Communication method and device applied to intelligent networked vehicle automatic driving

Abstract

The application provides a communication method and device applied to intelligent network connected vehicle automatic driving, and relates to the technical field of intelligent network connected vehicle automatic driving. The communication method obtains driving information of a vehicle, determines a network cell of a path to be passed through by the vehicle according to the driving information of the vehicle, further determines a prediction mechanism used for quality of service (QoS) prediction of the network cell, and then obtains a QoS prediction result of the network cell according to the cell information and the prediction mechanism. The embodiment of the application can help reduce the complexity of QoS prediction in intelligent network connected vehicle automatic driving and reduce the computing power consumption of QoS prediction.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology for intelligent connected vehicles, and in particular to a communication method and apparatus for autonomous driving of intelligent connected vehicles. Background Technology

[0002] Intelligent connected vehicles refer to the organic integration of vehicle networking and intelligent vehicles. They are not only equipped with advanced onboard sensors, controllers, actuators and other intelligent devices, but also enable intelligent information exchange and sharing between vehicles, people, roads and back-end systems through communication networks (such as 5G networks).

[0003] Intelligent connected vehicles can support autonomous driving. Autonomous driving, also known as automatic driving, typically involves a certain level of automation (L1-L5). Automated driving functions rely on communication networks, such as 5G connectivity for information assistance or even takeover. However, because automated driving depends on communication networks, the performance of these networks needs to be monitored. If the communication network is unreliable, the level of automation should be adjusted in advance based on road conditions and the vehicle's capabilities, such as relying on single-vehicle intelligence or stopping and parking in a safe location.

[0004] In related technologies, the Quality of Service (QoS) prediction process is highly complex and may consume significant computing resources. Therefore, there is an urgent need for a method to address the high computational cost and complexity of QoS prediction in autonomous driving of intelligent connected vehicles. Summary of the Invention

[0005] This application provides a communication method and apparatus for autonomous driving of intelligent connected vehicles, which can help reduce the complexity and / or computing power consumption of QoS prediction in autonomous driving of intelligent connected vehicles.

[0006] In a first aspect, embodiments of this application provide a communication method for autonomous driving of intelligent connected vehicles, comprising:

[0007] Obtain vehicle driving information;

[0008] Based on the driving information, determine the network cells along the route the vehicle needs to pass through;

[0009] Determine the prediction mechanism used for QoS prediction of the network cell;

[0010] Based on the prediction mechanism, the QoS prediction results of the network cell are obtained.

[0011] Secondly, embodiments of this application provide a communication device for autonomous driving of intelligent connected vehicles, comprising:

[0012] The acquisition unit is used to acquire vehicle driving information;

[0013] The processing unit is used to determine the network cells along the path that the vehicle needs to pass through based on the driving information.

[0014] The processing unit is also configured to determine the prediction mechanism used for QoS prediction of the network cell;

[0015] The acquisition unit is further configured to acquire the QoS prediction result of the network cell according to the prediction mechanism.

[0016] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the methods as described in the first aspect or its various implementations.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the methods as described in the first aspect or its various implementations.

[0018] Fifthly, embodiments of this application provide a computer program product including computer program instructions that cause a computer to perform the methods as described in the first aspect or its various implementations.

[0019] Sixthly, embodiments of this application provide a computer program that causes a computer to perform the methods as described in the first aspect or its various implementations.

[0020] In the embodiments of this application, by determining the prediction mechanism used when measuring QoS for network cells along the vehicle's path, QoS in autonomous driving can be predicted flexibly, which helps to reduce the computing power requirements and / or complexity of QoS prediction in autonomous driving. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of the solution in an embodiment of this application;

[0022] Figure 2 This is a schematic diagram of a QoS prediction process;

[0023] Figure 3 A schematic flowchart illustrating a communication method for autonomous driving of intelligent connected vehicles, provided as an embodiment of this application;

[0024] Figure 4A schematic flowchart illustrating another communication method for autonomous driving of intelligent connected vehicles provided in this application embodiment;

[0025] Figure 5 Another schematic diagram illustrating an application scenario provided in the embodiments of this application;

[0026] Figure 6 A schematic flowchart illustrating another communication method for autonomous driving of intelligent connected vehicles provided in this application embodiment;

[0027] Figure 7 An interactive flowchart illustrating a communication method for autonomous driving of intelligent connected vehicles, provided as an embodiment of this application;

[0028] Figure 8 A schematic diagram of a communication device for autonomous driving of intelligent connected vehicles provided in an embodiment of this application;

[0029] Figure 9 This is a schematic block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0032] The embodiments of this application are applied to the field of artificial intelligence technology.

[0033] Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that utilize digital computers or computers-controlled machines to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce new intelligent machines that can react in a way similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to possess perception, reasoning, and decision-making capabilities.

[0034] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0035] With the research and advancement of artificial intelligence (AI) technology, AI is being studied and applied in various fields, such as smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, autonomous driving, drones, robots, smart healthcare, and smart customer service. It is believed that with the development of technology, AI will be applied in more fields and play an increasingly important role.

[0036] This application's embodiments relate to autonomous driving technology within artificial intelligence. Autonomous driving technology relies on the collaborative efforts of artificial intelligence, computer vision, radar, monitoring devices, and a global positioning system to enable computers to automatically and safely operate motor vehicles without any human intervention. Autonomous driving technology typically includes high-precision mapping, environmental perception, behavioral decision-making, path planning, and motion control. Autonomous driving technology has broad application prospects. Specifically, the technical solution provided in this application's embodiments relates to a communication method applied to autonomous driving of intelligent connected vehicles, which can be used for QoS prediction in autonomous driving.

[0037] This application's embodiments may also relate to cloud computing technology within artificial intelligence. The communication method for autonomous driving provided in this application's embodiments can employ cloud computing for QoS prediction, and this method can be executed by network elements deployed on a cloud platform.

[0038] Cloud computing is a computing model that distributes computing tasks across a large pool of computers, enabling various application systems to access computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, resources in the "cloud" appear infinitely scalable, readily available, on-demand, and expandable, with payment based on usage.

[0039] As a provider of fundamental cloud computing capabilities, a cloud resource pool (referred to as a cloud platform, generally called an IaaS (Infrastructure as a Service) platform) is established. Various types of virtual resources are deployed in the resource pool for external customers to choose from. The cloud resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices.

[0040] Based on logical function, a Platform as a Service (PaaS) layer can be deployed on top of the Infrastructure as a Service (IaaS) layer, and a Software as a Service (SaaS) layer can be deployed on top of the PaaS layer. Alternatively, SaaS can be deployed directly on top of IaaS. PaaS is the platform on which software runs, such as databases and web containers. SaaS refers to various types of business software, such as web portals and bulk SMS senders. Generally speaking, SaaS and PaaS are upper layers compared to IaaS.

[0041] Figure 1 This is a schematic diagram illustrating an application scenario of the solution provided in this application. For example... Figure 1 As shown, intelligent connected vehicles rely on 5G networks for autonomous driving. The cloud platform can create service instances for intelligent connected vehicles, which can obtain the vehicle's location and status, as well as the network status of its surroundings. For example, the cloud platform can function as an application function (AF) to interact with the 5G network (e.g., the core network) to perform QoS monitoring of the 5G network surrounding the intelligent connected vehicle.

[0042] Optionally, the cloud platform can also obtain the location information of intelligent connected vehicles from a positioning system. For example, the positioning system can be the Global Positioning System (GPS), but this is not limited to that.

[0043] It should be noted that, Figure 1The example described uses a 5G network, but this 5G network can also be replaced by other wireless networks such as Global System for Mobile Communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G networks, next-generation networks, Bluetooth, Wi-Fi, and voice communication networks, without limitation.

[0044] The 3rd Generation Partnership Project (3GPP) introduced a QoS prediction mechanism in 5G networks. Figure 2 A schematic diagram of the QoS prediction process is shown. In this QoS prediction mechanism, the network data analytics function (NWDAF) can perform historical data analysis on the QoS characteristics of the 5G network and predict future trends. For example... Figure 2 As shown, the QoS prediction process may include steps 201 to 204.

[0045] 201. The network function (NF) consumer sends an analytics information request / analysis subscription (Nnwdaf_AnalyticsInfo_Request / Nnwdaf_AnalyticsSubscription_Subscribe) to the NWDAF. This analytics information request / analysis subscription may include an analytics identifier (Analytics_ID), which can be used to indicate QoS sustainability.

[0046] After receiving the analysis information request / analysis subscription, NWDAF can provide network data collection and analysis functions based on technologies such as big data and artificial intelligence, such as steps 202 and 203 below.

[0047] 202. NWDAF collects data from Operation Administration and Maintenance (OAM).

[0048] For example, the data here may refer to data generated from network operations, administration, maintenance, etc., such as data related to daily network and service analysis, prediction, planning and configuration, and / or data related to daily operational activities such as network and service testing and fault management, without limitation.

[0049] 203. NWDAF performs statistical analysis on network performance and QoS.

[0050] For example, NWDAF can perform statistical analysis on network performance and QoS based on the data collected in step 202 to monitor the parameters of different network elements and make predictions. For example, corresponding to the QoS sustainability analysis identifier mentioned above, NWDAF can perform statistical analysis of historical data and predict future trends of the QoS characteristics of 5G networks.

[0051] 204. NWDAF sends an analytics information response / analysis subscription notification (Nnwdaf_AnalyticsInfo_Response / Nnwdaf_AnalyticsSubscription_Notify) to NF consumers. This analytics information response / analysis subscription notification includes the QoS prediction results for the 5G network.

[0052] for Figure 1 In the application scenario of 5G-connected autonomous driving (also known as 5G-connected autonomous driving), the cloud platform will predict the network status of a large number of intelligent connected vehicles during their operation. If using... Figure 2 The QoS prediction mechanism shown performs statistical analysis of historical data and predicts future trends of the QoS characteristics of the 5G network for each intelligent connected vehicle. Due to the high complexity of the prediction algorithm, it will consume a large amount of computing resources. Especially when real-time operation of intelligent connected vehicles is required, the convergence time of the QoS prediction algorithm is also subject to high requirements.

[0053] In view of this, embodiments of this application provide a communication method for autonomous driving of intelligent connected vehicles, which can help reduce the complexity of QoS prediction in autonomous driving of intelligent connected vehicles, as well as reduce the computing power consumption of QoS prediction.

[0054] Specifically, in this embodiment of the application, the network cells along the path that the vehicle needs to pass through are determined based on the vehicle's driving information, and the prediction mechanism used for QoS prediction of the network cells is further determined. Then, based on the cell information and the prediction mechanism, the QoS prediction result of the network cells is obtained.

[0055] Therefore, by determining the prediction mechanism used when measuring QoS of network cells along the vehicle's path, the embodiments of this application can flexibly predict QoS in autonomous driving, which helps to reduce the computing power requirements and / or complexity of QoS prediction in autonomous driving.

[0056] Furthermore, when the computational power requirements and / or complexity of QoS prediction in autonomous driving are reduced, the convergence time of the QoS prediction algorithm can also be reduced accordingly, which can help meet the requirements of real-time operation of intelligent connected vehicles relying on the Internet.

[0057] The following description, in conjunction with the accompanying drawings, introduces a communication method for autonomous driving of intelligent connected vehicles, based on embodiments of this application.

[0058] Figure 3 This is a schematic flowchart illustrating a communication method 300 for autonomous driving in intelligent connected vehicles, provided in an embodiment of this application. Method 300 can be executed by any electronic device with data processing capabilities. For example, the electronic device can be implemented as a network element entity or functional entity, or a virtual machine or server deployed with network element functions. Exemplarily, method 300 can be used for QoS prediction in 5G-connected autonomous driving. As an example, method 300 is applied to an application function (AF) network element, for example, executed by the AF network element, which can be deployed on a cloud platform.

[0059] like Figure 3 As shown, method 300 includes steps 310 to 340.

[0060] 310, obtain vehicle driving information.

[0061] For example, the vehicle can be an intelligent connected vehicle, or other intelligent car capable of intelligent driving / autonomous driving via a network, without limitation.

[0062] For example, the vehicle's driving information includes at least one of the following: vehicle speed information, driving intention information, and driving trajectory prediction information.

[0063] As one possible implementation, the vehicle can report its speed information to the autopilot (AF). For example, the vehicle can utilize various installed sensors (such as millimeter-wave radar, lidar, monocular or binocular cameras, and satellite navigation) to sense its surroundings while driving, collect various data during the journey, and combine this data with navigation map data to perform systematic calculations and analysis to obtain and report real-time speed information to the AF. Correspondingly, the AF can obtain the vehicle's speed information based on the reported information.

[0064] As another possible implementation, AF can acquire the vehicle's location information at different times and calculate the vehicle's speed based on this location information. Optionally, AF can acquire the vehicle's location information at different times through GPS or data reported by roadside sensors.

[0065] In some optional embodiments, the AF can obtain first information input by the user from the user application, which includes the user's driving intention information and / or driving trajectory information. For example, the user application can be installed on the user client for the user to interact with the cloud platform. For instance, the user can input their driving intention through the user application, such as the user's desired destination and required driving speed; or the user can input their driving trajectory through the user application, such as the user's desired driving route.

[0066] In some embodiments, the user application may also display relevant vehicle information to the user, such as vehicle speed information, the route planned by the cloud platform for the user, etc., which is not limited in this application.

[0067] 320. Based on this driving information, obtain the network cells along the route the vehicle needs to take.

[0068] For example, the AF can determine the vehicle's driving path based on vehicle driving information, such as vehicle speed, driving intention information, and driving trajectory prediction information, and determine the network cell that can cover the driving path. As an example, the network cell has the strongest signal strength on the driving path, or the signal strength on the driving path is greater than a preset threshold; this application does not limit this.

[0069] It should be noted that the number of network cells on the path is not limited in the embodiments of this application; for example, it can be one, two, or more.

[0070] For example, the network cell can be a network cell of wireless networks such as GSM, WCDMA, 4G, 5G, future next-generation networks, Wi-Fi, and voice communication networks, without limitation.

[0071] Optionally, after determining the network cell through which the vehicle needs to traverse the path, the cell information of that network cell can be obtained. For example, the cell information may include at least one of the following: the road segment the vehicle traverses within the network cell, the cell identity, area identity (AI), and registering area identity (RAI). Here, the road segment the vehicle traverses within the network cell can be determined based on the coverage area of ​​the network cell and the path the vehicle needs to traverse. For instance, a road segment within the coverage area of ​​the network cell along the path the vehicle needs to traverse can be used as the road segment the vehicle traverses within that network cell.

[0072] The cell identifier is used to identify a cell and is a unique identifier for a cell within a Public Land Mobile Network (PLMN). For example, it can be a Physical Cell Identity (PCI) or an NR Cell Global Identifier (NCGI). The AI ​​is the identifier for an area within the PLMN, which can be used for the location management of terminal devices (such as intelligent connected vehicles). It is unique within the PLMN and can include, for example, an Area Code (AC). The RAI is the identifier for a registered area within the PLMN, which can also be used for the location management of terminal devices (such as intelligent connected vehicles). It is unique within the PLMN and can include, for example, a Registration Area Code (RAC).

[0073] 330. Determine the prediction mechanism used for QoS prediction of the network cell. The prediction algorithms of different prediction mechanisms have different complexity and / or computational requirements.

[0074] In some alternative embodiments, see Figure 4 The prediction mechanism used for QoS prediction of network cells can be determined through the following steps 331 and 332.

[0075] 331. Based on the vehicle's driving information, determine the time when the vehicle arrives at the first location area.

[0076] In some embodiments, the first location area may include road segments traversed by the vehicle within the network cell. See also Figure 5If the coverage area of ​​network cell 1 is area 501, then the road segment that the vehicle needs to pass through in area 501 can be used as an example of the first location area; if the coverage area of ​​network cell 2 is area 502, then the road segment that the vehicle needs to pass through in area 502 can be used as another example of the first location area.

[0077] In other embodiments, the first location region can be a location and / or area on the path that the vehicle needs to traverse. As a specific example, the first location region can be a location or area within a certain distance (e.g., 1m, 10m, 30m, 50m, etc.) from the vehicle's current location on the path that the vehicle will traverse.

[0078] Optionally, the first location area can be preset. For example, the first location area can be set as the road segment that the vehicle passes through in the network cell, or the first location area can be set as the location and / or area on the path that the vehicle needs to pass through. This application does not limit this.

[0079] Here, the shorter the time it takes for a vehicle to reach the first location area, for example, less than a certain threshold, the sooner the vehicle will reach the first location area; the longer the time it takes for a vehicle to reach the first location area, for example, greater than a certain threshold, the longer the vehicle will need to reach the first location area.

[0080] 332. Based on this time, determine the prediction mechanism used for QoS prediction of the network cell corresponding to the first location area.

[0081] For example, when the time when the vehicle arrives at the first location area indicates that the vehicle is about to arrive at the first location area, the QoS prediction data of the network cell corresponding to the first location area is very valuable for the safe driving of the vehicle. At this time, the prediction algorithm of the prediction mechanism used for the QoS prediction of the network cell can have high complexity and / or computing power requirements, so as to improve the accuracy of QoS prediction in autonomous driving.

[0082] When the time it takes for the vehicle to reach the first location area indicates that the vehicle still needs a relatively long time to reach the first location area, the QoS prediction data of the network cell corresponding to the first location area is not very meaningful for the vehicle's driving due to the time difference. At this time, the prediction algorithm of the prediction mechanism used for the QoS prediction of the network cell can have lower complexity and / or computing power requirements, which is conducive to reducing the computing power requirements and / or complexity of QoS prediction in autonomous driving.

[0083] It should be noted that the number of first location regions is not limited in the embodiments of this application; for example, it can be one, two, or more. When the number of first location regions is at least two, the prediction mechanism used for QoS prediction of the network cell can be determined separately when the vehicle is in these different location regions. Furthermore, since the time when the vehicle arrives at these different location regions is different relative to the current time, QoS prediction mechanisms with different computing power requirements and / or complexities can be adopted.

[0084] In some optional embodiments, if the time it takes for the vehicle to arrive at the first location area is greater than a first threshold, then the prediction mechanism used to predict the QoS of the network cell is determined to be the first prediction mechanism, which includes statistical analysis of historical data on the QoS characteristics of the network cell.

[0085] Specifically, when the vehicle still needs a considerable amount of time to reach the first location area, since the QoS prediction data of the network cell corresponding to that first location area is not very meaningful for the vehicle's driving, a first prediction mechanism can be used to predict the QoS of that network cell. This involves statistically analyzing historical data of the network cell's QoS characteristics without predicting future trends. For example, see [link to example]. Figure 5 For area 502, the first prediction mechanism can be used to measure the QoS of the network cell, that is, only the historical data of QoS characteristic parameters are statistically analyzed.

[0086] Since the algorithms used to predict the future trends of QoS characteristics of network cells are relatively more complex and consume more computing power, by using this first prediction mechanism to predict QoS for locations that require a long time to reach, the computing power requirements and / or complexity of QoS prediction in autonomous driving can be appropriately reduced without affecting the effectiveness and reliability of QoS prediction.

[0087] In some optional embodiments, if the time it takes for the vehicle to arrive at the first location area is less than or equal to a second threshold, then the prediction mechanism used to predict the QoS of the network cell is determined to be a second prediction mechanism. The second prediction mechanism includes statistical analysis of historical data on the QoS characteristics of the network cell and prediction of future trends.

[0088] Specifically, when the vehicle is about to reach the first location area, since the QoS prediction data of the network cell corresponding to the first location area is valuable for safe driving, a second prediction mechanism can be used to predict the QoS of the network cell. This involves statistically analyzing historical data on the QoS characteristics of the network cell and predicting future trends. For an example, see [link to example]. Figure 5For area 501, this second prediction mechanism can be used to measure the QoS of network cells, that is, to perform statistical analysis of historical data of QoS characteristic parameters and to predict future trends.

[0089] Therefore, the second prediction mechanism used in this application embodiment to predict the QoS of the upcoming location can help improve the accuracy of QoS prediction in autonomous driving, thereby improving the effectiveness and reliability of QoS prediction.

[0090] As an example, either the first threshold value or the second threshold value can be pre-configured. Optionally, the first threshold value and the second threshold value can be the same, and this application does not limit this.

[0091] 340. Based on this prediction mechanism, obtain the QoS prediction result for the network cell.

[0092] For example, the process of performing QoS prediction for this network cell can be based on the current QoS prediction framework, such as... Figure 2 The QoS prediction process shown is performed. Figure 2 The difference between the QoS prediction process shown is that, in step 340, the prediction mechanism can be different for different areas on the path that the vehicle needs to travel, and the prediction algorithm of different prediction mechanisms has different complexity and / or computing power requirements.

[0093] In some alternative embodiments, see Figure 6 The QoS prediction results for this network cell can be obtained through the following steps 341 and 342.

[0094] 341. Based on the aforementioned prediction mechanism, a QoS analysis subscription request is sent to the Network Data Analysis Function (NWDAF). Here, the prediction mechanism may be, for example, the first prediction mechanism or the second prediction mechanism described above. Optionally, the QoS analysis subscription request may include indication information to indicate the cell information and the prediction mechanism.

[0095] 342, retrieve the QoS analysis subscription notification sent by the NWDAF, which includes QoS prediction results.

[0096] For example, when the prediction mechanism in step 341 is a first prediction mechanism, the QoS prediction result includes only statistical QoS analysis results. When the prediction mechanism in step 341 is a second prediction mechanism, the QoS prediction result may include both statistical and predictive QoS analysis results. Here, the statistical QoS analysis results may include data obtained by statistically analyzing historical data of the QoS characteristics of network cells, and the predictive QoS analysis results may include data obtained by predicting future trends of the QoS characteristics of network cells.

[0097] For example, the QoS prediction result of the network cell includes at least one of the network cell's bandwidth, latency, reliability, and jitter, which is not limited in this application.

[0098] In some embodiments, if the QoS prediction result indicates that the communication network is unreliable, the level of autonomous driving should be adjusted according to the road conditions and the vehicle's own capabilities, such as relying on single-vehicle intelligence for driving, or stopping driving and parking in a safe location.

[0099] Therefore, the embodiments of this application can determine the prediction mechanism used when measuring QoS of network cells along the path of a vehicle, and can flexibly predict QoS in autonomous driving, which helps to reduce the computing power requirements and / or complexity of QoS prediction in autonomous driving.

[0100] Furthermore, when the computational power requirements and / or complexity of QoS prediction in autonomous driving are reduced, the convergence time of the QoS prediction algorithm can also be reduced accordingly, which can help meet the requirements of real-time operation of intelligent connected vehicles relying on the Internet.

[0101] Figure 7 This application provides an interactive flowchart of a communication method for autonomous driving in intelligent connected vehicles. This method can be jointly executed by user equipment (e.g., V-UEs), access network equipment (e.g., gNB), 5G core network (5GC), and AF. For example, the 5GC may include network elements or functions such as access and mobility management function (AMF) / user plane function (UPF), NWDAF, policy control function (PCF), and network exposure function (NEF). The AF can be deployed on a cloud platform.

[0102] It should be understood that Figure 7 The steps or operations of a communication method applied to autonomous driving of intelligent connected vehicles are illustrated, but these steps or operations are merely examples, and other operations may be performed in the embodiments of this application. Figure 7 Variations of various operations within it. Furthermore... Figure 7 The various steps in can be followed according to... Figure 7 The different orders in which they are presented may be executed, and it is possible that they are not intended to be executed. Figure 7 All operations within. For example... Figure 7 As shown, the communication method applied to autonomous driving of intelligent connected vehicles includes steps 701 to 706.

[0103] 701. An end-to-end 5G network connection is established between the V-UEs and the AF. In this way, the V-UEs and the AF can communicate wirelessly through this 5G network connection.

[0104] For example, V-UEs are, for instance, the intelligent connected vehicles mentioned above, without limitation.

[0105] 702, AF obtains the vehicle's speed, driving intention, and driving trajectory.

[0106] For example, AF can obtain vehicle speed information reported by V-UEs, or calculate vehicle speed information based on vehicle location. For example, AF can obtain driving intention information and driving trajectory information input by the user in the user application through interaction.

[0107] 703, AF interacts with 5GC to determine the 5G network cell.

[0108] For example, the AF can determine the 5G network cell along the path the vehicle will take based on the vehicle's speed information, driving intention information, and driving trajectory information, and further interact with the 5GC to obtain relevant information about the 5G network cell, such as the road segment the vehicle passes through in the network cell, the cell identifier of the network cell, and at least one of TAI and RAI.

[0109] 704, AF determines the QoS prediction mechanism for network cells.

[0110] For example, the AF can determine the QoS prediction mechanism used to perform QoS measurement on the network cell based on the time when the vehicle arrives at the first location area.

[0111] For example, if the time is greater than a first threshold, the QoS prediction mechanism is determined to be a first prediction mechanism, which includes statistical analysis of historical data on the QoS characteristics of the network cell. If the time is less than or equal to a second threshold, the QoS prediction mechanism is determined to be a second prediction mechanism, which includes statistical analysis of historical data on the QoS characteristics of the network cell and prediction of future trends.

[0112] 705, retrieves QoS analysis results for statistical categories only.

[0113] Specifically, when the QoS prediction mechanism is the first prediction mechanism, only statistical QoS analysis results are obtained.

[0114] By employing this first prediction mechanism to perform QoS prediction for locations that require a relatively long time to reach, the computational power requirements and / or complexity of QoS prediction in autonomous driving can be appropriately reduced without affecting the effectiveness and reliability of QoS prediction.

[0115] 706, retrieve QoS analysis results for both statistical and predictive categories.

[0116] Specifically, when the QoS prediction mechanism is the second prediction mechanism, the QoS analysis results of statistical class + prediction class are obtained.

[0117] By employing this second prediction mechanism to predict QoS for the upcoming location, the accuracy of QoS prediction in autonomous driving can be improved, thereby enhancing the effectiveness and reliability of QoS prediction.

[0118] Therefore, the embodiments of this application can determine the prediction mechanism used when measuring QoS of network cells along the path of a vehicle, and can flexibly predict QoS in autonomous driving, which helps to reduce the computing power requirements and / or complexity of QoS prediction in autonomous driving.

[0119] Furthermore, when the computational power requirements and / or complexity of QoS prediction in autonomous driving are reduced, the convergence time of the QoS prediction algorithm can also be reduced accordingly, which can help meet the requirements of real-time operation of intelligent connected vehicles relying on the Internet.

[0120] The specific embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this application, various simple modifications can be made to the technical solutions of this application, and these simple modifications all fall within the protection scope of this application. For example, the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this application will not describe the various possible combinations separately. Furthermore, various different embodiments of this application can also be arbitrarily combined, as long as they do not violate the spirit of this application, they should also be considered as the content disclosed in this application.

[0121] It should also be understood that, in the various method embodiments of this application, the sequence numbers of the above processes do not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. It should be understood that these sequence numbers can be interchanged where appropriate so that the embodiments of this application described can be implemented in a sequence other than those illustrated or described.

[0122] The method embodiments of this application have been described in detail above. The following description, in conjunction with... Figures 8 to 9 The following describes in detail the device embodiments of this application.

[0123] Figure 8This is a schematic block diagram of a communication device 800 for autonomous driving in intelligent connected vehicles, provided as an embodiment of this application. For example, the communication device 800 is an AF (Automatic Advancement) system. Figure 8 As shown, the device 800 may include an acquisition unit 810 and a processing unit 820.

[0124] Acquisition unit 810 is used to acquire vehicle driving information;

[0125] Processing unit 820 is used to determine the network cells along the path that the vehicle needs to pass through based on the driving information;

[0126] The processing unit 820 is further configured to determine the prediction mechanism used for QoS prediction of the network cell;

[0127] The acquisition unit 810 is further configured to acquire the QoS prediction result of the network cell according to the prediction mechanism.

[0128] In some alternative embodiments, the processing unit 820 is specifically used for:

[0129] Based on the driving information, determine the time when the vehicle arrives at the first location area;

[0130] Based on the time, determine the prediction mechanism used for QoS prediction of the network cell corresponding to the first location area.

[0131] In some alternative embodiments, the processing unit 820 is specifically used for:

[0132] If the time exceeds a first threshold, the prediction mechanism is determined to be a first prediction mechanism, which includes statistical analysis of historical data on the QoS characteristics of the network cell.

[0133] In some alternative embodiments, the processing unit 820 is specifically used for:

[0134] If the time is less than or equal to the second threshold, the prediction mechanism is determined to be the second prediction mechanism. The second prediction mechanism includes statistical analysis of historical data on the QoS characteristics of the network cell and prediction of future trends.

[0135] In some optional embodiments, the QoS prediction result of the network cell includes at least one of the network cell's bandwidth, latency, reliability, and jitter.

[0136] In some optional embodiments, the acquisition unit 810 is specifically used for:

[0137] Based on the prediction mechanism, a QoS analysis subscription request is sent to the Network Data Analysis Function (NWDAF);

[0138] Obtain the QoS analysis subscription notification sent by the NWDAF, wherein the QoS analysis subscription notification includes the QoS prediction result.

[0139] In some optional embodiments, the driving information includes at least one of vehicle speed information, driving intention information, and driving trajectory information.

[0140] In some optional embodiments, the acquisition unit 810 is specifically used for:

[0141] The first information input by the user is obtained from the user application as the driving information, and the first information includes the driving intention information and / or driving trajectory information.

[0142] It should be understood that the device embodiments and method embodiments can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, further details will not be provided here. Specifically, Figure 8 The apparatus 800 shown can execute the above-described method embodiments, and the aforementioned and other operations and / or functions of each module in the apparatus 800 are respectively for implementing the corresponding processes in the above-described methods. For the sake of brevity, they will not be described in detail here.

[0143] The apparatus 800 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, the steps of the method embodiments in this application can be completed by integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the method disclosed in this application embodiment can be directly embodied as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiments.

[0144] Figure 9 This is a schematic block diagram of the electronic device 900 provided in the embodiments of this application.

[0145] like Figure 9 As shown, the electronic device 900 may include:

[0146] The system includes a memory 910 and a processor 920. The memory 910 stores computer programs and transfers the program code to the processor 920. In other words, the processor 920 can retrieve and run the computer program from the memory 910 to implement the methods described in the embodiments of this application.

[0147] For example, the processor 920 can be used to execute the above-described method embodiments according to instructions in the computer program.

[0148] In some embodiments of this application, the processor 920 may include, but is not limited to:

[0149] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0150] In some embodiments of this application, the memory 910 includes, but is not limited to:

[0151] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0152] In some embodiments of this application, the computer program may be divided into one or more modules, which are stored in the memory 910 and executed by the processor 920 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0153] like Figure 9 As shown, the electronic device 900 may further include:

[0154] Transceiver 930, which can be connected to processor 920 or memory 910.

[0155] The processor 920 can control the transceiver 930 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 930 may include a transmitter and a receiver. The transceiver 930 may further include antennas, and the number of antennas may be one or more.

[0156] It should be understood that the various components in the electronic device are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0157] This application also provides a computer storage medium storing a computer program thereon, which, when executed by a computer, enables the computer to perform the methods of the above-described method embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the methods of the above-described method embodiments.

[0158] When implemented using software, it can be implemented entirely or partially as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0159] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0161] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0162] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A communication method for autonomous driving of intelligent connected vehicles, characterized in that, include: Obtain vehicle driving information; Based on the driving information, determine the network cells along the route the vehicle needs to pass through; Based on the driving information, determine the time when the vehicle arrives at the first location area; If the time exceeds the first threshold, the prediction mechanism is determined to be the first prediction mechanism, which includes statistical analysis of historical data on the QoS characteristics of the network cell. Based on the prediction mechanism, the QoS prediction results of the network cell are obtained.

2. The method according to claim 1, characterized in that, Also includes: If the time is less than or equal to the second threshold, the prediction mechanism is determined to be the second prediction mechanism. The second prediction mechanism includes statistical analysis of historical data on the QoS characteristics of the network cell and prediction of future trends.

3. The method according to claim 1 or 2, characterized in that, The QoS prediction result of the network cell includes at least one of the network cell's bandwidth, latency, reliability, and jitter.

4. The method according to claim 1 or 2, characterized in that, The step of obtaining the QoS prediction result of the network cell according to the prediction mechanism includes: Based on the prediction mechanism, a QoS analysis subscription request is sent to the Network Data Analysis Function (NWDAF); Obtain the QoS analysis subscription notification sent by the NWDAF, wherein the QoS analysis subscription notification includes the QoS prediction result.

5. The method according to claim 1 or 2, characterized in that, The driving information includes at least one of vehicle speed information, driving intention information, and driving trajectory information.

6. The method according to claim 1 or 2, characterized in that, The acquisition of vehicle driving information includes: The first information input by the user is obtained from the user application as the driving information, and the first information includes driving intention information and / or driving trajectory information.

7. A communication device for autonomous driving in intelligent connected vehicles, characterized in that, include: The acquisition unit is used to acquire vehicle driving information; The processing unit is used to determine the network cells along the path that the vehicle needs to pass through based on the driving information. The processing unit is further configured to determine the time when the vehicle arrives at the first location area based on the driving information; and if the time is greater than a first threshold, determine the prediction mechanism as a first prediction mechanism, wherein the first prediction mechanism includes statistical analysis of historical data on the QoS characteristics of the network cell. The acquisition unit is further configured to acquire the QoS prediction result of the network cell according to the prediction mechanism.

8. An electronic device, characterized in that, include: A processor and a memory, the memory being used to store a computer program, the processor being used to invoke and run the computer program stored in the memory to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Includes instructions that, when run on a computer, cause the computer to perform the method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the method as described in any one of claims 1 to 6.

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