Map construction method and related device

Abstract

A map construction method and a related device, which can be used in the field of intelligent driving in the field of artificial intelligence. The method comprises: a first device acquires first information, wherein the first information comprises first voice information for describing a first location area (i.e., a geographic location area), and the first voice information is inputted by a user; and inputting the first information into a machine learning model to obtain first map information of the first location area generated by the machine learning model. That is, another map information acquisition method is provided. Additionally, personalized map information generated by the machine learning model for the user on the basis of the voice information inputted by the user is conducive to meeting more diverse requirements of users on map information, thereby improving the user viscosity of the present solution.

Description

A map construction method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on January 31, 2024, with application number 202410144591.3 and invention name “A map construction method and related equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of artificial intelligence, and in particular to a map construction method and related equipment. Background Art

[0003] With the rapid development of intelligent driving technology, navigation systems have also developed rapidly. Navigation systems can provide users with accurate map information, and then provide users with positioning and navigation functions based on the aforementioned map information. However, current navigation systems only provide general map information and cannot provide users with personalized map information. Therefore, a method for constructing personalized map information is urgently needed. Summary of the Invention

[0004] This application provides a map construction method and related equipment, providing another way to obtain map information, which is conducive to meeting users' more diverse needs for map information, and further helps to improve the user stickiness of this solution.

[0005] This application provides the following technical solutions:

[0006] In the first aspect, the present application provides a map construction method that can be used in the field of intelligent driving in the field of artificial intelligence. In this method, a first device can obtain first information, and then input the first information into a machine learning model to obtain first map information of a first location area generated by the machine learning model. The first information includes first voice information for describing the first location area, and the first voice information is input by the user. For example, the specific product form of the first device includes but is not limited to: vehicles, mobile phones, smart wearable devices or other products that can use map information. Optionally, the first machine learning model can be a deep learning model; for example, the first machine learning model can specifically adopt a neural network based on an attention mechanism, a fully connected neural network or other types of neural networks. Optionally, the first machine learning model can be a large language model (LLM).

[0007] Exemplarily, in one implementation, if the first machine learning model is deployed on a first device, the first device inputs the first information into the machine learning model, and obtaining the first map information of the first location area generated by the machine learning model may include: the first device inputs the first information into the locally deployed first machine learning model, and obtains the first map information of the first location area generated by the locally deployed first machine learning model. In another implementation, if the first machine learning model is deployed on a device other than the first device, the first device inputs the first information into the machine learning model, and obtaining the first map information of the first location area generated by the machine learning model may include: the first device sends the first information to the device on which the first machine learning model is deployed, and then receives the first map information of the first location area sent by the device on which the first machine learning model is deployed.

[0008] In this implementation, the voice information input by the user to describe the first location area can be input into the machine learning model to obtain map information generated by the machine learning model. The user can then decide the content of the input voice information based on the brain's cognition of the geographical environment of the first location area. Correspondingly, the machine learning model generates personalized map information for the user based on the voice information input by the user. This solution provides another way to obtain map information, which is conducive to meeting users' more diversified needs for map information, and thus is conducive to improving the user stickiness of this solution.

[0009] In a possible implementation, the method may further include: the first device performs navigation based on first map information of the first location area and second map information of the first location area, where the second map information is common map information for different vehicles.

[0010] Furthermore, in one case, a third machine learning model can be deployed in the vehicle. When the vehicle obtains voice information input by the user (hereinafter referred to as "third voice information" for ease of distinction) expressing the user's navigation needs, the third voice information can be input into the third machine learning model to obtain prediction information shown by the third machine learning model. The prediction information is used to determine which map information to use between the first map information of the first location area and the second map information of the first location area, that is, the prediction information is used to determine whether to use a personalized map of the first location area or a universal map of the first location area.

[0011] In another scenario, after detecting a third voice message input by the user, the vehicle may determine whether the navigation request expressed in the third voice message points to a clear destination. If so, navigation may be performed directly using the second map information for the first location area. If the third voice message does not contain a clear destination, the first map information for the first location area may be used to determine the destination, and then the second map information for the first location area may be used to determine a navigation route from the vehicle's current location to the destination, and navigation may then be performed based on the determined navigation route.

[0012] In this implementation, the vehicle will also be deployed with second map information of the first location area. The second map information is universal map information in different vehicles. When the vehicle is navigating, it will not only be based on the personalized map information of the first location area (that is, the first map information), but also based on the universal map information of the first location area. The universal map information of the first location area can more accurately reflect the geographical environment information of the first location area, and the personalized map information of the first location area can better reflect the user's personalized preference for the first location area. The comprehensive use of the personalized map information and universal map information of the first location area is conducive to providing a navigation solution that better meets user expectations, thereby improving the user stickiness of this solution.

[0013] In one possible implementation, the second map information of the first location area is obtained based on at least two first map information of the first location area, and the objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area, that is, the second device can extract the objects that appear repeatedly in the at least two first map information of the first location area, and then put the objects that appear repeatedly in the at least two first map information of the first location area into the second map information of the first location area.

[0014] In this implementation, the universal map information of the first location area is obtained based on at least two first map information of the first location area. Since the user can experience the changes in the surrounding environment more accurately, the universal map information is obtained with the help of the voice information input by the user describing the geographical location area, which is conducive to more rapid adaptation to changes in urban planning and roads, and is conducive to shortening the update cycle of the universal map, so that the universal map information deployed in the vehicle can more accurately reflect the geographical environment, thereby improving the accuracy of the navigation process.

[0015] In one possible implementation, the method is applied to a first device, and third map information is deployed in the first device. The third map information includes second map information of multiple location areas. The third map information is obtained by fusing the second map information of multiple location areas, and the multiple location areas include the first location area. Exemplarily, "fusing the second map information of multiple location areas" can also be understood as "splicing the second map information of multiple location areas", that is, the second map information of each location area can be understood as fragmented map information, and the third map information includes universal map information of multiple location areas.

[0016] In this implementation, after fusing multiple first map information of a certain location area, universal second map information of the location area can be obtained. After fusing the second map information of multiple location areas, universal third map information can be obtained, that is, universal map information deployed in different vehicles. That is, by leveraging the capabilities of machine learning models to obtain universal map information deployed in vehicles, not only can the cost of mapping information be reduced, but also it is conducive to obtaining more accurate environmental information.

[0017] In one possible implementation, the first map information of the first location area includes relative position information of objects in the first location area. Exemplarily, in one case, the first map information of the first location area may include text information, that is, using text to describe which objects are included in the first location area and the relative positions between different objects; optionally, the first map information of the first location area may use a JavaScript Object Notation (JSON) file format. In another case, the first map information of the first location area may include image information, that is, using images to display which objects are included in the first location area and the relative positions between different objects. In another case, the first map information of the first location area may include both the above-mentioned text information and image information, the text information using text to describe which objects are included in the first location area and the relative positions between different objects, and the image information using images to display which objects are included in the first location area and the relative positions between different objects.

[0018] In this implementation, since the first map information of the first location area includes the relative position information of objects in the first location area, carrying the relative position information of objects in the map information can not only realize the navigation function, but also reduce the leakage of geographical environment information compared to the situation where the map information includes the absolute position information of the objects, that is, it realizes the output of the map information of the first location area in a safer manner.

[0019] In one possible implementation, in this method, after obtaining the first map information of the first location area generated by the machine learning model, the first device can also obtain second information, where the second information includes second voice information for describing the first location area; the first device inputs the second information into the machine learning model to obtain the fourth map information of the first location area generated by the machine learning model, and then updates the first map information of the first location area based on the fourth map information to obtain the updated first map information of the first location area.

[0020] Exemplarily, the vehicle updates the first map information of the first location area, which may include: if the fourth map information carries information that is not carried in the first map information of the first location area, the information not carried in the first map information of the first location area may be added to the first map information of the first location area; and / or, if the first location information of an object (hereinafter referred to as the "first object" for the convenience of description) carried in the fourth map information is incompatible with the second location information of the first object carried in the first map information, the second location information of the first object may be replaced with the first location information; and / or other update operations, etc.

[0021] In this implementation, after generating the first map information of the first location area, the second information can be obtained again, the second information including the second voice information describing the first location area, and the second information is input into the first machine learning model to obtain the fourth map information of the first location area generated by the first machine learning model. After updating the first map information of the first location area based on the fourth map information, the updated first map information of the first location area is obtained, that is, the voice information of the first location area described by the user can be used in time to update the map information of the first location area, so that when the geographical environment of the first location area changes, the map information corresponding to the changed first location area can be obtained in time, that is, not only personalized map information that is more in line with the user's description can be obtained, but also more accurate map information can be obtained.

[0022] In one possible implementation, the first information further includes at least one of the following: a captured image corresponding to the first location area, text describing the first location area, or a hand-drawn map of the first location area. In this implementation, the first information contains more information, which allows the first machine learning model to obtain more information, thereby helping the first machine learning model generate more accurate first map information.

[0023] On the second aspect, the present application provides a map construction method that can be used in the field of intelligent driving in the field of artificial intelligence. In this method, the second device obtains at least two first map information of the first location area, and then determines the second map information of the first location area based on the at least two first map information of the first location area. Wherein, each first map information is generated by a machine learning model, the input of the machine learning model includes first information, the first information includes first voice information for describing the first location area, the first voice information is input by the user, and the object in the second map information of the first location area is repeated in at least two first map information of the first location area. Exemplarily, the second device can be specifically manifested as a cloud server corresponding to the vehicle.

[0024] In one possible implementation, in this method, the second device may further fuse the second map information of multiple location areas to obtain third map information, wherein the third map information includes the second map information of the multiple location areas, and the multiple location areas include the first location area. Exemplarily, "fusing the second map information of the multiple location areas" can also be understood as "splicing the second map information of the multiple location areas," that is, the second map information of each location area can be understood as fragmented map information, and the third map information includes universal map information for the multiple location areas.

[0025] For the meaning of the nouns in the second aspect of this application and the various possible implementations of the second aspect, as well as the beneficial effects brought about by each possible implementation, please refer to the description of the various possible implementations in the first aspect, and will not be repeated here.

[0026] In a third aspect, the present application provides a map construction device that can be used in the field of intelligent driving within the field of artificial intelligence. The map construction device includes: an acquisition module for acquiring first information, the first information including first voice information describing a first location area, the first voice information being input by a user; and an input module for inputting the first information into a machine learning model to obtain first map information of the first location area generated by the machine learning model.

[0027] In the third aspect of this application, the map construction device is also used to execute the steps performed by the first device in the first aspect and various possible implementation methods of the first aspect. The specific implementation methods, meanings of terms and beneficial effects of the steps in the third aspect can all be referred to the first aspect and will not be repeated here.

[0028] In a fourth aspect, the present application provides a map construction device that can be used in the field of intelligent driving in the field of artificial intelligence. The map construction device includes: an acquisition module for acquiring at least two first map information of a first location area, wherein each first map information is generated by a machine learning model, and the input of the machine learning model includes first information, and the first information includes first voice information for describing the first location area, and the first voice information is input by a user; a determination module for determining second map information of the first location area based on the at least two first map information of the first location area, where an object in the second map information of the first location area appears repeatedly in the at least two first map information of the first location area.

[0029] In the fourth aspect of this application, the map construction device is also used to execute the steps performed by the second device in the second aspect and various possible implementation methods of the second aspect. The specific implementation methods, meanings of the terms and beneficial effects brought about by the steps in the fourth aspect can all be referred to the second aspect and will not be repeated here.

[0030] In a fifth aspect, an embodiment of the present application provides a device comprising a processor and a memory, wherein the processor is coupled to the memory, the memory is used to store programs, and the processor is used to execute the programs in the memory, so that the execution device executes the method described in the first or second aspect above.

[0031] In a sixth aspect, an embodiment of the present application provides a vehicle comprising a processor and a memory, wherein the processor is coupled to the memory, the memory being used to store programs; and the processor being used to execute the programs in the memory, so that the vehicle executes the method described in the first aspect above.

[0032] In a seventh aspect, an embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer executes the method described in the first or second aspect above.

[0033] In an eighth aspect, an embodiment of the present application provides a computer program product, which includes a program. When the program runs on a computer, it enables the computer to execute the method described in the first or second aspect above.

[0034] In a ninth aspect, the present application provides a chip system, which includes a processor for supporting the implementation of the functions involved in the above aspects, for example, sending or processing the data and / or information involved in the above methods. In one possible design, the chip system also includes a memory, which is used to store program instructions and data necessary for the terminal device or communication device. The chip system can be composed of a chip or can include a chip and other discrete devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] FIG1 is a schematic diagram of the structure of an artificial intelligence main framework provided in an embodiment of the present application;

[0036] FIG2 is a schematic diagram of an architecture of a map construction system provided in an embodiment of the present application;

[0037] FIG3 is a flow chart of a map construction method provided in an embodiment of the present application;

[0038] FIG4 is another flowchart of a map construction method provided in an embodiment of the present application;

[0039] FIG5 is a schematic diagram showing a comparison between first map information of a first location area and updated first map information provided in an embodiment of the present application;

[0040] FIG6 is a schematic diagram of a process for obtaining third map information according to an embodiment of the present application;

[0041] FIG7 is a schematic diagram of obtaining third map information according to an embodiment of the present application;

[0042] FIG8 is a schematic structural diagram of a map construction device provided in an embodiment of the present application;

[0043] FIG9 is another structural diagram of a map construction device provided in an embodiment of the present application;

[0044] FIG10 is a schematic structural diagram of a device provided in an embodiment of the present application;

[0045] FIG11 is another schematic diagram of the structure of the device provided in an embodiment of the present application;

[0046] FIG12 is a schematic structural diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged under appropriate circumstances. This is merely a way of distinguishing when describing objects with the same properties in the embodiments of the present application. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, so that a process, method, system, product or apparatus that includes a series of units is not necessarily limited to those units, but may include other units not expressly listed or inherent to these processes, methods, products or apparatuses.

[0048] In the embodiments of the present application, "sending" and "receiving" indicate the direction of signal transmission. For example, "sending information to XX device" can be understood as the destination of the information being XX device, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. "Receiving information from YY device" can be understood as the source of the information being YY device, which can include direct receiving from YY device through the air interface, as well as indirect receiving from YY device through the air interface from other units or modules. "Sending" can also be understood as the "output" of the chip interface, and "receiving" can also be understood as the "input" of the chip interface. In other words, sending and receiving can be performed between devices or within a device, for example, between components, modules, chips, software modules or hardware modules within the device through a bus, trace or interface. It is understandable that information may undergo necessary processing, such as encoding, modulation, etc., between the source and destination of the information, but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0049] In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association between the other information and the information to be indicated; it is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance, for example, the indication of specific information can be achieved with the help of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that, for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0050] First, let's describe the overall workflow of an AI system. See Figure 1, which shows a schematic diagram of the AI ​​framework. This framework will be explained from two perspectives: the "intelligent information chain" (horizontal axis) and the "IT value chain" (vertical axis). The "intelligent information chain" reflects the entire process from data acquisition to processing. For example, it could be the general process of intelligent information perception, intelligent information representation and formation, intelligent reasoning, intelligent decision-making, and intelligent execution and output. Throughout this process, data undergoes a condensed journey from "data-information-knowledge-wisdom." The "IT value chain," spanning the underlying infrastructure of human intelligence, information (provided and processed by technology), and the system's industrial ecosystem, reflects the value that AI brings to the information technology industry.

[0051] (1) Infrastructure

[0052] The infrastructure provides computing power for AI systems, enabling communication with the outside world and providing support through a basic platform. Communication with the outside world is achieved through sensors; computing power is provided by smart chips, which can specifically adopt hardware acceleration chips such as central processing units (CPUs), embedded neural network processing units (NPUs), graphics processing units (GPUs), application-specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs); the basic platform includes related platform guarantees and support such as distributed computing frameworks and networks, and can include cloud storage and computing, and interconnected networks. For example, sensors communicate with the outside world to obtain data, which is then provided to the smart chips in the distributed computing system provided by the basic platform for calculation.

[0053] (2) Data

[0054] Data above the infrastructure layer represents data sources for AI. This data includes graphics, images, voice, and text, as well as IoT data from traditional devices. This includes business data from existing systems and sensor data such as force, displacement, liquid level, temperature, and humidity.

[0055] (3) Data processing

[0056] Data processing generally includes data training, machine learning, deep learning, search, reasoning, decision-making, etc.

[0057] Among them, machine learning and deep learning can symbolize and formalize data for intelligent information modeling, extraction, preprocessing, and training.

[0058] Reasoning refers to the process of simulating human intelligent reasoning in computers or intelligent systems, using formalized information to perform machine thinking and solve problems based on reasoning control strategies. Typical functions are search and matching.

[0059] Decision-making refers to the process of making decisions after intelligent information is reasoned, and usually provides functions such as classification, sorting, and prediction.

[0060] (4) General ability

[0061] After the data has undergone the data processing mentioned above, some general capabilities can be further formed based on the results of the data processing, such as algorithms or a general system, for example, translation, text analysis, computer vision processing, speech recognition, image recognition, etc.

[0062] (5) Smart products and industry applications

[0063] Smart products and industry applications refer to the products and applications of artificial intelligence systems in various fields. They are the encapsulation of the overall artificial intelligence solution, which productizes intelligent information decision-making and realizes practical application. Its application areas mainly include: smart terminals, smart manufacturing, smart transportation, smart homes, smart medical care, smart security, smart driving, smart cities, etc.

[0064] The method provided in this application can be applied to various application scenarios using map information. For example, in the field of intelligent driving, vehicles can provide navigation functions based on the deployed map information. For another example, in the field of smart terminals, map information can be deployed on the smart terminal, and the smart terminal can provide navigation functions to users based on the deployed map information. The embodiments of this application do not enumerate the scenarios in which map information is used. However, since the currently provided navigation systems only provide general map information and cannot generate personalized map information for users, in order to solve the aforementioned problem, the embodiments of this application provide a map construction method that can generate personalized map information based on the voice information input by the user through a machine learning model (hereinafter referred to as the "first machine learning model" for the convenience of description).

[0065] Before describing the map construction method provided herein in detail, we will first describe the architecture of the map construction system provided herein. Please refer to Figure 2, which is a schematic diagram of the architecture of the map construction system provided herein. As shown in Figure 2, map construction system 200 includes a training device 210, a database 220, an execution device 230, a data storage system 240, and a first device 250. Execution device 230 includes a computing module 231.

[0066] Among them, the database 220 stores a training data set. During the training phase of the first machine learning model 201, the training device 210 generates the first machine learning model 201 and iteratively trains the first machine learning model 201 using the training data set to obtain the first machine learning model 201 that has been trained. The first machine learning model 201 can be specifically expressed as a neural network or a non-neural network model. In the embodiments of the present application, only the first machine learning model 201 expressed as a neural network is used as an example for description.

[0067] The first machine learning model 201 obtained by the training device 210 after performing the training operation can be deployed to the computing module 231 of the execution device 230. The execution device 230 can call data, code, etc. in the data storage system 240, or store data, instructions, etc. in the data storage system 240. The data storage system 240 can be placed in the execution device 230, or the data storage system 240 can be an external memory relative to the execution device 230.

[0068] During the application stage of the first machine learning model 201 that has performed the training operation, the execution device 230 obtains first information, which at least includes voice information input by the user to describe the geographical environment of the first location area. After the first information is input into the first machine learning model 201 in the calculation module 231, the first map information of the first location area generated by the first machine learning model 201 can be obtained.

[0069] In some embodiments of the present application, please refer to Figure 2, the execution device 230 and the first device 250 can be independent devices. The execution device 230 is configured with an input / output (I / O) interface to interact with the first device 250 for data. After determining the first information, the first device 250 sends the first information to the execution device 230 through the I / O interface. After the execution device 230 generates the first map information of the first location area through the first machine learning model 201 in the computing module 231, the execution device 230 can return the aforementioned second information to the client device through the I / O interface.

[0070] It is worth noting that Figure 2 is only an architectural diagram of the map construction system provided by an embodiment of the present invention, and the positional relationship between the devices, components, modules, etc. shown in the figure does not constitute any limitation. For example, in other embodiments of the present application, the execution device 230 and the client device 250 can be integrated into the same device, and the user can interact directly with the execution device 230. Exemplarily, the execution device 230 can be a module in the host processor (Host CPU) of the client device that uses the first deep learning model to process data. The execution device 230 can also be a graphics processing unit (GPU) or a neural network processor (NPU) in the client device. The GPU or NPU is mounted on the host processor as a coprocessor, and the host processor assigns tasks.

[0071] Specifically, see Figure 3. Figure 3 is a flowchart of a map construction method provided in an embodiment of the present application. The map construction method provided in an embodiment of the present application may include:

[0072] 301. A first device obtains first information, where the first information includes first voice information for describing a first location area, and the first voice information is input by a user.

[0073] In the embodiments of the present application, for example, the specific product form of the first device includes but is not limited to: vehicles, mobile phones, smart wearable devices or other products that can use map information, etc.; optionally, the first device also needs to have the ability to display map information to the user, for example, the vehicle can display map information to the user through the screen inside the vehicle, the mobile phone can display map information to the user through the mobile phone screen, the smart wearable device can use projection to display map information to the user, etc. It should be noted that the specific product forms of the first device can be determined in combination with the actual application scenario, and are not limited in the embodiments of the present application. In the subsequent embodiments of the present application, only the "first device" is expressed as a vehicle as an example to illustrate the map construction method provided in the present application. When the first device is expressed as other product forms, the specific implementation scheme can be understood by referring to the description of "the first device is a vehicle", and will not be repeated one by one in this application.

[0074] The map construction method provided in this application is used to generate personalized map information for users. Regarding the triggering method of the method provided in this application, in one implementation method, the method provided in this application can be automatically triggered by a first device. For example, the first device can obtain the voice information input by the user in real time. When it detects the first voice information input by the user to describe a certain location area (that is, the first location area), it triggers the start of the method provided in this application. The "first location area" in this application can also be called the "first geographical location" or "first geographical location area", etc., which refers to the location area in the geographical environment.

[0075] For example, a second machine learning model may be deployed in the first device. The second machine learning model is a machine learning model for performing classification or regression tasks. For example, the second machine learning model may be a recurrent neural network, a fully connected neural network, a neural network based on an attention mechanism, or other types of neural networks, the examples of which are not exhaustive here. The first device inputs the voice information acquired in real time into the second machine learning model to obtain category information generated by the second machine learning model. The category information is used to indicate whether the aforementioned voice information is voice information used to describe a geographic location area. The first device can then use the category information output by the second machine learning model to detect whether the voice information input by the user is voice information describing a geographic location area. It should be noted that the "detection of whether the voice information input by the user is voice information describing a geographic location area using a second machine learning model used to perform classification or regression tasks" shown here is only an example to prove the feasibility of this solution. The first device can also use other methods to detect whether the voice information input by the user is voice information describing a geographic location area, which will not be listed one by one in the embodiments of this application.

[0076] To further understand the present solution, an application scenario of the present application is given as an example here. For example, if user A in a vehicle wants to go to a nearby coffee shop, user B in the vehicle says: "Starting from the east gate of Park A, turn right and go straight for about 300 meters. There is an intersection. After going straight through this intersection, you can see a coffee shop on the right." The vehicle can detect that the aforementioned voice information of user B is the first voice information describing a certain location area (that is, the first location area), and thus can trigger the execution of the method provided by the present application, wherein the first location area may include the geographical location area from the east gate of Park A to the coffee shop. It should be understood that the example here is only for the convenience of understanding the present solution and is not used to limit the present solution.

[0077] In another implementation, the method provided in this application can be triggered based on a user's operation. That is, when the first device receives a first operation input by the user, the method provided in this application is triggered to start executing in response to the received first operation. For example, the first device can provide an icon for receiving the first operation, and the user can input the first operation by clicking on the aforementioned icon. For another example, the user can input the first operation by voice input, etc. The specific operation can be flexibly determined in combination with the actual application scenario and is not limited in the embodiments of this application.

[0078] Optionally, the first information further includes at least one of the following: a captured image corresponding to the first location area, text information for describing the first location area, a hand-drawn picture of the first location area, or other information.

[0079] Among them, the "captured images corresponding to the first location area" include images of the surrounding geographical environment that can be captured by the first device. Exemplarily, after the first device determines to start executing the map construction method provided by the present application, it can start capturing images of the surrounding geographical environment through the camera of the first device. In one case, the captured images corresponding to the first location area include all images captured by the first device after starting to execute the method provided by the present application. In another case, after acquiring the captured images, the first device can filter all the captured images to obtain filtered images, and the captured images corresponding to the first location area include the aforementioned filtered images.

[0080] Exemplarily, since the first voice information is voice information describing the first location area, the first device can filter all images taken by the first device according to the first voice information to obtain at least one filtered image, and the correlation between each filtered image and the first voice information satisfies the first condition.

[0081] For example, the first condition may be that the similarity between the image and the first voice information is greater than or equal to the first similarity. The first device may calculate the similarity between each image in all images captured by the first device and the first voice information, and then filter out at least one image that meets the first condition from all images captured by the first device, thereby obtaining at least one filtered image; exemplarily, the aforementioned similarity can be obtained by calculating the Euclidean distance, cosine similarity or L1 similarity between each image and the first voice information, or the similarity between each image and the first voice information may be calculated by other methods, which is not limited in the embodiments of the present application.

[0082] For another example, the first device may input the first voice information and all images captured by the first device into a machine learning model to perform the screening operation through the machine model, and use the image output by the machine learning model as the screened image. For another example, the first condition may be that the correlation between the image and the first voice information is greater than or equal to the first correlation. The first device may input the first voice information and all images captured by the first device into a machine learning model to generate the correlation between each input image and the first voice information through the machine model, and then determine at least one image that meets the first condition based on the correlation between each image captured by the first device and the first voice information, that is, obtain at least one screened image. It should be noted that the examples given here are only for the convenience of understanding this solution and are not used to limit this solution.

[0083] "Text information used to describe the first location area" may include: text information input by the user, text information downloaded from the network by the first device to describe the first location area, or other text information, etc., which is not limited here; for example, the text information used to describe the first location area may be text information describing the geographical environment of the first location area.

[0084] For example, the first voice information is "Starting from the east gate of Park A, turn right and go straight for about 300 meters, there is a crossroads. After going straight through this intersection, you can see a coffee shop on the right." Then the first location area may refer to the geographical location area from the east gate of Park A to the coffee shop. The first device can determine that the objects related to the first location area may include: Park A, the east gate of Park A, the traffic roads near Park A, the crossroads near the east gate of Park A, and the coffee shops around the crossroads based on the first voice information. Then the text information downloaded from the network by the first device to describe the first location area may include at least one of the following: text information describing Park A, text information describing the east gate of Park A, text information describing the traffic roads near Park A, text information describing the intersection, text information describing the coffee shop near the intersection, or text information describing other objects in the first location area, etc. It should be understood that the examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0085] The "hand-drawn map of the first location area" may include a hand-drawn map input by the user, a hand-drawn map of the first location area downloaded from the network by the first device, or other hand-drawn maps, etc. For example, the first voice message is "Starting from the east gate of Park A, turn right and go straight for about 300 meters, there is a crossroads, go straight through this crossroads, and you can see a coffee shop on the right", then the first location area may refer to the geographical location area from the east gate of Park A to the coffee shop, and the first device may determine that the objects related to the first location area may include: Park A, traffic roads and crossroads near the east gate of Park A, based on the first voice message, then the hand-drawn map of the first location area downloaded from the network by the first device may include at least one of the following: a hand-drawn map of Park A, a hand-drawn map of traffic roads near the east gate of Park A, a hand-drawn map of the geographical environment around the crossroads near the east gate of Park A, or other hand-drawn maps, etc. It should be understood that the examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0086] In an embodiment of the present application, the first information contains richer information, so the first machine learning model can obtain richer information, which is helpful in assisting the first machine learning model to generate more accurate first map information.

[0087] 302. The first device inputs the first information into the first machine learning model to obtain first map information of the first location area generated by the first machine learning model.

[0088] In an embodiment of the present application, after obtaining the first information, the first device may input the obtained first information into a first machine learning model to obtain first map information of the first location area generated by the first machine learning model. Optionally, the first machine learning model may be a deep learning model; for example, the first machine learning model may specifically adopt a neural network based on an attention mechanism, a fully connected neural network, or other types of neural networks, etc., which are not limited in the embodiment of the present application. Optionally, the first machine learning model may be a large language model (LLM).

[0089] In one implementation, if the first machine learning model is deployed on a first device, step 302 may include: the first device inputting the first information into the locally deployed first machine learning model to obtain first map information of the first location area generated by the locally deployed first machine learning model. In another implementation, if the first machine learning model is deployed on a device other than the first device, step 302 may include: the first device sending the first information to the device on which the first machine learning model is deployed, and then receiving the first map information of the first location area sent by the device on which the first machine learning model is deployed.

[0090] Optionally, the "first map information of the first location area" in this application may include relative position information of different objects in the first location area. For example, in one embodiment, the first map information of the first location area may include text information, that is, a text description of the objects included in the first location area and the relative positions of different objects; optionally, the first map information of the first location area may be in JavaScript Object Notation (JSON) file format.

[0091] In another case, the first map information of the first location area may include image information, that is, using an image to display objects contained in the first location area and the relative positions of different objects.

[0092] In another case, the first map information of the first location area may include the above-mentioned text information and image information at the same time. The text information uses text to describe which objects are included in the first location area and the relative positions between different objects. The image information uses images to show which objects are included in the first location area and the relative positions between different objects.

[0093] For example, the first device may be deployed with an intelligent voice assistant and a navigation module. The intelligent voice information uses the first map information in text form, and the navigation module uses the first map information in image form. It should be understood that the examples given here are only for the convenience of understanding the usage scenarios of the "first map information" and are not used to limit this solution.

[0094] In the embodiment of the present application, since the first map information of the first location area includes the relative position information of objects in the first location area, carrying the relative position information of the objects in the map information can not only realize the navigation function, but also reduce the leakage of geographical environment information compared to the situation where the map information includes the absolute position information of the objects, that is, it realizes the output of the map information of the first location area in a safer manner.

[0095] Optionally, based on the embodiment corresponding to FIG. 3 above, the method provided by the present application is further described below using the first device being a vehicle as an example. Specifically, please refer to FIG. 4 , which is another flowchart of the map construction method provided by the embodiment of the present application. The map construction method provided by the embodiment of the present application may include:

[0096] 401. A vehicle obtains first information, where the first information includes first voice information for describing a first location area, and the first voice information is input by a user.

[0097] 402. The vehicle inputs the first information into the machine learning model to obtain first map information of the first location area generated by the machine learning model.

[0098] In the embodiment of the present application, the specific implementation of steps 401 and 402 can refer to the description in the embodiment corresponding to Figure 3 above, and will not be repeated here.

[0099] 403. The vehicle navigates based on first map information of the first location area and second map information of the first location area, where the second map information is common map information among different vehicles.

[0100] In this embodiment of the present application, step 403 is optional. A third map information may also be deployed in the vehicle. This third map information is common to different vehicles and includes second map information for multiple location areas, where the multiple location areas include the first location area. This means that the second map information for the first location area is common to different vehicles.

[0101] After determining the first location area, the vehicle can obtain the second map information of the first location area from the third map information, and then navigate based on the first map information of the first location area and the second map information of the first location area.

[0102] Regarding the specific implementation method of determining the first position area in the present application, for example, the vehicle can determine the first position area based on the first voice information input by the user; for another example, the vehicle can determine the current position of the vehicle based on the positioning system, and determine the preset range around the current position of the vehicle as the first position area; for another example, the vehicle can determine the first position area based on the current position of the vehicle and the first voice information, etc., which is not limited in the embodiments of the present application.

[0103] A specific implementation method for navigating a vehicle based on first map information of a first location area and second map information of the first location area. In one case, a third machine learning model may be deployed in the vehicle. When the vehicle obtains voice information input by the user (hereinafter referred to as "third voice information" for ease of distinction) expressing the user's navigation needs, the vehicle may input the third voice information into the third machine learning model to obtain prediction information shown by the third machine learning model. The prediction information is used to determine which map information to use between the first map information of the first location area and the second map information of the first location area, that is, the prediction information is used to determine whether to use a personalized map of the first location area or a universal map of the first location area.

[0104] For example, if the third voice message is "Please navigate to the nearest coffee shop", the second map information of the first location area (that is, a universal map) can be used; if the third voice message is "Please navigate to my favorite coffee shop", the first map information of the first location area (that is, a personalized map) can be used. For another example, if the third voice message is "Please navigate to the largest coffee shop nearby", the second map information of the first location area (that is, a universal map) can be used; if the third voice message is "Please navigate to the coffee shop I often go to", the first map information of the first location area (that is, a personalized map) can be used. For another example, if the third voice message is "Please navigate to Coffee Shop A", the second map information of the first location area (that is, a universal map) can be used; if the third voice message is "Please navigate to the coffee shop I often go to", the first map information of the first location area (that is, a personalized map) can be used, etc. The examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0105] Exemplarily, the third machine learning model can be used to determine whether the third voice information carries the user's biased demand. If the third voice information carries the user's biased demand, the prediction information output by the third machine learning model can indicate the use of the first map information of the first location area. If the third voice information does not carry the user's biased demand, the prediction information output by the third machine learning model can indicate the use of the second map information of the first location area. Alternatively, the third machine learning model can be used to determine whether the third voice information has an emotional color. If the third voice information has an emotional color, the prediction information output by the third machine learning model can indicate the use of the first map information of the first location area. If the third voice information does not have an emotional color, the prediction information output by the third machine learning model can indicate the use of the second map information of the first location area, etc. The examples here are only to prove the feasibility of this solution and are not used to limit this solution.

[0106] In another case, after detecting the third voice message input by the user, the vehicle can determine whether the navigation demand expressed in the third voice message can point to a clear destination. If the third voice message can point to a clear destination, the second map information of the first location area can be directly used for navigation. If the third voice message does not carry a clear destination, the first map information of the first location area can be used to determine the destination first, and then the second map information of the first location area can be used to determine the navigation route between the current location of the vehicle and the destination, and then navigation can be performed based on the determined navigation route. Since the present solution uses a personalized map generated based on the user's voice information to determine the destination, the determined destination is more likely to meet the user's personalized needs, which is conducive to improving the user stickiness of the present solution; on this basis, the universal map of the first location area is used to determine the navigation route, which is conducive to obtaining a more accurate navigation route and reaching the destination faster.

[0107] For example, if the third voice message is "Please navigate to a nearby coffee shop," the third voice message does not clearly indicate which nearby coffee shop to navigate to. The vehicle can first determine to navigate to Coffee Shop A in the first location area based on the first map information of the first location area, and then determine the navigation route from the vehicle's current location to Coffee Shop A based on the second map information of the first location area, and then navigate based on the determined navigation route. For another example, if the third voice message is "Please navigate to Building C of Building B," since Building C of Building B is a clear destination, navigation can be performed directly based on the second map information of the first location area. The examples here are only for the convenience of understanding this solution and are not used to limit this solution.

[0108] It should be noted that other methods may be used in other situations to implement “navigation based on the first map information of the first location area and the second map information of the first location area”, which are not exhaustively listed in the embodiments of the present application.

[0109] In an embodiment of the present application, second map information of the first location area is also deployed in the vehicle. The second map information is universal map information in different vehicles. When the vehicle navigates, it will not only be based on the personalized map information of the first location area (that is, the first map information), but also based on the universal map information of the first location area. The universal map information of the first location area can more accurately reflect the geographical environment information of the first location area, and the personalized map information of the first location area can better reflect the user's personalized preference for the first location area. The comprehensive use of the personalized map information and universal map information of the first location area is conducive to providing a navigation solution that better meets user expectations, thereby improving the user stickiness of this solution.

[0110] 404. The vehicle obtains second information, where the second information includes second voice information for describing the first location area.

[0111] In this embodiment of the present application, steps 404 to 406 are optional. After navigation is performed based on the first map information of the first location area and the second map information of the first location area, the vehicle can drive according to the determined navigation route. During driving, the vehicle can also obtain voice information input by the user in real time, thereby being able to detect the second voice information used to describe the first location area.

[0112] Among them, the meaning of "second information" is similar to that of "first information", and the meaning of "second voice information" is similar to that of "first voice information". The specific implementation method of step 404 can refer to the above description of the specific implementation method of step 301, which will not be repeated here.

[0113] 405. The vehicle inputs the second information into the machine learning model to obtain fourth map information of the first location area generated by the machine learning model.

[0114] In the embodiment of the present application, the specific implementation of step 405 can be found in the description of the specific implementation of step 302 above, except that the "second information" in step 302 is replaced by the "first information" in step 405. The specific implementation of step 405 is not further described here. The meaning of "fourth map information for the first location area" is similar to the meaning of "first map information for the first location area" and is not further described here.

[0115] 406. The vehicle updates the first map information of the first location area according to the fourth map information to obtain updated first map information of the first location area.

[0116] In an embodiment of the present application, the vehicle updates the first map information of the first location area, which may include: if the fourth map information carries information that is not carried in the first map information of the first location area, the information not carried in the first map information of the first location area may be added to the first map information of the first location area; and / or, if the first location information of an object (hereinafter referred to as the "first object" for the convenience of description) carried in the fourth map information is incompatible with the second location information of the first object carried in the first map information, the second location information of the first object may be replaced with the first location information; and / or other update operations, etc.

[0117] For a more intuitive understanding of this solution, please refer to Figure 5, which is a comparative schematic diagram of the first map information of the first location area and the updated first map information provided in the embodiment of the present application. Figure 5 only takes the example of the first map information and the updated first map information both being in the form of images. Figure 5 includes two upper and lower sub-schematic diagrams. The upper sub-schematic diagram of Figure 5 shows the first map information of the first location area before the update, and the lower sub-schematic diagram of Figure 5 shows the first map information of the first location area after the update. By comparing the upper sub-schematic diagram and the lower sub-schematic diagram of Figure 5, it can be seen that the updated first map information shows richer geographical environment information of the first location area. It should be understood that the example in Figure 5 is only for the convenience of understanding this solution and is not used to limit this solution.

[0118] In an embodiment of the present application, after generating the first map information of the first location area, the second information can be obtained again, the second information including the second voice information describing the first location area, and the second information is input into the first machine learning model to obtain the fourth map information of the first location area generated by the first machine learning model. After updating the first map information of the first location area based on the fourth map information, the updated first map information of the first location area is obtained, that is, the voice information of the first location area described by the user can be used in time to update the map information of the first location area, so that when the geographical environment of the first location area changes, the map information corresponding to the changed first location area can be obtained in time, that is, not only personalized map information that is more in line with the user's description can be obtained, but also more accurate map information can be obtained.

[0119] 407. The vehicle navigates based on the updated first map information of the first location area.

[0120] In this embodiment of the present application, step 407 is an optional step. While the vehicle is driving according to the determined navigation route, the vehicle may update the first map information of the first location area through steps 404 to 406 to obtain updated first map information of the first location area, and then navigate based on the updated first map information of the first location area.

[0121] The specific implementation method of step 407 is similar to the specific implementation method of the above-mentioned step 403, that is, step 407 may include: the vehicle navigates based on the updated first map information of the first location area and the second map information of the first location area. The difference is that the "first map information of the first location area" in the above-mentioned step 403 is replaced by the "updated first map information of the first location area" in step 407, which will not be repeated here.

[0122] For example, if the vehicle determines a destination based on the first map information of the first location area in step 403, then in step 407, the vehicle may redetermine the destination based on the updated first map information of the first location area and the vehicle's current location. If the vehicle determines a navigation route based on the first map information of the first location area in step 403, then in step 407, the vehicle may redetermine the navigation route based on the updated first map information of the first location area and the vehicle's current location.

[0123] The embodiment of the present application further provides a method for obtaining second map information and third map information of a first location area. Optionally, the third map information includes second map information of multiple location areas. The third map information is obtained by fusing the first map information of the multiple location areas. The multiple location areas include the first location area. For details, please refer to FIG6 , which is a schematic diagram of a flow chart for obtaining the third map information provided in the embodiment of the present application. The map construction method provided in the embodiment of the present application may include:

[0124] 601. The second device obtains at least one first map information of the first location area, wherein each first map information is generated by a first machine learning model, the input of the first machine learning model includes first information, the first information includes first voice information for describing the first location area, and the first voice information is input by the user.

[0125] In the embodiments of the present application, for example, the second device may be specifically a cloud server corresponding to the vehicle. The meanings of "first map information of the first location area," "first machine learning model," "first information," and "first voice information" can all be found in the descriptions of the above embodiments and are not further elaborated here.

[0126] Specifically, in one implementation, step 601 may include: the second device is communicatively connected to at least one vehicle, and after each of the at least one vehicle generates the first map information (or updated first map information) of the first location area, it can send the first map information (or updated first map information) of the first location area to the second device. Correspondingly, step 601 may include: the second device receives the first map information (or updated first map information) of the first location area sent by each vehicle.

[0127] In another implementation, the second device may also autonomously generate first map information for the first location area. The second device may obtain voice information corresponding to each of the multiple location areas, and then obtain first voice information corresponding to the first location area from the first voice information corresponding to the multiple location areas. The voice information corresponding to each location area is used to describe the geographical environment of the location area.

[0128] Optionally, the second device is communicatively connected to at least one vehicle. After obtaining voice information input by a user describing the geographical environment of a certain location area, the vehicle can upload the voice information to the second device. The second device can then obtain voice information corresponding to each of the multiple location areas. Alternatively, the second device can obtain voice information specifically recorded by the user to describe the geographical environment of the location area. The method for obtaining the first voice information is not limited in the embodiments of the present application.

[0129] Optionally, if the first information also includes at least one of the following: a captured image of the first location area, textual information describing the first location area, or a hand-drawn map of the first location area, the vehicle may also obtain at least one of the following: a captured image of each of the multiple location areas, textual information describing each of the multiple location areas, and a hand-drawn map of each of the multiple location areas. The vehicle may then cluster the acquired voice information describing the multiple location areas, the captured image of each of the multiple location areas, the textual information describing each of the multiple location areas, and the hand-drawn map of each of the multiple location areas in a geographic spatial dimension, clustering information associated with the same location area to obtain clustered information corresponding to each of the multiple location areas. Exemplarily, the first location area is any one of the aforementioned multiple location areas, and "clustered information corresponding to the first location area" and "first information corresponding to the first location area" may have the same meaning, i.e., the clustered information corresponding to the first location area may include the first voice information describing the first location area, the captured image of the first location area, the textual information describing the first location area, and the hand-drawn map of the first location area.

[0130] Furthermore, in one implementation, the second device is communicatively connected to at least one vehicle, and the second device can receive the captured images of each of the multiple location areas captured by the vehicle; in another implementation, the second device can download the captured images of each of the multiple location areas from the network; or, the second device can also obtain the captured images of each location area through other methods, etc., which is not limited in the embodiments of the present application.

[0131] The specific implementation method of the second device obtaining "text information describing each location area in multiple location areas" and the specific implementation method of the second device obtaining "a hand-drawn picture of each location area in multiple location areas" can both refer to the specific implementation method of the second device obtaining the captured image of each location area in multiple location areas mentioned above, and will not be repeated in the embodiments of this application.

[0132] After obtaining the first information corresponding to the first location area, the second device can input the first information corresponding to the first location area into the first machine learning model to obtain the first map information of the first location area generated by the first machine learning model, thereby realizing the autonomous generation of the first map information of the first location area.

[0133] In another implementation, the second device not only receives the first map information of the first location area sent by the vehicle, but also autonomously generates the first map information of the first location area.

[0134] 602. The second device determines second map information of the first location area based on at least two first map information of the first location area, where an object in the second map information of the first location area appears repeatedly in the at least two first map information of the first location area.

[0135] In the embodiment of the present application, step 602 is an optional step. If the second device obtains at least two pieces of first map information for the first location area in step 601, the second map information for the first location area can be determined based on the at least two pieces of first map information for the first location area. The objects in the second map information for the first location area appear repeatedly in the at least two pieces of first map information for the first location area, that is, the second device can extract the objects that appear repeatedly in the at least two pieces of first map information for the first location area, and then place the objects that appear repeatedly in the at least two pieces of first map information for the first location area into the second map information for the first location area.

[0136] In an embodiment of the present application, the universal map information of the first location area is obtained based on at least two first map information of the first location area. Since the user can experience the changes in the surrounding environment more accurately, the universal map information is obtained with the help of the voice information describing the geographical location area input by the user, which is conducive to more rapid adaptation to changes in urban planning and roads, and is conducive to shortening the update cycle of the universal map, so that the universal map information deployed in the vehicle can more accurately reflect the geographical environment, thereby improving the accuracy of the navigation process.

[0137] It should be noted that if the second device obtains the first map information of the first location area in step 601, the obtained first map information of the first location area may also be determined as the second map information of the first location area.

[0138] 603. The second device fuses the second map information of the multiple location areas to obtain third map information, where the third map information includes the second map information of the multiple location areas, and the multiple location areas include the first location area.

[0139] In an embodiment of the present application, the second device repeatedly executes steps 601 and 602 multiple times, thereby obtaining the second map information of each of the multiple location areas. The second device can then fuse the second map information of the multiple location areas to obtain the third map information, where the multiple location areas include the first location area. For example, "fusing the second map information of the multiple location areas" can also be understood as "splicing the second map information of the multiple location areas", that is, the second map information of each location area can be understood as fragmented map information, and the third map information includes universal map information for the multiple location areas.

[0140] For a more intuitive understanding of this solution, please refer to Figure 7, which is a schematic diagram of obtaining the third map information provided in an embodiment of the present application. In Figure 7, the second device obtains the first map information corresponding to each location area from the vehicle as an example. As shown in Figure 7, after vehicle 1 generates map information 1 of a certain location area through the first machine learning model, it can send map information 1 to the second device; after vehicle 2 generates map information 2 of a certain location area through the first machine learning model, it can send map information 2 to the second device; after vehicle 3 generates map information 3 of a certain location area through the first machine learning model, it can send map information 3 to the second device; correspondingly, the second device can receive map information 1, map information 2 and map information 3 sent by the vehicle, and map information 1, map information 2 and map information 3 are all fragmented personalized map information, that is, map information 1, map information 2 and map information 3 are all generated by the first machine learning model based on the voice information of the geographical location area described by the user.

[0141] After obtaining multiple fragmented personalized map information sent by multiple vehicles, the second device can fuse the aforementioned multiple fragmented personalized map information to obtain universal third map information, and then send the universal third map information to each second device. The specific implementation steps of the second device in Figure 7 can refer to the description in the corresponding embodiment of Figure 6 above, and will not be repeated here. It should be understood that the example in Figure 7 is only for the convenience of understanding this solution and is not used to limit this solution.

[0142] It should be noted that steps 601 to 603 may be repeated multiple times to update the universal third map information deployed on the vehicle. For example, the second device may execute steps 602 and 603 once every preset time interval to obtain updated third map information, and then deploy the updated third map information to the vehicle.

[0143] In an embodiment of the present application, after fusing multiple first map information of a certain location area, universal second map information of the location area can be obtained. After fusing the second map information of multiple location areas, universal third map information can be obtained, that is, universal map information deployed in different vehicles. That is, by using the capabilities of the machine learning model to obtain universal map information deployed in the vehicle, not only can the mapping cost of the map information be reduced, but also it is conducive to obtaining more accurate environmental information.

[0144] In addition, the embodiment corresponding to Figure 6 above shows only one method of obtaining general map information. Other methods can also be used to generate general map information, such as using a large number of vehicles to collect geographic environment data offline to obtain general map information, etc. Other methods of obtaining general map information are no longer listed in the embodiments of this application.

[0145] Based on the embodiments corresponding to Figures 1 to 7 , in order to better implement the above-mentioned solutions of the embodiments of the present application, the following also provides related devices for implementing the above-mentioned solutions. Specifically, referring to Figure 8 , Figure 8 is a schematic structural diagram of a map construction device provided in an embodiment of the present application. Map construction device 800 includes: an acquisition module 801 for acquiring first information, the first information including first voice information describing a first location area, the first voice information being input by a user; and an input module 802 for inputting the first information into a machine learning model to obtain first map information of the first location area generated by the machine learning model.

[0146] Optionally, the map construction device 800 further includes: a navigation module 803 for performing navigation based on first map information of the first location area and second map information of the first location area, where the second map information is universal map information for different vehicles.

[0147] Optionally, the second map information of the first location area is obtained based on at least two first map information of the first location area, and an object in the second map information of the first location area appears repeatedly in the at least two first map information of the first location area.

[0148] Optionally, third map information is deployed in the device, the third map information includes second map information of multiple location areas, the third map information is obtained by fusing the second map information of multiple location areas, and the multiple location areas include the first location area.

[0149] Optionally, the first map information of the first location area includes relative location information of objects in the first location area.

[0150] Optionally, the acquisition module 801 is also used to obtain second information, the second information including second voice information for describing the first location area; the input module 802 is also used to input the second information into the machine learning model to obtain fourth map information of the first location area generated by the machine learning model; the map construction device 800 also includes: an update module 804, used to update the first map information of the first location area according to the fourth map information, to obtain the updated first map information of the first location area.

[0151] Optionally, the first information further includes at least one of the following: a captured image corresponding to the first location area, text information for describing the first location area, or a hand-drawn drawing of the first location area.

[0152] It should be noted that the information interaction, execution process, etc. between the modules / units in the map construction device 800 are based on the same concept as the various method embodiments corresponding to Figures 1 to 7 in this application. For specific contents, please refer to the description in the method embodiments shown above in this application, and will not be repeated here.

[0153] Please refer to Figure 9, which is another structural schematic diagram of the map construction device provided in an embodiment of the present application. The map construction device 900 includes: an acquisition module 901, used to obtain at least two first map information of a first location area, wherein each first map information is generated by a machine learning model, and the input of the machine learning model includes first information, and the first information includes first voice information for describing the first location area, and the first voice information is input by the user; a determination module 902, used to determine second map information of the first location area based on the at least two first map information of the first location area, where objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area.

[0154] Optionally, the map construction device 900 further includes: a fusion module 903 for fusing the second map information of multiple location areas to obtain third map information, wherein the third map information includes the second map information of the multiple location areas, and the multiple location areas include the first location area.

[0155] It should be noted that the information interaction, execution process, etc. between the modules / units in the map construction device 900 are based on the same concept as the various method embodiments corresponding to Figures 1 to 7 in this application. For specific contents, please refer to the description in the method embodiments shown above in this application, and will not be repeated here.

[0156] Next, a device provided in an embodiment of the present application is introduced. When the device is specifically manifested as a first device, please refer to Figure 10, which is a schematic structural diagram of a device provided in an embodiment of the present application. Specifically, the device 1000 includes: a receiver 1001, a transmitter 1002, a processor 1003, and a memory 1004 (wherein the number of processors 1003 in the device 1000 can be one or more, and Figure 10 takes one processor as an example). The processor 1003 may include an application processor 10031 and a communication processor 10032. In some embodiments of the present application, the receiver 1001, the transmitter 1002, the processor 1003, and the memory 1004 may be connected via a bus or other means.

[0157] The memory 1004 may include a read-only memory and a random access memory, and provides instructions and data to the processor 1003. A portion of the memory 1004 may also include non-volatile random access memory (NVRAM). The memory 1004 stores processor and operation instructions, executable modules, or data structures, or subsets or extended sets thereof. The operation instructions may include various operation instructions for implementing various operations.

[0158] Processor 1003 controls the operation of the device. In specific applications, the various components of the device are coupled together via a bus system. In addition to a data bus, the bus system may also include a power bus, a control bus, and a status signal bus. However, for clarity, all bus systems are referred to as a bus system in the figure.

[0159] The methods disclosed in the above embodiments of the present application can be applied to or implemented by processor 1003. Processor 1003 can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits or software instructions in processor 1003. The above processor 1003 can be a general-purpose processor, a digital signal processor (DSP), a microprocessor, or a microcontroller, and can further include an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The processor 1003 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present application can be directly implemented as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 1004, and processor 1003 reads the information in memory 1004 and, in conjunction with its hardware, completes the steps of the above method.

[0160] Receiver 1001 can be used to receive input digital or character information and generate signal input related to device settings and function control. Transmitter 1002 can be used to output digital or character information through the first interface. Transmitter 1002 can also be used to send instructions to the disk pack through the first interface to modify data in the disk pack. Transmitter 1002 can also include a display device such as a display screen.

[0161] In the embodiment of the present application, processor 1003 is configured to execute the map construction method performed by the first device in the embodiments corresponding to Figures 1 to 7. It should be noted that the specific manner in which application processor 10031 in processor 1003 executes the aforementioned steps is based on the same concept as the various method embodiments corresponding to Figures 1 to 7 of the present application, and the technical effects achieved are the same as the various method embodiments corresponding to Figures 1 to 7 of the present application. For details, please refer to the description of the method embodiments shown above in the present application, and will not be repeated here.

[0162] In the case where the device is specifically manifested as a second device, please refer to Figure 11, which is another structural diagram of the device provided in an embodiment of the present application. Specifically, the device 1100 is implemented by one or more servers. The device 1100 may have relatively large differences due to different configurations or performances. It may include one or more central processing units (CPUs) 1122 (for example, one or more processors) and a memory 1132, and one or more storage media 1130 (for example, one or more mass storage devices) storing application programs 1142 or data 1144. Among them, the memory 1132 and the storage medium 1130 can be temporary storage or permanent storage. The program stored in the storage medium 1130 may include one or more modules (not shown in the figure), each module may include a series of instruction operations on the device. Furthermore, the central processing unit 1122 can be configured to communicate with the storage medium 1130 to execute a series of instruction operations in the storage medium 1130 on the device 1100.

[0163] The device 1100 may also include one or more power supplies 1126, one or more wired or wireless network interfaces 1150, one or more input and output interfaces 1158, and / or one or more operating systems 1141, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0164] In the embodiment of the present application, the central processing unit 1122 is configured to execute the map construction method executed by the second device in the embodiments corresponding to Figures 1 to 7. It should be noted that the specific manner in which the central processing unit 1122 executes the aforementioned steps is based on the same concept as the various method embodiments corresponding to Figures 1 to 7 of the present application, and the technical effects achieved are the same as those of the various method embodiments corresponding to Figures 1 to 7 of the present application. For details, please refer to the description of the method embodiments shown above in the present application, and will not be repeated here.

[0165] The present application also provides a vehicle. Please refer to FIG12 , which is a schematic structural diagram of a vehicle provided in the present application. Vehicle 100 is configured in a fully or partially intelligent driving mode. For example, vehicle 100 can control itself while in intelligent driving mode, and can determine the current state of the vehicle and its surrounding environment through human operation, determine the possible behavior of at least one other vehicle in the surrounding environment, and determine the confidence level corresponding to the probability of the other vehicle executing the possible behavior, and control vehicle 100 based on the determined information. When vehicle 100 is in intelligent driving mode, vehicle 100 can also be set to operate without human interaction.

[0166] The vehicle 100 may include various subsystems, such as a travel system 102, a sensor system 104, a control system 106, one or more peripheral devices 108, a power source 110, a computer system 112, and a user interface 116. Alternatively, the vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple components. In addition, each subsystem and component of the vehicle 100 may be interconnected via wired or wireless connections.

[0167] Travel system 102 may include components that provide powered movement for vehicle 100. In one embodiment, travel system 102 may include engine 118, power source 119, transmission 120, and wheels / tires 121.

[0168] The engine 118 may be an internal combustion engine, an electric motor, an air compression engine, or a combination of other types of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. The engine 118 converts the energy source 119 into mechanical energy. Examples of the energy source 119 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. The energy source 119 may also provide energy for other systems of the vehicle 100. The transmission 120 may transmit the mechanical power from the engine 118 to the wheels 121. The transmission 120 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission 120 may also include other devices, such as a clutch. The drive shaft may include one or more shafts that can be coupled to one or more wheels 121.

[0169] Sensor system 104 may include several sensors that sense information about the environment surrounding vehicle 100. For example, sensor system 104 may include a positioning system 122 (the positioning system may be a global positioning system (GPS), a BeiDou system, or other positioning systems), an inertial measurement unit (IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130. Sensor system 104 may also include sensors for internal systems of monitored vehicle 100 (e.g., an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors may be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). This detection and recognition is a key function for the safe operation of autonomous vehicle 100.

[0170] Among them, the positioning system 122 can be used to estimate the geographic location of the vehicle 100. The IMU 124 is used to sense the position and orientation changes of the vehicle 100 based on inertial acceleration. In one embodiment, the IMU 124 can be a combination of an accelerometer and a gyroscope. The radar 126 can use radio signals to sense objects in the surrounding environment of the vehicle 100, and can specifically be a millimeter wave radar or a laser radar. In some embodiments, in addition to sensing objects, the radar 126 can also be used to sense the speed and / or direction of travel of objects. The laser rangefinder 128 can use lasers to sense objects in the environment in which the vehicle 100 is located. In some embodiments, the laser rangefinder 128 may include one or more laser sources, a laser scanner, and one or more detectors, as well as other system components. The camera 130 can be used to capture multiple images of the surrounding environment of the vehicle 100. The camera 130 can be a still camera or a video camera.

[0171] Control system 106 controls the operation of vehicle 100 and its components. Control system 106 may include various components, including a steering system 132 , a throttle 134 , a brake unit 136 , a computer vision system 140 , a lane control system 142 , and an obstacle avoidance system 144 .

[0172] The steering system 132 is operable to adjust the direction of travel of the vehicle 100. For example, in one embodiment, it may be a steering wheel system. The throttle 134 is used to control the operating speed of the engine 118 and, in turn, the speed of the vehicle 100. The brake unit 136 is used to control the deceleration of the vehicle 100. The brake unit 136 may use friction to slow the wheels 121. In other embodiments, the brake unit 136 may convert the kinetic energy of the wheels 121 into electrical current. The brake unit 136 may also take other forms to slow the rotation speed of the wheels 121 to control the speed of the vehicle 100. The computer vision system 140 is operable to process and analyze images captured by the camera 130 to identify objects and / or features in the environment surrounding the vehicle 100. These objects and / or features may include traffic signs, road boundaries, and obstacles. The computer vision system 140 may use object recognition algorithms, structure from motion (SFM) algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system 140 can be used to map the environment, track objects, estimate their speed, and so on. The route control system 142 is used to determine the route and speed of the vehicle 100. In some embodiments, the route control system 142 may include a lateral planning module 1421 and a longitudinal planning module 1422, which are respectively used to determine the route and speed for the vehicle 100 by combining data from the obstacle avoidance system 144, GPS 122, and one or more predetermined maps. The obstacle avoidance system 144 is used to identify, evaluate, and avoid or otherwise navigate obstacles in the environment of the vehicle 100. The aforementioned obstacles can specifically be represented by actual obstacles and virtual moving objects that may collide with the vehicle 100. In one embodiment, the control system 106 may include additional or alternative components other than those shown and described. Alternatively, some of the components shown above may be reduced.

[0173] Vehicle 100 interacts with external sensors, other vehicles, other computer systems, or users via peripheral devices 108. Peripheral devices 108 may include a wireless communication system 146, an onboard computer 148, a microphone 150, and / or a speaker 152. In some embodiments, peripheral devices 108 provide a means for the user of vehicle 100 to interact with user interface 116. For example, onboard computer 148 may provide information to the user of vehicle 100. User interface 116 may also operate onboard computer 148 to receive user input. Onboard computer 148 may be operated via a touchscreen. In other cases, peripheral devices 108 may provide a means for vehicle 100 to communicate with other devices located within the vehicle. For example, microphone 150 may receive audio (e.g., voice commands or other audio input) from the user of vehicle 100. Similarly, speaker 152 may output audio to the user of vehicle 100. Wireless communication system 146 may wirelessly communicate with one or more devices directly or via a communication network. For example, the wireless communication system 146 may utilize 3G cellular communications, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communications, such as LTE. Or 5G cellular communications. The wireless communication system 146 may utilize wireless local area network (WLAN) communications. In some embodiments, the wireless communication system 146 may utilize infrared links, Bluetooth, or ZigBee to communicate directly with devices. Other wireless protocols, such as various vehicle communication systems, may include one or more dedicated short range communications (DSRC) devices, which may include public and / or private data communications between vehicles and / or roadside stations.

[0174] Power source 110 can provide power to various components of vehicle 100. In one embodiment, power source 110 can be a rechargeable lithium-ion or lead-acid battery. One or more battery packs of such batteries can be configured as a power source to provide power to various components of vehicle 100. In some embodiments, power source 110 and energy source 119 can be implemented together, such as in some all-electric vehicles.

[0175] Some or all functions of vehicle 100 are controlled by computer system 112. Computer system 112 may include at least one processor 113 that executes instructions 115 stored in a non-transitory computer-readable medium, such as memory 114. Computer system 112 may also be a plurality of computing devices that control individual components or subsystems of vehicle 100 in a distributed manner. Processor 113 may be any conventional processor, such as a commercially available central processing unit (CPU). Alternatively, processor 113 may be a specialized device, such as an application-specific integrated circuit (ASIC) or other hardware-based processor. Although FIG12 functionally illustrates the processor, memory, and other components of computer system 112 in the same block, those skilled in the art will appreciate that the processor or memory may actually include multiple processors or memories that are not stored in the same physical housing. For example, memory 114 may be a hard drive or other storage medium located in a different housing than computer system 112. Therefore, references to processor 113 or memory 114 should be understood to include references to a collection of processors or memories that may or may not operate in parallel. Rather than using a single processor to perform the steps described herein, some components, such as the steering assembly and the retarding assembly, may each have its own processor that performs only calculations related to the functionality of the component specific component.

[0176] In various aspects described herein, the processor 113 may be located remotely from the vehicle 100 and in wireless communication with the vehicle 100. In other aspects, some of the processes described herein are performed on the processor 113 disposed within the vehicle 100 while others are performed by the remote processor 113, including taking the necessary steps to perform a single maneuver.

[0177] In some embodiments, memory 114 may contain instructions 115 (e.g., program logic) that are executable by processor 113 to perform various functions of vehicle 100, including those described above. Memory 114 may also contain additional instructions, including instructions for sending data to, receiving data from, interacting with, and / or controlling one or more of travel system 102, sensor system 104, control system 106, and peripherals 108. In addition to instructions 115, memory 114 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other such vehicle data, as well as other information. This information may be used by vehicle 100 and computer system 112 during operation of vehicle 100 in autonomous, semi-autonomous, and / or manual modes. A user interface 116 is provided for providing information to or receiving information from a user of vehicle 100. Optionally, user interface 116 may include one or more input / output devices within the set of peripherals 108, such as wireless communication system 146, onboard computer 148, microphone 150, and speaker 152.

[0178] Computer system 112 may control functions of vehicle 100 based on input received from various subsystems (e.g., travel system 102, sensor system 104, and control system 106) and from user interface 116. For example, computer system 112 may utilize input from control system 106 to control steering system 132 to avoid obstacles detected by sensor system 104 and obstacle avoidance system 144. In some embodiments, computer system 112 may be operable to provide control over many aspects of vehicle 100 and its subsystems.

[0179] Alternatively, one or more of the above components may be installed or associated separately from the vehicle 100. For example, the memory 114 may be partially or completely separate from the vehicle 100. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0180] Optionally, the above components are just an example. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 12 should not be understood as limiting the embodiments of the present application. A vehicle traveling on a road, such as vehicle 100 above, can identify objects in its surrounding environment to determine adjustments to the current speed. The objects can be other vehicles, traffic control devices, or other types of objects. In some examples, each identified object can be considered independently, and based on the respective characteristics of the object, such as its current speed, acceleration, distance from the vehicle, etc., it can be used to determine the speed to be adjusted for the vehicle.

[0181] Optionally, the vehicle 100 or a computing device associated with the vehicle 100, such as the computer system 112, computer vision system 140, and memory 114 of Figure 12, can predict the behavior of the identified objects based on the characteristics of the identified objects and the state of the surrounding environment (e.g., traffic, rain, ice on the road, etc.). Optionally, each identified object depends on the behavior of each other, so all identified objects can be considered together to predict the behavior of a single identified object. The vehicle 100 can adjust its speed based on the predicted behavior of the identified objects. In other words, the vehicle 100 can determine what stable state the vehicle will need to adjust to (e.g., accelerate, decelerate, or stop) based on the predicted behavior of the objects. In this process, other factors can also be considered to determine the speed of the vehicle 100, such as the lateral position of the vehicle 100 on the road it is traveling on, the curvature of the road, the proximity of static and dynamic objects, etc. In addition to providing instructions to adjust the speed of the vehicle, the computing device may also provide instructions to modify the steering angle of the vehicle 100 so that the vehicle 100 follows a given trajectory and / or maintains a safe lateral and longitudinal distance from objects near the vehicle 100 (e.g., cars in adjacent lanes on the road).

[0182] The vehicle 100 may be a car, truck, motorcycle, bus, ship, airplane, helicopter, lawn mower, recreational vehicle, amusement park vehicle, construction equipment, tram, golf cart, train, etc., and the embodiments of the present application do not impose any particular limitation thereto.

[0183] In the embodiment of the present application, the processor 113 in the vehicle 100 is configured to execute the method executed by the first device in the embodiments corresponding to Figures 1 to 7. It should be noted that the specific manner in which the processor 113 executes the aforementioned steps is based on the same concept as the various method embodiments corresponding to Figures 1 to 7 of the present application, and the technical effects achieved are the same as those of the various method embodiments corresponding to Figures 1 to 7 of the present application. For details, please refer to the description of the method embodiments shown above in the present application, and will not be repeated here.

[0184] A computer-readable storage medium is also provided in an embodiment of the present application. The computer-readable storage medium stores a program, which, when executed on a computer, enables the computer to execute the steps executed by the first device in the method described in the embodiments shown in Figures 1 to 7 above, or enables the computer to execute the steps executed by the second device in the method described in the embodiments shown in Figures 1 to 7 above.

[0185] Also provided in an embodiment of the present application is a computer program product, which includes a program. When the computer program product is run on a computer, it enables the computer to execute the steps executed by the first device in the method described in the embodiments shown in Figures 1 to 7 above, or enables the computer to execute the steps executed by the second device in the method described in the embodiments shown in Figures 1 to 7 above.

[0186] An embodiment of the present application further provides a circuit system, which includes a processing circuit, and the processing circuit is configured to execute the method described in the embodiments shown in Figures 1 to 7 above.

[0187] The first device, the second device, or the map construction device provided in the embodiments of the present application may specifically be a chip, and the chip includes: a processing unit and a communication unit. The processing unit may be, for example, a processor, and the communication unit may be, for example, an input / output interface, a pin, or a circuit. The processing unit may execute computer-executable instructions stored in the storage unit so that the chip executes the method described in the embodiments shown in Figures 1 to 7 above. Optionally, the storage unit is a storage unit within the chip, such as a register, a cache, etc. The storage unit may also be a storage unit located outside the chip within the wireless access device, such as a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM), etc.

[0188] The processor mentioned in any of the above places can be a general-purpose central processing unit, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the program of the above-mentioned first aspect method.

[0189] It should also be noted that the device embodiments described above are merely illustrative, wherein 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, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0190] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CLUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, disk or optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0191] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0192] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a server, or a data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode to another website, a computer, a server, or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium can be a magnetic medium, (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive (SSD)).

Claims

1. A map construction method, characterized in that: The method comprises: Acquire first information, where the first information includes first voice information for describing a first location area, and the first voice information is input by a user; The first information is input into a machine learning model to obtain first map information of the first location area generated by the machine learning model.

2. The method according to claim 1, characterized in that The method further comprises: Navigation is performed based on the first map information of the first location area and second map information of the first location area, where the second map information is common map information for different vehicles.

3. The method according to claim 2, characterized in that The second map information of the first location area is obtained based on at least two first map information of the first location area, and objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area.

4. The method according to claim 3, characterized in that The method is applied to a first device, in which third map information is deployed. The third map information includes the second map information of multiple location areas. The third map information is obtained by fusing the second map information of the multiple location areas, and the multiple location areas include the first location area.

5. The method according to any one of claims 1 to 4, characterized in that The first map information of the first location area includes relative location information of objects in the first location area.

6. The method according to any one of claims 1 to 4, characterized in that After obtaining the first map information of the first location area generated by the machine learning model, the method further includes: Acquire second information, where the second information includes second voice information for describing the first location area; Inputting the second information into a machine learning model to obtain fourth map information of the first location area generated by the machine learning model; The first map information of the first location area is updated according to the fourth map information to obtain updated first map information of the first location area.

7. The method according to any one of claims 1 to 4, characterized in that The first information further includes at least one of the following: a captured image corresponding to the first location area, text information for describing the first location area, or a hand-drawn picture of the first location area.

8. A map construction method, characterized in that: The method comprises: Obtaining at least two pieces of first map information for a first location area, wherein each piece of the first map information is generated by a machine learning model, and an input of the machine learning model includes first information, the first information includes first voice information for describing the first location area, and the first voice information is input by a user; Second map information of the first location area is determined based on the at least two first map information of the first location area, wherein objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area.

9. The method according to claim 8, characterized in that The method further comprises: The second map information of multiple location areas is merged to obtain third map information, wherein the third map information includes the second map information of the multiple location areas, and the multiple location areas include the first location area.

10. A map construction device, characterized in that: The device comprises: an acquisition module, configured to acquire first information, wherein the first information includes first voice information for describing a first location area, and the first voice information is input by a user; An input module is used to input the first information into a machine learning model to obtain first map information of the first location area generated by the machine learning model.

11. The device according to claim 10, characterized in that The device further comprises: The navigation module is used to perform navigation based on the first map information of the first location area and the second map information of the first location area, where the second map information is universal map information for different vehicles.

12. The device according to claim 11, characterized in that The second map information of the first location area is obtained based on at least two first map information of the first location area, and objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area.

13. The device according to claim 12, characterized in that The device is deployed with third map information, the third map information including the second map information of multiple location areas, the third map information being obtained by fusing the second map information of the multiple location areas including the first location area.

14. The device according to any one of claims 10 to 13, characterized in that The first map information of the first location area includes relative location information of objects in the first location area.

15. The device according to any one of claims 10 to 13, characterized in that The acquisition module is further configured to acquire second information, where the second information includes second voice information describing the first location area; The input module is further configured to input the second information into a machine learning model to obtain fourth map information of the first location area generated by the machine learning model; The device further includes an updating module configured to update the first map information of the first location area according to the fourth map information to obtain updated first map information of the first location area.

16. The device according to any one of claims 10 to 13, characterized in that The first information further includes at least one of the following: a captured image corresponding to the first location area, text information for describing the first location area, or a hand-drawn picture of the first location area.

17. A map construction device, characterized in that: The device comprises: an acquisition module, configured to acquire at least two pieces of first map information for a first location area, wherein each piece of the first map information is generated by a machine learning model, an input of the machine learning model includes first information, the first information includes first voice information for describing the first location area, and the first voice information is input by a user; A determination module is used to determine second map information of the first location area based on the at least two first map information of the first location area, where objects in the second map information of the first location area appear repeatedly in the at least two first map information of the first location area.

18. The device according to claim 17, characterized in that The device further comprises: A fusion module is used to fuse the second map information of multiple location areas to obtain third map information, wherein the third map information includes the second map information of the multiple location areas, and the multiple location areas include the first location area.

19. A device, characterized in that The method comprises a processor coupled to a memory, wherein the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the method according to any one of claims 1 to 9 is implemented.

20. A vehicle, characterized in that: The method comprises a processor coupled to a memory, wherein the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the method according to any one of claims 1 to 7 is implemented.

21. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, and when the program is run on a computer, the computer is caused to execute the method according to any one of claims 1 to 9.

22. A computer program product, characterized in that The computer program product comprises a program, which, when run on a computer, causes the computer to perform the method according to any one of claims 1 to 9 .

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