CN116012972A - Vehicle track processing method and vehicle track processing device - Google Patents
Vehicle track processing method and vehicle track processing device Download PDFInfo
- Publication number
- CN116012972A CN116012972A CN202211620639.0A CN202211620639A CN116012972A CN 116012972 A CN116012972 A CN 116012972A CN 202211620639 A CN202211620639 A CN 202211620639A CN 116012972 A CN116012972 A CN 116012972A
- Authority
- CN
- China
- Prior art keywords
- virtual
- actual
- sampling
- point set
- points
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title abstract description 11
- 238000005070 sampling Methods 0.000 claims abstract description 197
- 238000000034 method Methods 0.000 claims description 42
- 238000004590 computer program Methods 0.000 claims description 9
- 238000012935 Averaging Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 26
- 230000000295 complement effect Effects 0.000 description 5
- 238000005065 mining Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Traffic Control Systems (AREA)
Abstract
The embodiment of the application provides a vehicle track processing method and a vehicle track processing device, wherein a plurality of actual driving tracks are obtained, each actual driving track comprises an actual sampling point set, a plurality of actual sampling points in each actual sampling point set are selected to obtain a plurality of control point sets, each control point set is fitted to obtain a plurality of virtual driving tracks, each virtual driving track is sampled to obtain a plurality of virtual sampling point sets, a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets are sampled to obtain a median point set, the median point set comprises a plurality of median points, and an automatic driving track is obtained according to the median point set. According to the vehicle track processing method and the vehicle track processing device, the automatic driving track applicable to different tracked vehicles can be obtained according to the actual driving track.
Description
Technical Field
The present invention relates to the field of automatic driving, and in particular, to a vehicle track processing method and a vehicle track processing device.
Background
In recent years, with the continuous development of artificial intelligence technology, automatic driving technology has been increasingly used in vehicles. In the related art, one or more actual driving trajectories may be obtained from a start point to a destination through actual driving, and then the tracked vehicle is caused to automatically drive along the actual driving trajectories. However, in the case where the vehicle that performs actual driving and the following tracked vehicle are different, the tracked vehicle may not completely travel along the actual driving trajectory, and the actual driving vehicle may also generate a certain deviation while collecting the actual driving trajectory, so that a certain safety risk may be generated when the tracked vehicle travels along the actual driving trajectory.
Disclosure of Invention
The embodiment of the application provides a vehicle track processing method and a vehicle track processing device, which can obtain automatic driving tracks suitable for different tracked vehicles according to actual driving tracks.
In one aspect, an embodiment of the present application provides a method for processing a vehicle track, including: acquiring a plurality of actual driving tracks, wherein each actual driving track comprises an actual sampling point set, and each actual sampling point set comprises a plurality of actual sampling points which are sequentially arranged along the actual driving track; selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, wherein each control point set comprises a plurality of control points; fitting each control point set to obtain a plurality of virtual driving tracks; sampling each virtual driving track to obtain a plurality of virtual sampling point sets, wherein each virtual sampling point set comprises a plurality of virtual sampling points which are sequentially arranged along the corresponding virtual driving track; taking a plurality of virtual sampling points with the same sequence in a plurality of virtual sampling point sets to obtain a median point set, wherein the median point set comprises a plurality of median points; and obtaining the automatic driving track according to the median point set.
In some embodiments, obtaining a plurality of actual driving trajectories includes obtaining each actual driving trajectory; obtaining each actual driving track comprises the following steps: acquiring vehicle position information at regular time during driving to obtain a plurality of vehicle position information including time stamps; obtaining a plurality of actual acquisition points sequentially arranged along an actual driving track according to the plurality of vehicle position information; and obtaining an actual driving track according to the actual sampling points.
In some embodiments, each set of actual sampling points includes N actual sampling points arranged sequentially along the actual driving trajectory; selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, wherein the method comprises the steps of obtaining each control point set; acquiring each control point set, including: selecting an ith actual sampling point in the plurality of actual sampling points as a control point; calculating the distance between the (i+1) th actual sampling point and the control point; comparing the distance to a first threshold; selecting the (i+1) th actual sampling point as a control point under the condition that the distance is greater than or equal to a first threshold value; and returning to calculate the distance between the (i+1) th 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, selecting a plurality of actual sampling points in each set of actual sampling points to obtain a plurality of sets of control points, and further including: deleting the (i+1) th actual sampling point under the condition that the distance is smaller than a first threshold value to obtain an updated actual sampling point set; and returning to the step of calculating the distance between the (i+1) th actual sampling point and the control point until the distance is greater than or equal to a first threshold value.
In some embodiments, fitting each set of control points to obtain a plurality of virtual driving trajectories, including obtaining each virtual driving trajectory; obtaining each virtual driving track comprises the following steps: b spline interpolation is carried out on the control points, and a virtual driving track is obtained.
In some embodiments, sampling each virtual driving trajectory to obtain a plurality of virtual sampling point sets, including obtaining each virtual sampling point set; obtaining each virtual sampling point set comprises the following steps: acquiring a preset speed and a first sampling time; obtaining a first sampling distance according to a preset speed and a first sampling time; and selecting a plurality of virtual sampling points equidistantly along the virtual driving track according to the first sampling distance.
In some embodiments, obtaining each set of virtual sampling points further comprises: calculating curvature parameters of the virtual driving track at the virtual sampling point; under the condition that the curvature parameter is greater than or equal to a second threshold value, sampling is conducted from the virtual sampling point to the adjacent virtual sampling point according to a second sampling distance, and two supplementary virtual sampling points are obtained; obtaining an updated virtual sampling point set according to the virtual sampling points and the two supplementary virtual sampling points; wherein the first sampling distance is greater than the second sampling distance.
In some embodiments, the fetching a plurality of virtual sampling points in the same sequence in the plurality of virtual sampling point sets to obtain a median point set includes: averaging coordinates of a plurality of virtual sampling points with the same sequence in a plurality of virtual sampling point sets to obtain a median point set; or taking the intermediate value of the coordinates of a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a median point set.
Another aspect of the embodiments of the present application provides a device for processing a vehicle track, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of actual driving tracks, each actual driving track comprises an actual sampling point set, and each actual sampling point set comprises a plurality of actual sampling points which are sequentially arranged along the actual driving track; the selection module is used for selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, and each control point set comprises a plurality of control points; the fitting module is used for fitting each control point set to obtain a plurality of virtual driving tracks; the sampling module is used for sampling each virtual driving track to obtain a plurality of virtual sampling point sets, and each virtual sampling point set comprises a plurality of virtual sampling points which are sequentially arranged along the corresponding virtual driving track; the middle point collection module is used for collecting a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a middle point set, wherein the middle point set comprises a plurality of middle points; and the processing module is used for obtaining the automatic driving track according to the median point set.
In still another aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements a method of processing a vehicle track as described above.
In yet another aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement a method of processing a vehicle track as above.
According to the vehicle track processing method, a plurality of actual driving tracks are obtained, each actual driving track comprises one actual sampling point set, a plurality of actual sampling points in each actual sampling point set are selected to obtain a plurality of control point sets, each control point set is fitted to obtain a plurality of virtual driving tracks, each virtual driving track is sampled to obtain a plurality of virtual sampling point sets, a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets are sampled to obtain a median point set, the median point set comprises a plurality of median points, and the automatic driving track is obtained according to the median point set. According to the embodiment of the application, the plurality of actual sampling points are selected to obtain the plurality of control points capable of reflecting the trend of the actual driving track, the influence of redundant actual sampling points on the obtained virtual driving track is reduced, the automatic driving track is obtained by sampling the plurality of virtual driving tracks and taking the plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets, the accuracy of vehicle track processing can be improved, the automatic driving track can be suitable for different tracking vehicles, and the safety of the tracking vehicles in the automatic driving process is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a vehicle track processing device according to one embodiment of the present application;
FIG. 2 is a flow chart of a method of processing a vehicle track provided in accordance with one embodiment of the present application;
FIG. 3 illustrates an exemplary schematic diagram of some of the steps in the flow diagram of FIG. 2;
FIG. 4 illustrates an exemplary schematic diagram of some of the steps in the flow diagram of FIG. 2;
FIG. 5 illustrates an exemplary schematic diagram of some of the steps in the flow diagram of FIG. 2;
FIG. 6 illustrates an exemplary schematic diagram of some of the steps in the flow diagram of FIG. 2;
FIG. 7 illustrates an exemplary schematic diagram of the flow diagram of FIG. 6;
fig. 8 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2.
In the accompanying drawings:
100-a processing device of the vehicle track; 1-an acquisition module; 2-a selection module; 3-fitting a module; 4-sampling module; 5-a middle taking module; 6-a processing module.
Detailed Description
In order that the above objects, features and advantages of the present application may be more clearly understood, a further description of the aspects of the present application will be provided below. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the application.
It should be noted that unless otherwise indicated, technical or scientific terms used in the embodiments of the present application should be given the ordinary meanings as understood by those skilled in the art to which the embodiments of the present application belong.
Furthermore, the technical terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the embodiments of the present application, the meaning of "plurality" is two or more unless explicitly defined otherwise.
In the description of the embodiments of the present application, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly and may, for example, be fixedly connected, detachably connected, or be integrated; or may be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the embodiments of the present application will be understood by those of ordinary skill in the art according to the specific circumstances.
In general, automatic driving of a vehicle is achieved by acquiring high-precision map data and positioning data. However, in the case of automatic driving in a mining area, since the road environment of the mining area is complex and the road environment is constantly changing, high-precision map data of the driving area cannot be obtained, or the obtained high-precision map data has poor timeliness, and the conventional automatic driving planning method cannot be applied to the mining area environment. Therefore, in the case of automatic driving in a mining area, one or more actual driving tracks can be obtained from the start point to the destination through actual driving, and then the automatic driving tracks can be obtained by processing the actual driving tracks.
In view of this, an embodiment of the present application provides a method for processing a vehicle track, by acquiring a plurality of actual driving tracks, where each actual driving track includes an actual sampling point set, selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, fitting each control point set to obtain a plurality of virtual driving tracks, sampling each virtual driving track to obtain a plurality of virtual sampling point sets, and taking a plurality of virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain a median point set, where the median point set includes a plurality of median points, and obtaining an autopilot track according to the median point set. According to the embodiment of the application, the plurality of actual sampling points are selected to obtain the plurality of control points capable of reflecting the trend of the actual driving track, the influence of redundant actual sampling points on the obtained virtual driving track is reduced, the automatic driving track is obtained by sampling the plurality of virtual driving tracks and taking the plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets, the accuracy of vehicle track processing can be improved, the automatic driving track can be suitable for different tracking vehicles, and the safety of the tracking vehicles in the automatic driving process is improved.
Fig. 1 is a schematic structural diagram of a processing device 100 for vehicle track according to an embodiment of the present application. As shown in fig. 1, the embodiment of the present application further provides a device 100 for processing a vehicle track. The processing device 100 of the vehicle track comprises 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 acquisition module 1 is configured to acquire a plurality of actual driving trajectories, where 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 configured to select a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, where each control point set includes a plurality of control points. The fitting module 3 is used for fitting each control point set to obtain a plurality of virtual driving tracks. The sampling module 4 is configured to sample each virtual driving track to obtain a plurality of virtual sampling point sets, where 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 configured to center a plurality of virtual sampling points in the same sequence in a plurality of virtual sampling point sets, so as to obtain a median point set, where the median point set includes a plurality of median points. The processing module 6 is used for obtaining an automatic driving track according to the median point set.
Alternatively, the vehicle track processing device 100 may be installed in the tracking vehicle or may be disposed in a server, which is not limited in the embodiment of the present application.
Fig. 2 is a flow chart of a method for processing a vehicle track according to an embodiment of the present application. The vehicle track processing method of the embodiment of the present application is implemented by the vehicle track processing device 100 shown in fig. 1. As shown in fig. 2, the processing method of the vehicle track includes the following steps.
S100, acquiring a plurality of actual driving tracks, wherein each actual driving track comprises an actual sampling point set.
S200, selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets.
And S300, fitting each control point set to obtain a plurality of virtual driving tracks.
S400, sampling each virtual driving track to obtain a plurality of virtual sampling point sets.
S500, taking a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a median point set.
And S600, obtaining the automatic driving track according to the median point set.
In step S100, each actual sampling point set includes a plurality of actual sampling points sequentially arranged along the actual driving trajectory. It can be regarded as that an actual driving trajectory is obtained by sequentially connecting a plurality of actual sampling points.
Fig. 3 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2. As shown in fig. 2, in some embodiments of the present application, in step S100, acquiring a plurality of actual driving trajectories by acquiring each actual driving trajectory includes the following steps.
Step S110, collecting vehicle position information periodically during driving to obtain a plurality of vehicle position information including time stamps.
Step S120, a plurality of actual acquisition points sequentially arranged along the actual driving track are obtained according to the plurality of vehicle position information.
And step S130, obtaining an actual driving track according to the actual sampling points.
In step S110, in the case where the acquisition module 1 is provided to the actually driven vehicle, the acquisition module 1 may acquire the actually driven vehicle position information. The vehicle position information may be the position coordinates of the vehicle at the global positioning system (GPS, global Positioning System) level or the position coordinates of the vehicle at the start point and the end point. The method can record the position coordinates of the vehicle at a certain frequency in the process of driving along the actual driving track at regular time, and generate a time mark at the current time, namely, the time mark is used for representing the position of the actual driving vehicle on the actual driving track at different moments in the process of driving the actual driving vehicle. Alternatively, the vehicle position information may be collected periodically at a frequency of 10 Hz. It should be appreciated that random positioning errors may exist in the acquisition process of the acquisition module 1, and therefore, the frequency of acquisition performed by the acquisition module 1 should be as high as possible to reduce the probability of inaccuracy in the obtained vehicle position information.
In step S120, since a plurality of pieces of vehicle position information are acquired in step S110, the vehicle position information may include position coordinates of the vehicle and an order or time in which the position coordinates are recorded, and the vehicle position information may be represented in a coordinate system at the global positioning system level or in a coordinate system with respect to a start point and an end point. For example, the position coordinates are made to include a first coordinate in a first direction and a second coordinate in a second direction, the first direction and the second direction intersecting each other, and a plurality of pieces of vehicle position information are marked as points in a coordinate system based on the first coordinate and the second coordinate, that is, a plurality of actual collection points can be obtained.
In step S130, the plurality of actual acquisition points obtained in step S120 are connected in the order or time of recording the vehicle position information. Since the frequency of acquiring the plurality of vehicle position information is higher in step S110, the arrangement of the plurality of actual acquisition points is denser, and in this case, the actual driving track can be recorded more accurately by connecting the plurality of actual acquisition points.
Alternatively, step S110, step S120, and step S130 may be implemented by the acquisition module 1 of the vehicle track processing apparatus 100, and the vehicle track processing apparatus 100 may acquire the actual driving track from another apparatus or device through the acquisition module 1. For example, an actually driven vehicle includes an acquisition device that periodically acquires vehicle position information during traveling from a start point to an end point, and after reaching the end point, transmits acquired data, that is, an actual driving trajectory including an actual sampling point set, to the acquisition module 1 of the processing device 100 of the vehicle trajectory. The embodiments of the present application are not limited in this regard.
In step S200, each control point set 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 the step S100, and are coordinate points capable of reflecting the trend of the actual driving track in the plurality of actual sampling points. That is, since the frequency of the acquisition module 1 for periodically acquiring the vehicle position information is high, the arrangement of the plurality of actual sampling points is dense, and not every actual sampling point plays a role in determining the trend of the actual driving track, the selection module 2 selects the plurality of actual sampling points, that is, performs the thinning process on the plurality of actual sampling points, so as to remove redundant actual sampling points, and obtain a plurality of control points.
Fig. 4 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2. As shown in fig. 4, in some embodiments of the present application, in step S200, acquiring a plurality of control point sets by acquiring each control point set includes the following steps.
S210, selecting an ith actual sampling point in a plurality of actual sampling points as a control point.
S220, calculating the distance between the (i+1) th actual sampling point and the control point.
S230, comparing the distance with a first threshold.
S240, selecting the (i+1) th actual sampling point as a control point when the distance is larger than or equal to a first threshold value.
In step S210, an i-th actual sampling point among the plurality of actual sampling points is selected as a control point. For example, in a first cycle, a first one of 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 loop, the distance between the second actual sampling point and the control point selected in step S210 may be calculated, that is, the distance between the second actual sampling point and the first actual sampling point is calculated according to the position coordinates of the actual sampling point in the coordinate system at the global positioning system level or the coordinate system with respect to the start point and the end point. In this way, it can be determined whether the actual sampling point adjacent to the selected control point can reflect the trend of the actual driving trajectory by the distance between the plurality of actual sampling points.
In step S230, the distance between the control point obtained in step S220 and the i+1th actual sampling point is compared with a first threshold value. Alternatively, the first threshold may be related to the length of the body of the actually driven vehicle.
In step S240, when the distance between the control point and the (i+1) th actual sampling point is greater than or equal to the first threshold, the (i+1) th 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 value is the vehicle body length, the second actual sampling point is not within a range that can be reached by one vehicle body length, so the second actual sampling point can play a role in determining the trend of the driving track, and the second actual sampling point is the control point.
Each set of actual sampling points may be considered to include N actual sampling points arranged sequentially along the actual driving trajectory. After step S240, return to step S220 until i equals N-1. That is, after the first control point is obtained in step S210, in one case, step S220, step S230 and step S240 are looped, and the distance between the first actual sampling point and the second actual sampling point, the distance … … between the N-1 th actual sampling point and the N-th actual sampling point, and the distance between the second actual sampling point and the third actual sampling point are compared with the first threshold value, so as to obtain a plurality of control points.
In some embodiments of the present application, after step S230, in step S200, the following steps are also performed for each set of actual sampling points.
And S240', deleting the (i+1) th actual sampling point under the condition that the distance is smaller than the first threshold value, and obtaining an updated actual sampling point set.
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, so that the i+1th actual sampling point is deleted, and the updated actual sampling point set includes N-1 actual sampling points. For example, in the first cycle, the first actual sampling point is a control point, and if the first threshold is the length of the vehicle body, if the second actual sampling point is within a range that can be reached by one length of the vehicle body, the second actual sampling point fails to play a role in determining the trend of the actual driving track, that is, the second actual sampling point can be regarded as a redundant point, and the second actual sampling point can be omitted.
After step S240', the process returns 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 the second actual sampling point is deleted, an updated actual sampling point set is obtained, at this time, the second actual sampling point in the updated actual sampling point set is the third actual sampling point in the original actual sampling point set, and then step S220 is returned to calculate the distance between the first actual sampling point and the second actual sampling point again, so as to obtain, as a cycle, the next control point capable of determining the trend of the driving track. That is, after the first control point is obtained in step S210, in another case, step S220, step S230 and step S240' are made to be a loop.
In step S300, fitting is a method of connecting a series of points on a plane with a smooth curve. The fitting module 3 fits the control point set, so that a virtual driving track can be obtained.
Fig. 5 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2. As shown in fig. 5, in some embodiments of the present application, in step S300, acquiring a plurality of virtual driving trajectories by acquiring each virtual driving trajectory includes the following steps.
And S310, performing B spline interpolation on the control points to obtain a virtual driving track.
In step S310, in the present embodiment, the obtained plurality of control points are connected in a smooth curve by a B-spline interpolation method, so as to obtain a virtual driving track. B spline interpolation is a method for obtaining a smooth curve through weight recursion of a plurality of points, and the embodiments of the present application are not described herein.
In step S400, the virtual driving track is sampled by the sampling module 4, so that each virtual sampling point set includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving track.
Fig. 6 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2. As shown in fig. 6, in some embodiments of the present application, in step S400, obtaining a plurality of virtual sampling point sets by obtaining each virtual sampling point set includes the following steps.
S410, acquiring a preset speed and a first sampling time.
S420, obtaining a first sampling distance according to the preset speed and the first sampling time.
S430, selecting a plurality of virtual sampling points equidistantly along the virtual driving track according to the first sampling distance.
In step S410, the preset speed and the first sampling time may be preset. The preset speed may be a prescribed speed for the autonomous vehicle during subsequent autonomous operations. In addition, the process of the automatic driving vehicle running along the virtual driving track can be simulated, and the virtual driving track is sampled at intervals of the first sampling time similarly to the sampling of the actual driving track by the acquisition device. Alternatively, the first sampling time may be longer than the time for the acquisition device to sample the actual driving trajectory.
In step S420, a first sampling distance is obtained according to the preset speed and the first sampling time. And multiplying the preset speed and the first sampling time to obtain a first sampling distance.
In step S430, a plurality of virtual sampling points are equidistantly selected on the virtual driving track according to the first sampling distance. Alternatively, the virtual sampling points may also include virtual position coordinates in a coordinate system simulating the vehicle at the global positioning system level or in a coordinate system with respect to the start point and the end point, and a sampling sequence along the virtual driving trajectory.
In some alternative embodiments, since the virtual driving track is sampled equidistantly, it may not be possible to completely cover all control points having a determining effect on the virtual driving track, and in order to make the virtual driving track fit the actual driving track as much as possible, the selected plurality of virtual sampling points may be further sampled.
Fig. 7 shows an exemplary schematic diagram of the flow diagram of fig. 6. As shown in fig. 7, in step S400, obtaining each virtual sampling point set may further include the following steps.
S440, calculating curvature parameters of the virtual driving track at the virtual sampling points.
S450, under the condition that the curvature parameter is larger than or equal to a second threshold value, sampling is conducted from the virtual sampling point to the adjacent virtual sampling point according to the second sampling distance, and two supplementary virtual sampling points are obtained.
S460, obtaining an updated virtual sampling point set according to the virtual sampling points 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 track at the point is calculated according to the position coordinates of the virtual sampling point and the shape of the virtual driving track in the coordinate system of the global positioning system or the coordinate system relative to the start point and the end point.
In step S450, when the curvature parameter is greater than or equal to the second threshold, it is indicated that the section of virtual driving track where the current virtual sampling point is located is more tortuous, so that the single virtual sampling point cannot play a role in determining the virtual driving track. Therefore, the virtual sampling point can be sampled from the second sampling distance to two sides, and the supplementary virtual sampling point can be obtained. Optionally, the second sampling distance is smaller than the first sampling distance, so that the supplementary virtual sampling point can be ensured to be located between the current virtual sampling point and the adjacent virtual sampling point, and the trend of the virtual driving track at two sides of the current virtual sampling point can be determined.
In step S460, after the complementary virtual sampling points are obtained, the plurality of virtual sampling points and the two complementary virtual sampling points are used together as the plurality of virtual sampling points in the virtual sampling point set, so as to update the virtual sampling points.
Optionally, after step S460, curvature parameters of the virtual driving track at the plurality of complementary virtual sampling points may be further calculated, and if the curvature parameters are still greater than or equal to the second threshold, sampling is performed again from the complementary virtual sampling points to two sides until the obtained virtual sampling points and the complementary virtual sampling points can determine the trend of the virtual driving track.
In step S500, since each virtual sampling point set includes a plurality of virtual sampling points sequentially arranged along the corresponding virtual driving track, when the virtual sampling point set includes a plurality of virtual sampling points, the median point set is obtained by the centering module 5 centering a plurality of virtual sampling points in the same sequence in each virtual sampling point set, where the median point set includes a plurality of median points.
Fig. 8 shows an exemplary schematic diagram of some of the steps in the flow diagram shown in fig. 2. As shown in fig. 8, in some alternative embodiments, step S500 includes the following steps.
S510, taking an average value of coordinates of a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a median point set.
In step S510, since the virtual driving routes all extend from the start point to the end point and the sampling rules of the virtual sampling points are the same, the virtual sampling points in the virtual sampling point sets with the same sequence should be close to each other. The virtual position coordinates of the plurality of virtual sampling points in the same sequence are averaged to obtain one median point, and when the plurality of virtual sampling points arranged in sequence along the corresponding virtual driving track are included in the virtual sampling point set, a plurality of median points, namely, a median point set can be obtained.
In other alternative embodiments, step S500 includes the following steps.
S510', taking the intermediate value of the coordinates of a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a median point set.
In step S510', similarly to step S510, the virtual position coordinates of the plurality of virtual sampling points in the same order are intermediate values, and a plurality of median points can be obtained.
In step S600, the processing module 6 performs a connection or fitting process on the plurality of median points to obtain an autopilot track. Through the steps, the automatic driving track suitable for different tracking vehicles can be obtained according to the actual driving track, the accuracy of the automatic driving track is improved, and the safety of the automatic driving process is improved.
Based on the above description, embodiments of the present application also provide an electronic device including a processor and a memory storing computer program instructions. The processor, when executing the computer program instructions, implements the method for processing a vehicle track as described above.
The processor may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
The memory may include mass storage for data or instructions. For example, the memory may include a Hard Disk Drive (HDD), a floppy Disk Drive, a flash memory, an optical Disk, a magneto-optical Disk, a magnetic tape, or a universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of the foregoing. Where appropriate, the memory may include removable or non-removable (or fixed) media. The memory may be internal or external to the integrated gateway disaster recovery device, where appropriate. In a particular embodiment, the memory is a non-volatile solid state memory.
The 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, the 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 more processors) it is operable to perform the operations described with reference to a method according to an aspect of an embodiment of the present application.
In addition, in combination with the above description, the embodiments of the present application also provide a computer storage medium. The computer-readable storage medium stores computer program instructions that, when executed by the processor, enable the vehicle track processing method as described above to be implemented.
In summary, according to the vehicle track processing method provided by the embodiment of the present application, by obtaining a plurality of actual driving tracks, each actual driving track includes an actual sampling point set, selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, fitting each control point set to obtain a plurality of virtual driving tracks, sampling each virtual driving track to obtain a plurality of virtual sampling point sets, and taking a plurality of virtual sampling points in the same order in the plurality of virtual sampling point sets to obtain a median point set, where the median point set includes a plurality of median points, and obtaining an automatic driving track according to the median point set. According to the embodiment of the application, the plurality of actual sampling points are selected to obtain the plurality of control points capable of reflecting the trend of the actual driving track, the influence of redundant actual sampling points on the obtained virtual driving track is reduced, the automatic driving track is obtained by sampling the plurality of virtual driving tracks and taking the plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets, the accuracy of vehicle track processing can be improved, the automatic driving track can be suitable for different tracking vehicles, and the safety of the tracking vehicles in the automatic driving process is improved.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Claims (11)
1. A method of processing a vehicle track, comprising:
acquiring a plurality of actual driving tracks, wherein each actual driving track comprises an actual sampling point set, and each actual sampling point set comprises a plurality of actual sampling points sequentially arranged along the actual driving track;
selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, wherein each control point set comprises a plurality of control points;
fitting each control point set to obtain a plurality of virtual driving tracks;
sampling each virtual driving track to obtain a plurality of virtual sampling point sets, wherein each virtual sampling point set comprises a plurality of virtual sampling points which are sequentially arranged along the corresponding virtual driving track;
taking a plurality of virtual sampling points with the same sequence in a plurality of virtual sampling point sets to obtain a median point set, wherein the median point set comprises a plurality of median points;
and obtaining the automatic driving track according to the median point set.
2. The method of processing a vehicle trajectory according to claim 1, wherein the acquiring a plurality of actual driving trajectories includes acquiring each of the actual driving trajectories;
the obtaining each actual driving track includes:
acquiring vehicle position information at regular time during driving to obtain a plurality of pieces of vehicle position information including time marks;
obtaining a plurality of actual acquisition points sequentially arranged along the actual driving track according to the vehicle position information;
and obtaining the actual driving track according to the actual sampling points.
3. The method of processing a vehicle trajectory according to claim 2, wherein each of the actual sampling points includes N actual sampling points sequentially arranged along the actual driving trajectory;
selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, wherein the method comprises the steps of obtaining each control point set;
the acquiring each control point set includes:
selecting an ith actual sampling point in the actual sampling points as a control point;
calculating the distance between the (i+1) th actual sampling point and the control point;
comparing the distance to a first threshold;
selecting the (i+1) th actual sampling point as a control point under the condition that the distance is larger than or equal to the first threshold value;
and returning to the distance between the i+1th actual sampling point and the control point until i is equal to N-1.
4. A method of processing a vehicle track according to claim 3, wherein after said comparing said distance to a first threshold, said selecting a plurality of said actual sampling points in each of said actual sampling point sets results in a plurality of control point sets, further comprising:
deleting the (i+1) th actual sampling point under the condition that the distance is smaller than the first threshold value to obtain the updated actual sampling point set;
and returning to the step of calculating the distance between the (i+1) th actual sampling point and the control point until the distance is greater than or equal to the first threshold value.
5. The method for processing a vehicle track according to claim 1, wherein fitting each control point set to obtain a plurality of virtual driving tracks includes obtaining each virtual driving track;
the obtaining each virtual driving track includes:
b spline interpolation is carried out on the control points, and the virtual driving track is obtained.
6. The method for processing a vehicle track according to claim 1, wherein the step of sampling each of the virtual driving tracks to obtain a plurality of virtual sampling point sets includes obtaining each of the virtual sampling point sets;
the obtaining each virtual sampling point set includes:
acquiring a preset speed and a first sampling time;
obtaining a first sampling distance according to the preset speed and the first sampling time;
and selecting a plurality of virtual sampling points at equal intervals along the virtual driving track according to the first sampling distance.
7. The method for processing the vehicle track according to claim 6, wherein the obtaining each of the virtual sampling point sets further comprises:
calculating curvature parameters of the virtual driving track at the virtual sampling points;
under the condition that the curvature parameter is larger than or equal to a second threshold value, sampling is conducted from the virtual sampling point to an adjacent virtual sampling point according to a second sampling distance, and two supplementary virtual sampling points are obtained;
obtaining an updated virtual sampling point set according to the virtual sampling points and the two supplementary virtual sampling points;
wherein the first sampling distance is greater than the second sampling distance.
8. The method for processing a vehicle track according to claim 7, wherein the step of taking the plurality of virtual sampling points in the same order from the plurality of virtual sampling point sets to obtain a median point set includes:
averaging the coordinates of a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain the median point set; or (b)
And taking intermediate values of coordinates of a plurality of virtual sampling points in the same sequence in the plurality of virtual sampling point sets to obtain the median point set.
9. A processing apparatus for a vehicle track, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of actual driving tracks, each actual driving track comprises an actual sampling point set, and each actual sampling point set comprises a plurality of actual sampling points which are sequentially arranged along the actual driving track;
the selection module is used for selecting a plurality of actual sampling points in each actual sampling point set to obtain a plurality of control point sets, and each control point set comprises a plurality of control points;
the fitting module is used for fitting each control point set to obtain a plurality of virtual driving tracks;
the sampling module is used for sampling each virtual driving track to obtain a plurality of virtual sampling point sets, and each virtual sampling point set comprises a plurality of virtual sampling points which are sequentially arranged along the corresponding virtual driving track;
the middle point collection module is used for collecting a plurality of virtual sampling points with the same sequence in the plurality of virtual sampling point sets to obtain a middle point set, and the middle point set comprises a plurality of middle points; and
and the processing module is used for obtaining the automatic driving track according to the median point set.
10. An electronic device, comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a method of processing a vehicle track as claimed in any one of claims 1-8.
11. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement a method of processing a vehicle track according to any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211620639.0A CN116012972A (en) | 2022-12-15 | 2022-12-15 | Vehicle track processing method and vehicle track processing device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211620639.0A CN116012972A (en) | 2022-12-15 | 2022-12-15 | Vehicle track processing method and vehicle track processing device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116012972A true CN116012972A (en) | 2023-04-25 |
Family
ID=86032660
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211620639.0A Pending CN116012972A (en) | 2022-12-15 | 2022-12-15 | Vehicle track processing method and vehicle track processing device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116012972A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133473A (en) * | 2008-10-24 | 2014-11-05 | 格瑞股份公司 | Control method of autonomously driven vehicle |
| CN107346134A (en) * | 2017-08-29 | 2017-11-14 | 北京矿冶研究总院 | Unmanned control method and device for underground mining articulated vehicle |
| CN113022553A (en) * | 2020-11-20 | 2021-06-25 | 安徽海博智能科技有限责任公司 | Parking method and system for unmanned vehicle in mining area |
| US20220198096A1 (en) * | 2020-12-23 | 2022-06-23 | Woven Planet North America, Inc. | Generating Accurate and Diverse Simulations for Evaluation of Autonomous-Driving Systems |
| CN114664094A (en) * | 2022-05-26 | 2022-06-24 | 青岛慧拓智能机器有限公司 | Vehicle track prediction method, device, chip, terminal and computer equipment |
| CN115346288A (en) * | 2021-05-12 | 2022-11-15 | 前海七剑科技(深圳)有限公司 | Simulation driving record acquisition method and system, electronic equipment and storage medium |
-
2022
- 2022-12-15 CN CN202211620639.0A patent/CN116012972A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133473A (en) * | 2008-10-24 | 2014-11-05 | 格瑞股份公司 | Control method of autonomously driven vehicle |
| CN107346134A (en) * | 2017-08-29 | 2017-11-14 | 北京矿冶研究总院 | Unmanned control method and device for underground mining articulated vehicle |
| CN113022553A (en) * | 2020-11-20 | 2021-06-25 | 安徽海博智能科技有限责任公司 | Parking method and system for unmanned vehicle in mining area |
| US20220198096A1 (en) * | 2020-12-23 | 2022-06-23 | Woven Planet North America, Inc. | Generating Accurate and Diverse Simulations for Evaluation of Autonomous-Driving Systems |
| CN115346288A (en) * | 2021-05-12 | 2022-11-15 | 前海七剑科技(深圳)有限公司 | Simulation driving record acquisition method and system, electronic equipment and storage medium |
| CN114664094A (en) * | 2022-05-26 | 2022-06-24 | 青岛慧拓智能机器有限公司 | Vehicle track prediction method, device, chip, terminal and computer equipment |
Non-Patent Citations (1)
| Title |
|---|
| 孟德强: "智能车辆自主导航路径规划与跟踪控制方法研究", 中国优秀硕士学位论文全文数据库 (工程科技Ⅱ辑), 15 March 2022 (2022-03-15), pages 035 - 447 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106104656B (en) | Map information generating systems, method and program | |
| US7487017B1 (en) | Systems and methods for generating pattern keys for use in navigation systems to predict user destinations | |
| CN106371445A (en) | Unmanned vehicle planning control method based on topology map | |
| CN113916243A (en) | Vehicle positioning method, device, equipment and storage medium for target scene area | |
| CN108052101B (en) | Robot repositioning method and device | |
| CN107636751A (en) | Traveling lane discriminating gear and traveling lane method of discrimination | |
| CN109916421B (en) | Path planning method and device | |
| CN111914691B (en) | Rail transit vehicle positioning method and system | |
| CN113494917A (en) | Map construction method and system, method for making navigation strategy and storage medium | |
| CN113465615B (en) | Lane line generation method and related device | |
| CN113819917A (en) | Automatic driving path planning method, device, equipment and storage medium | |
| CN109086278A (en) | A kind of map constructing method, system, mobile terminal and storage medium for eliminating error | |
| EP3968609A1 (en) | Control method, vehicle, and server | |
| CN113029145A (en) | Visual topological navigation method based on reinforcement learning | |
| CN114754778B (en) | Vehicle positioning method and device, electronic equipment and storage medium | |
| CN113834489A (en) | Navigation path planning method and device | |
| CN104977016B (en) | Navigation processing method and mobile intelligent terminal | |
| CN116012972A (en) | Vehicle track processing method and vehicle track processing device | |
| CN116691659A (en) | Method and device for evaluating vehicle running track without GPS signal | |
| CN115393816A (en) | Lane line generation method, lane line generation device, computer device, and storage medium | |
| CN115683139A (en) | Vehicle-mounted map path planning method, system, electronic equipment and storage medium | |
| CN108896065A (en) | A kind of automobile paths planning method, system, equipment and computer storage medium | |
| US20180038696A1 (en) | A system for use in a vehicle | |
| CN113701768A (en) | Path determination method and device and electronic equipment | |
| JP5125240B2 (en) | Travel control data collection device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |