CN113607185A - Lane line information display method, lane line information display device, electronic device, and computer-readable medium - Google Patents
Lane line information display method, lane line information display device, electronic device, and computer-readable medium Download PDFInfo
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Abstract
The embodiment of the disclosure discloses a lane line information display method, a lane line information display device, an electronic device and a computer readable medium. One embodiment of the method comprises: acquiring a point cloud data set; constructing a point cloud road map based on the point cloud data set; in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set; dynamically adjusting the initial reflection intensity value to obtain an adjusted reflection intensity value; generating a first lane line information group based on the adjusted reflection intensity value and the point cloud road map; and sending the first lane line information group to a display terminal for displaying. This embodiment can improve the accuracy of the displayed lane line information.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a lane line information display method and device, electronic equipment and a computer readable medium.
Background
The method for displaying lane line information is a technology for determining and displaying a lane line from point cloud data. At present, when lane line information is displayed, the following methods are generally adopted: and screening the point cloud data by adopting a fixed threshold value to generate lane line information and display the lane line information.
However, when the lane line information display is performed in the above manner, there are often the following technical problems:
firstly, due to different wear conditions of lane lines on a road, the detected point cloud data comprises different laser reflection intensities, so that the point cloud data are screened by adopting a fixed threshold value, the quantity difference of the point cloud data screened at different lane line positions is large, the generated lane line information is not accurate enough, and the displayed lane line information is not accurate enough;
secondly, the number of the point cloud data is large, so that the efficiency of generating the lane line information is low, and further, the efficiency of displaying the lane line information is reduced.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose lane line information display methods, apparatuses, electronic devices, and computer-readable media to solve one or more of the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a lane line information display method, including: acquiring a point cloud data set, wherein point cloud data in the point cloud data set comprises point cloud coordinates and a laser reflection intensity value; constructing a point cloud road map based on the point cloud data set, wherein the point cloud road map is formed by point cloud data in the point cloud data set; in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set; dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map to obtain an adjusted reflection intensity value; generating a first lane line information group based on the adjusted reflection intensity value and the point cloud road map; and sending the first lane line information group to a display terminal for displaying.
In a second aspect, some embodiments of the present disclosure provide a lane line information display apparatus, including: the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is configured to acquire a point cloud data set, wherein point cloud data in the point cloud data set comprises point cloud coordinates and laser reflection intensity values; a construction unit configured to construct a point cloud road map based on the point cloud data set, wherein the point cloud road map is composed of point cloud data in the point cloud data set; a coordinate projection unit configured to project, in response to determining that a set of map lane line information corresponding to the point cloud data set exists in preset high-precision map information, each map lane line coordinate in a set of map lane line coordinates included in each map lane line information in the set of map lane line information into the point cloud road map, resulting in a set of post-projection lane line coordinate sets; a dynamic adjustment unit configured to dynamically adjust an initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the point cloud data in the point cloud road map and the projected lane line coordinate set to obtain an adjusted reflection intensity value; a generating unit configured to generate a first lane line information set based on the adjusted reflection intensity value and the point cloud road map; and the transmitting and displaying unit is configured to transmit the first lane line information group to a display terminal for displaying.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
In a fourth aspect, some embodiments of the present disclosure provide a computer readable medium on which a computer program is stored, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The above embodiments of the present disclosure have the following advantages: by the lane line information display method of some embodiments of the present disclosure, the accuracy of the displayed lane line information can be improved. Specifically, the reason why the accuracy of the displayed lane line information is reduced is that: due to the fact that the abrasion conditions of the lane lines on the road are different, the laser reflection intensity included in the detected point cloud data is different, and therefore the point cloud data are screened by adopting the fixed threshold value, the density of the point cloud data screened at different lane line positions is different, and therefore generated lane line information is not accurate enough. Based on this, the lane line information display method of some embodiments of the present disclosure first obtains a point cloud data set. And constructing a point cloud road map based on the point cloud data set. Therefore, the extraction of point cloud data can be facilitated. And then, in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in the preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set. And then, dynamically adjusting the initial reflection intensity value to obtain an adjusted reflection intensity value based on the point cloud coordinate and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map. By dynamically adjusting the initial reflection intensity value, the quantity difference of the point cloud data corresponding to different lane line positions can be reduced. Therefore, the accuracy of the screened point cloud data can be improved. And finally, generating a first lane line information set based on the adjusted reflection intensity value and the point cloud road map. And because the initial reflection intensity value is dynamically adjusted and high-precision map information is introduced, the accuracy of the screened point cloud data can be improved, and the accuracy of the generated lane line information can be further improved through the incidence relation between the point cloud data and the map lane line in the high-precision map information. Further, the accuracy of the displayed lane line information can be improved.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
Fig. 1 is a schematic view of one application scenario of a lane line information display method of some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of a lane line information display method according to the present disclosure;
FIG. 3 is a flow chart of further embodiments of lane line information display methods according to the present disclosure;
FIG. 4 is a schematic structural diagram of some embodiments of lane marking information display devices according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of an application scenario of a lane line information display method according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may obtain a point cloud data set 102, where the point cloud data in the point cloud data set 102 includes point cloud coordinates and laser reflection intensity values. Next, the computing device 101 may construct a point cloud road map 103 based on the point cloud data set 102, wherein the point cloud road map 103 is formed by the point cloud data in the point cloud data set 102. Then, in response to determining that a map lane line information set 1041 corresponding to the point cloud data set 102 exists in the preset high-precision map information 104, the computing device 101 may project each map lane line coordinate in a map lane line coordinate set included in each map lane line information in the map lane line information set 1041 into the point cloud road map, so as to obtain a projected lane line coordinate set 105. Then, the computing device 101 may dynamically adjust the initial reflection intensity value 106 based on the point cloud coordinates and the laser reflection intensity included in the point cloud data in the post-projection lane line coordinate set 105 and the point cloud road map 103, so as to obtain an adjusted reflection intensity value 107. Then, the computing device 101 may generate a first lane line information set 108 based on the adjusted reflection intensity value 107 and the point cloud road map 103. Finally, the computing device 101 may send the first lane line information group 108 to the display terminal 109 for display.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of lane line information display methods according to the present disclosure is shown. The flow 200 of the lane line information display method comprises the following steps:
In some embodiments, the executing subject of the lane marking information display method (e.g., the computing device 101 shown in fig. 1) may acquire the point cloud data set in a wired manner or in a wireless manner. The point cloud data in the point cloud data set may include point cloud coordinates and laser reflection intensity values. Each point cloud data set in the point cloud data set can be continuous frame point cloud data measured by a laser radar and around the vehicle. Each point cloud data set may correspond to a frame, i.e., a point in time.
In some embodiments, the executing subject may construct a point cloud road map based on the point cloud data set. The point cloud road map can be formed by point cloud data in the point cloud data set. The point cloud road map can be constructed by the following steps:
and step one, removing each point cloud data in the point cloud data group set to obtain a removed point cloud data set. The removing process may be removing a plurality of point cloud data corresponding to one target at different time points, so as to avoid duplication of the point cloud data.
And secondly, carrying out map construction on each point cloud data in the removed point cloud data set to obtain a point cloud road map. The map construction may be to project each point cloud data to a two-dimensional coordinate system to obtain a planar map.
And thirdly, carrying out road identification on the plane map to generate a point cloud road map. The road identification may be to identify a road area in the planar map by a detection algorithm (e.g., MNS (Non-Maximum Suppression) algorithm, a dual threshold method, or a Full Convolution Network (FCN), etc.), and determine the area as the point cloud road map.
In some embodiments, the executing entity may project, in response to determining that there is a map lane line information set corresponding to the point cloud data set in preset high-precision map information, each map lane line coordinate in a map lane line coordinate set included in each map lane line information in the map lane line information set into the point cloud road map, to obtain a set of projected lane line coordinate sets. The high-precision map information may be information in a high-precision map of the current position of the vehicle. The high-precision map information may include a set of map lane line information groups. Each map lane line information in the set of map lane line information groups may include a set of map lane line coordinates. The set of map lane line information sets may be used to characterize each lane line on the road on which the current vehicle is located in a high-precision map. First, an identification of the road on which the current vehicle is located may be determined. Then, the map lane line information group corresponding to the road sign in the map lane line information group set may be determined. Therefore, the map lane line information group corresponding to the point cloud data group set can be determined to exist in the preset high-precision map information. Finally, coordinate conversion can be carried out on each map lane line coordinate in the map lane line coordinate set included in the map lane line information so as to project the map lane line coordinates into the point cloud road map. Thus, a set of post-projection lane line coordinates can be obtained.
And 204, dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the point cloud data in the point cloud road map and the projected lane line coordinate set to obtain an adjusted reflection intensity value.
In some embodiments, the executing agent may dynamically adjust the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the point cloud data in the point cloud road map and the set of post-projection lane line coordinates, so as to obtain an adjusted reflection intensity value. Wherein the adjusted reflection intensity value can be obtained by:
firstly, determining a lane line area where each projected lane line coordinate set in the projected lane line coordinate set is located to obtain a lane line area set. The lane line area group can be used for representing a map lane line projected to the point cloud road map.
And secondly, determining point cloud data of each lane line area in the lane line area group in the point cloud road map to generate an area point cloud data group, and obtaining an area point cloud data group set. The area point cloud data set can also be used for representing map lane lines projected to the point cloud road map.
And thirdly, performing area division on each area point cloud data set in the area point cloud data set to generate a division point cloud data subgroup sequence. Wherein the map lane lines may be characterized by a width and a length due to the regional point cloud data set. Therefore, the area division may be to divide the map lane lines, that is, to divide the coordinates included in each area point cloud data in the area point cloud data set, according to a preset length value. Therefore, the point cloud data subgroup division can be used for representing the map lane line with the preset length.
And fourthly, determining the average value of the laser reflection intensity values included by each piece of divided point cloud data in the divided point cloud data subgroup as the reflection intensity value to be determined. Therefore, for each sub-group sequence of the divided point cloud data, a set of determined reflection intensity values can be obtained, that is, the set can correspond to one map lane line. From this a set of sets of reflection intensity values to be determined is obtained.
And fifthly, determining the minimum average value of the average values corresponding to all the to-be-determined reflection intensity value groups in the to-be-determined reflection intensity value group set as the adjusted reflection intensity value. The minimum average value is selected as the adjusted reflection intensity value, so that the point cloud data with continuity can be guaranteed to be selected to a certain extent, and the map lane line can be more accurately represented. This can improve the accuracy of the generated lane line information.
And step 205, generating a first lane line information set based on the adjusted reflection intensity value and the point cloud road map.
In some embodiments, the executing entity may generate a first lane line information set based on the adjusted reflection intensity value and the point cloud road map. The point cloud data which is larger than the adjusted reflection intensity value can be selected from each area point cloud data set in the area point cloud data set corresponding to the point cloud road map and used as the first road line information. Thereby, the first lane line information group can be obtained. The first lane line information in the first lane line information group may be used to characterize a lane line on a lane where the current vehicle is located.
And step 206, sending the first lane line information group to a display terminal for displaying.
In some embodiments, the execution main body may send the first lane line information group to a display terminal for displaying. The display terminal can be a display screen in the current vehicle and is used for displaying a lane line for a driver to view.
Optionally, the executing main body may further perform the following steps:
and step one, in response to the fact that a map lane line information group corresponding to the point cloud data group set does not exist in the preset high-precision map information, carrying out classification processing on point cloud data in the point cloud road map to generate a classified point cloud data group set. Wherein the road sign on which the current vehicle is located can be determined. If the map lane line information set corresponding to the road identifier does not exist in the map lane line information set, it may be determined that the map lane line information set corresponding to the point cloud data set does not exist in the preset high-precision map information. The road identification may be a number characterizing a road, corresponding to a road in the high precision map. Since the map lane line information is associated with the road in the high-precision map, it can be determined whether there is a map lane line information group corresponding to the road identifier in the map lane line information group set by the road identifier. The classification process may be to determine the objects (e.g., roads, vehicles, trees, etc.) represented by the point cloud data through the detection algorithm.
And secondly, screening the classified point cloud data group set to generate a screened point cloud data group set. The screening process may be to select point cloud data representing the lane line. Each screened point cloud data in the screened point cloud data set can represent a lane line of a road where the current vehicle is located.
And thirdly, determining the maximum laser reflection intensity value in the laser reflection intensities included in each screened point cloud data in the screened point cloud data group set as the reflection intensity value to be adjusted. The reflection intensity value to be adjusted can be used as an initial parameter, so that the subsequent reflection intensity value to be adjusted can be dynamically adjusted.
Optionally, the executing main body may further perform the following steps:
and step one, dynamically adjusting the reflection intensity value to be adjusted based on the point cloud coordinate and the laser reflection intensity included in the point cloud data set after screening and the point cloud data in the point cloud road map to obtain a target reflection intensity value. The reflection intensity value to be adjusted, the point cloud coordinate and the laser reflection intensity included in the point cloud data set after screening and the point cloud data in the point cloud road map can be input into a preset self-adaptive model, and a target reflection intensity value is obtained.
And secondly, generating a second lane line information group based on the target reflection intensity value and the point cloud road map. And selecting point cloud data larger than the target reflection intensity value from each region point cloud data set in the region point cloud data set corresponding to the point cloud road map as second lane line information.
And thirdly, sending the second lane line information group to the display terminal for displaying. In practice, due to the fact that the lane lines on the road are abraded, the detected point cloud data comprise different laser reflection intensities. Therefore, dynamic adjustment of a fixed threshold value is required, and then point cloud data screening is performed. Therefore, when the received point cloud data used for representing the lane line is less and/or the laser reflection intensity is weak, a proper amount of point cloud data can be selected for generating the lane line information. Therefore, the selected point cloud data are prevented from being too few, and the accuracy of the generated lane line information is prevented from being reduced. Further, the accuracy of the displayed lane line information can be improved.
The above embodiments of the present disclosure have the following advantages: by the lane line information display method of some embodiments of the present disclosure, the accuracy of the displayed lane line information can be improved. Specifically, the reason why the accuracy of the displayed lane line information is reduced is that: due to the fact that the abrasion conditions of the lane lines on the road are different, the laser reflection intensity included in the detected point cloud data is different, and therefore the point cloud data are screened by adopting the fixed threshold value, the density of the point cloud data screened at different lane line positions is different, and therefore generated lane line information is not accurate enough. Based on this, the lane line information display method of some embodiments of the present disclosure first obtains a point cloud data set. And constructing a point cloud road map based on the point cloud data set. Therefore, the extraction of point cloud data can be facilitated. And then, in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in the preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set. And then, dynamically adjusting the initial reflection intensity value to obtain an adjusted reflection intensity value based on the point cloud coordinate and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map. By dynamically adjusting the initial reflection intensity value, the quantity difference of the point cloud data corresponding to different lane line positions can be reduced. Therefore, the accuracy of the screened point cloud data can be improved. And finally, generating a first lane line information set based on the adjusted reflection intensity value and the point cloud road map. And because the initial reflection intensity value is dynamically adjusted and high-precision map information is introduced, the accuracy of the screened point cloud data can be improved, and the accuracy of the generated lane line information can be further improved through the incidence relation between the point cloud data and the map lane line in the high-precision map information. Further, the accuracy of the displayed lane line information can be improved.
With further reference to fig. 3, a flow 300 of further embodiments of lane line information display methods is illustrated. The flow 300 of the lane line information display method includes the following steps:
In some embodiments, the specific implementation manner and technical effects of step 301 may refer to step 201 in those embodiments corresponding to fig. 2, and are not described herein again.
In some embodiments, an executing entity (e.g., the computing device 101 shown in fig. 1) of the lane line information display method may perform motion compensation on each point cloud data set in the point cloud data set to generate a compensated point cloud data set, resulting in a compensated point cloud data set. The movement distance of the laser radar in the acquisition process of the point cloud data can be determined, and then the movement amount, including the compensation of rotation and translation, is compensated according to the relative time of two corresponding point cloud data in adjacent frames. Therefore, motion compensation can be performed on each point cloud data set in the point cloud data set, and a compensated point cloud data set is obtained.
And 303, generating a ground point cloud data set based on the compensated point cloud data set.
In some embodiments, the executing entity may generate a ground point cloud data set based on the compensated point cloud data set. Wherein the set of ground point cloud data sets may be generated by:
firstly, morphological filtering processing (for example, opening operation) is performed on each compensated point cloud data in each compensated point cloud data set in the compensated point cloud data set, and then a processed point cloud data set is obtained. And generating a corresponding morphological filtering value when each processed point cloud data in the processed point cloud data set is processed, and representing the difference value between the elevation value and the initial elevation value of each processed point cloud data in the corresponding processed point cloud data set. The elevation value may refer to a height value of a position of the cloud data at a certain point relative to the ground.
And secondly, determining the compensated point cloud data with the morphological filtering value smaller than a preset filtering threshold value corresponding to each compensated point cloud data group in the compensated point cloud data group set as ground point cloud data to generate a ground point cloud data group, so as to obtain a ground point cloud data group set.
And step 304, fusing each ground point cloud data in the ground point cloud data set to obtain a dense point cloud data set.
In some embodiments, the executing entity may fuse each ground point cloud data in the ground point cloud data set to obtain a dense point cloud data set. The point cloud coordinates included in each ground point cloud data in the ground point cloud data set can be projected into a preset two-dimensional coordinate system, so that a dense point cloud data set is obtained. The two-dimensional coordinate system may be established with the position coordinates of the current vehicle as an origin, a horizontal north direction passing through the origin as a horizontal axis, and a horizontal west direction perpendicular to the horizontal axis passing through the origin as a vertical axis.
In some embodiments, the executing subject may perform voxel filtering processing on each dense point cloud in the dense point cloud data set to generate a point cloud road map. Firstly, each dense point cloud in the dense point cloud data set can be subjected to voxelization to obtain a voxelized point cloud data set. Then, each point cloud data in the voxelized point cloud data set can be down-sampled by using an octree algorithm to obtain a road point cloud data set. And finally, determining the road point cloud data set and the two-dimensional coordinate system as a point cloud road map.
In some embodiments, the specific implementation manner and technical effects of step 306 may refer to step 203 in those embodiments corresponding to fig. 2, which are not described herein again.
And 307, dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the point cloud data in the projected lane line coordinate set and the point cloud road map to obtain an adjusted reflection intensity value.
In some embodiments, the executing entity may obtain the adjusted reflected intensity value by:
firstly, carrying out lane line fitting on each projected lane line coordinate in each projected lane line coordinate set in the projected lane line coordinate set to generate a projected lane line, and obtaining a projected lane line set.
And secondly, determining the point cloud data matched with each projection lane line in the projection lane line group in the point cloud road map as a matching point cloud data group to obtain a matching point cloud data group set. The matching may be that a distance value between a point cloud coordinate value included in the point cloud data and the projection lane line is smaller than a preset distance threshold.
Thirdly, executing the following dynamic adjustment steps for the initial reflection intensity value, each projection lane line in the projection lane line group and the corresponding matching point cloud data set:
and a first substep of determining the point cloud data with the laser reflection intensity greater than the initial reflection intensity value in the matching point cloud data set corresponding to the projection lane line as target point cloud data to obtain a target point cloud data set.
And a second substep of determining a residual value between the projected lane line and a point cloud coordinate included in each target point cloud data in the target point cloud data set.
And a third substep, in response to determining that the residual value is smaller than a preset residual threshold value, performing lane line fitting on point cloud coordinates included in each target point cloud data in the target point cloud data set to obtain a point cloud lane line, and determining an initial reflection intensity value corresponding to the cloud lane line as an adjusted reflection intensity value. The point cloud coordinates included in each target point cloud data in the target point cloud data set may be subjected to lane line fitting by a RANdom SAmple Consensus (RANSAC) algorithm to obtain a point cloud lane line.
Optionally, the executing body dynamically adjusts the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map, so as to obtain an adjusted reflection intensity value, and may further include the following steps:
and adjusting the initial reflection intensity value in response to the fact that the residual value is larger than or equal to a preset residual threshold value, and executing the dynamic adjusting step again by taking the adjusted initial reflection intensity value as the initial reflection intensity value. The adjusting of the initial reflection intensity value may be subtracting a preset adjustment parameter (e.g., 1) from the initial reflection intensity value to obtain an adjusted initial reflection intensity value.
In other embodiments, first, each point cloud data in the point cloud road map may be clustered according to the laser reflection intensity to obtain a clustered point cloud data group set. Then, the clustered point cloud data group set can be screened to obtain a screened point cloud data group set. The screening may be to select that the ratio of the width value to the width value of the area where each point cloud data in each point cloud data set is located is smaller than a preset proportional threshold, and the area is a strip-shaped point cloud area. Therefore, a clustering point cloud data set capable of representing the lane line is screened out to serve as a screening point cloud data set. And finally, executing the following adjusting steps for each screening point cloud data set:
the method comprises the following steps of firstly, selecting screening point cloud data with a laser reflection intensity value larger than an initial reflection intensity value from the screening point cloud data set to obtain an extracted point cloud data set.
And secondly, performing lane line fitting on point cloud coordinates included in each extracted point cloud data in the extracted point cloud data set through a RANdom SAmple Consensus (RANSAC) algorithm to generate extracted lane lines.
And thirdly, determining an initial residual value between each piece of screening point cloud data in the screening point cloud data set and the extracted lane line.
And fourthly, determining the ratio of the width value to the length value of the area where the screened point cloud data set is located and the sum of the initial residual values as a target residual value.
And fifthly, adjusting the initial reflection intensity value, and performing the adjusting step again by taking the adjusted initial reflection intensity value as the initial reflection intensity value. The termination condition of the adjusting step may be that the initial reflection intensity value is reduced to zero. Thus, a target set of residual values can be obtained. Finally, the initial reflection intensity value corresponding to the smallest target residual value in the set of target residual values can be determined as the adjusted reflection intensity value.
And 308, generating a first lane line information group based on the adjusted reflection intensity value and the point cloud road map.
In some embodiments, the executing entity may generate a first lane line information set based on the adjusted reflection intensity value and the point cloud road map. Wherein the first lane line information group may be generated by:
and step one, generating a left line group and a right line group of the lane line according to the adjusted reflection intensity value and the point cloud road map, wherein each left edge line of the lane line in the left line group of the lane line corresponds to each right edge line of the lane line in the right line group of the lane line. First, an adjusted point cloud data set having a laser reflection intensity value greater than the adjusted reflection intensity value may be selected from the point cloud road map. The adjusted point cloud data set can be used for representing a lane line of a road where the current vehicle is located. Then, the point cloud coordinates on the left side of the lane line and the point cloud coordinates on the right side of the lane line can be selected from the point cloud coordinates included in each adjusted point cloud data in the adjusted point cloud data set, so that the point cloud data set on the left side of the lane line and the point cloud data set on the right side of the lane line are obtained. And finally, performing lane line fitting on the point cloud coordinates included in the point cloud data on the left side of each lane line in the point cloud data group on the left side of the lane line to obtain a left side line of the lane line.
And secondly, fusing each left lane line in the left lane line group with the corresponding right lane line in the right lane line group to generate first lane line information, so as to obtain a first lane line information group. The target lane line can be obtained by fitting the lane line left side line and the lane line right side line, and the target lane line is used as the first lane line information.
Specifically, the target lane line is obtained by fitting the left side line and the right side line of the lane line. It is possible to reduce an error caused by passing only a boundary line on one side of the lane line as the target lane line. Thereby, the accuracy of generating the first lane line information can be improved.
In addition, when the preset high-precision map information does not have the map lane line information group corresponding to the point cloud data group set, the target reflection intensity value may be generated by adopting some optional implementation manners of some embodiments corresponding to fig. 2. Then, iteration can be continuously performed on the target reflection intensity values, and then an iteration residual value corresponding to each fitted lane line group set is generated. And finally, determining the fitted lane line corresponding to the minimum iteration residual value in each fitted lane line group as the first lane line information to obtain a first lane line information group.
Wherein the target reflection intensity value may be reduced to zero as an iteration termination condition. After each iteration, a set of lane line point cloud data sets can be selected by using the adjusted target reflection intensity values. And fitting each lane line point cloud data set in the lane line point cloud data set into a plurality of fitted lane lines so as to represent each lane line of the road where the current vehicle is located. Therefore, the iteration residual value between each fitting lane line and the non-coincident lane line point cloud data in the corresponding lane line point cloud data group can be determined.
In some embodiments, the specific implementation manner and technical effects of step 309 may refer to step 206 in those embodiments corresponding to fig. 2, and are not described herein again.
As can be seen from fig. 3, compared with the description of some embodiments corresponding to fig. 2, the flow 300 of the lane line information display method in some embodiments corresponding to fig. 3 embodies the steps of constructing the point cloud road map, dynamically adjusting the initial reflection intensity, and generating the first lane line information set. Firstly, the point cloud data can be conveniently compared with high-precision map information by constructing the point cloud road map. In the process of constructing the point cloud road map, the point cloud data are processed by motion step length, voxel filtering and the like so as to reduce the number of the point cloud data and the interference on the constructed point cloud road map, and therefore the accuracy of the generated first lane line information group can be improved. Then, different road conditions can be self-adapted by dynamically adjusting the initial reflection intensity. Therefore, the lane line can be more accurately extracted from the point cloud data. Finally, the problem of road coverage of high-precision maps is also considered. Therefore, under the condition of high-precision map coverage, the lane lines determined in the high-precision map can be used for participating in the selection of the point cloud data. Therefore, the screening efficiency of the point cloud data can be improved. Thus, the generation efficiency of the first lane line information group can be improved. Further, the display efficiency of the first lane line information group can be improved.
With further reference to fig. 4, as an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a lane marking information display apparatus, which correspond to those shown in fig. 2, and which may be particularly applied in various electronic devices.
As shown in fig. 4, the lane line information display apparatus 400 of some embodiments includes: an acquisition unit 401, a construction unit 402, a coordinate projection unit 403, a dynamic adjustment unit 404, a generation unit 405, and a transmission and display unit 406. The acquiring unit 401 is configured to acquire a point cloud data set, where point cloud data in the point cloud data set includes point cloud coordinates and laser reflection intensity values; a constructing unit 402 configured to construct a point cloud road map based on the point cloud data set, wherein the point cloud road map is composed of point cloud data in the point cloud data set; a coordinate projection unit 403 configured to project, in response to determining that a set of map lane line information corresponding to the point cloud data set exists in preset high-precision map information, each map lane line coordinate in a set of map lane line coordinates included in each map lane line information in the set of map lane line information into the point cloud road map, to obtain a set of post-projection lane line coordinate sets; a dynamic adjustment unit 404 configured to dynamically adjust the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the post-projection lane line coordinate set and the point cloud data in the point cloud road map, so as to obtain an adjusted reflection intensity value; a generating unit 405 configured to generate a first lane line information set based on the adjusted reflection intensity value and the point cloud road map; the transmitting and displaying unit 406 is configured to transmit the first lane line information group to a display terminal for displaying.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to FIG. 5, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring a point cloud data set, wherein point cloud data in the point cloud data set comprises point cloud coordinates and a laser reflection intensity value; constructing a point cloud road map based on the point cloud data set, wherein the point cloud road map is formed by point cloud data in the point cloud data set; in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set; dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map to obtain an adjusted reflection intensity value; generating a first lane line information group based on the adjusted reflection intensity value and the point cloud road map; and sending the first lane line information group to a display terminal for displaying.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a construction unit, a coordinate projection unit, a dynamic adjustment unit, a generation unit, and a transmission and display unit. The names of these units do not in some cases form a limitation on the unit itself, and for example, the acquiring unit may also be described as "a unit acquiring a set of point cloud data sets".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A lane line information display method includes:
acquiring a point cloud data set, wherein point cloud data in the point cloud data set comprises point cloud coordinates and a laser reflection intensity value;
constructing a point cloud road map based on the point cloud data set, wherein the point cloud road map is formed by point cloud data in the point cloud data set;
in response to the fact that a map lane line information group corresponding to the point cloud data group set exists in preset high-precision map information, projecting map lane line coordinates in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map to obtain a projected lane line coordinate group set;
dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the projected lane line coordinate set and the point cloud data in the point cloud road map to obtain an adjusted reflection intensity value;
generating a first lane line information group based on the adjusted reflection intensity value and the point cloud road map;
and sending the first lane line information group to a display terminal for display.
2. The method of claim 1, wherein the method further comprises:
in response to the fact that no map lane line information group corresponding to the point cloud data group set exists in the preset high-precision map information, carrying out classification processing on point cloud data in the point cloud road map to generate a classified point cloud data group set;
screening the classified point cloud data set to generate a screened point cloud data set;
and determining the maximum laser reflection intensity value in the laser reflection intensities included in each point cloud data after being screened in the point cloud data group set as the reflection intensity value to be adjusted.
3. The method of claim 2, wherein the method further comprises:
dynamically adjusting the reflection intensity value to be adjusted based on the point cloud coordinate and the laser reflection intensity included in the point cloud data in the point cloud road map data set after screening to obtain a target reflection intensity value;
generating a second lane line information group based on the target reflection intensity value and the point cloud road map;
and sending the second lane line information group to the display terminal for display.
4. The method of claim 1, wherein said constructing a point cloud road map based on said set of point cloud data sets comprises:
performing motion compensation on each point cloud data set in the point cloud data set to generate a compensated point cloud data set, and obtaining a compensated point cloud data set;
generating a ground point cloud data set based on the compensated point cloud data set;
fusing each ground point cloud data in the ground point cloud data set to obtain a dense point cloud data set;
and carrying out voxel filtering processing on each dense point cloud in the dense point cloud data set to generate a point cloud road map.
5. The method of claim 1, wherein the dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the post-projection lane line coordinate set and the point cloud data in the point cloud road map to obtain an adjusted reflection intensity value comprises:
performing lane line fitting on each projected lane line coordinate in each projected lane line coordinate group in the projected lane line coordinate group set to generate a projected lane line, and obtaining a projected lane line group;
determining point cloud data matched with each projection lane line in the projection lane line group in the point cloud road map as a matching point cloud data group to obtain a matching point cloud data group set;
and for the initial reflection intensity value, each projection lane line in the projection lane line group and the corresponding matching point cloud data set, executing the following dynamic adjustment steps:
determining point cloud data with laser reflection intensity greater than the initial reflection intensity value in a matching point cloud data set corresponding to the projection lane line as target point cloud data to obtain a target point cloud data set;
determining a residual error value between the projection lane line and a point cloud coordinate included in each target point cloud data in the target point cloud data set;
and in response to the fact that the residual value is smaller than a preset residual threshold value, performing lane line fitting on point cloud coordinates included in each target point cloud data in the target point cloud data set to obtain a point cloud lane line, and determining an initial reflection intensity value corresponding to the cloud lane line as an adjusted reflection intensity value.
6. The method of claim 5, wherein the dynamically adjusting the initial reflection intensity value based on the point cloud coordinates and the laser reflection intensity included in the post-projection lane line coordinate set and the point cloud data in the point cloud road map to obtain an adjusted reflection intensity value further comprises:
and adjusting the initial reflection intensity value in response to determining that the residual value is greater than or equal to a preset residual threshold value, and executing the dynamic adjustment step again with the adjusted initial reflection intensity value as the initial reflection intensity value.
7. The method of claim 6, wherein the generating a first lane line information set based on the adjusted reflected intensity values and the point cloud road map comprises:
generating a lane line left line group and a lane line right line group according to the adjusted reflection intensity value and the point cloud road map, wherein each lane line left side line in the lane line left line group corresponds to each lane line right side line in the lane line right line group;
and fusing each lane line left side line in the lane line left side line group with the corresponding lane line right side line in the lane line right side line group to generate first lane line information, so as to obtain a first lane line information group.
8. A lane line information display device comprising:
an acquisition unit configured to acquire a point cloud data set, wherein point cloud data in the point cloud data set includes point cloud coordinates and laser reflection intensity values;
a construction unit configured to construct a point cloud road map based on the point cloud data set, wherein the point cloud road map is composed of point cloud data in the point cloud data set;
a coordinate projection unit configured to project, in response to determining that a map lane line information group corresponding to the point cloud data group set exists in preset high-precision map information, each map lane line coordinate in a map lane line coordinate group included in each map lane line information in the map lane line information group into the point cloud road map, so as to obtain a projected lane line coordinate group set;
a dynamic adjustment unit configured to dynamically adjust an initial reflection intensity value based on a point cloud coordinate and a laser reflection intensity included in the post-projection lane line coordinate set and the point cloud data in the point cloud road map, so as to obtain an adjusted reflection intensity value;
a generating unit configured to generate a first set of lane line information based on the adjusted reflection intensity value and the point cloud road map;
and the transmitting and displaying unit is configured to transmit the first lane line information group to a display terminal for displaying.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
| CN114862808A (en) * | 2022-05-18 | 2022-08-05 | 亿咖通(湖北)技术有限公司 | Method, device and equipment for determining precision of dotted frame and storage medium |
| CN115239899A (en) * | 2022-06-29 | 2022-10-25 | 北京百度网讯科技有限公司 | Pose graph generation method and high-precision map generation method and device |
| CN115330923A (en) * | 2022-08-10 | 2022-11-11 | 小米汽车科技有限公司 | Point cloud data rendering method and device, vehicle, readable storage medium and chip |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127113A (en) * | 2016-06-15 | 2016-11-16 | 北京联合大学 | A kind of road track line detecting method based on three-dimensional laser radar |
| US20170039436A1 (en) * | 2015-08-03 | 2017-02-09 | Nokia Technologies Oy | Fusion of RGB Images and Lidar Data for Lane Classification |
| CN109191553A (en) * | 2018-08-29 | 2019-01-11 | 百度在线网络技术（北京）有限公司 | point cloud rendering method, device, terminal and storage medium |
| CN109506672A (en) * | 2017-09-15 | 2019-03-22 | 高德软件有限公司 | A kind of acquisition methods and device of pavement markers laser point cloud |
| CN109583313A (en) * | 2018-10-31 | 2019-04-05 | 百度在线网络技术（北京）有限公司 | Lane line extracting method, device and storage medium |
| CN109766878A (en) * | 2019-04-11 | 2019-05-17 | 深兰人工智能芯片研究院（江苏）有限公司 | A kind of method and apparatus of lane detection |
| CN110163930A (en) * | 2019-05-27 | 2019-08-23 | 北京百度网讯科技有限公司 | Lane line generation method, device, equipment, system and readable storage medium storing program for executing |
| CN110866449A (en) * | 2019-10-21 | 2020-03-06 | 北京京东尚科信息技术有限公司 | Method and device for identifying target object in road |
| CN112240772A (en) * | 2020-12-16 | 2021-01-19 | 北京赛目科技有限公司 | Lane line generation method and device |
| CN113255619A (en) * | 2021-07-09 | 2021-08-13 | 禾多科技（北京）有限公司 | Lane line recognition and positioning method, electronic device, and computer-readable medium |
| US20210270609A1 (en) * | 2020-03-02 | 2021-09-02 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Method, apparatus, computing device and computer-readable storage medium for positioning |
-
2021
- 2021-10-08 CN CN202111168373.6A patent/CN113607185B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170039436A1 (en) * | 2015-08-03 | 2017-02-09 | Nokia Technologies Oy | Fusion of RGB Images and Lidar Data for Lane Classification |
| CN106127113A (en) * | 2016-06-15 | 2016-11-16 | 北京联合大学 | A kind of road track line detecting method based on three-dimensional laser radar |
| CN109506672A (en) * | 2017-09-15 | 2019-03-22 | 高德软件有限公司 | A kind of acquisition methods and device of pavement markers laser point cloud |
| CN109191553A (en) * | 2018-08-29 | 2019-01-11 | 百度在线网络技术（北京）有限公司 | point cloud rendering method, device, terminal and storage medium |
| CN109583313A (en) * | 2018-10-31 | 2019-04-05 | 百度在线网络技术（北京）有限公司 | Lane line extracting method, device and storage medium |
| CN109766878A (en) * | 2019-04-11 | 2019-05-17 | 深兰人工智能芯片研究院（江苏）有限公司 | A kind of method and apparatus of lane detection |
| CN110163930A (en) * | 2019-05-27 | 2019-08-23 | 北京百度网讯科技有限公司 | Lane line generation method, device, equipment, system and readable storage medium storing program for executing |
| CN110866449A (en) * | 2019-10-21 | 2020-03-06 | 北京京东尚科信息技术有限公司 | Method and device for identifying target object in road |
| US20210270609A1 (en) * | 2020-03-02 | 2021-09-02 | Beijing Baidu Netcom Science And Technology Co., Ltd. | Method, apparatus, computing device and computer-readable storage medium for positioning |
| CN112240772A (en) * | 2020-12-16 | 2021-01-19 | 北京赛目科技有限公司 | Lane line generation method and device |
| CN113255619A (en) * | 2021-07-09 | 2021-08-13 | 禾多科技（北京）有限公司 | Lane line recognition and positioning method, electronic device, and computer-readable medium |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114034307A (en) * | 2021-11-19 | 2022-02-11 | 智道网联科技(北京)有限公司 | Lane line-based vehicle pose calibration method and device and electronic equipment |
| CN114034307B (en) * | 2021-11-19 | 2024-04-16 | 智道网联科技(北京)有限公司 | Vehicle pose calibration method and device based on lane lines and electronic equipment |
| CN114862808A (en) * | 2022-05-18 | 2022-08-05 | 亿咖通(湖北)技术有限公司 | Method, device and equipment for determining precision of dotted frame and storage medium |
| CN114862808B (en) * | 2022-05-18 | 2023-11-17 | 亿咖通(湖北)技术有限公司 | Determination method, device, equipment and storage medium for precision of dotted line frame |
| CN115239899A (en) * | 2022-06-29 | 2022-10-25 | 北京百度网讯科技有限公司 | Pose graph generation method and high-precision map generation method and device |
| CN115239899B (en) * | 2022-06-29 | 2023-09-05 | 北京百度网讯科技有限公司 | Pose map generation method, high-precision map generation method and device |
| CN115330923A (en) * | 2022-08-10 | 2022-11-11 | 小米汽车科技有限公司 | Point cloud data rendering method and device, vehicle, readable storage medium and chip |
| CN115330923B (en) * | 2022-08-10 | 2023-11-14 | 小米汽车科技有限公司 | Point cloud data rendering method and device, vehicle, readable storage medium and chip |
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