Vehicle navigation picture control method and device and vehicle

Abstract

The invention provides a vehicle navigation picture control method and device and a vehicle, and relates to the technical field of vehicle navigation. The method comprises the following steps: judging whether a current driving path of a vehicle has a preset navigation guide area or not; if the navigation guide area exists, acquiring real-time environment characteristics, and determining a target live-action guide map corresponding to the current moment based on the real-time environment characteristics and a preset map library; wherein a plurality of live-action guide maps are stored in the preset map library, and the live-action guide maps are associated with different environment feature tags; and sending the target live-action guide map to a navigation display interface for display. According to the control method, the real-scene guide map dynamically adaptive to the real-time environment characteristics can be obtained, and accurate and visual real-scene navigation guidance is provided.

Description

Technical Field

[0001] This application relates to the field of vehicle navigation technology, and in particular to a vehicle navigation screen control method, device and vehicle. Background Technology

[0002] With the development of vehicle navigation technology, users have placed higher demands on the accuracy and user experience of navigation systems. Traditional vehicle navigation systems mainly rely on two-dimensional electronic maps or simple three-dimensional models for route guidance. Although they can meet the basic needs of users in most scenarios, in complex road environments, especially in key navigation guidance areas such as urban interchanges, intersections with multiple forks in the road, and highway ramps, users are prone to misunderstand navigation information due to environmental changes, leading to wrong turns. Summary of the Invention

[0003] This application provides a vehicle navigation screen control method, device, and vehicle to solve the problem that existing vehicle navigation systems cannot dynamically adapt to real-time environmental features and provide accurate and intuitive real-scene navigation guidance, thereby effectively reducing the cognitive load and risk of wrong turns for drivers in complex road conditions.

[0004] In a first aspect, embodiments of this application provide a method for controlling a vehicle navigation screen, the method comprising: Determine if the vehicle's current driving path falls within a preset navigation guidance area; If the navigation guidance area exists, real-time environmental features are obtained, and based on the real-time environmental features and a preset image library, a target real-scene guidance image corresponding to the current time is determined; wherein, the preset image library stores several real-scene guidance images, and each real-scene guidance image is associated with different environmental feature tags; The target real-view guide map is sent to the navigation display interface for display.

[0005] This application utilizes the vehicle's current driving path to determine whether a navigation guidance area exists, thereby pre-determining whether real-view guidance is needed during navigation. Subsequently, real-time environmental features are used as key inputs for navigation screen decisions. From multiple real-view guidance images with different environmental feature labels, a target real-view guidance image matching the environmental features is determined. This upgrades the navigation system from static, fixed guidance to dynamic, intelligent guidance, achieving environmental adaptability for real-view guidance, ensuring that real-view guidance conforms to the user's visual perception of environmental changes. By displaying the target real-view guidance image, a real-view guidance map that highly matches the real environment is provided. This allows drivers to make correct driving decisions simply by comparing the screen image with the actual view outside the window, improving driving safety and comfort, and significantly reducing the probability of drivers making wrong turns at complex intersections due to difficulty understanding abstract map symbols or comparing them with the real scene.

[0006] In one implementation of this application, the method further includes: In response to an image acquisition command from a cloud server, when the vehicle travels to the target navigation guidance area and is within a specified acquisition time period, a corresponding real-scene image is acquired; wherein, the image acquisition command is generated by the cloud server after analyzing navigation historical data aggregated from multiple vehicles, and the image acquisition command includes at least the target navigation guidance area and the specified acquisition time period; The real-scene image is sent to the cloud server so that the cloud server can update the preset image library based on the real-scene image.

[0007] By responding to image acquisition commands, vehicles can participate in data acquisition, enabling distributed acquisition of real-scene images and dynamic updates of the preset image library. This effectively solves the problem of outdated guidance images in real-scene navigation caused by changes in road environment. Furthermore, the image acquisition is precisely targeted at the target navigation guidance area and the specified acquisition time period, effectively improving data acquisition efficiency.

[0008] In one implementation of this application, when the cloud server updates the preset image library based on the real-scene image, it specifically includes: The cloud server parses the real-world image and generates corresponding environmental feature labels; The cloud server establishes a mapping relationship between the real-world image and the navigation guidance area based on the environmental feature tags; The cloud server stores the mapping relationship and the real-scene image into the preset image library to update the preset image library.

[0009] The cloud server adds structured environmental feature tags to the images, transforming massive amounts of unstructured image data into structured data that can be efficiently retrieved and matched, thus enabling accurate matching. At the same time, the environmental feature tags establish a connection between the navigation guidance area and the real-world image, so as to call up the real-world guidance image in the preset image library.

[0010] In one implementation of this application, the method further includes: The cloud server obtains the frequency with which the vehicle calls each of the real-scene guidance maps; Based on the calling frequency, the cloud server generates image update tasks corresponding to each of the navigation guidance areas; wherein, the image update tasks are used to issue the image acquisition instructions according to different update frequencies.

[0011] Furthermore, based on the optimization strategy of usage frequency, the preset image library is differentiated and refined, so that limited resources are prioritized for updating the most frequently used and most important guidance area images, avoiding resource waste; and ensuring that the intersections that users pass through most often always have the latest and most accurate guidance images, thus achieving intelligent guarantee of service quality.

[0012] In one implementation of this application, obtaining real-time environmental features specifically includes: Determine the preset division area type corresponding to the navigation guidance area; Based on the preset region division type, a target strategy corresponding to the navigation guidance region is matched from a plurality of preset feature extraction strategies; wherein, different feature extraction strategies include different sets of environmental feature dimensions; Based on the target strategy, the real-time environmental features are determined from the pre-collected environmental perception data.

[0013] By setting different sets of environmental feature dimensions for different navigation guidance areas, real-time environmental features can be extracted differentially, avoiding unnecessary complex calculations in simple scenarios, thus optimizing computing resources and making the system response speed faster.

[0014] In one implementation of this application, before determining the real-scene guidance image corresponding to the current moment based on the real-time environmental features and the preset image library, the method further includes: During navigation, if the predefined conditions corresponding to the navigation guidance area are met, based on the current real-time environmental characteristics, a set of real-scene guidance maps corresponding to one or more subsequent navigation guidance areas on the navigation path is determined and cached locally.

[0015] By introducing a pre-caching mechanism for real-view guidance images of subsequent navigation guidance areas, matching operations can be performed locally, effectively avoiding network latency or computational lag issues that may occur when matching is performed when the vehicle arrives at the navigation guidance area.

[0016] In one implementation of this application, determining the target real-scene guidance image corresponding to the current moment based on the real-time environmental features and a preset image library specifically includes: Based on the real-time environmental features and the environmental feature labels of each of the real-scene guidance maps, determine the label matching degree corresponding to different environmental feature dimensions; Based on the environmental feature labels, a preset weight value is determined for each of the environmental feature dimensions; wherein, the sum of the preset weight values ​​for each of the environmental feature dimensions in the same environmental feature label is 1. Based on the weight values, the matching degree of each tag is calculated by weighting to obtain the comprehensive matching score corresponding to each real-scene guidance map; Based on the comprehensive matching scores, the target real-scene guidance image is selected from each of the real-scene guidance images.

[0017] By assigning weights to different environmental feature dimensions, the system can identify important environmental factors in order to match the optimal and targeted real-world guidance map, thereby improving the intelligence level of vehicle navigation screen control.

[0018] In one implementation of this application, before selecting the target real-world guidance image, the method further includes: Determine the vehicle's current navigation path and direction of travel; Based on the location of the navigation guidance area, a set of corresponding candidate real-scene guidance images is selected from the preset image library, and the navigation path identifier corresponding to each candidate real-scene guidance image is determined; the navigation path identifier includes the guidance direction pre-marked on the real-scene guidance image; The current navigation path driving direction is matched with the navigation path identifier to determine each candidate real-scene guidance map whose guidance direction is the same as the current navigation path driving direction based on the matching result, and the target real-scene guidance map is filtered based on each candidate real-scene guidance map.

[0019] By adding a direction consistency check during the target real-view guidance map screening process, on the one hand, it ensures that the obtained real-view guidance map has guidance signs that match the navigation path, preventing images that are not applicable to the current navigation path from being incorrectly displayed to the driver; on the other hand, combined with navigation path signs, it can achieve multi-dimensional and accurate screening of the target real-view guidance map.

[0020] Secondly, embodiments of this application also provide a vehicle navigation screen control device, the device comprising: The judgment module is used to determine whether the vehicle's current driving path contains a preset navigation guidance area; The determination module is used to, if the navigation guidance area exists, acquire real-time environmental features, and determine the target real-scene guidance image corresponding to the current time based on the real-time environmental features and a preset image library; wherein, the preset image library stores several real-scene guidance images, and each real-scene guidance image is associated with different environmental feature tags; The sending module is used to send the target real-scene guidance map to the navigation display interface for display.

[0021] Thirdly, embodiments of this application also provide a vehicle, the vehicle including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: execute the vehicle navigation screen control method described above. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a vehicle navigation screen control method according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a vehicle navigation screen control device according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a vehicle according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of a vehicle navigation screen control device according to an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Traditional vehicle navigation systems mainly rely on two-dimensional electronic maps or simple three-dimensional models for route guidance. While they can meet the basic needs of users in most scenarios, in complex road environments, especially in key navigation guidance areas such as urban interchanges, intersections with multiple forks in the road, and highway ramps, users are prone to misunderstand navigation information due to environmental changes, leading to wrong turns.

[0025] In existing technologies, traditional vehicle navigation screens often rely on abstract graphic symbols and arrow indicators, which differ significantly from the real world observed by the driver through the car window. Moreover, current navigation systems lack the ability to adapt to dynamic environmental changes; the guidance screens provided by the system are usually static and unchanging. This may cause drivers to be unable to quickly identify key landmarks referenced by the navigation when the environment changes, leading to misjudgments of the driving route.

[0026] For example, the view of the same intersection changes with the seasons. As the vehicle approaches a turn, a static image cannot provide sufficient detail, making it difficult for the driver to obtain accurate guidance at crucial turning moments. This increases the difficulty and risk of turning and fails to adequately meet the user's need for turning guidance at different stages of driving. While existing navigation systems may display real-view images of intersections, these images are often static. A picture of lush foliage in summer can look drastically different from one of bare trees in winter, making it difficult for the driver to quickly identify the same location, leading to confusion. In poor visibility conditions such as rain, fog, or snow, the displayed image shows clear weather, and the views and details may differ significantly from what the driver actually sees, reducing the guidance's value. Daytime images are difficult to discern at night due to lighting differences, making road signs and the surrounding environment hard to identify. In short, static real-view images cannot reflect dynamically changing environments, making navigation guidance unintuitive or even misleading in certain situations. Therefore, there is an urgent need for a real-view navigation solution that can adapt to environmental changes.

[0027] Based on this, embodiments of this application provide a vehicle navigation screen control method, device, and vehicle to solve the problem that existing vehicle navigation cannot dynamically adapt to real-time environmental characteristics and provide accurate and intuitive real-scene navigation guidance, thereby effectively reducing the cognitive load and risk of driving wrong in complex road conditions.

[0028] The various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0029] This application provides a method for controlling a vehicle navigation screen, such as... Figure 1 As shown, the method may include steps S101-S103: S101, determine whether the vehicle's current driving path has a preset navigation guidance area.

[0030] The navigation guidance area is a pre-marked section or location on the vehicle's navigation path that is prone to driver error due to complex road structure or variable environmental features. In other words, the navigation guidance area is a road location that requires enhanced visual guidance for the driver. This navigation guidance area can be determined based on the analysis of historical navigation data or other methods, without specific limitations here.

[0031] It should be noted that the executing entity of the vehicle navigation screen control method provided in this application can be the vehicle domain controller, or an in-vehicle computing platform including any one of intelligent cockpit systems, in-vehicle infotainment systems, or autonomous driving controllers, or a mobile terminal device that has a wired or wireless communication connection with the vehicle through an in-vehicle interconnection protocol, such as a user's smartphone or tablet computer, or other computing devices. This application does not make any specific limitations on these.

[0032] Before executing step S101, this application further includes: obtaining the current vehicle's navigation path, which at least includes a path start point, a path end point, and a corresponding recommended or selected arrival path. When executing the above vehicle navigation screen control method, it can be performed in real-time after the navigation path is determined, thereby identifying whether a navigation guidance area exists at each location along the navigation path. The navigation path can be generated by the user through a terminal device or vehicle navigation software that establishes a communication connection with the vehicle. The navigation software can synchronize the navigation path to the vehicle's domain controller or other onboard computing platform in real time, and simultaneously display the navigation path through the terminal's display interface. Navigation software includes, but is not limited to, Gaode Maps, Baidu Maps, and Tencent Maps.

[0033] The vehicle's current driving path can be obtained through the in-vehicle Global Positioning System (GPS) or determined through other positioning technologies; no specific limitation is made here. Once the current driving path is obtained, it is determined whether the vehicle is following the navigation route. If so, there is no need to wait for the navigation route to be switched; the navigation guidance area identification process is performed directly based on the currently navigated route. However, if it is determined that the vehicle's current driving path is not following the planned navigation route, the navigation software is waited for to switch the navigation route. After user confirmation or default selection of the navigation route, the original deviated navigation route is updated, and the navigation guidance area identification process is performed based on the updated navigation route.

[0034] Furthermore, the navigation guidance area can be understood as a pre-defined location or road segment where real-world guidance is required. This navigation guidance area can be generated by the vehicle service provider based on navigation history data analysis of user feedback, or it can be selected by the vehicle user and sent to the vehicle service provider's cloud server.

[0035] In some embodiments, the cloud server aggregates and analyzes historical navigation data, determines location coordinates or ranges that meet pre-defined navigation guidance demand criteria from the electronic map, designates these criteria as navigation guidance areas, and marks these areas on the electronic map to obtain a navigation guidance area distribution electronic map. The navigation guidance demand criteria include at least one or more of the following: curved intersections, three-way intersections, construction intersections, highway ramps, urban interchanges, and intersections where vehicles are prone to making mistakes. Intersections where vehicles are prone to making mistakes can be determined based on the statistical number of times vehicles deviate from the navigation path at intersections in historical navigation data. If the statistical number is greater than a first predetermined value, the intersection is determined to be an intersection where vehicles are prone to making mistakes. Similarly, if the statistical number is greater than a second predetermined value during a certain time period (e.g., 10 PM to 2 AM), the intersection is determined to be an intersection where vehicles are prone to making mistakes during that time period. The first and second predetermined values ​​can be set based on expert experience in actual usage scenarios and are not specifically limited here.

[0036] In some embodiments, in actual use scenarios, the electronic map of the navigation guidance area distribution on the road can be stored on a cloud server, or the user can pre-cache it to the vehicle's local storage module. This application does not make any specific limitations on this.

[0037] In this embodiment, when determining whether a preset navigation guidance area exists on the vehicle's current driving path, taking the vehicle domain controller as an example, the vehicle domain controller obtains the navigation path and, based on the vehicle's current real-time position, determines whether the vehicle is traveling along the navigation path. If so, it further traverses the preset range of the vehicle's current driving path to check whether a navigation guidance area exists. The preset range can be understood as the path range with the navigation path as the complete driving path interval, the vehicle's current position as the starting point, and the navigation path's end point as the ending point. Alternatively, the preset range can be a path sub-interval formed within the navigation path with the vehicle's current position as the starting point and a preset distance as the range boundary distance. When the ending boundary of the path sub-interval is the navigation end point, the ending boundary of the path sub-interval no longer changes.

[0038] Through the above scheme, this application can accurately identify the location information that needs to be given navigation guidance while the vehicle is traveling along the navigation path, and then execute steps S102-S103 to complete the control of the vehicle navigation screen.

[0039] When it is determined that the vehicle's current driving path does not fall within the preset navigation guidance area, including: In one embodiment of this application, if the application determines whether a preset navigation guidance area exists based on a complete driving route interval, the vehicle navigation screen control method will not continue to be executed if there is no change in the road route.

[0040] In another embodiment of this application, if this application determines whether a preset navigation guidance area exists based on a preset range where the vehicle's current location or path sub-interval is not the navigation endpoint, then the determination of whether a navigation guidance area exists in the vehicle's current driving path is performed in real time as the vehicle's driving position changes or its preset range changes.

[0041] Furthermore, it enables comprehensive identification and judgment of navigation guidance areas during vehicle operation, providing high-quality basic data for subsequent precise control of vehicle navigation screens.

[0042] S102, if a navigation guidance area exists, obtain real-time environmental features, and determine the target real-scene guidance map corresponding to the current time based on the real-time environmental features and the preset map library; wherein, the preset map library stores several real-scene guidance maps, and each real-scene guidance map is associated with different environmental feature labels.

[0043] In this embodiment of the application, obtaining real-time environmental features specifically includes: The system determines the preset region type corresponding to the navigation guidance area. Based on the preset region type, it matches the target strategy corresponding to the navigation guidance area from multiple preset feature extraction strategies. Different feature extraction strategies include different sets of environmental feature dimensions. Based on the target strategy, it determines real-time environmental features from pre-collected environmental perception data.

[0044] Specifically, real-time environmental characteristics can be collected through onboard sensors, including but not limited to temperature sensors, ambient light sensors, rain sensors, and optical cameras. The vehicle domain controller can access these onboard sensors to collect environmental perception data corresponding to the environmental characteristics, and then analyze the obtained environmental perception data to obtain the real-time environmental characteristics.

[0045] In some embodiments, when it is determined that the vehicle's current location is within the navigation guidance area or is less than a preset distance from the navigation guidance area, the aforementioned sensors will be invoked to collect environmental perception data. Subsequently, the real-time environmental features corresponding to the navigation guidance area will be obtained through analysis. The preset distance value can be set by the user according to the actual usage scenario, and is not specifically limited here.

[0046] The aforementioned real-time environmental features can include multiple feature dimensions and their corresponding feature values, such as season (summer), weather (sunny), time of day (day), etc. Season, weather, and time of day are feature dimensions, while summer, sunny, and day are the feature values ​​corresponding to those feature dimensions. This application pre-specifies the preset division type of navigation guidance areas at different locations. For example, navigation guidance area A is of type one, and its environmental feature dimension set is {season, weather, time of day, vegetation}. Navigation guidance area B's preset division type is type two, and its environmental feature dimension set is {season, weather, time of day}. The preset division type corresponding to different navigation guidance areas can be set based on expert experience in actual usage scenarios, and this application does not impose specific limitations on this. The feature extraction strategies corresponding to different preset division types and the set of environmental feature dimensions included in the feature extraction strategies are also set based on actual usage scenarios and are not specifically limited here.

[0047] For example, in a navigation guidance area in the city center, where there is little vegetation, vegetation may not be considered as an environmental feature dimension. However, in a navigation guidance area in a suburban location, where there is more vegetation, vegetation should be included in the set of environmental feature dimensions.

[0048] The above scheme can set a set of environmental feature dimensions that match the characteristics of different navigation guidance areas. On the one hand, it can avoid performing uniform environmental feature matching on all navigation guidance areas to a certain extent, which would cause unnecessary waste of computing resources. On the other hand, it can store environmental features and real-world images corresponding to navigation guidance areas in a targeted manner, without storing real-world images corresponding to irrelevant environmental feature dimensions, thus avoiding the waste of computing resources caused by storing data related to irrelevant environmental features.

[0049] In some embodiments, after determining the planned navigation path of the vehicle, based on the embodiment of step S101 above, all navigation guidance areas existing in the complete path can be determined. If there are many navigation guidance areas in a navigation path, performing complete real-time environmental feature collection only when the vehicle arrives at or is close to the corresponding navigation guidance area may result in excessively frequent calls to all onboard sensors, increasing unnecessary power consumption. Furthermore, at higher vehicle speeds, the collected environmental perception data may be unstable, leading to inaccurate or incomplete real-time environmental features. Therefore, before determining the real-scene guidance map corresponding to the current moment based on real-time environmental features and a preset image library, the method further includes: During navigation, if the predefined conditions corresponding to the navigation guidance area are met, based on the current real-time environmental characteristics, determine the set of real-scene guidance maps corresponding to one or more subsequent navigation guidance areas on the navigation path and cache them locally.

[0050] In other words, when it is determined that the navigation guidance area exists on the current driving path of the vehicle, including the navigation guidance area where the vehicle has not yet reached the location on the navigation path, a predefined condition judgment corresponding to the navigation guidance area can be performed, and a set of real-scene guidance maps can be pre-cached based on the judgment result.

[0051] Specifically, when determining that the remaining navigation path contains at least one navigation guidance area, starting from the vehicle's current location, the predefined conditions include one or more of the following: The distance between the vehicle's current location and the next navigation guidance area is less than a first preset threshold; The remaining navigation path length of the vehicle is greater than the second preset threshold.

[0052] The first and second preset thresholds are set based on expert experience and are not specifically limited here. The predefined conditions can also be set and adjusted separately based on expert experience in actual use scenarios. The principle for setting the predefined conditions is to ensure that there is a navigation guidance area in the remaining navigation path and that the navigation task has not been completed.

[0053] When the vehicle determines that it meets the aforementioned predefined conditions, it will invoke sensors to collect current environmental perception data and determine the current real-time environmental characteristics. Subsequently, based on the environmental feature dimensions corresponding to the current real-time environmental characteristics, it will match the real-scene guidance images corresponding to each subsequent navigation guidance area from a preset image library and construct a corresponding set of real-scene guidance images. Matching refers to calculating the matching degree between the current real-time environmental characteristics and the environmental feature labels of each real-scene guidance image corresponding to the subsequent navigation guidance area. The calculation methods include, but are not limited to, cosine similarity calculation and reciprocal Euclidean distance calculation. Since each navigation guidance area may correspond to a different set of environmental feature dimensions, there is a possibility that multiple real-scene guidance images for subsequent navigation guidance areas will be obtained based on the current real-time environmental characteristics. In this case, the real-scene guidance images of the same subsequent navigation guidance area will be added to their corresponding real-scene guidance image set, and this set of real-scene guidance images will be pre-cached locally in the vehicle.

[0054] The above solution utilizes the current real-time environmental characteristics to determine the corresponding real-scene guidance image for subsequent navigation guidance areas that shares commonalities with the current real-time environmental characteristics, and caches it locally in the vehicle. This eliminates the need for the vehicle to perform a cumbersome full matching process upon arrival at a subsequent navigation guidance area, where it must match multiple real-scene guidance images from a pre-set image library to the target real-scene guidance image corresponding to the real-time environmental characteristics. Instead, the pre-set image library is pre-screened to obtain one or more real-scene guidance images that most closely match the environmental characteristics, constructing a set of real-scene guidance images. This improves the efficiency of determining and loading the target real-scene guidance image after the vehicle arrives at the navigation guidance area.

[0055] For example, if a vehicle has arrived at location 'a', and the remaining navigation path includes navigation guidance areas A, B, and C, the vehicle's sensors collect environmental perception data to determine the real-time environmental characteristics of location 'a', assuming these include three dimensions: {season, weather, and vegetation}. Subsequently, these real-time environmental characteristics are matched against the environmental feature labels corresponding to the real-world guidance maps of each navigation guidance area. If the environmental feature dimension of navigation guidance area B does not include vegetation, then the matching degree for vegetation labels is not calculated during the matching process; only the matching degree for season and weather labels is calculated. Based on the label matching degree, a preliminary screening is performed on the real-world guidance maps corresponding to navigation guidance area B, constructing a set of real-world guidance maps corresponding to navigation guidance area B. Furthermore, this application achieves the pre-generation of a set of real-world guidance maps corresponding to each navigation guidance area using common environmental features, enabling the rapid acquisition of the target real-world guidance map later.

[0056] Furthermore, this application pre-sets an upper limit on the number of times the real-scene guidance map set can be constructed. For example, in the same trip along the same navigation path, the real-scene guidance map set generation operation is only performed once for each navigation guidance area in the path, avoiding frequent matching operations that consume controller computing resources.

[0057] Furthermore, if this application performs the aforementioned real-scene guidance map set construction operation, it will also record the environmental feature dimension set corresponding to the current real-time environmental features used for set construction, so as to compare this environmental feature dimension set with the environmental feature dimension set corresponding to each navigation guidance area, to obtain the common environmental feature dimension set for constructing the real-scene guidance map set for each navigation guidance area. For example, in the above example, the common environmental feature dimension set for navigation guidance area B is {season, weather}.

[0058] In this embodiment of the application, based on real-time environmental features and a preset image library, a real-scene guidance image corresponding to the current moment is determined, specifically including: Based on real-time environmental features and environmental feature labels of each real-scene guidance image, the label matching degree corresponding to different environmental feature dimensions is determined. Based on the environmental feature labels, a preset weight value is determined for each environmental feature dimension. The sum of the preset weight values ​​for each environmental feature dimension within the same environmental feature label is 1. Based on each weight value, the label matching degree is weighted and calculated to obtain a comprehensive matching score corresponding to each real-scene guidance image. Based on each comprehensive matching score, the target real-scene guidance image is selected from the real-scene guidance images.

[0059] In other words, this application can calculate the label matching degree between real-time environmental features and environmental feature labels across different environmental feature dimensions. The environmental feature labels correspond to real-world guidance images obtained from a preset image library. For example, image A1: environmental feature labels are {Season: Summer, Weather: Sunny, Time of Day, Vegetation: Lush}; image A2: environmental feature labels are {Season: Winter, Weather: Snow, Time of Day, Vegetation: Withered}; image A3: environmental feature labels are {Season: Any, Weather: Rain, Time of Day, Visibility: Low}. Simultaneously, this application can pre-set preset weight values ​​for each environmental feature dimension within different environmental feature labels, i.e., assigning corresponding weights to different environmental factors. The specific rules for setting these preset weight values ​​can be set based on actual usage scenarios and are not specifically limited here. The calculated label matching degree and preset weight values ​​are then weighted and summed to obtain the comprehensive matching score corresponding to the real-time environmental features and the real-world guidance image.

[0060] To illustrate the calculation process of tag matching degree and comprehensive matching score, consider a scenario where a vehicle is approaching navigation guidance area M (e.g., a bend in the road). The current real-time environmental characteristics are {Season: Winter, Weather: Snow, Time of Day}. The calculation is as follows: The candidate real-world guidance images include: Image A1: {Season: Summer, Weather: Sunny, Time of Day}; Image A2: {Season: Winter, Weather: Sunny, Time of Day}; Image A3: {Season: Winter, Weather: Snow, Time of Day}; Calculation process: Image A1 overall matching score calculation: Seasons (winter and summer): , The matching score for the seasonal environmental feature dimension is calculated to be 0.3 × 0 = 0. Weather (snow and sunny): , The matching score for the weather and environmental features dimension is calculated to be 0.5 × 0 = 0. Time period (day and day): , The matching score for the environmental feature dimension of the calculated time period is 0.15 × 1 = 0.15; The overall matching score = 0 + 0 + 0.15 = 0.15. Wherein, , , Preset weight values ​​for different environmental feature dimensions, Indicates the first Preset weight values ​​for each environmental feature dimension; , , These represent the label matching degree of image A1 in the three environmental feature dimensions mentioned above. Used to indicate the first The first real-scene guide map The label matching degree of each environmental feature dimension can be calculated by cosine similarity calculation, reciprocal Euclidean distance, etc., without specific limitations here.

[0061] Image A2 overall matching score calculation: Seasons (Winter and Winter): , The matching score for the seasonal environmental feature dimension is calculated to be 0.3 × 1 = 0.3; Weather (snow and sunny): , The matching score for the weather and environmental features dimension is calculated to be 0.5 × 0 = 0. Time period (day and day): , The matching score for the seasonal environmental feature dimension is calculated to be 0.15 × 1 = 0.15; The overall matching score is 0.3 + 0 + 0.15 = 0.45.

[0062] Image A3 overall matching score calculation: Seasons (Winter and Winter): , The matching score for the seasonal environmental feature dimension is calculated to be 0.3 × 1 = 0.3; Weather (snow and snow): , The matching score for the weather and environmental features dimension is calculated to be 0.5 × 1 = 0.5. Time period (day and day): , The matching score for the seasonal environmental feature dimension is calculated to be 0.15 × 1 = 0.15; Overall matching score = 0.3 + 0.5 + 0.15 = 0.95.

[0063] Therefore, based on the three comprehensive matching scores mentioned above, image A1 can be determined as the target real-world guidance image. The specific matching process for the target real-world guidance image is shown in Table 1.

[0064] Table 1. Real-world guidance map matching display table

[0065] Furthermore, in one embodiment of this application, when it is determined that a pre-cached set of real-scene guidance images exists in the current navigation guidance area and the set contains multiple (more than one) real-scene guidance images, the vehicle will determine the preset division area type corresponding to the current navigation guidance area to obtain the corresponding set of environmental feature dimensions. This set of environmental feature dimensions is then subjected to a set difference operation with a pre-recorded set of common environmental feature dimensions to obtain a set of differential environmental feature dimensions. Based on the differential environmental feature dimensions in this set, the vehicle-mounted sensors that need to be scheduled are determined, thereby scheduling the vehicle-mounted sensors corresponding to the differential environmental feature dimensions and collecting environmental perception data for those dimensions. For example, if the set of differential environmental feature dimensions is {vegetation}, then only the camera needs to be used to capture images of the environmental vegetation to obtain real-time environmental features. These real-time environmental features are then used to perform a comprehensive matching score calculation with each real-scene guidance image in the pre-cached set of real-scene guidance images to select the target real-scene guidance image.

[0066] The above scheme enables multi-level filtering of target real-scene guidance images, which can reduce power consumption caused by excessively frequent sensor calls and speed up the filtering of target real-scene guidance images.

[0067] In real-world navigation guidance scenarios, real-view guidance images not only need to display the actual environment of intersections but also provide directional guidance for the user's driving path. This means that real-view guidance images are not simply images taken directly from the outside world; they require rendering and other processing to provide path guidance functionality. Therefore, for a navigation guidance area, multiple real-view guidance images providing guidance in different directions may be stored. To improve the accuracy of the matched target real-view guidance image, the method includes the following steps before selecting the target real-view guidance image: Determine the vehicle's current navigation path direction. Based on the location of the navigation guidance area, select a set of candidate real-view guidance images from a pre-set image library and determine the navigation path identifier corresponding to each candidate real-view guidance image. The navigation path identifier includes the guidance direction pre-marked on the real-view guidance image. Match the current navigation path direction with the navigation path identifier to determine candidate real-view guidance images whose guidance direction is the same as the current navigation path direction, and perform target real-view guidance image selection based on each candidate real-view guidance image.

[0068] In other words, this application can pre-select real-scene guidance maps that match the driving direction of the current navigation path from a preset map library through the navigation path and the navigation path identifier in the real-scene guidance map. Based on the candidate real-scene guidance maps obtained by filtering by driving direction, a comprehensive matching score is further calculated to obtain the target real-scene guidance map mentioned above.

[0069] This technical solution ensures that the obtained real-world guidance map has guidance signs that match the navigation path, and, in combination with the navigation path signs, enables multi-dimensional and precise filtering of the target real-world guidance map.

[0070] It should also be noted that the aforementioned preset image library can be pre-stored on a cloud server or stored locally in the vehicle; no specific limitation is made here. The preset image library is built and updated based on the cloud server. For example, when the navigation guidance area is generated on the cloud server, a task to collect images of the navigation guidance area is simultaneously issued. This task can be carried out by a private vehicle that agrees to the task, or by a designated person taking photos on-site; no specific limitation is made here. The cloud server can use big data analytics to analyze each of the captured real-world images and generate real-world guidance maps and their corresponding environmental feature tags.

[0071] In real-world applications, road conditions are not static. Even with a large-scale pre-built map library, it may not be possible to fully cover all environmental changes in the navigation guidance area. Therefore, this application also provides the following embodiments, including: In response to an image acquisition command from the cloud server, when the vehicle travels to the target navigation guidance area and falls within the specified acquisition time period, corresponding real-scene images are acquired. The image acquisition command is generated by the cloud server after analyzing historical navigation data aggregated from multiple vehicles, and includes at least the target navigation guidance area and the specified acquisition time period. The real-scene images are then sent to the cloud server so that the cloud server can update its preset image library based on the real-scene images.

[0072] In other words, the cloud server can further analyze the aggregated navigation history data from multiple vehicles. For example, it can determine the statistical data of vehicles that recently experienced navigation path deviations (following the navigation but still taking the wrong route). If, within a certain time period T, the number of vehicles experiencing navigation path deviations in a particular navigation guidance area exceeds a preset number, then that navigation guidance area is designated as the target navigation guidance area, and the corresponding time period T is designated as the specified collection time period, generating an image collection command. This image collection command can be sent to vehicles for which the user has agreed to image collection. When a vehicle arrives at the area corresponding to the image collection command and is within the specified collection time period, the vehicle will take real-scene images of the target navigation guidance area to acquire real-scene images and update the preset image library.

[0073] The above solution allows for flexible updates to the preset image library, ensuring that it contains accurate and realistic real-world guide images.

[0074] In one embodiment of this application, when the cloud server updates the preset image library based on real-scene images, the process specifically includes: The cloud server analyzes the real-world images and generates corresponding environmental feature tags. Based on these tags, the cloud server establishes a mapping between the real-world images and the navigation guidance area. The cloud server then stores the mapping and the real-world images in a preset image library to update the library.

[0075] In other words, the cloud server extracts environmental feature tags and establishes a mapping relationship between real-world images and navigation guidance areas through these environmental feature tags, so as to establish an association between real-world images and navigation guidance areas, and uses the mapping relationship and real-world images as storage data for a preset image library.

[0076] Furthermore, by using environmental feature tags, a connection is established between the navigation guidance area and the real-world image, so as to call up the real-world guidance image in the preset image library.

[0077] In another embodiment of this application, the method further includes: The cloud server obtains the frequency with which the vehicle accesses each real-view guidance map. Based on the access frequency, the cloud server generates image update tasks corresponding to each navigation guidance area. These image update tasks are used to issue image acquisition commands according to different update frequencies.

[0078] In other words, the cloud server of this application can generate an update frequency that is positively correlated with the frequency of use of the real-view guidance map, and generate image acquisition instructions for the navigation guidance area corresponding to the real-view guidance map. That is, by combining the user's actual driving data, the corresponding real-view guidance map can be updated more frequently for navigation guidance areas with higher traffic volume, so as to avoid the problem of the real-view guidance map not being updated synchronously after road changes, thus providing incorrect guidance to the user.

[0079] The above solution allows for flexible updates to the preset map library, ensuring that users always receive the latest and most accurate guidance maps for the intersections they pass through most frequently, thus achieving intelligent assurance of service quality.

[0080] S103, send the target real-view guidance map to the navigation display interface for display.

[0081] The navigation display interface can be the display interface of the vehicle navigation software or the display interface of the navigation software running on the user's terminal (such as the user's mobile phone, tablet, etc.). The specific settings can be made by the user in actual use, and no specific restrictions are made here.

[0082] In summary, this application utilizes the vehicle's current driving path to determine whether a navigation guidance area exists, thereby pre-determining whether real-view guidance is needed during navigation. Subsequently, real-time environmental features are used as key inputs for navigation screen decisions. From multiple real-view guidance images with different environmental feature labels, a target real-view guidance image matching the environmental features is determined. This upgrades the navigation system from static, fixed guidance to dynamic, intelligent guidance, achieving environmental adaptability for real-view guidance, ensuring that real-view guidance conforms to the user's visual perception of environmental changes. By displaying the target real-view guidance image, a real-view guidance image highly consistent with the real environment is provided. This allows drivers to make correct driving decisions simply by comparing the screen image with the actual view outside the window, improving driving safety and comfort, and significantly reducing the probability of drivers making wrong turns at complex intersections due to difficulties in understanding abstract map symbols or comparing them with the actual view.

[0083] Corresponding to the above solution is a vehicle navigation screen control device, such as... Figure 2 As shown.

[0084] Figure 2 This is a schematic diagram of the structure of a vehicle navigation screen control device provided in an embodiment of this application, as shown below. Figure 2 As shown, the vehicle navigation screen control device 200 includes: The judgment module 201 is used to determine whether a preset navigation guidance area exists on the vehicle's current driving path. The determination module 202, if a navigation guidance area exists, acquires real-time environmental features and, based on these features and a preset image library, determines the target real-scene guidance image corresponding to the current moment. The preset image library stores several real-scene guidance images, each associated with different environmental feature tags. The sending module 203 is used to send the target real-scene guidance image to the navigation display interface for display.

[0085] In some embodiments, the vehicle navigation screen control device 200 can also: In response to an image acquisition command from the cloud server, when the vehicle travels to the target navigation guidance area and falls within the specified acquisition time period, corresponding real-scene images are acquired. The image acquisition command is generated by the cloud server after analyzing historical navigation data aggregated from multiple vehicles, and includes at least the target navigation guidance area and the specified acquisition time period. The real-scene images are then sent to the cloud server so that the cloud server can update its preset image library based on the real-scene images.

[0086] In some embodiments, when the cloud server updates the preset image library based on real-scene images, the vehicle navigation screen control device 200 can also: The cloud server analyzes the real-world images and generates corresponding environmental feature tags. Based on these tags, the cloud server establishes a mapping between the real-world images and the navigation guidance area. The cloud server then stores the mapping and the real-world images in a preset image library to update the library.

[0087] In some embodiments, the vehicle navigation screen control device 200 can also: The cloud server obtains the frequency with which the vehicle accesses each real-view guidance map. Based on the access frequency, the cloud server generates image update tasks corresponding to each navigation guidance area. These image update tasks are used to issue image acquisition commands according to different update frequencies.

[0088] In some embodiments, the determining module 202 is further configured to: The system determines the preset region type corresponding to the navigation guidance area. Based on the preset region type, it matches the target strategy corresponding to the navigation guidance area from multiple preset feature extraction strategies. Different feature extraction strategies include different sets of environmental feature dimensions. Based on the target strategy, it determines real-time environmental features from pre-collected environmental perception data.

[0089] In some embodiments, the vehicle navigation screen control device 200 can also: During navigation, if the predefined conditions corresponding to the navigation guidance area are met, based on the current real-time environmental characteristics, determine the set of real-scene guidance maps corresponding to one or more subsequent navigation guidance areas on the navigation path and cache them locally.

[0090] In some embodiments, the determining module 202 is further configured to: Based on real-time environmental features and environmental feature labels of each real-scene guidance image, the label matching degree corresponding to different environmental feature dimensions is determined. Based on the environmental feature labels, a preset weight value is determined for each environmental feature dimension. Based on each weight value, the label matching degree is weighted and calculated to obtain a comprehensive matching score corresponding to each real-scene guidance image. Based on each comprehensive matching score, the target real-scene guidance image is selected from the real-scene guidance images.

[0091] In some embodiments, the vehicle navigation screen control device 200 can also: Determine the vehicle's current navigation path direction. Based on the location of the navigation guidance area, select a set of candidate real-view guidance images from a pre-set image library and determine the navigation path identifier corresponding to each candidate real-view guidance image. The navigation path identifier includes the guidance direction pre-marked on the real-view guidance image. Match the current navigation path direction with the navigation path identifier to determine candidate real-view guidance images whose guidance direction is the same as the current navigation path direction, and perform target real-view guidance image selection based on each candidate real-view guidance image.

[0092] Regarding the apparatus in the above embodiments, the specific manner in which each unit performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0093] Corresponding to the above scheme is a type of vehicle, such as Figure 3 As shown.

[0094] Figure 3 This application provides a schematic diagram of the structure of a vehicle, as shown in the embodiment of the present application. Figure 3 As shown, the vehicle includes: at least one processor 301; and a memory 302 communicatively connected to the at least one processor 301. The memory 302 stores instructions executable by the at least one processor 301, which, when executed by the at least one processor 301, enable the at least one processor 301 to: The system determines whether a preset navigation guidance area exists on the vehicle's current driving path. If a navigation guidance area exists, it retrieves real-time environmental features and, based on these features and a preset image library, determines a target real-view guidance image corresponding to the current moment. The preset image library stores several real-view guidance images, each associated with different environmental feature tags. The target real-view guidance image is then sent to the navigation display interface for display.

[0095] Corresponding to the above solution, this application also provides a vehicle navigation screen control device. Figure 4 This is a schematic diagram of the structure of a vehicle navigation screen control device, such as... Figure 4 As shown, the device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: The system determines whether a preset navigation guidance area exists on the vehicle's current driving path. If a navigation guidance area exists, it retrieves real-time environmental features and, based on these features and a preset image library, determines a target real-view guidance image corresponding to the current moment. The preset image library stores several real-view guidance images, each associated with different environmental feature tags. The target real-view guidance image is then sent to the navigation display interface for display.

[0096] Corresponding to the above solution is a non-volatile computer storage medium that stores computer-executable instructions, which, when executed by a computer, can achieve the following: The system determines whether a preset navigation guidance area exists on the vehicle's current driving path. If a navigation guidance area exists, it retrieves real-time environmental features and, based on these features and a preset image library, determines a target real-view guidance image corresponding to the current moment. The preset image library stores several real-view guidance images, each associated with different environmental feature tags. The target real-view guidance image is then sent to the navigation display interface for display.

[0097] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments for apparatus, vehicles, equipment, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0098] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0099] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0101] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The aforementioned units can be implemented in hardware or software.

[0102] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0103] The beneficial effects of the above embodiments can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0104] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0105] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] In the description of this disclosure, it should be understood that if the terms "upper", "lower", "front", "rear", "left" and "right" are used to indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the position or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0108] The above are merely embodiments of this disclosure and are not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.

Claims

1. A method for controlling a vehicle navigation screen, characterized in that, The method includes: Determine if the vehicle's current driving path falls within a preset navigation guidance area; If the navigation guidance area exists, real-time environmental features are obtained, and based on the real-time environmental features and a preset image library, a target real-scene guidance image corresponding to the current time is determined; wherein, the preset image library stores several real-scene guidance images, and each real-scene guidance image is associated with different environmental feature tags; The target real-view guide map is sent to the navigation display interface for display.

2. The vehicle navigation screen control method according to claim 1, characterized in that, The method further includes: In response to an image acquisition command from a cloud server, when the vehicle travels to the target navigation guidance area and is within a specified acquisition time period, a corresponding real-scene image is acquired; wherein, the image acquisition command is generated by the cloud server after analyzing navigation historical data aggregated from multiple vehicles, and the image acquisition command includes at least the target navigation guidance area and the specified acquisition time period; The real-scene image is sent to the cloud server so that the cloud server can update the preset image library based on the real-scene image.

3. The vehicle navigation screen control method according to claim 2, characterized in that, When the cloud server updates the preset image library based on the real-scene image, it specifically includes: The cloud server parses the real-world image and generates corresponding environmental feature labels; The cloud server establishes a mapping relationship between the real-world image and the navigation guidance area based on the environmental feature tags; The cloud server stores the mapping relationship and the real-scene image into the preset image library to update the preset image library.

4. The vehicle navigation screen control method according to claim 2, characterized in that, The method further includes: The cloud server obtains the frequency with which the vehicle calls each of the real-scene guidance maps; Based on the calling frequency, the cloud server generates image update tasks corresponding to each of the navigation guidance areas; wherein, the image update tasks are used to issue the image acquisition instructions according to different update frequencies.

5. The vehicle navigation screen control method according to claim 1, characterized in that, Obtain real-time environmental characteristics, specifically including: Determine the preset division area type corresponding to the navigation guidance area; Based on the preset region division type, a target strategy corresponding to the navigation guidance region is matched from a plurality of preset feature extraction strategies; wherein, different feature extraction strategies include different sets of environmental feature dimensions; Based on the target strategy, the real-time environmental features are determined from the pre-collected environmental perception data.

6. The vehicle navigation screen control method according to claim 1, characterized in that, Before determining the real-world guidance map corresponding to the current moment based on the real-time environmental features and the preset image library, the method further includes: During navigation, if the predefined conditions corresponding to the navigation guidance area are met, based on the current real-time environmental characteristics, a set of real-scene guidance maps corresponding to one or more subsequent navigation guidance areas on the navigation path is determined and cached locally.

7. The vehicle navigation screen control method according to claim 1, characterized in that, Based on the real-time environmental features and the preset image library, a target real-scene guidance image corresponding to the current moment is determined, specifically including: Based on the real-time environmental features and the environmental feature labels of each of the real-scene guidance maps, determine the label matching degree corresponding to different environmental feature dimensions; Based on the environmental feature labels, determine the preset weight values ​​corresponding to each of the environmental feature dimensions; Based on the weight values, the matching degree of each tag is calculated by weighting to obtain the comprehensive matching score corresponding to each real-scene guidance map; Based on the comprehensive matching scores, the target real-scene guidance image is selected from each of the real-scene guidance images.

8. A vehicle navigation screen control method according to claim 7, characterized in that, Before selecting the target real-world guidance image, the method further includes: Determine the vehicle's current navigation path and direction of travel; Based on the location of the navigation guidance area, a set of corresponding candidate real-scene guidance images is selected from the preset image library, and the navigation path identifier corresponding to each candidate real-scene guidance image is determined; the navigation path identifier includes the guidance direction pre-marked on the real-scene guidance image; The current navigation path driving direction is matched with the navigation path identifier to determine each candidate real-scene guidance map whose guidance direction is the same as the current navigation path driving direction based on the matching result, and the target real-scene guidance map is filtered based on each candidate real-scene guidance map.

9. A vehicle navigation screen control device, characterized in that, The device includes: The judgment module is used to determine whether the vehicle's current driving path contains a preset navigation guidance area; The determination module is used to, if the navigation guidance area exists, acquire real-time environmental features, and determine the target real-scene guidance image corresponding to the current time based on the real-time environmental features and a preset image library; wherein, the preset image library stores several real-scene guidance images, and each real-scene guidance image is associated with different environmental feature tags; The sending module is used to send the target real-scene guidance map to the navigation display interface for display.

10. A vehicle, characterized in that, The vehicle includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: perform a vehicle navigation screen control method according to any one of claims 1-8.

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