Trajectory Processing Method, Device, Equipment, Storage Medium and Vehicle

Abstract

The present disclosure relates to a trajectory processing method, apparatus, device, storage medium, and vehicle. By calculating the distance between the set of vehicle driving trajectory points and the set of lane line points of a target lane group, the present disclosure excludes vehicle driving trajectories that are similar to the target lane group trajectory in the high-precision road network corresponding to the road to be analyzed but are actually far away, effectively differentiating the driving trajectories on the main road and the auxiliary road in the road, and improving the accuracy of data analysis based on vehicle driving trajectories.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and in particular, to a trajectory processing method, apparatus, device, storage medium, and vehicle. Background Art

[0002] The high-precision road network is an important part of the high-precision map. With the development of Internet information technology and positioning technology, the driving trajectories of vehicles on the high-precision road network are helpful for simulating the urban traffic operation status.

[0003] Since there are auxiliary roads in many roads, the driving trajectories of vehicles on the auxiliary roads are roughly the same as those on the main roads, but the driving trajectories on the auxiliary roads do not meet the statistical conditions of the driving trajectories of vehicles on the high-precision road network. However, in the prior art, it is impossible to effectively distinguish the driving trajectories on the main road and the auxiliary road. Summary of the Invention

[0004] To solve the above technical problems, the present disclosure provides a trajectory processing method, apparatus, device, storage medium, and vehicle, which can effectively distinguish the driving trajectories on the main road and the auxiliary road in the road, and improve the accuracy of data analysis based on the vehicle driving trajectories.

[0005] In a first aspect, an embodiment of the present disclosure provides a trajectory processing method, including:

[0006] Obtain a plurality of vehicle driving trajectories, each vehicle driving trajectory including a plurality of trajectory points;

[0007] For the vehicle driving trajectory among the plurality of vehicle driving trajectories, calculate the spatial centroid of the vehicle driving trajectory, and determine the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid;

[0008] Based on at least one lane line in the target lane group, determine a first set formed by the shape points corresponding to the at least one lane line;

[0009] Calculate the distance between a second set formed by the plurality of trajectory points in the vehicle driving trajectory and the first set;

[0010] Use at least one vehicle driving trajectory among the plurality of vehicle driving trajectories with a distance less than a first preset value as the target driving trajectory.

[0011] In some embodiments, before obtaining the plurality of vehicle driving trajectories, the method further includes:

[0012] Based on the center line of the high-precision road network corresponding to the road to be analyzed and a preset distance, determine a high-precision road network buffer zone;

[0013] Obtain a plurality of original trajectories, each original trajectory including at least one trajectory point;

[0014] Remove at least one original trajectory in which the positions of each trajectory point in the multiple original trajectories are not located in the high-precision road network buffer.

[0015] In some embodiments, after removing at least one original trajectory in which the positions of each trajectory point in the multiple original trajectories are not located in the high-precision road network buffer, the method further includes:

[0016] For each remaining original trajectory, obtain the length of the original trajectory and the number of trajectory points in the original trajectory;

[0017] Remove at least one original trajectory whose length is less than a preset length threshold or the number of trajectory points is continuously less than a preset quantity.

[0018] In some embodiments, calculating the spatial centroid of the vehicle driving trajectory includes:

[0019] Determine the coordinates of each trajectory point in the vehicle driving trajectory in the world coordinate system;

[0020] Calculate the mean value of the coordinates of the multiple trajectory points to obtain the coordinates of the spatial centroid of the vehicle driving trajectory.

[0021] In some embodiments, determining the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid includes:

[0022] Determine the projection point of the spatial centroid in the high-precision road network buffer where the high-precision road network is located;

[0023] According to the distance from the projection point to at least one lane group in the high-precision road network, determine the target lane group in the high-precision road network that is closest to the projection point.

[0024] In some embodiments, calculating the distance between the second set formed by the multiple trajectory points in the vehicle driving trajectory and the first set includes:

[0025] For each trajectory point in the second set, determine the target shape point in the first set that is farthest from the trajectory point, and use the distance between the trajectory point and the target shape point as the target distance of the trajectory point;

[0026] Determine the maximum target distance among the target distances corresponding to the multiple trajectory points in the second set as the distance between the second set and the first set.

[0027] In a second aspect, an embodiment of the present disclosure provides a trajectory processing device, including:

[0028] An acquisition module, configured to acquire a plurality of vehicle driving trajectories, each vehicle driving trajectory including a plurality of trajectory points;

[0029] A first determination module, configured to calculate a spatial centroid of a vehicle driving trajectory among the plurality of vehicle driving trajectories, and determine a target lane group in a high-precision road network corresponding to a road to be analyzed that is closest to the spatial centroid;

[0030] A second determination module, configured to determine a first set formed by shape points corresponding to at least one lane line in the target lane group;

[0031] A calculation module, configured to calculate a distance between a second set formed by the plurality of trajectory points in the vehicle driving trajectory and the first set;

[0032] A third determination module, configured to use at least one vehicle driving trajectory among the plurality of vehicle driving trajectories with a distance less than a first preset value as a target driving trajectory.

[0033] In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

[0034] A memory;

[0035] A processor; and

[0036] A computer program;

[0037] Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method as described in the first aspect.

[0038] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method as described in the first aspect.

[0039] In a fifth aspect, an embodiment of the present disclosure further provides a vehicle, and the vehicle includes the above-described trajectory processing device.

[0040] For the trajectory processing method, device, equipment, storage medium, and vehicle provided by the embodiments of the present disclosure, since the driving trajectories on the main road and the auxiliary road are similar in shape but have a relatively large actual distance between them, the actual distance between different driving trajectories is determined by calculating the distance between the set of vehicle driving trajectory points and the set of lane shape points of the target lane group, and the vehicle driving trajectories that are similar in trajectory to the target lane group in the high-precision road network corresponding to the road to be analyzed but have a relatively large actual distance are excluded, which can effectively distinguish the driving trajectories on the main road and the auxiliary road in the road, and improve the accuracy of data analysis based on vehicle driving trajectories. Description of the Drawings

[0041] The accompanying drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present disclosure, and are used together with the specification to explain the principles of the present disclosure.

[0042] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 Flowchart of the trajectory processing method provided by an embodiment of the present disclosure;

[0044] Figure 2 Schematic diagram of an application scenario provided by an embodiment of the present disclosure;

[0045] Figure 3 Flowchart of the trajectory processing method provided by another embodiment of the present disclosure;

[0046] Figure 4 Flowchart of the trajectory processing method provided by another embodiment of the present disclosure;

[0047] Figure 5 Schematic diagram of the structure of the trajectory processing device provided by an embodiment of the present disclosure;

[0048] Figure 6 Schematic diagram of the structure of the electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0049] In order to be able to more clearly understand the above objects, features, and advantages of the present disclosure, the following will further describe the solutions of the present disclosure. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other.

[0050] Many specific details are set forth in the following description to facilitate a thorough understanding of the present disclosure, but the present disclosure can also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of the present disclosure, rather than all embodiments.

[0051] The embodiments of the present disclosure provide a trajectory processing method, and the following will introduce this method in combination with specific embodiments.

[0052] Figure 1 Flowchart of the trajectory processing method provided by an embodiment of the present disclosure. This method can be applied to an application scenario as Figure 2 shown. As Figure 2As shown, this application scenario includes a terminal device 21 and a server 22, and the terminal device 21 is communicatively connected to the server 22. Among them, the terminal device 21 can be an in-vehicle terminal with real-time positioning function, such as a car head unit, an intelligent driving device, etc., for collecting the driving trajectory of the vehicle where the terminal is located. The terminal device 21 can also be other devices with real-time positioning function, such as a smart phone, a personal digital assistant, a tablet computer, a laptop computer, an all-in-one computer, etc., for collecting the driving trajectory of the moving carrier where the device is located. The server 22 can also be any device with data processing function. It can be understood that the trajectory processing method provided by the embodiments of the present disclosure can also be applied to other scenarios.

[0053] The following combines Figure 2 the application scenario shown, and Figure 1 introduces the trajectory processing method shown. The specific steps included in this method are as follows:

[0054] S101. Obtain multiple vehicle driving trajectories, and each vehicle driving trajectory includes multiple trajectory points.

[0055] The server 22 receives multiple original trajectories uploaded by the terminal device 21, and screens out multiple trajectories that meet the conditions as vehicle driving trajectories. Each vehicle trajectory respectively includes multiple trajectory points. A trajectory point, that is, a position point collected by the positioning system during the movement of the terminal device, is used to represent the position of the terminal device 21 or the moving carrier where it is located at a certain moment. A curve formed by multiple orderly arranged trajectory points is the trajectory. Among them, the positioning system can be the Global Positioning System (GPS) or any other positioning system, and the embodiments of the present disclosure do not limit this.

[0056] S102. For the vehicle driving trajectories among the multiple vehicle driving trajectories, calculate the spatial centroid of the vehicle driving trajectory, and determine the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid.

[0057] There may be auxiliary roads on some roads in the high-precision road network corresponding to the road to be analyzed, and the vehicle driving trajectories generated by vehicles driving on the auxiliary roads are not the trajectory data required for data analysis. Therefore, vehicle driving trajectories that are far from the high-precision road network, such as those generated by vehicles driving on auxiliary roads, can be excluded according to the distance between the vehicle driving trajectory and the target lane group. First, calculate the spatial centroid of the vehicle driving trajectory, and determine the target lane group in the high-precision road network that is closest to this spatial centroid.

[0058] S103. Based on at least one lane line in the target lane group, determine a first set composed of shape points corresponding to the at least one lane line.

[0059] The target lane group may include one or more lanes, and each lane has its corresponding lane line. Thus, a plurality of corresponding lane alignment points can be obtained, and the server 22 determines a first set composed of the alignment points corresponding to at least one lane line in the target lane group accordingly.

[0060] S104. Calculate the distance between the second set composed of the plurality of trajectory points in the vehicle driving trajectory and the first set.

[0061] The Hausdorff distance is a distance defined between any two sets in a metric space. The server 22 obtains the second set composed of the plurality of trajectory points of the vehicle driving trajectory and the first set composed of the alignment points corresponding to at least one lane line in the target lane group, and calculates the Hausdorff distance between the first set and the second set. The distance between the vehicle driving trajectory and the target lane group can be judged through the Hausdorff distance.

[0062] S105. Use at least one vehicle driving trajectory with a distance less than a first preset value among the plurality of vehicle driving trajectories as the target driving trajectory.

[0063] If the distance between the vehicle driving trajectory and the target lane group is greater than or equal to the first preset value, it can be determined that the vehicle driving trajectory is far from the target lane group, i.e., the high-precision road network, and is not suitable as the basis for data analysis; if the distance between the vehicle driving trajectory and the target lane group is less than the first preset value, it can be determined that the vehicle driving trajectory is the target driving trajectory on the high-precision road network and can be used as the vehicle driving trajectory data for data analysis.

[0064] In the embodiment of the present disclosure, by obtaining a plurality of vehicle driving trajectories, each vehicle driving trajectory includes a plurality of trajectory points; for the vehicle driving trajectories among the plurality of vehicle driving trajectories, calculate the spatial centroid of the vehicle driving trajectory, and determine the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid; based on at least one lane line in the target lane group, determine a first set composed of the alignment points corresponding to the at least one lane line; calculate the distance between the second set composed of the plurality of trajectory points in the vehicle driving trajectory and the first set; use at least one vehicle driving trajectory with a distance less than a first preset value among the plurality of vehicle driving trajectories as the target driving trajectory, by calculating the distance between the set of vehicle driving trajectory points and the set of lane alignment points of the target lane group, excluding the vehicle driving trajectories that are similar to the target lane group trajectory in the high-precision road network but are actually far away, effectively distinguishing the driving trajectories on the main road and the auxiliary road in the road, and improving the accuracy of data analysis based on vehicle driving trajectories.

[0065] Figure 3 It is a flowchart of a trajectory processing method provided by another embodiment of the present disclosure. AsFigure 3 As shown in Figure 3 , the method includes the following steps:

[0066] S301. Establish a high-precision road network buffer zone with a width of a preset distance based on the center line of the high-precision road network corresponding to the road to be analyzed. The center line of the high-precision road network can be the center line of the road or lane to be analyzed specified in advance, and the position of the center line of the high-precision road network can be obtained according to the high-precision map. A buffer zone is a range of influence or service range of a geographical space object. Specifically, it refers to a polygon with a certain width automatically established around a point, line, or surface entity. The high-precision road network buffer zone is a polygon with a certain width automatically established around the route in the high-precision road network, or a polygon with a certain width automatically established around a certain lane on the route in the high-precision road network. Specifically, it can be a rectangle with a width of a preset distance based on the center line of the high-precision road network. A preset distance is set in advance, and this preset distance is the most suitable buffer zone width for the high-precision road network. The area within the preset distance around the center line of the high-precision road network is the high-precision road network buffer zone. For example, for a straight road with a length of 10 kilometers, a high-precision road network buffer zone is established with a preset distance of 5 meters, and finally a rectangular high-precision road network buffer zone with a length of 10 kilometers and a width of 5 meters and the center line of the road as the center line of the high-precision road network is obtained. S302. Obtain a plurality of original trajectories, and each original trajectory includes at least one trajectory point.

[0067] The server 22 obtains a plurality of original trajectories from the terminal device 21, and each original trajectory includes one or more trajectory points.

[0068] S303. Remove at least one original trajectory in which the positions of each trajectory point in the plurality of original trajectories are not located in the high-precision road network buffer zone.

[0069] For each original trajectory, any trajectory point included therein may be located within the high-precision road network buffer zone or outside the high-precision road network buffer zone. If there is an original trajectory in which all trajectory points are located outside the high-precision road network buffer zone, it means that there is no intersection between this original trajectory and the high-precision road network buffer zone, that is, there is no intersection between this original trajectory and the high-precision road network corresponding to the high-precision road network buffer zone. Remove such original trajectories that have no intersection with the high-precision road network buffer zone from the plurality of original trajectories.

[0070] S304. For each remaining original trajectory, obtain the length of the original trajectory and the number of trajectory points in the original trajectory.

[0071] S305. Remove at least one original trajectory in which the length of the original trajectory is less than a preset length threshold or the number of trajectory points is less than a preset quantity.

[0072] For multiple original trajectories where any trajectory point is located in the high-precision road network buffer, that is, multiple original trajectories that intersect with the high-precision road network buffer, further obtain the length of each original trajectory and the number of trajectory points it contains. If the length of the original trajectory is too short or the number of trajectory points is small, then the original trajectory is short and may be noise data, without general reference. Therefore, remove the original trajectories with an original trajectory length less than a preset length threshold or the number of trajectory points less than a preset quantity. The original trajectories with a length greater than or equal to the preset length threshold and the number of trajectory points in the original trajectory greater than or equal to the preset quantity can be used as vehicle driving trajectories to participate in subsequent data analysis and exploration.

[0073] S306. Determine multiple projection points of multiple trajectory points in each remaining original trajectory in the high-precision road network buffer, where the multiple trajectory points and the multiple projection points correspond one by one.

[0074] S307. For each remaining original trajectory, calculate the distance between each trajectory point in the original trajectory and its corresponding projection point.

[0075] The original trajectory may not completely coincide with the high-precision road network, and not all of the multiple trajectory points in the original trajectory are located in the high-precision road network buffer. Therefore, it is necessary to determine multiple projection points of multiple trajectory points in each original trajectory in the high-precision road network buffer. For example, for each trajectory point, project it onto the center line of the high-precision road network buffer to obtain projection points in the high-precision road network buffer that correspond one by one to the trajectory points.

[0076] S308. Based on the distance between each trajectory point in the original trajectory and its corresponding projection point, determine the coefficient of variation of the original trajectory.

[0077] The coefficient of variation, that is, the coefficient of dispersion, is a normalized measure of the degree of dispersion of a probability distribution, and is defined as the ratio of the standard deviation to the mean. For each original trajectory, after determining the projection points corresponding to each trajectory point in the original trajectory, the server 22 calculates the distance between the trajectory point and its corresponding projection point. The same operation is performed for each vehicle driving trajectory until the distances between the trajectory points of each vehicle trajectory and the corresponding projection points are obtained. Further calculate the coefficient of variation of the original trajectory, that is, the ratio of the standard deviation to the mean of the distances between multiple trajectory points in the original trajectory and their corresponding projection points. The coefficient of variation can characterize the degree of dispersion of the original trajectory relative to the high-precision road network buffer.

[0078] S309. Remove at least one original trajectory with a coefficient of variation greater than a second preset value from the remaining original trajectories.

[0079] When the coefficient of variation of the original trajectory is greater than the second preset value, it means that the coincidence or similarity between the original trajectory and the high-precision road network buffer is small. It can be determined that the original trajectory is not obtained by driving on the high-precision road network, and these trajectories with a coefficient of variation greater than the second preset value need to be removed from multiple original trajectories. For example, in three-dimensional space, there is still a road similar to a section of the high-precision road network below a section of the high-precision road network, but the driving trajectories of vehicles on different roads will not be exactly the same. Therefore, the coefficient of variation of the trajectories generated by vehicles driving on other roads is relatively large compared to the coefficient of variation of the trajectories generated by vehicles driving on the high-precision road network. Remove these original trajectories generated by vehicles driving on other roads, that is, at least one original trajectory with a coefficient of variation greater than the second preset value from multiple original trajectories.

[0080] S310. For each remaining original trajectory, determine the second set and the third set corresponding to the original trajectory.

[0081] Among them, the second set includes multiple trajectory points of the original trajectory, and the third set includes the trajectory points of the original trajectory located in the high-precision road network buffer.

[0082] The original trajectory may not completely coincide with the high-precision road network, and not all of the multiple trajectory points in the original trajectory are located in the high-precision road network buffer. For each remaining original trajectory, that is, each vehicle driving trajectory in one or more original trajectories with a coefficient of variation less than or equal to the second preset value, the server 22 determines the second set composed of multiple trajectory points in the original trajectory, and the third trajectory set composed of the trajectory points located in the high-precision road network buffer in the vehicle driving trajectory.

[0083] S311. Calculate the similarity between the second set and the third set.

[0084] Optionally, the similarity between the two sets is determined by calculating the Jaccard Distance between the second set and the third set. The Jaccard Distance is used to measure the difference between two sets and is defined as 1 minus the Jaccard similarity coefficient; the Jaccard similarity index is used to measure the similarity between two sets and is defined as the number of elements in the intersection of the two sets divided by the number of elements in the union. When the Jaccard similarity index between the second set and the third set is larger, its Jaccard Distance is smaller, indicating that the similarity between the second set and the third set is higher, that is, the coincidence degree of the original trajectory and the high-precision road network buffer is higher; when the Jaccard similarity index between the second set and the third set is smaller, its Jaccard Distance is larger, indicating that the similarity between the second set and the third set is lower, that is, the coincidence degree of the original trajectory and the high-precision road network buffer is lower.

[0085] S312. Determine that at least one of the remaining original trajectories with a similarity greater than or equal to a third preset value is a vehicle driving trajectory.

[0086] For each of the remaining original trajectories, the higher the similarity between the corresponding second set and the third set, the higher the degree of overlap between the original trajectory and the buffer zone of the high-precision road network. Conversely, it means that the degree of overlap between the original trajectory and the buffer zone of the high-precision road network is lower. For example, after a vehicle travels a certain distance in the high-precision road network corresponding to the buffer zone of the high-precision road network and then drives into other roads, although some of the multiple trajectory points included in the generated original trajectory are located in the buffer zone of the high-precision road network, the proportion of these trajectory points in the total number of trajectory points in the original trajectory is small. Therefore, the similarity between the second set corresponding to the original trajectory and the third set is low. The original trajectory with a low degree of overlap with the buffer zone of the high-precision road network is also not suitable as the basis for subsequent data analysis. Therefore, at least one of the original trajectories with a similarity greater than or equal to the third preset value among multiple original trajectories can be used as the vehicle driving trajectory to participate in the subsequent data analysis process.

[0087] In the embodiments of the present disclosure, by calculating the coefficient of variation of the original trajectory with respect to the buffer zone of the high-precision road network corresponding to the road to be analyzed and the similarity between the second set corresponding to the original trajectory and the third set, suitable trajectories are selected from a large number of original trajectories as vehicle driving trajectories, removing the influence of noise data on trajectory data analysis, improving the accuracy of the trajectory processing method, and at the same time reducing the amount of data and calculation in the trajectory processing process, greatly improving the efficiency of the trajectory processing method.

[0088] Figure 4 It is a flowchart of a trajectory processing method provided by another embodiment of the present disclosure. As Figure 4 shown, the method includes the following steps:

[0089] S401. Obtain multiple vehicle driving trajectories, and each vehicle driving trajectory includes multiple trajectory points.

[0090] S402. Determine the coordinates of each trajectory point in the vehicle driving trajectory in the world coordinate system.

[0091] S403. Calculate the mean value of the coordinates of the multiple trajectory points to obtain the coordinates of the spatial centroid of the vehicle driving trajectory.

[0092] Each trajectory point collected by the positioning system during the movement of the terminal device has its corresponding position information, such as the coordinates of each trajectory point in the world coordinate system. For a vehicle driving trajectory, first determine the coordinates of each trajectory point in the world coordinate system, and further calculate the mean value of each coordinate to obtain the coordinates of the spatial centroid of the vehicle driving trajectory. Specifically, calculate the mean value of the coordinates of the trajectory points in each axis direction of the world coordinate system to obtain the coordinates of the spatial centroid of the vehicle driving trajectory in each axis direction.

[0093] S404. Determine the projection point of the spatial centroid in the high-precision road network buffer where the high-precision road network is located.

[0094] The spatial centroid of the vehicle driving trajectory calculated based on the coordinates of multiple trajectory points may be located outside the high-precision road network buffer. Therefore, first determine the projection point of the spatial centroid in the high-precision road network buffer. Specifically, the spatial centroid can be vertically projected onto the center line of the high-precision road network buffer to obtain the projection point of the spatial centroid in the high-precision road network buffer.

[0095] In some embodiments, according to different determination methods of the high-precision road network buffer, the reference for determining the projection point can be adjusted accordingly. For example, the point on the boundary line of the high-precision road network buffer that is closest to the spatial centroid can also be used as the projection point of the spatial centroid in the high-precision road network buffer. The embodiments of the present disclosure do not limit this.

[0096] S405. Determine the target lane group in the high-precision road network that is closest to the projection point according to the distance from the projection point to at least one lane group in the high-precision road network.

[0097] Specifically, determine the target lane group in the high-precision road network that is closest to the projection point according to the distance from the projection point to the center line of at least one lane group in the high-precision road network.

[0098] S406. Based on at least one lane line in the target lane group, determine the first set composed of the shape points corresponding to the at least one lane line.

[0099] The target lane group may include one or more lanes, and each lane has its corresponding lane line. Therefore, multiple corresponding lane shape points can be obtained, and the server 22 determines the first set composed of the shape points corresponding to at least one lane line in the target lane group accordingly.

[0100] S407. For each trajectory point in the second set, determine the target shape point in the first set that is farthest from the trajectory point, and use the distance between the trajectory point and the target shape point as the target distance of the trajectory point.

[0101] S408: Determine the maximum target distance among the target distances respectively corresponding to the multiple trajectory points in the second set as the distance between the second set and the first set.

[0102] The distance between each shape point in the first set and each trajectory point in the second set is calculated, and the distance between a shape point and a trajectory point with the farthest distance is selected as the distance between the first set and the second set.

[0103] S409: Use at least one vehicle driving trajectory among the multiple vehicle driving trajectories whose distance is less than a first preset value as a target driving trajectory.

[0104] If the distance between the vehicle driving trajectory and the target lane group is greater than or equal to the first preset value, it can be determined that the vehicle driving trajectory is far away from the target lane group, i.e., the high-precision road network, and is not suitable as a basis for data analysis, and the vehicle driving trajectory is removed; if the distance between the vehicle driving trajectory and the target lane group is less than the first preset value, it can be determined that the vehicle driving trajectory is the target driving trajectory on the high-precision road network and can be used as vehicle driving trajectory data for data analysis.

[0105] The disclosed embodiment calculates the distance between the set of vehicle driving trajectory points and the set of lane line points of the target lane group, thereby excluding vehicle driving trajectories that are similar to the target lane group trajectory in the high-precision road network but are actually farther away, effectively distinguishing the driving trajectories of the main road and the secondary road in the road, improving the accuracy of data analysis based on vehicle driving trajectories, and further improving the accuracy of trajectory data processing. Figure 5 Schematic diagram of the structure of the trajectory processing device provided in the embodiment of the present disclosure. The trajectory processing device may be the server as described in the above embodiment, or the trajectory processing device may be a component or assembly in the server. The trajectory processing device provided in the embodiment of the present disclosure may execute the processing flow provided in the trajectory processing method embodiment, such as Figure 5 As shown, the trajectory processing device 50 includes: an acquisition module 51, a first determination module 52, a second determination module 53, a calculation module 54, and a third determination module 55; wherein the acquisition module 51 is used to acquire multiple vehicle driving trajectories, each vehicle driving trajectory includes multiple trajectory points; the first determination module 52 is used to calculate the spatial centroid of the vehicle driving trajectory for the vehicle driving trajectories in the multiple vehicle driving trajectories, and determine the target lane group closest to the spatial centroid in the high-precision road network; the second determination module 53 is used to determine a first set of shape points corresponding to at least one lane line in the target lane group based on the at least one lane line in the target lane group; the calculation module 54 is used to calculate the distance between the second set composed of the multiple trajectory points in the vehicle driving trajectory and the first set; the third determination module 55 is used to take at least one vehicle driving trajectory in the multiple vehicle driving trajectories whose distance is less than a first preset value as the target driving trajectory.

[0106] Optionally, the obtaining module 51 is further configured to determine a high-precision road network buffer based on the center line of the high-precision road network and a preset distance; obtain a plurality of original trajectories, each original trajectory including at least one trajectory point; remove at least one original trajectory in which the positions of each trajectory point in the plurality of original trajectories are not located in the high-precision road network buffer.

[0107] Optionally, the obtaining module 51 is further configured to, for each remaining original trajectory, obtain the length of the original trajectory and the number of trajectory points in the original trajectory; remove at least one original trajectory in which the length of the original trajectory is less than a preset length threshold or the number of trajectory points is less than a preset quantity.

[0108] Optionally, the first determining module 52 is further configured to determine the coordinates of each trajectory point in the vehicle driving trajectory in the world coordinate system; calculate the mean value of the coordinates of the plurality of trajectory points to obtain the coordinates of the spatial centroid of the vehicle driving trajectory.

[0109] Optionally, the first determining module 52 is further configured to determine a projection point of the spatial centroid in the high-precision road network buffer where the high-precision road network is located; determine a target lane group in the high-precision road network that is closest to the projection point according to the distance from the projection point to at least one lane group in the high-precision road network.

[0110] Optionally, the calculating module 54 is further configured to, for each trajectory point in the second set, determine a target shape point in the first set that is farthest from the trajectory point, and use the distance between the trajectory point and the target shape point as the target distance of the trajectory point; determine the maximum target distance among the target distances corresponding to the plurality of trajectory points in the second set as the distance between the second set and the first set.

[0111] Remove Figure 5 The trajectory processing device in the illustrated embodiment can be used to execute the technical solutions in the above method embodiments, and its implementation principle and technical effects are similar, which will not be elaborated here.

[0112] In addition, an embodiment of the present disclosure further provides a vehicle, which includes the trajectory processing device as described in the above embodiment.

[0113] Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. The electronic device may be the server as described in the above embodiment. The electronic device provided by the embodiment of the present disclosure can execute the processing flow provided by the trajectory processing method embodiment, as Figure 6 shown, the electronic device 60 includes: a memory 61, a processor 62, a computer program, and a communication interface 63; wherein, the computer program is stored in the memory 61 and is configured to be executed by the processor 62 to perform the trajectory processing method as described above.

[0114] In addition, an embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the trajectory processing method described in the above embodiment.

[0115] Furthermore, an embodiment of the present disclosure also provides a computer program product, which includes a computer program or instruction, and when the computer program or instruction is executed by a processor, it implements the trajectory processing method as described above.

[0116] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The above programming languages include, but are not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., connected through the Internet using an Internet service provider).

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0118] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising said element.

[0119] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A trajectory processing method, characterized in that, The method includes: Obtain multiple vehicle driving trajectories, where each vehicle driving trajectory includes multiple trajectory points; For a vehicle driving trajectory among the multiple vehicle driving trajectories, calculate the spatial centroid of the vehicle driving trajectory, and determine the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid; Based on at least one lane line in the target lane group, determine a first set composed of the shape points corresponding to the at least one lane line; Calculate the distance between a second set composed of the multiple trajectory points in the vehicle driving trajectory and the first set; Use at least one vehicle driving trajectory among the multiple vehicle driving trajectories with a distance less than a first preset value as the target driving trajectory.

2. The method according to claim 1, characterized in that, Before obtaining the multiple vehicle driving trajectories, the method further includes: Establish a high-precision road network buffer zone with a width of a preset distance based on the center line of the high-precision road network corresponding to the road to be analyzed; Obtain multiple original trajectories, where each original trajectory includes at least one trajectory point; Remove at least one original trajectory in which the positions of each trajectory point in the multiple original trajectories are not located in the high-precision road network buffer zone.

3. The method according to claim 2, wherein After removing at least one original trajectory in which the positions of each trajectory point in the multiple original trajectories are not located in the high-precision road network buffer zone, the method further includes: For each remaining original trajectory, obtain the length of the original trajectory and the number of trajectory points in the original trajectory; Remove at least one original trajectory whose length is less than a preset length threshold or the number of trajectory points is continuously less than a preset quantity.

4. The method according to claim 1, wherein Calculating the spatial centroid of the vehicle driving trajectory includes: Determine the coordinates of each trajectory point in the vehicle driving trajectory in the world coordinate system; Calculate the mean value of the coordinates of the multiple trajectory points to obtain the coordinates of the spatial centroid of the vehicle driving trajectory.

5. The method according to claim 1, wherein Determining the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid includes: Determine the projection point of the spatial centroid in the high-precision road network buffer zone where the high-precision road network is located; According to the distance from the projection point to at least one lane group in the high-precision road network, determine the target lane group in the high-precision road network that is closest to the projection point.

6. The method according to claim 1, characterized in that Calculating the distance between the second set composed of the multiple trajectory points in the vehicle driving trajectory and the first set includes: For each trajectory point in the second set, determine the target shape point in the first set that is farthest from the trajectory point, and use the distance between the trajectory point and the target shape point as the target distance of the trajectory point; Determine the maximum target distance among the target distances corresponding to the multiple trajectory points in the second set as the distance between the second set and the first set.

7. A trajectory processing device, characterized in that, The device includes: An acquisition module, configured to obtain multiple vehicle driving trajectories, where each vehicle driving trajectory includes multiple trajectory points; A first determination module, configured to calculate the spatial centroid of a vehicle driving trajectory among the multiple vehicle driving trajectories and determine the target lane group in the high-precision road network corresponding to the road to be analyzed that is closest to the spatial centroid; A second determination module, configured to determine a first set formed by shape points corresponding to the at least one lane line based on the at least one lane line in the target lane group; A calculation module, configured to calculate a distance between a second set formed by the plurality of trajectory points in the vehicle driving trajectory and the first set; A third determination module, configured to use at least one vehicle driving trajectory with a distance less than a first preset value among the plurality of vehicle driving trajectories as a target driving trajectory.

8. An electronic device, characterized in that, Comprising: A memory; A processor; And A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1-6.

10. A vehicle, characterized in that, Comprising: The trajectory processing device according to claim 7.

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