Road boundary detection method, device, equipment and medium

Abstract

The present invention discloses a road boundary detection method, device, equipment, and medium. The method comprises: acquiring road boundary point cloud data within an ROI in real time and determining the height of each pre-divided grid within the ROI; determining the road boundary point sets corresponding to the left and right sides of the vehicle, respectively, based on the height of each grid within the ROI and the left or right area to which each grid belongs, and generating the complete road curve equation and the effective road curve equation corresponding to the left and right sides of the vehicle, respectively; determining an inlier point set based on the effective road curve equations updated historically within a specified time period, and determining multiple valid inliers within the inlier point set based on the complete road curve equation and / or the effective road curve equation; adding the valid inliers to the road boundary point set, updating the effective road curve equation, and generating a road boundary detection result. The above technical solution can accurately distinguish between road boundaries and road gaps, effectively improving the accuracy of road boundary detection.

Description

Technical Field

[0001] The present invention relates to the field of automobile intelligent technology, and in particular to a road boundary detection method, device, equipment and medium. Background Art

[0002] With the development of intelligent automotive technology, more and more cars are equipped with autonomous driving systems. In road scenes, road boundaries composed of curbs, guardrails, etc. are one of the important road features and are also the focus of the perception module in autonomous driving systems.

[0003] Existing LiDAR-based road boundary detection methods generally fit the overall road boundary curve equation based on all potential points when fitting the road curve. However, road scenes are complex and changeable, and the actual road boundary may have relatively complex scenes such as gaps and intersections. Taking a typical T-shaped intersection as an example, one side usually has a continuous road boundary, while the other side has a gap of a certain length. In addition, when the road boundary is blocked by vehicles or other objects, it may also cause the identified road boundary to be missing. Therefore, it is difficult to effectively describe the actual road boundary using only the overall curve equation, which may cause misjudgment of subsequent functions and affect the safety of autonomous driving functions. Summary of the Invention

[0004] The present invention provides a road boundary detection method, device, equipment and medium, which can accurately distinguish road boundaries from road gaps and effectively improve the accuracy of road boundary detection.

[0005] According to one aspect of the present invention, a road boundary detection method is provided, comprising:

[0006] Acquire road boundary point cloud data within the ROI in real time, and determine the height of each pre-divided grid within the ROI based on the road boundary point cloud data;

[0007] Determining the road boundary point sets corresponding to the left and right sides of the vehicle, respectively, based on the height of each grid within the ROI region and the left or right region to which each grid belongs, and generating the complete road curve equation and the effective road curve equation corresponding to the left and right sides of the vehicle, respectively, based on the road boundary point sets; wherein the effective road curve equation is determined by whether there is a road gap on the left or right side of the vehicle;

[0008] Determine an inlier point set based on each historically updated road effective curve equation within a specified time period, and determine a plurality of effective inliers in the inlier point set based on the road complete curve equation and / or the road effective curve equation;

[0009] The valid interior points are added to the road boundary point set, and according to the road boundary point set after the valid interior points are added, the road valid curve equation is updated to generate a road boundary detection result.

[0010] According to another aspect of the present invention, there is provided a road boundary detection device, comprising:

[0011] A grid height acquisition module is used to acquire the road boundary point cloud data within the ROI in real time, and determine the height of each grid pre-divided within the ROI based on the road boundary point cloud data;

[0012] a curve equation generation module, configured to determine, based on the height of each grid within the ROI region and the left or right region to which each grid belongs, a set of road boundary points corresponding to the left and right sides of the vehicle, respectively, and to generate, based on the road boundary point sets, a complete road curve equation and a valid road curve equation corresponding to the left and right sides of the vehicle, respectively; wherein the valid road curve equation is determined by whether there is a road gap on the left or right side of the vehicle;

[0013] an effective inlier determination module, configured to determine an inlier point set based on each historically updated effective curve equation of the road within a specified time period, and to determine a plurality of effective inliers in the inlier point set based on the complete road curve equation and / or the effective road curve equation;

[0014] The road boundary detection result generating module is used to add the valid inner points to the road boundary point set, and update the road valid curve equation according to the road boundary point set after adding the valid inner points, so as to generate the road boundary detection result.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the road boundary detection method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the road boundary detection method according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains road boundary point cloud data within the ROI, determines the height of each pre-divided grid within the ROI, and determines the road boundary point sets corresponding to the left and right sides of the vehicle respectively based on the height of each grid within the ROI area and the left or right area to which each grid belongs. Then, based on the road boundary point sets, a complete road curve equation and a road effective curve equation corresponding to the left and right sides of the vehicle are generated respectively. Then, based on the historically updated road effective curve equation, an inlier point set is obtained, and a valid inlier point is determined in the inlier point set. The road effective curve equation is updated based on the valid inlier point, thereby generating a road boundary detection result. This method can eliminate the influence of obstacles and special road sections on road boundary detection, and solves the problem of inaccurate road boundary detection results caused by fitting only the point cloud collected by the lidar in the prior art. At the same time, updating the road effective boundary curve based on the valid inlier point can solve the problem of the collected data at a certain moment being affected by the surrounding environment, reduce the road gap size error, and effectively improve the accuracy of road boundary detection.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flowchart of a road boundary detection method provided according to the first embodiment of the present invention;

[0024] Figure 2 is a schematic diagram of a vehicle region of interest provided according to an embodiment of the present invention;

[0025] Figure 3 is a flowchart of another road boundary detection method provided according to the second embodiment of the present invention;

[0026] Figure 4 2 is a schematic structural diagram of a road boundary detection device provided according to a third embodiment of the present invention;

[0027] Figure 5 2 is a schematic diagram of the structure of an electronic device for implementing the road boundary detection method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] Example 1

[0031] Figure 1 This is a flow chart of a road boundary detection method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of accurately distinguishing normal road boundaries and road boundary gaps. The method can be executed by a road boundary detection device, which can be implemented in the form of hardware and / or software and can generally be configured in a vehicle computer or a processor of an autonomous driving system. Figure 1 As shown, the method includes:

[0032] S110 , acquiring road boundary point cloud data within the ROI in real time, and determining the height of each pre-divided grid within the ROI based on the road boundary point cloud data.

[0033] Optionally, the point cloud data may be collected in real time by a laser radar carried by a vehicle, and the road boundary point cloud data may refer to point cloud data that can represent the road boundary.

[0034] Optionally, the ROI (Region of Interest) can be a preset area, or an area within a specified range around the vehicle. Generally, the ROI will be smaller than the maximum area that can be collected by the lidar. The ROI can be determined based on the current vehicle coordinate system.

[0035] Figure 2 is a schematic diagram of an optional vehicle area of ​​interest. Figure 2 As shown, xoy is the vehicle coordinate system, the origin o of the coordinate system can be the vehicle center of mass or the center of the vehicle, the x-axis can point to the direction of the vehicle head, the y-axis can point to the direction perpendicular to the vehicle head, and the shaded area is the area of ​​interest of the vehicle.

[0036] Optionally, after the laser radar obtains the point cloud data at the current moment, the point cloud data within the ROI can be filtered out according to the pre-divided ROI, and then the point cloud data belonging to the interference objects in the ROI can be filtered out, and only the point cloud data that can represent the road boundary is retained, and the point cloud data that can represent the road boundary is downsampled to generate the road boundary point cloud data.

[0037] Optionally, the ROI can be divided into multiple grids according to a predetermined grid division rule. Figure 2 As shown, in the ROI shown in the shaded area, each grid divided by white lines is a pre-divided grid within the ROI.

[0038] Optionally, after determining each grid within the ROI, the road boundary point cloud data can be divided into point cloud data within each grid, and then the point clouds in each grid that obviously do not belong to the road boundary are deleted, and the point cloud with the highest height is determined in the remaining point clouds, and its height is used as the grid height to determine the height of each pre-divided grid within the ROI.

[0039] S120. Determine the road boundary point sets corresponding to the left and right sides of the vehicle, respectively, based on the height of each grid within the ROI area and the left or right area to which each grid belongs. Generate a complete curve equation and a valid curve equation of the road corresponding to the left and right sides of the vehicle, respectively, based on the road boundary point sets.

[0040] The effective road curve equation is determined by whether there is a road gap on the left or right side of the vehicle.

[0041] Before respectively determining the road boundary point sets corresponding to the left side and the right side of the vehicle according to the height of each grid in the ROI area and the left or right area to which each grid belongs, the method may further include:

[0042] Obtaining motion data of the vehicle, and calculating the current position of the vehicle based on the motion data;

[0043] Calculating a motion trajectory equation of the vehicle based on the vehicle's historical position within a specified time period and the vehicle's current position, and determining the vehicle's motion trajectory based on the motion trajectory equation;

[0044] The grid passed by the motion trajectory is determined as the middle grid, and the grids located on the left side of the middle grid in the ROI are divided into the left area, and the grids located on the right side of the middle grid are divided into the right area.

[0045] Optionally, the motion data of the vehicle may include the linear velocity, yaw angular velocity, vehicle position of the vehicle at the last data collection moment and the time interval between each motion data collection.

[0046] Optionally, you can use the formula Calculate the position variable Δp of the vehicle on the horizontal axis from the last data collection moment to the current data collection moment x , according to the formula Calculate the position variable Δp of the vehicle on the vertical coordinate from the last data collection moment to the current data collection moment y , where v k is the linear velocity at the last data collection moment, ω k is the yaw angular velocity at the last data collection moment, and T is the time interval between each motion data collection.

[0047] Furthermore, after determining the position variable of the vehicle, the current position of the vehicle can be calculated based on the vehicle position at the last data collection moment and the position variable of the vehicle.

[0048] Optionally, the designated time period can generally be a smaller time period, and the designated time period can be determined according to the vehicle speed. For example, when the vehicle speed is 20 km / h, the designated time period can be 5 seconds, and when the vehicle speed is 80 km / h, the designated time period can be 2 seconds. However, this is only an example and does not limit the specific selection method of the designated time period.

[0049] Optionally, the historical position can refer to the vehicle position at each data collection moment within a specified time period. Based on each historical position and the current position of the vehicle, the vehicle positions can be fitted to generate the vehicle's motion trajectory equation, and then the vehicle's motion trajectory can be determined based on the curve of the motion trajectory equation. Figure 2 In the example shown, the dotted line portion represents the motion trajectory of the vehicle.

[0050] It is understandable that the size of the grid is generally much smaller than the size of the vehicle body. Therefore, after determining the middle grid that the vehicle trajectory passes through, there is no need to process the point cloud data of the middle grid. The road boundaries are generally located on the left and right sides of the vehicle.

[0051] Optionally, after dividing the left and right areas of the vehicle, the left and right sides can be searched separately starting from the middle grid, and the height differences between two adjacent grids can be compared one by one. When the height difference is higher than a certain value, there may be a road boundary such as a curb or a fence. The higher grid in the adjacent grid may fall on the road boundary. The highest point in the grid can be used as a road boundary point. After determining the grid where the road boundary point is located, stop continuing to compare the height differences of the grids outside the grid.

[0052] Furthermore, the road boundary points belonging to the left area may be combined to generate a road boundary point set corresponding to the left side of the vehicle, and the road boundary points belonging to the right area may be combined to generate a road boundary point set corresponding to the right side of the vehicle.

[0053] Optionally, after obtaining the road boundary point set, the road boundary point sets corresponding to the left area and the right area respectively may be fitted to obtain complete curve equations of the roads on the left and right sides of the vehicle.

[0054] Optionally, after obtaining the road boundary point set, the distance difference between each adjacent road boundary point in each road boundary point set can be calculated respectively, that is, the distance difference between the road boundary points closest to each other in the road boundary point set is calculated. When the distance difference between two road boundary points is greater than a certain value, it can be determined that there is a gap between the two road boundary points. At this time, the two road boundary points can be used as the endpoints on both sides of the gap.

[0055] Optionally, for a single-sided road, there may be multiple gaps, one gap, or no gap. When there is at least one gap in the single-sided road, the effective road curve equation may include multiple road segment curve equations. When there is no gap in the single-sided road, the effective road curve equation is the complete road curve equation.

[0056] Optionally, when there is a gap in the road, the road boundary point set on the vehicle side can be divided into multiple subsets according to each gap, and the road boundary points in each subset are fitted to generate a road segment curve equation.

[0057] S130. Determine an inlier point set based on each historically updated road effective curve equation within a specified time period, and determine a plurality of effective inliers in the inlier point set based on the road complete curve equation and / or the road effective curve equation.

[0058] Optionally, each historically updated road effective curve equation within a specified time period may refer to a road effective curve finally determined by the road boundary detection at a historical moment. It can be understood that the road effective curve generated in step S120 is a preliminary generated road effective curve, and the road effective curve determined at a certain moment may not be able to reflect the real and complete road conditions. Therefore, it can be updated through the historically confirmed road effective curve, so that the updated road effective curve equation is used as the final road effective curve.

[0059] Optionally, the interior points of each historically updated road effective curve may be determined based on each historically updated road effective curve and a set of road boundary points used to update each road effective curve.

[0060] Optionally, if the distance between a road boundary point in the road boundary point set and the road effective curve is less than a certain value, then the road boundary point may be used as an interior point of the road effective curve.

[0061] Optionally, the interior point set may include all interior points of each historically updated road effective curve equation, and the interior point set may also be divided into an interior point set on the left side of the vehicle and an interior point set on the right side of the vehicle.

[0062] Optionally, after determining the inner point set, each inner point in the inner point set can be compared with the road complete curve equation and / or the road effective curve equation. When there is no road gap on the target side of the vehicle, it is only necessary to compare the inner points on the target side of the vehicle with the road complete curve equation. If the inner point falls within the inner point range of the road complete curve equation, the inner point is determined to be a valid inner point; when there is a gap on the target side of the vehicle, it is necessary to compare the inner point with the road complete curve equation and the road effective curve equation respectively. If the inner point falls within the inner point range of the road complete curve equation or the road effective curve equation, the inner point is determined to be a valid inner point.

[0063] S140: Add the valid inliers to the road boundary point set, and update the road valid curve equation according to the road boundary point set to which the valid inliers are added, to generate a road boundary detection result.

[0064] Optionally, the valid inner point can be added to the road boundary point set that matches the target vehicle side where the valid inner point is located, and based on the updated road boundary point set on the target vehicle side, the road effective curve equation on the target vehicle side is re-fitted and generated, and based on the regenerated road effective curve equation, the road effective curve equation generated in step S120 is updated.

[0065] Optionally, after obtaining the updated road effective curve equation, if the road effective curve equation is a complete road curve equation, a curve matching the complete road curve equation can be output as the road boundary; if the road effective curve equation is a segmented road curve equation, each segmented curve is used as the road boundary, and the gap between each road boundary is the road gap.

[0066] The technical solution of the embodiment of the present invention obtains road boundary point cloud data within the ROI, determines the height of each pre-divided grid within the ROI, and determines the road boundary point sets corresponding to the left and right sides of the vehicle respectively based on the height of each grid within the ROI area and the left or right area to which each grid belongs. Then, based on the road boundary point sets, a complete road curve equation and a road effective curve equation corresponding to the left and right sides of the vehicle are generated respectively. Then, based on the historically updated road effective curve equation, an inlier point set is obtained, and a valid inlier point is determined in the inlier point set. The road effective curve equation is updated based on the valid inlier point, thereby generating a road boundary detection result. This method can eliminate the influence of obstacles and special road sections on road boundary detection, and solves the problem of inaccurate road boundary detection results caused by fitting only the point cloud collected by the lidar in the prior art. At the same time, updating the road effective boundary curve based on the valid inlier point can solve the problem of the collected data at a certain moment being affected by the surrounding environment, reduce the road gap size error, and effectively improve the accuracy of road boundary detection.

[0067] Example 2

[0068] Figure 3 This is a flow chart of a road boundary detection method provided by the second embodiment of the present invention. This embodiment specifically describes the road boundary detection method based on the above embodiment. Figure 3 As shown, the method includes:

[0069] S210 , deleting the point cloud data of each interfering object from the initial point cloud data within the ROI acquired in real time, and performing downsampling processing on the remaining initial point cloud data to generate road boundary point cloud data within the ROI.

[0070] Optionally, the initial point cloud data may refer to each point cloud data belonging to the ROI in the point cloud data acquired by the laser radar, that is, the initial point cloud data may include all point cloud data within the vehicle ROI.

[0071] Optionally, the interfering objects may include pedestrians, vehicles, obstacles, and other objects that can be clearly distinguished from road boundaries.

[0072] Optionally, the benefit of downsampling the remaining initial point cloud data is that the main purpose of this application is to fit curves related to road boundaries, so as to determine road boundaries based on the curve conditions. Curve fitting does not require a large amount of point cloud data, only universal or representative point clouds are required. Therefore, downsampling can effectively improve the calculation speed and meet the need to obtain road boundary detection results in real time when the vehicle is driving.

[0073] S220 , dividing the road boundary point cloud data into pre-divided grids, and obtaining point cloud data of each grid respectively.

[0074] S230 , filtering the point clouds exceeding a first preset threshold in the point cloud data of each grid, and using the height value of the highest point cloud in each grid after filtering as the height of each grid.

[0075] Optionally, the first preset threshold may be used to filter out point cloud data in each grid that clearly does not belong to a road boundary.

[0076] It is understandable that by screening and deleting the point cloud data of interfering objects, it is possible to delete obstacles with known relatively fixed shapes. However, in actual road scenes, for overpasses, tunnels, etc., point cloud data may also be collected for the suspended parts of overpasses and tunnels, which may result in the presence of some high-altitude point clouds in the point cloud data of each grid. However, these point clouds cannot represent the road boundaries. Therefore, point clouds with a height greater than the first preset threshold can be filtered, and only the point clouds determined as road boundaries in each grid are retained.

[0077] S240: Starting from each middle grid, respectively calculate the height difference of each adjacent grid in the left area and the right area. When it is determined that the height difference of the target adjacent grid is greater than a second preset threshold, determine the road boundary point in the outer grid of the target adjacent grid, and stop calculating the height difference related to the outer grid.

[0078] Optionally, a second preset threshold can be used to determine whether each grid can be used as a road boundary. If the height difference between adjacent target grids is greater than the second preset threshold, the grid with a higher height in the target grid can be used as a road boundary, and the point with the highest height in the grid can be used as a road boundary point.

[0079] It is understandable that the height difference of each adjacent grid is searched outward from the middle grid because after the vehicle travels on the road, the road boundary is generally outside the vehicle. Therefore, the outer grid of the target adjacent grid is the grid with a higher height among the target adjacent grids.

[0080] S250 , summarizing the road boundary points in the left area and the right area respectively, and determining the road boundary point sets corresponding to the left and right sides of the vehicle respectively.

[0081] S260 , fitting the road boundary point set corresponding to the left side of the vehicle and the road boundary point set corresponding to the right side of the vehicle respectively to generate complete curve equations of the road corresponding to the left side and the right side of the vehicle respectively.

[0082] S270. Determine whether there is a road gap on the left or right side of the vehicle. For the vehicle side with a road gap, generate a road segment curve equation corresponding to the vehicle side, and use the road segment curve equation as the effective road curve equation for the vehicle side; for the vehicle side without a road gap, use the road complete curve equation corresponding to the vehicle side as the effective road curve equation.

[0083] The process of determining whether there is a road gap on the left or right side of the vehicle and generating a road segment curve equation corresponding to the vehicle side with the road gap may include:

[0084] respectively calculating the distance between each adjacent road boundary point in each road boundary point set, and determining that a road gap exists between the target adjacent road boundary points when the distance between the target adjacent road boundary points is greater than a third preset threshold;

[0085] According to each determined road gap, the road boundary point set on the vehicle side where the road gap exists is split to generate multiple boundary sub-point sets;

[0086] The boundary sub-point sets on the vehicle side where the road gap exists are fitted to generate the road segment curve equation corresponding to the vehicle side.

[0087] Optionally, a third preset threshold may be used to determine whether there is a gap in the road. When the distance between target adjacent road boundary points in the road boundary point set is greater than the third preset threshold, it may be determined that there is a road gap between the target adjacent road boundary points.

[0088] S280: Acquire multiple interior points of each historically updated road effective curve equation within a specified time period, convert each interior point to the current vehicle coordinate system, and combine them to form an interior point set.

[0089] S290. Calculate first distances between each inner point in the inner point set located on the vehicle target side and the complete curve equation of the vehicle target side road and / or second distances between each inner point and the valid curve equation of each road on the vehicle target side.

[0090] S2100: When the first distance or the second distance of the target interior point is less than a fourth preset threshold, determine the target interior point as a valid interior point on the vehicle target side.

[0091] Optionally, the fourth preset threshold is a preset inlier range value, which can be used to determine whether the inlier is a valid inlier.

[0092] S2110. Add each valid interior point on the vehicle target side to the road boundary point set that matches the vehicle target side, and update the road effective curve equation on the vehicle target side based on the road boundary point set to which the valid interior points are added.

[0093] The advantage of this setting is that if there is a gap in the road, the effective boundary curve of the road obtained by only fitting the point cloud data once may cause deviations due to other environmental factors, and the accuracy of the identification of the road gap cannot be guaranteed. Therefore, by adding valid internal points, the scope of the road gap can be re-determined.

[0094] S2120: Mark the updated curves of the valid road curve equations as valid road boundaries, and mark the gaps between the valid road boundaries as road boundary gaps.

[0095] The technical solution of the embodiment of the present invention obtains road boundary point cloud data within the ROI, determines the height of each pre-divided grid within the ROI, and determines the road boundary point sets corresponding to the left and right sides of the vehicle respectively based on the height of each grid within the ROI area and the left or right area to which each grid belongs. Then, based on the road boundary point sets, a complete road curve equation and a road effective curve equation corresponding to the left and right sides of the vehicle are generated respectively. Then, based on the historically updated road effective curve equation, an inlier point set is obtained, and a valid inlier point is determined in the inlier point set. The road effective curve equation is updated based on the valid inlier point, thereby generating a road boundary detection result. This method can eliminate the influence of obstacles and special road sections on road boundary detection, and solves the problem of inaccurate road boundary detection results caused by fitting only the point cloud collected by the lidar in the prior art. At the same time, updating the road effective boundary curve based on the valid inlier point can solve the problem of the collected data at a certain moment being affected by the surrounding environment, reduce the road gap size error, and effectively improve the accuracy of road boundary detection.

[0096] Example 3

[0097] Figure 4 This is a schematic diagram of the structure of a road boundary detection device provided by the third embodiment of the present invention. Figure 4 As shown, the device includes: a grid height acquisition module 310, a curve equation generation module 320, a valid interior point determination module 330 and a road boundary detection result generation module 340.

[0098] The grid height acquisition module 310 is used to acquire the road boundary point cloud data within the ROI in real time, and determine the height of each grid pre-divided within the ROI based on the road boundary point cloud data.

[0099] The curve equation generation module 320 is used to determine the road boundary point sets corresponding to the left and right sides of the vehicle respectively based on the height of each grid in the ROI area and the left or right area to which each grid belongs, and generate the complete curve equation and the effective curve equation of the road corresponding to the left and right sides of the vehicle respectively based on the road boundary point sets.

[0100] The effective road curve equation is determined by whether there is a road gap on the left or right side of the vehicle.

[0101] The valid inlier determination module 330 is used to determine an inlier point set based on each historically updated road valid curve equation within a specified time period, and to determine multiple valid inliers in the inlier point set based on the road complete curve equation and / or the road valid curve equation.

[0102] The road boundary detection result generating module 340 is configured to add the valid inliers to the road boundary point set, and update the road valid curve equation according to the road boundary point set after adding the valid inliers, so as to generate a road boundary detection result.

[0103] The technical solution of the embodiment of the present invention obtains road boundary point cloud data within the ROI, determines the height of each pre-divided grid within the ROI, and determines the road boundary point sets corresponding to the left and right sides of the vehicle respectively based on the height of each grid within the ROI area and the left or right area to which each grid belongs. Then, based on the road boundary point sets, a complete road curve equation and a road effective curve equation corresponding to the left and right sides of the vehicle are generated respectively. Then, based on the historically updated road effective curve equation, an inlier point set is obtained, and a valid inlier point is determined in the inlier point set. The road effective curve equation is updated based on the valid inlier point, thereby generating a road boundary detection result. This method can eliminate the influence of obstacles and special road sections on road boundary detection, and solves the problem of inaccurate road boundary detection results caused by fitting only the point cloud collected by the lidar in the prior art. At the same time, updating the road effective boundary curve based on the valid inlier point can solve the problem of the collected data at a certain moment being affected by the surrounding environment, reduce the road gap size error, and effectively improve the accuracy of road boundary detection.

[0104] Based on the above embodiments, the grid height acquisition module 310 can be specifically used to:

[0105] The point cloud data of each interfering object is deleted from the initial point cloud data within the ROI acquired in real time, and the remaining initial point cloud data is downsampled to generate the road boundary point cloud data within the ROI;

[0106] Dividing the road boundary point cloud data into pre-divided grids, and obtaining point cloud data of each grid respectively;

[0107] The point clouds exceeding a first preset threshold value in the point cloud data of each grid are filtered, and the height value of the highest point cloud in each grid after filtering is used as the height of each grid.

[0108] On the basis of the above embodiments, a grid division module may be further included, specifically for:

[0109] Obtaining motion data of the vehicle, and calculating the current position of the vehicle based on the motion data;

[0110] Calculating a motion trajectory equation of the vehicle based on the vehicle's historical position within a specified time period and the vehicle's current position, and determining the vehicle's motion trajectory based on the motion trajectory equation;

[0111] The grid passed by the motion trajectory is determined as the middle grid, and the grids located on the left side of the middle grid in the ROI are divided into the left area, and the grids located on the right side of the middle grid are divided into the right area.

[0112] Based on the above embodiments, the curve equation generating module 320 may include:

[0113] a height difference calculation unit, configured to calculate, starting from each middle grid, the height difference of each adjacent grid in the left area and the right area, and when it is determined that the height difference of the target adjacent grid is greater than a second preset threshold, determine a road boundary point in an outer grid of the target adjacent grid and stop the height difference calculation related to the outer grid;

[0114] a road boundary point set determination unit, configured to respectively summarize the road boundary points in the left area and the right area to determine the road boundary point sets corresponding to the left and right sides of the vehicle;

[0115] a complete curve equation generating unit, configured to respectively fit a set of road boundary points corresponding to the left side of the vehicle and a set of road boundary points corresponding to the right side of the vehicle, to generate complete curve equations of the road corresponding to the left side and the right side of the vehicle respectively;

[0116] The road effective curve equation generating unit is used to determine whether there is a road gap on the left and right sides of the vehicle. For the vehicle side with a road gap, a road segmented curve equation corresponding to the vehicle side is generated, and the road segmented curve equation is used as the road effective curve equation for the vehicle side; for the vehicle side without a road gap, the road complete curve equation corresponding to the vehicle side is used as the road effective curve equation.

[0117] Based on the above embodiments, the road effective curve equation generating unit can be specifically used to:

[0118] respectively calculating the distance between each adjacent road boundary point in each road boundary point set, and determining that a road gap exists between the target adjacent road boundary points when the distance between the target adjacent road boundary points is greater than a third preset threshold;

[0119] According to each determined road gap, the road boundary point set on the vehicle side where the road gap exists is split to generate multiple boundary sub-point sets;

[0120] The boundary sub-point sets on the vehicle side where the road gap exists are fitted to generate the road segment curve equation corresponding to the vehicle side.

[0121] Based on the above embodiments, the effective interior point determination module 330 can be specifically used to:

[0122] Obtain multiple interior points of each historically updated road effective curve equation within a specified time period, convert each interior point to the current vehicle coordinate system, and combine them to form an interior point set;

[0123] Calculating first distances between each interior point in the interior point set located on the vehicle target side and a complete curve equation of the vehicle target side road and / or second distances between each interior point and each effective curve equation of the vehicle target side road;

[0124] When the first distance or the second distance of the target interior point is less than a fourth preset threshold, the target interior point is determined as a valid interior point on the vehicle target side.

[0125] Based on the above embodiments, the road boundary detection result generating module 340 can be specifically used to:

[0126] Add each valid interior point on the target side of the vehicle to the road boundary point set that matches the target side of the vehicle, and update the road effective curve equation on the target side of the vehicle based on the road boundary point set after adding the valid interior points;

[0127] The curves where the updated valid curve equations of each road are located are marked as valid road boundaries, and the gaps between the valid road boundaries are marked as road boundary gaps.

[0128] The road boundary detection device provided in the embodiment of the present invention can execute the road boundary detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0129] Example 4

[0130] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0131] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0132] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0133] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the road boundary detection method described in the embodiment of the present invention. That is,

[0134] Acquire road boundary point cloud data within the ROI in real time, and determine the height of each pre-divided grid within the ROI based on the road boundary point cloud data;

[0135] Determining the road boundary point sets corresponding to the left and right sides of the vehicle, respectively, based on the height of each grid within the ROI region and the left or right region to which each grid belongs, and generating the complete road curve equation and the effective road curve equation corresponding to the left and right sides of the vehicle, respectively, based on the road boundary point sets; wherein the effective road curve equation is determined by whether there is a road gap on the left or right side of the vehicle;

[0136] Determine an inlier point set based on each historically updated road effective curve equation within a specified time period, and determine a plurality of effective inliers in the inlier point set based on the road complete curve equation and / or the road effective curve equation;

[0137] The valid interior points are added to the road boundary point set, and according to the road boundary point set after the valid interior points are added, the road valid curve equation is updated to generate a road boundary detection result.

[0138] In some embodiments, the road boundary detection method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the road boundary detection method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the road boundary detection method in any other suitable manner (e.g., via firmware).

[0139] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0142] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0143] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0144] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0145] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0146] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A road boundary detection method, characterized in that: include: Acquire road boundary point cloud data within a region of interest (ROI) in real time, and determine the height of each pre-divided grid within the ROI based on the road boundary point cloud data; Determining the road boundary point sets corresponding to the left and right sides of the vehicle, respectively, based on the height of each grid within the ROI region and the left or right region to which each grid belongs, and generating the complete road curve equation and the effective road curve equation corresponding to the left and right sides of the vehicle, respectively, based on the road boundary point sets; wherein the effective road curve equation is determined by whether there is a road gap on the left or right side of the vehicle; Determine an inlier point set based on each historically updated road effective curve equation within a specified time period, and determine a plurality of effective inliers in the inlier point set based on the road complete curve equation and / or the road effective curve equation; The valid interior points are added to the road boundary point set, and according to the road boundary point set after the valid interior points are added, the road valid curve equation is updated to generate a road boundary detection result.

2. The method according to claim 1, characterized in that Acquire the road boundary point cloud data within the ROI in real time, and determine the height of each pre-divided grid within the ROI based on the road boundary point cloud data, including: The point cloud data of each interfering object is deleted from the initial point cloud data within the ROI acquired in real time, and the remaining initial point cloud data is downsampled to generate the road boundary point cloud data within the ROI; Dividing the road boundary point cloud data into pre-divided grids, and obtaining point cloud data of each grid respectively; The point clouds exceeding a first preset threshold value in the point cloud data of each grid are filtered, and the height value of the highest point cloud in each grid after filtering is used as the height of each grid.

3. The method according to claim 1, characterized in that Before respectively determining the road boundary point sets corresponding to the left and right sides of the vehicle according to the heights of the grids in the ROI region and the left or right regions to which the grids belong, the method further includes: Obtaining motion data of the vehicle, and calculating the current position of the vehicle based on the motion data; Calculating a motion trajectory equation of the vehicle based on the vehicle's historical position within a specified time period and the vehicle's current position, and determining the vehicle's motion trajectory based on the motion trajectory equation; The grid passed by the motion trajectory is determined as the middle grid, and the grids located on the left side of the middle grid in the ROI are divided into the left area, and the grids located on the right side of the middle grid are divided into the right area.

4. The method according to claim 3, characterized in that According to the height of each grid in the ROI area and the left or right area to which each grid belongs, the road boundary point sets corresponding to the left and right sides of the vehicle are determined respectively, and the complete curve equations and the road segment curve equations corresponding to the left and right sides of the vehicle are generated respectively based on the road boundary point sets, including: Starting from each middle grid, the height difference of each adjacent grid in the left area and the right area is calculated respectively, and when it is determined that the height difference of the target adjacent grid is greater than a second preset threshold, the road boundary point is determined in the outer grid of the target adjacent grid, and the height difference calculation related to the outer grid is stopped; Summarize the road boundary points in the left area and the right area respectively to determine the road boundary point sets corresponding to the left and right sides of the vehicle; Fitting the road boundary point set corresponding to the left side of the vehicle and the road boundary point set corresponding to the right side of the vehicle respectively to generate complete road curve equations corresponding to the left side and the right side of the vehicle respectively; Determine whether there is a road gap on the left or right side of the vehicle. For the vehicle side with a road gap, generate a road segment curve equation corresponding to the vehicle side, and use the road segment curve equation as the effective road curve equation for the vehicle side; for the vehicle side without a road gap, use the road complete curve equation corresponding to the vehicle side as the effective road curve equation.

5. The method according to claim 4, characterized in that Determine whether there is a road gap on the left or right side of the vehicle. For the vehicle side with a road gap, generate a road segment curve equation corresponding to the vehicle side, including: respectively calculating the distance between each adjacent road boundary point in each road boundary point set, and determining that a road gap exists between the target adjacent road boundary points when the distance between the target adjacent road boundary points is greater than a third preset threshold; According to each determined road gap, the road boundary point set on the vehicle side where the road gap exists is split to generate multiple boundary sub-point sets; The boundary sub-point sets on the vehicle side where the road gap exists are fitted to generate the road segment curve equation corresponding to the vehicle side.

6. The method according to claim 1, characterized in that Determine an inlier point set based on each historically updated road effective curve equation within a specified time period, and determine multiple effective inliers in the inlier point set based on the complete road curve equation and / or the road effective curve equation, including: Obtain multiple interior points of each historically updated road effective curve equation within a specified time period, convert each interior point to the current vehicle coordinate system, and combine them to form an interior point set; Calculating first distances between each interior point in the interior point set located on the vehicle target side and a complete curve equation of the vehicle target side road and / or second distances between each interior point and each effective curve equation of the vehicle target side road; When the first distance or the second distance of the target interior point is less than a fourth preset threshold, the target interior point is determined as a valid interior point on the vehicle target side.

7. The method according to claim 6, characterized in that Adding the valid inlier point to the road boundary point set, and updating the road valid curve equation according to the road boundary point set after adding the valid inlier point, to generate a road boundary detection result, including: Add each valid interior point on the target side of the vehicle to the road boundary point set that matches the target side of the vehicle, and update the road effective curve equation on the target side of the vehicle based on the road boundary point set after adding the valid interior points; The curves where the updated valid curve equations of each road are located are marked as valid road boundaries, and the gaps between the valid road boundaries are marked as road boundary gaps.

8. A road boundary detection device, characterized in that: include: A grid height acquisition module is used to acquire the road boundary point cloud data within the region of interest (ROI) in real time, and determine the height of each grid pre-divided within the ROI based on the road boundary point cloud data; a curve equation generation module, configured to determine, based on the height of each grid within the ROI region and the left or right region to which each grid belongs, a set of road boundary points corresponding to the left and right sides of the vehicle, respectively, and to generate, based on the road boundary point sets, a complete road curve equation and a valid road curve equation corresponding to the left and right sides of the vehicle, respectively; wherein the valid road curve equation is determined by whether there is a road gap on the left or right side of the vehicle; an effective inlier determination module, configured to determine an inlier point set based on each historically updated effective curve equation of the road within a specified time period, and to determine a plurality of effective inliers in the inlier point set based on the complete road curve equation and / or the effective road curve equation; The road boundary detection result generating module is used to add the valid inner points to the road boundary point set, and update the road valid curve equation according to the road boundary point set after adding the valid inner points, so as to generate the road boundary detection result.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the road boundary detection method according to any one of claims 1 to 7 of the present invention.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the road boundary detection method according to any one of claims 1 to 7 when executed.

Images