CN116012972A - Vehicle track processing method and vehicle track processing device - Google Patents

Abstract

The embodiment of the present application provides a processing method of a vehicle trajectory and a processing device of a vehicle trajectory. By obtaining multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and a plurality of actual sampling point sets in each actual sampling point set Select the actual sampling points to obtain multiple sets of control points, fit each set of control points to obtain multiple virtual driving trajectories, and sample each virtual driving trajectory to obtain multiple sets of virtual sampling points. A plurality of virtual sampling points in the same order in the virtual sampling point set are centered to obtain a median point set, the median point set includes a plurality of median points, and an automatic driving trajectory is obtained according to the median point set. According to the vehicle trajectory processing method and the vehicle trajectory processing device of the embodiments of the present application, automatic driving trajectories suitable for different tracking vehicles can be obtained according to actual driving trajectories.

Description

Vehicle track processing method and vehicle track processing device

technical field

The present application relates to the field of automatic driving, and in particular to a method for processing vehicle trajectories and a processing device for vehicle trajectories.

Background technique

In recent years, with the continuous development of artificial intelligence technology, autonomous driving technology has received more and more functional applications on vehicles. In related technologies, one or more actual driving trajectories can be obtained from the starting point to the destination through actual driving first, and then the tracking vehicle can be driven automatically along the actual driving trajectories. However, when the actual driving vehicle and the subsequent tracking vehicle are different, the tracking vehicle may not completely drive along the actual driving track, and the actual driving vehicle may also collect the actual driving track. There will be a certain deviation, so that when the tracking vehicle drives along the actual driving track, there will be a certain safety risk.

Contents of the invention

Embodiments of the present application provide a vehicle trajectory processing method and a vehicle trajectory processing device, which can obtain automatic driving trajectories suitable for different tracking vehicles according to actual driving trajectories.

Embodiments of the present application provide a method for processing vehicle trajectories on the one hand, including: obtaining multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and each actual sampling point set includes Multiple actual sampling points; multiple actual sampling points in each actual sampling point set are selected to obtain multiple control point sets, each control point set includes multiple control points; each control point set is fitted , to obtain a plurality of virtual driving trajectories; each virtual driving trajectory is sampled to obtain a plurality of virtual sampling point sets, and each virtual sampling point set includes a plurality of virtual sampling points arranged in sequence along the corresponding virtual driving trajectories; A plurality of virtual sampling points with the same order in a set of virtual sampling points are centered to obtain a set of median points, and the set of median points includes multiple median points; according to the set of median points, an automatic driving trajectory is obtained.

In some embodiments, acquiring multiple actual driving trajectories includes acquiring each actual driving trajectory; acquiring each actual driving trajectory includes: regularly collecting vehicle position information during driving to obtain multiple vehicle positions including time stamps information; obtain multiple actual collection points arranged sequentially along the actual driving trajectory according to multiple vehicle position information; obtain the actual driving trajectory according to multiple actual sampling points.

In some embodiments, each actual sampling point set includes N actual sampling points arranged sequentially along the actual driving trajectory; multiple actual sampling points in each actual sampling point set are selected to obtain multiple control point sets, Including obtaining each set of control points; obtaining each set of control points, including: selecting the i-th actual sampling point among multiple actual sampling points as a control point; calculating the distance between the i+1th actual sampling point and the control point Distance; compare the distance with the first threshold; if the distance is greater than or equal to the first threshold, select the i+1th actual sampling point as the control point; return to calculate the distance between the i+1th actual sampling point and the control point , until i is equal to N-1.

In some embodiments, after comparing the distance with the first threshold, a plurality of actual sampling points in each actual sampling point set are selected to obtain a plurality of control point sets, further including: when the distance is less than the first threshold Next, delete the i+1th actual sampling point to obtain an updated set of actual sampling points; the return step calculates the distance between the i+1th actual sampling point and the control point until the distance is greater than or equal to the first threshold.

In some embodiments, each set of control points is fitted to obtain multiple virtual driving trajectories, including obtaining each virtual driving trajectory; obtaining each virtual driving trajectory includes: performing B-spline interpolation on multiple control points , to get the virtual driving trajectory.

In some embodiments, each virtual driving trajectory is sampled to obtain a plurality of virtual sampling point sets, including obtaining each virtual sampling point set; obtaining each virtual sampling point set includes: obtaining a preset speed and the first sampling point time; the first sampling distance is obtained according to the preset speed and the first sampling time; according to the first sampling distance, a plurality of virtual sampling points are selected equidistantly along the virtual driving track.

In some embodiments, obtaining each set of virtual sampling points also includes: calculating the curvature parameter of the virtual driving track at the virtual sampling point; and when the curvature parameter is greater than or equal to the second threshold, according to the second sampling distance, from the virtual sampling point Points are sampled to adjacent virtual sampling points to obtain two supplementary virtual sampling points; according to the virtual sampling points and the two supplementary virtual sampling points, an updated set of virtual sampling points is obtained; wherein, the first sampling distance is greater than the second sampling distance .

In some embodiments, taking a plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets is centered to obtain a set of median points, including: a plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets The coordinates of the points are averaged to obtain a set of median points; or the coordinates of multiple virtual sampling points with the same order in the set of multiple virtual sampling points are taken as an intermediate value to obtain a set of median points.

Another aspect of the embodiment of the present application provides a processing device for vehicle trajectories, including: an acquisition module for acquiring multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and each actual sampling point set includes A plurality of actual sampling points arranged sequentially in the actual driving trajectory; a selection module, used to select a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, each control point set including multiple control points points; the fitting module is used to fit each set of control points to obtain multiple virtual driving trajectories; the sampling module is used to sample each virtual driving trajectory to obtain multiple sets of virtual sampling points, and each virtual The set of sampling points includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving trajectory; the centering module is used to center a plurality of virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain a median value A set of points, the set of median points includes a plurality of median points; and a processing module, configured to obtain an automatic driving trajectory according to the set of median points.

Another aspect of the embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the above vehicle trajectory processing method is realized.

Another aspect of the embodiment of the present application provides a computer-readable storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the above method for processing vehicle trajectories is realized.

The processing method of the vehicle trajectory provided by the embodiment of the present application obtains multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, selects a plurality of actual sampling points in each actual sampling point set, and obtains multiple A set of control points, fitting each set of control points to obtain multiple virtual driving trajectories, sampling each virtual driving trajectory, and obtaining multiple sets of virtual sampling points, for multiple sets of virtual sampling points in the same order A plurality of virtual sampling points are centered to obtain a set of median points, and the set of median points includes a plurality of median points, and an automatic driving trajectory is obtained according to the set of median points. According to the embodiment of the present application, a plurality of actual sampling points are selected to obtain a plurality of control points that can reflect the trend of the actual driving trajectory, which reduces the influence of redundant actual sampling points on the obtained virtual driving trajectory. Sampling virtual driving trajectories and taking the center of multiple virtual sampling points in the same order in multiple virtual sampling point sets to obtain automatic driving trajectories, which can improve the accuracy of vehicle trajectory processing and make automatic driving trajectories applicable to different Tracking vehicles, and improving the safety of tracking vehicles in the process of automatic driving.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings that need to be used in the embodiments of the present application. Obviously, the accompanying drawings described below are only some embodiments of the present application. Those of ordinary skill in the art can also obtain other drawings based on these drawings without making creative efforts.

Fig. 1 is a schematic structural diagram of a vehicle track processing device provided according to an embodiment of the present application;

FIG. 2 is a schematic flowchart of a method for processing a vehicle track according to an embodiment of the present application;

Fig. 3 shows an exemplary schematic diagram of some steps in the flow diagram shown in Fig. 2;

Fig. 4 shows an exemplary schematic diagram of some steps in the flow diagram shown in Fig. 2;

Fig. 5 shows an exemplary schematic diagram of some steps in the flowchart schematic diagram shown in Fig. 2;

FIG. 6 shows an exemplary schematic diagram of some steps in the schematic flow diagram shown in FIG. 2;

FIG. 7 shows an exemplary schematic diagram of the flowchart shown in FIG. 6;

FIG. 8 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 .

In the attached picture:

100-vehicle trajectory processing device; 1-acquisition module; 2-selection module; 3-fitting module; 4-sampling module; 5-centering module; 6-processing module.

Detailed ways

In order to better understand the above purpose, features and advantages of the present application, the solution of the present application will be further described below. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

In the following description, a lot of specific details have been set forth in order to fully understand the present application, but the present application can also be implemented in other ways different from those described here; obviously, the embodiments in the description are only a part of the present application, and Not all examples.

It should be noted that, unless otherwise specified, the technical terms or scientific terms used in the embodiments of the present application shall have ordinary meanings understood by those skilled in the art to which the embodiments of the present application belong.

In addition, the technical terms "first", "second" and so on are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. In the description of the embodiments of the present application, "plurality" means two or more, unless otherwise specifically defined.

In the description of the embodiments of this application, unless otherwise clearly specified and limited, the technical terms "installation", "connection", "connection", "fixation" and other terms should be understood in a broad sense, for example, it can be a fixed connection, or It can be a detachable connection, or integrated; it can also be a mechanical connection, it can also be an electrical connection; it can be directly connected or indirectly connected through an intermediary, and it can be the internal communication of two components or the interaction of two components relation. Those of ordinary skill in the art can understand the specific meanings of the above terms in the embodiments of the present application according to specific situations.

Generally speaking, the automatic driving of vehicles is realized by obtaining high-precision map data and positioning data. However, in the case of automatic driving in the mining area, due to the complex road environment in the mining area and the constantly changing road environment, it is impossible to obtain high-precision map data of the driving area, or the timeliness of the obtained high-precision map data is poor. The traditional automatic driving planning method cannot be applied to the mining environment. Therefore, in the case of automatic driving in the mining area, one or more actual driving trajectories can be obtained from the starting point to the destination through actual driving, and then the automatic driving trajectory can be obtained by processing the actual driving trajectories.

In view of this, the embodiment of the present application provides a method for processing vehicle trajectories. By obtaining multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and performs a process for multiple actual sampling points in each actual sampling point set. Select to obtain multiple sets of control points, fit each set of control points to obtain multiple virtual driving trajectories, sample each virtual driving trajectory, obtain multiple sets of virtual sampling points, and set multiple virtual sampling points A plurality of virtual sampling points with the same median order are centered to obtain a median point set, the median point set includes a plurality of median points, and an automatic driving trajectory is obtained according to the median point set. According to the embodiment of the present application, a plurality of actual sampling points are selected to obtain a plurality of control points that can reflect the trend of the actual driving trajectory, which reduces the influence of redundant actual sampling points on the obtained virtual driving trajectory. Sampling virtual driving trajectories and taking the center of multiple virtual sampling points in the same order in multiple virtual sampling point sets to obtain automatic driving trajectories, which can improve the accuracy of vehicle trajectory processing and make automatic driving trajectories applicable to different Tracking vehicles, and improving the safety of tracking vehicles in the process of automatic driving.

Fig. 1 is a schematic structural diagram of a vehicle track processing device 100 provided according to an embodiment of the present application. As shown in FIG. 1 , the embodiment of the present application also provides a vehicle track processing device 100 . The vehicle trajectory processing device 100 includes an acquisition module 1 , a selection module 2 , a fitting module 3 , a sampling module 4 , a centering module 5 and a processing module 6 .

The acquiring module 1 is used to acquire a plurality of actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and each actual sampling point set includes a plurality of actual sampling points sequentially arranged along the actual driving trajectory. The selection module 2 is used to select multiple actual sampling points in each actual sampling point set to obtain multiple control point sets, and each control point set includes multiple control points. The fitting module 3 is used to fit each set of control points to obtain multiple virtual driving trajectories. The sampling module 4 is used to sample each virtual driving track to obtain a plurality of virtual sampling point sets, and each virtual sampling point set includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving track. The centering module 5 is used to center a plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets to obtain a set of median points, and the set of median points includes a plurality of median points. The processing module 6 is used to obtain the automatic driving trajectory according to the set of median points.

Optionally, the vehicle track processing device 100 may be installed in a tracking vehicle, or may be set in a server, which is not limited in this embodiment of the present application.

Fig. 2 is a schematic flowchart of a method for processing vehicle trajectories according to an embodiment of the present application. The vehicle trajectory processing method in the embodiment of the present application is implemented by the vehicle trajectory processing device 100 shown in FIG. 1 . As shown in Fig. 2, the processing method of the vehicle trajectory includes the following steps.

S100. Acquire multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set.

S200. Select multiple actual sampling points in each actual sampling point set to obtain multiple control point sets.

S300. Fit each set of control points to obtain multiple virtual driving trajectories.

S400. Sampling each virtual driving trajectory to obtain multiple sets of virtual sampling points.

S500. Center a plurality of virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain a median point set.

S600. Obtain an automatic driving trajectory according to the set of median points.

In step S100, each set of actual sampling points includes a plurality of actual sampling points arranged sequentially along the actual driving trajectory. It can be regarded as the actual driving trajectory obtained by sequentially connecting multiple actual sampling points.

FIG. 3 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 . As shown in FIG. 2 , in some embodiments of the present application, in step S100 , multiple actual driving trajectories are acquired by acquiring each actual driving trajectory, and acquiring each actual driving trajectory includes the following steps.

Step S110 , regularly collect vehicle location information during driving to obtain a plurality of vehicle location information including time stamps.

Step S120, obtaining a plurality of actual collection points sequentially arranged along the actual driving trajectory according to the plurality of vehicle position information.

Step S130, obtaining an actual driving trajectory according to a plurality of actual sampling points.

In step S110 , if the acquisition module 1 is set in the vehicle that is actually driven, the acquisition module 1 can collect the location information of the vehicle that is actually driven. The vehicle location information may be the location coordinates of the vehicle at the Global Positioning System (GPS) level, or the location coordinates of the vehicle relative to the starting point and the ending point. During the process of the vehicle driving along the actual driving track, the position coordinates of the vehicle can be recorded at a certain frequency, and the current time can be generated as a time stamp, that is, during the actual driving process of the vehicle, the time stamp indicates that the actual driving vehicle is at different times position on the actual driving trajectory. Optionally, the vehicle location information may be regularly collected at a frequency of 10 Hz. It should be understood that the acquisition module 1 may have random positioning errors during the acquisition process. Therefore, the frequency of acquisition by the acquisition module 1 should be as high as possible to reduce the probability of inaccurate vehicle position information obtained.

In step S120, since a plurality of vehicle position information is obtained in step S110, the vehicle position information may include the position coordinates of the vehicle and the order or time of recording the position coordinates, and the vehicle position information may be expressed in a coordinate system at the global positioning system level, or In the coordinate system relative to the starting point and the ending point. For example, make the position coordinates include first coordinates in a first direction and second coordinates in a second direction, the first direction and the second direction intersect each other, according to the first coordinates and the second coordinates, multiple vehicle positions The information is marked as points in the coordinate system, that is, multiple actual collection points can be obtained.

In step S130, the multiple actual collection points obtained in step S120 are connected in the order or time of the vehicle position information records. Since in step S110, the frequency of acquiring multiple vehicle position information is relatively high, and the arrangement of multiple actual collection points is relatively dense, in this case connecting them can record the actual driving trajectory more accurately.

Optionally, step S110 , step S120 and step S130 can be implemented by the acquisition module 1 of the vehicle trajectory processing device 100 , and the vehicle trajectory processing device 100 can also obtain the actual driving trajectory from other devices or equipment through the acquisition module 1 . For example, the actual driving vehicle includes a collection device that regularly collects vehicle position information during the driving process from the starting point to the destination, and transmits the collected data, that is, the actual driving trajectory including the actual sampling point set, to the vehicle after reaching the destination. The acquisition module 1 of the track processing device 100 . This embodiment of the present application does not limit this.

In step S200, each set of control points includes a plurality of control points. The plurality of control points are obtained by selecting a plurality of actual sampling points in the actual sampling point set obtained in step S100, and are coordinate points among the plurality of actual sampling points that can reflect the direction of the actual driving trajectory. That is to say, because the acquisition module 1 regularly collects vehicle location information with a high frequency, resulting in a relatively dense arrangement of multiple actual sampling points, not every actual sampling point plays a decisive role in the direction of the actual driving trajectory. Therefore, The selection module 2 selects multiple actual sampling points, that is, performs thinning processing on the multiple actual sampling points, so as to remove redundant actual sampling points and obtain multiple control points.

FIG. 4 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 . As shown in Fig. 4, in some embodiments of the present application, in step S200, multiple control point sets are obtained by obtaining each control point set, and obtaining each control point set includes the following steps.

S210. Select an i-th actual sampling point among the plurality of actual sampling points as a control point.

S220. Calculate the distance between the i+1th actual sampling point and the control point.

S230. Compare the distance with a first threshold.

S240. When the distance is greater than or equal to the first threshold, select the i+1th actual sampling point as a control point.

In step S210, the ith actual sampling point among the plurality of actual sampling points is selected as a control point. For example, in the first cycle, the first actual sampling point among the plurality of actual sampling points may be selected as the control point.

In step S220, the distance between the i+1th actual sampling point and the control point is calculated. For example, in the first cycle, the distance between the second actual sampling point and the control point selected in step S210 can be calculated, that is, according to the coordinate system or the relative starting point and the ending point of the actual sampling point at the global positioning system level Calculate the distance between the second actual sampling point and the first actual sampling point. In this way, the distance between multiple actual sampling points can be used to determine whether the actual sampling points adjacent to the selected control point can reflect the trend of the actual driving trajectory.

In step S230, the distance between the control point obtained in step S220 and the (i+1)th actual sampling point is compared with the first threshold. Optionally, the first threshold may be related to the body length of the actually driven vehicle.

In step S240, when the distance between the control point and the i+1th actual sampling point is greater than or equal to the first threshold, the i+1th actual sampling point is taken as the next control point. For example, in the first cycle, the first actual sampling point is the control point, and when the first threshold is the body length, the second actual sampling point is not within the reachable range of a body length, therefore, the second The first actual sampling point can play a decisive role in the direction of the driving trajectory, and the second actual sampling point is used as the control point.

Each set of actual sampling points can be regarded as including N actual sampling points arranged sequentially along the actual driving trajectory. After step S240, return to step S220 until i is equal to N-1. That is to say, after the first control point is obtained in step S210, in one situation, step S220, step S230 and step S240 are made into a cycle, and the difference between the first actual sampling point and the second actual sampling point The distance between the second actual sampling point and the third actual sampling point... The distance between the N-1th actual sampling point and the Nth actual sampling point is compared with the first threshold to determine Get multiple control points.

In some embodiments of the present application, after step S230, in step S200, the following steps are further performed on each actual sampling point set.

S240'. If the distance is less than the first threshold, delete the i+1th actual sampling point to obtain an updated set of actual sampling points.

In step S240', when the distance between the control point and the i+1th actual sampling point is smaller than the first threshold, the i+1th actual sampling point is used as a redundant point, thereby deleting the i+1th actual sampling point actual sampling points, so that the updated set of actual sampling points includes N-1 actual sampling points. For example, in the first cycle, the first actual sampling point is the control point, and when the first threshold is the body length, if the second actual sampling point is within the reachable range of a body length, the second If the first actual sampling point fails to play a decisive role in the direction of the actual driving trajectory, the second actual sampling point can be regarded as a redundant point, and the second actual sampling point can be deleted.

After step S240', return to step S220 until the distance between the control point and the i+1th actual sampling point is greater than or equal to the first threshold. For example, after deleting the second actual sampling point, an updated actual sampling point set is obtained. At this time, the second actual sampling point in the updated actual sampling point set is also the third actual sampling point set in the original actual sampling point set. The actual sampling point, after that, return to step S220, calculate the distance between the first actual sampling point and the second actual sampling point again, and use this as a loop to obtain the next control point that can play a decisive role in the direction of the driving trajectory . That is to say, after the first control point is obtained in step S210, in another situation, make step S220, step S230 and step S240' a loop.

In step S300, fitting is a method of connecting a series of points on the plane with a smooth curve. The fitting module 3 fits the set of control points to obtain a virtual driving trajectory.

FIG. 5 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 . As shown in FIG. 5 , in some embodiments of the present application, in step S300 , multiple virtual driving trajectories are acquired by acquiring each virtual driving trajectory, and acquiring each virtual driving trajectory includes the following steps.

S310. Perform B-spline interpolation on multiple control points to obtain a virtual driving track.

In step S310, in this embodiment, a plurality of obtained control points are connected with a smooth curve by means of B-spline interpolation, so as to obtain a virtual driving trajectory. B-spline interpolation is a method of recursively obtaining a smooth curve through the weights of multiple points, which will not be described in detail in this embodiment of the present application.

In step S400, the virtual driving trajectory is sampled by the sampling module 4, so that each set of virtual sampling points includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving trajectory.

FIG. 6 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 . As shown in FIG. 6 , in some embodiments of the present application, in step S400 , multiple sets of virtual sampling points are obtained by obtaining each set of virtual sampling points, and obtaining each set of virtual sampling points includes the following steps.

S410. Acquire a preset speed and a first sampling time.

S420. Obtain a first sampling distance according to the preset speed and the first sampling time.

S430. Select a plurality of virtual sampling points equidistantly along the virtual driving track according to the first sampling distance.

In step S410, a preset speed and a first sampling time may be preset. The preset speed may be a prescribed speed for the automatic driving vehicle in the subsequent automatic driving process. In addition, the process of driving the autonomous vehicle along the virtual driving trajectory can be simulated, similar to the acquisition device sampling the actual driving trajectory, sampling the virtual driving trajectory at intervals of the first sampling time. Optionally, the first sampling time may be longer than the time when the acquisition device samples the actual driving trajectory.

In step S420, the first sampling distance is obtained according to the preset speed and the first sampling time. The first sampling distance is obtained by multiplying the preset speed and the first sampling time.

In step S430, a plurality of virtual sampling points are selected equidistantly on the virtual driving track according to the first sampling distance. Optionally, the virtual sampling points may also include virtual position coordinates in the simulated vehicle's coordinate system at the global positioning system level or in a coordinate system relative to the start point and the end point, and the sampling sequence along the virtual driving trajectory.

In some optional embodiments, since the virtual driving trajectory is sampled at equal distances, it may not be possible to completely cover all the control points that have a decisive effect on the virtual driving trajectory. In order to make the virtual driving trajectory fit the actual driving trajectory as much as possible, you can Further sampling is performed on the selected plurality of virtual sampling points.

FIG. 7 shows an exemplary schematic diagram of the flowchart shown in FIG. 6 . As shown in FIG. 7, in step S400, obtaining each virtual sampling point set may further include the following steps.

S440. Calculate the curvature parameter of the virtual driving trajectory at the virtual sampling point.

S450. When the curvature parameter is greater than or equal to the second threshold, perform sampling from the virtual sampling point to adjacent virtual sampling points according to the second sampling distance to obtain two supplementary virtual sampling points.

S460. Obtain an updated virtual sampling point set according to the virtual sampling point and the two supplementary virtual sampling points.

Wherein, the first sampling distance is greater than the second sampling distance.

In step S440, the curvature parameter of the virtual driving trajectory at this point is calculated according to the position coordinates of the virtual sampling point and the shape of the virtual driving trajectory in the coordinate system at the global positioning system level or in the coordinate system relative to the starting point and the ending point.

In step S450, if the curvature parameter is greater than or equal to the second threshold, it means that the section of the virtual driving track where the current virtual sampling point is located is relatively tortuous, so that a single virtual sampling point cannot play a decisive role in the virtual driving track. Therefore, sampling can be performed from the virtual sampling point to both sides according to the second sampling distance to obtain supplementary virtual sampling points. Optionally, making the second sampling distance smaller than the first sampling distance can ensure that the supplementary virtual sampling point is located between the current virtual sampling point and adjacent virtual sampling points, so as to determine the direction of the virtual driving trajectory on both sides of the current virtual sampling point .

In step S460, after the supplementary virtual sampling points are obtained, the plurality of virtual sampling points and the two supplementary virtual sampling points are jointly used as a plurality of virtual sampling points in the virtual sampling point set, so as to update the virtual sampling points.

Optionally, after step S460, the curvature parameters of the virtual driving trajectory at multiple supplementary virtual sampling points can be further calculated, and in the case that the curvature parameters are still greater than or equal to the second threshold, from the supplementary virtual sampling points to the two Sampling is performed until the obtained virtual sampling points and supplementary virtual sampling points can determine the direction of the virtual driving trajectory.

In step S500, since each set of virtual sampling points includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving track, in the case of including multiple sets of virtual sampling points, each virtual sampling point is sampled by the centering module 5 A plurality of virtual sampling points with the same sequence in the point set are centered to obtain a median point set, and the median point set includes a plurality of median points.

FIG. 8 shows an exemplary schematic diagram of some steps in the schematic flowchart shown in FIG. 2 . As shown in FIG. 8, in some optional embodiments, step S500 includes the following steps.

S510. Average the coordinates of multiple virtual sampling points in the same order in the multiple virtual sampling point sets to obtain a median point set.

In step S510, since a plurality of virtual driving routes all extend from the starting point to the end point, and the sampling rules of the plurality of virtual sampling points are the same, therefore, the plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets should be mutually near. The virtual position coordinates of multiple virtual sampling points in the same order are averaged to obtain a median point. When the virtual sampling point set includes multiple virtual sampling points arranged in sequence along the corresponding virtual driving trajectory, A plurality of median points can be obtained, that is, a set of median points.

In other optional embodiments, step S500 includes the following steps.

S510'. Take the median value for the coordinates of multiple virtual sampling points with the same order in the multiple virtual sampling point sets to obtain a median point set.

In step S510', similar to step S510, intermediate values are obtained for the virtual position coordinates of multiple virtual sampling points in the same order, and multiple median points can also be obtained.

In step S600, the processing module 6 performs connection or fitting processing on a plurality of median points to obtain the automatic driving trajectory. After the above steps, the automatic driving trajectory suitable for different tracking vehicles can be obtained according to the actual driving trajectory, the accuracy of the automatic driving trajectory can be improved, and the safety of the automatic driving process can be improved.

Based on the above description, an embodiment of the present application further provides an electronic device, and the electronic device includes a processor and a memory storing computer program instructions. When the processor executes the computer program instructions, the above vehicle trajectory processing method is realized.

The processor may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more integrated circuits in the embodiments of the present application.

Memory may include mass storage for data or instructions. For example, memory may include a Hard Disk Drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of the above. Storage may include removable or non-removable (or fixed) media, where appropriate. Where appropriate, the storage can be inside or outside the integrated gateway disaster recovery device. In a particular embodiment, the memory is non-volatile solid-state memory.

Memory may include read only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical/tangible memory storage devices. Thus, in general, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions, and when the software is executed (e.g., by one or multiple processors), it is operable to perform the operations described with reference to the method according to an aspect of the embodiments of the present application.

In addition, in combination with the above description, the embodiment of the present application further provides a computer storage medium. Computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by the processor, the above method for processing vehicle trajectories can be realized.

To sum up, the vehicle trajectory processing method provided by the embodiment of the present application obtains multiple actual driving trajectories, each actual driving trajectory includes an actual sampling point set, and multiple actual sampling points in each actual sampling point set Make selections to obtain multiple control point sets, fit each control point set to obtain multiple virtual driving trajectories, sample each virtual driving trajectory, and obtain multiple virtual sampling point sets, and multiple virtual sampling point sets A plurality of virtual sampling points in the same order in the set are centered to obtain a set of median points, the set of median points includes a plurality of median points, and an automatic driving trajectory is obtained according to the set of median points. According to the embodiment of the present application, a plurality of actual sampling points are selected to obtain a plurality of control points that can reflect the trend of the actual driving trajectory, which reduces the influence of redundant actual sampling points on the obtained virtual driving trajectory. Sampling virtual driving trajectories and taking the center of multiple virtual sampling points in the same order in multiple virtual sampling point sets to obtain automatic driving trajectories, which can improve the accuracy of vehicle trajectory processing and make automatic driving trajectories applicable to different Tracking vehicles, and improving the safety of tracking vehicles in the process of automatic driving.

It should be noted that in this article, relative 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 these No such actual relationship or order exists between entities or operations. Moreover, the term "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements but also other elements not expressly listed, Or also include elements inherent in such a process, method, article or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

Claims (11)

1. A processing method for a vehicle track, comprising: Acquiring a plurality of actual driving trajectories, each of the actual driving trajectories includes an actual sampling point set, and each of the actual sampling point sets includes a plurality of actual sampling points arranged sequentially along the actual driving trajectories; selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, each of which includes a plurality of control points; Fitting each set of control points to obtain multiple virtual driving trajectories; Sampling each of the virtual driving trajectories to obtain a plurality of sets of virtual sampling points, each of which includes a plurality of virtual sampling points arranged sequentially along the corresponding virtual driving trajectories; Taking the plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets to obtain a median point set, the median point set including a plurality of median points; According to the set of median points, an automatic driving trajectory is obtained. 2. The processing method of the vehicle track according to claim 1, wherein said obtaining a plurality of actual driving tracks comprises obtaining each said actual driving track; The acquisition of each actual driving trajectory includes: Collecting vehicle location information regularly during driving to obtain a plurality of vehicle location information including time stamps; Obtaining a plurality of actual collection points sequentially arranged along the actual driving trajectory according to the plurality of vehicle position information; The actual driving trajectory is obtained according to the plurality of actual sampling points. 3. The processing method of the vehicle trajectory according to claim 2, wherein each said actual sampling point set comprises N said actual sampling points arranged in sequence along said actual driving trajectory; The selecting a plurality of the actual sampling points in each of the actual sampling point sets to obtain a plurality of control point sets includes acquiring each of the control point sets; The acquisition of each set of control points includes: Selecting the i-th actual sampling point among the plurality of actual sampling points as a control point; Calculate the distance between the i+1th actual sampling point and the control point; comparing the distance to a first threshold; When the distance is greater than or equal to the first threshold, select the i+1th actual sampling point as a control point; Return to the calculation of the distance between the i+1th actual sampling point and the control point until i is equal to N-1. 4. The processing method of vehicle trajectory according to claim 3, characterized in that, after said comparing said distance with a first threshold, said multiple said actual sampling points in each said actual sampling point set Sampling points are selected to obtain multiple sets of control points, including: When the distance is less than the first threshold, delete the i+1th actual sampling point to obtain an updated set of actual sampling points; Return to the step of calculating the distance between the i+1th actual sampling point and the control point until the distance is greater than or equal to the first threshold. 5. The processing method of the vehicle track according to claim 1, wherein said fitting each set of control points to obtain a plurality of virtual driving tracks comprises obtaining each said virtual driving track; The acquisition of each virtual driving track includes: B-spline interpolation is performed on a plurality of control points to obtain the virtual driving trajectory. 6. The processing method of vehicle trajectory according to claim 1, wherein said sampling each said virtual driving trajectory to obtain a plurality of virtual sampling point sets comprises obtaining each said virtual sampling point set ; The obtaining of each set of virtual sampling points includes: Obtain the preset speed and the first sampling time; obtaining a first sampling distance according to the preset speed and the first sampling time; According to the first sampling distance, a plurality of virtual sampling points are selected equidistantly along the virtual driving trajectory. 7. the processing method of vehicle trajectory according to claim 6, is characterized in that, described obtaining each described virtual sampling point set, also comprises: calculating a curvature parameter of the virtual driving trajectory at the virtual sampling point; In the case where the curvature parameter is greater than or equal to a second threshold, according to a second sampling distance, sampling is performed from the virtual sampling point to an adjacent virtual sampling point to obtain two supplementary virtual sampling points; Obtain an updated set of virtual sampling points according to the virtual sampling point and the two supplementary virtual sampling points; Wherein, the first sampling distance is greater than the second sampling distance. 8. The processing method of the vehicle trajectory according to claim 7, characterized in that, the plurality of virtual sampling points with the same order in the plurality of virtual sampling point sets are centered to obtain a median point collection, including: Taking the average value of the coordinates of multiple virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain the median point set; or Taking an intermediate value for the coordinates of the plurality of virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain the median point set. 9. A processing device for a vehicle trajectory, comprising: An acquisition module, configured to acquire a plurality of actual driving trajectories, each of the actual driving trajectories includes an actual sampling point set, and each of the actual sampling point sets includes a plurality of actual sampling points sequentially arranged along the actual driving trajectories; A selection module, configured to select a plurality of actual sampling points in each of the actual sampling point sets to obtain a plurality of control point sets, each of which includes a plurality of control points; A fitting module, configured to fit each set of control points to obtain multiple virtual driving trajectories; A sampling module, configured to sample each of the virtual driving trajectories to obtain a plurality of sets of virtual sampling points, each of which includes a plurality of virtual sampling points arranged sequentially along the corresponding virtual driving trajectories ; A centering module, configured to center a plurality of virtual sampling points in the same order in a plurality of virtual sampling point sets to obtain a median point set, and the median point set includes a plurality of median points; as well as A processing module, configured to obtain an automatic driving trajectory according to the set of median points. 10. An electronic device, comprising: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the vehicle trajectory processing method according to any one of claims 1-8 is realized. 11. A computer-readable storage medium, characterized in that computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the computer program instructions described in any one of claims 1-8 are implemented. The processing method of the vehicle trajectory described above.

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