Information processing method, apparatus, device, storage medium, and program product

Abstract

The present disclosure provides an information processing method, device, equipment, storage medium and program product, which can be applied to the field of automatic driving. The method comprises: obtaining a virtual measurement position corresponding to a real measurement position in a real environment in a simulation model; obtaining virtual moving object quantity information of a virtual moving object passing through the virtual measurement position in the simulation model in a target time period; obtaining real moving object information of a real moving object passing through the real measurement position in the target time period, including real moving object quantity information, which is measured in the real environment; obtaining information of a candidate destination road covered by the real measurement position in the real environment; determining a predicted destination road according to the candidate destination road based on the real moving object information and the information of the candidate destination road; and adjusting the quantity of virtual moving objects on a virtual road corresponding to the predicted destination road in the simulation model according to the virtual moving object quantity information and the real moving object quantity information, thereby improving the accuracy of simulation analysis.

Description

Technical Field

[0001] This disclosure relates to the field of data processing, and more specifically, to an information processing method, apparatus, electronic device, computer-readable storage medium, and computer program product. Background Technology

[0002] Using computer technology to simulate and analyze traffic data can provide strong data support for fields such as autonomous driving and traffic management. However, simulation analysis of offline traffic data in related technologies has relatively low accuracy.

[0003] As mentioned above, improving the accuracy of traffic data simulation analysis has become an urgent problem to be solved.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this disclosure is to provide an information processing method, apparatus, device, readable storage medium, and program product that improves the accuracy of traffic data simulation analysis to at least a certain extent.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] This disclosure provides an information processing method, comprising: obtaining a virtual measurement location in a simulation model corresponding to a real measurement location in a real environment; obtaining information on the number of virtual moving objects passing through the virtual measurement location in the simulation model during a target time period; obtaining information on real moving objects measured in the real environment that pass through the real measurement location during the target time period, the real moving object information including real moving object quantity information; obtaining information on candidate destination roads, the candidate destination roads being roads covered by the real measurement location in the real environment; determining a predicted destination road based on the real moving object information and the candidate destination road information; and adjusting the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the virtual moving object quantity information and the real moving object quantity information.

[0008] This disclosure provides an information processing apparatus, comprising: an information acquisition module for acquiring a virtual measurement location in a simulation model corresponding to a real measurement location in a real environment; an information acquisition module for acquiring information on the number of virtual moving objects passing through the virtual measurement location in the simulation model during a target time period; the information acquisition module is further configured to acquire information on real moving objects measured in the real environment that pass through the real measurement location during the target time period, the real moving object information including real moving object quantity information; the information acquisition module is further configured to acquire information on candidate destination roads, the candidate destination roads being roads covered by the real measurement location in the real environment; a processing module for determining a predicted destination road based on the real moving object information and the candidate destination road information; the processing module is further configured to adjust the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the virtual moving object quantity information and the real moving object quantity information.

[0009] According to one embodiment of this disclosure, the information of the real moving object further includes the moving speed information of the real moving object when passing the real measurement location; the information of the candidate destination road includes the length of the candidate destination road; the information acquisition module is further configured to: obtain the moving distance of the real moving object in the target time period based on the moving speed information of the real moving object and the target time period; obtain the tracking length of the candidate destination road based on the length of the candidate destination road, wherein the tracking length of the candidate destination road is the road length for tracking the real moving object on the candidate destination road after the target time period; the processing module is further configured to compare the moving distance with the tracking length of the candidate destination road, and determine the predicted destination road based on the comparison result and the candidate destination road.

[0010] According to an embodiment of this disclosure, the processing module is further configured to: if the moving distance is less than or equal to the tracking length of the candidate destination road, compare the moving distance with a first target ratio of the tracking length of the candidate destination road; if the moving distance is greater than or equal to the first target ratio of the tracking length of the candidate destination road, determine the candidate destination road as the predicted destination road.

[0011] According to one embodiment of this disclosure, the actual measurement location includes an actual measurement node, and the plurality of candidate destination roads include the road where the actual measurement node is located; the processing module is further configured to: if the moving distance is less than a first target ratio of the tracking length of the candidate destination road, then determine the road where the actual measurement node is located as the predicted destination road.

[0012] According to an embodiment of this disclosure, the information acquisition module is further configured to obtain the downstream road length of the candidate destination road if the moving distance is greater than the tracking length of the candidate destination road; the processing module is further configured to compare the length difference obtained by subtracting the tracking length of the candidate destination road from the moving distance with the downstream road length of the candidate destination road, and determine the predicted destination road from the candidate destination road and its downstream road based on the comparison result.

[0013] According to an embodiment of this disclosure, the processing module is further configured to: if the length difference is less than or equal to the length of the downstream road of the candidate destination road, compare the distance difference with a second target ratio of the length of the downstream road of the candidate destination road; if the length difference is greater than or equal to the second target ratio of the length of the downstream road of the candidate destination road, determine that the downstream road of the candidate destination road is the predicted destination road.

[0014] According to one embodiment of this disclosure, the processing module is further configured to: if the length difference is less than a second target ratio of the downstream road length of the candidate destination road, then determine the candidate destination road as the predicted destination road.

[0015] According to one embodiment of this disclosure, the information of the candidate destination road further includes the starting position of the candidate destination road; the information obtaining module is further configured to: obtain the road length between the actual measurement position and the starting position of the candidate destination road; when the actual measurement position is on the candidate destination road, subtract the road length between the actual measurement position and the starting position of the candidate destination road from the length of the candidate destination road to obtain the tracking length of the candidate destination road.

[0016] According to one embodiment of this disclosure, the information of the candidate destination road further includes the starting position of the candidate destination road; the information obtaining module is further configured to: obtain the road length between the actual measurement position and the starting position of the candidate destination road; when the actual measurement position is not on the candidate destination road, add the road length between the actual measurement position and the starting position of the candidate destination road to the length of the candidate destination road to obtain the tracking length of the candidate destination road.

[0017] According to an embodiment of this disclosure, the processing module is further configured to: compare the number of virtual moving objects with the number of real moving objects based on the virtual moving object quantity information and the real moving object quantity information; if the number of virtual moving objects is less than the number of real moving objects, then add virtual moving objects to the virtual road corresponding to the predicted destination road in the simulation model, so that the number of virtual moving objects after the addition is equal to the number of real moving objects.

[0018] According to one embodiment of this disclosure, the processing module is further configured to: determine a template virtual mobile object based on the virtual mobile objects on the virtual road; copy the template virtual mobile object to obtain a new virtual mobile object; determine whether the virtual road includes a placement position that is farther than a target distance threshold from the template virtual mobile object; and if the virtual road includes the placement position, place the new virtual mobile object at the placement position.

[0019] According to one embodiment of this disclosure, the real measurement location includes a real measurement node; the information acquisition module is further configured to: acquire the identifier of a first road where the real measurement node is located; acquire the road length between the real measurement node and the starting position of the first road; the information acquisition module is further configured to: acquire the virtual measurement location based on the identifier of the first road and the road length between the real measurement node and the starting position of the first road; and acquire information on the number of virtual moving objects passing through the virtual measurement location during the target time period using a target sensor set at the virtual measurement location.

[0020] This disclosure provides an electronic device, including: a memory, a processor, and executable instructions stored in the memory and executable in the processor, wherein the processor executes the executable instructions to implement any of the methods described above.

[0021] This disclosure provides a computer-readable storage medium having computer-executable instructions stored thereon, which, when executed by a processor, implement any of the methods described above.

[0022] This disclosure provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0023] The information processing method provided in the embodiments of this disclosure is based on information about real moving objects that pass through real measurement locations in a target time period, measured in a real environment, and information about candidate destination roads covered by the real measurement locations in the real environment. It determines a predicted destination road based on the candidate destination roads, and adjusts the number of virtual moving objects on the virtual roads corresponding to the predicted destination road in the simulation model based on the number of virtual moving objects that pass through virtual measurement locations corresponding to the real measurement locations in the target time period and the number of real moving objects. This method can predict the predicted destination road where real moving objects that pass through real measurement locations will be located after the target time period based on real-time traffic data, and then adjust the number of virtual moving objects on the virtual roads corresponding to the predicted destination road in the simulation model. This enables rapid and real-time adjustment of the traffic simulation model based on real-time data, thereby improving the accuracy of traffic data simulation analysis.

[0024] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0025] The above and other objects, features and advantages of this disclosure will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0026] Figure 1 A schematic diagram of a system structure according to an embodiment of this disclosure is shown.

[0027] Figure 2 A flowchart of an information processing method according to an embodiment of this disclosure is shown.

[0028] Figure 3 It shows Figure 2 The steps S202 and S204 shown are schematic diagrams of the processing in one embodiment.

[0029] Figure 4 An example illustration shows a schematic diagram of the road covered by a real measurement location.

[0030] Figure 5 It shows Figure 2 The step S210 shown is a schematic diagram of the processing procedure in one embodiment.

[0031] Figure 6 It shows Figure 5 The step S504 shown is a schematic diagram of the processing procedure in one embodiment.

[0032] Figure 7 An exemplary embodiment illustrates a schematic diagram of the positional relationship between a real measured location and a candidate destination road.

[0033] Figure 8 An exemplary embodiment illustrates another schematic diagram of the positional relationship between a real measured location and a candidate destination road.

[0034] Figure 9 It shows Figure 5 The step S506 shown is a schematic diagram of the processing procedure in one embodiment.

[0035] Figure 10 according to Figure 9 A schematic diagram of a predicted destination road is shown.

[0036] Figure 11 according to Figure 9 A schematic diagram of another predicted destination road is shown.

[0037] Figure 12 It shows Figure 5 The step S506 shown is a schematic diagram of the processing procedure in another embodiment.

[0038] Figure 13 according to Figure 12 A schematic diagram of a predicted destination road is shown.

[0039] Figure 14 It is based on Figures 5 to 13 The diagram shows a flowchart of a method for predicting and obtaining a destination road.

[0040] Figure 15 It shows Figure 2 The step S212 shown is a schematic diagram of the processing procedure in one embodiment.

[0041] Figure 16 It shows Figure 15 The step S15042 shown is a schematic diagram of the processing procedure in one embodiment.

[0042] Figure 17 It is based on Figure 16 This diagram illustrates the addition of a vehicle to a simulation model.

[0043] Figure 18 It is based on Figures 2 to 17 The diagram shows a flowchart of a traffic flow update method in a traffic simulation model.

[0044] Figure 19 It is based on Figures 2 to 18 The diagram shows a flowchart of a traffic simulation model adjustment method.

[0045] Figure 20 A block diagram of an information processing apparatus according to an embodiment of the present disclosure is shown.

[0046] Figure 21A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0047] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0048] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0049] Furthermore, in the description of this disclosure, unless otherwise expressly specified and limited, terms such as "connection" should be interpreted broadly, for example, meaning an electrical connection or the ability to communicate with each other; it can mean a direct connection or an indirect connection through an intermediate medium. "A plurality of" means at least two, such as two, three, etc., unless otherwise expressly and specifically limited. Those skilled in the art can understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0050] The terms used in the embodiments of this disclosure are explained below.

[0051] Checkpoint: refers to a sensor installed at a certain location on a road that can sense traffic conditions, such as cameras, geomagnetic coils, laser scanners, etc.

[0052] Traffic flow: The number of vehicles passing through a certain location within a certain period of time. The location can be, for example, the location of a checkpoint, a road, or an area, etc. The vehicles can be, for example, motor vehicles, non-motor vehicles, etc.

[0053] Origin-Destination matrix: A matrix representing the traffic demand of a road network within a specific time period. Origin-Destination estimation involves using measurement data from various sensors on the road to obtain the OD (Origin-Destination) for a past period.

[0054] Tracking length: In traffic simulation models, the length of a road segment on which a simulated vehicle is likely to appear within a certain period of time in the future is the tracking length of that road.

[0055] As mentioned above, some traffic simulation models in related technologies primarily process offline data, such as Vissim, Sumo, and Dynast software, resulting in relatively low accuracy in traffic simulation analysis. Other related technologies use offline data to calibrate model parameters in traffic simulations, typically requiring data such as vehicle travel time, average vehicle speed, and road vehicle occupancy. This significantly limits their application scenarios and makes it difficult to quickly and in real-time adjust traffic simulations based on real-time data. Furthermore, because the influence of model parameters on the simulation is indirect, the accuracy of specific checkpoints cannot be guaranteed.

[0056] Other related technologies extract the OD matrix from real-time data for simulation analysis. However, these technologies have high data requirements, requiring data from devices such as mobile phones, floating cars, RFID devices, and vehicle detectors to operate. Moreover, they can only guarantee the accuracy of traffic simulation at the OD level, and it is difficult to guarantee the accuracy at the checkpoint level.

[0057] Therefore, this disclosure provides an information processing method that predicts the destination road of a real moving object that has passed through a real measurement location after a target time period based on real-time traffic data, and then adjusts the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model. This enables rapid and real-time adjustment of the traffic simulation model based on real-time data, thereby improving the accuracy of traffic data simulation analysis.

[0058] Figure 1 An exemplary system architecture 10 is shown that the information processing methods and apparatus of this disclosure can be applied.

[0059] like Figure 1 As shown, system architecture 10 may include terminal device 102, network 104, and server 106. Terminal device 102 may be various electronic devices with a display screen and supporting input and output, including but not limited to smartphones, tablets, laptops, desktop computers, wearable devices, in-vehicle terminals, virtual reality devices, smart home devices, etc. Network 104 is used as a medium to provide a communication link between terminal device 102 and server 106. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc. Server 106 may be a server or server cluster providing various services, such as a backend processing server, database server, etc.

[0060] Terminal device 102 can interact with server 106 via network 104 to receive or send data. For example, information about real moving objects measured by terminal device 102 in a real environment can be uploaded to server 106 via network 104 for processing. Alternatively, server 106 can determine a predicted destination road based on candidate destination roads and send the predicted destination road information to terminal device 102 via network 104 for display. Furthermore, users can operate on terminal device 102 to view the simulation model after server 106 adjusts the number of virtual moving objects.

[0061] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0062] Figure 2 This is a flowchart illustrating an information processing method according to an exemplary embodiment. For example... Figure 2 The method shown can be applied, for example, to the server of the above system, or to the terminal device of the above system.

[0063] refer to Figure 2 The method 20 provided in this embodiment may include the following steps S202 to S212.

[0064] In step S202, the virtual measurement position corresponding to the real measurement position in the simulation model and the real measurement position in the real environment is obtained.

[0065] In some embodiments, the simulation model may be, for example, a traffic simulation model, which may include simulations of roads based on real-world maps, simulations of moving objects based on historical traffic data, etc. Real roads have corresponding virtual roads in the simulation model, and the map in the simulation model is drawn at a certain scale. Moving objects may include vehicles, pedestrians, etc., where vehicles may include motor vehicles, non-motor vehicles, etc.

[0066] In this embodiment of the disclosure, the (virtual or real) moving object is described using a vehicle as an example, but is not limited thereto.

[0067] In some embodiments, the actual measurement location can be the actual measurement node, such as the location of a checkpoint (hereinafter referred to as the checkpoint location). In this disclosure, unless otherwise explained, the actual measurement location is taken as the checkpoint location for illustration, but it is not limited thereto.

[0068] In other embodiments, the actual measurement location can also be a region, such as a certain street, etc., for example, a certain street includes multiple roads corresponding to that street.

[0069] In step S204, the number of virtual moving objects that passed through the virtual measurement location in the simulation model during the target time period is obtained.

[0070] In some embodiments, sensors can be placed at locations on the virtual road corresponding to the checkpoint location in the simulation model to obtain measurement data over a target time period, thereby obtaining information on the number of virtual moving objects passing through the virtual measurement location. Specific implementation details can be found in [reference needed]. Figure 3 .

[0071] In other embodiments, for example, if the current simulation model is an initial model, the initial placement direction of the roads around the virtual measurement location in the initial settings can be obtained, pointing towards the vehicles at the virtual measurement location.

[0072] In step S206, information on real moving objects that passed through the actual measurement location during the target time period, measured in the real environment, is obtained. The information on real moving objects includes the number of real moving objects.

[0073] In some embodiments, a sensor positioned at the actual measurement location can be used to collect the movement speed of a real moving object passing through the actual measurement location within a target time period. The number of speeds collected by the sensor during the target time period represents the number of real moving objects passing through the actual measurement location during that time period. The information on real moving objects may also include their movement speed information when passing through the actual measurement location, such as the speeds of all motor vehicles passing through the actual measurement location during the target time period.

[0074] In step S208, information about candidate destination roads is obtained. The candidate destination roads are roads covered by actual measured locations in the real environment.

[0075] In some embodiments, for example, when the actual measurement location is the checkpoint location, the road covered by the actual measurement location can be the road where the checkpoint is located and its downstream roads, that is, the road where the vehicle is most likely to be after passing the checkpoint in the real environment.

[0076] Figure 4 An example diagram illustrates the road coverage of a real-world measurement location. (See example...) Figure 4 As shown, the roads covered by checkpoint a may include road 01, road 02, road 03 and road 04.

[0077] In some embodiments, the information about the candidate destination road includes the length of the candidate destination road. For example, such as... Figure 7 As shown, the road length of candidate destination road 01 at checkpoint a is L1.

[0078] In some embodiments, the information of the candidate destination road may further include the starting position of the candidate destination road. The starting position of the candidate destination road may be the position where a real moving object, having passed through the actual measured location, enters the candidate destination road.

[0079] The embodiments disclosed herein all use the right-hand traffic rule as an example, but are not limited thereto.

[0080] For example, such as Figure 7 As shown, vehicles passing through checkpoint a travel from right to left. Therefore, the starting position of candidate destination road 01 at checkpoint a is the right end point A of the road. 01 .

[0081] In step S210, based on the information of the real moving object and the information of the candidate destination roads, the predicted destination road is determined according to the candidate destination roads.

[0082] In some embodiments, for example, the movement distance of the real moving object in the target time period can be estimated based on the collected speed of the real moving object, and then the movement distance of the real moving object in the target time period can be compared with the tracking length of the candidate destination road to predict the road where the real moving object will be located after the target time period. Detailed implementation methods can be found in [reference needed]. Figures 5 to 14 .

[0083] In other embodiments, for example, sensors such as lidar can be used to directly collect the movement distance of the actual moving object during the target time period, and then the movement distance of the actual moving object during the target time period can be compared with the tracking length of the candidate destination road to predict the road where the actual moving object will be after the target time period.

[0084] In other embodiments, for example, the location of the real moving object after the target time period can be determined based on the speed and direction of movement of the real moving object.

[0085] In other embodiments, for example, when the actual measurement location is a region, the candidate destination road is all the roads in that region, and the predicted destination road can be the candidate destination road.

[0086] In step S212, the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model is adjusted based on the virtual moving object quantity information and the real moving object quantity information.

[0087] In some embodiments, the number of virtual moving objects can be compared with the number of real moving objects. If the number of virtual moving objects is less than the number of real moving objects, virtual moving objects can be added to the road segments covered by the virtual measurement locations in the simulation model to make the traffic conditions denser. Specific implementation methods can be found in [reference needed]. Figures 15 to 17 .

[0088] In other embodiments, if the number of virtual moving objects is greater than the number of real moving objects, then some virtual moving objects can be removed from the road segment covered by the virtual measurement location in the simulation model. Specific implementation details can be found in [reference needed]. Figure 15 .

[0089] In other embodiments, if the number of virtual moving objects is equal to the number of real moving objects, then there is no need to change the virtual moving objects covering the road segment in the simulation model where the virtual measurement location is located.

[0090] According to the information processing method provided in this disclosure, based on the information of real moving objects that pass through the real measurement location in the target time period and the information of candidate destination roads covered by the real measurement location in the real environment, a predicted destination road is determined according to the candidate destination roads. Then, based on the number of virtual moving objects that pass through the virtual measurement location corresponding to the real measurement location in the simulation model in the target time period and the number of real moving objects, the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model is adjusted. By predicting the predicted destination road where the real moving objects that pass through the real measurement location will be located after the target time period based on real-time traffic data, and then adjusting the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model, the traffic simulation model can be quickly and in real-time adjusted according to real-time data, thereby improving the accuracy of traffic data simulation analysis.

[0091] Figure 3 It shows Figure 2 The steps S202 and S204 shown are schematic diagrams of the processing in one embodiment. Figure 3 The example used is a real measurement node (i.e., a checkpoint location on a real road). Figure 3 As shown in the embodiments of this disclosure, the above method 20 may further include the following steps S302 to S308.

[0092] Step S302: Obtain the identifier of the first road where the actual measurement node is located.

[0093] In some embodiments, the road identifier can be a unique road ID, which can be used to uniquely number the roads on the map (as simulation targets). For example, the identifier of the first road can be 01, R01001, R0402, etc.

[0094] Step S304: Obtain the road length between the actual measurement node and the starting position of the first road.

[0095] In some embodiments, for example, refer to Figure 7 ,like Figure 7 If road 01 is the first road and a is the actual measurement node, then the road length between the actual measurement node and the starting position of the first road is S1.

[0096] Step S306: Obtain the virtual measurement position based on the identification of the first road and the road length between the actual measurement node and the starting position of the first road.

[0097] In some embodiments, the virtual roads in the simulation model correspond to real roads. Therefore, according to the scale of the simulation model, the corresponding virtual measurement position on the virtual road corresponding to the first road can be obtained based on the road length between the real measurement node and the starting position of the first road.

[0098] Step S308: Obtain information on the number of virtual moving objects that pass through the virtual measurement location during the target time period by using the target sensor set at the virtual measurement location.

[0099] In some embodiments, for example, the target sensor set at the virtual measurement location can be a counter, which can be set to accumulate the number of virtual moving objects passing through the virtual measurement location within a certain period of time. The type of virtual moving object can also be set, for example, it can be set to accumulate the number of virtual motor vehicles passing through the virtual measurement location within a target time period.

[0100] According to the method provided in the embodiments of this disclosure, by setting a target sensor at a virtual measurement location corresponding to the real measurement location, the number of virtual moving objects passing through the virtual measurement location during a target time period is obtained, thereby improving the accuracy of the obtained number of virtual moving objects.

[0101] Figure 5 It shows Figure 2 The step S210 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 5 As shown in the present embodiment, step S210 may further include steps S502 to S506.

[0102] Step S502: Based on the moving speed information of the real moving object and the target time period, obtain the moving distance of the real moving object during the target time period.

[0103] In some embodiments, for example, the average speed of vehicles collected during the target time period can be calculated to obtain the average vehicle speed, and then the average vehicle speed can be multiplied by the target time period to obtain an estimate of the distance the vehicle traveled during the target time period.

[0104] Step S504: Based on the candidate destination road length, obtain the tracking length of the candidate destination road. The tracking length of the candidate destination road is the road length of the real moving object tracked on the candidate destination road after the target time period.

[0105] In some embodiments, the tracking length of the candidate destination road is the distance that the actual moving object can move on the candidate destination road after the target time period. This can be divided into two cases: the checkpoint location is on the candidate destination road and the checkpoint location is not on the candidate destination road. For specific implementation details, please refer to... Figures 6 to 8 .

[0106] Step S506: Compare the travel distance with the tracking length of the candidate destination road, and determine the predicted destination road based on the comparison results and the candidate destination road.

[0107] In some embodiments, the distance traveled by a real moving object within a target time period can be compared with the tracking length of a candidate destination road to predict whether the real moving object will be on the candidate destination road after the target time period. Specific implementation methods can be found in [reference needed]. Figures 9 to 14 .

[0108] Figure 6 It shows Figure 5 The step S504 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 6 As shown in the present embodiment, step S504 may further include steps S602 to S6044.

[0109] Step S602: Obtain the road length between the actual measurement location and the starting location of the candidate destination road.

[0110] Step S6042: When the actual measurement position is on the candidate destination road, subtract the road length between the actual measurement position and the starting position of the candidate destination road from the length of the candidate destination road to obtain the tracking length of the candidate destination road.

[0111] In some embodiments, Figure 7 An exemplary embodiment illustrates a schematic diagram showing the positional relationship between a real measured location and a candidate destination road, such as... Figure 7 As shown, Figure 7The actual measurement location is checkpoint a, the candidate destination road is road 01, the length of candidate destination road 01 is L1, and the starting position of checkpoint a and road 01 is A. 01 If the length of the road between them is S1, then the tracking length of road 01 is L1-S1.

[0112] Step S6044: When the actual measurement position is not on the candidate destination road, add the length of the road between the actual measurement position and the starting position of the candidate destination road to obtain the tracking length of the candidate destination road.

[0113] In some embodiments, Figure 8 An exemplary embodiment illustrates another schematic diagram showing the positional relationship between a real measured location and a candidate destination road, such as... Figure 8 As shown in the figure, the actual measurement location is checkpoint a, the candidate destination road is road 02, the length of candidate destination road 02 is L2, and the starting position of checkpoint a and road 02 is A. 02 If the length of the road between them is S2, then the tracking length of road 02 is L2+S2.

[0114] Figure 9 It shows Figure 5 The diagram illustrates step S506 in one embodiment. Figure 9 In this context, the actual measurement location is used as the actual measurement node, and multiple candidate destination roads include the roads where the actual measurement nodes are located. For example... Figure 9 As shown in the present embodiment, step S506 may further include steps S902 to S9044.

[0115] Step S902: If the moving distance is less than or equal to the tracking length of the candidate destination road, compare the first target ratio of the moving distance to the tracking length of the candidate destination road.

[0116] In some embodiments, if the moving distance is less than the tracking length of the candidate destination road, it indicates that if the actual moving object moved to the candidate destination road and did not leave the road after the target time period, it may still be on the road. Then, a first target ratio k1 is compared between the moving distance and the tracking length of the candidate destination road. This first target ratio can be, for example, 1 / 3, 1 / 2, or 2 / 3, etc.

[0117] In some embodiments, if the travel distance is equal to the tracking length of the candidate destination road, the candidate destination road can be considered as the predicted destination road.

[0118] Step S9042: If the moving distance is greater than or equal to the first target ratio of the tracking length of the candidate destination road, then the candidate destination road is determined as the predicted destination road.

[0119] In some embodiments, when the moving distance is less than the tracking length of the candidate destination road, if the moving distance is greater than or equal to the first target ratio k1 of the tracking length of the candidate destination road, it means that the moving distance is between k1 times the tracking length of the candidate destination road and the tracking length of the candidate destination road. At this time, the candidate destination road can be determined as the predicted destination road.

[0120] Figure 10 According to Figure 9 FIG. shows a schematic diagram of predicting the destination road. As Figure 10 shown, the real measurement position in the figure is checkpoint a, the candidate destination road is road 02, the tracking length of the candidate destination road 02 is L2 + S2, and the moving distance of checkpoint a in the target time period is D a1 , D a1 < L2 + S2, then compare D a1 with k1(L2 + S2) ( Figure 10 taking k1 as 1 / 2 as an example in a ), if D

[0121] > k1(L2 + S2), then determine the candidate destination road 02 as the predicted destination road.

[0122] In some embodiments, if the moving distance is less than the first target ratio k1 of the tracking length of the candidate destination road, it means that the moving distance is less than k1 times the tracking length of the candidate destination road. At this time, it can be considered that the real moving object may not have moved to the candidate destination road and may still be on the road where the real measurement node is located. Therefore, the candidate destination road is determined as the predicted destination road.

[0123] Figure 11 According to Figure 9 FIG. shows another schematic diagram of predicting the destination road. As Figure 11 shown, the road where the real measurement position checkpoint a is located in the figure is road 01, the candidate destination road is road 02, the tracking length of the candidate destination road 02 is L2 + S2, and the moving distance of checkpoint a in the target time period is D a1 A , D a1 < L2 + S2, then compare D a1 with k1(L2 + S? ( Figure 11 taking k1 as 1 / 2 as an example in a ), if D

[0124] Figure 12 shows Figure 5The diagram illustrates step S506 in another embodiment. Figure 12 In this context, the actual measurement location includes the actual measurement node, and multiple candidate destination roads include the roads where the actual measurement nodes are located. For example... Figure 12 As shown in the embodiments of this disclosure, step S506 may further include steps S1202 to S12046.

[0125] Step S1202: If the moving distance is greater than the tracking length of the candidate destination road, obtain the downstream road length of the candidate destination road.

[0126] In some embodiments, if the moving distance is greater than the tracking length of the candidate destination road, it means that if the real moving object moves to the candidate destination road, it may have moved out of the road after the target time period has elapsed, or may have moved to a downstream road. In this case, the length of the downstream road can be obtained.

[0127] Step S1204: Compare the length difference obtained by subtracting the tracking length of the candidate destination road from the moving distance with the length of the downstream road of the candidate destination road, and determine the predicted destination road from the candidate destination road and its downstream road based on the comparison result.

[0128] In some embodiments, the original travel distance can be subtracted from the tracking length of the candidate destination road, and the resulting length difference is the travel distance of the actual moving object on that downstream road. Then, according to... Figure 9 The steps are compared as described in steps S12042 to S12048 below.

[0129] Step S12042: If the length difference is less than or equal to the downstream road length of the candidate destination road, compare the distance difference with the second target ratio of the downstream road length of the candidate destination road.

[0130] Step S12044: If the length difference is less than the second target ratio of the downstream road length of the candidate destination road, then the candidate destination road is determined as the predicted destination road.

[0131] Step S12046: If the length difference is greater than or equal to the second target ratio of the length of the downstream road of the candidate destination road, then the downstream road of the candidate destination road is determined as the predicted destination road.

[0132] In some embodiments, for example, Figure 13 according to Figure 12 A schematic diagram of a predicted destination road is shown, such as... Figure 13 As shown in the figure, the actual measurement location is checkpoint a, the candidate destination road is road 02, the tracking length of candidate destination road 02 is L2+S2, and the movement distance of checkpoint a during the target time period is D. a1 D a1If L2 + S2, then the length of the downstream road 021 of the candidate destination road 02 is L. 21 Then compare D a1 –(L2+S2) and L 21 D can be obtained a1 –(L2+S2) <L 21 Then compare D a1 –(L2+S2) and k2L 21 ( Figure 13 Taking k2 as 1 / 2 as an example, we have D a1 –(L2+S2)>k2L 21 If so, then the downstream road 021 of the candidate destination road 02 is determined as the predicted destination road.

[0133] Step S12048: If the length difference is greater than the length of the downstream road of the candidate destination road, subtract the length of the downstream road from the length difference to obtain the current length difference, take the downstream road as the current candidate destination road, and return to step S1204.

[0134] Figure 14 It is based on Figures 5 to 13 The diagram illustrates a method for predicting the destination road. In the method provided in this embodiment, the input parameters are the location of the checkpoint, i.e., the road identifiers (IDs) of the road where the checkpoint is located and its downstream roads (candidate destination roads), and the road lengths between the checkpoint and the starting positions of each road. The output result is the road ID of the predicted destination road, which indicates which roads the vehicles will be distributed on, as represented by the traffic state data measured by the checkpoint.

[0135] like Figure 14 As shown, the method provided in this embodiment may include steps S1402 to S1414.

[0136] Step S1402: First, calculate the average vehicle speed measured at the checkpoint * target time period = average vehicle travel distance (corresponding to the travel distance of the actual moving object in the target time period).

[0137] Step S1404: Initialize the current track length to be tracked as the average vehicle travel distance, and obtain the current road segment. The current road segment is a road randomly selected from the candidate destination roads.

[0138] Step S1406: Determine whether the current tracking length is greater than the current road segment tracking length. The method for obtaining the current road segment tracking length can be found in [reference needed]. Figure 6 and Figure 8 .

[0139] Step S1408: If the current track length is greater than the current road segment track length, update the current track length = current track length - current road segment track length, and take each downstream road of the current road segment as the current road segment in sequence, then return to step S1406.

[0140] Step S1410: If the current tracking length is less than or equal to the current road segment tracking length, check if the current tracking length is greater than half of the current road segment tracking length. (Refer to...) Figures 9 to 11 In this method 1, the proportion of the first target is 1 / 2.

[0141] Step S1412: If the current track length to be tracked is greater than half of the current road segment track length, then the current road segment is determined to be the predicted target road.

[0142] Step S1414: If the current track length is not greater than half of the current road segment track length, update the current road segment. That is, select another road from the candidate destination roads that has not been traversed in step S1406, and return to step S1406.

[0143] According to the method provided in the embodiments of this disclosure, when the moving distance of the real moving object in the target time period is between the (first or second) target ratio multiplied by the tracking length of the candidate destination road (or its downstream road) and the tracking length of the candidate destination road, the candidate destination road (or its downstream road) is determined as the predicted destination road. This simplifies the calculation and improves the efficiency of obtaining the predicted destination road.

[0144] Figure 15 It shows Figure 2 The step S212 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 15 As shown in the present embodiment, step S212 may further include steps S1502 to S15044.

[0145] Step S1502: Compare the number of virtual moving objects with the number of real moving objects based on the virtual moving object quantity information and the real moving object quantity information.

[0146] Step S15042: If the number of virtual moving objects is less than the number of real moving objects, then add virtual moving objects to the virtual road corresponding to the predicted destination road in the simulation model so that the number of virtual moving objects after the addition is equal to the number of real moving objects.

[0147] In some embodiments, the number of real moving objects can be subtracted from the number of virtual moving objects to obtain the difference in quantity, and then the difference in quantity of virtual moving objects can be added to the corresponding virtual road in the simulation model.

[0148] In some embodiments, if there are multiple target roads, one target road can be randomly selected each time, and a virtual moving object can be placed on it. Taking a virtual moving object as an example, if there are currently no virtual vehicles on the selected target road, a newly generated virtual vehicle can be placed at a random position on that road, and the speed of the newly generated vehicle can be set to the average speed of vehicles actually measured at the checkpoint.

[0149] In other embodiments, if a virtual vehicle already exists on the selected target road, it can be first determined whether there is a "gap" (i.e., placement position) on the road where a virtual vehicle can be placed. If a "gap" exists, the virtual vehicle can be placed. Specific implementation details can be found in [reference needed]. Figure 16 .

[0150] Step S15044: If the number of virtual moving objects is greater than the number of real moving objects, then reduce the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model, so that the increased number of virtual moving objects is equal to the number of real moving objects.

[0151] In some embodiments, if the number of virtual moving objects is greater than the number of real moving objects, the number of virtual moving objects can be subtracted from the number of real moving objects to obtain the difference in quantity. Then, virtual moving objects with the difference in quantity can be randomly selected from all virtual moving objects on all predicted destination roads and removed.

[0152] Figure 16 It shows Figure 15 The step S15042 shown is a schematic diagram of the processing procedure in one embodiment. (See attached diagram.) Figure 16 As shown in the present embodiment, step S15042 may further include steps S1602 to S16084.

[0153] Step S1602: Determine the template virtual moving object based on the virtual moving object on the virtual road.

[0154] In some embodiments, a vehicle can be randomly selected from all virtual vehicles on the virtual road in the simulation model as a template vehicle, and then that vehicle can be copied as a newly generated vehicle.

[0155] Step S1604: Copy the template virtual movement object to obtain the newly added virtual movement object.

[0156] Step S1606: Determine whether the virtual road includes placement positions that are farther away from the template virtual moving object than the target distance threshold.

[0157] In some embodiments, the target distance threshold can be a set safe distance between the virtual moving object and the template, such as 18 meters, 20 meters, 25 meters, etc. The placement location can be the length of the road used for placement and expected to accommodate a virtual vehicle, such as 5 meters, 6 meters, or 7 meters, etc.

[0158] Step S16082: If the virtual road includes a placement location, place the new virtual moving object at the placement location.

[0159] Step S16084: If the virtual road does not include a placement location, select another predicted destination road, obtain the virtual road corresponding to the predicted destination road in the simulation model, and return to step S1602.

[0160] According to the method provided in this disclosure, the newly generated virtual vehicle is placed in the placement position closest to the template virtual vehicle. If no placement position can be found on the predicted destination road, the newly generated virtual vehicle is abandoned on the predicted destination road. This can prevent the newly generated virtual vehicle from affecting the operation of the original template vehicle in the simulation model.

[0161] Figure 17 It is based on Figure 16 This diagram illustrates how to add a vehicle to a simulation model. Figure 17 As shown, Road 05, Road 06, and Road 07 are three predicted destination roads. On Road 05, M00 is a template virtual vehicle, and M001 is a newly added virtual vehicle. On Road 06, M01 is a template virtual vehicle, C03 is an existing virtual vehicle, and M011 is a newly added virtual vehicle. On Road 07, M02 is a template virtual vehicle, while C01 and C02 are existing virtual vehicles with no placement space, so no new virtual vehicles were added on Road 07.

[0162] Figure 18 It is based on Figures 2 to 17 This diagram illustrates a traffic flow update method in a traffic simulation model. Figure 18 As shown, firstly, the predicted destination road corresponding to the actual measurement location is determined (S1802). Then, it is determined whether the actual traffic flow at the actual measurement location is greater than the simulated traffic flow at the corresponding location in the simulation model (S1804). If the actual traffic flow is greater than the simulated traffic flow, virtual vehicles are added to the simulation model according to the difference between the traffic flow in the simulation model and the actual traffic flow (S1806). Otherwise, virtual vehicles are reduced in the simulation model (S1808).

[0163] Figure 19 It is based on Figures 2 to 18 The diagram shows a flowchart of a traffic simulation model adjustment method. Figure 19The provided method can be used to adjust traffic simulations in the "real-time simulation" function of traffic simulation products by obtaining real-time checkpoint data from traffic management departments. For example... Figure 19 As shown, the system can periodically query the server for real-time checkpoint data input (S1904). If so, the real-time checkpoint data is used as input, and the running traffic simulation model is used (S1902) to call the method provided in the above embodiment to generate a real-time adjusted traffic simulation that is closer to the real traffic state using the checkpoint data (S1906).

[0164] According to the method provided in this disclosure, when real traffic data from traffic management departments can be obtained, the traffic state in the simulation can be efficiently modified based on real checkpoint data, making the simulation closer to reality. The information obtained when extrapolating through traffic simulation is also more accurate. Furthermore, it is highly flexible and can handle real-time data of arbitrary length and location.

[0165] Figure 20 This is a block diagram illustrating an information processing apparatus according to an exemplary embodiment. Figure 20 The device shown can be applied, for example, to the server side of the above system, or to the terminal device of the above system.

[0166] refer to Figure 20 The apparatus 200 provided in this embodiment may include an information acquisition module 2002, an information acquisition module 2004, and a processing module 2006.

[0167] The information acquisition module 2002 can be used to obtain the virtual measurement position in the simulation model that corresponds to the real measurement position in the real environment.

[0168] The information acquisition module 2002 can also be used to acquire information about candidate destination roads, which are roads covered by actual measured locations in a real environment.

[0169] The actual measurement location can include the actual measurement node.

[0170] The information acquisition module 2002 can also be used to obtain the moving distance of the real moving object in the target time period based on the moving speed information of the real moving object and the target time period; and to obtain the tracking length of the candidate destination road based on the length of the candidate destination road, wherein the tracking length of the candidate destination road is the road length of the real moving object tracked on the candidate destination road after the target time period.

[0171] Information about candidate destination roads may include the length of the candidate destination road.

[0172] Information about candidate destination roads may also include the starting location of the candidate destination road.

[0173] Information about candidate destination roads may also include the starting location of the candidate destination road.

[0174] Multiple candidate destination roads can include the roads where the actual measurement nodes are located.

[0175] The information acquisition module 2002 can also be used to obtain the downstream road length of the candidate destination road if the moving distance is greater than the tracking length of the candidate destination road.

[0176] The information acquisition module 2002 can also be used to obtain the road length between the actual measurement location and the starting location of the candidate destination road; when the actual measurement location is not on the candidate destination road, the length of the candidate destination road is added to the road length between the actual measurement location and the starting location of the candidate destination road to obtain the tracking length of the candidate destination road.

[0177] The information acquisition module 2004 can be used to acquire information on the number of virtual moving objects that pass through the virtual measurement location in the simulation model during the target time period.

[0178] The information acquisition module 2004 can also be used to acquire information on real moving objects that have passed through the actual measurement location in the target time period, as measured in the real environment. The information on real moving objects may include the number of real moving objects.

[0179] The information acquisition module 2004 can also be used to acquire the identifier of the first road where the actual measurement node is located; and to acquire the road length between the actual measurement node and the starting position of the first road.

[0180] The information acquisition module 2004 can also be used to obtain a virtual measurement location based on the identification of the first road and the road length between the actual measurement node and the starting position of the first road; and to obtain information on the number of virtual moving objects that pass through the virtual measurement location during the target time period by using a target sensor set at the virtual measurement location.

[0181] The information acquisition module 2004 can also be used to obtain the road length between the actual measurement location and the starting location of the candidate destination road; when the actual measurement location is on the candidate destination road, the length of the candidate destination road is subtracted from the road length between the actual measurement location and the starting location of the candidate destination road to obtain the tracking length of the candidate destination road.

[0182] Information about a real moving object can also include the object's speed when it passes the actual measured location.

[0183] The processing module 2006 can be used to determine the predicted destination road based on the information of the real moving object and the information of the candidate destination road.

[0184] The processing module 2006 can also be used to adjust the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the virtual moving object quantity information and the real moving object quantity information.

[0185] The processing module 2006 can also be used to compare the travel distance with the tracking length of the candidate destination road, and determine the predicted destination road based on the comparison results and the candidate destination road.

[0186] The processing module 2006 can also be used to compare the moving distance with the first target ratio of the tracking length of the candidate destination road if the moving distance is less than or equal to the tracking length of the candidate destination road; if the moving distance of the real moving object in the target time period is greater than or equal to the first target ratio of the tracking length of the candidate destination road, then the candidate destination road is determined as the predicted destination road.

[0187] The processing module 2006 can also be used to determine the road where the actual measurement node is located as the predicted target road if the moving distance of the real moving object in the target time period is less than the first target ratio of the tracking length of the candidate target road.

[0188] The processing module 2006 can also be used to compare the length difference obtained by subtracting the tracking length of the candidate destination road from the travel distance with the length of the downstream road of the candidate destination road, and determine the predicted destination road from the candidate destination road and its downstream road based on the comparison result.

[0189] The processing module 2006 can also be used to compare the distance difference with the second target ratio of the downstream road length of the candidate destination road if the length difference is less than or equal to the downstream road length of the candidate destination road; if the length difference is greater than or equal to the second target ratio of the downstream road length of the candidate destination road, then the downstream road of the candidate destination road is determined as the predicted destination road.

[0190] The processing module 2006 can also be used to determine the candidate target road as the predicted target road if the length difference is less than the second target ratio of the downstream road length of the candidate target road.

[0191] The processing module 2006 can also be used to compare the number of virtual moving objects with the number of real moving objects based on the information on the number of virtual moving objects and the information on the number of real moving objects; if the number of virtual moving objects is less than the number of real moving objects, then add virtual moving objects to the virtual road corresponding to the predicted destination road in the simulation model so that the number of virtual moving objects after the addition is equal to the number of real moving objects.

[0192] The processing module 2006 can also be used to determine a template virtual moving object based on the virtual moving objects on the virtual road; copy the template virtual moving object to obtain a new virtual moving object; determine whether the virtual road includes a placement position that is farther away from the template virtual moving object than the target distance threshold; if the virtual road includes a placement position, place the new virtual moving object at the placement position.

[0193] The specific implementation of each module in the device provided in this embodiment can be referred to the content of the above method, and will not be repeated here.

[0194] Figure 21 A schematic diagram of the structure of an electronic device according to an embodiment of this disclosure is shown. It should be noted that... Figure 21 The devices shown are merely examples of computer systems and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0195] like Figure 21 As shown, device 2100 includes a central processing unit (CPU) 2101, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 2102 or a program loaded from storage section 2108 into random access memory (RAM) 2103. The RAM 2103 also stores various programs and data required for the operation of device 2100. CPU 2101, ROM 2102, and RAM 2103 are interconnected via bus 2104. Input / output (I / O) interface 2105 is also connected to bus 2104.

[0196] The following components are connected to I / O interface 2105: an input section 2106 including a keyboard, mouse, etc.; an output section 2107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 2108 including a hard disk, etc.; and a communication section 2109 including a network interface card such as a LAN card, modem, etc. The communication section 2109 performs communication processing via a network such as the Internet. A drive 2110 is also connected to I / O interface 2105 as needed. Removable media 2111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 2110 as needed so that computer programs read from them can be installed into storage section 2108 as needed.

[0197] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 2109, and / or installed from removable medium 2111. When the computer program is executed by central processing unit (CPU) 2101, it performs the functions defined above in the system of this disclosure.

[0198] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0199] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0200] The modules described in the embodiments of this disclosure can be implemented in software or hardware. The described modules can also be housed in a processor; for example, a processor may be described as including an information acquisition module, an information gathering module, and a processing module. The names of these modules do not necessarily limit the module itself; for example, the information acquisition module may also be described as "a module for acquiring information about virtual measurement locations and candidate destination roads."

[0201] This disclosure also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include:

[0202] The simulation model obtains virtual measurement locations corresponding to real measurement locations in the real environment; it also obtains information on the number of virtual moving objects passing through the virtual measurement locations in the simulation model during the target time period; it obtains information on real moving objects passing through the real measurement locations in the real environment during the target time period, including the number of real moving objects; it obtains information on candidate destination roads, which are roads covered by the real measurement locations in the real environment; based on the information of real moving objects and candidate destination roads, it determines the predicted destination road; and it adjusts the number of virtual moving objects on the virtual roads corresponding to the predicted destination road in the simulation model based on the number of virtual moving objects and the number of real moving objects.

[0203] This disclosure provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0204] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. An information processing method, characterized in that, include: Obtain the virtual measurement position in the simulation model that corresponds to the real measurement position in the real environment; Obtain the number of virtual moving objects that pass through the virtual measurement location in the simulation model during the target time period; Acquire information on real moving objects that have passed through the real measurement location in the target time period, as measured in the real environment; the information on real moving objects includes information on the number of real moving objects. Information on candidate destination roads is obtained, wherein the candidate destination roads are roads covered by the actual measurement location in the real environment, and the information on the candidate destination roads includes the length of the candidate destination roads; Based on the information of the real moving object and the information of the candidate destination roads, a predicted destination road is determined according to the candidate destination roads; Based on the virtual moving object quantity information and the real moving object quantity information, adjust the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model; Based on the information of the actual moving object and the information of the candidate destination roads, the predicted destination road is determined according to the candidate destination roads, including: Obtain the distance the actual moving object has moved from the actual measured position during the target time period; Based on the candidate destination road length and the actual measurement location, the tracking length of the candidate destination road is obtained. The tracking length of the candidate destination road is the distance from the actual measurement location to the end of the candidate destination road that is furthest from the actual measurement location. The travel distance is compared with the tracking length of the candidate destination road, and the predicted destination road is determined based on the comparison result and the candidate destination road. Based on the virtual moving object quantity information and the real moving object quantity information, adjust the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model, including: Based on the virtual mobile object quantity information and the real mobile object quantity information, compare the size of the virtual mobile object quantity and the real mobile object quantity; Based on the comparison results, the number of virtual moving objects on the virtual road corresponding to the predicted target road in the simulation model may be increased, decreased, or maintained.

2. The method according to claim 1, characterized in that, The information of the real moving object also includes the moving speed information of the real moving object when it passes the real measurement position; Obtaining the distance traveled by the actual moving object during the target time period includes: Based on the movement speed information of the actual moving object and the target time period, the movement distance of the actual moving object during the target time period is obtained.

3. The method according to claim 1 or 2, characterized in that, Based on the comparison results and according to the candidate destination roads, the predicted destination road is determined, including: If the moving distance is less than or equal to the tracking length of the candidate destination road, compare the product of the moving distance and the tracking length of the candidate destination road with the proportion of the first target. If the moving distance is greater than or equal to the product of the tracking length of the candidate destination road and the proportion of the first target, then the candidate destination road is determined to be the predicted destination road.

4. The method according to claim 3, characterized in that, The actual measurement location includes the actual measurement node, and the multiple candidate destination roads include the roads where the actual measurement node is located; Based on the comparison results, determining the predicted destination road according to the candidate destination roads also includes: If the moving distance is less than the product of the tracking length of the candidate destination road and the proportion of the first target, then the road where the actual measurement node is located is determined to be the predicted destination road.

5. The method according to claim 1 or 2, characterized in that, Based on the comparison results, a predicted destination road is determined according to the candidate destination roads, including: If the travel distance is greater than the tracking length of the candidate destination road, the downstream road length of the candidate destination road is obtained; The predicted destination road is determined by comparing the length difference obtained by subtracting the tracking length of the candidate destination road from the travel distance with the length of the downstream road of the candidate destination road, and then determining the predicted destination road from the candidate destination road and its downstream road based on the comparison result.

6. The method according to claim 5, characterized in that, Determining the predicted destination road from the candidate destination roads and their downstream roads based on the comparison results includes: If the length difference is less than or equal to the length of the downstream road of the candidate destination road, compare the length difference with the product of the length of the downstream road of the candidate destination road and the proportion of the second target. If the length difference is greater than or equal to the product of the length of the downstream road of the candidate destination road and the proportion of the second target, then the downstream road of the candidate destination road is determined to be the predicted destination road.

7. The method according to claim 6, characterized in that, Determining the predicted destination road from the candidate destination roads and their downstream roads based on the comparison results further includes: If the length difference is less than the product of the downstream road length of the candidate destination road and the proportion of the second target, then the candidate destination road is determined to be the predicted destination road.

8. The method according to claim 2, characterized in that, The information of the candidate destination road also includes the starting position of the candidate destination road; Based on the length of the candidate destination road, the tracking length of the candidate destination road is obtained, including: Obtain the road length between the actual measurement location and the starting location of the candidate destination road; When the actual measurement position is on the candidate destination road, the tracking length of the candidate destination road is obtained by subtracting the road length between the actual measurement position and the starting position of the candidate destination road from the length of the candidate destination road.

9. The method according to claim 2, characterized in that, The information of the candidate destination road also includes the starting position of the candidate destination road; Based on the length of the candidate destination road, the tracking length of the candidate destination road is obtained, including: Obtain the road length between the actual measurement location and the starting location of the candidate destination road; When the actual measurement position is not on the candidate destination road, the tracking length of the candidate destination road is obtained by adding the length of the road between the actual measurement position and the starting position of the candidate destination road to the length of the candidate destination road.

10. The method according to claim 1, characterized in that, Adjusting the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the virtual moving object quantity information and the real moving object quantity information, further includes: If the number of virtual moving objects is less than the number of real moving objects, then virtual moving objects are added to the virtual road corresponding to the predicted destination road in the simulation model, so that the number of virtual moving objects after the addition is equal to the number of real moving objects.

11. The method according to claim 10, characterized in that, Adding virtual moving objects to the virtual road corresponding to the predicted destination road in the simulation model, including: Based on the virtual moving objects on the virtual road, determine the template virtual moving object; The template virtual movement object is copied to obtain a new virtual movement object; Determine whether the virtual road includes a placement position that is farther than the target distance threshold from the template virtual moving object; If the virtual road includes the placement location, place the newly added virtual mobile object at the placement location.

12. The method according to claim 1, characterized in that, The actual measurement location includes the actual measurement node; Obtain the virtual measurement position in the simulation model that corresponds to the real measurement position in the real environment, including: Obtain the identifier of the first road where the actual measurement node is located; Obtain the road length between the actual measurement node and the starting position of the first road; The virtual measurement position is obtained based on the identification of the first road and the road length between the actual measurement node and the starting position of the first road; Obtaining information on the number of virtual moving objects that pass through the virtual measurement location in the simulation model during the target time period includes: By using a target sensor set at the virtual measurement location, information on the number of virtual moving objects that passed through the virtual measurement location during the target time period is obtained.

13. An information processing device, characterized in that, include: The information acquisition module is used to obtain the virtual measurement position in the simulation model that corresponds to the real measurement position in the real environment; The information acquisition module is used to acquire information on the number of virtual moving objects that pass through the virtual measurement location in the simulation model during the target time period; The information acquisition module is also used to acquire information about real moving objects that have passed through the real measurement location in the target time period as measured in the real environment. The information about the real moving objects includes information about the number of real moving objects. The information acquisition module is further configured to acquire information about candidate destination roads, wherein the candidate destination roads are roads covered by the actual measurement location in the real environment, and the information about the candidate destination roads includes the length of the candidate destination roads; The processing module is used to determine the predicted destination road based on the information of the real moving object and the information of the candidate destination road; The information acquisition module is further configured to: acquire the moving distance of the real moving object from the real measurement position during the target time period; and acquire the tracking length of the candidate destination road based on the candidate destination road length and the real measurement position, wherein the tracking length of the candidate destination road is the distance from the real measurement position to the end of the candidate destination road furthest from the real measurement position. The processing module is further configured to compare the travel distance with the tracking length of the candidate destination road, and determine the predicted destination road based on the comparison result and the candidate destination road; The processing module is further configured to adjust the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the virtual moving object quantity information and the real moving object quantity information. The processing module is further configured to compare the number of virtual moving objects with the number of real moving objects based on the virtual moving object quantity information and the real moving object quantity information; and to increase, decrease, or maintain the number of virtual moving objects on the virtual road corresponding to the predicted destination road in the simulation model based on the comparison result.

14. An electronic device comprising: A memory, a processor, and executable instructions stored in the memory and executable in the processor, characterized in that the processor, when executing the executable instructions, implements the method as described in any one of claims 1-12.

15. A computer-readable storage medium having computer-executable instructions stored thereon, characterized in that, When the executable instructions are executed by the processor, they implement the method as described in any one of claims 1-12.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-12.

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