified, wherein the single set of acquired data includes at least one of point cloud data and an image obtained by scanning the road surface once.

[0046] In step S240, a reference lane line that matches the second lane line is determined from at least one first lane line.

[0047] In step S250, the second lane line is corrected based on the baseline lane line.

[0048] According to embodiments of this disclosure, lane line information in a reflectance map obtained by stitching together multiple sets of point cloud data is used to correct lane line information in a single set of point cloud data or an image. Since the point cloud data collected by LiDAR is relatively accurate, the reflectance map obtained by fusing multiple sets of point cloud data includes accurate lane line information for the entire road surface, and the first lane line identified from it is also relatively accurate. Correcting the second lane line based on the first lane line can improve the accuracy of the second lane line.

[0049] The following details each step of method 200.

[0050] In step S210, a reflectance base map of the road surface is obtained. The reflectance base map includes reflectance information of multiple sampling points on the road surface. The reflectance base map is generated by stitching together multiple sets of point cloud data of the road surface. Each set of point cloud data is obtained by scanning the road surface once.

[0051] According to some embodiments, by means of a vehicle (e.g., Figure 1 The vehicle (110) is equipped with data acquisition devices (e.g., lidar) to collect point cloud data of the surrounding environment while in motion. Point cloud data typically includes the three-dimensional spatial coordinates (x, y, z), reflectivity information, timestamps, and other data of each sampling point in the surrounding environment. A single scan of the road surface by the acquisition device yields a set of point cloud data; multiple scans at a preset frequency yield multiple sets of point cloud data.

[0052] Because the acquisition range of the data acquisition equipment is very limited, each scan can only collect point cloud data of a portion of the road surface, and cannot collect the complete point cloud data of the road surface at once. Therefore, it is necessary to transform the point cloud data collected multiple times to a unified coordinate system and stitch them together to form the complete point cloud data of the road surface. In this disclosure, any suitable method can be selected to achieve point cloud stitching. By stitching together multiple sets of point cloud data of the road surface, and using the reflectivity information in the point cloud data, a reflectivity base map including lane line information of the complete road surface can be generated. The reflectivity base map includes reflectivity information of multiple sampling points on the road surface, that is, the reflection intensity of each sampling point.

[0053] In step S220, at least one first lane line in the reflectivity base map is identified.

[0054] According to some embodiments, lane line pixels are extracted from a reflectance base map, and the lane line pixels are fitted to obtain at least one first lane line. According to some embodiments, lane line pixels can be extracted from the reflectance base map using methods such as semantic segmentation. Generating at least one lane line (i.e., the first lane line) from the reflectance base map by fitting the lane line pixels can improve the positional accuracy of the lane lines in the reflectance base map. The first lane line can be a solid line or a dashed line, and can be a white lane line or a yellow lane line; no limitation is imposed here.

[0055] Semantic segmentation is a computer vision problem that extracts pixel information from an image and classifies each pixel. Therefore, semantic segmentation can be used to distinguish lane line pixels from the background, extracting lane line pixels from the road surface reflectance map.

[0056] By fitting the lane line pixels extracted from the reflectance base map, a first lane line can be obtained. Based on the three-dimensional spatial coordinates of the sampling points corresponding to the first lane line, the first position information of the first lane line can be determined. Typically, the least squares method, i.e., the mean square error method, can be used to fit the lane line pixels. It should be noted that in this disclosure, any suitable lane line fitting method can be chosen to fit the lane line pixels to obtain the first lane line, and is not limited to the least squares method.

[0057] By identifying the first lane line in the reflectance base map, the first attribute information of the first lane line can also be determined. The first attribute information includes, for example, first color information (such as white and yellow mentioned above) and first line type information (such as solid line and dashed line mentioned above).

[0058] In step S230, the second lane line in a single set of acquired data is identified, wherein the single set of acquired data includes at least one of point cloud data and an image obtained by scanning the road surface once.

[0059] According to some embodiments, a single set of acquired data may include point cloud data obtained from a single scan of the road surface by a LiDAR, or image data obtained from a single scan of the road surface by a camera, or a single set of acquired data generated by fusing point cloud data and image data. Point cloud data includes the three-dimensional spatial coordinates (x, y, z), reflectivity information, timestamps, and other data for each sampling point. The image includes lane lines on the road surface.

[0060] According to some embodiments, the second lane line can be identified from a single set of collected data using semantic segmentation, or it can be identified from a single set of collected data using feature extraction methods.

[0061] By identifying the second lane line in a single set of collected data, the second lane line's second position information and second attribute information can be determined. The second attribute information includes, for example, second color information (e.g., white, yellow) and second line type information (e.g., solid line, dashed line). The second lane line can also have confidence information. Confidence information is used to indicate the accuracy of the second position information and the second attribute information.

[0062] Since each acquisition can only collect point cloud data and / or image data of a portion of the road surface, the second lane line may be a portion of a lane line.

[0063] In step S240, a reference lane line that matches the second lane line is determined from at least one first lane line.

[0064] According to some embodiments, each of the at least one first lane line has first position information, and the second lane line has second position information and confidence information. Accordingly, based on the corresponding first position information, second position information, and confidence information, the matching distance between any of the at least one first lane lines and the second lane line can be determined; the first lane line with the smallest matching distance to the second lane line among the at least one first lane lines is determined as the reference lane line.

[0065] According to some embodiments, the accuracy of the position and attribute information of a lane line can be determined based on the confidence information of the second lane line. When a single set of acquired data includes only point cloud data, the second lane line is determined based on the point cloud data (which can be denoted as the point cloud lane line), and its confidence level can be determined by the reflectance of the corresponding sampling points. For example, the higher the reflectance of the sampling points corresponding to the second lane line, the greater the confidence level of the second lane line. When a single set of acquired data includes only images, the second lane line is determined based on image data (which can be denoted as the image lane line), and its confidence level can be determined based on the distance of the lane line from the optical axis of the acquisition device (e.g., a camera). For example, the greater the angular distance between the second lane line and the camera's optical axis (i.e., the second lane line is located at the edge of the camera's field of view), the more likely distortion is to occur, and the lower the confidence level of the second lane line. When a single set of acquired data includes both point cloud data and images, the confidence level of the second lane line can be determined by combining the reflectance of the sampling points corresponding to the point cloud lane line and the angular distance between the image lane line and the camera's optical axis. The higher the confidence level of the second lane line, the higher the accuracy of its position and attribute information.

[0066] The first and second position information can be coordinates within the same coordinate system. Using the first position information of the first lane line and the second position information of the second lane line, the matching distance between the two lane lines can be calculated. The smaller the matching distance, the higher the matching degree between the two lane lines. Using the first lane line with the smallest matching distance (i.e., the highest matching degree) as the reference lane line to correct the position information of the second lane line can improve the accuracy of the lane line position information.

[0067] According to some embodiments, in response to the confidence information of the second lane line being greater than a threshold, the lateral distance and longitudinal distance between the first lane line and the second lane line are calculated based on the first location information and the second location information; and the matching distance is determined based on the lateral distance and the longitudinal distance.

[0068] When the confidence level of the second lane line is greater than a threshold, the accuracy of its position and attribute information is relatively high. Accordingly, when calculating the matching distance, only the difference in position information between the first and second lane lines needs to be considered; that is, the lateral and longitudinal distances between the two lane lines can be calculated using the first and second position information. This improves computational efficiency. The matching distance D can be calculated using the following formula:

[0069]

[0070] Among them, Dist lateral Dist is the lateral distance between the first lane line and the second lane line. longitudinal This represents the longitudinal distance between the first lane line and the second lane line. A smaller matching distance indicates a higher degree of matching between the second lane line and the first lane line. It is understandable that the matching distance can also be determined by a weighted sum of the lateral and longitudinal distances.

[0071] According to some embodiments, each of the at least one first lane line further has first attribute information, and the second lane line further has second attribute information. In response to the confidence information of the second lane line being less than or equal to a threshold, the lateral distance and longitudinal distance between the first lane line and the second lane line are calculated based on the first location information and the second location information; the attribute difference degree between the first lane line and the second lane line is determined based on the corresponding first attribute information and second attribute information; and the matching distance is determined based on the lateral distance, the longitudinal distance, and the attribute difference degree.

[0072] When the confidence level of the second lane line is less than or equal to the threshold, the accuracy of its position and attribute information is low. Accordingly, when calculating the matching distance, not only the difference in position information between the first and second lane lines must be considered, but also the difference in attribute information. That is, the lateral and longitudinal distances between the two lane lines are calculated using the first and second position information, and the attribute difference between the two lane lines is calculated using the first and second attribute information. Therefore, for the second lane line with a confidence level less than or equal to the threshold, the matching distance can be determined based on the lateral and longitudinal distances and attribute difference between the first and second lane lines. This can improve the accuracy of the baseline lane line determined based on the matching distance. The matching distance D can be calculated using the following formula:

[0073] D = Dist lateral +Dist longitudinal +Dist property (2)

[0074] Among them, Dist lateralDist is the lateral distance between the first lane line and the second lane line. longitudinal Dist is the longitudinal distance between the first lane line and the second lane line. property This represents the attribute difference between the first lane line and the second lane line. A smaller matching distance indicates a higher degree of matching between the second lane line and the first lane line. It is understandable that this matching distance can also be determined by a weighted sum of lateral distance, longitudinal distance, and attribute difference.

[0075] According to some embodiments, the first attribute information includes first color information and first line type information, and the second attribute information includes second color information and second line type information. The system determines the color difference between the first color information and the second color information; determines the line type difference between the first line type information and the second line type information; and determines the attribute difference by weighting the color difference and the line type difference. This enables the attribute difference to comprehensively and accurately express the differences in attributes between the first lane line and the second lane line.

[0076] Lane line attribute information can include color information and line type information. Color information refers to the color of the lane line (e.g., white, yellow, etc.), and line type information refers to the line type of the lane line (e.g., solid line, dashed line, etc.). The attribute difference is determined based on the color and line type differences between the first and second lane lines. The attribute difference can be calculated using the following formula:

[0077] Dist property =C1·LineType(L1,L2)+C2·Colortype(L1,L2) (3)

[0078] In this model, C1 and C2 represent the weights of line type difference and color difference, respectively, while L1 and L2 represent the first lane line and the second lane line, respectively. `LineType(L1, L2)` indicates whether the first and second line type information are the same, i.e., whether the line types of the first and second lane lines are identical. `LineType(L1, L2)` can be set to 0 if the first and second line type information are the same, and set to 1 if they are different. `ColorType(L1, L2)` indicates whether the first and second color type information are the same, i.e., whether the colors of the first and second lane lines are the same. `ColorType(L1, L2)` can be set to 0 if the first and second color type information are the same, and set to 1 if they are different. It can be understood that the closer the color and line type of the first and second lane lines are, the smaller the attribute difference.

[0079] It should be noted that the weights of line type difference and color difference in the above formula (e.g., C1 and C2) can be set and changed according to the actual situation.

[0080] According to some embodiments, the first lane line with the highest matching degree (i.e., the smallest matching distance) with the second lane line is used as the reference lane line.

[0081] In step S250, the second lane line is corrected based on the baseline lane line.

[0082] According to some embodiments, the second position information of the second lane line is corrected based on the position information of the reference lane line. This improves the accuracy of the second position information of the second lane line.

[0083] When correcting lane markings, the second lane marking is first corrected based on the position information of the baseline lane marking. For example, the position of the second lane marking is corrected to the position of the baseline lane marking.

[0084] According to some embodiments, there are multiple second lane lines. These multiple second lane lines are spliced ​​together according to the corrected second position information to generate a third lane line. Based on the third attribute information of the third lane line, the second attribute information of each of the multiple second lane lines is corrected. This improves the accuracy of the second attribute information of the second lane lines.

[0085] After correcting the second position information of multiple second lane lines, the second position information is used to stitch the multiple second lane lines together to obtain the stitched third lane line. The second position information can include the spatial location information of the second lane lines and the timestamp information of the data collection. Therefore, multiple second lane lines can be sorted according to their spatial location or according to the order of their data collection timestamps to generate the third lane line. The third attribute information of the third lane line can be determined based on the attributes of the vast majority of the second lane lines included in the third lane line. For example, if the color of the vast majority of the second lane lines in the third lane line is white, then the color of the third lane line can be determined to be white. Based on the third attribute information of the third lane line, the second attribute information of the second lane lines in the third lane line can be corrected. For example, if the color of the third lane line is white, and a yellow second lane line appears among the multiple second lane lines included in the third lane line, then the color attribute of that second lane line is corrected to white. Stitching multiple second lane lines together to correct the second attribute information can improve the accuracy of the second lane line attributes.

[0086] Figure 3 , Figure 4 The images show a comparison of the second lane lines before and after correction of the second position information according to embodiments of the present disclosure (the left image shows the second lane lines before correction, and the right image shows the second lane lines after correction). Figure 3 , Figure 4 The second lane line in the image is a point cloud lane line generated based on a single acquisition of road surface point cloud data. For example... Figure 3 As shown, the outline of the second lane line 310A before correction was unclear, and its positional error was relatively large. According to an embodiment of this disclosure, the second lane line 310A was corrected to obtain the second lane line 310B. The corrected second lane line 310B has a clear outline and a more accurate position. Figure 4 As shown, the outline of the second lane line 410A before correction was unclear, and its positional error was relatively large. According to an embodiment of this disclosure, the second lane line 410A was corrected to obtain the second lane line 410B. The corrected second lane line 410B has a clear outline and a more accurate position.

[0087] Figure 5 , Figure 6 The images show a comparison of the effects of second lane lines before and after correction of the second position information according to embodiments of the present disclosure. Figure 5 The above image and Figure 6 The left image shows the second lane line before the correction. Figure 5 The image below and Figure 6 The image on the right shows the corrected second lane markings. Figure 5 , Figure 6 The second lane line in the image is an image lane line generated based on a single acquired image. For example... Figure 5 As shown, the outline of the second lane line 510A before correction was unclear, and there was a problem of missing lane lines. According to an embodiment of this disclosure, the second lane line 510A was corrected to obtain the second lane line 510B. The corrected second lane line 510B has a clear outline, and the problem of missing lane lines is effectively improved. The outline of the second lane line 520A before correction was unclear, and there was a significant ghosting problem. According to an embodiment of this disclosure, the second lane line 520A was corrected to obtain the second lane line 520B. The corrected second lane line 520B has a clear outline, and the ghosting problem is effectively improved. Figure 6 As shown, the outline of the second lane line 610A before correction was unclear, and its position was significantly off (ghosting occurred in the lower left). According to an embodiment of this disclosure, the second lane line 610A was corrected to obtain the second lane line 610B. The corrected second lane line 610B has a clear outline and a more accurate position.

[0088] pass Figures 3-6 It can be seen that after position information correction, the problems of unclear lane line outlines due to errors in lane line position or distortion are effectively improved. The lane line correction method of this disclosure can improve the accuracy of lane line position.

[0089] Figure 7A comparison diagram is shown before and after the correction of the second attribute information of the second lane line according to an embodiment of the present disclosure. For example... Figure 7 As shown, the second lane line 710A before correction was identified as a dashed line (represented by the letter b). According to an embodiment of this disclosure, the attribute information of the second lane line 710A is corrected to obtain the second lane line 710B. The corrected second lane line 710B is displayed as a solid line (represented by the letter s). After attribute information correction, the previously incorrectly identified attribute information is optimized into correct attribute information. The lane line correction method of this disclosure can optimize the attribute information of lane lines and improve the accuracy of lane line attributes.

[0090] According to embodiments of this disclosure, a lane line correction device is provided. Figure 8 A structural block diagram of a lane line correction device 800 according to an embodiment of the present disclosure is shown. Figure 8 As shown, the device 800 includes an acquisition module 810, a first identification module 820, a second identification module 830, a determination module 840, and a correction module 850.

[0091] The acquisition module 810 is configured to acquire a reflectance base map of the road surface. The reflectance base map includes reflectance information of multiple sampling points on the road surface. The reflectance base map is generated by stitching together multiple sets of point cloud data of the road surface. Each set of point cloud data is obtained by scanning the road surface once.

[0092] The first recognition module 820 is configured to recognize at least one first lane line in the reflectivity base map.

[0093] The second recognition module 830 is configured to recognize a second lane line in a single set of acquired data, wherein the single set of acquired data includes at least one of point cloud data and an image obtained by scanning the road surface once.

[0094] The determination module 840 is configured to determine a reference lane line that matches the second lane line from at least one first lane line.

[0095] The correction module 850 is configured to correct the second lane line based on the baseline lane line.

[0096] According to some embodiments, the first identification module 820 includes: an extraction unit configured to extract lane line pixels from a reflectance base map; and a fitting unit configured to fit the lane line pixels to obtain at least one first lane line.

[0097] According to some embodiments, each of the at least one first lane line has first position information, and the second lane line has second position information and confidence information. The determining module includes: a first determining unit configured to determine a matching distance between any of the at least one first lane lines and the second lane line based on the corresponding first position information, second position information, and confidence information; and a second determining unit configured to determine the first lane line with the smallest matching distance to the second lane line among the at least one first lane lines as the reference lane line.

[0098] According to some embodiments, the first determining unit includes: a first calculation subunit configured to, in response to confidence information being greater than a threshold, calculate the lateral distance and longitudinal distance between the first lane line and the second lane line based on the first location information and the second location information; and the first determining subunit configured to determine the matching distance based on the lateral distance and the longitudinal distance.

[0099] According to some embodiments, each of the at least one first lane line further has first attribute information, and the second lane line further has second attribute information. The first determining unit further includes: a second calculation subunit configured to calculate the lateral distance and longitudinal distance between the first lane line and the second lane line based on the first position information and the second position information in response to the confidence information being less than or equal to a threshold; the second determining subunit configured to determine the attribute difference degree between the first lane line and the second lane line based on the corresponding first attribute information and second attribute information; and a third determining subunit configured to determine the matching distance based on the lateral distance, the longitudinal distance, and the attribute difference degree.

[0100] According to some embodiments, the first attribute information includes first color information and first line type information, the second attribute information includes second color information and second line type information, and the second determining subunit includes: a first difference determination subunit configured to determine the color difference between the first color information and the second color information; a second difference determination subunit configured to determine the line type difference between the first line type information and the second line type information; and a third difference determination subunit configured to determine the attribute difference by weighting the color difference and the line type difference.

[0101] According to some embodiments, the correction module 850 includes: a position correction unit configured to correct a second position information of a second lane line based on a first position information of a reference lane line.

[0102] According to some embodiments, there are multiple second lane lines, and the correction module 850 further includes: a generation unit configured to splice the multiple second lane lines according to the corrected second position information to generate a third lane line; and an attribute correction unit configured to correct the second attribute information of each of the multiple second lane lines based on the third attribute information of the third lane line.

[0103] It should be understood that Figure 8 The various modules or units of the device 800 shown can be connected with Figure 2 The steps in method 200 described correspond to each other. Therefore, the operations, features, and advantages described in method 200 are also applicable to device 800 and its constituent modules and units. For the sake of brevity, some operations, features, and advantages will not be repeated here.

[0104] Although specific functions have been discussed with reference to specific modules above, it should be noted that the functions of the various modules discussed in this article can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module.

[0105] It should also be understood that the various techniques described herein can be implemented in software, hardware, components, or program modules. The above... Figure 8 The various modules described herein may be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules may be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules may be implemented as hardware logic / circuit. For example, in some embodiments, one or more modules of modules 810-850 may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes a processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and / or one or more components of other circuitry) and may optionally execute received program code and / or include embedded firmware to perform functions.

[0106] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0107] According to embodiments of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing 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 lane correction method of the present disclosure embodiments.

[0108] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the lane correction method of the present disclosure embodiments is also provided.

[0109] According to embodiments of the present disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the lane line correction method of the embodiments of the present disclosure.

[0110] refer to Figure 9 The present invention describes a structural block diagram of an electronic device 900 that can serve as a server or client of the present disclosure, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, 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.

[0111] like Figure 9 As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded into a random access memory (RAM) 903 from a storage unit 908. The RAM 903 may also store various programs and data required for the operation of the electronic device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0112] Multiple components in electronic device 900 are connected to I / O interface 905, including: input unit 906, output unit 907, storage unit 908, and communication unit 909. Input unit 906 can be any type of device capable of inputting information to electronic device 900. Input unit 906 can receive input digital or character information and generate key signal input related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touch screen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 907 can be any type of device capable of presenting information, and can include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 908 can include, but is not limited to, disk and optical disk. Communication unit 909 allows electronic device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth. TM Equipment, 802.11 equipment, Wi-Fi equipment, WiMAX equipment, cellular communication equipment and / or the like.

[0113] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 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 901 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations 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., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), the Internet, and blockchain networks.

[0119] 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, servers in distributed systems, or servers incorporating blockchain technology.

[0120] 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 performed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0121] While embodiments or examples of this disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of this disclosure is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many elements described herein can be replaced by equivalents that appear after this disclosure.

Claims

1. A lane line correction method, comprising: A reflectance base map of the road surface is obtained, wherein the reflectance base map includes reflectance information of multiple sampling points on the road surface. The reflectance base map is generated by stitching together multiple sets of point cloud data of the road surface. Each set of point cloud data is obtained by scanning the road surface once by a lidar deployed on the vehicle during the vehicle's driving process. Identify at least one first lane line in the reflectivity base map, wherein each of the at least one first lane line has first location information and first attribute information; Identify a second lane line in a single set of acquired data, wherein the single set of acquired data includes point cloud data obtained by scanning the road surface once by the lidar during the vehicle's travel, and the second lane line has second location information, second attribute information and confidence information, wherein the confidence information indicates the accuracy of the second location information and the second attribute information; Based on the corresponding first location information, second location information, and confidence information, determining the matching distance between any of the at least one first lane lines and the second lane line includes: In response to the confidence information being less than or equal to a threshold, the lateral distance and longitudinal distance between the first lane line and the second lane line are calculated based on the first location information and the second location information; Based on the corresponding first attribute information and second attribute information, determine the attribute difference degree between the first lane line and the second lane line; and The matching distance is determined based on the horizontal distance, the vertical distance, and the attribute difference. The lane line with the smallest matching distance to the second lane line among the at least one first lane line is determined as the reference lane line matching the second lane line; and The second lane line is corrected based on the baseline lane line.

2. The method according to claim 1, wherein, The identification of at least one first lane line in the reflectivity base map includes: Extract lane line pixels from the reflectance base map; and The lane line pixels are fitted to obtain the at least one first lane line.

3. The method according to claim 1, wherein, Determining the matching distance between any of the at least one first lane line and the second lane line based on the corresponding first location information, second location information, and confidence information includes: In response to the confidence information being greater than a threshold, based on the first location information and the second location information, the lateral distance and longitudinal distance between the first lane line and the second lane line are calculated; and The matching distance is determined based on the horizontal distance and the vertical distance.

4. The method according to claim 1, wherein, The first attribute information includes first color information and first line type information, and the second attribute information includes second color information and second line type information. Determining the attribute difference between the first lane line and the second lane line based on the corresponding first attribute information and second attribute information includes: Determine the color difference between the first color information and the second color information; Determine the linetype difference between the first linetype information and the second linetype information; and The attribute difference is determined by the weighted sum of the color difference and the line type difference.

5. The method according to claim 1, wherein, The step of correcting the second lane line based on the baseline lane line includes: Based on the first position information of the reference lane line, the second position information of the second lane line is corrected.

6. The method according to claim 5, wherein, There are multiple second lane lines, and the correction of the second lane lines based on the reference lane lines further includes: Multiple second lane lines are spliced ​​together according to the corrected second position information to generate a third lane line; and Based on the third attribute information of the third lane line, the second attribute information of each of the multiple second lane lines is corrected.

7. A lane line correction device, comprising: The acquisition module is configured to acquire a reflectance base map of the road surface, wherein the reflectance base map includes reflectance information of multiple sampling points on the road surface. The reflectance base map is generated by stitching together multiple sets of point cloud data of the road surface. Each set of point cloud data is obtained by scanning the road surface once by a lidar deployed on the vehicle during the vehicle's driving process. The first identification module is configured to identify at least one first lane line in the reflectivity base map, each of the at least one first lane line having first location information and first attribute information; The second recognition module is configured to recognize the second lane line in a single set of acquired data, wherein the single set of acquired data includes point cloud data obtained by scanning the road surface once by the lidar during the vehicle's driving process, and the second lane line has second location information, second attribute information and confidence information, wherein the confidence information indicates the accuracy of the second location information and the second attribute information; A first determining unit is configured to determine, based on corresponding first location information, second location information, and the confidence information, the matching distance between any one of the at least one first lane line and the second lane line, wherein the first determining unit includes: The second calculation subunit is configured to calculate the lateral distance and longitudinal distance between the first lane line and the second lane line based on the first location information and the second location information in response to the confidence information being less than or equal to a threshold. The second determining subunit is configured to determine the attribute difference degree between the first lane line and the second lane line based on corresponding first attribute information and second attribute information; and The third determining subunit is configured to determine the matching distance based on the horizontal distance, the vertical distance, and the attribute difference degree; The second determining unit is configured to determine the first lane line among the at least one first lane line that has the smallest matching distance to the second lane line as the reference lane line that matches the second lane line; and The correction module is configured to correct the second lane line based on the baseline lane line.

8. The apparatus according to claim 7, wherein, The first identification module includes: The extraction unit is configured to extract lane line pixels from the reflectance base map; and The fitting unit is configured to fit the lane line pixels to obtain the at least one first lane line.

9. The apparatus according to claim 7, wherein, The first determining unit includes: A first calculation subunit is configured to, in response to the confidence information being greater than a threshold, calculate the lateral distance and longitudinal distance between the first lane line and the second lane line based on the first location information and the second location information; and The first determining subunit is configured to determine the matching distance based on the lateral distance and the longitudinal distance.

10. The apparatus according to claim 7, wherein, The first attribute information includes first color information and first line type information; the second attribute information includes second color information and second line type information; the second determining subunit includes: The first difference determination subunit is configured to determine the color difference between the first color information and the second color information; The second difference determination subunit is configured to determine the line type difference between the first line type information and the second line type information; and The third difference determination subunit is configured to determine the attribute difference by weighting the color difference and the line type difference.

11. The apparatus according to claim 7, wherein, The correction module includes: The position correction unit is configured to correct the second position information of the second lane line based on the first position information of the reference lane line.

12. The apparatus according to claim 11, wherein, The second lane has multiple lane lines, and the correction module further includes: The generation unit is configured to stitch together multiple second lane lines according to the corrected second position information to generate a third lane line; and The attribute correction unit is configured to correct the second attribute information of each of the plurality of second lane lines based on the third attribute information of the third lane line.

13. An electronic device, comprising: At least one processor; as well as A memory that is communicatively connected to the at least one processor; in 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 1-6.

14. 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 1-6.

15. A computer program product comprising a computer program, wherein, The computer program, when executed by a processor, implements the method of any one of claims 1-6.

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