Quality control methods, devices, electronic equipment and media for high-precision map trajectory acquisition

Abstract

This disclosure provides a quality control method, apparatus, electronic device, and medium for high-precision map trajectory acquisition, relating to the field of computer technology, and particularly to the fields of autonomous driving, high-precision maps, map navigation, and intelligent transportation. The method includes: acquiring the current trajectory and other trajectories on the same road from the trajectory acquired in the high-precision map; determining whether the current trajectory is abnormal based on the elevation difference between points on the current trajectory and corresponding points on the other trajectories; if the current trajectory is abnormal, filtering it out from the acquired trajectories and stopping the output of the current trajectory to the high-precision map production. This technical solution can quickly identify abnormal trajectories, reduce the risk of accuracy anomalies, improve the accuracy of field data, prevent abnormal trajectories from flowing into the high-precision map production process, and improve the reliability of the high-precision map.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to the fields of autonomous driving, high-precision maps, map navigation, and intelligent transportation. Specifically, it relates to a quality control method, device, electronic device, and medium for high-precision map trajectory acquisition. Background Technology

[0002] The field data collected for high-precision maps includes field trajectory information and corresponding point images and laser point cloud information. For wider roads, multiple trajectories need to be collected in the field to make the point cloud coverage more complete. The fused point cloud and images are then provided to the office for the production of high-precision map data.

[0003] Currently, the collection of high-precision map field trajectories relies entirely on the accuracy of the field data collection equipment, with multiple data collection runs used to minimize accuracy deviations. However, for scenarios with high accuracy requirements, such as autonomous driving, multiple data collection runs cannot effectively control the collection accuracy. When the field data collection equipment is unstable or malfunctions, there is a risk of accuracy anomalies, which in turn reduces the reliability of high-precision map data. Summary of the Invention

[0004] This disclosure provides a quality control method, apparatus, electronic device, storage medium, and computer program product for high-precision map trajectory acquisition.

[0005] According to one aspect of this disclosure, a quality control method for high-precision map trajectory acquisition is provided, comprising:

[0006] From the trajectory collected by the high-precision map, obtain the current trajectory and other trajectories on the road;

[0007] Based on the elevation difference between points on the current trajectory and points on other corresponding trajectories, determine whether the current trajectory is abnormal;

[0008] If the current trajectory is abnormal, then the current trajectory is filtered out from the collected trajectories, and the output of the current trajectory to the production of the high-precision map is stopped.

[0009] According to another aspect of this disclosure, a quality control device for high-precision map trajectory acquisition is provided, comprising:

[0010] The acquisition module is used to acquire the current trajectory and other trajectories on the same road from the trajectory collected by the high-precision map;

[0011] The determination module is used to determine whether there is an anomaly in the current trajectory based on the elevation difference between a point on the current trajectory and a point on the corresponding other trajectories;

[0012] The control module is used to filter out the current trajectory from the collected trajectories and stop outputting the current trajectory to the production of the high-precision map if the current trajectory is abnormal.

[0013] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods in any embodiment of this disclosure.

[0017] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause the computer to perform the methods of any embodiment of this disclosure.

[0018] According to another aspect of this disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the methods in any embodiment of this disclosure.

[0019] The technical solution of this disclosure can perform quality control on the trajectory collected by high-precision map based on elevation difference, providing an effective control means, which can quickly identify abnormal trajectories, reduce the risk of accuracy abnormalities, improve the accuracy of field data, prevent abnormal trajectories from flowing into the high-precision map production process, effectively avoid waste of production capacity, improve the reliability of high-precision map, and safeguard autonomous driving.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0021] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0022] Figure 1 This is a schematic diagram of a quality control method for high-precision map trajectory acquisition according to an embodiment of the present disclosure;

[0023] Figure 2a This is a schematic diagram of a quality control method for high-precision map trajectory acquisition according to an embodiment of the present disclosure;

[0024] Figure 2bThis is a schematic diagram of trajectory drift according to one embodiment of the present disclosure;

[0025] Figure 3a This is a schematic diagram illustrating the determination of abnormal trajectories based on the elevation difference of trajectories in the same direction according to an embodiment of this disclosure;

[0026] Figure 3b This is a schematic diagram of the elevation difference of the same trajectory according to an embodiment of the present disclosure;

[0027] Figure 4a This is a schematic diagram illustrating the determination of an abnormal trajectory based on the elevation difference between opposing trajectories according to an embodiment of this disclosure;

[0028] Figure 4b This is a schematic diagram of the elevation difference of opposing trajectories according to an embodiment of the present disclosure;

[0029] Figure 5a This is a schematic diagram illustrating the determination of an abnormal trajectory based on planar distance according to an embodiment of this disclosure;

[0030] Figure 5b This is a schematic diagram of trajectory intersection according to an embodiment of the present disclosure;

[0031] Figure 6 This is a schematic diagram of the elevation difference of a single trajectory according to an embodiment of the present disclosure;

[0032] Figure 7 This is a block diagram of a quality control device for high-precision map trajectory acquisition according to an embodiment of the present disclosure;

[0033] Figure 8 This is a block diagram of an electronic device used to implement the quality control method for high-precision map trajectory acquisition in the embodiments of this disclosure. Detailed Implementation

[0034] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0035] The technical solution of this disclosure is applied to the fields of autonomous driving, high-precision maps, map navigation, and intelligent transportation, especially to the quality control of field data collected for high-precision maps. It addresses the stage between field data collection and office production, performing accuracy quality control on multiple trajectories collected from the field based on elevation differences. This technical solution can be applied to quality control equipment for high-precision map trajectory collection. The collected trajectories can be displayed on the interface of the quality control equipment, and abnormal trajectories can also be marked on the interface, including static or dynamic display methods, facilitating viewing and monitoring by quality control personnel. The entire quality control process is automated, enabling rapid identification of abnormal trajectories and reducing the risk of accuracy anomalies. Furthermore, the quality control results can be re-identified manually to further improve the accuracy of the field data. The quality-controlled trajectories are then output to the office production stage, preventing abnormal trajectories from flowing into the high-precision map production process, effectively avoiding waste of production capacity, improving the reliability of high-precision maps, and ensuring the safety of autonomous driving.

[0036] In this embodiment of the disclosure, during the field data collection for high-precision maps, the data collection device can automatically acquire and record the position information of the trajectory along the driving direction, including x, y, and z values. The two-dimensional map used in the quality control process can be a standard two-dimensional road network (also known as a base map). Based on the two dimensions of elevation and horizontal distance, the two-dimensional road network is matched within the range of the collected trajectory to identify abnormal trajectories. This ensures that trajectories whose elevation or horizontal accuracy does not meet the quality requirements will not enter the production stage of high-precision maps, thus avoiding quality abnormalities in high-precision map product data caused by unqualified field data.

[0037] The elevation difference involved in this embodiment refers to the distance between two points in the Z direction, which can be calculated using the absolute value of the difference between the Z coordinate values ​​of the two points, i.e., ΔH = |Z1 - Z2|; or it can be calculated using the formula The calculation is not limited to any specific value; where Z1 and Z2 are the Z coordinates of the two points, and ΔH is the elevation difference between the two points.

[0038] In this embodiment, the planar distance between two points refers to the distance between two points within a single plane, ignoring the distance along the Z-axis (height). However, in real-world scenarios, two points may have different coordinates along the Z-axis, meaning they may lie on planes at different heights. This planar distance can be calculated using the following formula: Where X1 and Y1 are the X and Y coordinates of a point, X2 and Y2 are the X and Y coordinates of another point, and ΔL is the planar distance between the two points, which will not be elaborated further below.

[0039] Figure 1 This is a schematic diagram of a quality control method for high-precision map trajectory acquisition in one embodiment of this disclosure. Figure 1 As shown, the method includes:

[0040] S101: Obtain the current trajectory and other trajectories on the road from the trajectory collected by the high-precision map;

[0041] S102: Determine whether there is an anomaly in the current trajectory based on the elevation difference between points on the current trajectory and points on other corresponding trajectories;

[0042] S103: If the current trajectory is abnormal, filter out the current trajectory from the collected trajectories and stop outputting the current trajectory to the high-precision map production.

[0043] In one implementation, S102 may include:

[0044] When there are multiple trajectories, locate a reference line on the 2D map corresponding to the road, and determine the intersection point of the reference line with the current trajectory and multiple trajectories;

[0045] Based on the elevation difference between each intersection point and the preset first threshold, it is determined whether there is an anomaly in the current trajectory.

[0046] This method of determining abnormal trajectories based on the elevation difference between the current trajectory and multiple other trajectories is easy to calculate and implement. By comparing thresholds, it can be determined whether the current trajectory is abnormal, providing an effective quality control method for trajectories collected by high-precision maps.

[0047] In one implementation, determining whether the current trajectory is abnormal based on the elevation difference between the obtained intersection points and a preset first threshold includes:

[0048] If multiple trajectories are two trajectories in the same direction as the current trajectory, and the intersection of the current trajectory and the reference line is the first intersection point, and the intersections of the two trajectories and the reference line are the second and third intersection points, then calculate the elevation difference between any two points among the first, second, and third intersection points.

[0049] If the elevation difference between the first and second intersection points and the elevation difference between the first and third intersection points both exceed a preset first threshold, and the elevation difference between the second and third intersection points does not exceed the first threshold, then it is determined that there is an anomaly in the current trajectory.

[0050] The above-mentioned method for detecting whether the current trajectory is abnormal can effectively identify the abnormal current trajectory based on the elevation difference by using two trajectories collected in the same direction. This situation indicates that the elevation difference between the current trajectory and other trajectories in the same direction is significantly too large. In the scenario of the same road, it is an abnormal trajectory. Therefore, the abnormal trajectory is filtered out and the output of the abnormal trajectory to the production of high-precision map is stopped, which reduces the risk of accuracy abnormality and improves the accuracy of field data and the reliability of high-precision map.

[0051] In one implementation, determining whether the current trajectory is abnormal based on the elevation difference between the obtained intersection points and a preset first threshold includes:

[0052] If multiple trajectories are two trajectories opposite to the current trajectory, and the intersection point of the current trajectory with the reference line is the fourth intersection point, and the intersection points of the two trajectories with the reference line are the fifth and sixth intersection points, then calculate the elevation difference between the fourth and fifth intersection points and the elevation difference between the fourth and sixth intersection points.

[0053] If the elevation difference between the fourth and fifth intersection points or the elevation difference between the fourth and sixth intersection points exceeds a preset first threshold, then the current trajectory is determined to be abnormal.

[0054] The above-mentioned method for detecting whether the current trajectory is abnormal can effectively identify abnormal current trajectories based on the elevation difference by using two trajectories collected from the opposite direction. This situation indicates that the elevation difference between the current trajectory and other opposing trajectories is significantly too large, and it is an abnormal trajectory in the same road scenario. Therefore, the abnormal trajectory is filtered out, and the output of the abnormal trajectory to the production of high-precision maps is stopped, which reduces the risk of accuracy anomalies and improves the accuracy of field data and the reliability of high-precision maps.

[0055] In one embodiment, the above method may further include:

[0056] Locate a reference point on the current trajectory, and obtain a circular region with the reference point as the center and a specified length as the radius;

[0057] Determine the centerline of the road on the corresponding 2D map;

[0058] If the centerline does not intersect with the circular area, it is determined that the current trajectory has an anomaly of drift.

[0059] The above-mentioned method for detecting whether the current trajectory is abnormal is based on the current trajectory and the road centerline. It can effectively identify the current trajectory that is drifting. Such trajectories are obviously outside the road area and are considered abnormal trajectories. Stopping the output of this type of abnormal trajectory to the production of high-precision maps reduces the risk of accuracy anomalies and improves the accuracy of field data and the reliability of high-precision maps.

[0060] In one embodiment, the above method may further include:

[0061] When other trajectories are opposing trajectories, the anomaly of whether the current trajectory intersects with the opposing trajectory is determined based on the planar distance between points on the current trajectory and points on the opposing trajectory.

[0062] The above method, based on the planar distance between the corresponding points of the current trajectory and the opposing trajectory, can effectively identify the current trajectory that intersects with the opposing trajectory. In the scenario of the same road, the trajectories traveling in opposite directions cannot intersect. Therefore, the current trajectory that intersects with the opposing trajectory is obviously an abnormal trajectory. Stopping the output of such abnormal trajectories to the production of high-precision maps reduces the risk of accuracy anomalies and improves the accuracy of field data and the reliability of high-precision maps.

[0063] In one implementation, determining whether the current trajectory has an anomaly of intersecting with the opposing trajectory based on the planar distance between points on the current trajectory and points on the corresponding opposing trajectory includes:

[0064] Locate the reference line on the 2D map corresponding to the road, and obtain the seventh intersection point between the reference line and the current trajectory, as well as the eighth intersection point between the reference line and the opposite trajectory.

[0065] Calculate the planar distance between the seventh and eighth intersection points;

[0066] If the planar distance is less than the preset second threshold, it is determined that the current trajectory has an anomaly of intersecting with the opposite trajectory.

[0067] The above method, based on the planar distance between the corresponding points of the current trajectory and the opposing trajectory, can effectively identify the current trajectory that intersects with the opposing trajectory. Such trajectories are obviously abnormal trajectories. Stopping the output of such abnormal trajectories to the production of high-precision maps reduces the risk of accuracy anomalies and improves the accuracy of field data and the reliability of high-precision maps.

[0068] In one embodiment, the above method may further include:

[0069] Two points are determined on the current trajectory for a specified duration or length. The elevation difference between the two points is calculated. If the elevation difference between the two points exceeds a preset third threshold, it is determined that there is an elevation anomaly in the current trajectory.

[0070] The above method is applicable to single-track recognition scenarios. It does not require the use of other tracks. Based on the elevation difference between two points on the current track, it can determine whether there is an elevation anomaly in the current track, thereby reducing the risk of accuracy anomalies and improving the accuracy of field data and the reliability of high-precision maps.

[0071] The method provided in this disclosure can perform quality control on the accuracy of trajectories collected from high-precision maps based on elevation differences. It provides an effective control means, can quickly identify abnormal trajectories, reduce the risk of accuracy abnormalities, improve the accuracy of field data, prevent abnormal trajectories from flowing into the high-precision map production process, effectively avoid waste of production capacity, improve the reliability of high-precision maps, and safeguard autonomous driving.

[0072] Figure 2aThis is a schematic diagram of a quality control method for high-precision map trajectory acquisition in one embodiment of this disclosure. Figure 2a As shown, the method includes:

[0073] S201: Obtain the current trajectory and other trajectories on the road from the trajectory collected by the high-precision map;

[0074] The high-precision map typically collects multiple trajectories, which can be obtained from a single collection or from multiple collections, with no specific limitation. The current trajectory is located on the same road as other trajectories, which can be trajectories traveling in the same direction as the current trajectory or trajectories traveling in the opposite direction.

[0075] S202: When there are multiple trajectories, locate a reference line on the two-dimensional map corresponding to the above roads, and determine the intersection point of the reference line with the current trajectory and multiple trajectories;

[0076] A two-dimensional map is a map that uses horizontal and vertical coordinates to reflect road information on a plane. After a high-precision map captures the current trajectory, the road containing that trajectory can be found on the two-dimensional map. The reference line mentioned above can be a horizontal line perpendicular to the current road, etc.

[0077] S203: Based on the elevation difference between each intersection point and the preset first threshold, determine whether there is an anomaly in the current trajectory;

[0078] The obtained intersection points include the intersection of the reference line and the current trajectory, as well as the intersection of the reference line and every other trajectory. The size of the first threshold can be set as needed, such as 2 meters or 2.5 meters, etc., and the specific value is not limited.

[0079] S204: Locate a reference point on the current trajectory, obtain a circular area with the reference point as the center and a specified length as the radius, determine the center line of the road on the two-dimensional map corresponding to the above road, and determine whether there is an anomaly of drift in the current trajectory based on the center line and the circular area;

[0080] For example, the aforementioned reference points can be determined on the current trajectory according to a specified time interval or length, such as positioning a reference point every 1 second or 2 seconds, or every 100 meters or 200 meters, etc. The size of the aforementioned radius can also be set as needed, and can be set with reference to the road width, such as setting it to half the road width, etc. This disclosure does not make specific limitations in this regard. In addition, the aforementioned centerline can also be set according to the road width, such as setting it to half the road width and allowing a certain error range, etc., this disclosure does not make specific limitations in this regard.

[0081] In one implementation, determining whether there is an anomaly of drift in the current trajectory based on the centerline and the circular region in S204 above may specifically include:

[0082] Determine if the center line intersects with the circular area. If there is no intersection, it indicates that the current trajectory is drifting abnormally. If there is an intersection, it indicates that the current trajectory is normal.

[0083] Furthermore, the above-mentioned S204 can also be replaced by the following steps:

[0084] Multiple reference points are located on the current trajectory. A circular area is obtained with each reference point as the center and a specified length as the radius. The centerline of the road is determined on the two-dimensional map corresponding to the road. The centerline is matched with each circular area to see if there is an intersection. If the number of non-intersecting matching results reaches a specified number, it is determined that there is an anomaly of drift in the current trajectory.

[0085] For example, the intersection of the center line and the circular region includes at least one point on the center line falling into the circular region, including falling on the boundary line of the circular region.

[0086] Figure 2b This is a schematic diagram of trajectory drift in one embodiment of this disclosure. See also... Figure 2b In the 2D map, the solid black line represents the center line of the current road, typically half the road width, but can be fine-tuned within acceptable error ranges based on actual conditions. The dashed line with arrows represents the current trajectory captured by the high-precision map. Point A is one of the reference points located on the current trajectory every 1 second or every 20 meters. A circular area is drawn in the image with A as the center and half the road width as the radius. It is clearly visible in the image that the center line is far from the circular area, and the two do not intersect. Therefore, it can be determined that the current trajectory has drifted and deviated outside the road surface area, constituting an abnormal trajectory. Alternatively, if no intersecting circular area is found within 10 consecutive seconds or 200 meters (i.e., the current trajectory does not match the 2D map for 10 consecutive times), then the current trajectory is determined to be drifting, indicating abnormal planar accuracy.

[0087] S205: When other trajectories are opposing trajectories, determine whether there is an anomaly where the current trajectory intersects with the opposing trajectory based on the planar distance between points on the current trajectory and points on the opposing trajectory;

[0088] S206: If an anomaly is determined in the current trajectory, filter out the current trajectory from the collected trajectories and stop outputting the current trajectory to the production of the high-precision map.

[0089] Figure 3a This is a schematic diagram illustrating the determination of abnormal trajectories based on the elevation difference of trajectories traveling in the same direction, according to one embodiment of this disclosure. Figure 3aAs shown, in one embodiment, step S203 may specifically include:

[0090] S301: If multiple trajectories are two trajectories in the same direction as the current trajectory, and the intersection of the current trajectory and the reference line is the first intersection point, and the intersections of the two trajectories and the reference line are the second and third intersection points, then calculate the elevation difference between any two points among the first, second, and third intersection points.

[0091] S302: If the elevation difference between the first intersection point and the second intersection point and the elevation difference between the first intersection point and the third intersection point both exceed the preset first threshold, and the elevation difference between the second intersection point and the third intersection point does not exceed the first threshold, then it is determined that there is an anomaly in the current trajectory.

[0092] Figure 3b This is a schematic diagram of the elevation difference of trajectories in the same direction in one embodiment of this disclosure. For example... Figure 3b As shown, the current trajectory and two other trajectories traveling in the same direction on the same road are obtained from the high-precision map data, namely trajectory 1 and trajectory 2 in the same direction shown in the figure. A reference line, a straight line perpendicular to the current road, is located on the 2D map corresponding to the aforementioned road. The intersection point of this reference line with the current trajectory is A, with trajectory 1 in the same direction is B, and with trajectory 2 in the same direction is C. The elevation difference between any two points A, B, and C is calculated. If the elevation difference between points A and B exceeds a specified threshold of 2 meters, and the elevation difference between points A and C also exceeds 2 meters, but the elevation difference between points B and C does not exceed 2 meters, then the current trajectory containing point A is determined to have an abnormal elevation and is considered an abnormal trajectory (i.e., this trajectory is in the minority), while the trajectories containing points B and C are both normal (i.e., these two trajectories are in the majority). In other words, the majority of the collected trajectories are normal trajectories, and only a minority of trajectories are abnormal trajectories, meaning that only trajectories with elevations significantly inconsistent with the majority of other trajectories are identified as abnormal trajectories, conforming to the principle of majority rule.

[0093] Figure 4a This is a schematic diagram illustrating the determination of abnormal trajectories based on the elevation difference between opposing trajectories in one embodiment of this disclosure. For example... Figure 4a As shown, in one embodiment, step S203 may specifically include:

[0094] S401: If multiple trajectories are two trajectories opposite to the current trajectory, and the intersection point of the current trajectory with the reference line is the fourth intersection point, and the intersection points of the two trajectories with the reference line are the fifth and sixth intersection points, then calculate the elevation difference between the fourth and fifth intersection points and the elevation difference between the fourth and sixth intersection points.

[0095] S402: If the elevation difference between the fourth and fifth intersection points or the elevation difference between the fourth and sixth intersection points exceeds the preset first threshold, then it is determined that there is an anomaly in the current trajectory.

[0096] Figure 4bThis is a schematic diagram of the elevation difference between opposing trajectories in one embodiment of this disclosure. See also... Figure 4b The current trajectory and two opposing trajectories on the same road are obtained from the trajectory collected by the high-precision map, namely opposing trajectory 1 and opposing trajectory 2 in the figure. A reference line, i.e., a straight line perpendicular to the current road, is located on the 2D map corresponding to the aforementioned road. The intersection point of this reference line with the current trajectory is A, the intersection point with opposing trajectory 1 is B, and the intersection point with opposing trajectory 2 is C. The elevation differences between points A and B, and between points A and C, are calculated respectively. If the elevation difference between points A and B does not exceed a specified threshold of 2 meters, and the elevation difference between points A and C also does not exceed 2 meters, then the elevation of the current trajectory containing point A is normal. If the elevation difference between points A and B exceeds 2 meters, or the elevation difference between points A and C exceeds 2 meters, then the elevation of the current trajectory containing point A is abnormal.

[0097] Figure 5a This is a schematic diagram illustrating the determination of abnormal trajectories based on planar distance in one embodiment of this disclosure. Figure 5a As shown, in one embodiment, step S205 may specifically include:

[0098] S501: When other trajectories are opposing trajectories, locate the reference line on the two-dimensional map corresponding to the above roads, and obtain the seventh intersection point of the reference line with the current trajectory and the eighth intersection point of the reference line with the opposing trajectory.

[0099] S502: Calculate the planar distance between the seventh and eighth intersection points;

[0100] S503: If the planar distance is less than the preset second threshold, it is determined that the current trajectory has an anomaly of intersecting with the opposite trajectory.

[0101] The second threshold can be set to a size as needed, and the specific value is not limited.

[0102] Figure 5b This is a schematic diagram of trajectory intersections in one embodiment of this disclosure. See also... Figure 5b The high-precision map collects the current trajectory and the opposing trajectory on the current road. Reference lines are positioned on the corresponding 2D map every 2 seconds or 200 meters. Each positioning operation yields the intersection point M between the reference line and the current trajectory, and the intersection point N between the reference line and the opposing trajectory. The planar distance between M and N is calculated. If this planar distance is less than a specified threshold of 1 meter, it can be determined that the current trajectory intersects with the opposing trajectory, resulting in abnormal accuracy. Point A in the figure illustrates the intersection point where the two trajectories intersect.

[0103] In any of the embodiments provided in this disclosure, the following steps may also be included:

[0104] Two points are identified on the current trajectory for a specified duration or length. The elevation difference between the two points is calculated. If the elevation difference between the two points exceeds a preset third threshold, it is determined that there is an elevation anomaly in the current trajectory.

[0105] Figure 6 This is a schematic diagram of the elevation difference of a single trajectory in one embodiment of this disclosure. See also... Figure 6 The current trajectory captured by the high-precision map is shown by the dotted line in the figure. Points are identified on the current trajectory every 1 second or 100 meters, resulting in two points, A and B. The elevation difference between points A and B is calculated. If this elevation difference exceeds a specified threshold of 20 meters, an elevation anomaly is determined to exist in the current trajectory.

[0106] The method provided in this disclosure can perform quality control on the accuracy of trajectories collected from high-precision maps based on elevation differences. It provides an effective control means, can quickly identify abnormal trajectories, reduce the risk of accuracy abnormalities, improve the accuracy of field data, prevent abnormal trajectories from flowing into the high-precision map production process, effectively avoid waste of production capacity, improve the reliability of high-precision maps, and safeguard autonomous driving.

[0107] Figure 7 This is a block diagram of a quality control device for high-precision map trajectory acquisition in one embodiment of this disclosure. Figure 7 As shown, the device includes:

[0108] The acquisition module 701 is used to acquire the current trajectory and other trajectories on the road in the trajectory collected by the high-precision map;

[0109] The determination module 702 is used to determine whether there is an anomaly in the current trajectory based on the elevation difference between points on the current trajectory and points on other corresponding trajectories;

[0110] The control module 703 is used to filter out the current trajectory from the collected trajectories and stop outputting the current trajectory to the production of the high-precision map if there is an anomaly in the current trajectory.

[0111] In one implementation, the determining module 702 may include:

[0112] The intersection point determination unit is used to locate a reference line on the two-dimensional map corresponding to the road when there are multiple trajectories, and to determine the intersection point of the reference line with the current trajectory and multiple trajectories;

[0113] The anomaly determination unit is used to determine whether there is an anomaly in the current trajectory based on the elevation difference between each intersection point and a preset first threshold.

[0114] In one implementation, the anomaly determination unit can be used to:

[0115] If multiple trajectories are two trajectories in the same direction as the current trajectory, and the intersection of the current trajectory and the reference line is the first intersection point, and the intersections of the two trajectories and the reference line are the second and third intersection points, then calculate the elevation difference between any two points among the first, second, and third intersection points.

[0116] If the elevation difference between the first and second intersection points and the elevation difference between the first and third intersection points both exceed a preset first threshold, and the elevation difference between the second and third intersection points does not exceed the first threshold, then it is determined that there is an anomaly in the current trajectory.

[0117] In another implementation, the anomaly determination unit can be used for:

[0118] If multiple trajectories are two trajectories opposite to the current trajectory, and the intersection point of the current trajectory with the reference line is the fourth intersection point, and the intersection points of the two trajectories with the reference line are the fifth and sixth intersection points, then calculate the elevation difference between the fourth and fifth intersection points and the elevation difference between the fourth and sixth intersection points.

[0119] If the elevation difference between the fourth and fifth intersection points or the elevation difference between the fourth and sixth intersection points exceeds a preset first threshold, then the current trajectory is determined to be abnormal.

[0120] In one embodiment, the above-mentioned apparatus may further include:

[0121] The drift module is used to locate a reference point on the current trajectory, and obtain a circular area with the reference point as the center and a specified length as the radius; determine the center line of the road on the corresponding 2D map; if the center line does not intersect with the circular area, it is determined that there is a drift anomaly in the current trajectory.

[0122] In one embodiment, the above-mentioned apparatus may further include:

[0123] The intersection module is used to determine whether the current trajectory has any anomalies that intersect with the opposing trajectory, based on the planar distance between points on the current trajectory and points on the opposing trajectory, when other trajectories are opposing trajectories.

[0124] In one implementation, the cross module can be used for:

[0125] When other trajectories are opposing trajectories, locate the reference line on the two-dimensional map corresponding to the road, and obtain the seventh intersection point of the reference line with the current trajectory and the eighth intersection point of the reference line with the opposing trajectory.

[0126] Calculate the planar distance between the seventh and eighth intersection points;

[0127] If the planar distance is less than the preset second threshold, it is determined that the current trajectory has an anomaly of intersecting with the opposite trajectory.

[0128] In one implementation, the determining module 702 can also be used for:

[0129] Two points are determined on the current trajectory for a specified duration or length. The elevation difference between the two points is calculated. If the elevation difference between the two points exceeds a preset third threshold, it is determined that there is an elevation anomaly in the current trajectory.

[0130] The apparatus provided in this embodiment can perform quality control on the trajectory collected by high-precision map based on elevation difference, providing an effective control means, which can quickly identify abnormal trajectories, reduce the risk of accuracy abnormalities, improve the accuracy of field data, prevent abnormal trajectories from flowing into the high-precision map production process, effectively avoid waste of production capacity, improve the reliability of high-precision map, and safeguard autonomous driving.

[0131] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0132] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0133] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0134] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0135] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0136] The computing unit 801 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the quality control method for high-precision map trajectory acquisition. For example, in some embodiments, the quality control method for high-precision map trajectory acquisition can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the quality control method for high-precision map trajectory acquisition described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured by any other suitable means (e.g., by means of firmware) to perform a quality control method for high-precision map trajectory acquisition.

[0137] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0140] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0141] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0142] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0143] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0144] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A quality control method for high-precision map trajectory acquisition, comprising: From the trajectory collected by the high-precision map, obtain the current trajectory and other trajectories on the road; Based on the elevation difference between points on the current trajectory and points on other corresponding trajectories, determine whether the current trajectory is abnormal; If the current trajectory is abnormal, then the current trajectory is filtered out from the collected trajectories, and the output of the current trajectory to the high-precision map production is stopped; The step of determining whether there is an anomaly in the current trajectory based on the elevation difference between a point on the current trajectory and a corresponding point on another trajectory includes: In the case where there are multiple other trajectories, a reference line is located on the two-dimensional map corresponding to the road, and the intersection point of the reference line with the current trajectory and the multiple trajectories is determined. Based on the elevation difference between each intersection point and a preset first threshold, it is determined whether the current trajectory is abnormal.

2. The method according to claim 1, wherein, The step of determining whether the current trajectory is abnormal based on the elevation difference between each intersection point and a preset first threshold includes: If the multiple trajectories are two trajectories in the same direction as the current trajectory, and the intersection of the current trajectory and the reference line is the first intersection point, and the intersections of the two trajectories and the reference line are the second and third intersection points, then calculate the elevation difference between any two points among the first, second, and third intersection points; If the elevation difference between the first intersection point and the second intersection point, and the elevation difference between the first intersection point and the third intersection point both exceed a preset first threshold, and the elevation difference between the second intersection point and the third intersection point does not exceed the first threshold, then it is determined that the current trajectory is abnormal.

3. The method according to claim 1, wherein, The step of determining whether the current trajectory is abnormal based on the elevation difference between each intersection point and a preset first threshold includes: If the multiple trajectories are two trajectories opposite to the current trajectory, and the intersection point of the current trajectory and the reference line is the fourth intersection point, and the intersection points of the two trajectories and the reference line are the fifth and sixth intersection points, then calculate the elevation difference between the fourth and fifth intersection points and the elevation difference between the fourth and sixth intersection points. If the elevation difference between the fourth and fifth intersection points or the elevation difference between the fourth and sixth intersection points exceeds a preset first threshold, then it is determined that the current trajectory is abnormal.

4. The method according to claim 1, further comprising: A reference point is located on the current trajectory, and a circular region is obtained with the reference point as the center and a specified length as the radius. Determine the centerline of the road on the corresponding two-dimensional map; If the center line does not intersect with the circular region, then it is determined that the current trajectory has an anomaly of drift.

5. The method according to claim 1, further comprising: In the case that the other trajectories are opposing trajectories, the anomaly of whether the current trajectory intersects with the opposing trajectory is determined based on the planar distance between the points on the current trajectory and the points on the opposing trajectory.

6. The method according to claim 5, wherein, The step of determining whether the current trajectory has an anomaly of intersecting with the opposing trajectory based on the planar distance between points on the current trajectory and points on the corresponding opposing trajectory includes: Locate a reference line on the two-dimensional map corresponding to the road, and obtain the seventh intersection point of the reference line with the current trajectory and the eighth intersection point of the reference line with the opposing trajectory; Calculate the planar distance between the seventh and eighth intersection points; If the planar distance is less than a preset second threshold, it is determined that the current trajectory has an anomaly of intersecting with the opposing trajectory.

7. The method according to any one of claims 1-6, further comprising: Two points are determined on the current trajectory for a specified duration or length, and the elevation difference between the two points is calculated. If the elevation difference between the two points exceeds a preset third threshold, it is determined that there is an elevation anomaly in the current trajectory.

8. A quality control device for high-precision map trajectory acquisition, comprising: The acquisition module is used to acquire the current trajectory and other trajectories on the same road from the trajectory collected by the high-precision map; The determination module is used to determine whether there is an anomaly in the current trajectory based on the elevation difference between a point on the current trajectory and a point on the corresponding other trajectories; The control module is used to filter out the current trajectory from the collected trajectory and stop outputting the current trajectory to the production of the high-precision map if the current trajectory is abnormal. The determining module includes: an intersection point determining unit, used to locate a reference line on a two-dimensional map corresponding to the road when the other trajectories are multiple trajectories, and to determine the intersection point of the reference line with the current trajectory and the multiple trajectories; An anomaly determination unit is used to determine whether there is an anomaly in the current trajectory based on the elevation difference between the obtained intersection points and a preset first threshold.

9. The apparatus according to claim 8, wherein, The anomaly determination unit is used to: if the multiple trajectories are two trajectories in the same direction as the current trajectory, and the intersection of the current trajectory and the reference line is the first intersection point, and the intersections of the two trajectories and the reference line are the second and third intersection points, then calculate the elevation difference between any two points among the first intersection point, the second intersection point, and the third intersection point; If the elevation difference between the first intersection point and the second intersection point, and the elevation difference between the first intersection point and the third intersection point both exceed a preset first threshold, and the elevation difference between the second intersection point and the third intersection point does not exceed the first threshold, then it is determined that the current trajectory is abnormal.

10. The apparatus according to claim 8, wherein, The anomaly determination unit is used to: if the multiple trajectories are two trajectories opposite to the current trajectory, and the intersection point of the current trajectory and the reference line is the fourth intersection point, and the intersection points of the two trajectories and the reference line are the fifth and sixth intersection points, then calculate the elevation difference between the fourth and fifth intersection points and the elevation difference between the fourth and sixth intersection points; If the elevation difference between the fourth and fifth intersection points or the elevation difference between the fourth and sixth intersection points exceeds a preset first threshold, then it is determined that the current trajectory is abnormal.

11. The apparatus according to claim 8, further comprising: The drift module is used to locate a reference point on the current trajectory, obtain a circular area with the reference point as the center and a specified length as the radius; determine the center line of the road on the two-dimensional map corresponding to the road; if the center line does not intersect with the circular area, it is determined that the current trajectory has a drift anomaly.

12. The apparatus according to claim 8, further comprising: The intersection module is used to determine whether the current trajectory has an anomaly of intersecting with the opposing trajectory, based on the planar distance between points on the current trajectory and points on the opposing trajectory, when the other trajectories are opposing trajectories.

13. The apparatus according to claim 12, wherein, The intersection module is used to: locate a reference line on the two-dimensional map corresponding to the road when the other trajectories are opposing trajectories, and obtain the seventh intersection point of the reference line with the current trajectory and the eighth intersection point of the reference line with the opposing trajectory; Calculate the planar distance between the seventh and eighth intersection points; If the planar distance is less than a preset second threshold, it is determined that the current trajectory has an anomaly of intersecting with the opposing trajectory.

14. The apparatus according to any one of claims 8-13, wherein the determining module is further configured to: determine two points on the current trajectory for a specified duration or a specified length, calculate the elevation difference between the two points, and if the elevation difference between the two points exceeds a preset third threshold, determine that there is an elevation anomaly on the current trajectory.

15. An electronic device comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

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

17. A computer program product comprising a computer program / instructions that, when executed by a processor, implement the method of any one of claims 1-7.

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