Lane line extraction method, device, vehicle and storage medium

Abstract

The present invention discloses a lane line extraction method, device, vehicle and storage medium. The method comprises: obtaining the current position point and high-precision map data of the vehicle, wherein the high-precision map data comprises lane line data and the planned path data of the vehicle; based on the current position point and the planned path data, extracting the target lane line data within the set lane line length in front of the lane where the vehicle is located from the lane line data; and performing curve fitting on the target lane line data to obtain the lane line. Through the technical solution of the present invention, it is possible to extract lane lines based on high-precision maps, solve the problem that the traditional method of identifying lane lines on the ground through image acquisition equipment has low lane line recognition accuracy due to the constraints of weather, ground light reflection and lane line wear, and can be used alone or in combination with traditional lane line recognition methods to achieve the effect of reducing the constraints of lane line recognition and improving the recognition accuracy of lane lines.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of lane line detection, and in particular, to a lane line extraction method, device, vehicle and storage medium. Background Art

[0002] With the development of artificial intelligence technology, autonomous driving technology has developed rapidly. In the field of autonomous driving, vehicles automatically collect environmental information and drive automatically based on environmental information. Lane detection is an essential basic function in autonomous driving vehicles.

[0003] At present, the lane line recognition system based on machine vision mainly uses image sensors such as cameras installed in front of the vehicle to collect images of the road ahead and identify and extract lane lines from the images. In the process of lane line recognition, due to factors such as weather, ground light reflection or high degree of lane line wear, the lane line images collected by the image sensor have serious problems with incomplete lane lines, which has affected the accuracy of lane line recognition. Summary of the invention

[0004] The embodiments of the present invention provide a lane line extraction method, device, vehicle and storage medium to achieve the extraction of lane lines based on high-precision maps, and solve the problem that the traditional method of identifying lane lines on the ground through image acquisition equipment is constrained by weather, ground light reflection and the degree of lane line wear, and the lane line recognition accuracy is not high. It can be used alone or in combination with traditional lane line recognition methods to reduce the constraints of lane line recognition and improve the recognition accuracy of lane lines.

[0005] In a first aspect, an embodiment of the present invention provides a lane line extraction method, comprising:

[0006] Obtaining the current location of the vehicle and high-precision map data, wherein the high-precision map data includes lane line data and planned path data of the vehicle;

[0007] Based on the current position point and the planned path data, extracting target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data;

[0008] The target lane line data is subjected to curve fitting to obtain the lane line.

[0009] In a second aspect, an embodiment of the present invention further provides a lane line extraction device, the device comprising:

[0010] The acquisition module is used to obtain the current location of the vehicle and high-precision map data, which includes lane line data and the planned path data of the vehicle;

[0011] An extraction module, configured to extract target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data of the high-precision map data based on the current position point and the planned path data;

[0012] The fitting module is used to perform curve fitting on the target lane line data to obtain the lane line.

[0013] In a third aspect, an embodiment of the present invention further provides a vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a lane line extraction method as described in any one of the embodiments of the present invention is implemented.

[0014] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a lane line extraction method as described in any one of the embodiments of the present invention.

[0015] The embodiment of the present invention obtains the current position point and high-precision map data of the vehicle, wherein the high-precision map data includes lane line data and planned path data of the vehicle; based on the current position point and the planned path data, extracts target lane line data within a set lane line length in front of the lane where the vehicle is located from the lane line data; curve fitting is performed on the target lane line data to obtain lane lines. The lane lines can be extracted based on the high-precision map, thereby solving the problem that the traditional method of identifying lane lines on the ground through image acquisition equipment has low lane line recognition accuracy due to the constraints of weather, ground light reflection and the degree of lane line wear. The method can be used alone or in combination with traditional lane line recognition methods to reduce the constraints on lane line recognition and improve the recognition accuracy of lane lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of a lane line extraction method in Embodiment 1 of the present invention;

[0018] Figure 2 is a flow chart of a lane line extraction method in Embodiment 2 of the present invention;

[0019] Figure 3 is a structural schematic diagram of a lane line extraction device in Embodiment 3 of the present invention;

[0020] Figure 4 It is a structural schematic diagram of a vehicle in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0022] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0023] Embodiment 1

[0024] Figure 1 A flowchart of a lane line extraction method provided in Example 1 of the present invention. This embodiment is applicable to the case of extracting lane lines based on high-precision maps. The method can be executed by the lane line extraction device in the embodiment of the present invention, and the device can be implemented in software and / or hardware.

[0025] like Figure 1 As shown, the method specifically comprises the following steps:

[0026] S110, obtaining the current position point and high-precision map data of the vehicle, where the high-precision map data includes lane line data and planned path data of the vehicle.

[0027] A high-precision map refers to a map with high precision and detailed definition, which can reach the decimeter level and can distinguish each lane. The detailed definition means that the high-precision map uses a formatted storage method to store various traffic elements in the traffic scene, that is, the high-precision map data can include the road network data, lane network data, lane line data, traffic signs and other data of traditional maps. After the vehicle determines the planned path in the high-precision map based on the vehicle's current position, the destination to be reached by the vehicle, and the road environment information, the high-precision map data also includes the vehicle's planned path data, which is used to indicate the lane corresponding to the vehicle's planned path in the high-precision map.

[0028] It should be noted that in high-precision maps, the formatting and storage method of high-precision map data is specifically as follows: based on changes in lane straight length, diversion, merging, curvature, etc., lane line data is stored in the high-precision map in the form of layers, and lane identification numbers are assigned to lanes in each layer.

[0029] For example, the lane data of 0-500m and 500m-1000m of a straight lane with a length of 1000m are stored in the lane layers with lane identification numbers R01 and R02 respectively; the right turn lane with a length of 400m on the right side of lane R02 is stored in the lane layer with lane identification number R03, and the left turn lane with a length of 300m on the left side of lane R02 is stored in the lane layer with lane identification number R04. The high-precision map also stores a lane line layer for storing lane line data on both sides of the lane.

[0030] The high-precision map data includes lane line data and vehicle planned path data. The lane line data is a sequence of boundary points on the boundary line of the lane, and the planned path data is a sequence of vehicle planned trajectory points. The current position point of the vehicle is the two-dimensional position coordinate of the vehicle in the high-precision map. Specifically, any existing method can be used to determine the current position point of the vehicle and the planned path, and the present invention does not limit this.

[0031] S120, based on the current position point and the planned path data, extract the target lane line data within the set lane line length in front of the lane where the vehicle is located from the lane line data of the high-precision map data.

[0032] The target lane line data is a sequence of boundary points on the boundary line of the lane within a set lane line length extracted from the lane line data of the high-precision map data.

[0033] The lane line length is set as the length of the lane line to be extracted within each cycle time, and can be set according to the actual situation of the road or the needs of the user.

[0034] Specifically, the lane layer corresponding to the planned path is determined based on the planned path data and the current position point, the lane line layer data stored in the lane line layer is obtained based on the lane identification number ID corresponding to the lane layer, and the lane line data of the set lane line length is intercepted from the lane line layer data as the target lane line data.

[0035] Optionally, the lane line length is set based on at least one of the following conditions: the vehicle speed and the degree of change in road curvature.

[0036] For example, in a high-speed scenario, the vehicle speed is relatively fast and the road curvature changes relatively little, so the lane line length that needs to be fitted is larger, so the lane line length is set to the first lane line length; in a low-speed scenario, the vehicle speed is relatively slow and the road curvature changes relatively large, so fitting a shorter lane line can meet the requirements of vehicle control, and there is no need to fit a lane line that is too long and waste computing power, so the lane line length is set to the second lane line length, and the second lane line length is less than the first lane line length.

[0037] S130, performing curve fitting on the target lane line data to obtain the lane line.

[0038] Specifically, the target lane line data can be described as a sequence of points in the vehicle coordinates (x0, y0), (x1, y1), (x2, y2), ..., (x n ,y n ), where n is the number of data points in the target lane line data, and n can be determined by the length of the target lane line and the sampling frequency of the high-precision map data. Any existing curve fitting method can be used to perform curve fitting on the target lane line data to obtain the lane line, such as calling a curve fitting function or a custom curve fitting algorithm, and the embodiment of the present invention does not impose any limitation on this.

[0039] For example, a cubic curve fitting method is used to fit the target lane line data to obtain the lane line. The cubic curve fitting equation is y′=C0+C1x+C2x 2 +C3x 3 , C0, C1, C2, and C3 are all fitting parameters. Substitute the data points of the target lane line data into the secondary curve fitting equation in turn to obtain: Based on the least squares method, the fitting parameters C0, C1, C2, and C3 are calculated so that Σ|y i -y′ i |Minimum, (x i ,y i ) is the coordinate of the i-th data point in the target lane line data, y′ i for y i The corresponding expected value.

[0040] The technical solution of this embodiment is to obtain the current position point and high-precision map data of the vehicle, wherein the high-precision map data includes lane line data and planned path data of the vehicle; based on the current position point and the planned path data, extract the target lane line data within the set lane line length in front of the lane where the vehicle is located from the lane line data; curve fitting is performed on the target lane line data to obtain the lane line, which can extract the lane line based on the high-precision map, and solve the problem that the traditional method of identifying lane lines on the ground through image acquisition equipment has low lane line recognition accuracy due to the constraints of weather, ground light reflection and lane line wear. It can be used alone or in combination with traditional lane line recognition methods to reduce the constraints on lane line recognition and improve the recognition accuracy of lane lines.

[0041] Embodiment 2

[0042] Figure 2This is a flow chart of a lane line extraction method in Example 2 of the present invention. This embodiment optimizes step S120 based on the above embodiment and extracts target lane line data within a set lane line length in front of the lane where the vehicle is located from the lane line data based on the current position point and the planned path data.

[0043] like Figure 2 As shown, the method of this embodiment specifically includes the following steps:

[0044] S210, obtaining the current position point and high-precision map data of the vehicle, where the high-precision map data includes lane line data and planned path data of the vehicle.

[0045] S220, extracting current lane line layer data from the lane line data, where the current lane line layer data is lane line layer data corresponding to the current lane determined based on the current position point and the planned path data.

[0046] Specifically, the current lane of the vehicle is determined according to the current position point and the planned path data, and the current lane line layer data corresponding to the current lane is extracted from the lane line data based on the current lane ID.

[0047] S230: Determine the distance between the current position point and the end position point in the current lane line layer data as the current lane line length.

[0048] The data size corresponding to the current lane line layer data is determined by the storage method of the high-precision map, and the lane line layer data sizes corresponding to different lane IDs can be different. The end position point in the current lane line layer data refers to the position point farthest from the current position point in the current vehicle's forward direction stored in the current lane line layer data.

[0049] Specifically, the current lane line layer data is a sequence composed of a series of position points. The end position point of the current lane line layer data is determined. According to the number of data points between the current position point and the end position point, and the sampling frequency (unit, meters / second) of the lane line layer data in the high-precision map, the distance between the current position point and the end position point is calculated, and the distance is determined as the current lane line length.

[0050] S240, if the current lane line length is greater than or equal to the set lane line length, obtain the first current lane line data with a length of the set lane line length from the current lane line layer data, and determine the first current lane line data as the target lane line data.

[0051] Specifically, if the current lane line length is greater than or equal to the set lane line length, the first current lane line data with a length of the set lane line length is obtained from the current lane line layer data along the vehicle's forward direction as the target lane line data. The target lane line data is used to determine the target lane line, that is, to obtain a target lane line with a length of the set lane line length.

[0052] S250, if the current lane line length is less than the set lane line length, determine the predicted lane of the vehicle based on the current position point and the planned path data, determine whether the predicted lane identification number corresponding to the predicted lane is empty, and obtain the target lane line data from the lane line data based on the judgment result.

[0053] The predicted lane refers to the lane in front of the vehicle determined by the planned path.

[0054] Specifically, if the current lane line length is less than the set lane line length, it means that the current lane line length is not enough to determine the lane line, and it is necessary to determine the predicted lane of the vehicle in the forward direction according to the current position point and the planned path data, and extract the lane line from the predicted lane so that the length of the target lane line data reaches the set lane line length. However, there may be a situation where there is no road ahead, so it is judged whether the predicted lane identification number corresponding to the predicted lane is empty, and the target lane line data is further obtained from the lane line data according to the judgment result.

[0055] S260, performing curve fitting on the target lane line data to obtain the lane line.

[0056] The technical solution of this embodiment is to obtain the current position point and high-precision map data of the vehicle, wherein the high-precision map data includes lane line data and planned path data of the vehicle; based on the current position point and the planned path data, extract the target lane line data within the set lane line length in front of the lane where the vehicle is located from the lane line data; curve fitting is performed on the target lane line data to obtain the lane line, which can extract the lane line based on the high-precision map, and solve the problem that the traditional method of identifying lane lines on the ground through image acquisition equipment has low lane line recognition accuracy due to the constraints of weather, ground light reflection and lane line wear. It can be used alone or in combination with traditional lane line recognition methods to reduce the constraints on lane line recognition and improve the recognition accuracy of lane lines.

[0057] Optionally, step S240, obtaining current lane line data corresponding to the set lane line length from the current lane line layer data, includes:

[0058] Determine a projection point corresponding to the current position point in the current lane line layer data, where the projection point is a boundary point projected from the current position point onto the current lane boundary line;

[0059] Obtaining the boundary points included in the current lane boundary line starting from the projection point and having a length equal to the set lane line length in the current lane line layer data;

[0060] The sequence formed by the boundary points is determined as the first current lane line data.

[0061] Specifically, the current position point of the vehicle is projected onto the lane line, the projection point of the current position point in the current lane line layer data is determined, and the projection point is used as the starting point to intercept the boundary points contained in the lane boundary line whose length in the current lane line layer data is the set lane line length, and the sequence formed by all boundary points is determined as the first current lane line data.

[0062] For example, if the coordinates of the current position point are (x0, y0), then the coordinates of the projection point corresponding to the current position point in the current lane line layer data are (x1, y1), and y0 = y1. If the lane line length is set to 100 meters, the boundary points in the current lane line layer data are obtained: (x1, y1), (x2, y2), ..., (x m ,y m ). (x m ,y m ) is the end point in the first current lane line data. If the data sampling frequency of the current lane line layer data is f s , then (m-1)f s =100 meters.

[0063] Optionally, step S250: acquiring the target lane line data from the lane line data based on the judgment result, includes:

[0064] Step S251: if the judgment result is that the predicted lane identification number is empty, obtaining second current lane line data having a length equal to the length of the current lane line from the current lane line layer data, and determining the second current lane line data as the target lane line data;

[0065] Specifically, if the predicted lane identification number is empty, it means that there is no predicted road in front of the current lane. The second current lane line data with the projection point as the starting point and the length of the current lane line is obtained from the current lane line layer data, and the second current lane line data is determined as the target lane line data.

[0066] Exemplarily, if the current lane line layer data is the position coordinates of a boundary point on the current lane boundary line with a length of 100 meters, and the current position point is a data point at the 45th meter in the current lane line layer data, then the second current lane line data consisting of the position coordinates of the boundary points of the current lane boundary line from the 45th meter to the 100th meter in the current lane line layer data is obtained, and the second current lane line data is determined as the target lane line data.

[0067] S252: If the judgment result is that the predicted lane identification number is not empty, extracting the predicted lane line layer data corresponding to the predicted lane from the lane line data; and determining the predicted lane line length corresponding to the predicted lane line layer data.

[0068] The predicted lane line layer data refers to the data stored in the predicted lane line layer, which is the position coordinates of the boundary points on the boundary line of the predicted lane.

[0069] Specifically, if the predicted lane identification number is not empty, indicating that there is a predicted road in front of the current lane, the predicted lane line layer data is extracted from the lane line data to determine the predicted lane line length corresponding to the predicted lane line layer data. The predicted lane line length is determined based on the number of predicted lane line layer data and the data sampling frequency of the predicted lane line layer.

[0070] S253: Determine whether the sum of the predicted lane line length and the current lane line length is less than the set lane line length; if not, execute step S254; if so, execute S255.

[0071] S254: Obtain first predicted lane line data having a first length from the predicted lane line layer data, where the first length is the difference between the set lane line length and the current lane line length, determine the set of the second current lane line data and the first predicted lane line data as the target lane line data, and end the judgment process.

[0072] For example, if the predicted lane line length is 150 meters, the current lane line length is 45 meters, the set lane line length is 100 meters, and the sum of the predicted lane line length and the current lane line length is greater than or equal to the set lane line length, then the length difference between the set lane line length and the current lane line length is determined to be 55 meters, and the predicted lane line data of 0-55 meters is obtained from the predicted lane line layer data. The current lane line data and the predicted lane line data are combined to obtain the target lane line data corresponding to the lane line with a length of 100 meters.

[0073] S255: Determine the prepared predicted lane of the vehicle based on the planned path data, extract the prepared predicted lane line layer data corresponding to the prepared predicted lane from the lane line data, and determine the prepared predicted lane line length corresponding to the prepared predicted lane line layer data; add the predicted lane line length and the prepared predicted lane length to obtain the total predicted lane line length; determine the total predicted lane line length as the predicted lane line length, until the sum of the total predicted lane line length and the current lane line length is greater than the set lane line length, and execute step S256.

[0074] In a specific example, if the predicted lane line length is 10 meters, the current lane line length is 45 meters, the set lane line length is 100 meters, and the sum of the predicted lane line length and the current lane line length is less than the set lane line length, the vehicle's prepared predicted lane is determined based on the planned path, the prepared predicted lane line layer data is obtained, and the prepared predicted lane line length corresponding to the lane line layer data is determined. If the prepared predicted lane line length is 50 meters, the predicted lane line length and the prepared predicted lane line length are accumulated to obtain a total predicted lane line length of 55 meters, that is, the predicted lane line length is updated to 55 meters. The sum of the total predicted lane line length and the current lane line length is 105 meters, which is greater than the set lane line length of 100 meters, and step S254 is executed, so that the target lane line data corresponding to the lane line with a length of 100 meters is obtained.

[0075] In another specific example, if the predicted lane line length is 10 meters, the current lane line length is 45 meters, the set lane line length is 100 meters, and the sum of the predicted lane line length and the current lane line length is less than the set lane line length, then the first prepared predicted lane of the vehicle is determined based on the planned path, the first prepared predicted lane line layer data is obtained, and the first prepared predicted lane line length corresponding to the lane line layer data is determined. If the first prepared predicted lane line length is 30 meters, the predicted lane line length and the first prepared predicted lane line length are accumulated to obtain a total predicted lane line length of 55 meters. If it is determined that the sum of the predicted lane line length and the current lane line length is 85 meters, step S255 is performed again: the second prepared predicted lane of the vehicle is determined based on the planned path, the second prepared predicted lane line layer data is obtained, and the second prepared predicted lane line length corresponding to the lane line layer data is determined. If the second prepared predicted lane line length is 40 meters, the predicted lane line length and the second prepared predicted lane line length are accumulated to obtain a total predicted lane line length of 85 meters. The sum of the total predicted lane line length and the current lane line length is 125 meters, which is greater than the set lane line length of 100 meters.

[0076] Step S256: Obtain second predicted lane line data having a length equal to the predicted lane line length from the predicted lane line layer data; obtain preparation predicted lane line data having a second length from the preparation predicted lane line layer data; the second length is the set lane line length minus the length difference between the current lane line length and the second predicted lane line length; determine the set of the second current lane line data, the second predicted lane line data and the preparation predicted lane line data as the target lane line data.

[0077] Exemplarily, in the above example, if the current lane line length is 45 meters, the predicted lane line length is 10 meters, the first prepared predicted lane line length is 30 meters, the second prepared predicted lane line length is 40 meters, and the set lane line length is 100 meters, then the second predicted lane line data with a length of 10 meters is obtained from the predicted lane line layer data; the first prepared predicted lane line data with a length of 30 meters is obtained from the first prepared predicted lane line layer data; the second prepared predicted lane line data with a length of 15 meters is obtained from the second prepared predicted lane line layer data. The set of the second current lane line data, the predicted lane line data, and the prepared predicted lane line data is determined as the target lane line data.

[0078] Optionally, before extracting the prepared predicted lane line layer data corresponding to the prepared predicted lane from the lane line data, the method further includes:

[0079] If the lane identification number corresponding to the prepared predicted lane is empty, obtaining predicted lane line data having a length equal to the length of the predicted lane line from the predicted lane line layer data;

[0080] A set of the second current lane line data and the predicted lane line data is determined as the target lane line data.

[0081] For example, in the above example, if the current lane line length is 45 meters, the predicted lane line length is 10 meters, the first prepared predicted lane line length is 30 meters, the set lane line length is 100 meters, and the lane identification number corresponding to the second prepared predicted lane is empty, then the second current lane line data with a length of 45 meters is obtained from the current lane line layer data, and the second predicted lane line data with a length of 10 meters is obtained from the predicted lane line layer data; the first prepared predicted lane line data with a length of 30 meters is obtained from the first prepared predicted lane line layer data. The set of the second current lane line data, the predicted lane line data, and the first prepared predicted lane line data is determined as the target lane line data with a length of 85 meters.

[0082] Embodiment 3

[0083] Figure 3This is a schematic diagram of the structure of a lane line extraction device provided in the third embodiment of the present invention. This embodiment is applicable to the case of extracting lane lines based on high-precision maps. The device can be implemented in software and / or hardware. The device can be integrated in a vehicle, such as Figure 3 As shown, the lane line extraction device specifically includes: an acquisition module 310, an extraction module 320 and a fitting module 330.

[0084] The acquisition module 310 is used to acquire the current position of the vehicle and high-precision map data, where the high-precision map data includes lane line data and planned path data of the vehicle;

[0085] An extraction module 320 is used to extract target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data of the high-precision map data based on the current position point and the planned path data;

[0086] The fitting module 330 is used to perform curve fitting on the target lane line data to obtain the lane line.

[0087] Optionally, the extraction module 320 includes:

[0088] An extraction unit, configured to extract current lane line layer data from the lane line data, wherein the current lane line layer data is lane line layer data corresponding to the current lane determined based on the current position point and the planned path data;

[0089] A first determining unit, configured to determine the distance between the current position point and the end position point in the current lane line layer data as the current lane line length;

[0090] A second determination unit is used for obtaining first current lane line data having a length of the set lane line length from the current lane line layer data if the current lane line length is greater than or equal to the set lane line length, and determining the first current lane line data as the target lane line data;

[0091] An acquisition unit is used to determine the predicted lane of the vehicle based on the current position point and the planned path data if the current lane line length is less than the set lane line length, determine whether the predicted lane identification number corresponding to the predicted lane is empty, and acquire the target lane line data from the lane line data based on the judgment result.

[0092] Optionally, the second determining unit is specifically configured to:

[0093] Determine a projection point corresponding to the current position point in the current lane line layer data, where the projection point is a boundary point projected from the current position point onto the current lane boundary line;

[0094] Obtaining the boundary points included in the current lane boundary line starting from the projection point and having a length equal to the set lane line length in the current lane line layer data;

[0095] The sequence formed by the boundary points is determined as the first current lane line data.

[0096] Optionally, the acquisition unit includes:

[0097] a determination subunit, configured to obtain second current lane line data having a length equal to the length of the current lane line from the current lane line layer data if the judgment result is that the predicted lane identification number is empty, and determine the second current lane line data as the target lane line data;

[0098] An acquisition subunit is used to extract the predicted lane line layer data corresponding to the predicted lane from the lane line data if the judgment result is that the predicted lane identification number is not empty; determine the predicted lane line length corresponding to the predicted lane line layer data, and acquire the target lane line data from the predicted lane line layer data based on the predicted lane line length and the current lane line length.

[0099] Optionally, the acquisition subunit is specifically used to:

[0100] Determining whether the sum of the predicted lane line length and the current lane line length is less than the set lane line length;

[0101] If the sum of the predicted lane line length and the current lane line length is greater than or equal to the set lane line length, first predicted lane line data having a first length is obtained from the predicted lane line layer data, where the first length is the length difference between the set lane line length and the current lane line length;

[0102] A set of the second current lane line data and the first predicted lane line data is determined as the target lane line data.

[0103] Optionally, the acquisition subunit is further used to:

[0104] If the sum of the predicted lane line length and the current lane line length is less than the set lane line length, the following steps are repeated:

[0105] Determine a prepared predicted lane of the vehicle based on the planned path data, extract prepared predicted lane line layer data corresponding to the prepared predicted lane from the lane line data, and determine the prepared predicted lane line length corresponding to the prepared predicted lane line layer data;

[0106] The predicted lane line length and the prepared predicted lane line length are added together to obtain a total predicted lane line length;

[0107] Until the sum of the total predicted lane line length and the current lane line length is greater than the set lane line length;

[0108] Acquire second predicted lane line data having a length equal to the predicted lane line length from the predicted lane line layer data; acquire preliminary predicted lane line data having a length equal to a second length from the preliminary predicted lane line layer data; the second length is the set lane line length minus the difference between the current lane line length and the second predicted lane line length;

[0109] A set of the second current lane line data, the second predicted lane line data and the preliminary predicted lane line data is determined as the target lane line data.

[0110] Optionally, the acquisition subunit is further used to:

[0111] If the lane identification number corresponding to the prepared predicted lane is empty, obtaining second predicted lane line data having a length equal to the length of the predicted lane line from the predicted lane line layer data;

[0112] A set of the second current lane line data and the second predicted lane line data is determined as the target lane line data.

[0113] Optionally, the set lane line length is determined based on at least one of the following conditions: the vehicle speed and the degree of change of road curvature.

[0114] The above-mentioned product can execute the method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] Embodiment 4

[0116] Figure 4 A structural block diagram of a vehicle provided in Embodiment 4 of the present invention, such as Figure 4 As shown, the vehicle includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of processors 410 in the vehicle can be one or more. Figure 4 A processor 410 is taken as an example; the processor 410, the memory 420, the input device 430 and the output device 440 in the vehicle can be connected via a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0117] The memory 420, as a computer-readable storage medium, can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the lane line extraction method in the embodiment of the present invention (for example, the acquisition module 310, the extraction module 320 and the fitting module 330 in the lane line extraction device). The processor 410 executes various functional applications and data processing of the vehicle by running the software programs, instructions and modules stored in the memory 420, that is, realizing the above-mentioned lane line extraction method.

[0118] The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 420 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 420 may further include a memory remotely arranged relative to the processor 410, and these remote memories may be connected to the vehicle via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0119] The input device 430 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the vehicle. The output device 440 may include a display device such as a display screen.

[0120] Embodiment 5

[0121] Embodiment five of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the lane line extraction method provided in all the embodiments of the invention of this application is implemented: obtaining a current position point and high-precision map data of a vehicle, wherein the high-precision map data includes lane line data and planned path data of the vehicle; based on the current position point and the planned path data, extracting target lane line data within a set lane line length in front of the lane where the vehicle is located from the lane line data; performing curve fitting on the target lane line data to obtain the lane line.

[0122] Any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0123] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0124] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0125] Computer program code for performing the operation of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0126] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A lane line extraction method, characterized in that: include: Obtaining the current location of the vehicle and high-precision map data, wherein the high-precision map data includes lane line data and planned path data of the vehicle; Based on the current position point and the planned path data, extracting target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data; Performing curve fitting on the target lane line data to obtain a lane line; The step of extracting target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data based on the current position point and the planned path data includes: Extracting current lane line layer data from the lane line data, wherein the current lane line layer data is lane line layer data corresponding to the current lane determined based on the current position point and the planned path data; Determine the distance between the current position point and the end position point in the current lane line layer data as the current lane line length; If the current lane line length is greater than or equal to the set lane line length, first current lane line data having a length of the set lane line length is obtained from the current lane line layer data, and the first current lane line data is determined as the target lane line data; If the current lane line length is less than the set lane line length, the predicted lane of the vehicle is determined based on the current position point and the planned path data, and it is determined whether the predicted lane identification number corresponding to the predicted lane is empty, and the target lane line data is obtained from the lane line data based on the judgment result.

2. The method according to claim 1, characterized in that Acquiring the current lane line data corresponding to the set lane line length from the current lane line layer data, including: Determine a projection point corresponding to the current position point in the current lane line layer data, where the projection point is a boundary point projected from the current position point onto the current lane boundary line; Obtaining the boundary points included in the current lane boundary line starting from the projection point and having a length equal to the set lane line length in the current lane line layer data; The sequence formed by the boundary points is determined as the first current lane line data.

3. The method according to claim 1, characterized in that Acquiring the target lane line data from the lane line data based on the judgment result, comprising: If the judgment result is that the predicted lane identification number is empty, obtaining second current lane line data having a length equal to the length of the current lane line from the current lane line layer data, and determining the second current lane line data as the target lane line data; If the judgment result is that the predicted lane identification number is not empty, the predicted lane line layer data corresponding to the predicted lane is extracted from the lane line data; the predicted lane line length corresponding to the predicted lane line layer data is determined, and the target lane line data is obtained from the predicted lane line layer data based on the predicted lane line length and the current lane line length.

4. The method according to claim 3, characterized in that Acquiring the target lane line data from the predicted lane line layer data based on the predicted lane line length and the current lane line length, comprising: Determining whether the sum of the predicted lane line length and the current lane line length is less than the set lane line length; If the sum of the predicted lane line length and the current lane line length is greater than or equal to the set lane line length, first predicted lane line data having a first length is obtained from the predicted lane line layer data, where the first length is the length difference between the set lane line length and the current lane line length; A set of the second current lane line data and the first predicted lane line data is determined as the target lane line data.

5. The method according to claim 4, characterized in that If the sum of the predicted lane line length and the current lane line length is less than the set lane line length, the following steps are repeated: Determine a prepared predicted lane of the vehicle based on the planned path data, extract prepared predicted lane line layer data corresponding to the prepared predicted lane from the lane line data, and determine the prepared predicted lane line length corresponding to the prepared predicted lane line layer data; The predicted lane line length and the prepared predicted lane line length are added together to obtain a total predicted lane line length; Until the sum of the total predicted lane line length and the current lane line length is greater than the set lane line length; Acquire second predicted lane line data having a length equal to the predicted lane line length from the predicted lane line layer data; acquire preliminary predicted lane line data having a length equal to a second length from the preliminary predicted lane line layer data; the second length is the set lane line length minus the difference between the current lane line length and the second predicted lane line length; A set of the second current lane line data, the second predicted lane line data and the preliminary predicted lane line data is determined as the target lane line data.

6. The method according to claim 5, characterized in that Before extracting the prepared predicted lane line layer data corresponding to the prepared predicted lane from the lane line data, the method further includes: If the lane identification number corresponding to the prepared predicted lane is empty, obtaining second predicted lane line data having a length equal to the length of the predicted lane line from the predicted lane line layer data; A set of the second current lane line data and the second predicted lane line data is determined as the target lane line data.

7. The method according to any one of claims 1 to 6, characterized in that: The set lane line length is determined according to at least one of the following conditions: the vehicle speed and the degree of change of the road curvature.

8. A lane line extraction device, characterized in that: include: The acquisition module is used to obtain the current location of the vehicle and high-precision map data, which includes lane line data and the planned path data of the vehicle; An extraction module, configured to extract target lane line data within a set lane line length ahead of the lane where the vehicle is located from the lane line data of the high-precision map data based on the current position point and the planned path data; A fitting module, used for performing curve fitting on the target lane line data to obtain a lane line; The extraction module comprises: An extraction unit, configured to extract current lane line layer data from the lane line data, wherein the current lane line layer data is lane line layer data corresponding to the current lane determined based on the current position point and the planned path data; A first determining unit, configured to determine the distance between the current position point and the end position point in the current lane line layer data as the current lane line length; A second determination unit is used for obtaining first current lane line data having a length of the set lane line length from the current lane line layer data if the current lane line length is greater than or equal to the set lane line length, and determining the first current lane line data as the target lane line data; An acquisition unit is used to determine the predicted lane of the vehicle based on the current position point and the planned path data if the current lane line length is less than the set lane line length, determine whether the predicted lane identification number corresponding to the predicted lane is empty, and acquire the target lane line data from the lane line data based on the judgment result.

9. A vehicle, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the lane line extraction method as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the lane line extraction method as described in any one of claims 1-7 is implemented.

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