Vehicle navigation method and system based on intelligent prediction, vehicle, and medium

Abstract

A vehicle navigation method and system based on intelligent prediction, a vehicle, and a storage medium. The method comprises the following steps: configuring at least one destination control on an interactive interface, each destination control pointing to a destination containing a specific orientation; and upon receiving a trigger response of one of the at least one destination control, starting vehicle navigation on the basis of the specific orientation of the destination to which the triggered destination control points. In the method, navigation can be directly started when a user inputs a destination, or navigation can be directly started when a user says a destination, thereby achieving one-step trigger in intelligent cabins, improving user operation experience, and enhancing the convenience.

Description

A vehicle navigation method, system, vehicle and medium based on intelligent prediction TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle navigation, and in particular to a vehicle navigation method, system, vehicle and medium based on intelligent prediction. BACKGROUND

[0002] With the development of intelligent cockpit of automobile, front-mounted vehicle navigation has become an essential online application of central control vehicle system, and is an indispensable function in intelligent automobile. With the development and popularization of intelligentization of intelligent cockpit, the use frequency of vehicle navigation is also increasing, and the way of using support to navigate by mobile phone is gradually reduced, but the overall vehicle navigation function can be optimized to improve the convenience of navigation function.

[0003] Currently, starting vehicle navigation usually involves three steps: first, searching for a destination in the home page; second, selecting one from the destination list provided by navigation; and third, the system provides three routes according to the selected destination, and the user needs to select one of them to start navigation. In the whole process, the user needs to perform at least two selection operations, which are selecting a destination and determining a navigation route. However, due to the inconvenience of operation of vehicle large screen, compared with mobile phone navigation operation, this multi-step process is more likely to cause accidental touch in driving, which not only affects driving safety, but also reduces the overall experience of users.

[0004] SUMMARY

[0005] To overcome the problems in the related art, the embodiments of the present application provide a vehicle navigation method, system, vehicle and medium based on intelligent prediction.

[0006] The first aspect of the embodiments of the present application provides a vehicle navigation method based on intelligent prediction, comprising the following steps:

[0007] At least one destination control is set on the interactive interface, and each destination control points to a destination containing a specific direction;

[0008] After receiving the trigger response of one of the destination controls, the vehicle navigation is started with the specific direction of the destination pointed by the triggered destination control.

[0009] Further, the trigger response of the destination control includes a click response, a voice response and a cloud response;

[0010] The click response refers to the response triggered by the user's click operation on the interactive interface at the position corresponding to the destination control;

[0011] The voice response refers to the response triggered by the user inputting voice information related to the selected destination control to the vehicle computer.

[0012] The cloud response refers to a response triggered by a user sending a control instruction related to the selected destination control to the on-board computer through the cloud.

[0013] Further, the destination control is generated by the following steps:

[0014] Obtaining user historical travel data;

[0015] According to the user historical travel data, the historical destination of the user and the corresponding direction and historical travel trajectory of each historical destination are determined; the historical destination of the user refers to the place name in the user historical travel data as the end point of the travel, and each historical destination has a unique corresponding direction and at least one historical travel trajectory;

[0016] According to the historical travel trajectory corresponding to the historical destination, the historical route preference of each historical destination is generated;

[0017] The direction and historical route preference of the historical destination are encapsulated to obtain the destination control.

[0018] Further, the step of generating the historical route preference of each historical destination according to the historical travel trajectory corresponding to the historical destination is realized by a smart prediction model stored in the cloud, which specifically includes the following steps:

[0019] The on-board computer establishes a data transmission channel with the cloud with the user as an identifier;

[0020] The on-board computer uploads the user historical travel data to the cloud;

[0021] The cloud calls the stored smart prediction model, and performs feature analysis on the user historical travel data as input data by the smart prediction model to obtain the historical route preference of each historical destination in the user historical travel data;

[0022] The cloud downloads the historical route preference output by the smart prediction model to the on-board computer.

[0023] Further, in the vehicle navigation, the direction recorded in the destination control serves as the end point of the travel, and the historical route preference recorded in the destination control serves as the travel path of the vehicle navigation.

[0024] Further, the following steps are further included:

[0025] After receiving the cancellation response of the vehicle navigation, a destination input window and / or a destination selection window are displayed on the interactive interface;

[0026] After receiving the destination selection response, the destination input window and / or destination selection window will take the specific location of the destination pointed to by the destination selection response as the trip end point.

[0027] Based on the vehicle's current location and the destination, select the specific location of the destination to be indicated by the response, establish multiple navigation routes, and display them on the interactive interface;

[0028] Upon receiving a route selection response, the navigation route indicated by the route selection response is used as the vehicle navigation route, and vehicle navigation begins.

[0029] Furthermore, it also includes the following steps:

[0030] The specific location of the destination indicated by the destination selection response and the navigation route indicated by the route selection response are added to the user's historical trip data.

[0031] A second aspect of the present invention provides a vehicle navigation system based on intelligent prediction, mounted on an in-vehicle computer, the system including an interactive interface and a navigation module;

[0032] The interactive interface is provided with at least one destination control, and each destination control points to a destination with a specific location.

[0033] The navigation module is used to start vehicle navigation based on the specific location of the destination pointed to by the triggered destination control after receiving a trigger response from one of the destination controls in the interactive interface.

[0034] Furthermore, the location recorded in the destination control serves as the destination of the vehicle navigation trip, and the historical route preferences recorded in the destination control serve as the route path of the vehicle navigation trip.

[0035] Furthermore, it also includes a cloud communication module; the cloud communication module is used to interact with the cloud for data, specifically including the following steps:

[0036] The cloud communication module establishes a data transmission channel with the cloud, using the user as the identifier.

[0037] The cloud communication module uploads the user's historical travel data to the cloud;

[0038] The cloud calls the stored intelligent prediction model, and uses the user's historical travel data as input data to perform feature analysis to obtain the historical route preference for each historical destination in the user's historical travel data.

[0039] The cloud sends the historical route preferences output by the intelligent prediction model to the cloud communication module.

[0040] A third aspect of the present invention provides a vehicle equipped with an on-board computer, the on-board computer including a processor, a memory, and computer program instructions stored in the memory and executable on the processor, wherein the processor executes the computer program instructions to implement the aforementioned intelligent prediction-based vehicle navigation method.

[0041] A fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the aforementioned intelligent prediction-based vehicle navigation method.

[0042] The embodiments of the present invention have the following beneficial effects: The vehicle navigation method, system, vehicle and medium based on intelligent prediction of the present invention can intelligently select navigation route preferences for users according to user habits through intelligent prediction models; the present invention can start navigation directly after the user inputs a destination, or start navigation directly after the user speaks a destination, realizing one-step departure in the intelligent cockpit, improving the user operation experience and increasing convenience.

[0043] Additional aspects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description or may be learned by practice of the invention. Attached Figure Description

[0044] Figure 1 is a flowchart of the main steps of a vehicle navigation method based on intelligent prediction according to the present invention;

[0045] Figure 2 is a schematic diagram of the destination control structure of the present invention;

[0046] Figure 3 is a schematic diagram of the destination control generation steps of the present invention;

[0047] Figure 4 is a schematic diagram of the historical itinerary data structure of the present invention;

[0048] Figure 5 is a schematic diagram of the cloud communication module of the vehicle computer of the present invention and the cloud communication effect;

[0049] Figure 6 is a schematic diagram of the vehicle-mounted computer cloud communication module and cloud communication process of the present invention;

[0050] Figure 7 is a schematic diagram of the navigation route reselection steps of the present invention;

[0051] Figure 8 is a schematic diagram of the structure of a vehicle navigation system based on intelligent prediction according to the present invention;

[0052] Figure 9 is a structural schematic diagram of a vehicle according to the present invention;

[0053] Figure 10 is a schematic diagram of the structure of a computer storage medium according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. At the same time, it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0055] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0056] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0057] Existing in-car navigation systems typically require three steps to activate: First, the user needs to search for a specific destination on the navigation's home screen. This process may involve entering a specific address, selecting landmarks, or searching for nearby businesses. Next, from a list of multiple possible destinations automatically provided by the navigation system, the user needs to carefully filter and select the exact destination. Finally, once the destination is determined, the system presents the user with three or more alternative routes based on factors such as real-time traffic conditions, route length, and estimated travel time. The user must then carefully compare these routes again and choose one as the final navigation route.

[0058] However, this multi-step navigation startup process has revealed some problems in practical applications. Since in-car navigation systems are typically installed on a large car screen, their user interface and touch response may not be as convenient or sensitive as those of portable devices like smartphones. This design significantly increases the risk of accidental triggering while driving, potentially leading to navigation errors and distracting the driver, thus affecting driving safety. Furthermore, this multi-step operation also degrades the user experience to some extent, especially in emergency situations where quick navigation is urgently needed; users may experience anxiety or dissatisfaction due to the cumbersome process. Therefore, optimizing the in-car navigation system's operation process, reducing unnecessary selection steps, and improving the system's convenience and safety have become important directions for the improvement of current in-car navigation systems.

[0059] To simplify user operation, this invention proposes a vehicle navigation method, system, vehicle, and medium based on intelligent prediction, enabling one-step triggering of in-vehicle navigation, improving user experience, and increasing convenience.

[0060] As shown in Figure 1, the first embodiment of the present invention provides a vehicle navigation method based on intelligent prediction, including the following steps:

[0061] S1. Set at least one destination control on the interactive interface, with each destination control pointing to a destination containing a specific location;

[0062] S2. Upon receiving a trigger response from one of the destination controls, begin vehicle navigation based on the specific location of the destination pointed to by the triggered destination control.

[0063] The vehicle navigation method provided in this invention only requires receiving a trigger response from the destination control to start vehicle navigation, achieving one-step triggering and quick initiation of in-vehicle navigation. This invention eliminates additional user operations, allowing users to concentrate on driving and improving driving safety.

[0064] The implementation process of each step of this invention is described in detail below.

[0065] S1. Set at least one destination control on the interactive interface, with each destination control pointing to a destination containing a specific location.

[0066] The structure of the destination control in this embodiment of the method is shown in Figure 2. In this embodiment, each destination control on the interactive interface is uniquely identified by its destination name. The destination controls seen by the user on the interactive interface are individual destination names, facilitating quick destination selection. In this embodiment, the destination names are user-defined names, including "Home," "Company," "School," "Hospital," etc. Users can name their destinations according to their needs to quickly select their frequently visited destinations. If the user does not set a destination name, the destination control in this embodiment will use the name marked on the map as the destination name.

[0067] As shown in Figure 3, the destination control in this embodiment of the method is generated through the following steps:

[0068] S1-1. Obtain user's historical travel data;

[0069] S1-2. Based on the user's historical travel data, determine the user's historical destinations and the corresponding location and historical travel trajectory for each historical destination; the user's historical destination refers to the name of the location in the user's historical travel data that is used as the end point of the trip, and each historical destination has a unique corresponding location and at least one historical travel trajectory.

[0070] S1-3. Generate historical route preferences for each historical destination based on the historical travel trajectory corresponding to the historical destination;

[0071] S1-4. Encapsulate the location and route preferences of historical destinations to obtain the destination control.

[0072] In this embodiment of the method, the user's historical travel data is used to extract one or more destinations that the user has previously visited, and destination controls are created for these destinations. When the user needs to go to these previously visited destinations again, there is no need to repeatedly input the destination and select the navigation route. Instead, the user can directly start vehicle navigation through the destination controls on the interactive interface, saving the user from repetitive operations and improving the user navigation experience.

[0073] The user trip data structure in this embodiment of the method is shown in Figure 4. The user trip data is compiled based on the user's historical vehicle navigation information. Every vehicle navigation trip previously taken by the user is recorded and stored as user trip data by the in-vehicle system. Each piece of user trip data includes the location of the destination and the travel trajectory. The location of the destination represents the satellite latitude and longitude positioning of the destination (aa°bb'cc”N / S, dd°ee'ff”E / W). Using latitude and longitude to represent the destination location allows for geometric positioning accuracy within 2 meters, meeting vehicle navigation requirements. The travel trajectory of the destination represents the specific driving route, driving time, driving speed, and other information for that navigation trip. The driving route includes which roads, intersections, bridges, etc., the vehicle passed through from the start of navigation to the destination; the driving time and speed represent the time taken to reach the destination and the vehicle speed.

[0074] The parameters of the user's travel trajectory in the user's travel data can be provided to the intelligent prediction model, enabling the model to analyze the user's driving habits and road conditions on various road segments, ultimately extracting the user's route preference to their destination. As shown in Figure 5, in this embodiment of the method, the intelligent prediction model is loaded in the cloud. The onboard computer establishes a data transmission channel with the cloud via a wireless network, sending user travel data to the cloud through this channel. The intelligent prediction model then performs feature analysis on the user travel data in the cloud. In this embodiment, the onboard computer establishes a timed response mechanism with the cloud, periodically sending the latest user travel data to the cloud and simultaneously receiving historical route preferences from the cloud, which are then encapsulated in the destination control. Thus, when the user triggers the destination control, the onboard computer can directly retrieve the historical route preferences set in the destination control for vehicle navigation, without waiting for the intelligent prediction model in the cloud to complete feature analysis or for the onboard computer and cloud to complete data interaction. This significantly reduces the user's waiting time and achieves efficient vehicle navigation.

[0075] The communication process between the in-vehicle computer and the cloud in an embodiment of the present invention is shown in Figure 6. First, the in-vehicle computer and the cloud perform a user account authentication process, allowing the user to log in. In this embodiment, the cloud may interact with multiple in-vehicle computers simultaneously, therefore, a different data storage space needs to be set for each user. This storage space is used to store uploaded user trip data and historical route preferences output by the intelligent prediction model. After logging in, the user immediately uploads the user trip data stored in the in-vehicle computer, rather than waiting for the user to trigger the destination control before uploading the user trip data for feature analysis, improving the convenience of navigation operations and reducing user waiting time. After receiving the user trip data, the cloud stores it in the user's corresponding data storage space. The intelligent prediction model periodically calls the user trip data stored in the data storage space for feature analysis and stores the historical route preferences as the feature analysis results in the data storage space. In this embodiment, the intelligent prediction model can be implemented using LSTM (Long Short-Term Memory) neural network models, GNN (Graph Neural Network) neural network models, SRN (Simple Recurrent Network) neural network models, etc. One or more intelligent prediction models can be set in the cloud for users to choose from. Different users may choose the same or different intelligent prediction models, so the historical route preferences obtained by each user may vary. In the embodiments of the present invention, the feature analysis of the intelligent prediction model can be triggered in a timed or untimed manner. Timed triggering means that the intelligent prediction model reads user trip data from the data storage space for feature analysis within a certain time period; untimed triggering means that the intelligent prediction model is triggered to perform feature analysis each time the user logs into the cloud and uploads user trip data. When the user triggers the destination control on the in-vehicle computer, the in-vehicle computer directly calls the historical route preferences generated by the model from the user's corresponding data storage space in the cloud for vehicle navigation, achieving fast and convenient vehicle navigation control.

[0076] S2. Upon receiving a trigger response from one of the destination controls, begin vehicle navigation based on the specific location of the destination pointed to by the triggered destination control.

[0077] In this embodiment of the method, the triggering response of the destination control includes a click response, a voice response, and a cloud response. A click response refers to the response triggered when the user clicks on the corresponding location of the destination control on the interactive interface; a voice response refers to the response triggered when the user inputs voice information related to the selected destination control into the in-vehicle computer; and a cloud response refers to the response triggered when the user sends control commands related to the selected destination control to the in-vehicle computer via the cloud. Users can use a suitable triggering method to activate the destination control according to their needs; for example, if both hands cannot leave the steering wheel while driving, the destination control can be triggered via voice without affecting the user's vehicle operation.

[0078] Once the destination control is triggered, the vehicle's computer reads the location and historical route preferences encapsulated in the destination control; it uses the location recorded in the destination control as the destination of the vehicle navigation trip and the historical route preferences recorded in the destination control as the route path of the vehicle navigation trip to start the vehicle navigation directly, without needing to receive user responses again during the process, thus achieving fast navigation.

[0079] In some embodiments, the navigation route automatically provided by the vehicle system may not meet the user's needs. In this case, the user needs to send a cancellation response to the vehicle computer, and the vehicle computer will perform an additional S3. Vehicle navigation reset step.

[0080] As shown in Figure 7, the navigation route reselection includes the following steps:

[0081] S3-1. Upon receiving a cancellation response from the vehicle navigation system, display the destination input window and / or destination selection window on the interactive interface;

[0082] S3-2. After receiving the destination selection response, the destination input window and / or destination selection window shall take the specific location of the destination pointed to by the destination selection response as the trip end point;

[0083] S3-3. Based on the vehicle's current location and destination, select the specific location of the destination to be responded to, establish multiple navigation routes, and display them on the interactive interface;

[0084] S3-4. Upon receiving the route selection response, use the navigation route indicated by the route selection response as the travel path for vehicle navigation and begin vehicle navigation.

[0085] In the navigation reselection step of this invention's method embodiment, after the user cancels the vehicle navigation automatically provided by the onboard computer, they select a destination through the destination input window and / or destination selection window. The onboard computer automatically calculates the route from the starting point to the destination and provides it to the user. After the user confirms the navigation route, the onboard computer performs vehicle navigation based on the user's selected destination and navigation route. Through the design of step S3 of this invention, the user can choose whether to accept the navigation suggestions automatically provided by the onboard computer, or manually input or select the destination and navigation route, increasing the flexibility and personalization of the user experience.

[0086] Meanwhile, the user's operation process in the S3 navigation reselection step can also be used as part of the user's historical trip data. This navigation is stored in the vehicle's computer, and when the vehicle's computer connects to the cloud, the data of this navigation operation is fed back to the cloud. This data can be used as the input for the next intelligent prediction model to trigger feature analysis, further optimizing the historical route preferences recommended by the intelligent prediction model and providing users with better navigation suggestions.

[0087] The second embodiment of this invention discloses a vehicle navigation system based on intelligent prediction, which is mounted on an in-vehicle computer. As shown in Figure 8, the system includes an interactive interface and a navigation module. The interactive interface has at least one destination control, each pointing to a destination with a specific location. The navigation module, upon receiving a trigger response from one of the destination controls on the interactive interface, begins vehicle navigation based on the specific location of the destination pointed to by the triggered destination control.

[0088] In this system embodiment, each destination control on the interactive interface is uniquely identified by its destination name. The destination controls displayed to the user on the interactive interface are individual destination names, facilitating quick destination selection. In this system embodiment, the destination names are user-defined names, including "Home," "Company," "School," "Hospital," etc. Users can name their destinations according to their needs for quick selection of frequently visited destinations. If the user does not set a destination name, the destination controls in this system embodiment will use the names marked on the map as the destination names.

[0089] In this system embodiment, the system extracts one or more destinations previously visited by the user from the user's historical travel data and establishes destination controls for these destinations. When the user needs to travel to these previously visited destinations again, there is no need to repeatedly input the destination and select the navigation route. Instead, the user can directly start vehicle navigation through the destination controls on the interactive interface, saving the user from repetitive operations and improving the user navigation experience.

[0090] In this system embodiment of the invention, user trip data is generated by summarizing the user's historical vehicle navigation information. Every vehicle navigation trip previously taken by the user is recorded and stored as user trip data by the in-vehicle system. Each piece of user trip data includes the location of the destination and the travel trajectory. The location of the destination represents the satellite latitude and longitude positioning of the destination (aa°bb'cc”N / S, dd°ee'ff”E / W). Using latitude and longitude to represent the destination location allows for geometric positioning accuracy within 2 meters, meeting vehicle navigation requirements. The travel trajectory of the destination represents the specific driving route, driving time, driving speed, and other information for that navigation trip. The driving route includes which roads, intersections, bridges, etc., the vehicle passed through from the start of navigation to the destination; the driving time and speed represent the time taken to reach the destination and the vehicle speed.

[0091] The parameters of the user's travel trajectory in the user's travel data can be provided to the intelligent prediction model, enabling the model to analyze the user's driving habits and road conditions on various road segments, ultimately extracting the user's route preference to their destination. In this system embodiment, the intelligent prediction model is loaded in the cloud. The onboard computer establishes a data transmission channel with the cloud via a wireless network, sending user travel data to the cloud through this channel. The intelligent prediction model then performs feature analysis on the user's travel data in the cloud. In this system embodiment, the onboard computer and the cloud establish a timed response mechanism. The onboard computer periodically sends the latest user travel data to the cloud and simultaneously receives historical route preferences from the cloud, encapsulating them in the destination control. Thus, when the user triggers the destination control, the onboard computer can directly retrieve the historical route preferences set in the destination control for vehicle navigation, without waiting for the intelligent prediction model in the cloud to complete feature analysis or for the onboard computer and cloud to complete data interaction. This significantly reduces the user's waiting time and achieves efficient vehicle navigation.

[0092] In this embodiment of the invention, communication between the in-vehicle computer and the cloud is achieved through a cloud communication module. First, the in-vehicle computer's cloud communication module performs a user account authentication process with the cloud, allowing the user to log in. In this system embodiment, the cloud may simultaneously interact with multiple in-vehicle computers; therefore, a different data storage space needs to be set for each user. This storage space is used to store uploaded user trip data and historical route preferences output by the intelligent prediction model. After the user logs in, the cloud communication module immediately uploads the user trip data stored in the in-vehicle computer, rather than waiting for the user to trigger the destination control before uploading the user trip data for feature analysis, improving the convenience of navigation operations and reducing user waiting time. After receiving the user trip data, the cloud stores it in the user's corresponding data storage space. The intelligent prediction model periodically calls the user trip data stored in the data storage space for feature analysis, storing the historical route preferences as the feature analysis results in the data storage space. In the system embodiments of this invention, the intelligent prediction model can be implemented using models such as LSTM (Long Short-Term Memory), GNN (Graph Neural Network), and SRN (Simple Recurrent Network). One or more intelligent prediction models can be set up in the cloud for users to choose from. Different users may choose the same or different intelligent prediction models, thus each user's historical route preferences may differ. The feature analysis of the intelligent prediction model in the system embodiments of this invention can be triggered periodically or irregularly. Periodic triggering means that the intelligent prediction model reads user trip data from the data storage space for feature analysis within a certain time period; irregular triggering means that the intelligent prediction model is triggered to perform feature analysis each time the user logs into the cloud and uploads user trip data. When the user triggers the destination control on the in-vehicle computer, the in-vehicle computer's cloud communication module directly calls the historical route preferences generated by the model from the user's corresponding data storage space in the cloud for vehicle navigation, achieving fast and convenient vehicle navigation control.

[0093] In this system embodiment, the triggering response of the destination control includes a click response, a voice response, and a cloud response. A click response refers to the response triggered when the user clicks on the corresponding location of the destination control on the interactive interface; a voice response refers to the response triggered when the user inputs voice information related to the selected destination control into the vehicle's computer; and a cloud response refers to the response triggered when the user sends control commands related to the selected destination control to the vehicle's computer via the cloud. Users can use a suitable triggering method to activate the destination control according to their needs; for example, if both hands cannot leave the steering wheel while driving, the destination control can be triggered via voice without affecting the user's vehicle operation.

[0094] Once the destination control is triggered, the vehicle's computer reads the location and historical route preferences encapsulated in the destination control; it uses the location recorded in the destination control as the destination of the vehicle navigation trip and the historical route preferences recorded in the destination control as the route path of the vehicle navigation trip to start the vehicle navigation directly, without needing to receive user responses again during the process, thus achieving fast navigation.

[0095] In some embodiments, the navigation route automatically provided by the vehicle system may not meet the user's needs. In this case, the user needs to send a cancellation response to the vehicle computer, and the vehicle computer navigation module will perform additional vehicle navigation reset steps.

[0096] In the navigation reselection step of this invention's system embodiment, after the user cancels the vehicle navigation automatically provided by the onboard computer, they select a destination through the destination input window and / or destination selection window. The onboard computer automatically calculates the route from the origin to the destination and provides it to the user. After the user confirms the navigation route, the onboard computer performs vehicle navigation based on the user's selected destination and navigation route. Through the design of the vehicle navigation reselection step of this invention, the user can choose whether to accept the navigation suggestions automatically provided by the onboard computer, or manually input or select the destination and navigation route, increasing the flexibility and personalization of the user experience.

[0097] Meanwhile, the user's navigation reselection process can also be used as part of the user's historical trip data. This navigation is stored in the vehicle's computer, and when the vehicle's computer connects to the cloud, the data of this navigation operation is fed back to the cloud. This data can be used as input for the next intelligent prediction model to trigger feature analysis, further optimizing the historical route preferences recommended by the intelligent prediction model and providing users with better navigation suggestions.

[0098] Figure 9 is a schematic diagram of the vehicle structure proposed in the third embodiment of the present invention. Exemplarily, the vehicle includes a processor and a memory coupled to the processor. Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV, or pickup truck. The vehicle can also be a commercial vehicle, such as a van, bus, small truck, or large trailer. The vehicle can be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle. In this embodiment, the memory stores program instructions for implementing the intelligent prediction-based vehicle navigation method of any of the above embodiments. The processor executes the program instructions stored in the memory to perform intelligent prediction-based vehicle navigation. The processor can also be called a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor can also be a general-purpose processor, a digital signal processor (DSP), 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 general-purpose processor can be a microprocessor or any conventional processor.

[0099] Figure 10 is a schematic diagram of the structure of the storage medium according to the fourth embodiment of the present invention. The storage medium of the fourth embodiment of the present invention stores program instructions capable of implementing the above-described intelligent prediction-based vehicle navigation method. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0100] In summary, the technical effects of this invention include:

[0101] 1. The embodiments of the present invention can intelligently select destinations and navigation route preferences for users based on their usage habits and big data input. This allows users to start navigation directly after entering a destination, or to start navigation directly by speaking a destination, thus improving the user experience and increasing convenience.

[0102] 2. This invention establishes a unique intelligent prediction model for each driver based on their driving habits, historical itineraries, and frequently visited locations. Different drivers can have different intelligent prediction models, thus the recommended locations and routes will vary from person to person. Therefore, when using in-vehicle navigation, different drivers will receive location recommendations and route planning tailored to their individual preferences, greatly improving the practicality of the navigation system and user satisfaction.

[0103] 3. To ensure the security and convenience of user data, the intelligent prediction model of the in-vehicle navigation system is stored in the cloud and is strictly encrypted. Because the model is encrypted and stored in the cloud, users can use the same intelligent prediction method by logging into the same account in different vehicles. Therefore, in this embodiment of the invention, as long as a user logs into the same account in different vehicles, they can obtain their own personalized intelligent prediction solution. This design not only facilitates users switching between navigation systems in different vehicles but also ensures the security and privacy of user data. Furthermore, because the model is stored in the cloud, the system can be updated and optimized in real time to adapt to the driver's constantly changing driving habits and preferences.

[0104] 4. If the route recommended by this embodiment of the invention does not meet the user's needs, the user can perform a navigation reselection operation. This embodiment of the invention will record the user's navigation reselection operation and use it as reference data for the next navigation route prediction. In this way, the in-vehicle navigation system can continuously learn and adapt to changes in the driver's preferences, providing more accurate and personalized services.

[0105] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement the intelligent prediction-based vehicle navigation method provided in the above embodiment.

[0106] Those skilled in the art will understand that modules in the device of the embodiments of the present invention can be adaptively modified and placed in one or more devices different from those embodiments. Modules, units, or components in the embodiments of the present invention can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the corresponding claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the corresponding claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0107] 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.

[0108] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0109] Furthermore, the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. In particular, for embodiments such as apparatus and devices, since they are basically similar to the method embodiments, the relevant parts can be referred to the description of the method embodiments. The apparatus, devices, and other embodiments described above are merely illustrative, and the modules, units, etc., described as separate components may or may not be physically separate, that is, they may be located in one place or distributed in multiple places, such as nodes in a system network. Specifically, some or all of the modules and units can be selected according to actual needs to achieve the purpose of the above-described embodiment solutions. Those skilled in the art can understand and implement this without creative effort.

[0110] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0111] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0112] Furthermore, the terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance, or implicitly specifying the number of technical features indicated in this embodiment. Therefore, features defined with terms such as "first" and "second" in the embodiments of this invention can explicitly or implicitly indicate that the embodiment includes at least one of those features. In the description of this invention, the word "multiple" means at least two or more, such as two, three, four, etc., unless otherwise explicitly specified in the embodiments.

[0113] In embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover a 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 a process, method, article, or apparatus. Without further limitations, 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 that element. Furthermore, components, features, and elements with the same names in different embodiments of the present invention may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0114] Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention. Other embodiments of the present invention will readily conceive of by considering the specification and practicing the invention. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

Claims

1. A method for intelligent prediction based vehicle navigation, the method comprising: The method comprises the following steps: setting at least one destination control on an interactive interface, each of the destination controls pointing to a destination with a specific orientation; starting navigation of the vehicle with the specific orientation of the destination pointed by the triggered destination control in response to the triggering of one of the destination controls.

2. The intelligent prediction based vehicle navigation method as claimed in claim 1 wherein, The triggering of the destination control includes click response, voice response and cloud response; The click response refers to the response triggered by the click operation of the user on the interactive interface at the position corresponding to the destination control; The voice response refers to the response triggered by the input of the voice information related to the selected destination control by the user to the vehicle computer; The cloud response refers to the response triggered by the control instruction related to the selected destination control sent by the user to the vehicle computer through the cloud.

3. The intelligent prediction based vehicle navigation method of claim 1, wherein, The destination control is generated by the following steps: obtaining historical travel data of the user; determining historical destinations of the user, the orientation corresponding to each of the historical destinations and the historical travel trajectory according to the historical travel data of the user; the historical destination of the user refers to the name of the place in the historical travel data of the user as the end point of the travel, each of the historical destinations has a unique corresponding orientation and at least one historical travel trajectory; generating the historical route preference of each of the historical destinations according to the historical travel trajectory corresponding to the historical destination; encapsulating the orientation of the historical destination and the historical route preference to obtain the destination control.

4. The intelligent prediction based vehicle navigation method of claim 3, wherein, The step of generating the historical route preference of each of the historical destinations according to the historical travel trajectory corresponding to the historical destination is realized by an intelligent prediction model stored in the cloud, and specifically comprises the following steps: the vehicle computer establishes a data transmission channel with the cloud with the user as an identifier; the vehicle computer uploads the historical travel data of the user to the cloud; the cloud calls the stored intelligent prediction model, performs feature analysis on the historical travel data of the user by the intelligent prediction model to obtain the historical route preference of each of the historical destinations in the historical travel data of the user; the cloud sends the historical route preference output by the intelligent prediction model to the vehicle computer.

5. The intelligent prediction based vehicle navigation method as claimed in claim 3, wherein, In the vehicle navigation, the orientation recorded in the destination control serves as the end point of the travel, and the historical route preference recorded in the destination control serves as the travel path of the vehicle.

6. The intelligent prediction based vehicle navigation method of claim 1, wherein, The method further comprises the following steps: after receiving the cancellation response of the vehicle navigation, displaying a destination input window and / or a destination selection window on the interactive interface; after receiving the destination selection response of the destination input window and / or the destination selection window, taking the specific orientation of the destination pointed by the destination selection response as the end point of the travel; establishing a plurality of navigation routes according to the current orientation of the vehicle and the specific orientation of the destination pointed by the destination selection response and displaying the navigation routes on the interactive interface; after receiving the route selection response, taking the navigation route pointed by the route selection response as the travel path of the vehicle navigation and starting the vehicle navigation.

7. The intelligent prediction based vehicle navigation method of claim 6, wherein, The method further comprises the following steps: adding the specific orientation of the destination pointed by the destination selection response and the navigation route pointed by the route selection response into the historical travel data of the user.

8. An intelligent prediction based vehicle navigation system characterized by, The system is mounted on the vehicle computer and comprises an interactive interface and a navigation module. The interactive interface is provided with at least one destination control, each of the destination controls respectively pointing to a destination with a specific orientation; The navigation module is configured to start the vehicle navigation with the specific orientation of the destination pointed by the triggered destination control after receiving the trigger response of one of the destination controls on the interactive interface.

9. A smart prediction based vehicle navigation system as claimed in claim 8, wherein, The orientation recorded in the destination control is used as the end point of the vehicle navigation, and the historical route preference recorded in the destination control is used as the path of the vehicle navigation.

10. The intelligent prediction based vehicle navigation system as claimed in claim 8, wherein, The cloud communication module is further included, and the cloud communication module is configured to interact with the cloud, and the cloud communication module comprises the following steps: The cloud communication module establishes a data transmission channel with the cloud by taking the user as an identifier; The cloud communication module uploads the historical travel data of the user to the cloud; The cloud calls the stored intelligent prediction model, and performs feature analysis on the historical travel data of the user by taking the historical travel data of the user as input data by the intelligent prediction model to obtain the historical route preference of each historical destination in the historical travel data of the user; The cloud downloads the historical route preference output by the intelligent prediction model to the cloud communication module.

11. A vehicle characterized by comprising: The vehicle is provided with a vehicle-mounted computer, and the vehicle-mounted computer comprises a processor, a memory, and computer program instructions stored in the memory and executable on the processor, and the processor executes the computer program instructions to implement the intelligent prediction-based vehicle navigation method according to any one of claims 1 to 7.

12. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, and the computer execution instructions are executed by the processor to implement the intelligent prediction-based vehicle navigation method according to any one of claims 1 to 7.

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