Automobile perception service information processing method, core network element and storage medium

Abstract

The application provides a kind of automobile perception service information processing method, core network element and storage medium, it is related to vehicle networking technical field.The method comprises: receiving the perception service analysis request for target automobile that AF network element sends, and according to the real-time positioning data of target automobile and perception service analysis request, perception monitoring request is sent to base station, and perception monitoring data returned by base station is received;According to the historical positioning data of target automobile, whether target automobile is driven out of the range of perception service is predicted;Perception service analysis result is sent to AF network element, and historical perception service information is sent to target automobile in the case where AF network element target automobile drives out of the range of perception service;In the case where target automobile does not drive out of the range of perception service, perception monitoring data is sent to target automobile, and driving reference information is provided for target automobile.The application can provide perception data for automobile under various conditions, guarantee the safety of automobile driving.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Vehicles, in particular to a vehicle perception service information processing method, a core network element and a storage medium. BACKGROUND

[0002] With the continuous development of science and technology, the public is increasingly pursuing a convenient and intelligent lifestyle. Environmental perception is an indispensable technical system for vehicles, especially for self-driving vehicles. 5G New Radio (5G NR) wireless sensing services are also used to assist vehicle driving.

[0003] During vehicle driving and navigation, real-time road conditions, surrounding objects, obstacles, traffic signs and pedestrian crossings can be obtained by using environmental perception technology and NR wireless sensing services, thereby providing timely and accurate traffic environment information for vehicle driving. Therefore, environmental perception results play an important role in vehicle driving decision-making, ensuring driving safety and stability.

[0004] In the prior art, NR wireless sensing services can only be received when the vehicle is within the coverage of the NR wireless sensing services. When the vehicle drives into remote areas or beyond the coverage of the NR wireless sensing services, the vehicle may experience a perception service interruption or be unable to perceive, which is not conducive to vehicle driving safety. SUMMARY

[0005] The present application aims to overcome the deficiencies in the prior art and provide a vehicle perception service information processing method, a core network element and a storage medium to provide perception data for vehicles under various conditions and ensure vehicle driving safety.

[0006] To achieve the above-mentioned purpose, the technical solutions adopted by the embodiments of the present application are as follows:

[0007] In a first aspect, the embodiments of the present application provide a vehicle perception service information processing method applied to a network data analysis function (NWDAF) element, which comprises the following steps:

[0008] receiving a perception service analysis request for a target vehicle sent by an application function (AF) element, wherein the perception service analysis request comprises a vehicle identifier and a perception type of the target vehicle;

[0009] sending a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receiving perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station;

[0010] predicting whether the target vehicle drives out of a perception service range of the preset perception device according to historical positioning data of the target vehicle;

[0011] sending the perception service analysis result to the AF network element, the AF network element being configured to send historical perception service information to the target vehicle if the perception service analysis result indicates that the target vehicle drives out of the perception service range, and send the perception monitoring data to the target vehicle to provide driving reference information for the target vehicle if the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, the driving reference information being the historical perception service information or the perception monitoring data.

[0012] Optionally, the step of predicting whether the target vehicle drives out of a perception service range of the preset perception device according to historical positioning data of the target vehicle comprises:

[0013] obtaining a plurality of first historical positioning data of the target vehicle and a plurality of second historical positioning data of other vehicles in a perception service range in which the target vehicle is located;

[0014] calculating a driving similarity between the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data;

[0015] calculating a probability of the target vehicle going to each region in a preset range according to the driving similarity;

[0016] determining whether the target vehicle drives out of the perception service range according to the region with the maximum probability.

[0017] Optionally, the step of calculating a driving similarity between the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data comprises:

[0018] establishing a first transition probability matrix of the target vehicle according to the plurality of first historical positioning data;

[0019] establishing a second transition probability matrix of the other vehicles according to the plurality of second historical positioning data;

[0020] calculating the driving similarity according to the first transition probability matrix and the second transition probability matrix.

[0021] Optionally, the step of establishing a first transition probability matrix of the target vehicle according to the plurality of first historical positioning data comprises:

[0022] According to the plurality of first historical positioning data, a transfer matrix and a transfer vector of the target vehicle are established, the transfer matrix is used to indicate a number of times from a position corresponding to each first historical positioning data to a position corresponding to another first historical positioning data, and the transfer vector is used to indicate a total number from the position corresponding to the each first historical positioning data to the positions corresponding to the other first historical positioning data.

[0023] According to the transfer matrix and the transfer vector, the first transfer probability matrix is calculated.

[0024] Optionally, the calculating the probability of the target vehicle going to each region in a preset range according to the driving similarity comprises:

[0025] According to the driving similarity, a similarity matrix of the target vehicle and the other vehicles is established.

[0026] According to the similarity matrix, the target vehicle and the other vehicles are classified.

[0027] According to the transfer matrix and the transfer vector of the vehicles included in each category, a transfer probability matrix of each category is determined.

[0028] According to the transfer probability matrices of the plurality of categories, a target category to which the target vehicle belongs is calculated.

[0029] According to the transfer probability matrix of the target category, the probability of the target vehicle going to each region in the preset range is determined.

[0030] In a second aspect, the embodiments of the present application further provide a method for processing vehicle perception service information, applied to an application function (AF) network element, and the method comprises the following steps:

[0031] According to a first perception service request sent by a target vehicle, a perception service analysis request is sent to a network data analysis function (NWDAF) network element, and the perception service analysis request comprises a vehicle identifier and a perception type of the target vehicle.

[0032] A perception service analysis result sent by the NWDAF network element is received, wherein the NWDAF network element sends a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receives perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station; and whether the target vehicle drives out of a perception service range of the preset perception device is predicted according to historical positioning data of the target vehicle.

[0033] If the perception service analysis result indicates that the target vehicle drives out of the perception service range, historical perception service information is sent to the target vehicle.

[0034] if the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, sending the perception monitoring data to the target vehicle;

[0035] wherein the target vehicle takes the historical perception service information or the perception monitoring data as driving reference information.

[0036] Optionally, if the perception service analysis result indicates that the target vehicle drives out of the perception service range, the historical perception service information is sent to the target vehicle, including:

[0037] if the perception service analysis result indicates that the target vehicle drives out of the perception service range, sending a perception service information acquisition request to an edge computing server;

[0038] forwarding the historical perception service information outside the perception service range sent by the edge computing server to the target vehicle.

[0039] Optionally, after the historical perception service information is sent to the target vehicle, the method further includes:

[0040] sending a vehicle-mounted sensor opening instruction to the target vehicle, so that the vehicle-mounted sensor of the target vehicle acquires vehicle-mounted perception data outside the perception service range;

[0041] when the target vehicle reenters the perception service range, receiving a second perception service request sent by the target vehicle, wherein the second perception service request includes the vehicle-mounted perception data;

[0042] sending the vehicle-mounted perception data to an edge computing server.

[0043] In a third aspect, an embodiment of the present application provides an automobile perception service information processing apparatus applied to a network data analysis function (NWDAF) network element, and the apparatus includes:

[0044] an analysis request receiving module configured to receive a perception service analysis request for a target vehicle sent by an application function (AF) network element, wherein the perception service analysis request includes a vehicle identifier of the target vehicle and a perception type;

[0045] a monitoring request sending module configured to send a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receive perception monitoring data corresponding to the perception type collected by a preset perception device and returned by the base station;

[0046] The service range judgment module is configured to predict whether the target vehicle drives out of a perception service range of the preset perception device according to historical positioning data of the target vehicle.

[0047] The analysis result sending module is configured to send a perception service analysis result to an AF network element, and the AF network element is configured to send historical perception service information to the target vehicle if the perception service analysis result indicates that the target vehicle drives out of the perception service range, and send the perception monitoring data to the target vehicle to provide driving reference information for the target vehicle if the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, wherein the driving reference information is the historical perception service information or the perception monitoring data.

[0048] Optionally, the service range judgment module comprises:

[0049] The positioning data obtaining unit is configured to obtain a plurality of first historical positioning data of the target vehicle and a plurality of second historical positioning data of other vehicles in a perception service range in which the target vehicle is located.

[0050] The similarity calculation unit is configured to calculate a driving similarity between the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data.

[0051] The probability calculation unit is configured to calculate a probability of the target vehicle going to each region in a preset range according to the driving similarity.

[0052] The service range judgment unit is configured to determine whether the target vehicle drives out of the perception service range according to the region with the maximum probability.

[0053] Optionally, the similarity calculation unit comprises:

[0054] The first transition probability matrix establishing sub-unit is configured to establish a first transition probability matrix of the target vehicle according to the plurality of first historical positioning data.

[0055] The second transition probability matrix establishing sub-unit is configured to establish a second transition probability matrix of the other vehicles according to the plurality of second historical positioning data.

[0056] The similarity calculation sub-unit is configured to calculate the driving similarity according to the first transition probability matrix and the second transition probability matrix.

[0057] Optionally, the first transition probability matrix establishing subunit is specifically configured to establish a transition matrix and a transition vector of the target vehicle according to the plurality of first historical positioning data, the transition matrix being used to indicate the number of times of transition from a position corresponding to each first historical positioning data to a position corresponding to another first historical positioning data, and the transition vector being used to indicate the total number of transitions from the position corresponding to the each first historical positioning data to the positions corresponding to the other first historical positioning data; and the first transition probability matrix is calculated according to the transition matrix and the transition vector.

[0058] Optionally, the probability calculating unit is specifically configured to establish a similarity matrix of the target vehicle and the other vehicles according to the driving similarity; classify the target vehicle and the other vehicles according to the similarity matrix; determine a transition probability matrix of each category according to a transition matrix and a transition vector of the vehicles included in each category; calculate a target category to which the target vehicle belongs according to the transition probability matrices of the plurality of categories; and determine the probability of the target vehicle going to each region within the preset range according to the transition probability matrix of the target category.

[0059] In a fourth aspect, an apparatus for processing vehicle perception service information is also provided. The apparatus is applied to an application function (AF) network element and includes:

[0060] The analysis request sending module is configured to send a perception service analysis request to a network data analysis function (NWDAF) network element according to a first perception service request sent by a target vehicle, the perception service analysis request including a vehicle identifier and a perception type of the target vehicle.

[0061] The analysis result receiving module is configured to receive a perception service analysis result sent by the NWDAF network element, wherein the NWDAF network element sends a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receives perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station; and the target vehicle is predicted to determine whether the target vehicle drives out of a perception service range of the perception device according to historical positioning data of the target vehicle.

[0062] The service information sending module is configured to send historical perception service information to the target vehicle if the perception service analysis result indicates that the target vehicle drives out of the perception service range.

[0063] The monitoring data sending module is configured to send the perception monitoring data to the target vehicle if the perception service analysis result indicates that the target vehicle does not drive out of the perception service range.

[0064] The target vehicle uses the historical perception service information or the perception monitoring data as driving reference information.

[0065] Optionally, the service information sending module is specifically configured to: if the perception service analysis result indicates that the target vehicle drives out of the perception service range, send a perception service information acquisition request to an edge computing server; and forward historical perception service information outside the perception service range sent by the edge computing server to the target vehicle.

[0066] Optionally, after the service information sending module, the apparatus further includes:

[0067] a start instruction sending module configured to send a vehicle-mounted sensor start instruction to the target vehicle, so that the vehicle-mounted sensor of the target vehicle acquires vehicle-mounted perception data outside the perception service range;

[0068] a service request receiving module configured to receive a second perception service request sent by the target vehicle when the target vehicle drives into the perception service range again, the second perception service request including the vehicle-mounted perception data.

[0069] a perception data sending module configured to send the vehicle-mounted perception data to an edge computing server.

[0070] In a fifth aspect, an embodiment of the present application further provides a core network element, including: a processor, a storage medium and a bus, the storage medium stores program instructions executable by the processor, when the core network element runs, the processor and the storage medium communicate through the bus, and the processor executes the program instructions to perform the steps of the automobile perception service information processing method in the first aspect or the second aspect.

[0071] In a sixth aspect, an embodiment of the present application further provides a computer readable storage medium, the storage medium stores a computer program, and the computer program is run by a processor to perform the steps of the automobile perception service information processing method in the first aspect or the second aspect.

[0072] The present application has the following beneficial effects:

[0073] The automobile perception service information processing method, the core network element and the storage medium provided by the application, the NWDAF network element acquires perception monitoring data from a base station according to a perception service analysis request for a target automobile, and predicts whether the target automobile drives out of a perception service range of a preset perception device according to historical positioning data of the target automobile, sends historical perception service information to the target automobile in the case of predicting that the target automobile drives out of the perception service range, and sends perception monitoring data collected by the base station to the target automobile in the case of predicting that the target automobile does not drive out of the perception service range, so that the target automobile can utilize perception data as driving reference information in or out of the perception service range, thereby enhancing automobile driving safety. BRIEF DESCRIPTION OF DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0075] Figure 1 The network architecture diagram provided for the embodiments of the application;

[0076] Figure 2 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 1 ;

[0077] Figure 3 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 2 ;

[0078] Figure 4 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 3 ;

[0079] Figure 4 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 5 ;

[0080] Figure 4 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 5 ;

[0081] Figure 6 The flowchart of the automobile perception service information processing method provided for the embodiments of the application Figure 5 ;

[0082] Figure 6A flowchart illustrating the vehicle perception service information processing method provided in this application embodiment. Figure 7 ;

[0083] Figure 6 A flowchart illustrating the vehicle perception service information processing method provided in this application embodiment. Figure 7 ;

[0084] Figure 8 A schematic diagram illustrating the target vehicle leaving the sensing service range, provided in an embodiment of this application;

[0085] Figure 7 Interactive illustration of the vehicle perception service information processing method provided in the embodiments of this application Figure 8 ;

[0086] Figure 9 A schematic diagram illustrating the target vehicle entering the sensing service range as provided in an embodiment of this application;

[0087] Figure 8 Interactive illustration of the vehicle perception service information processing method provided in the embodiments of this application Figure 9 ;

[0088] Figure 10 A schematic diagram of the structure of the vehicle perception service information processing device provided in the embodiments of this application. Figure 11 ;

[0089] Figure 1 Schematic diagram of the structure of the vehicle perception service information processing device provided in the embodiments of this application Figure 12 ;

[0090] Figure 13 A schematic diagram of the core network elements provided in the embodiments of this application. Detailed Implementation

[0091] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0092] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0093] Moreover, the terms "first", "second", and the like, herein do not necessarily have an ordinal or chronological significance, but are used for the purpose of differentiating between two or more objects, and thus can be interchanged under appropriate circumstances. It is to be understood that the phraseology or terminology employed herein encompasses the use of generics, derivatives, and equivalents. The various embodiments of the application can be implemented in software, hardware, or a combination thereof, and can be implemented in methods or apparatuses such as the steps and / or functions illustrated in the accompanying drawings and described in the appended embodiments. Although the various embodiments of the application have been disclosed in connection with the embodiments presented and illustrated, the various embodiments of the application are not intended to be limited to these described embodiments, but rather, are intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the various embodiments of the application. The words "comprise", "comprising", "include", "including", and the like, specify the presence of stated features, integers, steps, or components but do not preclude the presence or addition of one or more other features, integers, steps, components, or groups thereof. The singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise.

[0094] It should be noted that the features of the embodiments of the present application can be combined if there is no conflict.

[0095] Reference is made to Figure 2 The network architecture provided by the embodiments of the present application can include the following components, as shown in Figure 14 The network architecture can include the following components, as shown in

[0096] 1. User Equipment (UE): can also be referred to as a user device, a terminal, an access terminal, a user unit, a user station, a mobile station, a mobile station, a remote station, a remote terminal, a mobile device, a user terminal, a wireless communication device, a user agent, or a user apparatus. In the automotive driving application scenario of the present application, the terminal device can be a car.

[0097] 2. Access Network (AN): provides network access functions for authorized users in a specific area, and can use transmission tunnels of different qualities according to the level of users, the needs of services, etc. An access network that implements access network functions based on wireless communication technology can be referred to as a Radio Access Network (RAN). The radio access network can manage radio resources and provide access services for terminals, thereby completing the forwarding of control signals and user data between terminals and core networks, and generally provides a radio access network through a base station.

[0098] 3. Access and Mobility Management Function (AMF) network element: mainly used for mobility management and access management, etc., and can be used to implement other functions in the mobility management entity (MME) function except session management, such as lawful interception, or access authorization (or authentication) functions.

[0099] 4. A Location Management Function (LMF) network element, configured to select a corresponding positioning method according to a positioning accuracy requirement, a time delay requirement, and the like, and select a corresponding communication protocol to complete interaction of information required for positioning, and further configured to provide other information required for positioning service or a positioning strategy.

[0100] 5. A Network Data Analytics Function (NWDAF) network element, configured to provide a network analysis service according to requested data of a network service.

[0101] 6. An Application Function (AF) network element, similar to an application server, configured to interact with a control plane network function network element in a 5G core network and provide a service, and the AF can exist for different application services and can be owned by an operator or a trusted third party.

[0102] 7. A Multi-access / Mobile Edge Computing server, deployed at an edge of a NR perception service coverage area, configured to provide perception service information for a vehicle after the vehicle drives out of the NR perception service coverage area.

[0103] It should be understood that the network elements described above can communicate with each other through preset interfaces, which will not be described herein. It should also be understood that these core network elements can be independent computer devices or servers, or can be integrated into the same computer device or server to implement different functions, and the present application does not limit this.

[0104] The following describes a specific implementation of the vehicle perception service information processing method applied to the NWDAF network element with reference to the embodiments.

[0105] Please refer to Figure 1 , a flowchart of the vehicle perception service information processing method provided by the embodiments of the present application is shown. Figure 15 As shown in Figure 2 , the method can include the following steps.

[0106] S101: receiving a perception service analysis request for a target vehicle sent by an AF network element, the perception service analysis request including a vehicle identifier of the target vehicle and a perception type.

[0107] In the embodiment, when the target vehicle needs to obtain road related data, a first perception service request is sent to an AF network element of an operator or a third party, and the first perception service request can include a vehicle identifier of the target vehicle and a perception type, the perception type being used to indicate a type of monitoring data that the target vehicle needs to perceive, such as road condition data, traffic sign data, pedestrian crossing data, etc.

[0108] The AF network element sends a perception service analysis request to a NWDAF network element according to the first perception service request, so that the NWDAF network element processes the perception service analysis request, and the vehicle identifier of the target vehicle and the perception type can also be included in the perception service analysis request.

[0109] In some embodiments, if the AF network element can provide multiple types of services, the first perception service request can further include a perception service identifier, the perception service identifier being used to indicate that the target vehicle needs to request to provide an NR wireless perception service.

[0110] In some embodiments, the first perception service request can further include a public land mobile network (PLMN), so that the AF network element can send the perception service analysis request to the NWDAF network element in the core network network element constructed by the corresponding operator according to the operator to which the PLMN belongs.

[0111] Further, the first perception service request can further include a perception service demand level, according to which the NWDAF network element can determine a time interval for providing the target vehicle with perception service analysis.

[0112] Based on the information contained in the above first perception service request, the perception service analysis request can include, in addition to the vehicle identifier of the target vehicle and the perception type, a perception service analysis identifier, an identifier of the AF network element, and a perception service demand level. The perception service analysis identifier is used to indicate that the NWDAF provides the target vehicle with perception service analysis, and the identifier of the AF network element is used to indicate that the NWDAF network element sends the perception service analysis result to the corresponding AF network element.

[0113] S102: According to the real-time positioning data of the target vehicle and the perception service analysis request, a perception monitoring request for the target vehicle is sent to a base station, and perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station is received.

[0114] In the embodiment, the NWDAF network element requests the LMF network element to locate the target vehicle, obtains real-time positioning data of the target vehicle, and sends a perception monitoring request to the AMF network element according to the real-time positioning data of the target vehicle and the perception service analysis request. The perception monitoring request can at least include: the vehicle identifier of the target vehicle, the perception type, the identifier of the NWDAF network element, the perception monitoring identifier, and the real-time positioning data of the target vehicle. Further, the perception monitoring request can further include: a perception monitoring time interval, which can be determined according to the perception service demand level.

[0115] The AMF network element determines the RAN that provides services for the target vehicle according to the real-time positioning data of the target vehicle, forwards the perception monitoring request to the corresponding RAN, and obtains the perception monitoring data corresponding to the perception type collected by the NR perception service device around the location of the target vehicle. The RAN sends the perception monitoring data to the AMF network element, and the AMF network element sends the perception monitoring data to the NWDAF network element.

[0116] In some embodiments, when the NWDAF network element sends a positioning request to the LMF network element, the positioning request includes: the vehicle identifier of the target vehicle, the identifier of the MWDAF network element, and a positioning time interval, which can be determined according to the perception service demand level.

[0117] S103: According to the historical positioning data of the target vehicle, it is predicted whether the target vehicle drives out of the perception service range of the preset perception device.

[0118] In the embodiment, the historical positioning data of the target vehicle is the positioning data of the target vehicle provided by the LMF network element stored by the NWDAF network element in a timely manner. The NWDAF network element needs to predict whether the target vehicle drives out of the perception service range of the preset perception device according to the historical positioning data in a preset time period after receiving the perception monitoring data returned by the base station each time.

[0119] In some embodiments, the NWDAF network element can determine the driving trajectory, driving direction, and driving speed of the target vehicle according to the historical positioning data of the target vehicle in a preset time period, determine the distance between the target vehicle and the edge of the perception service range along the driving direction according to the driving trajectory, determine the time required for the target vehicle to drive out of the perception service range according to the driving speed and the distance, and determine whether the target vehicle drives out of the perception service range according to the required time. If the required time is less than a preset time, it is determined that the target vehicle will drive out of the perception service range. If the required time is greater than or equal to the preset time, it is determined that the target vehicle does not drive out of the perception service range.

[0120] S104: send the perception service analysis result to the AF network element, the AF network element is configured to send the historical perception service information to the target vehicle in the case that the perception service analysis result indicates that the target vehicle has driven out of the perception service range; and send the perception monitoring data to the target vehicle in the case that the perception service analysis result indicates that the target vehicle has not driven out of the perception service range, so as to provide the driving reference information for the target vehicle, the driving reference information being the historical perception service information or the perception monitoring data.

[0121] In the embodiment, the NWDAF network element sends the perception service analysis result to the AF network element according to the prediction result, and the AF network element determines the perception data to be sent to the target vehicle according to the perception service analysis result, wherein, if the perception service analysis result indicates that the target vehicle has driven out of the perception service range, the target vehicle can no longer use the perception monitoring data returned by the base station as the reference information for safe driving, therefore, the AF network element needs to obtain the historical perception service information outside the perception service range and send the historical perception service information to the target vehicle. If the perception service analysis result indicates that the target vehicle has not driven out of the perception service range, the perception monitoring data is included in the perception service analysis result, and the target vehicle can still use the perception monitoring data returned by the base station as the reference information for safe driving, therefore, the AF network element sends the perception monitoring data to the target vehicle.

[0122] The target vehicle uses the perception monitoring data as the driving parameter information when driving in the perception service range, and uses the historical perception service information as the driving reference information when driving outside the perception service range.

[0123] The automobile perception service information processing method provided by the above embodiment can ensure that the target vehicle can use the perception data as the driving reference information when driving in the perception service range or outside the perception service range, thereby enhancing the driving safety of the automobile.

[0124] In a possible implementation manner, please refer to Figure 16 The flowchart of the automobile perception service information processing method provided by the embodiment of the present application is shown in Figure 1 As shown in Figure 1 The process of predicting whether the target vehicle has driven out of the perception service range of the preset perception device according to the historical positioning data of the target vehicle in S103 can include:

[0125] S201: Obtain a plurality of first historical positioning data of the target vehicle and a plurality of second historical positioning data of other vehicles in a preset range where the target vehicle is located.

[0126] In this embodiment, the preset range can be any range in the map, and the size of the preset range can be defined according to requirements. The plurality of vehicles in the preset range includes the target vehicle and other vehicles. In the case where the plurality of vehicles all request the NWDAF network element to perform perception service analysis, the NWDAF network element stores the plurality of first historical positioning data of the target vehicle and the plurality of second historical positioning data of the other vehicles provided by the LMF network element. The positioning period in which the LMF network element provides the first historical positioning data of the target vehicle and the second historical positioning data of the other vehicles to the NWDAF network element can be determined according to the perception service demand level of the target vehicle and the perception service demand level of the other vehicles.

[0127] S202: Calculate the driving similarity of the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data.

[0128] In this embodiment, the driving trajectory of the target vehicle is determined according to the plurality of first historical positioning data, the driving trajectory of the other vehicles is determined according to the plurality of historical historical positioning data, and the driving similarity of the target vehicle and the other vehicles is determined by comparing and analyzing the driving trajectory of the target vehicle and the driving trajectory of the other vehicles.

[0129] In some embodiments, the calculation of the driving similarity can be determined according to the overlap degree of the driving trajectory of the target vehicle and the driving trajectory of the other vehicles.

[0130] S203: Calculate the probability of the target vehicle going to each region in the preset range according to the driving similarity.

[0131] In this embodiment, the driving trajectory of the other vehicles with a driving similarity to the target vehicle within a preset range is determined according to the driving similarity of the target vehicle and the other vehicles, and the probability of the target vehicle going to the region where the other vehicles are located in the preset range from the position in the real-time positioning data is determined according to the driving trajectory of the other vehicles with a driving similarity within the preset range. The higher the driving similarity of the driving trajectory of the target vehicle and the other vehicles, the greater the probability of the target vehicle going to the region where the other vehicles are located.

[0132] In some embodiments, the preset range is divided into regions of the same size, and the last region where the other vehicles are located is determined according to the plurality of historical second positioning data of the other vehicles.

[0133] S204: Determine whether the target vehicle exits the perception service range according to the region with the maximum probability.

[0134] In the embodiment, the region with the highest probability is taken as a region where the target vehicle is about to go, and it is determined whether the region is within the perception service range. If the region is within the perception service range, it is determined that the target vehicle does not exit the perception service range. If the region is outside the perception service range, it is determined that the target vehicle is about to exit the perception service range.

[0135] In a possible implementation, referring to Figure 2 , a flowchart of a vehicle perception service information processing method provided by the embodiment of the application is shown. Figure 1 As shown in Figure 2 , the process of calculating the driving similarity of the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data in S202 can include:

[0136] S301: establishing a first transition probability matrix of the target vehicle according to the plurality of first historical positioning data.

[0137] In the embodiment, the preset range is divided into grid regions, the plurality of first grid regions corresponding to the plurality of first historical positioning data are determined according to the positions of the plurality of first historical positioning data within the preset range, the number of times of mutual transition of the target vehicle within the plurality of first network regions is determined according to the driving trajectories corresponding to the plurality of first historical positioning data, and the first transition probability matrix P i of the target vehicle is established according to the number of times of mutual transition of the target vehicle within the plurality of first network regions.

[0138] S302: establishing a second transition probability matrix of the other vehicles according to the plurality of second historical positioning data.

[0139] In the embodiment, the preset range is divided into grid regions, the plurality of second grid regions corresponding to the plurality of second historical positioning data are determined according to the positions of the plurality of second historical positioning data within the preset range, the number of times of mutual transition of the other vehicles within the plurality of second network regions is determined according to the driving trajectories corresponding to the plurality of second historical positioning data, and the second transition probability matrix P j of the other vehicles is established according to the number of times of mutual transition of the other vehicles within the plurality of first network regions.

[0140] S303: calculating the driving similarity according to the first transition probability matrix and the second transition probability matrix.

[0141] In the embodiment, the driving similarity is calculated according to the first transition probability matrix P i and the second transition probability matrix P jCalculate the driving differences of the target car relative to other cars, and the driving differences of other cars relative to the target car. Based on the driving differences of the target car relative to other cars and the driving differences of other cars relative to the target car, calculate the driving similarity.

[0142] For example, relative differences can be measured using KL divergence for the target vehicle UE. i Compared to other automotive UEs j The driving differences can be expressed as: Other automotive UEs j Relative to the target vehicle UE i The driving differences can be expressed as: Then the driving similarity S ij It can be represented as:

[0143] In one possible implementation, please refer to Figure 3 This is a flowchart illustrating the vehicle perception service information processing method provided in the embodiments of this application. Figure 2 ,like Figure 3 As shown, the process of S301 above, which establishes the first transition probability matrix of the target vehicle based on multiple first historical positioning data, may include:

[0144] S401: Based on multiple first historical positioning data, establish the transfer matrix and transfer vector of the target vehicle. The transfer matrix is ​​used to indicate the number of times from the position corresponding to each first historical positioning data to the position corresponding to another first historical positioning data. The transfer vector is used to indicate the total number of times from the position corresponding to each first historical positioning data to the position corresponding to other first historical positioning data.

[0145] S402: Calculate the first transition probability matrix based on the transition matrix and the transition vector.

[0146] In this embodiment, based on multiple first grid areas corresponding to multiple first historical positioning data, the number of times the target vehicle moves from each grid area to other grid areas is determined, and the target vehicle UE is calculated. i The transition probability matrix P i The transfer vector V of the target car is calculated based on the total number of times the target car moves from each grid region to all other grid regions. i .

[0147] Example, target vehicle UE i The transition matrix Q i It can be represented as:

[0148]

[0149] Where N is the total number of grid regions divided by the preset range, and q ab The target vehicle UE is indicated i The number of times a person moves from grid region a to grid region b in multiple first network regions.

[0150] The transition vector Vi of the target car UEi can be represented as: V i = (v1, v2, ..., v N ), where v k Indicates the target vehicle UE i The sum of the number of times the target vehicle UE moves from grid region k to all grid regions within the multiple first grid regions. i The first transition probability matrix P i It can be represented as:

[0151]

[0152] It should be noted that other automotive UEs j The second transition probability matrix P j The calculation method and the target vehicle UE i The first transition probability matrix P i The calculation method is the same, so it will not be repeated here.

[0153] In one possible implementation, please refer to Figure 4 This is a flowchart illustrating the vehicle perception service information processing method provided in the embodiments of this application. Figure 3 ,like Figure 4 As shown, the process of S203 above, which calculates the probability of a target vehicle traveling to various areas within a preset range based on driving similarity, may include:

[0154] S501: Based on driving similarity, establish a similarity matrix between the target car and other cars.

[0155] S502: Classify the target car and other cars based on the similarity matrix.

[0156] S503: Determine the transition probability matrix for each category based on the transition matrix and transition vector of the cars included in each category.

[0157] S504: Calculate the target category to which the target car belongs based on the transition probability matrix of multiple categories.

[0158] S505: Based on the transition probability matrix of the target category, determine the probability that the target car will travel to each area within the preset range.

[0159] In this embodiment, the target vehicle UE i Other automotive UEs jThe similarity matrix U of the target vehicle UE

[0160]

[0161] where S ij is the driving similarity of the target vehicle UE i and other vehicles UE j , and m is the number of vehicles in a preset range.

[0162] The specific steps of classifying the target vehicle and other vehicles and calculating the transition probability matrix of each category are as follows:

[0163] 1) Calculate the similarity matrix U;

[0164] 2) Set r = 1 and i = 1;

[0165] 3) When the UE set is not empty, iteratively perform steps 4) to 7), and the UE set includes m vehicles in a preset range;

[0166] 4) Construct the category vector probability W, which can be expressed as: W = (w1, w2, …, w m ), and the given category closeness g(W) = W'UW, where the larger g(W) is, the closer the relationship between the vehicles UE W is, and further the optimal category probability vector W is obtained, that is, m i axg(W) = W'UW is solved.

[0167] 5) Classify the UE corresponding to the non-zero element in the vector W into category r;

[0168] 6) Delete the information of the UE that has been classified into category r from the UE set, the category probability vector W, and the similarity matrix U, to obtain a new UE set, a category probability vector W, and a similarity matrix U;

[0169] 7) r = r + 1;

[0170] 8) When i <= r, iteratively perform steps 9) to 10);

[0171] 9) Sum all the transition matrices and region vectors of the UE in category i, and calculate the transition probability matrix of category i from the sum of the two parts;

[0172] 10) i = i + 1;

[0173] 11) Output the category set and the transition probability matrix set of the categories.

[0174] According to the plurality of first grid regions of the target vehicle UE i , the current driving region trajectory of the target vehicle UE i is determined as (S1, S2, …, Sl ), the current driving area trajectory of the target vehicle UE i is calculated by using the Bayesian formula. l ) respectively belong to categories C1, C2, …, C r .

[0175] In an example, the formula for calculating the probability that the current driving area trajectory of the target vehicle UE i is (S1, S2, …, S l ) respectively belong to categories C1, C2, …, C r may be expressed as:

[0176]

[0177] wherein p(C=C r ) is the prior probability of the category C r , which is obtained by the ratio of the number of UEs in the category C r to the total number of UEs, p(S1, S2, …, S l |C=C r ) is the conditional probability of the driving trajectory (S1, S2, …, S r ) under the condition of the category C l , which is obtained by the product of the region transition probabilities corresponding to the driving trajectory in the transition probability matrix of the category C r . Specifically, the probability of each driving trajectory corresponding to two grid regions in the first transition probability matrix P i of the target vehicle UE i is determined, the product of the probabilities corresponding to multiple driving trajectories is calculated, and the conditional probability of the driving trajectory (S1, S2, …, S l ) is obtained.

[0178] The category with the maximum probability value is determined as the belonging category of the target vehicle UE i , and according to the probability matrix of the belonging category, the column corresponding to the maximum probability value of the row in which the current position S i of the target vehicle UE i is located is determined as the grid region to which the target vehicle UE i will go next.

[0179] In an example, if the belonging category of the target vehicle UE i is C k , and the transition probability matrix of the category C k is set as:

[0180]

[0181] If the target vehicle UE iThe probability maximum in the ith row is p ij The region corresponding to j is the target automobile UE i The next most likely to go to the grid area.

[0182] For the plurality of grid areas obtained by dividing the preset range, determine the grid area belonging to the perception service range and the grid area not belonging to the perception service range, and determine the next most likely to go to the grid area according to the target automobile UE i The next most likely to go to the grid area, determine whether the grid area is located within the perception service range.

[0183] The automobile perception service information processing method provided in the above embodiment, according to a plurality of first historical positioning data of the target automobile and a plurality of second historical positioning data of other automobiles, calculates the driving similarity of the target automobile and the other automobiles, determines the probability of the target automobile going to each region in the preset range according to the driving similarity, determines whether the target automobile drives out of the perception service range according to the region with the maximum probability, and predicts the driving trajectory of the target automobile by using the historical positioning data of the other automobiles, which can improve the accuracy of the prediction of the driving trajectory of the target automobile, so as to ensure that the target automobile can receive accurate perception information as driving reference information and ensure driving safety.

[0184] The following describes the specific implementation mode of the automobile perception service information processing method applied to the AF network element in combination with the embodiments.

[0185] Please refer to Figure 5 The flowchart of the automobile perception service information processing method provided in the embodiments of the present application is shown in Figure 4 As shown in Figure 5 The method can include the following steps.

[0186] S601: According to the first perception service request sent by the target automobile, a perception service analysis request is sent to the NWDAF network element, and the perception service analysis request includes the vehicle identifier of the target automobile and the perception type.

[0187] S602: Receive the perception service analysis result sent by the NWDAF network element, wherein the NWDAF network element sends a perception monitoring request for the target automobile to the base station according to the real-time positioning data of the target automobile and the perception service analysis request, and receives the perception monitoring data corresponding to the perception type collected by the preset perception device returned by the base station; and according to the historical positioning data of the target automobile, it is predicted whether the target automobile drives out of the perception service range of the preset perception device.

[0188] S603: If the perception service analysis result indicates that the target automobile drives out of the perception service range, historical perception service information is sent to the target automobile.

[0189] S604: If the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, the perception monitoring data is sent to the target vehicle.

[0190] The target vehicle uses the historical perception service information or the perception monitoring data as driving reference information.

[0191] In this embodiment, when the target vehicle needs to obtain road-related data, a first perception service request is sent to the AF network element of the operator or a third party, and the first perception service request can include the vehicle identifier of the target vehicle and the perception type. The AF network element sends a perception service analysis request to the NWDAF network element according to the first perception service request. The NWDAF network element requests the LMF network element to locate the target vehicle and obtain real-time positioning data of the target vehicle. The NWDAF network element sends a perception monitoring request to the AMF network element according to the real-time positioning data of the target vehicle and the perception service analysis request. The AMF network element determines the RAN that provides services for the target vehicle according to the real-time positioning data of the target vehicle, forwards the perception monitoring request to the corresponding RAN, and obtains the perception monitoring data of the perception type collected by the NR perception service device around the position of the target vehicle by the RAN. The RAN sends the perception monitoring data to the AMF network element, and the AMF network element sends the perception monitoring data to the NWDAF network element.

[0192] The NWDAF network element can determine the driving trajectory, driving direction, and driving speed of the target vehicle according to the historical positioning data of the target vehicle in a preset time period, determine the distance between the target vehicle and the edge of the perception service range along the driving direction according to the driving trajectory, determine the time required for the target vehicle to drive out of the perception service range according to the driving speed and the distance, and determine whether the target vehicle drives out of the perception service range according to the required time.

[0193] The NWDAF network element sends the perception service analysis result to the AF network element according to the prediction result, and the AF network element determines the perception data sent to the target vehicle according to the perception service analysis result. If the perception service analysis result indicates that the target vehicle drives out of the perception service range, the target vehicle cannot use the perception monitoring data returned by the base station as reference information for safe driving at this time. Therefore, the AF network element needs to obtain the historical perception service information outside the perception service range and send the historical perception service information to the target vehicle.

[0194] If the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, the perception service analysis result includes the perception monitoring data, at this time, the target vehicle can still use the perception monitoring data returned by the base station as the reference information for safe driving, therefore, the AF network element sends the perception monitoring data to the target vehicle. When the target vehicle drives in the perception service range, the target vehicle uses the perception monitoring data as the driving parameter information, and when the target vehicle drives out of the perception service range, the target vehicle uses the historical perception service information as the driving reference information.

[0195] The vehicle perception service information processing method provided by the above embodiment, the AF network element sends a perception service analysis request to the NWDAF network element based on the first perception service request of the target vehicle, so that the NWDAF network element acquires the perception monitoring data from the base station according to the perception service analysis request of the target vehicle, and predicts whether the target vehicle drives out of the perception service range of the preset perception device according to the historical positioning data of the target vehicle, the AF network element sends the historical perception service information to the target vehicle in the case that the NWDAF network element predicts that the target vehicle drives out of the perception service range, and sends the perception monitoring data collected by the base station to the target vehicle in the case that the target vehicle does not drive out of the perception service range, so that the target vehicle can use the perception data as the driving reference information in or out of the perception service range, thereby enhancing the driving safety of the vehicle.

[0196] In a possible implementation manner, please refer to Figure 6 The flowchart of the vehicle perception service information processing method provided by the embodiment of the present application is shown in Figure 5 As shown in Figure 6 The process of sending the historical perception service information to the target vehicle in the case that the perception service analysis result indicates that the target vehicle drives out of the perception service range in the above S603 can include:

[0197] S701: If the perception service analysis result indicates that the target vehicle drives out of the perception service range, send a perception service information acquisition request to the edge computing server.

[0198] S702: Forward the historical perception service information outside the perception service range sent by the edge computing server to the target vehicle.

[0199] In the embodiment, the edge computing server is deployed at the edge of the perception service range, and is used to store the perception data outside the perception service range as historical perception service information. When the target vehicle is about to drive out of the perception service range, it is meaningless to send the perception monitoring data in the perception service range to the target vehicle at this time. In order to ensure that the target vehicle has driving reference information for assisting driving when driving outside the perception service range, the AF network element sends a perception service information acquisition request to the edge computing server to acquire the historical perception service information between the perception service ranges from the edge computing server and sends the historical perception service information to the target vehicle when it is determined that the target vehicle drives out of the perception service range according to the analysis result of the perception service.

[0200] The vehicle perception service information processing method provided in the above embodiment can ensure that the target vehicle can use the historical perception service information as driving reference information when driving outside the perception service range, thereby enhancing the driving safety of the vehicle.

[0201] In a possible implementation, refer to Figure 7 The flowchart of the vehicle perception service information processing method provided in the embodiment of the application is shown in Figure 6 As shown in Figure 7 After the above S604 sends the perception monitoring data to the target vehicle, the method can further include the following steps:

[0202] S801: Send a vehicle-mounted sensor opening instruction to the target vehicle, so that the vehicle-mounted sensor of the target vehicle acquires vehicle-mounted perception data outside the perception service range.

[0203] S802: When the target vehicle drives into the perception service range again, receive a second perception service request sent by the target vehicle, and the second perception service request includes the vehicle-mounted perception data.

[0204] S803: Send the vehicle-mounted perception data to the edge computing server.

[0205] In the embodiment, when the target vehicle drives outside the perception service range, in order to collect the perception information outside the perception service range, the vehicle-mounted sensor of the target vehicle can be used to collect the vehicle-mounted perception data.

[0206] Specifically, the AF network element sends a vehicle-mounted sensor opening instruction to the target vehicle at the same time of sending the historical perception service information to the target vehicle, and the target vehicle opens the vehicle-mounted sensor based on the vehicle-mounted sensor opening instruction, so as to collect the vehicle-mounted perception data corresponding to the driving area when the target vehicle drives outside the perception service range.

[0207] When the target vehicle re-enters the perception service range, an initial access is triggered, a connection is established with the 5G core network, and when the target vehicle sends a second perception service request to the AF network element, in addition to sending the same content as the first perception service request, vehicle-mounted perception data collected by the vehicle-mounted sensor is also sent. After the AF network element receives the vehicle-mounted perception data, the vehicle-mounted perception data is uploaded to the edge computing server, so that when any vehicle exits the perception service range, the vehicle-mounted perception data is sent to the vehicle exiting the perception service range as historical perception service information.

[0208] The vehicle perception service information processing method provided by the above embodiment can collect vehicle-mounted perception data outside the perception service range by the vehicle-mounted server of the target vehicle when the target vehicle exits the perception service range, and upload the vehicle-mounted perception data to the edge computing server for storage when the target vehicle re-enters the perception service range, so that the vehicle-mounted perception data can be used as driving reference information when other vehicles exit the perception service range, improving the utilization rate of data and ensuring the safety of vehicle driving.

[0209] In some embodiments, please refer to Figure 8 The schematic diagram of the target vehicle exiting the perception service range provided by the embodiment of the application is shown in Figure 7 The interaction schematic of the vehicle perception service information processing method provided by the embodiment of the application is shown in Figure 8 As shown in Figure 9 and Figure 8 , the interaction process of the vehicle perception service information processing can include:

[0210] 1. The UE initiates a perception service request to the AF network element, and the message of the perception service request includes: UE ID, service ID (perception service), PLMN, perception type (road condition, traffic sign, pedestrian crossing, etc.), perception service demand level, etc. The perception service demand level can be the safety requirement level of the target vehicle;

[0211] 2. The AF network element initiates a perception service analysis request to the NWDAF network element, and the message of the perception service analysis request includes: UE ID, analysis ID (perception service analysis), AF ID, perception type, perception service demand level, etc.

[0212] 3. The NWDAF network element initiates a positioning request to the LMF network element, and the message of the positioning request includes: UE ID, NWDAF ID, positioning time interval, etc.

[0213] 4. The LMF network element positions the UE according to the positioning request of the NWDAF network element;

[0214] 5. The LMF network element uploads the positioning data to the NWDAF network element;

[0215] 6. The NWDAF network element initiates a perception monitoring request to the AMF network element, and the message of the perception monitoring request includes UE ID, NWDAF ID, request ID (perception monitoring), perception type, monitoring time interval, and the like information;

[0216] 7. The AMF network element forwards the perception monitoring request to the RAN currently serving the UE;

[0217] 8. The RAN regularly performs perception monitoring and uploads the perception monitoring data to the AMF network element;

[0218] 9. The AMF network element forwards the perception monitoring data to the NWDAF network element;

[0219] 10. The NWDAF network element predicts whether the UE drives out of the NR perception service range according to the historical positioning data of the UE;

[0220] 11. The NWDAF network element notifies the AF network element of the perception service analysis result, and if it is predicted that the UE does not drive out of the NR perception service range, the perception service analysis result also carries the perception monitoring data, and steps 12 and 13 are skipped;

[0221] 12. If it is predicted that the UE drives out of the NR perception service range, the AF network element requests the MEC for historical perception service information outside the perception service range (collected by the vehicle-mounted receiver on the UE);

[0222] 13. The MEC returns the historical perception service information outside the perception service range to the AF network element;

[0223] 14. The AF network element sends the perception monitoring data or the historical perception service information to the UE, and if the historical perception service information is sent, the AF network element also sends a vehicle-mounted sensor opening instruction to the UE.

[0224] In some embodiments, please refer to Figure 9 , the schematic diagram of the target automobile driving into the perception service range provided by the embodiment of the application, please refer to Figure 10 , the interaction schematic of the automobile perception service information processing method provided by the embodiment of the application Figure 11 , as shown in Figure 1 and Figure 10 , the interaction process of the automobile perception service information processing can include:

[0225] 1. When the UE triggers the vehicle-mounted sensor switch and drives outside the NR perception service range, collect the vehicle-mounted perception data based on the vehicle-mounted sensor;

[0226] 2. When the UE drives into the NR perception service range and triggers initial access, establish a connection with the 5GS;

[0227] 3. The UE initiates a perception service request to the AF network element, and the perception service request message includes, in addition to the information in step 1, Figure 11 the vehicle-mounted perception data collected by the vehicle-mounted sensor.

[0228] 4. The AF network element stores the vehicle-mounted perception data to the MEC.

[0229] 5. Steps 2-14 in Figure 12 .

[0230] On the basis of the method embodiments, the embodiments of the present application provide an automobile perception service information processing device, which is applied to the NWDAF network element. Please refer to Figure 13 , the structure of the automobile perception service information processing device provided by the embodiments of the present application is shown in Figure 2 , as shown in Figure 12 , the device can include:

[0231] The analysis request receiving module 101 is configured to receive a perception service analysis request for a target automobile sent by an application function (AF) network element, and the perception service analysis request includes a vehicle identifier of the target automobile and a perception type.

[0232] The monitoring request sending module 102 is configured to send a perception monitoring request for the target automobile to a base station according to real-time positioning data of the target automobile and the perception service analysis request, and receive perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station.

[0233] The service range judgment module 103 is configured to predict whether the target automobile drives out of a perception service range of the preset perception device according to historical positioning data of the target automobile.

[0234] The analysis result sending module 104 is configured to send a perception service analysis result to the AF network element, and the AF network element is configured to send historical perception service information to the target automobile in the case that the perception service analysis result indicates that the target automobile drives out of the perception service range, and send the perception monitoring data to the target automobile in the case that the perception service analysis result indicates that the target automobile does not drive out of the perception service range, so as to provide driving reference information for the target automobile, the driving reference information being the historical perception service information or the perception monitoring data.

[0235] Optionally, the service range judgment module 103 includes:

[0236] The positioning data acquisition unit is configured to acquire a plurality of first historical positioning data of the target automobile and a plurality of second historical positioning data of other automobiles in the perception service range of the target automobile.

[0237] The similarity calculation unit is used to calculate the driving similarity between the target vehicle and other vehicles based on multiple first historical positioning data and multiple second historical positioning data.

[0238] The probability calculation unit is used to calculate the probability of a target vehicle traveling to various areas within a preset range based on driving similarity.

[0239] The service range determination unit is used to determine whether the target vehicle has left the perception service range based on the area with the highest probability.

[0240] Optional, similarity calculation unit includes:

[0241] The first transition probability matrix establishment sub-unit is used to establish the first transition probability matrix of the target vehicle based on multiple first historical positioning data.

[0242] The second transition probability matrix establishment sub-unit is used to establish the second transition probability matrix of other vehicles based on multiple second historical positioning data.

[0243] The similarity calculation subunit is used to calculate the driving similarity based on the first transition probability matrix and the second transition probability matrix.

[0244] Optionally, the first transition probability matrix establishment sub-unit is specifically used to establish the transition matrix and transition vector of the target vehicle based on multiple first historical positioning data. The transition matrix is ​​used to indicate the number of times from the position corresponding to each first historical positioning data to the position corresponding to another first historical positioning data, and the transition vector is used to indicate the total number of times from the position corresponding to each first historical positioning data to the position corresponding to other first historical positioning data. The first transition probability matrix is ​​calculated based on the transition matrix and transition vector.

[0245] Optionally, the probability calculation unit is specifically used to: establish a similarity matrix between the target car and other cars based on driving similarity; classify the target car and other cars based on the similarity matrix; determine the transition probability matrix of each category based on the transition matrix and transition vector of the cars included in each category; calculate the target category to which the target car belongs based on the transition probability matrices of multiple categories; and determine the probability of the target car traveling to various areas within a preset range based on the transition probability matrix of the target category.

[0246] Based on the above method embodiments, this application also provides a vehicle perception service information processing device applied to an AF network element. Please refer to... Figure 13 A schematic diagram of the structure of the vehicle perception service information processing device provided in the embodiments of this application. Figure 11 ,like Figure 11 As shown, the device may include:

[0247] The analysis request sending module 201 is configured to send, according to the first perception service request sent by the target automobile, a perception service analysis request to a network data analysis function (NWDAF) network element, wherein the perception service analysis request comprises a vehicle identifier of the target automobile and a perception type;

[0248] The analysis result receiving module 202 is configured to receive a perception service analysis result sent by the NWDAF network element, wherein the NWDAF network element sends, according to real-time positioning data of the target automobile and the perception service analysis request, a perception monitoring request for the target automobile to a base station, and receives perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station; and the analysis result receiving module 202 is further configured to predict, according to historical positioning data of the target automobile, whether the target automobile drives out of a perception service range of the preset perception device.

[0249] The service information sending module 203 is configured to send, if the perception service analysis result indicates that the target automobile drives out of the perception service range, historical perception service information to the target automobile.

[0250] The monitoring data sending module 204 is configured to send, if the perception service analysis result indicates that the target automobile does not drive out of the perception service range, perception monitoring data to the target automobile.

[0251] The target automobile uses the historical perception service information or the perception monitoring data as driving reference information.

[0252] Optionally, the service information sending module 203 is specifically configured to send, if the perception service analysis result indicates that the target automobile drives out of the perception service range, a perception service information acquisition request to an edge computing server; and forward historical perception service information outside the perception service range sent by the edge computing server to the target automobile.

[0253] Optionally, after the service information sending module 203, the apparatus further comprises:

[0254] The start instruction sending module is configured to send a vehicle-mounted sensor starting instruction to the target automobile, so that the vehicle-mounted sensor of the target automobile acquires vehicle-mounted perception data outside the perception service range.

[0255] The service request receiving module is configured to receive a second perception service request sent by the target automobile when the target automobile reenters the perception service range, wherein the second perception service request comprises vehicle-mounted perception data.

[0256] The perception data sending module is configured to send the vehicle-mounted perception data to the edge computing server.

[0257] The apparatus is used for executing the method provided in the foregoing embodiments, and has similar implementation principles and technical effects, which are not described herein again.

[0258] The above modules can be one or more integrated circuits configured to implement the above methods, for example, one or more application specific integrated circuits (ASICs), or one or more microprocessors, or one or more field programmable gate arrays (FPGAs), etc. For another example, when a certain module above is implemented in the form of a processing element scheduling code, the processing element can be a general purpose processor, such as a central processing unit (CPU) or other processor that can invoke program code. For another example, the modules can be integrated together to implement in the form of a system on a chip (SOC).

[0259] Reference is made to Figure 14 Figure 1 Figure 14 Figure 15 Figure 2 Figure 15 Figure 16 A schematic diagram of a core network element provided by an embodiment of the present application is shown in FIG. 3. The core network element 300 includes a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the core network element is running, the processor and the storage medium communicate through the bus. The processor executes the program instructions to perform the above method embodiments applied to the NWDAF network element or the above method embodiments applied to the AF network element. The specific implementation and technical effects are similar, and will not be repeated here.

[0260] Optionally, the present application also provides a computer readable storage medium, which stores a computer program. When the computer program is run by a processor, the above method embodiments applied to the NWDAF network element or the above method embodiments applied to the AF network element are executed.

[0261] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of the units is merely a logical function division. There can be another division manner for the 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. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0262] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme.

[0263] In addition, each functional unit in various embodiments of the application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional unit.

[0264] The integrated unit realized in the form of software functional unit can be stored in a computer readable storage medium. The software functional unit stored in a storage medium includes a plurality of instructions for making a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) execute part of the steps of the method described in various embodiments of the application. The foregoing storage medium includes a variety of program code storage media such as a U disk, a mobile hard disk, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk.

[0265] The above is only a specific embodiment of the application, but the protection scope of the application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the application, which should be covered within the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A method of processing information for a car perception service, characterized by, The method is applied to a network data analysis function (NWDAF) network element, and the method comprises the following steps: receiving a perception service analysis request for a target vehicle sent by an application function (AF) network element, wherein the perception service analysis request comprises a vehicle identifier of the target vehicle and a perception type; sending a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receiving perception monitoring data corresponding to the perception type collected by a preset perception device and returned by the base station; predicting whether the target vehicle drives out of a perception service range of the preset perception device according to historical positioning data of the target vehicle; sending a perception service analysis result to the AF network element, wherein the AF network element is configured to send historical perception service information to the target vehicle in a case where the perception service analysis result indicates that the target vehicle drives out of the perception service range, and to send the perception monitoring data to the target vehicle in a case where the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, so as to provide driving reference information for the target vehicle, wherein the driving reference information is the historical perception service information or the perception monitoring data.

2. The method of claim 1, wherein, The method further comprises the following steps: obtaining a plurality of first historical positioning data of the target vehicle and a plurality of second historical positioning data of other vehicles in a perception service range in which the target vehicle is located; calculating a driving similarity between the target vehicle and the other vehicles according to the plurality of first historical positioning data and the plurality of second historical positioning data; calculating a probability of the target vehicle going to each region in a preset range according to the driving similarity; determining whether the target vehicle drives out of the perception service range according to a region with the maximum probability.

3. The method of claim 2, wherein, The method further comprises the following steps: establishing a first transition probability matrix of the target vehicle according to the plurality of first historical positioning data; establishing a second transition probability matrix of the other vehicles according to the plurality of second historical positioning data; calculating the driving similarity according to the first transition probability matrix and the second transition probability matrix.

4. The method of claim 3, wherein, The method further comprises the following steps: establishing a transition matrix and a transition vector of the target vehicle according to the plurality of first historical positioning data, wherein the transition matrix is used to indicate a number of times of transition from a position corresponding to each first historical positioning data to a position corresponding to another first historical positioning data, and the transition vector is used to indicate a total number of transitions from the position corresponding to each first historical positioning data to positions corresponding to other first historical positioning data; calculating the first transition probability matrix according to the transition matrix and the transition vector.

5. The method of claim 2, wherein, The method further comprises the following steps: According to the driving similarity, a similarity matrix of the target vehicle and the other vehicles is established; According to the similarity matrix, the target vehicle and the other vehicles are classified; According to a transfer matrix and a transfer vector of the vehicles included in each category, a transfer probability matrix of each category is determined; According to the transfer probability matrices of multiple categories, a target category to which the target vehicle belongs is calculated; According to the transfer probability matrix of the target category, a probability of the target vehicle going to each region within the preset range is determined.

6. An automobile perception service information processing method characterized by comprising: Applied to an application function (AF) network element, the method comprises: According to a first perception service request sent by a target vehicle, a perception service analysis request is sent to a network data analysis function (NWDAF) network element, wherein the perception service analysis request comprises a vehicle identifier and a perception type of the target vehicle; A perception service analysis result sent by the NWDAF network element is received, wherein the NWDAF network element sends a perception monitoring request for the target vehicle to a base station according to real-time positioning data of the target vehicle and the perception service analysis request, and receives perception monitoring data corresponding to the perception type collected by a preset perception device returned by the base station; whether the target vehicle drives out of a perception service range of the perception device is predicted according to historical positioning data of the target vehicle; If the perception service analysis result indicates that the target vehicle drives out of the perception service range, historical perception service information is sent to the target vehicle; If the perception service analysis result indicates that the target vehicle does not drive out of the perception service range, the perception monitoring data is sent to the target vehicle; The target vehicle takes the historical perception service information or the perception monitoring data as driving reference information.

7. The method of claim 6, wherein, If the perception service analysis result indicates that the target vehicle drives out of the perception service range, the historical perception service information is sent to the target vehicle, comprising: If the perception service analysis result indicates that the target vehicle drives out of the perception service range, a perception service information acquisition request is sent to an edge computing server; The historical perception service information outside the perception service range sent by the edge computing server is forwarded to the target vehicle.

8. The method of claim 6, wherein, After the historical perception service information is sent to the target vehicle, the method further comprises: A vehicle-mounted sensor opening instruction is sent to the target vehicle, so that vehicle-mounted sensors of the target vehicle acquire vehicle-mounted perception data outside the perception service range; When the target vehicle reenters the perception service range, a second perception service request sent by the target vehicle is received, wherein the second perception service request comprises the vehicle-mounted perception data; The vehicle-mounted perception data is sent to an edge computing server.

9. A core network element characterized by, Comprise: A processor, a storage medium, and a bus, the storage medium storing program instructions executable by the processor, the processor in communication with the storage medium via the bus when the core network element is running, the processor executing the program instructions to perform the steps of the method for processing vehicle perception service information according to any one of claims 1 to 5, or the steps of the method for processing vehicle perception service information according to any one of claims 6 to 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, the computer program being executed by the processor to perform the steps of the method for processing vehicle perception service information according to any one of claims 1 to 5, or the steps of the method for processing vehicle perception service information according to any one of claims 6 to 8.

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