Lane selection label generation method and related equipment

Abstract

The invention discloses a lane selection label generation method and related equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining scene data, traversing all frames in the scene data, determining a corresponding future frame for trajectory planning based on a current frame, and obtaining the map information of the future frame and the corresponding lane center line information, the map information comprises vehicle coordinates of a vehicle in a future frame, and the lane center line information comprises all lane lines corresponding to the future frame, a step of determining a minimum projection distance of the vehicle coordinates projected to all lane center lines, and a step of determining a target lane line where the vehicle is located in the future based on the minimum projection distance. According to the method, the map information corresponding to the future frame is obtained based on the scene data, the target lane where the vehicle is located at a certain moment in the future is accurately determined by combining the minimum projection distance of the vehicle coordinate projected to the center line of all lanes, and then the accurate lane selection label is generated.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for generating lane selection labels and related equipment. Background Technology

[0002] In modern transportation systems, lane label data is used to reconstruct travel routes, assist in determining liability for accidents, optimize operational behavior, and improve operational efficiency and safety, making it crucial for post-mortem analysis.

[0003] However, with rapid economic development, the number of vehicles is constantly increasing, and the collected historical driving data of vehicles is also growing exponentially, making it very difficult to generate accurate lane selection labels based on a large amount of historical driving data.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is related technology. Summary of the Invention

[0005] The main purpose of this application is to provide a method and related equipment for generating lane selection labels, aiming to solve the technical problem of how to generate accurate lane selection labels.

[0006] To achieve the above objectives, this application proposes a method for generating lane selection labels, the method comprising:

[0007] Acquire scene data;

[0008] Traverse all frames in the scene data and determine the corresponding future frames for trajectory planning based on the current frame;

[0009] Obtain map information and corresponding lane centerline information for the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame;

[0010] The minimum projection distance of the vehicle coordinates onto the center lines of all lanes is determined, and based on the minimum projection distance, the target lane line where the vehicle will be located in the future is determined to generate a lane selection label.

[0011] In one embodiment, the step of determining the corresponding future frame for trajectory planning based on the current frame includes any one of the following:

[0012] Based on the speed of the vehicle in the current frame, determine the corresponding future frame for trajectory planning;

[0013] Based on the current frame, determine the frame velocity of different frames, and based on the frame velocity, determine the corresponding future frames for trajectory planning.

[0014] In one embodiment, before the step of obtaining the future frame, the map information of the future frame, and the corresponding lane centerline information, the following steps are included:

[0015] Based on the scenario data, determine multiple sets of coordinate sequences for lane centerlines;

[0016] Based on the coordinate sequence, a global grid is constructed, and each coordinate in the coordinate sequence is mapped to the global grid to obtain a global perception map with a preset accuracy, so as to determine the vehicle coordinates of the vehicle in the future frame based on the global perception map.

[0017] In one embodiment, the step of constructing a global grid based on the coordinate sequence and mapping each coordinate in the coordinate sequence to the global grid to obtain a globally perceived map with a preset accuracy includes:

[0018] Based on the coordinate sequence, the boundary values ​​of the lane centerline are determined, and a global grid is constructed based on the boundary values.

[0019] Each coordinate in the coordinate sequence is mapped to the global grid to obtain the target grid;

[0020] Based on the target grid, find the center lines of the first lane and the second lane that have overlapping areas, determine the number of times the center lines of the first lane and the second lane overlap, and determine whether the number of overlaps is greater than or equal to a preset overlap threshold.

[0021] If the number of overlaps is greater than or equal to a preset overlap threshold, the first lane centerline and the second lane centerline are added to the same lane centerline set; or, if the number of overlaps is less than the preset overlap threshold, the first lane centerline and the second lane centerline are added to different lane centerline sets respectively.

[0022] Based on the set of all lane centerlines, determine the corresponding perception map for the current frame.

[0023] In one embodiment, the step of mapping each coordinate in the coordinate sequence to the global grid to obtain the target grid further includes any one of the following:

[0024] Map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain the initial grid. Perform a floor-down thinning operation on the initial grid to obtain the target grid.

[0025] Each coordinate in the coordinate sequence is mapped to its corresponding position in the grid to obtain the initial grid. The initial grid is then subjected to a rounding-based thinning operation to obtain the target grid.

[0026] In one embodiment, the step of determining the coordinate sequence of multiple lane centerlines based on the scene data further includes:

[0027] Based on the scene data, the vehicle location information is determined, wherein the vehicle location information includes the vehicle coordinates and the vehicle heading angle;

[0028] Based on the vehicle location information, a vehicle coordinate system is constructed;

[0029] Obtain multiple coordinates corresponding to the center line of each lane, and determine the first distance from each coordinate to the vehicle based on the vehicle coordinate system;

[0030] Each coordinate is saved sequentially into a coordinate sequence based on the first distance in ascending order, resulting in multiple sets of lane centerline coordinate sequences.

[0031] In one embodiment, after the step of saving each coordinate sequentially into a coordinate sequence based on a first distance in ascending order to obtain multiple sets of lane centerline coordinate sequences, the method further includes:

[0032] Determine the first coordinate in each lane centerline coordinate sequence, and determine the second coordinate adjacent to the first coordinate, wherein the distance from the second coordinate to the vehicle is less than the distance from the first coordinate to the vehicle;

[0033] Calculate the second distance between the first coordinate and the second coordinate, and determine whether the second distance is less than a preset distance threshold;

[0034] If the value is less than the first coordinate, delete the first coordinate and obtain a new sequence of lane centerline coordinates for each lane based on the sequence of lane centerline coordinates after the deletion operation.

[0035] In one embodiment, the step of constructing a vehicle coordinate system based on the vehicle location information further includes:

[0036] Extract the vehicle coordinates and vehicle heading angle from the vehicle location information;

[0037] The vehicle coordinates are set as the origin, and the direction corresponding to the vehicle heading angle is set as the positive y-axis. Based on the origin and the positive y-axis, a vehicle coordinate system is constructed for each set of local perception type map data.

[0038] Furthermore, to achieve the above objectives, this application also proposes a lane selection label generation device, which includes:

[0039] The first acquisition module is used to acquire scene data;

[0040] The first determining module is used to traverse all frames in the scene data and determine the corresponding future frame for trajectory planning based on the current frame.

[0041] The second acquisition module is used to acquire map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame.

[0042] The second determining module is used to determine the minimum projection distance of the vehicle coordinates onto the center lines of all lanes, and based on the minimum projection distance, to determine the target lane line where the vehicle will be located in the future, so as to generate a lane selection label.

[0043] In one embodiment, the first determining module includes:

[0044] The first determining unit is used to determine the corresponding future frame for trajectory planning based on the speed of the corresponding vehicle in the current frame.

[0045] The second determining unit is used to determine the frame rate of different frames based on the current frame, and to determine the corresponding future frames for trajectory planning based on the frame rate.

[0046] In one embodiment, the lane label generation device further includes a construction module, the construction module comprising:

[0047] The third determining unit is used to determine multiple sets of lane centerline coordinate sequences based on the scene data;

[0048] The first construction unit is used to construct a global grid based on the coordinate sequence, and map each coordinate in the coordinate sequence to the global grid to obtain a global perception map with a preset accuracy, so as to determine the vehicle coordinates of the vehicle in the future frame based on the global perception map.

[0049] In one embodiment, the building module further includes:

[0050] The second construction unit is used to determine the boundary values ​​of the lane centerline based on the coordinate sequence, and to construct a global mesh based on the boundary values;

[0051] A mapping unit is used to map each coordinate in the coordinate sequence to the global grid to obtain the target grid;

[0052] The search unit is used to search for the center lines of the first lane and the center lines of the second lane that have overlapping areas based on the target grid, and to determine the number of times the center lines of the first lane and the center lines of the second lane overlap, and to determine whether the number of overlaps is greater than or equal to a preset number of overlaps threshold.

[0053] The addition unit is used to add the first lane centerline and the second lane centerline to the same lane centerline set if the number of overlaps is greater than or equal to a preset overlap number threshold; or, if the number of overlaps is less than the preset overlap number threshold, add the first lane centerline and the second lane centerline to different lane centerline sets respectively.

[0054] The acquisition unit is used to determine the corresponding perception map for the current frame based on the set of all lane centerlines.

[0055] In one embodiment, the building module further includes:

[0056] The first thinning unit is used to map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain an initial grid, and to perform a thinning operation based on rounding down on the initial grid to obtain the target grid;

[0057] The second thinning unit is used to map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain an initial grid, and to perform a rounding-based thinning operation on the initial grid to obtain the target grid.

[0058] In one embodiment, the building module further includes:

[0059] The fourth determining unit is used to determine vehicle position information based on the scene data, wherein the vehicle position information includes vehicle coordinates and vehicle heading angle;

[0060] The third construction unit is used to construct a vehicle coordinate system based on the vehicle position information;

[0061] The fifth determining unit is used to obtain multiple coordinates corresponding to the center line of each lane, and determine the first distance from each coordinate to the vehicle based on the vehicle coordinate system;

[0062] The storage unit is used to save each coordinate in ascending order based on the first distance to the coordinate sequence, thereby obtaining multiple sets of lane centerline coordinate sequences.

[0063] In one embodiment, the building module further includes:

[0064] The sixth determining unit is used to determine the first coordinate in each group of lane centerline coordinate sequences, and to determine the second coordinate adjacent to the first coordinate, wherein the distance from the second coordinate to the vehicle is less than the distance from the first coordinate to the vehicle;

[0065] The calculation unit is used to calculate the second distance between the first coordinate and the second coordinate, and to determine whether the second distance is less than a preset distance threshold.

[0066] The deletion unit is used to delete the first coordinate if it is less than the first coordinate, and to obtain a new sequence of lane centerline coordinates for each group of lane centerline coordinates based on the sequence of lane centerline coordinates after the deletion operation.

[0067] In one embodiment, the building module further includes:

[0068] The extraction unit is used to extract the vehicle coordinates and vehicle heading angle from the vehicle location information;

[0069] The setting unit is used to set the vehicle coordinates as the origin, set the direction corresponding to the vehicle heading angle as the positive y-axis direction, and construct the vehicle coordinate system corresponding to each set of local perception type map data based on the origin and the positive y-axis direction.

[0070] In addition, to achieve the above objectives, this application also proposes a lane selection label generation device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the lane selection label generation method as described above.

[0071] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the lane selection label generation method described above.

[0072] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the lane selection label generation method described above.

[0073] One or more technical solutions proposed in this application have at least the following technical effects:

[0074] This application proposes a method and related equipment for generating lane selection labels, relating to the field of data processing technology. Compared to related technologies where the number of vehicles is constantly increasing and the collected historical vehicle driving data is growing exponentially, making it extremely difficult to generate accurate lane selection labels based on a large amount of historical vehicle driving data, this application first acquires scene data. Then, it iterates through all frames in the scene data, determines the corresponding future frame for trajectory planning based on the current frame, further acquires the map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame. Finally, it determines the minimum projection distance of the vehicle coordinates onto all lane centerlines, and based on the minimum projection distance, determines the target lane line where the vehicle will be located in the future.

[0075] Understandably, this application obtains map information corresponding to future frames based on scene data, and then combines the minimum projection distance of the vehicle coordinates onto the center line of all lanes to accurately determine the target lane where the vehicle is at a certain time in the future, thereby generating an accurate lane selection label. Attached Figure Description

[0076] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0077] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0078] Figure 1 This is a flowchart illustrating an embodiment of the method for generating lane selection labels in this application.

[0079] Figure 2 This is a flowchart illustrating Embodiment 2 of the method for generating lane selection labels in this application.

[0080] Figure 3 This is a flowchart illustrating Embodiment 3 of the method for generating lane selection labels in this application.

[0081] Figure 4 This is a schematic diagram of the module structure of the lane selection label generation device according to an embodiment of this application;

[0082] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the lane selection label generation method in this application embodiment.

[0083] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0084] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0085] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0086] The main solution in this application embodiment is:

[0087] In this embodiment, for ease of description, the following description will focus on the lane selection label generation device as the execution subject.

[0088] Due to the current technology, with the rapid economic development, the number of vehicles is constantly increasing, and the collected historical driving data of vehicles is also growing exponentially, making it very difficult to generate accurate lane selection labels based on a large amount of historical driving data.

[0089] This application provides a solution that: acquires scene data, traverses all frames in the scene data, determines the corresponding future frame for trajectory planning based on the current frame, acquires map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates in the future frame, the lane centerline information includes all lane lines corresponding to the future frame, determines the minimum projection distance of the vehicle coordinates onto all lane centerlines, and determines the target lane line where the vehicle will be located in the future based on the minimum projection distance. This application, based on scene data, acquires map information corresponding to future frames, and combines this with the minimum projection distance of the vehicle coordinates onto all lane centerlines to accurately determine the target lane where the vehicle will be located at a certain time in the future, thereby generating an accurate lane selection label.

[0090] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, grid communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or lane selection label generation device capable of performing the above functions. The following uses lane determination as an example to describe this embodiment and the subsequent embodiments.

[0091] Based on this, embodiments of this application provide a method for generating lane selection labels, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the lane selection label generation method of this application.

[0092] In this embodiment, the lane selection label generation method includes steps S100 to S400:

[0093] Step S100: Obtain scene data;

[0094] It should be noted that scene data refers to the vehicle's historical driving data. Specifically, scene data is data collected by the vehicle's sensors during the vehicle's driving process. Scene data includes lane data, pedestrian data on the roadside, etc., and the vehicle's sensors include vision sensors, radar, etc.

[0095] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, grid communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or lane selection label generation device capable of performing the above functions. The following description uses a lane selection label generation device as an example to illustrate this embodiment and the subsequent embodiments.

[0096] In this application, specific application scenarios may include:

[0097] Vehicles traveling on the road collect road data, vehicle driving data, and traffic environment data through sensors such as lidar, radar, and cameras. Furthermore, the lane selection tag generation device receives the road data, vehicle driving data, and traffic environment data collected by the sensors.

[0098] Step S200: Traverse all frames in the scene data and determine the corresponding future frames for trajectory planning based on the current frame;

[0099] It should be noted that in this application, the future frame is determined based on the current frame, and the vehicle's travel distance is an important reference factor in determining the future frame.

[0100] Based on the specific vehicle driving scenario, there should be a minimum value for the distance the vehicle travels from the current frame to the future frame. If the calculated predicted driving distance is less than the minimum value, then the minimum value is used as the actual driving distance, and the new future frame is determined based on the actual driving distance.

[0101] For example, suppose the current frame is frame 10. The minimum distance the vehicle travels in the time interval from the current frame to the next 60 frames is 50 meters. If the calculated predicted distance is 30 meters, then 50 meters is used as the actual distance the vehicle travels. Furthermore, if it is determined that the time required for the vehicle to travel 50 meters is 70 frames, then frame 80 is used as the actual future frame. Consequently, the lane label generation device needs to determine the lane the vehicle is in at frame 80.

[0102] Specifically, the step of determining the corresponding future frame for trajectory planning based on the current frame includes any one of steps S210 to S220:

[0103] Step S210: Based on the speed of the vehicle in the current frame, determine the future frame for trajectory planning;

[0104] It is understood that this embodiment uses the speed of the vehicle corresponding to the current frame as the average speed, and then determines the predicted driving distance of the vehicle. Based on the predicted driving distance and the minimum driving distance, the actual driving distance of the vehicle is determined. The predicted driving distance is 6*curr_v (since the inter-frame frequency is 10HZ, that is, the next 60 frames are the next 6 seconds), and curr_v refers to the speed of the vehicle corresponding to the current frame.

[0105] Furthermore, the lane label generation device uses the initialized defined distance accumulator to move frame by frame starting from the current frame, calculates the travel distance of the vehicle between two adjacent frames in turn, and accumulates the travel distance until the accumulated result is greater than or equal to the actual travel distance, thus obtaining the actual future frame.

[0106] Step S220: Based on the current frame, determine the frame speed of different frames, and based on the frame speed, determine the corresponding future frames for trajectory planning.

[0107] It is understood that this embodiment uses the speed of the corresponding vehicle in different frames to determine the travel distance of the vehicle between adjacent frames, and then determines the predicted travel distance of the vehicle. Based on the predicted travel distance and the minimum travel distance, the actual travel distance of the vehicle is determined.

[0108] Furthermore, the lane label generation device uses the initialized defined distance accumulator to move frame by frame starting from the current frame, calculates the travel distance of the vehicle between two adjacent frames in turn, and accumulates the travel distance until the accumulated result is greater than or equal to the actual travel distance, thus obtaining the actual future frame.

[0109] Step S300: Obtain the map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame.

[0110] It should be noted that the lane centerline is a characteristic line formed by connecting the center points of each lane in sequence. The lane centerline can reflect the horizontal position and curvature of the road.

[0111] Step S400: Determine the minimum projection distance of the vehicle coordinates onto the center lines of all lanes. Based on the minimum projection distance, determine the target lane line where the vehicle will be located in the future, so as to generate a lane selection label.

[0112] It should be noted that if the projection distance meets the projection threshold, it indicates that the vehicle is driving in the lane, and the lane corresponding to the minimum projection is set as the target lane. If the projection distance does not meet the projection threshold, it indicates that the vehicle is not driving in the lane.

[0113] One or more technical solutions proposed in this application have at least the following technical effects:

[0114] This application proposes a method and related equipment for generating lane selection labels, relating to the field of data processing technology. Compared to related technologies where the number of vehicles is constantly increasing and the collected historical vehicle driving data is growing exponentially, making it extremely difficult to generate accurate lane selection labels based on a large amount of historical vehicle driving data, this application first acquires scene data. Then, it iterates through all frames in the scene data, determines the corresponding future frame for trajectory planning based on the current frame, further acquires the map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame. Finally, it determines the minimum projection distance of the vehicle coordinates onto all lane centerlines, and based on the minimum projection distance, determines the target lane line where the vehicle will be located in the future.

[0115] Understandably, this application obtains map information corresponding to future frames based on scene data, and then combines the minimum projection distance of the vehicle coordinates onto the center line of all lanes to accurately determine the target lane where the vehicle is at a certain time in the future, thereby generating an accurate lane selection label.

[0116] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Before step S300, the lane selection label generation method further includes steps A100 to A200:

[0117] Step A100: Based on the scene data, determine multiple sets of coordinate sequences for lane centerlines;

[0118] It should be noted that, specifically, the step of determining the coordinate sequence of multiple lane centerlines based on the scene data further includes steps A110 to A140:

[0119] Step A110: Based on the scene data, determine the vehicle position information, wherein the vehicle position information includes the vehicle coordinates and the vehicle heading angle;

[0120] It should be noted that vehicle position information typically includes the vehicle's coordinates (x, y) and heading angle. Specifically, the steps for determining vehicle position information based on the aforementioned scene data include:

[0121] First, the lane confirmation device collects all information about the vehicle's position from the scene data. This information includes the vehicle's GPS coordinates, the vehicle's IMU (Inertial Measurement Unit) data, the vehicle's wheel speed information, the vehicle's steering angle, and so on.

[0122] Understandably, lane confirmation devices use data fusion techniques to improve the accuracy of location information because a single data source may not be accurate enough. Common data fusion techniques include Kalman filtering and particle filtering.

[0123] Furthermore, the lane confirmation device uses GPS data to obtain the vehicle's global coordinates (longitude, latitude, and altitude).

[0124] The vehicle's heading angle can be determined using magnetometer data from the IMU (Important Measure) or estimated using the vehicle's steering angle and trajectory. If the scene data includes IMU data, the lane confirmation device can directly use the magnetometer data to determine the heading angle. If there is no IMU data, the lane confirmation device may need to use other methods (such as visual odometry, wheel speed measurement, etc.) to estimate the heading angle.

[0125] Finally, the lane label generation device obtains vehicle position information with the vehicle's coordinates and heading angle.

[0126] Step A120: Based on the vehicle location information, construct a vehicle coordinate system;

[0127] It's important to note that constructing the Vehicle Coordinate System (VCS) is a crucial step in autonomous driving and vehicle navigation, aiding in understanding and controlling the vehicle's position and movement in space. The VCS is typically a two-dimensional coordinate system with its origin at the center of the vehicle's rear axle, the x-axis pointing forward, and the y-axis pointing to the left. The steps for constructing the VCS are as follows:

[0128] Understandably, since the vehicle is in motion, the vehicle coordinate system needs to be updated in real time to reflect the vehicle's latest position and orientation, which is usually achieved through vehicle sensor data (such as GPS, IMU, wheel speedometer, etc.).

[0129] Specifically, the step of constructing a vehicle coordinate system based on the vehicle location information further includes steps A121 to A122:

[0130] Step A121: Extract the vehicle coordinates and vehicle heading angle from the vehicle location information;

[0131] Step A122: Set the vehicle coordinates as the origin, set the direction corresponding to the vehicle heading angle as the positive y-axis direction, and construct the vehicle coordinate system corresponding to each set of local perception type map data based on the origin and the positive y-axis direction.

[0132] It is understandable that the lane centerline may be a curved curve. In this case, to save the coordinates of the lane centerline sequentially into the coordinate sequence, a reference coordinate is needed. As vehicles move in the same direction in the lane over time, using the vehicle as the origin to construct a coordinate system can provide a basis for subsequent steps to save the coordinates of the lane centerline sequentially into the coordinate sequence.

[0133] Step A130: Obtain multiple coordinates corresponding to the center line of each lane, and determine the first distance from each coordinate to the vehicle based on the vehicle coordinate system;

[0134] It should be noted that the multiple coordinates corresponding to the center line of each lane are not obtained based on the vehicle coordinate system.

[0135] Step A140: Save each coordinate in ascending order of the first distance to the coordinate sequence to obtain multiple sets of lane centerline coordinate sequences.

[0136] Specifically, after the step of saving each coordinate in ascending order based on the first distance to a coordinate sequence to obtain multiple sets of lane centerline coordinate sequences, the method further includes steps A150 to A170:

[0137] Step A150: Determine the first coordinate in each lane centerline coordinate sequence, and determine the second coordinate adjacent to the first coordinate, wherein the distance from the second coordinate to the vehicle is less than the distance from the first coordinate to the vehicle;

[0138] Step A160: Calculate the second distance between the first coordinate and the second coordinate, and determine whether the second distance is less than a preset distance threshold;

[0139] Step A170: If the value is less than the first coordinate, delete the first coordinate. Based on the lane centerline coordinate sequence after the deletion operation, obtain a new lane centerline coordinate sequence.

[0140] Understandably, performing a thinning operation on the coordinates in the lane coordinate sequence reduces the number of coordinates in the sequence, thereby reducing the computational load in subsequent steps and improving real-time performance.

[0141] Step A200: Based on the coordinate sequence, construct a global grid and map each coordinate in the coordinate sequence to the global grid to obtain a global perception map with a preset accuracy, so as to determine the vehicle coordinates of the vehicle in the future frame based on the global perception map.

[0142] Specifically, the step of constructing a global grid based on the coordinate sequence and mapping each coordinate in the coordinate sequence to the global grid to obtain a globally perceived map with a preset accuracy includes steps A210 to A250:

[0143] Step A210: Based on the coordinate sequence, determine the boundary values ​​of the lane centerline, and construct a global mesh based on the boundary values;

[0144] It should be noted that the size of the global grid is related to the boundary values ​​of the lane centerline. For example, if the horizontal coordinate range of the boundary values ​​is [0, 5] and the vertical coordinate range is [0, 5], then the size of the global grid is 5x5.

[0145] Step A220: Map each coordinate in the coordinate sequence to the global grid to obtain the target grid;

[0146] Step A230: Based on the target grid, find the center lines of the first lane and the second lane that have overlapping areas, determine the number of times the center lines of the first lane and the second lane overlap, and determine whether the number of overlaps is greater than or equal to a preset overlap threshold.

[0147] Step A240: If the number of overlaps is greater than or equal to a preset overlap threshold, add the first lane centerline and the second lane centerline to the same lane centerline set; or, if the number of overlaps is less than the preset overlap threshold, add the first lane centerline and the second lane centerline to different lane centerline sets respectively.

[0148] It should be noted that in the perception map, the perception results of the same sensor for the same lane line are not the same at different times. For example, at the first time, the perception result corresponding to lane line 1 is lane line 1, while at the second time, the perception result corresponding to lane line 1 becomes lane line 2, which reduces the accuracy of the perception animation.

[0149] It is understandable that if the number of overlaps between the center lines of the first lane and the center lines of the second lane is greater than or equal to a preset overlap threshold, it indicates that the two lane lines may be the same lane line. Therefore, the center lines of the first lane and the center lines of the second lane are placed in the same set of lane center lines.

[0150] Step A250: Determine the corresponding perception map for the current frame based on the set of all lane centerlines.

[0151] Understandably, placing lane centerlines with high overlap into the same set of lane centerlines can result in a more accurate perception map.

[0152] Based on the first and second embodiments of this application, in the third embodiment of this application, the content that is the same as or similar to that in embodiments one and two above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3The step of mapping each coordinate in the coordinate sequence to the global grid to obtain the target grid further includes any one of steps A221 to A222:

[0153] Step A221: Map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain the initial grid; perform a floor-down thinning operation on the initial grid to obtain the target grid.

[0154] Step A222: Map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain the initial grid. Perform a rounding-based thinning operation on the initial grid to obtain the target grid.

[0155] Taking rounding down as an example, firstly, the int (round down) operator is used to transform the coordinate point to the corresponding position (temp_x, temp_y) in the grid, where temp_x = int(temp_x) - int(x_min), temp_y = int(temp_y) - int(y_min), and (temp_x, temp_y) represents the grid position corresponding to the previous coordinate point.

[0156] A list `polyline` is constructed to store the coordinates of the new lane centerline after thinning. If the list `polyline` is empty, `point` is added to the list, and the (temp_x, temp_y) position in the grid is set to 1. No further checks are performed on this coordinate point. Here, `point` represents the coordinates on the lane centerline. If the list `polyline` is not empty, further checks are required on this coordinate point. The specific steps are as follows:

[0157] Check the grid position value (temp_x, temp_y) corresponding to the current coordinate point. If it is 0, it means that the coordinate point appears for the first time at the grid position (temp_x, temp_y), and this position is marked as 1. If it is 1, it means that the coordinate point has already been marked, so skip this point. If the coordinate point appears for the first time at the grid position (temp_x, temp_y), further determination is needed to determine whether to retain the coordinate point.

[0158] Furthermore, if the distance between the current coordinate point and the previously added point in the polyline list is less than 1, the two points are considered too close, and the current point can be ignored, thus achieving thinning. Otherwise, the current coordinate point is added to the polyline list and marked in the grid.

[0159] Understandably, in path planning, excessively dense waypoints not only increase the burden of computation and storage, but may also lead to path redundancy and reduced efficiency. Therefore, it is necessary to perform sparse processing while preserving the shape features and trajectory information of the path to ensure the accuracy and real-time performance of path planning.

[0160] In this embodiment, by thinning the computation, the amount of computation is reduced, the computational efficiency is improved, and thus, real-time performance is guaranteed.

[0161] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the lane selection label generation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0162] This application also provides a lane selection label generation device, please refer to... Figure 4 The lane selection label generating device includes:

[0163] First acquisition module 10, the first acquisition module is used to acquire scene data;

[0164] The first determining module 20 is used to traverse all frames in the scene data and determine the corresponding future frame for trajectory planning based on the current frame.

[0165] The second acquisition module 30 is used to acquire map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame.

[0166] The second determining module 40 is used to determine the minimum projection distance of the vehicle coordinates onto the center lines of all lanes, and based on the minimum projection distance, determine the target lane line where the vehicle will be located in the future.

[0167] In one embodiment, the first determining module includes:

[0168] The first determining unit is used to determine the corresponding future frame for trajectory planning based on the speed of the corresponding vehicle in the current frame.

[0169] The second determining unit is used to determine the frame rate of different frames based on the current frame, and to determine the corresponding future frames for trajectory planning based on the frame rate.

[0170] In one embodiment, the lane label generation device further includes a construction module, the construction module comprising:

[0171] The third determining unit is used to determine multiple sets of lane centerline coordinate sequences based on the scene data;

[0172] The first construction unit is used to construct a global grid based on the coordinate sequence, and map each coordinate in the coordinate sequence to the global grid to obtain a global perception map with a preset accuracy, so as to determine the vehicle coordinates of the vehicle in the future frame based on the global perception map.

[0173] In one embodiment, the building module further includes:

[0174] The second construction unit is used to determine the boundary values ​​of the lane centerline based on the coordinate sequence, and to construct a global mesh based on the boundary values;

[0175] A mapping unit is used to map each coordinate in the coordinate sequence to the global grid to obtain the target grid;

[0176] The search unit is used to search for the center lines of the first lane and the center lines of the second lane that have overlapping areas based on the target grid, and to determine the number of times the center lines of the first lane and the center lines of the second lane overlap, and to determine whether the number of overlaps is greater than or equal to a preset number of overlaps threshold.

[0177] The merging unit is used to add the first lane centerline and the second lane centerline to the same lane centerline set if the number of overlaps is greater than or equal to a preset overlap number threshold; or, if the number of overlaps is less than the preset overlap number threshold, add the first lane centerline and the second lane centerline to different lane centerline sets respectively.

[0178] The acquisition unit is used to determine the corresponding perception map for the current frame based on the set of all lane centerlines.

[0179] In one embodiment, the building module further includes:

[0180] The first thinning unit is used to map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain an initial grid, and to perform a thinning operation based on rounding down on the initial grid to obtain the target grid;

[0181] The second thinning unit is used to map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain an initial grid, and to perform a rounding-based thinning operation on the initial grid to obtain the target grid.

[0182] In one embodiment, the building module further includes:

[0183] The fourth determining unit is used to determine vehicle position information based on the scene data, wherein the vehicle position information includes vehicle coordinates and vehicle heading angle;

[0184] The third construction unit is used to construct a vehicle coordinate system based on the vehicle position information;

[0185] The fifth determining unit is used to obtain multiple coordinates corresponding to the center line of each lane, and determine the first distance from each coordinate to the vehicle based on the vehicle coordinate system;

[0186] The storage unit is used to save each coordinate in ascending order based on the first distance to the coordinate sequence, thereby obtaining multiple sets of lane centerline coordinate sequences.

[0187] In one embodiment, the building module further includes:

[0188] The sixth determining unit is used to determine the first coordinate in each group of lane centerline coordinate sequences, and to determine the second coordinate adjacent to the first coordinate, wherein the distance from the second coordinate to the vehicle is less than the distance from the first coordinate to the vehicle;

[0189] The calculation unit is used to calculate the second distance between the first coordinate and the second coordinate, and to determine whether the second distance is less than a preset distance threshold.

[0190] The deletion unit is used to delete the first coordinate if it is less than the first coordinate, and to obtain a new sequence of lane centerline coordinates for each group of lane centerline coordinates based on the sequence of lane centerline coordinates after the deletion operation.

[0191] In one embodiment, the building module further includes:

[0192] The extraction unit is used to extract the vehicle coordinates and vehicle heading angle from the vehicle location information;

[0193] The setting unit is used to set the vehicle coordinates as the origin, set the direction corresponding to the vehicle heading angle as the positive y-axis direction, and construct the vehicle coordinate system corresponding to each set of local perception type map data based on the origin and the positive y-axis direction.

[0194] The lane selection label generation device provided in this application, employing the lane selection label generation method in the above embodiments, can solve the technical problem of lane selection label generation. Compared with the prior art, the beneficial effects of the lane selection label generation device provided in this application are the same as those of the lane selection label generation method provided in the above embodiments, and other technical features in the lane selection label generation device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0195] This application provides a lane selection label generation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the lane selection label generation method in Embodiment 1 above.

[0196] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a lane selection tag generation device suitable for implementing embodiments of this application. The lane selection tag generation device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The lane label generation device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0197] like Figure 5 As shown, the lane selection tag generation device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the lane selection tag generation device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the lane selection tag generating device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show lane selection tag generating devices with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0198] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0199] The lane selection label generation device provided in this application, employing the lane selection label generation method in the above embodiments, can solve the technical problem of lane selection label generation. Compared with the prior art, the beneficial effects of the lane selection label generation device provided in this application are the same as those of the lane selection label generation method provided in the above embodiments, and other technical features in this lane selection label generation device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0200] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0201] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0202] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the lane label generation method in the above embodiments.

[0203] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0204] The aforementioned computer-readable storage medium may be included in the lane selection label generating device; or it may exist independently and not be assembled into the lane selection label generating device.

[0205] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the lane selection label generating device, cause the lane selection label generating device to:

[0206] Acquire scene data;

[0207] Traverse all frames in the scene data and determine the corresponding future frames for trajectory planning based on the current frame;

[0208] Obtain map information and corresponding lane centerline information for the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame;

[0209] The minimum projection distance of the vehicle coordinates onto the center lines of all lanes is determined, and based on the minimum projection distance, the target lane line where the vehicle will be located in the future is determined to generate a lane selection label.

[0210] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can 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 can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0211] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0212] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0213] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described lane selection label generation method, thereby solving the technical problem of lane selection label generation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the lane selection label generation method provided in the above embodiments, and will not be repeated here.

[0214] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the lane label generation method described above.

[0215] The computer program product provided in this application can solve the technical problem of lane selection label generation. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the lane selection label generation method provided in the above embodiments, and will not be repeated here.

[0216] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for generating lane selection labels, characterized in that, The method for generating lane selection labels includes: Acquire scene data; Traverse all frames in the scene data and determine the corresponding future frames for trajectory planning based on the current frame; Obtain map information and corresponding lane centerline information for the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame; The minimum projection distance of the vehicle coordinates onto the center lines of all lanes is determined, and based on the minimum projection distance, the target lane line where the vehicle will be located in the future is determined to generate a lane selection label.

2. The method for generating lane selection labels as described in claim 1, characterized in that, The step of determining the corresponding future frame for trajectory planning based on the current frame includes any one of the following: Based on the speed of the vehicle in the current frame, determine the corresponding future frame for trajectory planning; Based on the current frame, determine the frame velocity of different frames, and based on the frame velocity, determine the corresponding future frames for trajectory planning.

3. The method for generating lane selection labels as described in claim 1, characterized in that, Before the steps of obtaining the future frame, the map information of the future frame, and the corresponding lane centerline information, the following steps are included: Based on the scenario data, determine multiple sets of coordinate sequences for lane centerlines; Based on the coordinate sequence, a global grid is constructed, and each coordinate in the coordinate sequence is mapped to the global grid to obtain a global perception map with a preset accuracy, so as to determine the vehicle coordinates of the vehicle in the future frame based on the global perception map.

4. The method for generating lane selection tags as described in claim 3, characterized in that, The step of constructing a global grid based on the coordinate sequence and mapping each coordinate in the coordinate sequence to the global grid to obtain a globally perceived map with a preset accuracy includes: Based on the coordinate sequence, the boundary values ​​of the lane centerline are determined, and a global grid is constructed based on the boundary values. Each coordinate in the coordinate sequence is mapped to the global grid to obtain the target grid; Based on the target grid, find the center lines of the first lane and the second lane that have overlapping areas, determine the number of times the center lines of the first lane and the second lane overlap, and determine whether the number of overlaps is greater than or equal to a preset overlap threshold. If the number of overlaps is greater than or equal to a preset overlap threshold, the first lane centerline and the second lane centerline are added to the same lane centerline set; or, if the number of overlaps is less than the preset overlap threshold, the first lane centerline and the second lane centerline are added to different lane centerline sets respectively. Based on the set of all lane centerlines, determine the corresponding perception map for the current frame.

5. The method for generating lane selection tags as described in claim 4, characterized in that, The step of mapping each coordinate in the coordinate sequence to the global grid to obtain the target grid further includes any one of the following: Map each coordinate in the coordinate sequence to the corresponding position in the grid to obtain the initial grid. Perform a floor-down thinning operation on the initial grid to obtain the target grid. Each coordinate in the coordinate sequence is mapped to its corresponding position in the grid to obtain the initial grid. The initial grid is then subjected to a rounding-based thinning operation to obtain the target grid.

6. The method for generating lane selection labels as described in claim 3, characterized in that, The step of determining the coordinate sequence of multiple lane centerlines based on the scene data further includes: Based on the scene data, the vehicle location information is determined, wherein the vehicle location information includes the vehicle coordinates and the vehicle heading angle; Based on the vehicle location information, a vehicle coordinate system is constructed; Obtain multiple coordinates corresponding to the center line of each lane, and determine the first distance from each coordinate to the vehicle based on the vehicle coordinate system; Each coordinate is saved sequentially into a coordinate sequence based on the first distance in ascending order, resulting in multiple sets of coordinate sequences for the lane centerlines.

7. The method for generating lane selection labels as described in claim 6, characterized in that, After the step of saving each coordinate sequentially into a coordinate sequence based on the first distance in ascending order to obtain multiple sets of lane centerline coordinate sequences, the method further includes: Determine the first coordinate in each lane centerline coordinate sequence, and determine the second coordinate adjacent to the first coordinate, wherein the distance from the second coordinate to the vehicle is less than the distance from the first coordinate to the vehicle; Calculate the second distance between the first coordinate and the second coordinate, and determine whether the second distance is less than a preset distance threshold; If the value is less than the first coordinate, delete the first coordinate and obtain a new sequence of lane centerline coordinates for each lane based on the sequence of lane centerline coordinates after the deletion operation.

8. The method for generating lane selection labels as described in claim 6, characterized in that, The step of constructing a vehicle coordinate system based on the vehicle location information further includes: Extract the vehicle coordinates and vehicle heading angle from the vehicle location information; The vehicle coordinates are set as the origin, and the direction corresponding to the vehicle heading angle is set as the positive y-axis. Based on the origin and the positive y-axis, a vehicle coordinate system is constructed for each set of local perception type map data.

9. A lane selection label generating device, characterized in that, The lane selection label generation device includes: The first acquisition module is used to acquire scene data; The first determining module is used to traverse all frames in the scene data and determine the corresponding future frame for trajectory planning based on the current frame. The second acquisition module is used to acquire map information and corresponding lane centerline information of the future frame, wherein the map information includes the vehicle coordinates of the vehicle in the future frame, and the lane centerline information includes all lane lines corresponding to the future frame. The second determining module is used to determine the minimum projection distance of the vehicle coordinates onto the center lines of all lanes, and based on the minimum projection distance, to determine the target lane line where the vehicle will be located in the future, so as to generate a lane selection label.

10. A lane selection label generation device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the lane label generation method as described in any one of claims 1 to 8.

11. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the lane label generation method as described in any one of claims 1 to 8.

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