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

Abstract

The invention provides a lane line marking method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the field of data processing, image processing and automatic driving. According to the specific implementation scheme, point cloud data collected on a road are displayed; in response to a selection operation for a target point on the point cloud data, determining a first lane line based on the target point; and adjusting the first lane line based on the pavement information to obtain a second lane line. According to the embodiment of the invention, the efficiency and accuracy of lane line marking can be improved.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to the fields of data processing, image processing and autonomous driving. Background Art

[0002] As point cloud technology matures, point cloud data is increasingly used in the field of autonomous driving. In order to obtain lane line information on the road, it is necessary to accurately mark the lane lines on the collected point cloud data. However, the current lane line marking results have poor consistency and quality, and the marking efficiency is low. Summary of the invention

[0003] The present disclosure provides a lane marking method, device, electronic device and storage medium.

[0004] According to one aspect of the present disclosure, a lane marking method is provided, comprising:

[0005] Display point cloud data collected on the road;

[0006] In response to a selection operation on a target point on the point cloud data, determining a first lane line based on the target point;

[0007] The first lane line is adjusted based on the road surface information to obtain a second lane line.

[0008] According to another aspect of the present disclosure, a lane marking device is provided, comprising:

[0009] A display module, used for displaying point cloud data collected on the road;

[0010] A lane line determination module, configured to determine a first lane line based on the target point in response to a selection operation on the point cloud data;

[0011] The lane line adjustment module is used to adjust the first lane line based on the road surface information to obtain the second lane line.

[0012] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores instructions that can be executed 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 execute any method in the embodiments of the present disclosure.

[0016] 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 enable the computer to execute any method according to the embodiments of the present disclosure.

[0017] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which implements any method according to the embodiments of the present disclosure when executed by a processor.

[0018] According to the technical solution of the disclosed embodiment, the user can select the target point in the displayed point cloud data to quickly mark the lane line, while ensuring that the lane line can fit the road surface, thereby improving the efficiency and accuracy of lane line marking.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0021] Figure 1 is a flowchart of a lane marking method provided by an embodiment of the present disclosure;

[0022] Figure 2 is a schematic diagram of an application example of an embodiment of the present disclosure;

[0023] Figure 3 is a schematic block diagram of a lane marking device provided by an embodiment of the present disclosure;

[0024] Figure 4 is a schematic block diagram of a lane marking device provided by another embodiment of the present disclosure;

[0025] Figure 5 is a schematic block diagram of a lane marking device provided by another embodiment of the present disclosure;

[0026] Figure 6 It is a block diagram of an electronic device used to implement the lane marking method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0028] Figure 1 A lane marking method provided by an embodiment of the present disclosure is shown. The method can be applied to a lane marking device, which can be deployed in an electronic device. The electronic device is, for example, a single or multi-machine terminal, server or other processing device. Among them, the terminal can be a user equipment (User Equipment, UE) such as a mobile device, a personal digital assistant (Personal Digital Assistant, PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc.; the server can be a stand-alone server or a server cluster. In some possible implementations, the method can also be implemented by a processor calling computer-readable instructions stored in a memory. For example Figure 1 As shown, the method may include the following steps S110 to S130.

[0029] Step S110: Displaying the point cloud data collected on the road.

[0030] Optionally, the point cloud data may include 4D (4-Dimensions) point clouds, which can be understood as being based on a large number of point sets in a three-dimensional coordinate system with the addition of time dimension information. In other words, the point cloud data includes multiple frames of 3D (3-Dimensions) point clouds in continuous time. An exemplary scenario is that a vehicle can obtain continuous multiple frames of 3D point clouds (i.e., point cloud data) through an on-board radar while driving on a road.

[0031] Exemplarily, the point cloud data collected on the road may include point clouds corresponding to lane lines, point clouds corresponding to the road surface, and point clouds corresponding to obstacles.

[0032] Optionally, the display of point cloud data can be to display each frame of 3D point cloud in the 4D point cloud separately. For example, for each frame of 3D point cloud, the 3D point cloud can be projected onto a specific plane, and the plane projection image can be displayed on the interactive interface; or, each frame of 3D point cloud can be displayed on the 3D display space on the interactive interface.

[0033] Optionally, an algorithm may be used to identify the category of each point in the point cloud data, and different display styles may be used to display points of different categories.

[0034] Step S120: In response to a selection operation on a target point on the point cloud data, determining a first lane line based on the target point.

[0035] In the disclosed embodiment, the user can find the position of the lane line by observing the point cloud data, regard the points on the lane line as target points, and complete the selection operation for the target points in the interactive interface. The lane line marking device can read the target points selected by the user and determine the first lane line based on the target points.

[0036] In the disclosed embodiment, there may be multiple target points, and the first lane line may be determined based on the multiple target points. For example, the minimum circumscribed figure containing the multiple target points may be determined as the first lane line, or the lane line may be determined by fitting.

[0037] In some examples of actual applications, multiple functions can be provided on the interactive interface. The user can select the line drawing function on the interactive interface, enter the sub-interface corresponding to the function, and then mark points on the point cloud data corresponding to the lane line. The lane line marking device can automatically generate the first lane line based on the user's markings.

[0038] Step S130: Adjust the first lane line based on the road surface information to obtain the second lane line.

[0039] In the disclosed embodiment, the road surface information may include position information of a road surface point cloud in the point cloud data. Optionally, the height of the first lane line may be adjusted according to the road surface information to ensure that the second lane line fits the road surface.

[0040] Optionally, the point cloud data may be preprocessed based on an algorithm (e.g., semantic segmentation) to obtain a road area in the point cloud data, and a road segmentation mark may be added to the road area. In the case where the point cloud data contains a road segmentation mark preprocessed by the algorithm, the point cloud with the mark is used as the point cloud corresponding to the road surface, and the first lane line is attached to the point cloud corresponding to the road surface.

[0041] In some examples of actual applications, the automatic ground suction switch for drawing lines can be set on the interactive interface, and the user can choose to turn it on, or the automatic ground suction switch for drawing lines can be turned on by default. When the automatic ground suction switch for drawing lines is turned on, when the line drawing function is used to draw a target point on the line, the algorithm function will be automatically called to return the road surface information predicted by the algorithm, and then after the first lane line is determined based on the target point, the first lane line can be fitted to the road surface based on the road surface information.

[0042] In some embodiments, the user can observe the point cloud data and / or the simulation image corresponding to the point cloud data, identify the lane line attributes (such as virtuality, color, etc.), and select or input the lane line attributes in the interactive interface, and the attributes of the second lane line can be determined based on the lane line attributes. Of course, the lane line marking device can also identify the point cloud data and / or the simulation image corresponding to the point cloud data, determine the lane line attributes, and then determine the attributes of the second lane line.

[0043] In the related art, the 4D point cloud lane line labeling process requires personal experience and skill level, the consistency and quality of the labeling results are poor, the labeling efficiency is low, the labor cost is high, it is not suitable for large-scale data processing, and it is difficult to process complex or fuzzy point cloud data. In order to solve at least one of the above problems, the user of the disclosed embodiment only needs to select the target point, and the labeled lane lines can be generated immediately. According to the above method of the disclosed embodiment, the user can realize the rapid labeling of lane lines through simple interface operation, while ensuring that the lane lines can fit the road surface, which can improve the efficiency and accuracy of lane line labeling, provide more reliable road information for the automatic driving assistance system, and help improve the safety and reliability of the automatic driving assistance system.

[0044] Figure 2 FIG. 2 is a schematic diagram showing an application example of the lane marking method in an embodiment of the present disclosure. Figure 2 As shown, a point cloud acquisition device (such as a radar) configured on a vehicle 21 collects point cloud data on the road. The point cloud data is transmitted or input to a lane line marking device 22, which provides a user interaction interface, on which a lane line drawing function is provided, under which point cloud data is displayed to the user, and the user's selection of a target point is detected. For example, a brush tool may be provided on the interface, and the user may drag the brush to select a target point. A large number of points within the brush coverage range are target points, and a large number of target points form a first lane line. If the user turns on the automatic ground suction switch, the first lane line will be adjusted according to the road surface information, for example, the height information of each point in the first lane line is adjusted to be consistent with the height information of the corresponding point on the road surface, and a second lane line is obtained. It can be understood that the second lane line is a more accurate marking result that fits the road surface. The relevant information of the second lane line is transmitted or input to other electronic devices, such as a server 23, for providing lane line information to assist in autonomous driving decisions, or used as sample data to train a high-precision lane line recognition model.

[0045] In some embodiments, displaying point cloud data collected on a road includes:

[0046] Based on the point cloud data collected on the road, determine the point cloud top view;

[0047] After the point cloud top view is color adjusted based on a preset color adjustment method, the point cloud data is displayed by displaying the point cloud top view; wherein the color adjustment method is used to highlight the lines on the point cloud top view.

[0048] In the disclosed embodiment, the point cloud top view may be a two-dimensional image formed by projecting the point cloud data in the three-dimensional space onto a top-view plane. In some examples of practical applications, the point cloud data may be projected onto multiple planes to obtain multiple views corresponding to the point cloud data to provide different display perspectives. The user may select the display perspective of the point cloud data so that the interactive interface displays the point cloud top view. Using the point cloud top view to present the point cloud data may intuitively present the lane lines from a top-down perspective, thereby preventing the height information of the point cloud from affecting the user's observation and judgment of the lane lines.

[0049] Optionally, the preset color adjustment method may include sharpening the point cloud top view, adjusting contrast, adjusting hue saturation, removing shadows, etc. Optionally, a plurality of preset color adjustment methods may be provided in the interactive interface for the user to select, and the point cloud top view is color adjusted according to the color adjustment method selected by the user to highlight the lines on the point cloud top view, so that the user can accurately observe and find the lane lines on the point cloud top view, which is convenient for selecting the target point on the lane line later.

[0050] Optionally, the user can trigger the preset color adjustment method multiple times. The display effect after each color adjustment is different. The color adjustment can be triggered multiple times until the user accurately finds the lane line on the point cloud top view.

[0051] According to the above embodiment, by adjusting the color of the point cloud top view, the lines on the point cloud top view are highlighted, so that the user can accurately observe and find the lane lines on the point cloud top view, thereby improving the user experience and further improving the efficiency and accuracy of lane line marking.

[0052] In some embodiments, the road surface information includes a ground model;

[0053] Lane marking methods also include:

[0054] Based on the target point, an initialized ground point cloud is determined in the point cloud data;

[0055] Fitting is performed based on the initialized ground point cloud to obtain the ground model.

[0056] In the disclosed embodiment, all points in the point cloud data can be roughly filtered based on the position of the target point, and points far away from the target point can be filtered out. Optionally, points that are obviously inconsistent with the ground height can also be filtered out, so as to determine the initialized ground point cloud. Exemplarily, the point cloud near the target point can be determined as the initialized ground point cloud in the point cloud data.

[0057] In the disclosed embodiment, a plane model can be fitted from a random portion of sample points in the initialized ground point cloud, and the fitted plane model is regarded as a ground model. The ground model can be understood as a mathematical model for representing the ground, and the ground model can be converted into a plane in a coordinate system.

[0058] Optionally, after determining the first lane line based on the target point, the height information (z coordinate) of each point in the first lane line can be re-determined based on the plane coordinates (e.g., x and y coordinates) of each point in the first lane line and the ground model, thereby adjusting the height of the first lane line to obtain the second lane line so that the second lane line can fit the ground model.

[0059] In one example, the standard deviation can be calculated based on the distance between each point in the ground point cloud and the plane corresponding to the ground model. When the standard deviation meets the requirements (i.e., the standard deviation is small), the plane coordinates of each point in the first lane line are substituted into the ground model to obtain the predicted height close to the ground.

[0060] According to the above embodiment, a ground model with high fitting accuracy and robustness can be obtained, so that the height of the first lane line is adjusted based on the ground model, which can further improve the efficiency and accuracy of lane line marking.

[0061] Optionally, when the above-mentioned standard deviation is large, a cylindrical space surrounding the target point can be determined in the point cloud data, and the cylindrical space can be bucketed according to the height direction to obtain multiple bucket windows. The bucket windows with the number of points exceeding the threshold are taken from low height to high height, and the lowest point in the bucket window is used as the predicted height close to the ground. The first lane line is adjusted based on the predicted height so that the obtained second lane line is close to the ground.

[0062] In some embodiments, fitting is performed based on the initialized ground point cloud to obtain a ground model, including:

[0063] Based on the initialized ground point cloud, N iteration operations are performed to obtain a ground model; wherein N is an integer not less than 2, and the i-th iteration operation in the N iteration operations includes:

[0064] Determine the i-th ground point cloud; when i is equal to 1, the i-th ground point cloud is point cloud data randomly sampled from the initialized ground point cloud; when i is an integer not less than 2 and not greater than N, the i-th ground point cloud is screened from the i-1-th ground point cloud based on the distance between the point in the i-1-th ground point cloud and the i-1-th plane model;

[0065] Fitting is performed based on the i-th ground point cloud to obtain the i-th plane model;

[0066] When the i-th plane model meets the preset conditions, the i-th plane model is determined as the ground model; wherein i is an integer not less than 2 and not greater than N.

[0067] In the disclosed embodiment, some points are randomly sampled from the initialized ground point cloud as the first ground point cloud; fitting is performed based on the first ground point cloud to obtain the first plane model. If the first plane model meets the preset conditions, it is used as the ground model. If it does not meet the conditions, some points close to the first ground model are selected in the first ground point cloud to obtain the second ground point cloud; fitting is performed based on the second point cloud to obtain the second plane model, and then it is determined whether it meets the conditions, and so on, until a plane model that meets the conditions is obtained.

[0068] Optionally, the method of screening out the i-th ground point cloud from the i-1th ground point cloud may include: calculating the residual of each point in the i-1th ground point cloud based on the distance between each point in the i-1th ground point cloud and the i-1th plane model, determining whether each point is an in-plane point based on the residual (for example, a residual less than a threshold is an in-plane point), and obtaining the i-th ground point cloud based on the in-plane points.

[0069] Optionally, in the i-th iteration operation, some points may be randomly sampled from the i-1-th plane point cloud or the initialized plane point cloud, and the points are taken as a union with the above-mentioned plane points to obtain the i-th ground point cloud.

[0070] According to the above embodiment, the fitting accuracy and robustness of the ground model can be improved through multiple iterative operations.

[0071] In one example, the ground model may be determined by RANSAC (Random Sample Consensus), which may include:

[0072] Step 1: Within the maximum number of iterations (maxIterations), perform the following calculations in each round to update the plane model:

[0073] Step 11, randomly sample some points from the initialized ground point cloud as the ground point cloud (sampleIndices);

[0074] Step 12: Fitting the plane model with randomly sampled points;

[0075] Step 13: Calculate the residual according to the distance from all points in the ground point cloud to the plane model, and those within the threshold are considered to be points in this round of plane;

[0076] Step 14: Compare whether the current model is better, update the plane model parameters and the inlier points.

[0077] Step 2: Output the optimal linear model parameters and the optimal inlier as the ground point result (i.e., ground model).

[0078] In some embodiments, based on the target point, determining an initialized ground point cloud in the point cloud data includes:

[0079] Based on the target point, a cylindrical space surrounding the target point is determined in the point cloud data;

[0080] Bucket the cylindrical space in the height direction and determine M bucket windows, where M is an integer not less than 2;

[0081] Determine the target bucket window based on the number of points in the bucket window;

[0082] Based on multiple points within the target bucket window, an initialized ground point cloud is determined.

[0083] Optionally, the cylindrical space may be the minimum cylindrical space surrounding all target points. Alternatively, a reasonable radius value may be calculated or preset according to the position of each target point, and the cylindrical space may be determined based on the center of all target points and the radius value, and the cylindrical space may surround at least some of the target points.

[0084] In the disclosed embodiment, the point clouds contained in different bucket windows may be non-overlapping or partially overlapping. For example, M bucket windows are determined by sliding windows. Here, the sliding window method is, for example, after determining the first bucket window, sliding upward a certain distance to obtain the second bucket window, and so on to determine the M bucket windows. Specifically, the number of bucket windows can be preset by the user, or it can be the ground thickness / bucket height. The ground thickness can be determined based on the distance between the highest point and the lowest point in the cylindrical space, and the bucket height (i.e., the distance to slide upward when bucketing) can be set by the user.

[0085] In an example, when the actual ground environment is relatively simple, the number of points in each of the M bucket windows can be traversed to determine the bucket window with the largest number of points among the M bucket windows, that is, the target bucket window.

[0086] In one example, a method of determining a target bucket may include:

[0087] Step 1: Determine the points within the plane radius (radius) around the target point, that is, the points within a cylindrical space.

[0088] Step 2: Divide the data into buckets according to the height direction to obtain multiple bucket windows. The number of buckets can be ground thickness / bucket height (ground_height / height_bin_size). The ground thickness can be determined based on the distance between the highest point and the lowest point in the cylindrical space, and the bucket height can be set by the user.

[0089] Step 3: For normal ground point clouds (i.e., the actual ground environment is relatively simple), start from the bucket window with the lowest height, slide the bucket window upward, and find the window with the largest number of points.

[0090] According to the above embodiment, by coarsely filtering all points in the point cloud data through the bucketing strategy, the point clouds far away from the road surface can be accurately filtered out, reducing the number of subsequent fitting point clouds, thereby improving the efficiency and accuracy of fitting point clouds and reducing the requirements for computing power.

[0091] In the embodiments of the present disclosure, a variety of different methods for determining the target bucket window are also provided, and an exemplary method is provided below.

[0092] Method 1:

[0093] Based on the number of points in the bucket window, determine the target bucket window, including:

[0094] When the ratio between the number of points in the jth bucket window among the M bucket windows and the number of points in the cylindrical space is greater than a first threshold, the jth bucket window is determined as the target bucket window, where j is a positive integer not greater than M.

[0095] Exemplarily, when the actual ground environment is relatively complex (for example, the point cloud data includes underground reflection points, curbs, vegetation point clouds, etc.), starting from the bucket window with the lowest height, the point cloud quantity ratio is determined based on the ratio of the number of points in the first bucket window to the number of points in the cylindrical space. When the point cloud quantity ratio is less than the first threshold, the bucket window can be slid upward, and the point cloud quantity ratio is determined based on the ratio of the number of points in the second bucket window to the number of points in the cylindrical space, and so on until the point cloud quantity ratio corresponding to the j-th bucket window is greater than the first threshold, which can trigger the early determination of the j-th bucket window as the target bucket window. That is, each bucket window in the M bucket windows can be determined as the target bucket window without traversing each bucket window.

[0096] According to the above embodiment, while accurately obtaining the target bucket window, compared with the number of points traversed in each bucket window, terminating the determination of the target bucket window in advance can improve efficiency and reduce the requirement for computing power.

[0097] Method 2:

[0098] Based on the number of points in the bucket window, determine the target bucket window, including:

[0099] When the ratio of the number of points in the kth bucket window among the M bucket windows to the maximum number of window points is greater than a second threshold, the kth bucket window is determined as the target bucket window; wherein the maximum number of window points is the maximum value of the number of points in the 1st bucket window among the M bucket windows to the number of points in the k-1th bucket window, and k is a positive integer not greater than M.

[0100] In the disclosed embodiment, when the local actual ground environment is relatively complex (for example, the point cloud data includes underground reflection points, curbs, vegetation point clouds, etc.), starting from the bucket window with the lowest height, the window growth rate threshold (min_gradient_rate) is determined based on the ratio of the number of points in the second bucket window to the number of points in the first bucket window. When the window growth rate threshold is less than the second threshold, the bucket window can be slid upward, and the window growth rate threshold is determined based on the ratio of the number of points in the third bucket window to the bucket window with the largest number of midpoints between the first bucket window and the second bucket window, and so on until the ratio between the number of points in the kth bucket window and the maximum number of window points is greater than the second threshold, which means that the kth bucket window has encountered a peak, which can trigger the early determination of the kth bucket window as the target bucket window. That is, each bucket window in the M bucket windows can be traversed without traversing each bucket window, and the bucket window corresponding to the window growth rate threshold greater than the second threshold is determined as the target bucket window.

[0101] According to the above embodiment, while accurately obtaining the target bucket window, the determination of the target bucket window is terminated in advance compared to the number of points traversed in each bucket window, thereby reducing the requirement for computing power.

[0102] It can be seen that the lane marking method provided by the embodiment of the present disclosure significantly improves the efficiency and accuracy of lane marking, minimizes the requirements for computing power, and can also optimize the user interaction experience. The lane marking method of the embodiment of the present disclosure can provide more reliable road information for the automatic driving assistance system, which helps to improve the safety and reliability of the system.

[0103] According to an embodiment of the present disclosure, the present disclosure also provides a lane marking device, Figure 3 A schematic block diagram of a lane marking device provided by an embodiment of the present disclosure is shown. Figure 3 As shown, the device comprises:

[0104] A display module 310, for displaying point cloud data collected on the road;

[0105] A lane line determination module 320, configured to determine a first lane line based on the target point in response to a selection operation on the point cloud data;

[0106] The lane line adjustment module 330 is used to adjust the first lane line based on the road surface information to obtain the second lane line.

[0107] In some embodiments, the road surface information includes a ground model;

[0108] like Figure 4 As shown, the lane marking device may further include:

[0109] A point cloud determination module 420 is used to determine an initialized ground point cloud in the point cloud data based on the target point;

[0110] The point cloud processing module 430 is used to perform fitting based on the initialized ground point cloud to obtain a ground model.

[0111] In some embodiments, the point cloud processing module 430 is used to:

[0112] Based on the initialized ground point cloud, N iteration operations are performed to obtain a ground model; wherein N is an integer not less than 2, and the i-th iteration operation in the N iteration operations includes:

[0113] Determine the i-th ground point cloud; when i is equal to 1, the i-th ground point cloud is point cloud data randomly sampled from the initialized ground point cloud; when i is an integer not less than 2 and not greater than N, the i-th ground point cloud is screened from the i-1-th ground point cloud based on the distance between the point in the i-1-th ground point cloud and the i-1-th plane model;

[0114] Fitting is performed based on the i-th ground point cloud to obtain the i-th plane model;

[0115] When the i-th plane model meets the preset conditions, the i-th plane model is determined as the ground model; wherein i is an integer not less than 2 and not greater than N.

[0116] In some embodiments, Figure 5 As shown, the point cloud determination module 420 includes:

[0117] A space determination submodule 510 is used to determine a cylindrical space surrounding the target point in the point cloud data based on the target point;

[0118] The bucketing submodule 520 is used to bucket the cylindrical space in the height direction and determine M bucketing windows, where M is an integer not less than 2;

[0119] A window determination submodule 530 is used to determine a target bucket window based on the number of points in the bucket window;

[0120] The point cloud determination submodule 540 is used to determine the initialized ground point cloud based on multiple points in the target bucket window.

[0121] In some embodiments, the window determination submodule 530 is configured to:

[0122] When the ratio between the number of points in the jth bucket window among the M bucket windows and the number of points in the cylindrical space is greater than a first threshold, the jth bucket window is determined as the target bucket window, where j is a positive integer not greater than M.

[0123] In some embodiments, the window determination submodule 530 is configured to:

[0124] When the ratio of the number of points in the kth bucket window among the M bucket windows to the maximum number of window points is greater than a second threshold, the kth bucket window is determined as the target bucket window; wherein the maximum number of window points is the maximum value of the number of points in the 1st bucket window among the M bucket windows to the number of points in the k-1th bucket window, and k is a positive integer not greater than M.

[0125] In some embodiments, the display module 310 is used to:

[0126] Based on the point cloud data collected on the road, determine the point cloud top view;

[0127] After the point cloud top view is color adjusted based on a preset color adjustment method, the point cloud data is displayed by displaying the point cloud top view; wherein the color adjustment method is used to highlight the lines on the point cloud top view.

[0128] For the description of specific functions and examples of each module and submodule of the device in the embodiment of the present disclosure, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0129] In the technical solution disclosed herein, the acquisition, storage and application of the target object's personal information are in compliance with the relevant laws and regulations and do not violate public order and good morals.

[0130] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0131] Figure 6A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment 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 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0132] like Figure 6 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 607 to a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 804. An input / output (I / O) interface 605 is also connected to the bus 604.

[0133] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSP), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as the lane marking method. For example, in some embodiments, the lane marking method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the lane marking method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the lane marking method in any other appropriate manner (eg, by means of firmware).

[0135] 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 chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0136] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0137] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0138] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a cathode ray tube (CRT) or a liquid crystal display (LCD) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0139] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

[0140] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0141] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0142] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A lane marking method, comprising: Display point cloud data collected on the road; In response to a selection operation on a target point on the point cloud data, determining a first lane line based on the target point; The first lane line is adjusted based on road surface information to obtain a second lane line.

2. The method according to claim 1, wherein: The road surface information includes a ground model; The method further comprises: Based on the target point, determining an initialized ground point cloud in the point cloud data; Fitting is performed based on the initialized ground point cloud to obtain the ground model.

3. The method according to claim 2, wherein: The fitting based on the initialized ground point cloud to obtain the ground model includes: Performing N iteration operations based on the initialized ground point cloud to obtain the ground model; wherein N is an integer not less than 2, and the i-th iteration operation in the N iteration operations includes: Determine the i-th ground point cloud; wherein, when i is equal to 1, the i-th ground point cloud is point cloud data randomly sampled in the initialized ground point cloud; when i is an integer not less than 2 and not greater than N, the i-th ground point cloud is screened in the i-1-th ground point cloud based on the distance between the point in the i-1-th ground point cloud and the i-1-th plane model; Performing fitting based on the i-th ground point cloud to obtain an i-th plane model; When the i-th plane model meets a preset condition, the i-th plane model is determined as the ground model; wherein i is an integer not less than 2 and not greater than N.

4. The method according to claim 2 or 3, wherein: The step of determining an initialized ground point cloud in the point cloud data based on the target point includes: Based on the target point, determining a cylindrical space surrounding the target point in the point cloud data; Bucketing the cylindrical space in the height direction to determine M bucketing windows, where M is an integer not less than 2; Determine a target bucket window based on the number of points in the bucket window; Based on the multiple points in the target bucket window, an initialized ground point cloud is determined.

5. The method according to claim 4, wherein: The step of determining a target bucket window based on the number of points in the bucket window includes: When the ratio of the number of points in the jth bucket window among the M bucket windows to the number of points in the cylindrical space is greater than a first threshold, the jth bucket window is determined as the target bucket window, where j is a positive integer not greater than M.

6. The method according to claim 4, wherein: The step of determining a target bucket window based on the number of points in the bucket window includes: When the ratio of the number of points in the kth bucket window among the M bucket windows to the maximum number of window points is greater than a second threshold, the kth bucket window is determined as the target bucket window; wherein the maximum number of window points is the maximum value of the number of points in the 1st bucket window among the M bucket windows to the number of points in the k-1th bucket window, and k is a positive integer not greater than M.

7. The method according to any one of claims 1 to 6, wherein: The point cloud data collected on the road is displayed, including: Based on the point cloud data collected on the road, determine the point cloud top view; After the point cloud top view is color adjusted based on a preset color adjustment method, the point cloud data is displayed by displaying the point cloud top view; wherein the color adjustment method is used to highlight the lines on the point cloud top view.

8. A lane marking device, comprising: A display module, used for displaying point cloud data collected on the road; A lane line determination module, configured to determine a first lane line based on the target point in response to a selection operation on the point cloud data; The lane line adjustment module is used to adjust the first lane line based on road surface information to obtain a second lane line.

9. The device according to claim 8, wherein: The road surface information includes a ground model; The device also includes: A point cloud determination module, used to determine an initialized ground point cloud in the point cloud data based on the target point; The point cloud processing module is used to perform fitting based on the initialized ground point cloud to obtain the ground model.

10. The device according to claim 9, wherein: The point cloud processing module is used to: Performing N iteration operations based on the initialized ground point cloud to obtain the ground model; wherein N is an integer not less than 2, and the i-th iteration operation in the N iteration operations includes: Determine the i-th ground point cloud; wherein, when i is equal to 1, the i-th ground point cloud is point cloud data randomly sampled in the initialized ground point cloud; when i is an integer not less than 2 and not greater than N, the i-th ground point cloud is screened in the i-1-th ground point cloud based on the distance between the point in the i-1-th ground point cloud and the i-1-th plane model; Performing fitting based on the i-th ground point cloud to obtain an i-th plane model; When the i-th plane model meets a preset condition, the i-th plane model is determined as the ground model; wherein i is an integer not less than 2 and not greater than N.

11. The device according to claim 9 or 10, wherein: The point cloud determination module comprises: A space determination submodule, configured to determine, based on the target point, a cylindrical space surrounding the target point in the point cloud data; A bucketing submodule, used to bucket the cylindrical space in the height direction and determine M bucketing windows, where M is an integer not less than 2; A window determination submodule, used to determine a target bucket window based on the number of points in the bucket window; The point cloud determination submodule is used to determine the initialized ground point cloud based on multiple points in the target bucket window.

12. The device according to claim 11, wherein: The window determination submodule is used for: When the ratio of the number of points in the jth bucket window among the M bucket windows to the number of points in the cylindrical space is greater than a first threshold, the jth bucket window is determined as the target bucket window, where j is a positive integer not greater than M.

13. The device according to claim 11, wherein: The window determination submodule is used for: When the ratio of the number of points in the kth bucket window among the M bucket windows to the maximum number of window points is greater than a second threshold, the kth bucket window is determined as the target bucket window; wherein the maximum number of window points is the maximum value of the number of points in the 1st bucket window among the M bucket windows to the number of points in the k-1th bucket window, and k is a positive integer not greater than M.

14. The device according to any one of claims 8 to 13, wherein: The display module is used for: Based on the point cloud data collected on the road, determine the point cloud top view; After the point cloud top view is color adjusted based on a preset color adjustment method, the point cloud data is displayed by displaying the point cloud top view; wherein the color adjustment method is used to highlight the lines on the point cloud top view.

15. 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, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

16. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-7.

17. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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