A simulation method, device, equipment and storage medium for vehicle trajectory tracking

Abstract

The present invention relates to a simulation method, device, equipment and storage medium for vehicle trajectory tracking, which includes: constructing a track model based on image information of a target track; constructing a corresponding vehicle model based on actual vehicle information, the vehicle model including vehicle attributes; planning trajectory points of a preset route in the track model; setting simulation parameters of the vehicle model so that the vehicle model performs simulation tracking according to the trajectory points of the preset route based on the simulation parameters and vehicle attributes. The present invention constructs a track model based on image information of the target track, and constructs a corresponding vehicle model based on actual vehicle information, plans a path in the track model, and then sets simulation parameters so that the vehicle model tracks according to the preset path in the track model. The present invention can obtain more detailed motion parameters of the vehicle, effectively judge the motion state of the car when following the track, and judge the quality of the planned trajectory by the degree of jitter of the motion result.

Description

Technical Field

[0001] The present invention relates to the field of tracking simulation technology, and in particular to a simulation method, device, equipment and storage medium for vehicle trajectory tracking. Background Art

[0002] Currently, in the fields of vehicle path recognition, path planning, and tracking, more attention is paid to the research of algorithms for recognition, planning, and following. For example, Zhang Lin, Zhang Xinjie, and others added 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 this path is also the path after crossing the obstacle; Shi and Eberhart used fuzzy systems in the particle swarm algorithm to adjust the inertia weight, and then adjust the search range, thereby improving the convergence performance; Siding Li, Xin Xu, and others combined the greedy algorithm with the Boltzmann search algorithm in the Q-Learning algorithm to narrow the search area and accelerate the learning process.

[0003] Although improvements have been made to the algorithms in these studies, the vehicle models used are basically mathematical models, which simplify the complex vehicle system and ignore the actual driving conditions of the vehicle on the road. There is no need to consider the complex characteristics of the entire vehicle. It only requires the mathematical model to be able to drive on the road according to a pre-planned trajectory. Summary of the Invention

[0004] In view of this, it is necessary to provide a vehicle trajectory tracking simulation method, device, equipment and storage medium to solve the problem of ignoring the actual driving conditions of the vehicle and the complex vehicle characteristics when performing vehicle trajectory simulation in the prior art.

[0005] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a simulation method for vehicle trajectory tracking, comprising:

[0007] Construct a track model based on the image information of the target track;

[0008] Construct a corresponding vehicle model based on actual vehicle information, where the vehicle model includes vehicle attributes;

[0009] Plan the trajectory points of the preset route in the track model;

[0010] The simulation parameters of the vehicle model are set so that the vehicle model can simulate and track the trajectory points of the preset route according to the simulation parameters and vehicle properties.

[0011] Preferably, constructing a track model based on the image information of the target track includes:

[0012] Obtain image information of the target track;

[0013] Performing binarization processing on the image information of the target track to obtain a binary image of the target track;

[0014] According to the binary image of the target track, obtain the midpoint coordinates of the target track;

[0015] Construct a track model based on the midpoint coordinates of the target track.

[0016] Preferably, the image information of the target track includes annotation information and road contour information. Binarization processing is performed on the image information of the target track to obtain a binary image of the target track, including:

[0017] Use different colors to distinguish marking information and road contour information;

[0018] Assigning values ​​to the image information of the target track according to the different colors corresponding to the annotation information and the road contour information, thereby obtaining the assigned image information of the target track;

[0019] Inverting the image information of the target track after the assignment to obtain a data set of the image information of the target track;

[0020] By using a preset function, isolated points and isolated areas in the dataset of the image information of the target track are eliminated to obtain image information containing only the road outline;

[0021] The image information containing only the road outline is set to a preset pixel size to obtain a binary image of the target track.

[0022] Preferably, obtaining the midpoint coordinates of the target track according to the binary image of the target track includes:

[0023] Invert the data in the binary image of the target track and save it;

[0024] Add data points to the inner and outer road contours of the road contour information and record the coordinates of the data points;

[0025] Calculate the midpoint coordinates of the target track based on the coordinates of the data points.

[0026] Preferably, the image information of the target track also includes road surface width, road surface height, road surface adhesion coefficient, and road section length; and constructing a track model based on the midpoint coordinates of the target track includes:

[0027] Use the preset modeling tools to set the road width, road height, road adhesion coefficient and road section length parameters, and build a three-dimensional track model based on the midpoint coordinates of the road.

[0028] Preferably, planning the trajectory points of the preset route in the track model includes:

[0029] The binary image of the target track is processed by a preset algorithm to determine the coordinates of the track points of the preset route.

[0030] Preferably, setting the simulation parameters of the vehicle model includes:

[0031] Set the driving file of the vehicle model, which includes the coordinates of the trajectory points, road width, road height, lateral acceleration and vehicle speed;

[0032] Set the simulation time and step size according to the preset conditions. The road surface file is the track model. Set the power and braking force of the vehicle model.

[0033] In a second aspect, the present invention further provides a vehicle trajectory tracking simulation device, comprising:

[0034] A track model building module is used to build a track model based on the image information of the target track;

[0035] A vehicle model building module is used to build a corresponding vehicle model based on actual vehicle information, and the vehicle model includes vehicle attributes;

[0036] Path planning module, used to plan the trajectory points of the preset route in the track model;

[0037] The simulation module is used to set the simulation parameters of the vehicle model so that the vehicle model can simulate and track the trajectory points of the preset route according to the simulation parameters and vehicle properties.

[0038] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein:

[0039] Memory, used to store programs;

[0040] The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the simulation method for vehicle trajectory tracking in any of the above implementations.

[0041] In a fourth aspect, the present invention further provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, the steps in the simulation method for vehicle trajectory tracking in any of the above-mentioned implementation methods can be implemented.

[0042] The beneficial effects of the above embodiment are as follows: the present invention constructs a track model using image information of the target track, constructs a corresponding vehicle model based on actual vehicle information, performs path planning in the track model, and then sets simulation parameters to allow the vehicle model to track along a preset path in the track model. Through the present invention, more detailed motion parameters of the vehicle and the driving conditions of the vehicle model on the actual road model can be obtained, the motion state of the car when following the track can be effectively judged, and the quality of the planned trajectory can be evaluated by the degree of jitter of the motion results. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic flow chart of an embodiment of a vehicle trajectory tracking simulation method provided by the present invention;

[0044] Figure 2 for Figure 1 A method flow chart of an embodiment of S101;

[0045] Figure 3 for Figure 2 A method flow chart of an embodiment of S202;

[0046] Figure 4 for Figure 2 A method flow chart of an embodiment of S203;

[0047] Figure 5 This is a practical rendering of an embodiment of the vehicle tracking trajectory provided by the present invention;

[0048] Figure 6 for Figure 1 A method flow chart of an embodiment of S104;

[0049] Figure 7 A schematic structural diagram of an embodiment of a vehicle trajectory tracking device provided by the present invention;

[0050] Figure 8 A schematic diagram of the structure of a vehicle trajectory tracking device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0052] In the description of the present application, “plurality” means two or more, unless otherwise clearly defined.

[0053] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0054] The present invention provides a simulation method, device, equipment and storage medium for vehicle trajectory tracking, which are described below respectively.

[0055] See also Figure 1 , Figure 1 A flow chart of an embodiment of a vehicle trajectory tracking simulation method provided by the present invention. A specific embodiment of the present invention discloses a vehicle trajectory tracking simulation method, comprising:

[0056] S101, constructing a track model based on image information of the target track;

[0057] S102: Construct a corresponding vehicle model based on actual vehicle information, where the vehicle model includes vehicle attributes;

[0058] S103, planning trajectory points of a preset route in the track model;

[0059] S104: Setting simulation parameters of the vehicle model so that the vehicle model performs simulation tracking along the trajectory points of the preset route according to the simulation parameters and vehicle attributes.

[0060] In step S101, in order to improve the reference value and authenticity of the simulation, it is necessary to construct a track model based on the image information of the actual track, process the image information of the target track, and then set the various parameters contained in the image information to the parameters of the actual track.

[0061] In step S102, parameter information of the actual simulated vehicle is obtained, and then a corresponding vehicle model is constructed through software. The vehicle model includes vehicle attributes such as volume parameters, speed parameters, acceleration parameters, etc.

[0062] In a preferred embodiment provided by the present invention, the data of the vehicle model comes from the CATIA digital model, and the model parameters are measured in CATIA. The model parameters include the coordinates of key hard points and the mass inertia of each component. Based on the obtained parameters, a vehicle model for following the trajectory is established in ADAMS.

[0063] In step S103, based on the constructed track model, a simulation path of the vehicle model is planned, and the coordinates of the trajectory points constituting the path are calculated so that the vehicle model can perform tracking simulation according to the expected path during the actual simulation process.

[0064] In step S104, simulation parameters and conditions are set according to the vehicle model and the track model, so that the constructed vehicle model can track the track points of the preset track route on the constructed track model and observe the simulation results.

[0065] In the above embodiment, a track model and a vehicle model are constructed based on actual track image information and actual vehicle information, and a simulation route is planned in the constructed track model. Then, the vehicle model performs trajectory tracking simulation along the planned route on the track model. The model is established based on actual track information and vehicle information, and the simulation results are more realistic and have reference value.

[0066] Compared with the prior art, the present invention provided in this embodiment constructs a track model using image information of the target track, and constructs a corresponding vehicle model based on actual vehicle information, performs path planning in the track model, and then sets simulation parameters to allow the vehicle model to track along a preset path in the track model. Through the present invention, more detailed motion parameters of the vehicle and the driving conditions of the vehicle model on the actual road model can be obtained, the motion state of the car when following the track can be effectively judged, and the quality of the planned trajectory can be evaluated by the degree of jitter of the motion results.

[0067] See also Figure 2 , Figure 2 for Figure 1 In some embodiments of the present invention, constructing a track model based on image information of the target track includes:

[0068] S201, obtaining image information of a target track;

[0069] S202, performing binarization processing on the image information of the target track to obtain a binary image of the target track;

[0070] S203, obtaining the midpoint coordinates of the target track according to the binary image of the target track;

[0071] S204: Construct a track model according to the midpoint coordinates of the target track.

[0072] In step S201, to model the target track, relevant image information of the target track needs to be obtained. In one embodiment provided by the present invention, the target track is the Xiangyang track officially announced by the China Formula Student car competition. A drawing of the target track can be obtained to construct a track model of the Xiangyang track officially announced by the China Formula Student car competition.

[0073] In step S202 , in order to accurately extract the track from the image information of the target track, the target track needs to be binarized. The target track image information after binarization makes it easier to accurately extract the track.

[0074] In step S203 , the structure of the target track can be quickly determined based on the midpoint coordinates of the target track. Therefore, the midpoint coordinates of the target track are calculated based on the binarized image for subsequent modeling.

[0075] In step S204 , the position coordinates and direction of the target track are determined based on the calculated midpoint coordinates of the target track, thereby completing the construction of the track model.

[0076] In the above embodiment, the image information of the target track is first obtained, and then the image information of the target track is binarized to calculate the midpoint coordinates of the target track. Then, a track model is constructed based on the midpoint coordinates of the target track. This can establish a realistic target track model, restore the actual situation of the track, and improve the authenticity of the simulation results.

[0077] See also Figure 3 , Figure 3 for Figure 2 In some embodiments of the present invention, the image information of the target track includes annotation information and road contour information, and the image information of the target track is binarized to obtain a binarized image of the target track, including:

[0078] S301, distinguishing the marking information and the road contour information by using different colors;

[0079] S302, assigning values ​​to the image information of the target track according to different colors corresponding to the annotation information and the road contour information, to obtain the assigned image information of the target track;

[0080] S303, inverting the image information of the target track after the assignment to obtain a data set of the image information of the target track;

[0081] S304: Eliminate isolated points and isolated areas in the data set of the image information of the target track using a preset function to obtain image information containing only the road outline;

[0082] S305 : Setting the image information containing only the road outline to a preset pixel size to obtain a binary image of the target track.

[0083] In step S301, the officially released drawing of the Xiangyang track from the China Formula Student racing competition is imported into MATLAB. The image information of the target track includes annotation information and road profile information. The annotation information includes dimensional information such as the length, width, height, and curve angles of the target track, while the road profile information includes information such as the structure and direction of the target track's roads. In a preferred embodiment provided by the present invention, the annotations on the drawing are displayed in red, and the road profiles in the drawing are displayed in green. These different colors prevent the standard information from interfering with the subsequent extraction of the track image.

[0084] It should be noted that there is no limitation on the colors used to distinguish the annotation information and the road profile information. However, in this embodiment, red is used to represent the annotation information, and green is used to represent the road profile information, which is more convenient for subsequent calculations.

[0085] In step S302, two sets of data are generated based on the different RGB values ​​of the annotations and road outlines. The first set of data sets the value of R greater than 200 to 1, otherwise to 0; and sets the value of G greater than 155, B greater than 155, and R greater than 155 to 1, otherwise to 0. This distinguishes the annotations and road outlines in the target drawing and assigns them values, eliminating some useless information in the drawing.

[0086] In step S303, the image information of the target track after the assignment is inverted, and then the union is calculated with the image after the assignment in step S302. The union is reassigned to the image information of the target track and inverted again to obtain a dataset of the image information of the target track. At this time, the dataset removes the annotation information and some useless information due to the different assignments.

[0087] In step S304, the preset function is the bwareaopen function, which eliminates isolated points and isolated areas in the data set of the image information of the target track. At this time, the image information only contains the contour information of the road, eliminating possible interference factors in the subsequent calculation process.

[0088] In step S305, the image containing only the road contour information in step S304 is set to a pixel size of 1000*1000 to obtain a binary image of the target track and save it.

[0089] In the above embodiment, the image information of the target track is binarized by MATLAB, and the annotations and other useless information in the initial image are removed to obtain image information containing only the road outline. The image information containing only the road outline is set to a pixel size of 1000*1000 and saved for future use.

[0090] See Figure 4 , Figure 4 for Figure 2 In some embodiments of the present invention, obtaining the midpoint coordinates of the target track according to the binary image of the target track includes:

[0091] S401, inverting and saving the data in the binary image of the target track;

[0092] S402, adding data points to the inner and outer road contours of the road contour information, and recording the coordinates of the data points;

[0093] S403: Calculate the midpoint coordinates of the target track according to the coordinates of the data points.

[0094] In step S401, the image containing only road profile information obtained in step S305 is read by MATLAB, and the assigned values ​​therein are inverted and saved to avoid affecting the road profile in subsequent operations.

[0095] In step S402, the webplotdigitizer tool is read into MATLAB. In the webplotdigitizer tool, data points are added to the outer line of the road contour. After sorting by distance, the horizontal and vertical coordinates of the read points are saved in the first and second columns of "data.xlsx" sheet1; in the webplotdigitizer tool, data points are added to the inner line of the road contour. After sorting by distance, the horizontal and vertical coordinates of the read points are saved in the first and second columns of "data.xlsx" sheet2. The coordinates of the data points of the inner and outer contours of the road can more accurately reflect the road contour.

[0096] In step S403, open the CATIA macro file GSD_PointSplineLoftFromExcel, write the previously obtained data points into the macro file, open CATIA, import the data points in the macro file, and connect these scattered points into splines. Use the parallel curve command to translate the inner and outer lines of the road profile 15.617 mm toward the middle respectively. Use the point extraction function for the two curves that appear after the translation, and extract 1000 points for each. In the CATIA-digitized shape editor module, import the obtained point coordinates into the "data1.xls" file sheet1 and sheet2. Read the exported points into MATLAB and calculate the midpoint coordinates.

[0097] In the above embodiment, MATLAB is used to add data points to the inner and outer contours of the road based on an image containing only road contour information to determine the inner and outer contour coordinates of the target track road. The midpoint coordinates of the target track can be calculated based on the coordinates of the added data points.

[0098] In some embodiments of the present invention, the image information of the target track also includes road surface width, road surface height, road surface adhesion coefficient, and road section length; constructing a track model based on the midpoint coordinates of the target track includes:

[0099] Use the preset modeling tools to set the road width, road height, road adhesion coefficient and road section length parameters, and build a three-dimensional track model based on the midpoint coordinates of the road.

[0100] In the above embodiment, the preset modeling tool is ROAD BUILDER. Based on the road surface width, road surface height, road surface adhesion coefficient, road segment length in the image information of the target track and the midpoint coordinates calculated in step S403, ROAD BUILDER is opened in ADAMS to build a three-dimensional track model.

[0101] In some embodiments of the present invention, planning trajectory points of a preset route in a track model includes:

[0102] The binary image of the target track is processed by a preset algorithm to determine the coordinates of the track points of the preset route.

[0103] In the above embodiment, the preset algorithm is preferably a path planning algorithm written in QT CREATOR, which performs static planning on the binary image to obtain the coordinates of the trajectory points; the written path planning algorithm mainly uses the A star algorithm and the expansion corrosion algorithm. The header file in the entire pathplanning.pro program contains 3 contents, "astar.h", "mainwindow.h", and "mapdisp.h"; sources contains four contents: "astar.cpp", "main.cpp", "mainwindow.cpp", and "mapdisp.cpp"; run the program, and the trajectory display can be seen in the following figure. Figure 5 , Figure 5 This is an actual effect diagram of an embodiment of the vehicle tracking trajectory provided by the present invention. The coordinates of the trajectory points after static planning are obtained in the window, and the coordinates of the trajectory points are copied to "data3.xls".

[0104] Specifically, the path planning algorithm written in QT CREATOR is used to determine the coordinates of the trajectory points of the preset route, including:

[0105] The first step is to read the path from the binary image and set the expansion coefficient in mapdisp to replace the vehicle parameters and add the starting point to the open list.

[0106] Step 2: a. Traverse the open list and find the node with the smallest F value, and use it as the current node to be processed.

[0107] b. Move this node to the close list.

[0108] c. Determine each of the eight adjacent squares of the current square:

[0109] If it is unreachable or it is in the close list, ignore it. Otherwise, do the following.

[0110] If it is not in the open list, add it to the open list, set the current square as its parent, and record the F, G, and H values ​​of the square.

[0111] If it's already on the open list, check if this path (i.e., reaching it via the current square) is better, using the G value as a guide. A lower G value indicates 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, re-sorting may be necessary after this change.

[0112] d. If the destination is added to the open list, the path has been found. If the destination search fails and the open list is empty, there is no path. If the destination search fails and the open list is not empty, repeat steps ad.

[0113] The third step is to start from the end point and move each square along the parent node until it reaches the starting point. This is the resulting path.

[0114] See Figure 6 , Figure 6 for Figure 1 In some embodiments of the present invention, setting simulation parameters of the vehicle model and performing simulation tracking according to trajectory points of a preset route based on the simulation parameters and vehicle attributes includes:

[0115] S601: Setting a driving file of a vehicle model, where the driving file of the vehicle model includes track point coordinates, road width, road height, lateral acceleration, and vehicle speed;

[0116] S602: Set the simulation time and step size according to preset conditions. The road surface file is the track model. Set the power and braking force of the vehicle model.

[0117] S603: Based on the simulation parameters and vehicle attributes, the vehicle model is made to simulate and track the trajectory points of the preset route.

[0118] In step S601, use Notepad to write a drd file named "guiji.drd" and set the driver file of the vehicle model. In addition to the coordinates of the trajectory points, it is also necessary to determine the path based on the road surface width and height in the road model, and set the lateral acceleration and speed of the vehicle model so that the vehicle can travel on the track model as required.

[0119] In step S602, the driving 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 to the track model constructed in step S101, the driver file is set to the driver file written in step S601, and the Smart Driver Task is set to be restricted by the vehicle.

[0120] It can be understood that the simulation time is set to 200S and the step size is set to 2000, which means that the simulated vehicle operation status is output every 0.1s. The smart Driver Task is set to be subject to vehicle constraints, which sets the power and braking force of the vehicle model according to the actual vehicle model.

[0121] In step S603, the simulation is run according to the set simulation parameters, the vehicle model follows the trajectory points, and the post-processing interface is opened to read the driving trajectory and movement status of the vehicle model.

[0122] In the above embodiment, in addition to the time-dependent curves of yaw rate and sideslip angle, time-dependent curves of acceleration, velocity, and force can also be obtained. Through the present invention, more detailed motion parameters of the vehicle can be obtained, effectively judging the motion state of the racing car when following the trajectory, and the quality of the planned trajectory can be evaluated by the degree of jitter in the motion results.

[0123] In order to better implement the simulation method of vehicle trajectory tracking in the embodiment of the present invention, based on the simulation method of vehicle trajectory tracking, correspondingly, please refer to Figure 7 , Figure 7 This is a schematic structural diagram of an embodiment of a vehicle trajectory tracking simulation device provided by the present invention. The embodiment of the present invention provides a vehicle trajectory tracking device 700, comprising:

[0124] A track model building module 701 is used to build a track model based on the image information of the target track;

[0125] A vehicle model building module 702 is used to build a corresponding vehicle model based on actual vehicle information, where the vehicle model includes vehicle attributes;

[0126] The path planning module 703 is used to plan the trajectory points of the preset route in the track model;

[0127] The simulation module 704 is used to set simulation parameters of the vehicle model so that the vehicle model performs simulation tracking according to the trajectory points of the preset route based on the simulation parameters and vehicle properties.

[0128] It should be noted here that the device 700 provided in the above embodiment can implement the technical solutions described in the above method embodiments. The specific implementation principles of the above modules or units can be found in the corresponding contents in the above method embodiments, which will not be repeated here.

[0129] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of a vehicle trajectory tracking device provided by an embodiment of the present invention. Based on the above-described vehicle trajectory tracking simulation method, the present invention also provides a vehicle trajectory tracking device. The vehicle trajectory tracking device can be a computing device such as a mobile terminal, desktop computer, notebook, PDA, or server. The vehicle trajectory tracking device includes a processor 810, a memory 820, and a display 830. Figure 8 Only some of the components of the electronic device are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0130] In some embodiments, the memory 820 may be an internal storage unit of the vehicle trajectory tracking device, such as a hard drive or memory of the vehicle trajectory tracking device. In other embodiments, the memory 820 may also be an external storage device of the vehicle trajectory tracking device, such as a plug-in hard drive, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the vehicle trajectory tracking simulation device. Furthermore, the memory 820 may include both an internal storage unit of the vehicle trajectory tracking simulation device and an external storage device. The memory 820 is used to store application software and various types of data installed in the vehicle trajectory tracking device, such as program code for installing the vehicle trajectory tracking device. The memory 820 may also be used to temporarily store data that has been output or is about to be output. In one embodiment, the memory 820 stores a vehicle trajectory tracking program 840, which can be executed by the processor 810 to implement the vehicle trajectory tracking simulation method of each embodiment of the present application.

[0131] In some embodiments, the processor 810 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 820, such as executing a simulation method for vehicle trajectory tracking.

[0132] In some embodiments, the display 830 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 830 is used to display information on the vehicle trajectory tracking device and to display a visual user interface. The components 810-830 of the vehicle trajectory tracking device communicate with each other via a system bus.

[0133] In one embodiment, when the processor 810 executes the vehicle trajectory tracking program 840 in the memory 820 , the steps in the above vehicle trajectory tracking simulation method are implemented.

[0134] The present invention provides a simulation method, device, equipment and storage medium for vehicle trajectory tracking, which obtains image information of the target track, performs binarization processing on the image information of the target track, calculates the midpoint coordinates of the target track, constructs a track model, and then plans a path through the actual track model, and constructs a corresponding vehicle model based on the actual vehicle information, sets simulation parameters, writes a driver file, and allows the vehicle model to track along a preset path in the track model. The present invention can obtain more detailed motion parameters of the vehicle and the driving conditions of the vehicle model on the actual road model based on the actual road information and the entire vehicle model, effectively judge the motion state of the car when following the track, and judge the quality of the planned trajectory by the degree of jitter of the motion result. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A simulation method for vehicle trajectory tracking, characterized in that: include: Acquiring image information of a target track, wherein the image information of the target track further includes road surface width, road surface height, road surface adhesion coefficient, and road section length; performing binarization processing on the image information of the target track to obtain a binary image of the target track; Obtaining the midpoint coordinates of the target track according to the binary image of the target track; By using a preset modeling tool, the road surface width, road surface height, road surface adhesion coefficient and road section length parameters are set, and a three-dimensional track model is established according to the midpoint coordinates of the target track; Constructing a corresponding vehicle model according to actual vehicle information, wherein the vehicle model includes vehicle attributes, wherein the vehicle attributes include a volume parameter, a speed parameter, and an acceleration parameter of the vehicle; Planning trajectory points of a preset route in the track model; Setting simulation parameters of the vehicle model so that the vehicle model performs simulation tracking according to the trajectory points of the preset route based on the simulation parameters and vehicle attributes; The setting of the simulation parameters of the vehicle model includes: Setting a driving file of the vehicle model, wherein the driving file of the vehicle model includes trajectory point coordinates, road surface width, road surface height, lateral acceleration, and vehicle speed; The simulation time and step size are set according to the preset conditions. The road surface file is the track model. The power and braking force of the vehicle model are set.

2. The vehicle trajectory tracking simulation method according to claim 1, characterized in that: The image information of the target track includes annotation information and road contour information. Binarizing the image information of the target track to obtain a binary image of the target track includes: Differentiating the marking information and the road profile information by using different colors; Assigning values ​​to the image information of the target track according to the different colors corresponding to the annotation information and the road contour information, to obtain the assigned image information of the target track; Inverting the image information of the target track after the assignment 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 by a preset function to obtain image information containing only the road contour; The image information containing only the road outline is set to a preset pixel size to obtain a binary image of the target track.

3. The vehicle trajectory tracking simulation method according to claim 2, characterized in that: The step of obtaining the midpoint coordinates of the target track according to the binary image of the target track includes: Inverting and saving the data in the binary image of the target track; Add data points to the inner and outer road contours of the road contour information, and record the coordinates of the data points; The midpoint coordinates of the target track are calculated according to the coordinates of the data points.

4. The vehicle trajectory tracking simulation method according to claim 1, characterized in that: The track points of the preset route are planned in the track model, including: The binary image of the target track is processed by a preset algorithm to determine the coordinates of the track points of the preset route.

5. A vehicle trajectory tracking simulation device, characterized in that: include: A track model building module is used to obtain image information of a target track, wherein the image information of the target track also includes road surface width, road surface height, road surface adhesion coefficient, and road section length; Binarizing the image information of the target track to obtain a binary image of the target track; obtaining the midpoint coordinates of the target track based on the binary image of the target track; setting the road surface width, road surface height, road surface adhesion coefficient, and road section length parameters using a preset modeling tool, and establishing a three-dimensional track model based on the midpoint coordinates of the target track; A vehicle model building module, configured to build a corresponding vehicle model based on actual vehicle information, wherein the vehicle model includes vehicle attributes, wherein the vehicle attributes include a volume parameter, a speed parameter, and an acceleration parameter of the vehicle; A path planning module, used to plan trajectory points of a preset route in the track model; A simulation module, configured to set simulation parameters of the vehicle model so that the vehicle model performs simulation tracking according to the trajectory points of the preset route based on the simulation parameters and vehicle attributes; The setting of the simulation parameters of the vehicle model includes: Setting a driving file of the vehicle model, wherein the driving file of the vehicle model includes trajectory point coordinates, road surface width, road surface height, lateral acceleration, and vehicle speed; The simulation time and step size are set according to the preset conditions. The road surface file is the track model. The power and braking force of the vehicle model are set.

6. An electronic device, characterized in that: comprising a memory and a processor, wherein, The memory is used to store programs; The processor is coupled to the memory and is used to execute the program stored in the memory to implement the steps in the vehicle trajectory tracking simulation method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the vehicle trajectory tracking simulation method described in any one of claims 1 to 4.

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