CN114065490A - Vehicle trajectory tracking simulation method, device, equipment and storage medium - Google Patents
Vehicle trajectory tracking simulation method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a simulation method, a device, equipment and a storage medium for vehicle trajectory tracking, which comprises the following steps: constructing a track model according to the image information of the target track; constructing a corresponding vehicle model according to actual vehicle information, wherein the vehicle model comprises vehicle attributes; planning track points of a preset route in the track model; and setting simulation parameters of the vehicle model, and enabling the vehicle model to carry out simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route. The invention constructs the track model through the image information of the target track, constructs the corresponding vehicle model according to the actual vehicle information, plans the path in the track model, sets the simulation parameter, and leads the vehicle model to seek the track in the track model according to the preset path.
Description
Technical Field
The invention relates to the technical field of tracing simulation, in particular to a vehicle trajectory tracking simulation method, device, equipment and storage medium.
Background
At present, in the fields of vehicle path identification, path planning and tracking driving, more attention is paid to algorithm research of identification, planning and following, for example, Zhanglin, Zhangxinjie and the like add a circular detection area after considering local obstacle avoidance; the D-algorithm (Dynamic a) proposed by Stentz can replan the path after encountering an obstacle, and the path is also the path after passing the obstacle; shi and Eberhart use fuzzy system to adjust inertia weight in particle swarm optimization, and then adjust search range, convergence is promoted; siding Li, Xin Xu et al combine the greedy algorithm with the Boltzmann search algorithm in the Q-Learning algorithm to narrow the search area and speed up the Learning process.
Although the algorithm is improved in the researches, the adopted vehicle models are basically mathematical models, the complex system of the vehicle is simplified, the actual running condition of the vehicle on the road is ignored, the complex characteristics of the whole vehicle are not required to be considered, and only the mathematical model is required to be capable of running on the road according to the pre-planned track.
Disclosure of Invention
In view of the above, it is necessary to provide a simulation method, device, apparatus and storage medium for vehicle trajectory tracking, so as to solve the problems of neglecting the actual driving condition of the vehicle and complicated vehicle characteristics when performing vehicle trajectory simulation in the prior art.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a simulation method for vehicle trajectory tracking, including:
constructing a track model according to the image information of the target track;
constructing a corresponding vehicle model according to the actual vehicle information, wherein the vehicle model comprises vehicle attributes;
planning track points of a preset route in the track model;
and setting simulation parameters of the vehicle model, so that the vehicle model carries out simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route.
Preferably, the constructing the track model according to the image information of the target track includes:
acquiring image information of a target track;
carrying out binarization processing on the image information of the target track to obtain a binarization picture of the target track;
acquiring a midpoint coordinate of the target track according to the binary image of the target track;
and constructing a track model according to the midpoint coordinates of the target track.
Preferably, the image information of the target track includes label information and road contour information, and the binarizing processing is performed on the image information of the target track to obtain a binarized picture of the target track, including:
distinguishing marking information and road contour information in different colors;
assigning the image information of the target track according to the different colors corresponding to the labeling information and the road profile information respectively to obtain the image information of the target track after assignment;
inverting the image information of the assigned target track to obtain a data set of the image information of the target track;
eliminating isolated points and isolated areas in a data set of the image information of the target track through a preset function to obtain the image information only containing the road profile;
and setting the image information only containing the road profile as a preset pixel size to obtain a binary image of the target track.
Preferably, the obtaining the midpoint coordinate of the target track according to the binarized picture of the target track includes:
inverting and storing data in the binarization picture of the target track;
adding data points for the inner and outer road profiles of the road profile information, and recording coordinates of the data points;
and calculating the midpoint coordinate of the target track according to the coordinate of the data point.
Preferably, the image information of the target track further includes a road width, a road height, a road adhesion coefficient, and a road length; according to the midpoint coordinates of the target track, constructing a track model, comprising the following steps:
setting road surface width, road surface height, road surface adhesion coefficient and road section length parameters through a preset modeling tool, and establishing a three-dimensional track model according to the road midpoint coordinate.
Preferably, the planning of the track points of the preset route in the track model includes:
and processing the binary image of the target track through a preset algorithm, and determining the track point coordinates of a preset route.
Preferably, the setting of simulation parameters of the vehicle model comprises:
setting a driving file of the vehicle model, wherein the driving file of the vehicle model comprises track point coordinates, road surface width, road surface height, lateral acceleration and vehicle speed;
and setting simulation time and step length according to preset conditions, setting a road surface file as the track model, and setting power and braking force of the vehicle model.
In a second aspect, the present invention further provides a simulation apparatus for tracking a vehicle trajectory, including:
the track model building module is used for building a track model according to the image information of the target track;
the vehicle model building module is used for building a corresponding vehicle model according to the actual vehicle information, and the vehicle model comprises vehicle attributes;
the path planning module is used for planning track points of a preset route in the track model;
and the simulation module is used for setting simulation parameters of the vehicle model and enabling the vehicle model to carry out simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route.
In a third aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein,
a memory for storing a program;
and a processor, coupled to the memory, for executing the program stored in the memory to implement the steps of the simulation method for vehicle trajectory tracking in any of the above-mentioned implementations.
In a fourth aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program or instruction, which when executed by a processor, is capable of implementing the steps in the simulation method for vehicle trajectory tracking in any one of the above-mentioned implementations.
The beneficial effects of adopting the above embodiment are: the invention constructs the track model through the image information of the target track, constructs the corresponding vehicle model according to the actual vehicle information, plans the path in the track model, sets the simulation parameter, and leads the vehicle model to seek the track in the track model according to the preset path.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a simulation method for vehicle trajectory tracking according to the present invention;
FIG. 2 is a flowchart of a method of one embodiment of S101 of FIG. 1;
FIG. 3 is a flowchart of a method of one embodiment of S202 of FIG. 2;
FIG. 4 is a flowchart of a method of one embodiment of S203 of FIG. 2;
FIG. 5 is a diagram illustrating the actual effect of one embodiment of the vehicle tracking trajectory provided by the present invention;
FIG. 6 is a flowchart of a method of one embodiment of S104 of FIG. 1;
FIG. 7 is a schematic structural diagram of an embodiment of a vehicle trajectory tracking device provided by the present invention;
fig. 8 is a schematic structural diagram of a vehicle trajectory tracking device according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides a simulation method, a simulation device, a simulation equipment and a storage medium for vehicle track tracking, which are respectively explained below.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a simulation method for tracking a vehicle trajectory according to an embodiment of the present invention. The invention discloses a simulation method for vehicle track tracking, which comprises the following steps:
s101, constructing a track model according to image information of a target track;
s102, constructing a corresponding vehicle model according to actual vehicle information, wherein the vehicle model comprises vehicle attributes;
s103, planning track points of a preset route in the track model;
s104, setting simulation parameters of the vehicle model, and enabling the vehicle model to perform simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route.
In step S101, in order to improve the reference value and the reality of the simulation, it is necessary to construct a track model from the image information of the actual track, process the image information of the target track, and then set each parameter included in the image information as a parameter of the actual track.
In step S102, parameter information of the simulated actual vehicle is acquired, and then a corresponding vehicle model is constructed through software, where the vehicle model includes vehicle attributes such as a volume parameter, a speed parameter, and an acceleration parameter of the vehicle.
In a preferred embodiment provided by the invention, data of the vehicle model is derived from a CATIA digital analogy, model parameters are measured in the CATIA, the model parameters comprise key hard point coordinates and mass inertia of each part, and the vehicle model for following the track to run is established in the ADAMS according to the obtained parameters.
In step S103, a simulation path of the vehicle model is planned according to the constructed track model, and coordinates of track points constituting the path are calculated, so that the vehicle model can perform tracking simulation according to an expected path in an actual simulation process.
In step S104, parameters and conditions of the simulation are set according to the vehicle model and the track model, so that the constructed vehicle model can track on the constructed track model according to track points of a preset track route, and a simulation result is observed.
In the embodiment, the track model and the vehicle model are constructed according to the actual track image information and the actual vehicle information, the simulation route is planned in the constructed track model, then the vehicle model carries out track tracking simulation on the track model according to the planned route, the model is established according to the actual track information and the vehicle information, and the simulation result has authenticity and reference value.
Compared with the prior art, the method and the device provided by the embodiment of the invention construct the track model through the image information of the target track, construct the corresponding vehicle model according to the actual vehicle information, plan the path in the track model, set the simulation parameters, and make the vehicle model track in the track model according to the preset path.
Referring to fig. 2, fig. 2 is a flowchart illustrating a method of S101 in fig. 1 according to an embodiment. In some embodiments of the present invention, constructing the course model according to the image information of the target course includes:
s201, acquiring image information of a target track;
s202, carrying out binarization processing on the image information of the target track to obtain a binarization picture of the target track;
s203, acquiring a midpoint coordinate of the target track according to the binarization picture of the target track;
and S204, constructing a track model according to the midpoint coordinates of the target track.
In step S201, a target course is modeled, and related image information of the target course needs to be acquired, and the target course of the embodiment of the present invention is an Xiangyang course published by the officer of the Chinese university student equation automobile tournament, and a drawing of the target course can be acquired, so as to construct a course model of the Xiangyang course published by the officer of the Chinese university student equation automobile tournament.
In step S202, the track is accurately extracted from the image information of the target track, and the target track needs to be binarized, so that the track can be more easily and accurately extracted from the image information of the target track after the binarization processing.
In step S203, the structure of the target track may be determined quickly according to the midpoint coordinates of the target track, and therefore, the midpoint coordinates of the target track are calculated according to the binarized picture, so as to facilitate subsequent modeling.
In step S204, the position coordinates and the trend of the target track are determined according to the calculated midpoint coordinates of the target track, and the building of the track model is completed.
In the above embodiment, the image information of the target track is first obtained, then the image information of the target track is subjected to binarization processing, the midpoint coordinate of the target track is calculated, and then the track model is constructed according to the midpoint coordinate of the target track, so that a real target track model can be established, the actual condition of the track is restored, and the authenticity of the simulation result is improved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a method of S202 in fig. 2 according to an embodiment. In some embodiments of the present invention, the image information of the target track includes label information and road contour information, and the binarizing processing is performed on the image information of the target track to obtain a binarized picture of the target track, including:
s301, distinguishing the marking information and the road contour information in different colors;
s302, assigning the image information of the target track according to the different colors corresponding to the labeling information and the road contour information to obtain the image information of the target track after assignment;
s303, inverting the image information of the assigned target track to obtain a data set of the image information of the target track;
s304, eliminating isolated points and isolated areas in the data set of the image information of the target track through a preset function to obtain image information only containing road contours;
s305, setting the image information only containing the road contour to be a preset pixel size, and obtaining a binary image of the target track.
In step S301, the acquired xiangyang racing track drawing published by the official of the chinese university student' S equation automobile tournament is imported into the MATLAB, and the image information of the target racing track includes label information and road profile information, where the label information is dimension information such as length, width, and height of the target racing track, and size information such as curve angle, and the road profile information is information such as structure and trend of the target racing track road. In the preferred embodiment provided by the invention, the marks in the drawing are represented by red, the road outline in the drawing is represented by green, and the marks are marked by different colors, so that the influence of standard information during the subsequent extraction of the track image is avoided.
It should be noted that, what color is used to distinguish the labeling information and the road contour information is not limited herein, and the red color adopted in the embodiment represents the labeling information, and the green color represents the road contour information, which is more convenient for the subsequent calculation.
In step S302, two sets of data are obtained according to the difference between the label and the RGB to which the road contour belongs, the first set of data: changing the region with the R value larger than 200 into 1, otherwise changing into 0; the portion where the G value is greater than 155 and the B value is greater than 155 and the R value is greater than 155 is changed to 1, otherwise to 0. The method realizes the distinguishing and assigning of the labels and the road profiles in the target drawing, and removes some useless information in the drawing.
In step S303, the image information of the target track after being assigned is inverted, then a union set is obtained with the image after being assigned in step S302, the union set is assigned to the image information of the target track again, and inversion processing is performed again, so that a data set of the image information of the target track is obtained, and in this case, the annotation information and some useless information are removed through the difference of the assignments.
In step S304, the preset function is a bwaeopen function, and the isolated point and the isolated area in the data set of the image information of the target track are eliminated through the bwaeopen function, where the image information only includes the contour information of the road, and possible interference factors in the subsequent calculation process are removed.
In step S305, the image containing only the road contour information in step S304 is set to a pixel size of 1000 × 1000, and a binarized image of the target track is obtained and stored.
In the above embodiment, the image information of the target track is binarized by MATLAB, so as to remove the labels and other unnecessary information in the initial image, obtain the image information only containing the road contour, set the image information only containing the road contour to be 1000 × 1000 pixels, and store for later use.
Referring to fig. 4, fig. 4 is a flowchart illustrating a method of S203 in fig. 2 according to an embodiment. In some embodiments of the present invention, obtaining the midpoint coordinate of the target track according to the binarized image of the target track includes:
s401, inverting and storing data in the binarized picture of the target track;
s402, adding data points for the inner and outer road profiles of the road profile information, and recording coordinates of the data points;
and S403, calculating the center point coordinate of the target track according to the coordinate of the data point.
In step S401, the image obtained in step S305 and containing only the road contour information is read by MATLAB, and the assigned value is inverted and stored, so as to avoid the influence on the road contour in the subsequent operation.
In step S402, reading the weblob digitor tool in the MATLAB, adding data points to the road outline in the weblobdititor tool, sorting according to distance, and storing the horizontal and vertical coordinates of the read points in the first and second rows of the "data. In the webplottizer tool, data points are added to the lines in the road profile, the read horizontal and vertical coordinates of the points are stored in a first row and a second row of 'data.xlsx' sheet2 after the lines are sorted according to distance, and the coordinates of the data points of the inner and outer profiles of the road can more accurately represent the road profile.
In step S403, the CATIA macro file GSD _ pointscribelftfrommexecel is opened, the previously obtained data points are compiled into the macro file, the CATIA is opened, the data points in the macro file are imported, the scatter points are connected into sample lines, the parallel curve command is used to translate the inner line and the outer line of the road contour 15.617mm towards the middle, the point fetching function is used for the two curves after translation, 1000 points are respectively fetched, the obtained point coordinates are imported into the "data1. xls" files sheet1 and sheet2 by the CATIA-differentiated sheet model, the exported points are read into the MATLAB, and the coordinates are calculated.
In the above embodiment, data points are added to the inner and outer contours of the road by the MATLAB according to the image only including the road contour information, the coordinates of the inner and outer contours of the target track road are determined, and the coordinates of the midpoint of the target track can be calculated according to the coordinates of the added data points.
In some embodiments of the present invention, the image information of the target course further includes a road width, a road height, a road adhesion coefficient, and a road segment length; according to the midpoint coordinates of the target track, constructing a track model, comprising the following steps:
setting road surface width, road surface height, road surface adhesion coefficient and road section length parameters through a preset modeling tool, and establishing a three-dimensional track model according to the road midpoint coordinate.
In the above embodiment, the preset modeling tool is a ROAD building model, and the ROAD building model is opened in the ADAMS according to the ROAD width, the ROAD height, the ROAD adhesion coefficient, the ROAD length in the image information of the target track and the midpoint coordinate calculated in step S403, so as to build a three-dimensional track model.
In some embodiments of the present invention, the planning of the track points of the preset route in the track model includes:
and processing the binary image of the target track through a preset algorithm, and determining the track point coordinates of a preset route.
In the above embodiment, the preset algorithm is preferably a path planning algorithm written in QT CREATOR, and static planning is performed on the binarized picture to obtain coordinates of the track points; the written path planning algorithm mainly uses an A star algorithm and an expansion corrosion algorithm, and the whole inside header file of the pathplanning pro program contains 3 contents, namely 'astar.h', 'mainwindow.h' and 'mapdisp.h'; sources contains four things: "astar.cpp", "main.cpp", "mainwindow.cpp", "mapdisep.cpp"; referring to fig. 5, fig. 5 is a diagram of an actual effect of an embodiment of the vehicle tracking track provided by the present invention, a statically-planned track point coordinate is obtained in a window, and the track point coordinate is copied to "data 3. xls".
Specifically, the determining the track point coordinates of the preset route by the path planning algorithm written in the QT CREATOR includes:
firstly, setting an expansion coefficient in mapdisp according to a binary image reading path so as to replace vehicle parameters and add a starting point into an open list.
And step two, traversing the open list, searching the node with the minimum F value, and taking the node as the current node to be processed.
b. Move this node to close list.
c. Judging each square of 8 adjacent squares of the current square:
if it (adjacent square) is not reachable or it is in close list, it is ignored. Otherwise, the following is done.
If it is not in the open list, it is added to the open list and the current tile is set to its parent and the F, G and H values for that tile are recorded.
If it is already in the open list, check if this path (i.e. arriving at it via the current cell) is better, use the value of G as a reference. A smaller G value indicates that this is a better path. If so, set its parent to the current square and recalculate its G and F values. If the open list is sorted by F value, reordering may be required after change.
d. If the end point is added into the open list, the path is found at the moment; if the end point search fails and the open list is empty, no path exists at the moment; if the end point search fails and the open list is not empty, steps a-d are repeated.
Third, starting from the end point, each cell moves along the parent node to the start point, which is the resulting path.
Referring to fig. 6, fig. 6 is a flowchart illustrating a method of S104 in fig. 1 according to an embodiment. In some embodiments of the present invention, setting simulation parameters of a vehicle model, and performing simulation tracing according to the simulation parameters and vehicle attributes and track points of a preset route, includes:
s601, setting a driving file of the vehicle model, wherein the driving file of the vehicle model comprises track point coordinates, road surface width, road surface height, lateral acceleration and vehicle speed;
s602, setting simulation time and step length according to preset conditions, setting a road file as the track model, and setting power and braking force of the vehicle model;
and S603, according to the simulation parameters and the vehicle attributes, enabling the vehicle model to perform simulation tracing according to the tracing points of the preset route.
In step S601, a notebook is used to write drd file named "guiji.drd", a driving file of the vehicle model is set, besides the track point coordinates, a path is determined according to the road surface width and the road surface height in the road model, and the lateral acceleration and the vehicle speed of the vehicle model are set so that the vehicle can run on the track model according to the requirements.
In step S602, the running vehicle is selected as the vehicle model constructed in step S102, the simulation time is set to 200S, the step size is set to 2000, the road surface file is set as the track model constructed in step S101, the Driver file is set as the Driver file written in step S601, and the Smart Driver Task is set as being restricted by the vehicle.
It is understood that the simulation time is set to 200S, the step size is set to 2000, which represents the case where the simulated vehicle behavior is output every 0.1S, and the mart Driver Task is set to be limited by the vehicle to set the power and braking force of the vehicle model according to the actual vehicle model.
In step S603, the simulation is run according to the set simulation parameters, the vehicle model runs along the track points, the post-processing interface is opened, and the running track and the motion status of the vehicle model are read.
In the embodiment, in addition to the curves of the yaw angular velocity and the centroid slip angle along with time, the curves of acceleration, velocity, force and the like along with time can be obtained, more detailed motion parameters of the vehicle can be obtained through the method, the motion state of the racing car when the racing car runs along the track can be effectively judged, and the quality of the planned track can be judged through the jitter degree of the motion result.
In order to better implement the simulation method for vehicle trajectory tracking in the embodiment of the present invention, on the basis of the simulation method for vehicle trajectory tracking, please refer to fig. 7, where fig. 7 is a schematic structural diagram of an embodiment of the simulation device for vehicle trajectory tracking provided by the present invention, and an embodiment of the present invention provides a vehicle trajectory tracking device 700, including:
a track model building module 701, configured to build a track model according to image information of a target track;
a vehicle model construction module 702, configured to construct a corresponding vehicle model according to actual vehicle information, where the vehicle model includes vehicle attributes;
a path planning module 703, configured to plan track points of a preset route in the track model;
and the simulation module 704 is used for setting simulation parameters of the vehicle model, so that the vehicle model carries out simulation tracing according to the simulation parameters and the vehicle attributes and according to the tracing points of the preset route.
Here, it should be noted that: the apparatus 700 provided in the foregoing embodiments may implement the technical solutions described in the foregoing method embodiments, and the specific implementation principles of the modules or units may refer to the corresponding contents in the foregoing method embodiments, which are not described herein again.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a vehicle trajectory tracking device according to an embodiment of the present invention. Based on the simulation method for vehicle trajectory tracking, the invention also correspondingly provides a vehicle trajectory tracking device, which can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and other computing devices. The vehicle trajectory tracking device includes a processor 810, a memory 820, and a display 830. Fig. 8 shows only some of the components of the electronic device, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The memory 820 may be an internal storage unit of the vehicle trajectory tracking device in some embodiments, such as a hard disk or memory of the vehicle trajectory tracking device. The memory 820 may also be an external storage device of the vehicle trajectory tracking device in other embodiments, such as a plug-in hard disk provided on an emulation device of the vehicle trajectory tracking, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 820 may also include both an internal storage unit of the vehicle trajectory tracking simulation device and an external storage device. The memory 820 is used for storing application software installed in the vehicle trajectory tracking device and various kinds of data, such as program codes installed in the vehicle trajectory tracking device. The memory 820 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 820 stores a vehicle trajectory tracking program 840, and the vehicle trajectory tracking program 840 may be executed by the processor 810 to implement the vehicle trajectory tracking simulation method according to the embodiments of the present application.
The display 830 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. Display 830 is used to display information at the vehicle trajectory tracking device and to display a visual user interface. The components 810 and 830 of the vehicle trajectory tracking device communicate with each other via a system bus.
In one embodiment, the steps in the simulation method of vehicle trajectory tracking described above are implemented when the processor 810 executes the vehicle trajectory tracking program 840 in the memory 820.
The invention provides a simulation method, a device, equipment and a storage medium for vehicle track tracking, which are used for obtaining image information of a target track, carrying out binarization processing on the image information of the target track, calculating the midpoint coordinate of the target track, constructing a track model, planning a path through the actual track model, constructing a corresponding vehicle model according to the actual vehicle information, setting simulation parameters, compiling a drive file, and enabling the vehicle model to seek tracks in the track model according to a preset path. The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A simulation method for vehicle trajectory tracking, comprising:
constructing a track model according to the image information of the target track;
constructing a corresponding vehicle model according to actual vehicle information, wherein the vehicle model comprises vehicle attributes;
planning track points of a preset route in the track model;
and setting simulation parameters of the vehicle model, and enabling the vehicle model to carry out simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route.
2. The simulation method for vehicle trajectory tracking according to claim 1, wherein constructing a track model according to image information of a target track comprises:
acquiring image information of the target track;
carrying out binarization processing on the image information of the target track to obtain a binarization picture of the target track;
acquiring a midpoint coordinate of the target track according to the binarization picture of the target track;
and constructing a track model according to the midpoint coordinates of the target track.
3. The vehicle trajectory tracking simulation method according to claim 2, wherein the image information of the target track includes label information and road contour information, and the binarizing processing the image information of the target track to obtain a binarized picture of the target track includes:
distinguishing the marking information and the road contour information in different colors;
assigning the image information of the target track according to the different colors respectively corresponding to the labeling information and the road contour information to obtain the image information of the target track after assigning;
inverting the image information of the assigned target track to obtain a data set of the image information of the target track;
eliminating isolated points and isolated areas in the data set of the image information of the target track through a preset function to obtain image information only containing road contours;
and setting the image information only containing the road profile as a preset pixel size to obtain a binary image of the target track.
4. The vehicle trajectory tracking simulation method according to claim 2, wherein the obtaining of the midpoint coordinate of the target track according to the binarized picture of the target track comprises:
inverting and storing the data in the binarization picture of the target track;
adding data points for the inner and outer road profiles of the road profile information, and recording coordinates of the data points;
and calculating the midpoint coordinate of the target track according to the coordinate of the data point.
5. The simulation method for vehicle trajectory tracking according to claim 2, wherein the image information of the target course further includes a road surface width, a road surface height, a road surface adhesion coefficient, and a road segment length; the building of the track model according to the midpoint coordinates of the target track comprises the following steps:
and setting the road width, the road height, the road adhesion coefficient and the road section length parameters through a preset modeling tool, and establishing a three-dimensional track model according to the road midpoint coordinate.
6. The simulation method for tracking the vehicle track according to claim 2, wherein the planning of the track points of the preset route in the track model comprises:
and processing the binary image of the target track through a preset algorithm, and determining the track point coordinates of a preset route.
7. The method for simulating vehicle trajectory tracking according to claim 1, wherein the setting of the simulation parameters of the vehicle model comprises:
setting a driving file of a vehicle model, wherein the driving file of the vehicle model comprises track point coordinates, road surface width, road surface height, lateral acceleration and vehicle speed;
and setting simulation time and step length according to preset conditions, setting the power and braking force of the vehicle model by using the road surface file as the track model.
8. A simulation apparatus for vehicle trajectory tracking, comprising:
the track model building module is used for building a track model according to the image information of the target track;
the vehicle model building module is used for building a corresponding vehicle model according to actual vehicle information, and the vehicle model comprises vehicle attributes;
the path planning module is used for planning track points of a preset route in the track model;
and the simulation module is used for setting simulation parameters of the vehicle model and enabling the vehicle model to carry out simulation tracing according to the simulation parameters and the vehicle attributes and the track points of the preset route.
9. An electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, is configured to execute the program stored in the memory to implement the steps in the simulation method for vehicle trajectory tracking according to any one of the preceding claims 1 to 7.
10. A computer-readable storage medium storing a computer-readable program or instructions, which when executed by a processor, is capable of implementing the steps in the simulation method for vehicle trajectory tracking according to any one of claims 1 to 7.
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