Vehicle relay tracking method based on holographic road network

Abstract

The invention relates to the technical field of holographic road networks, in particular to a vehicle relay tracking method based on a holographic road network. The method comprises the following steps: in a process of tracking a vehicle based on a holographic road network, according to a real-time track of the vehicle on a current monitored road section, predicting a driving track of the vehicle on a next non-monitored road section to obtain a predicted track; displaying the moving process of the vehicle model from the first display area to the second display area according to the splicing track on the holographic road network; wherein the vehicle model is used for indicating the vehicle in the holographic road network; the first display area is used for indicating a current monitored road section in the holographic road network, and the second display area is used for indicating a next non-monitored road section in the holographic road network; the technical problem that comprehensive and accurate vehicle relay tracking cannot be achieved based on a holographic road network due to the fact that the coverage rate of monitoring equipment is low in the prior art can be solved.

Description

Technical Field

[0001] The present application relates to the technical field of holographic road networks, and in particular to a vehicle relay tracking method based on a holographic road network. Background Art

[0002] In the existing technology, the holographic road network is a technology that deploys monitoring equipment at intersections and key sections, and then uses the video data and radar data collected by the monitoring equipment to achieve all-weather accurate collection of information such as lane-level flow at intersections, queue length, vehicle speed, driving trajectory, number of stops, and accidents.

[0003] In practical applications, the holographic road network can realize relay tracking of vehicles and display the movement process of the vehicle in real time. The relay tracking of vehicles relies on the video data and radar data provided by the monitoring equipment.

[0004] In many existing areas, the coverage rate of monitoring equipment is still relatively low, which makes it impossible to achieve comprehensive and accurate vehicle relay tracking based on the holographic road network. Summary of the invention

[0005] In view of this, the purpose of this application is to provide a vehicle relay tracking method based on a holographic road network to solve the technical problem in the prior art that the coverage of monitoring equipment is still relatively low, resulting in the inability to achieve comprehensive and accurate vehicle relay tracking based on a holographic road network.

[0006] In a first aspect, the present application provides a vehicle relay tracking method based on a holographic road network, the method comprising: In the process of tracking the vehicle based on the holographic road network, according to the real-time trajectory of the vehicle on the current monitored road section, the driving trajectory of the vehicle on the next non-monitored road section is predicted to obtain a predicted trajectory; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section; In response to the vehicle driving from the current monitored section into the next non-monitored section, splicing the real-time trajectory and the predicted trajectory to obtain a spliced ​​trajectory; Displaying on the holographic road network the movement process of the vehicle model from the first display area to the second display area according to the splicing trajectory; The vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

[0007] In a second aspect, the present application provides a vehicle relay tracking device based on a holographic road network, the device comprising: a trajectory prediction module, a trajectory splicing module and a trajectory display module; The trajectory prediction module is used to predict the driving trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory of the vehicle on the current monitored road section to obtain a predicted trajectory during the process of tracking the vehicle based on the holographic road network; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section; The trajectory splicing module is used for splicing the real-time trajectory and the predicted trajectory to obtain a spliced ​​trajectory in response to the vehicle entering the next non-monitored road section from the current monitored road section; The track display module is used to display the movement process of the vehicle model from the first display area to the second display area according to the splicing track on the holographic road network; The vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

[0008] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory is used to store an application program, and the processor runs or executes a software program stored in the memory so that the electronic device implements the above-mentioned vehicle relay tracking method based on a holographic road network.

[0009] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store program codes executed by a processor, and the program codes are used to implement the above-mentioned vehicle relay tracking method based on a holographic road network.

[0010] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are run on an electronic device, the electronic device implements the above-mentioned vehicle relay tracking method based on a holographic road network.

[0011] Beneficial effects: The present application provides a vehicle relay tracking method based on a holographic road network. The present application provides a vehicle relay tracking method based on a holographic road network, and the method comprises: in the process of tracking a vehicle based on a holographic road network, determining a predicted trajectory of the vehicle on a non-monitored section according to the real-time trajectory of the vehicle on the monitored section; wherein the predicted trajectory is obtained by prediction based on the real-time trajectory; in response to the vehicle entering a monitored section from a non-monitored section, determining a spliced ​​trajectory according to the real-time trajectory and the predicted trajectory; displaying a vehicle model on the holographic road network, and moving from a first display area to a second display area according to the spliced ​​trajectory; wherein the vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the monitored section in the holographic road network, and the second display area is used to indicate the non-monitored section in the holographic road network.

[0012] In summary, the vehicle relay tracking method for the holographic road network provided in the present application can determine the predicted trajectory of the non-monitored section based on the real-time trajectory of the monitored section, and splice the real-time trajectory and the predicted trajectory, so that the movement process of the vehicle model can be displayed in real time on the holographic road network according to the spliced ​​trajectory. Therefore, the present application can solve the technical problem existing in the prior art that the coverage rate of monitoring equipment is still relatively low, resulting in the inability to achieve comprehensive and accurate vehicle relay tracking based on the holographic road network. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. The following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1 A flow chart of a vehicle relay tracking method based on a holographic road network provided in an embodiment of the present application; Figure 2 A schematic diagram of the settings of the monitoring section and the non-monitoring section provided in the embodiment of the present application; Figure 3 A schematic diagram of a moving process of a vehicle model displayed in a holographic road network provided in an embodiment of the present application; Figure 4 Another schematic diagram of displaying the movement process of a vehicle model in a holographic road network provided in an embodiment of the present application; Figure 5 A structural diagram of a vehicle relay tracking device based on a holographic road network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] In the existing technology, the holographic road network is based on the Digital Twin technology, which uses radar cameras to conduct full coverage perception of road sections and intersections, and gather and integrate the trajectories of vehicles at intersections. It combines three-dimensional maps and equipment modeling to build a digital base, complete the digitization and visualization of transportation elements such as vehicles, roads, and facilities, and display a virtual transportation network.

[0016] Based on the holographic road network, vehicles can be tracked. The vehicles can be vehicles with special purposes that need to be tracked, etc. However, in many existing areas, the coverage rate of radar cameras is still relatively low, which makes it impossible to achieve comprehensive and accurate vehicle relay tracking based on the holographic road network.

[0017] In order to solve the above technical problems, the present application provides a technical solution for vehicle tracking based on a holographic road network. In this technical solution, the driving trajectory of the vehicle on a non-monitored section is predicted based on the real-time trajectory of the vehicle on the monitored section to obtain a predicted trajectory; when the vehicle enters the monitored section from the non-monitored section, the real-time trajectory and the predicted trajectory can be spliced ​​to obtain a spliced ​​trajectory; after obtaining the spliced ​​trajectory, the vehicle model displayed on the holographic road network can be made to move from the first display area to the second display area according to the spliced ​​trajectory.

[0018] Among them, the vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the monitored section in the holographic road network, and the second display area is used to indicate the non-monitored section in the holographic road network.

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0020] First, the present application provides a vehicle relay tracking method based on a holographic road network, such as Figure 1 As shown, Figure 1 The flowchart of the vehicle relay tracking method based on the holographic road network provided in the embodiment of the present application includes: S110~S130, the details are as follows: S110: In the process of tracking the vehicle based on the holographic road network, according to the real-time trajectory of the vehicle on the current monitored road section, the driving trajectory of the vehicle on the next non-monitored road section is predicted to obtain a predicted trajectory; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section.

[0021] Specifically, the “current monitored road section” is the monitored road section that the vehicle is traveling on at the current moment, and the “next non-monitored road section” is the non-monitored road section that the vehicle enters after leaving the current monitored road section.

[0022] In actual operation, the "monitored sections" and "non-monitored sections" are arranged at intervals. When a vehicle leaves a "monitored section", it enters a "non-monitored section", and when a vehicle leaves a "non-monitored section", it enters a "monitored section".

[0023] In an embodiment of the present application, an advance prediction can be made for the vehicle at the "next non-monitored section", for example, the vehicle's driving trajectory on the "next non-monitored section" can be predicted when there are multiple sections between the vehicle and the "next non-monitored section", but the number of vehicles and traffic conditions on different sections are likely to be different. Therefore, when the vehicle is traveling on the "current monitored section", the predicted trajectory obtained by predicting the vehicle's driving trajectory on the "next non-monitored section" is the closest to the vehicle's actual trajectory on the "next non-monitored section", because the "current monitored section" is closest to the "next non-monitored section".

[0024] After the predicted trajectory is determined, the vehicle model can be made to move on the highway displayed by the holographic road network according to the predicted trajectory, so that the user can intuitively observe the driving status of the vehicle on the holographic road network and make decisions based on the driving status.

[0025] In actual operation, both “real-time trajectory” and “predicted trajectory” are trajectory data sets, and the elements in the trajectory data set are their real-time coordinates and timestamps in the coordinate system where the holographic road network is located.

[0026] In one implementation, before S110, the method further includes: Step (1) to Step (2), the details of which are as follows: Step (1): using a radar camera installed on the current monitored road section to collect vehicle driving data to obtain real-time driving data; Among them, the monitored section is the monitoring and identification area of ​​the radar camera, and the non-monitored section is the monitoring blind area of ​​the radar camera.

[0027] Specifically, in the embodiments of the present application, Figure 2 As shown, Figure 2 A schematic diagram of the settings of monitoring sections and non-monitoring sections provided in an embodiment of the present application, wherein the monitoring section is a section where a radar camera is installed and is within the monitoring identification area of ​​the radar camera, and the monitoring section is a section where no radar camera is installed and is within the monitoring blind area of ​​the radar camera.

[0028] In actual operation, when a vehicle is traveling on a monitored road section, the real-time driving data of the vehicle can be collected through the radar camera, such as the vehicle's specific position coordinates, instantaneous speed and average speed, as well as the model, color, size and license plate of each vehicle; among which, the vehicle's specific position coordinates are the coordinates of a certain feature point detected by the radar camera. The feature point is usually the location where the radar beam intersects the vehicle surface. It may be located at the front, side or rear of the vehicle, depending on the installation position of the radar camera, the detection angle and the shape and size of the vehicle.

[0029] Step (2): Determine the real-time trajectory based on the real-time driving data.

[0030] Specifically, after the real-time driving data is determined, the real-time trajectory of the vehicle can be determined based on the real-time driving data; the holographic road network can refer to the real-time trajectory to enable the holographic road network to display the movement process of the vehicle model in the first display area according to the real-time trajectory. The movement process is the process in which the vehicle model that the user can observe on the holographic road network continuously moves on the holographic road network.

[0031] Among them, the model used to indicate the vehicle in the holographic road network is the vehicle model; the display area used to indicate the monitored section in the holographic road network is the first display area, and the display area used to indicate the non-monitored section in the holographic road network is the second display area.

[0032] like Figure 3 and Figure 4 As shown, Figure 3 A schematic diagram of a moving process of a vehicle model displayed in a holographic road network provided in an embodiment of the present application, Figure 4 Another schematic diagram of the movement process of a vehicle model displayed in a holographic road network provided in an embodiment of the present application is shown in FIG. Figure 3 In the holographic road network, when the vehicle model is displayed on the moving process, the vehicle license plate will also be displayed. Figure 4 In addition to displaying the movement of the vehicle model on the holographic road network, a display interface for executing other functions of the holographic road network is also displayed.

[0033] It should be emphasized that whether to distinguish and display the first display area and the second display area in the holographic road network can be determined according to the actual situation. It should also be emphasized that the background server is fully aware of the monitored sections and non-monitored sections in the actual road network, because the above information can be determined through urban infrastructure data.

[0034] In one implementation, S110 includes: step (3), details of which are as follows: Step (3): Based on the real-time trajectory, the trajectory of the vehicle on the next non-monitored road section is predicted by the trajectory prediction model to obtain a predicted trajectory; Among them, the trajectory prediction model is obtained by training the big data model.

[0035] Specifically, when applying the trajectory prediction model, it is also necessary to input the section length of the current monitored section corresponding to the real-time trajectory and the section length of the next non-monitored section, so that the trajectory prediction model outputs a predicted trajectory corresponding to the section length of the next non-monitored section.

[0036] In the embodiment of the present application, the big data model is an AI (Artificial Intelligence) big model; in actual operation, in order to enable the AI ​​big model to have the prediction ability to determine the predicted trajectory, it is necessary to pre-train and fine-tune the AI ​​big model. In the prior art, the predicted trajectories of multiple vehicles can be predicted by a traffic micro-simulation model for the driving trajectories of multiple vehicles on non-monitored sections. After the predicted trajectories of multiple vehicles are determined, the predicted trajectories of each of the multiple vehicles can be determined. However, the above operation method has application problems such as insufficient original data, high requirements for data processing technology, long data processing cycle, and very difficult correction of parameters and variables, resulting in the process of predicting the driving trajectories of multiple vehicles on non-monitored sections through a traffic micro-simulation model. It is very slow and difficult to meet the real-time prediction requirements. In particular, in actual operation, it is often not necessary to obtain the predicted trajectories of multiple vehicles, but only to determine the driving trajectory of the target vehicle among the multiple vehicles on the holographic road network, which further reduces the efficiency of obtaining the predicted trajectory of the target vehicle.

[0037] In an embodiment of the present application, in order to meet the real-time prediction requirements and real-time prediction efficiency and improve the prediction accuracy of the predicted trajectory of the target vehicle, the present application trains a trajectory prediction model that only predicts the predicted trajectory of the target vehicle.

[0038] In some implementations, step (3) includes: step (3.1) to step (3.4), the details of which are as follows: Step (3.1): Determine the first parameter according to the real-time trajectory of the current monitored section.

[0039] Wherein, the first parameter indicates the influence of non-road section factors on the driving state of the vehicle; Specifically, in the embodiment of the present application, non-road section factors refer to factors that usually do not change in real time except the length and curvature of the road section. Non-road section factors mainly include: weather conditions and traffic congestion conditions, etc.; in actual applications, non-road section factors can be accurately obtained through the radar camera on the current monitored section.

[0040] In actual situations, real-time weather conditions will affect both the number of vehicles on a road section and the driving state of the target vehicle. Since the trajectory prediction model in the embodiment of the present application only focuses on the prediction of the driving trajectory of the target vehicle, and the number of vehicles on a road section will also affect the driving state of the target vehicle, the impact of weather conditions on the number of vehicles on a road section (and the driving state of the vehicles) are abstracted into the impact on the driving state of the target vehicle, so that the trajectory prediction model only considers the final impact of the weather conditions on the predicted trajectory of the target vehicle; wherein, the weather conditions can be input into the trajectory prediction model by using meteorological professional terms as prompt words (i.e., the first parameter), so that the trajectory prediction model can perform efficient and accurate real-time prediction of the vehicle's driving trajectory on the next non-monitored section according to the weather conditions of the current monitored section.

[0041] Traffic congestion may spread from the current monitored section to the next non-monitored section, so it is necessary to consider the impact of the traffic accident on the driving state of the target vehicle. In the prior art, traffic congestion is used to describe the driving conditions of multiple vehicles in a section, but in an embodiment of the present application, the traffic congestion is abstracted into an impact on the driving state of the target vehicle, so that the trajectory prediction model only considers the final impact of the traffic congestion on the predicted trajectory of the target vehicle; wherein, the traffic accident can be achieved by inputting the specific information of the traffic congestion as a prompt word (i.e., the first parameter) into the trajectory prediction model, so that the trajectory prediction model can make an efficient and accurate real-time prediction of the vehicle's driving trajectory on the next non-monitored section based on the traffic congestion of the current monitored section.

[0042] Step (3.2): according to the road section type of the next non-monitored road section, a reference monitoring road section is determined from the current monitoring road section traveled by the vehicle and at least one historical monitoring road section traveled by the vehicle; Among them, the road section types include: straight sections and curved sections; the section type of the reference monitoring section is the same as the section type of the next non-monitoring section.

[0043] Specifically, in the embodiment of the present application, determining the predicted trajectory requires retrieving the historical movement trajectory of the monitoring section that has the same section type as the next non-monitoring section.

[0044] In the embodiment of the present application, the road section types are divided into two categories, straight sections and curved sections. Among them, the straight section refers to the section where the vehicle travels straight along the center line of the road or the main road without changing the driving direction; on the straight section, the driver needs to keep the vehicle driving straight and not deviate from the center line of the road or the main road; the accident risk is low.

[0045] A curved section refers to the curved part of the road, that is, the part where the route is not straight; on a curved section, the driver needs to pay special attention to his or her line of sight and the vehicle's driving stability; the risk of accidents is higher.

[0046] In the embodiment of the present application, the reason for selecting the reference monitoring section is that the driving behavior and trajectory patterns of vehicles on the same type of sections have a high degree of similarity. By analyzing the driving data of the vehicle on the reference monitoring section, the typical driving habits and speed patterns of the vehicle on this type of section can be extracted, providing a strong basis for predicting the trajectory of the vehicle on the next non-monitoring section.

[0047] Step (3.3): retrieve the current second parameter of the next non-monitored section, the reference moving trajectory and the reference second parameter of the reference monitored section from the database; The second parameter indicates the influence of the road section on the driving state of the vehicle.

[0048] Specifically, in actual operation, due to the lack of real-time vehicle position and speed data, the prediction of the vehicle's driving trajectory in non-monitored sections becomes particularly complicated. Therefore, accurate prediction requires comprehensive consideration of the vehicle's historical driving behavior, current state, and the impact of the second parameter on the vehicle's driving state.

[0049] In the embodiment of the present application, the second parameter should include a series of factors that affect the driving state of the vehicle, such as the length, width and speed limit of the road section, etc. The second parameter and the first parameter jointly determine the possibility of the vehicle's driving speed and direction change on the road section. In the embodiment of the present application, the trajectory prediction model is also connected to the database, and can retrieve the corresponding second parameter from the record in the database according to the field and can store the second parameter in the record corresponding to the field.

[0050] Step (3.4): input the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter into the trajectory prediction model, and let the trajectory prediction model output the predicted trajectory.

[0051] Specifically, after the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the trajectory prediction model, the trajectory prediction model can be adaptively adjusted for the reference moving trajectory according to the first parameter, the current second parameter and the reference second parameter to obtain the most likely driving trajectory of the target vehicle on the next non-monitored road section. The most likely driving trajectory is the predicted trajectory.

[0052] It should be emphasized that, although the present application does not limit the specific structure of the trajectory prediction model, the model structure of the trajectory prediction model should have a neural network structure for processing time series data and capturing time dependencies.

[0053] In one implementation, between step (3.3) and step (3.4), the method further includes: step (3.5) to step (3.6), the details of which are as follows: Step (3.5): if the current number m of the reference monitoring sections is less than the preset number N, retrieve (Nm) historical second parameters and corresponding (Nm) historical movement trajectories from the database according to the current second parameter; The difference between each of the (Nm) historical second parameters and the current second parameter is less than or equal to a preset threshold.

[0054] Specifically, in actual operation, the accuracy and reliability of the predicted trajectory output by the trajectory prediction model depends on the reference second parameters and reference moving trajectories of a rich reference monitoring section to cover as comprehensive scenarios as possible. Therefore, when the current number m of reference monitoring sections is less than the preset number N, it is necessary to supplement the number of reference second parameters and reference moving trajectories of the reference monitoring sections to the preset number N; the preset number N can be determined according to actual needs, and this application does not make any specific limitations on this.

[0055] In actual operation, the setting of the preset threshold can ensure that the retrieved historical second parameter has sufficient similarity with the current second parameter; the preset threshold can be determined according to actual needs, but if the threshold is set too high, it may cause the retrieved data to be significantly different from the target section, affecting the prediction accuracy; if the threshold is set too low, it may limit the amount of data that can be retrieved.

[0056] Step (3.6): Determine the (Nm) historical movement trajectories and the (Nm) historical second parameters as (Nm) reference movement trajectories and (Nm) reference second parameters.

[0057] In one implementation, the trajectory prediction model includes: a first trajectory prediction model and a second trajectory prediction model; step (3.4) includes: step (3.4.1) to step (3.4.2), the details of which are as follows: Step (3.4.1): If the next non-monitored road section is a straight section, the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the first trajectory prediction model, and the trajectory prediction model is made to output the predicted trajectory of the vehicle on the straight section.

[0058] Specifically, on sections of different road types, there may be significant differences in the vehicle driving status and behavior patterns, so it is necessary to apply two special trajectory prediction models - a first trajectory prediction model and a second trajectory prediction model, to predict the vehicle driving trajectory for straight sections and curved sections respectively; the first trajectory prediction model is specially designed to predict the vehicle's driving trajectory on straight sections; the second trajectory prediction model is specially designed to predict the vehicle's driving trajectory on curved sections.

[0059] Step (3.4.2): If the next non-monitored road section is a curved road section, the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the second trajectory prediction model, so that the trajectory prediction model outputs the predicted trajectory of the vehicle on the curved road section.

[0060] Specifically, in actual operation, compared with the fine-tuning samples of the first trajectory prediction model during the fine-tuning process, the fine-tuning samples of the second trajectory prediction model during the fine-tuning process also need to include the second parameters unique to the curved road section, such as curvature and turning radius.

[0061] S120: In response to the vehicle driving from the current monitored section into the next non-monitored section, the real-time trajectory and the predicted trajectory are spliced ​​to obtain a spliced ​​trajectory.

[0062] Specifically, in actual operation, by effectively splicing the predicted trajectory of the vehicle in the next non-monitored section with the real-time trajectory in the current monitored section, a complete movement trajectory can be formed, allowing users to intuitively observe the continuous movement process of the vehicle model on the holographic road network.

[0063] In actual operation, it is necessary to perform time synchronization processing on the real-time trajectory and the predicted trajectory, specifically, to determine the end timestamp of the predicted trajectory and the start timestamp of the real-time trajectory as the time points for splicing; on the basis of time synchronization, it is necessary to compare the last position point of the predicted trajectory with the first position point of the real-time trajectory. If the distance between the two position points is within the preset distance, it is considered that the position match is successful and the trajectory can be spliced. If the distance between the two position points is not within the preset distance, it is necessary to adjust the predicted trajectory so that the predicted trajectory can be spliced ​​with the real-time trajectory.

[0064] S130: Displaying the movement process of the vehicle model from the first display area to the second display area according to the splicing trajectory on the holographic road network; Among them, the vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

[0065] Specifically, the holographic road network can accurately display the movement process of the vehicle model from the first display area into the second display area, ensuring that the user can clearly see the movement process of the vehicle model.

[0066] In one implementation, the method further includes: Step (4) to Step (7), the details of which are as follows: Step (4): During the process of tracking the vehicle based on the holographic road network, if there is an intersection in the current non-monitored road section where the vehicle is traveling, in response to the vehicle entering the intersection, the current signal light state of the signal light in the intersection is obtained.

[0067] Specifically, the holographic road network provides real-time road condition information and vehicle dynamic display. In order to more accurately track the driving conditions of vehicles on non-monitored sections and adjust the predicted trajectory of vehicles according to the state of traffic lights when encountering intersections; in actual operation, it is necessary to continuously determine whether there is an intersection in the non-monitored section where the vehicle is currently located during vehicle tracking; if there is an intersection, proceed to the next step; if there is no intersection, continue to track according to the current predicted trajectory. When a vehicle enters an intersection, the current signal light state (such as red light, green light, yellow light, etc.) of the current intersection signal light is obtained through the communication interface or data sharing platform with the intersection signal light system; it should be emphasized that the obtained signal light state data must be accurate and timely to reflect the actual traffic conditions at the intersection.

[0068] Step (5): Determine the predicted traffic status of the vehicle at the intersection based on the current traffic light status.

[0069] Specifically, in actual operation, the traffic status of the vehicle in the current traffic light cycle can be judged based on the current traffic light state obtained; for example, if the traffic light is green, the vehicle may not need to stop to pass the intersection; if the traffic light is red, the vehicle needs to stop and wait at the intersection.

[0070] In actual operation, the specific time when the vehicle passes through the intersection or the time it stops and waits can be predicted by combining the traffic light status and the vehicle's current position, speed and other information. Confirmation of the above information will help to accurately adjust the vehicle's trajectory later.

[0071] Step (6): If the predicted traffic status indicates that the vehicle needs to stop at the intersection, the current predicted trajectory of the vehicle is adjusted to obtain an adjusted predicted trajectory.

[0072] Specifically, based on the predicted traffic status, determine whether the vehicle needs to stop at the intersection; if it needs to stop, proceed to the next step; if it does not need to stop, continue tracking according to the current predicted trajectory.

[0073] In actual operation, when a vehicle needs to stop at an intersection, the vehicle's current predicted trajectory is adjusted based on the parking location and parking time.

[0074] It should be emphasized that the adjusted predicted trajectory should reflect the process of the vehicle stopping and waiting at the intersection and then continuing to drive according to the change of the traffic light. It should also be emphasized that the adjusted predicted trajectory should be optimized to ensure that it conforms to the actual road conditions and vehicle driving rules.

[0075] Step (7): In the second display area corresponding to the current non-monitored road section in the holographic road network, the movement process of the vehicle model moving according to the adjusted predicted trajectory is displayed.

[0076] Specifically, the adjusted predicted trajectory is updated in real time to the corresponding second display area in the holographic road network, i.e., the current non-monitored road section, to ensure that the updated trajectory is consistent with the actual driving conditions of the vehicle. In the holographic road network, the process of the vehicle model moving according to the adjusted predicted trajectory is dynamically displayed. The movement of the vehicle model intuitively reflects the parking, waiting, and subsequent driving conditions of the vehicle at the intersection.

[0077] In one implementation, the first time when the vehicle model is displayed at the target location on the holographic road network is later than the second time when the real-time driving data of the vehicle at the target location is received; the method further includes: steps (8) to (10), the details of which are as follows: Step (8): In the process of tracking the vehicle based on the holographic road network, in response to the vehicle entering the current monitored section from the historical non-monitored section at the third moment, real-time driving data of the vehicle on the current monitored section is obtained.

[0078] Specifically, "the first moment of displaying the vehicle model at the target location on the holographic road network is later than the second moment of receiving the real-time driving data of the vehicle at the target location" means that the first moment of receiving the real-time driving data of the vehicle from the background server of the holographic road network and the second moment of displaying the vehicle model on the holographic road network are not the same, and the first moment is earlier than the second moment, that is, "the moment of displaying the trajectory" has a time delay relative to "the moment of receiving the driving data". Therefore, in actual applications, when the vehicle model on the holographic road network is still in the second display area corresponding to the historical non-monitored section, the target vehicle has already entered the current monitored section.

[0079] In actual operation, after predicting the driving trajectory of the vehicle on the non-monitored section, the vehicle model in the holographic road network is moved according to the predicted trajectory. However, if the predicted trajectory is different from the actual trajectory of the vehicle, when the vehicle enters the monitored section and the real-time trajectory is determined according to the real-time driving data of the vehicle, the actual trajectory and the real-time trajectory mentioned above are not completely aligned, resulting in the vehicle model on the holographic road network to "jump" or even cause the entire screen to be stuck, which is very unfavorable for tracking the vehicle. Therefore, the embodiment of the present application uses the real-time trajectory of the vehicle in the current monitored section obtained by the above-mentioned time delay to correct the predicted trajectory of the vehicle model in the holographic road network to obtain a repaired trajectory, so that the vehicle model on the holographic road network can move smoothly according to the repaired trajectory.

[0080] Step (9): The remaining predicted trajectory of the vehicle on the historical non-monitored section and the real-time trajectory on the current monitored section are input into the trajectory repair model, and the trajectory repair model is instructed to repair the vehicle on the current monitored section based on the spliced ​​trajectory of the remaining predicted trajectory and the real-time trajectory.

[0081] Among them, the remaining predicted trajectory is the part of the predicted trajectory of the historical non-monitored section corresponding to the time period after the third moment; the trajectory repair model is obtained by training the big data model.

[0082] Specifically, when applying the trajectory repair model, it is also necessary to input the section length of the non-monitored section corresponding to the remaining predicted trajectory, so that the trajectory repair model can output a repaired trajectory corresponding to the section length of the remaining predicted trajectory.

[0083] In practical applications, since the "real-time trajectory" and "predicted trajectory" in the embodiments of the present application are both trajectory data sets, the elements in the trajectory data set are their real-time coordinates and timestamps in the coordinate system of the holographic road network. Therefore, when the vehicle model in the holographic road network moves according to the repaired trajectory, it may exhibit behaviors such as "acceleration", "deceleration" or "change of driving direction", allowing it to move "smoothly" in the repaired trajectory corresponding to the remaining predicted trajectory, rather than "jumping" and other phenomena.

[0084] Step (10): Displaying the movement process of the vehicle model on the holographic road network according to the repair trajectory, at the fourth moment, from the second display area corresponding to the historical non-monitored section to the first display area corresponding to the current monitored section.

[0085] Among them, the fourth moment is later than the third moment.

[0086] Specifically, after the repair trajectory is determined, the vehicle model in the holographic road network can be driven from the second display area corresponding to the historical non-monitored section into the first display area corresponding to the current monitored section according to the repair trajectory. However, the moment when the vehicle model enters the corresponding first display area in the holographic road network is the fourth moment, which is later than the third moment because the "moment of displaying the trajectory" has a time delay relative to the "moment of receiving the driving data".

[0087] Second, the present application provides a vehicle relay tracking device based on a holographic road network, such as Figure 5 As shown, Figure 5 A structural diagram of a vehicle relay tracking device based on a holographic road network provided in an embodiment of the present application, the device comprises: a trajectory prediction module 310, a trajectory splicing module 320 and a trajectory display module 330; The trajectory prediction module 310 is used to predict the driving trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory of the vehicle on the current monitored road section to obtain a predicted trajectory during the process of tracking the vehicle based on the holographic road network; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section; A trajectory splicing module 320, configured to splice the real-time trajectory and the predicted trajectory to obtain a spliced ​​trajectory in response to the vehicle entering the next non-monitored section from the current monitored section; The track display module 330 is used to display the movement process of the vehicle model from the first display area to the second display area according to the splicing track on the holographic road network; Among them, the vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

[0088] In one implementation, the device further includes: a data acquisition module; The data acquisition module is used to collect the vehicle's driving data through the radar camera set on the current monitoring section to obtain real-time driving data; Among them, the monitored road section is the monitoring and identification area of ​​the radar camera, and the non-monitored road section is the monitoring blind area of ​​the radar camera; The data acquisition module is also used to determine the real-time trajectory based on the real-time driving data.

[0089] In one implementation, the trajectory prediction module 310 is further configured to predict the trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory using a trajectory prediction model to obtain a predicted trajectory; Among them, the trajectory prediction model is obtained by training the big data model.

[0090] In one implementation, the trajectory prediction module 310 is further configured to determine the first parameter according to the real-time trajectory of the current monitored road section; Wherein, the first parameter indicates the influence of non-road section factors on the driving state of the vehicle; The trajectory prediction module 310 is further used to determine a reference monitoring section from a current monitoring section traveled by the vehicle and at least one historical monitoring section traveled by the vehicle according to the section type of the next non-monitoring section; The road section types include: straight road sections and curved road sections; the road section type of the reference monitoring road section is the same as the road section type of the next non-monitoring road section; The trajectory prediction module 310 is further used to retrieve the current second parameter of the next non-monitored section, the reference moving trajectory and the reference second parameter of the reference monitored section from the database; The second parameter indicates the influence of the road section factor on the driving state of the vehicle; The trajectory prediction module 310 is further configured to input the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter into the trajectory prediction model, so that the trajectory prediction model outputs a predicted trajectory.

[0091] In one implementation, the trajectory prediction module 310 is further configured to retrieve (Nm) historical second parameters and corresponding (Nm) historical moving trajectories from a database according to the current second parameter if the current number m of the reference monitoring sections is less than the preset number N; Wherein, the difference between each historical second parameter of the (Nm) historical second parameters and the current second parameter is less than or equal to a preset threshold; The trajectory prediction module 310 is further configured to determine (Nm) historical movement trajectories and (Nm) historical second parameters as (Nm) reference movement trajectories and (Nm) reference second parameters.

[0092] In one implementation, the trajectory prediction model includes: a first trajectory prediction model and a second trajectory prediction model; The trajectory prediction module 310 inputs the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter into the first trajectory prediction model if the next non-monitored road section is a straight road section, and enables the trajectory prediction model to output the predicted trajectory of the vehicle on the straight road section; The trajectory prediction module 310 is further used to input the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter into the second trajectory prediction model if the next non-monitored road section is a curved road section, so that the trajectory prediction model outputs the predicted trajectory of the vehicle on the curved road section.

[0093] In one implementation, the trajectory prediction module 310 is further configured to, during the process of tracking the vehicle based on the holographic road network, if there is an intersection in the current non-monitored road section where the vehicle is traveling, obtain the current signal light state of the signal light in the intersection in response to the vehicle entering the intersection; The trajectory prediction module 310 is also used to determine the predicted traffic state of the vehicle at the intersection according to the current signal light state; The trajectory prediction module 310 is further configured to adjust the current predicted trajectory of the vehicle to obtain an adjusted predicted trajectory if the predicted traffic status indicates that the vehicle needs to stop at an intersection; The trajectory prediction module 310 is also used to display the movement process of the vehicle model according to the adjusted predicted trajectory in the second display area corresponding to the current non-monitored road section in the holographic road network.

[0094] In one implementation, the first time when the vehicle model is displayed at the target location on the holographic road network is later than the second time when the real-time driving data of the vehicle at the target location is received; the trajectory prediction module 310 is further used to obtain the real-time trajectory of the vehicle driving on the current monitored road section in response to the vehicle entering the current monitored road section from the historical non-monitored road section at the third time during the process of tracking the vehicle based on the holographic road network; The trajectory prediction module 310 is further used to input the remaining predicted trajectory of the vehicle on the historical non-monitored section and the real-time trajectory on the current monitored section into the trajectory repair model, so that the trajectory repair model repairs the remaining predicted trajectory and the real-time trajectory according to the spliced ​​trajectory to obtain the repaired trajectory of the vehicle on the current monitored section; The remaining predicted trajectory is the part of the predicted trajectory of the historical non-monitored road section corresponding to the period after the third moment; the trajectory repair model is obtained by training the big data model; The trajectory prediction module 310 is further used to display on the holographic road network the movement process of the vehicle model driving from the second display area corresponding to the historical non-monitored road section into the first display area corresponding to the current monitored road section at the fourth moment according to the repaired trajectory; Among them, the fourth moment is later than the third moment.

[0095] Third, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, steps S110 to S130 provided in the above embodiment are implemented.

[0096] Fourth, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, steps S110 to S130 of the above embodiment are executed.

[0097] Fifth, the computer program product provided in the present application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can refer to steps S110~S130 of the method embodiment, which will not be repeated here.

[0098] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0099] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0100] Furthermore, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0101] It should be noted that if the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0102] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0103] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle relay tracking method based on a holographic road network, characterized in that: The method comprises: In the process of tracking the vehicle based on the holographic road network, according to the real-time trajectory of the vehicle on the current monitored road section, the driving trajectory of the vehicle on the next non-monitored road section is predicted to obtain a predicted trajectory; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section; In response to the vehicle driving from the current monitored section into the next non-monitored section, splicing the real-time trajectory and the predicted trajectory to obtain a spliced ​​trajectory; Displaying on the holographic road network the movement process of the vehicle model from the first display area to the second display area according to the splicing trajectory; The vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

2. The method according to claim 1, characterized in that Before predicting the driving trajectory of the vehicle on the next non-monitored road section to obtain a predicted trajectory based on the real-time trajectory of the vehicle on the current monitored road section, the method further includes: The real-time driving data is obtained by collecting the driving data of the vehicle through a radar camera arranged on the currently monitored road section; The monitored road section is the monitoring identification area of ​​the radar camera, and the non-monitored road section is the monitoring blind area of ​​the radar camera; The real-time trajectory is determined according to the real-time driving data.

3. The method according to claim 2, characterized in that The step of predicting the driving trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory of the vehicle on the current monitored road section to obtain a predicted trajectory includes: According to the real-time trajectory, a trajectory of the vehicle on the next non-monitored road section is predicted by a trajectory prediction model to obtain the predicted trajectory; The trajectory prediction model is obtained by training a big data model.

4. The method according to claim 3, characterized in that The step of predicting the trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory by using a trajectory prediction model to obtain the predicted trajectory includes: Determining a first parameter according to the real-time trajectory of the current monitored road section; Wherein, the first parameter indicates the influence of non-road section factors on the driving state of the vehicle; Determine, according to the road section type of the next non-monitored road section, a reference monitored road section from a current monitored road section traveled by the vehicle and at least one historical monitored road section traveled by the vehicle; The road section types include: straight road sections and curved road sections; the road section type of the reference monitoring road section is the same as the road section type of the next non-monitoring road section; Retrieving the current second parameter of the next non-monitored section, the reference moving trajectory and the reference second parameter of the reference monitored section from the database; Wherein, the second parameter indicates the influence of the road section factor on the driving state of the vehicle; The first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the trajectory prediction model, so that the trajectory prediction model outputs the predicted trajectory.

5. The method according to claim 4, characterized in that After determining the reference monitoring section, the method further includes: If the current number m of the reference monitoring sections is less than the preset number N, according to the current second parameter, (Nm) historical second parameters and corresponding (Nm) historical movement trajectories are retrieved from the database; Wherein, the difference between each of the (Nm) historical second parameters and the current second parameter is less than or equal to a preset threshold; The (Nm) historical movement trajectories and the (Nm) historical second parameters are determined as (Nm) reference movement trajectories and (Nm) reference second parameters.

6. The method according to claim 4, characterized in that The trajectory prediction model includes: a first trajectory prediction model and a second trajectory prediction model; the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the trajectory prediction model, and the trajectory prediction model outputs the predicted trajectory, including: If the next non-monitored road section is the straight road section, input the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter into the first trajectory prediction model, so that the trajectory prediction model outputs the predicted trajectory of the vehicle on the straight road section; If the next non-monitored road section is the curved road section, the first parameter, the current second parameter, the reference moving trajectory and the reference second parameter are input into the second trajectory prediction model, so that the trajectory prediction model outputs the predicted trajectory of the vehicle in the curved road section.

7. The method according to claim 1, characterized in that The method further comprises: In the process of tracking the vehicle based on the holographic road network, if there is an intersection in the current non-monitored road section where the vehicle is traveling, in response to the vehicle entering the intersection, obtaining the current signal light state of the signal light in the intersection; Determining a predicted traffic state of the vehicle at the intersection according to the current traffic light state; If the predicted traffic status indicates that the vehicle needs to stop at the intersection, adjusting the current predicted trajectory of the vehicle to obtain an adjusted predicted trajectory; In the second display area corresponding to the current non-monitored road section in the holographic road network, the movement process of the vehicle model moving according to the adjusted predicted trajectory is displayed.

8. The method according to claim 2, characterized in that: The first time when the vehicle model is displayed at the target location on the holographic road network is later than the second time when the real-time driving data of the vehicle at the target location is received; the method further comprises: In the process of tracking the vehicle based on the holographic road network, in response to the vehicle entering the current monitored road section from the historical non-monitored road section at a third moment, obtaining a real-time trajectory of the vehicle traveling on the current monitored road section; Inputting the remaining predicted trajectory of the vehicle on the historical non-monitored section and the real-time trajectory on the current monitored section into a trajectory repair model, and allowing the trajectory repair model to repair the vehicle on the current monitored section according to the spliced ​​trajectory of the remaining predicted trajectory and the real-time trajectory; The remaining predicted trajectory is a portion of the predicted trajectory of the historical non-monitored road section corresponding to the period after the third moment; the trajectory repair model is obtained by training the big data model; Displaying on the holographic road network a moving process of the vehicle model according to the repair trajectory, at a fourth moment, driving from the second display area corresponding to the historical non-monitored road section into the first display area corresponding to the current monitored road section; The fourth moment is later than the third moment.

9. A vehicle relay tracking device based on a holographic road network, characterized in that: The device comprises: a trajectory prediction module, a trajectory splicing module and a trajectory display module; The trajectory prediction module is used to predict the driving trajectory of the vehicle on the next non-monitored road section according to the real-time trajectory of the vehicle on the current monitored road section to obtain a predicted trajectory during the process of tracking the vehicle based on the holographic road network; The next non-monitored road section is the non-monitored road section that the vehicle enters after leaving the current monitored road section; The trajectory splicing module is used for splicing the real-time trajectory and the predicted trajectory to obtain a spliced ​​trajectory in response to the vehicle entering the next non-monitored road section from the current monitored road section; The track display module is used to display the movement process of the vehicle model from the first display area to the second display area according to the splicing track on the holographic road network; The vehicle model is used to indicate the vehicle in the holographic road network; the first display area is used to indicate the current monitored section in the holographic road network, and the second display area is used to indicate the next non-monitored section in the holographic road network.

10. An electronic device, characterized in that: The electronic device includes a processor and a memory, the memory is used to store an application, and the processor runs or executes a software program stored in the memory so that the electronic device implements the vehicle relay tracking method based on a holographic road network as described in any one of claims 1 to 8.

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