Vehicle mode control method and device, medium and vehicle

Abstract

The invention provides a vehicle mode control method and device, a medium and a vehicle, and can respond to a vehicle starting signal to obtain current environment information; under the condition that the current environment information meets the preset commuting condition, the vehicle is controlled to enter a commuting mode; and controlling the intelligent cabin of the vehicle based on the personalized service parameters in the commuting mode. Therefore, by setting the commuting mode and actively triggering the commuting function according to the use scene, the corresponding personalized service is actively provided for the user, the convenience and comfort of the user in the commuting process are improved, and then the vehicle use experience of the user is improved.

Description

Technical Field

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

[0002] With economic development, transportation systems have become the lifeline and hub of travel activities. People's ever-increasing living standards have led to higher and higher demands for travel efficiency and convenience. The development of intelligent vehicles has received increasing attention and importance, and communication between people and cars has become increasingly important. Therefore, intelligent cockpits have emerged.

[0003] Currently, although smart cockpits have implemented functions such as navigation and entertainment, traditional smart cockpits often use fixed settings and content, and cannot provide personalized functions and services according to different users' preferences and habits. This causes users to have to manually change relevant vehicle settings during their commute, which affects the user experience. Summary of the Invention

[0004] This application provides a vehicle mode control method, device, medium, and vehicle that can proactively provide users with personalized commuting services and improve the user's driving experience.

[0005] The first aspect of this application provides a vehicle mode control method, the method comprising:

[0006] In response to the vehicle start signal, obtain current environmental information;

[0007] If the current environmental information meets the preset commuting conditions, the vehicle is controlled to enter commuting mode;

[0008] The vehicle's smart cockpit is controlled based on the personalized service parameters in the commuting mode.

[0009] Optionally, the current environmental information includes current time information, vehicle location information, and / or navigation destination;

[0010] Before controlling the vehicle to enter commuting mode when the current environmental information meets preset commuting conditions, the method further includes:

[0011] If the time information is detected to be within a preset time range and / or the vehicle location information is within a preset area range and / or the navigation destination is a preset target location, the current environmental parameters are determined to meet preset commuting conditions.

[0012] Optionally, the personalized service parameters include vehicle setting parameters, intelligent voice service parameters, navigation service parameters, and / or multimedia service parameters;

[0013] The control of the vehicle's smart cockpit based on the personalized service parameters includes:

[0014] Adjust multiple in-vehicle devices according to the vehicle setting parameters; and / or,

[0015] Provide dialogue services to the user according to the intelligent voice service parameters; and / or,

[0016] Provide navigation display information to the user according to the navigation service parameters; and / or,

[0017] Multimedia entertainment content is provided to users in accordance with the multimedia service parameters.

[0018] Optionally, before controlling the vehicle's smart cockpit based on personalized service parameters in the commuting mode, the method further includes:

[0019] Acquire historical behavioral data of the user regarding the smart cockpit;

[0020] The user's historical behavior data is analyzed to obtain user preference information;

[0021] Based on the user preference information, personalized service parameters for the vehicle in commuting mode are generated.

[0022] Optionally, the user's historical behavior data is analyzed to obtain user preference information, including:

[0023] The historical behavior data is input into a preset preference model, and the user preference information is output.

[0024] The preference model is obtained through the following training method:

[0025] Acquire control data from different users regarding the smart cockpit to form a user sample set; the control data includes: user settings for in-vehicle devices and / or user-vehicle interaction data.

[0026] The control data in the user sample set is preprocessed to obtain feature input data;

[0027] Based on the feature input data, a neural network model is trained to obtain the preset preference model; wherein, the preference model is used to determine the user's corresponding preference label according to the control data of different users, and the preference label includes in-vehicle device setting label and / or interaction label.

[0028] Optionally, after controlling the vehicle to enter commuter mode, the method further includes:

[0029] Acquire user behavior data related to the smart cockpit;

[0030] Based on the user's behavior data, update the personalized service parameters of the vehicle in commuting mode.

[0031] Optionally, the method further includes:

[0032] In response to user input, personalized service parameters for the vehicle in commuting mode are generated.

[0033] Based on the same inventive concept, a second aspect of the present application provides a vehicle mode control device, the device comprising:

[0034] The information acquisition module is used to obtain current environmental information in response to the vehicle start signal;

[0035] The condition judgment module is used to control the vehicle to enter commuting mode when the current environmental information meets the preset commuting conditions;

[0036] A personalized control module is used to control the vehicle's smart cockpit based on personalized service parameters in the commuting mode.

[0037] Based on the same inventive concept, a third aspect of the present application provides a storage medium storing machine-executable instructions, which, when executed by a processor, implement the vehicle mode control method proposed in the first aspect of the present application.

[0038] Based on the same inventive concept, a fourth aspect of this application provides a vehicle including a processor and a memory; the memory stores machine-executable instructions that can be executed by the processor, and the processor is used to execute the machine-executable instructions to implement the vehicle mode control method proposed in the first aspect of this application.

[0039] Compared with the prior art, this application has the following advantages:

[0040] This application provides a vehicle mode control method that, in response to a vehicle start signal, acquires current environmental information; when the current environmental information meets preset commuting conditions, it controls the vehicle to enter commuting mode; and based on personalized service parameters in commuting mode, it controls the vehicle's intelligent cockpit. Thus, by setting a commuting mode and proactively triggering commuting functions according to usage scenarios, it actively provides users with corresponding personalized services, improving the convenience and comfort of users during commutes, thereby enhancing the user's driving experience and brand loyalty. Attached Figure Description

[0041] Figure 1 This is a flowchart of a vehicle mode control method according to an embodiment of this application;

[0042] Figure 2 This is a schematic diagram of the connection between the service function module and the cloud in commuting mode in one embodiment of this application;

[0043] Figure 3 This is a schematic diagram of the process for providing personalized services in commuting mode according to one embodiment of this application;

[0044] Figure 4 This is a schematic diagram of the functional modules of a vehicle mode control device according to an embodiment of this application;

[0045] Figure 5 This is a structural schematic diagram of a vehicle according to one embodiment of this application. Detailed Implementation

[0046] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] Currently, although smart cockpits have achieved functions such as navigation and entertainment, some problems still need to be solved. For example:

[0048] 1. Existing smart cockpits lack the ability to intelligently customize to meet individual user needs. Traditional smart cockpits often use fixed settings and content, failing to provide personalized functions and services based on different users' preferences and habits.

[0049] 2. While existing navigation systems can provide route planning, they lack a deep understanding of user preferences and cannot proactively offer users the most suitable navigation methods and content. Furthermore, the provision of multimedia content is limited and lacks diversity, failing to meet the diverse needs of users.

[0050] 3. Due to the lack of personalized customization capabilities, users need to perform frequent and repetitive operations during each commute, which often occurs when users are under time pressure. This undoubtedly increases users' psychological pressure and anxiety.

[0051] In modern urban life, commuting has become a daily challenge for many. The fixed times, locations, and high frequency of commutes bring numerous inconveniences. Traditional commuting methods require users to constantly adjust the smart cockpit themselves, including setting up in-car equipment, using the navigation system, and playing multimedia content. This not only wastes time but also increases the user's mental burden and safety risks. Therefore, current smart cockpits have certain limitations and shortcomings in terms of personalization, navigation methods, multimedia content provision, and user operation for users' commuting needs.

[0052] In view of this, this application proposes a vehicle mode control method, which sets a commuting mode and actively triggers the commuting function according to the usage scenario, thereby proactively providing users with corresponding personalized services, improving the convenience and comfort of users during commuting, and thus enhancing the user's car-using experience and brand loyalty.

[0053] Please refer to Figure 1 , Figure 1 This is a flowchart of a vehicle mode control method proposed in one embodiment of this application. Figure 1 As shown, the method includes the following steps:

[0054] S101: In response to the vehicle start signal, obtain current environmental information.

[0055] It should be noted that the execution subject in this embodiment can be a computing service device with data processing, network communication and program running functions, or an electronic device with the above functions, such as a vehicle computer, an in-vehicle computer, such as an ECU (Electronic Control Unit), a BCM (Body Control Module), a VCU (Vehicle Control Unit), an HCU (Hybrid Control Unit), etc. This embodiment will use a VCU as the execution subject for description. It should be noted that this embodiment does not impose specific limitations on the execution subject.

[0056] In this embodiment, by setting a personalized commuting mode on the vehicle side, when the personalized commuting mode is enabled (i.e., when the user allows its use), once the VCU detects the vehicle starting, it collects current environmental information and determines whether to trigger the personalized commuting function based on this information. It then controls the vehicle to enter commuting mode, proactively providing personalized services tailored to the user's preferences during their commute without requiring manual user intervention. For example, the user can enable or disable the intelligent operation of the personalized commuting mode in the vehicle settings - scenario modes.

[0057] The current environmental information includes the current time, vehicle location, and navigation destination. By detecting this information and comparing it with preset conditions in the commuting mode, the system determines whether commuting conditions are met and decides whether to trigger the personalized commuting function.

[0058] S102: When the current environmental information meets the preset commuting conditions, control the vehicle to enter commuting mode.

[0059] In this embodiment, when the current environmental information is detected to meet the preset commuting conditions, the personalized commuting function is triggered, and the vehicle is controlled to enter commuting mode. After entering commuting mode, the relevant vehicle functions will run automatically, and personalized content and services will also be provided automatically. Users do not need to operate manually or spend any effort, and can enjoy all the conveniences provided by the smart cockpit in the commuting scenario without any pressure.

[0060] For example, if the current environmental information includes the current time and vehicle location information, assuming the detected current time is 8:00 AM and the vehicle location is location A, the VCU compares the current time with the preset conditions for commuting mode. If it finds that the current time is between the preset commuting time of 7:30 AM and 8:30 AM, and the vehicle's location A is within the preset commuting area, then it considers the current environmental information to meet the preset commuting conditions and controls the vehicle to enter commuting mode.

[0061] It should be noted that since different users have different actual commuting scenarios, the preset conditions for their commuting modes are different for different users. For example, the preset time conditions for user 1's commuting mode are 7:30-8:30, while the preset time conditions for user 2's commuting mode are 7:00-8:00, thus realizing personalized commuting settings for users.

[0062] S103: Controls the vehicle's intelligent cockpit based on personalized service parameters during commuting.

[0063] In this embodiment, the personalized service parameters in commuting mode can be sourced from the cloud. These personalized service parameters are obtained by the cloud through preference analysis of the user's historical behavioral data, tailored to the user's commuting scenario. These service parameters better match the user's preferences and can improve the user's commuting experience.

[0064] In practice, after the VCU controls the vehicle to enter commuting mode, it sends a data request message to the cloud to obtain personalized service parameters from the cloud, and controls the vehicle's smart cockpit accordingly based on these personalized service parameters. This allows users to enjoy customized functions, content, and services during their commute, improving the comfort and convenience of their commute.

[0065] Furthermore, the control of the vehicle's intelligent cockpit includes several aspects: 1. Control of in-vehicle equipment. For example, the settings for the seats, including position, angle, ventilation, and heating; the steering wheel, including position and heating; the air conditioning, including mode (cooling or heating), airflow, and temperature; and the fragrance components, including fragrance type and concentration. 2. Control of intelligent voice services. For example, setting greetings during morning and evening commutes. 3. Control of navigation services. For example, setting voice broadcast content and navigation interface display information. 4. Control of multimedia services. For example, setting music playlists, audiobooks, podcasts, and radio. Thus, by providing users with a variety of personalized services during commuting, the diverse needs of users can be met.

[0066] This application sets a commuting mode on the vehicle and proactively triggers the commuting function based on the vehicle's usage scenario, thereby proactively providing users with corresponding personalized services. This allows users to enjoy the functions and services of the smart cockpit more easily and comfortably during their commute without having to frequently operate the vehicle, thus improving the convenience and comfort of users during their commute, enhancing their driving experience and brand loyalty.

[0067] Optionally, the current environmental information includes current time information, vehicle location information, and / or navigation destination. Before controlling the vehicle to enter commuting mode, if the current environmental information meets preset commuting conditions, the method further includes: determining that the current environmental parameters meet preset commuting conditions if the time information is detected to be within a preset time range and / or the vehicle location information is within a preset area range and / or the navigation destination is a preset destination.

[0068] In this embodiment, the triggering conditions for the commuting function include one or more of time, vehicle location, and navigation destination. After the vehicle is started, if one or more of the current time, vehicle location, and navigation destination meet the corresponding preset conditions, the current environmental parameters are considered to meet the preset commuting conditions, and the commuting function is triggered, controlling the vehicle to enter commuting mode to provide users with personalized services in their commuting mode.

[0069] The preset time range, preset area range, and preset destination are user behavior patterns identified in the cloud through deep learning and preference analysis of historical user behavior data (including commuting time, location, frequency, and application functions and content used). For example, if a user navigates from location A to location B between 7:30 and 8:30 AM for five consecutive workdays, then the user's commuting behavior pattern information would correspond to: a preset time range of 7:30-8:30 AM, a preset area range including a small area of ​​location A, and a preset destination of location B.

[0070] In practice, one or more of the user's behavior patterns in commuting mode can be used as preset commuting conditions. When the current time information is detected to be within the preset time range and / or the vehicle location information is within the preset area range and / or the navigation destination is the preset destination, the commuting function is triggered.

[0071] For example, suppose the current vehicle location is detected as location A, and the user manually enters the navigation destination as location B. The VCU compares this location with the preset commuting conditions in commuting mode. It finds that location A is within the preset commuting area, and the navigation destination B is the same as the preset destination. Therefore, it determines that the current environmental information meets the preset commuting conditions and controls the vehicle to enter commuting mode. By using vehicle location and navigation destination as trigger conditions for the commuting function, personalized services can be provided not only during normal commutes but also when there is a temporary need to go to destination B, thus addressing different usage scenarios.

[0072] Optionally, personalized service parameters include vehicle setting parameters, intelligent voice service parameters, navigation service parameters, and / or multimedia service parameters. Controlling the vehicle's intelligent cockpit based on these personalized service parameters includes: adjusting multiple in-vehicle devices according to the vehicle setting parameters; and / or providing dialogue services to the user according to the intelligent voice service parameters; and / or providing navigation display information to the user according to the navigation service parameters; and / or providing multimedia entertainment content to the user according to the multimedia service parameters.

[0073] Please refer to Figure 2 , Figure 2 This is a schematic diagram illustrating the connection between the service function module and the cloud in commuting mode according to one embodiment of this application. For example... Figure 2As shown, the vehicle-mounted system can provide various service functions, including vehicle settings, intelligent voice control, navigation, multimedia, time, and weather services. These service modules are connected to the VCU via a CAN bus and to the cloud via a CAN transceiver. The cloud can analyze users' historical behavior data to obtain and store users' commuting preferences and habits, providing a basis for personalized services.

[0074] In this embodiment, after the commuting function is triggered, the VCU can obtain the user's personalized service parameters in commuting mode from the cloud, and control the corresponding service function modules based on these personalized service parameters to provide the user with customized services and content in commuting mode, thereby improving the user's commuting comfort and convenience. The personalized service parameters include vehicle setting parameters, intelligent voice service parameters, navigation service parameters, and / or multimedia service parameters.

[0075] like Figure 2 As shown, the vehicle settings service module can automatically adjust multiple in-vehicle devices (seats, air conditioning, steering wheel, etc.) based on relevant vehicle settings parameters, including adjusting seat posture and interior temperature. For example, it can adjust the seat backrest to level two and the interior air conditioning temperature to 18℃ to provide users with a more comfortable commuting environment. The intelligent voice service module, as the emotional interaction interface between the user and the intelligent cockpit, can provide dialogue services to the user based on relevant intelligent voice service parameters, engaging in commuting-related emotional interactions during the commute. For example, at the start of the morning commute, it can greet the user with a welcoming greeting in a local dialect, "Good morning, a wonderful life begins from this moment." The navigation service module can provide navigation display information to the user based on relevant navigation service parameters. For example, if the navigation service parameters determine that the route, time, and real-time traffic conditions need to be displayed, it will automatically retrieve the time and real-time traffic information from the cloud for display. The multimedia service module can provide multimedia entertainment content to the user based on relevant multimedia service parameters. For example, if the multimedia service parameters reveal a user's preference for energetic or upbeat songs, the system can automatically play relevant popular songs to improve the user experience.

[0076] In addition, the vehicle settings service module can also be set to start on a scheduled basis. For example, it can be set to start 5 minutes earlier than the start time of the previous day's commute, so as to ensure that the vehicle settings and the in-car environment are in the most comfortable state for the user when they use the car, thereby improving the user's driving experience.

[0077] Optionally, before controlling the vehicle's smart cockpit based on personalized service parameters in the commuting mode, the method further includes:

[0078] S201: Obtain historical user behavior data for the smart cockpit.

[0079] Please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the process of providing personalized services in commuting mode according to one embodiment of this application. For example... Figure 3 As shown, the provision of personalized commuting services mainly includes three stages: first, data collection and analysis; second, training and learning of cloud-based preference models; and third, using preference models to obtain personalized service parameters in order to provide users with personalized services in commuting mode.

[0080] In this embodiment, during the data collection and analysis phase, the smart cockpit can collect users' historical behavior data through various sensors and user interfaces, including users' commuting time, location, frequency, and preferred functions and services. Then, the VCU uploads this historical behavior data to the cloud for analysis.

[0081] S202: Analyze users' historical behavior data to obtain user preference information.

[0082] In this embodiment, during the training and learning phase of the preference model, after collecting the user's historical behavior data in the cloud, deep learning technology is used to analyze the information related to navigation applications, vehicle settings, multimedia content, and voice services that the user inputs during commuting events. From this, the user's commuting preference settings and habits are learned, and preference information under the user's commuting mode is obtained, providing a basis for personalized services.

[0083] In practical implementation, historical behavioral data can be input into a pre-defined preference model, which outputs user preference information. This preference model can be a deep learning model such as a Convolutional Neural Network (CNN) or a Recurrent Neural Network (RNN). It utilizes algorithms like neural networks, decision trees, and support vector machines to train the machine learning model, enabling it to learn the user's commuting time, route, vehicle availability, and service content during the commute, based on their commuting habits and service preferences, thus obtaining the final preference model. Building upon this deep learning model, similarity-based or clustering-based recommendation algorithms can be used to match multimedia content with commuting data, providing users with personalized multimedia recommendations.

[0084] Furthermore, the preference model is obtained through the following training method:

[0085] S202-1: Obtain control data for the smart cockpit from different users to form a user sample set.

[0086] In this embodiment, by collecting control data from different users regarding the smart cockpit to form a user sample set, and training the preference model based on this user sample set, the generalization ability of the preference model can be improved, making the model more robust and more conducive to realizing personalized commuting service functions tailored to each individual.

[0087] The control data includes user settings for in-vehicle devices and / or user-vehicle interaction data. For example, settings for in-vehicle devices include configurations for seats, steering wheel, air conditioning, and fragrance systems, used to provide a comfortable in-vehicle environment. User-vehicle interaction data includes: dialogue data under intelligent voice services, such as user requests to speak in a dialect; navigation information displayed and voice broadcast methods input by the user under navigation services, such as selectively displaying traffic information, travel time, and traffic camera violations on the navigation interface, and choosing a concise, standard, or detailed voice broadcast method; and multimedia content preferred by the user under multimedia services, such as commuting playlists, audiobooks, podcasts, and radio, for example, a user's preference for music and audiobooks from the most popular commuting playlists.

[0088] S202-2: Preprocess the control data in the user sample set to obtain feature input data.

[0089] In this embodiment, before feature extraction from these control data, preprocessing is required, including cleaning and processing the data such as noise removal, standardization of audio format and sampling rate, to remove invalid data and obtain commuting data to be extracted. This better meets the requirements of machine learning algorithms, making subsequent training of machine learning models more targeted and accurate, and improving the accuracy of preference models.

[0090] Furthermore, the preprocessed commuting data undergoes dimensionality reduction and normalization to enable machine learning algorithms to analyze and process it, obtaining data that better reflects users' commuting characteristics and facilitating the analysis of users' commuting preferences.

[0091] In addition, user characteristics can be extracted from user information data simultaneously. By comprehensively analyzing user characteristics and commuting characteristics, a commuting pattern suitable for similar users can be obtained. Furthermore, when providing personalized commuting services to new users, the commuting pattern settings of similar users can be used as the user's initial commuting settings, solving the cold start problem of preference models. User information data can be obtained from the user's mobile device connected to the vehicle.

[0092] S202-3: Based on the feature input data, a neural network model is trained to obtain a preset preference model; wherein, the preference model is used to determine the user's corresponding preference label according to the control data of different users, and the preference label includes in-vehicle equipment setting label and / or interaction label.

[0093] In this embodiment, a neural network model is trained using feature input data from different users to learn their commuting preferences and habits, thereby obtaining a preference model for predicting user commuting preferences. The output of the preference model is the user's corresponding preference labels, including in-vehicle device setting labels and / or interaction labels. For example, a user's commuting preference labels might be: backrest angle level 2, air conditioning strong fan speed, dialect dialogue, concise announcements, popular playlists, and upbeat songs.

[0094] In this embodiment, a neural network model is trained using control data from different users for the smart cockpit. After obtaining the preference model, the preference model can be directly used to analyze and predict the user's commuting preference information, i.e., preference label, based on the recent historical behavior data of the user of the vehicle to be predicted.

[0095] S203: Generate personalized service parameters for the vehicle in commuting mode based on user preference information.

[0096] In this implementation, during the personalized service provision stage, after predicting user preference information using a preference model, personalized service parameters for the current commuting mode can be generated based on the user's specific settings data from the previous day's or week's commuting mode. Examples include backrest setting at level two, air conditioning temperature at 18°C, a welcoming greeting "Good morning, a wonderful life begins now," concise navigation announcements, and access to the most popular audiobooks. Then, the corresponding service function modules are controlled according to the personalized service parameters to provide users with customized services and content during the commuting mode, improving the user's comfort and convenience during commutes.

[0097] Optionally, after controlling the vehicle to enter commuting mode, the method further includes: acquiring user behavior data regarding the smart cockpit; and updating the personalized service parameters of the vehicle in commuting mode based on the user behavior data.

[0098] In this embodiment, if Figure 3 As shown, during a user's commute, changes in user behavior and preferences will be collected in real time. If a user's behavior or preferences change, the personalized commuting mode will be adjusted in a timely manner, and the personalized service parameters will be updated to ensure that the commuting mode can continuously meet the user's needs.

[0099] For example, regarding vehicle settings services, if it is detected that a user has adjusted a certain in-vehicle device setting (such as air conditioning temperature, seat ventilation level, etc.) three times consecutively in commuting mode, the new setting will be automatically recorded as the user's preference and automatically changed when the personalized commuting mode is triggered next time. Regarding intelligent voice services, based on changes in the user's tone of voice, speaking habits, and verbal tics, the system continuously learns the user's speaking habits and dialogue preferences, engaging in emotional communication and becoming a close and emotional assistant to the user. Regarding navigation services, if it is detected that a user has adjusted a certain navigation setting three times consecutively in commuting mode, such as muting the navigation volume, turning off navigation routes, or retaining traffic and time information, the new setting will be automatically recorded as the user's preference and automatically changed when the personalized commuting mode is triggered next time. Regarding multimedia services, if it is detected that a user has switched multimedia content three times consecutively in commuting mode, including playlists, audiobooks, podcasts, radio, etc., similar recommendations will be made based on the content the user switched to.

[0100] In this embodiment, as users' commuting habits change and the system learns more deeply, the intelligent cockpit will continuously optimize the user experience to better match user preferences and needs. This enables the intelligent cockpit to proactively learn and adapt to user preferences, and to autonomously adjust its functions, content, and service provision based on real-time user needs. Such an intelligent cockpit can better meet users' personalized commuting needs and improve their commuting experience.

[0101] Optionally, the method further includes:

[0102] In response to user input, personalized service parameters for the vehicle in commuting mode are generated.

[0103] In this embodiment, if Figure 3 As shown, during the intelligent cockpit's active learning process, users can also manually change personalized service parameters in commuting mode. For example, users can manually adjust their personalized commuting mode through the interactive interface provided on the screen or voice commands, setting specific navigation preferences, music playlists, and emotional voice interaction methods. Users can modify settings at any time according to actual needs to meet commuting requirements during specific periods or under special circumstances.

[0104] In this embodiment, users can customize the cockpit according to their personal preferences and specific needs. Simultaneously, the intelligent cockpit can learn and adapt to changes in user preferences in a timely manner, providing more precise and personalized services. Therefore, by combining manual user settings with intelligent learning, the intelligent cockpit can adapt to changes in user needs more quickly, providing more accurate and personalized services and more comprehensively meeting users' commuting needs.

[0105] Please refer to Figure 4Based on the same inventive concept, a second aspect of this application provides a vehicle mode control device, the vehicle mode control device 400 comprising:

[0106] Information acquisition module 401 is used to acquire current environmental information in response to the vehicle start signal;

[0107] The condition judgment module 402 is used to control the vehicle to enter commuting mode when the current environmental information meets the preset commuting conditions.

[0108] The personalized control module 403 is used to control the vehicle's smart cockpit based on personalized service parameters in commuting mode.

[0109] Optionally, the current environmental information includes the current time, vehicle location, and / or navigation destination;

[0110] Condition judgment module 402 includes:

[0111] If the time information is detected to be within a preset time range and / or the vehicle location information is within a preset area range and / or the navigation destination is a preset destination, the current environmental parameters are determined to meet the preset commuting conditions.

[0112] Optionally, personalized service parameters include vehicle setting parameters, intelligent voice service parameters, navigation service parameters, and / or multimedia service parameters;

[0113] Personalization control module 403 includes:

[0114] Adjust multiple in-vehicle devices according to vehicle settings parameters; and / or,

[0115] Provide dialogue services to users according to the intelligent voice service parameters; and / or,

[0116] Provide navigation display information to the user according to navigation service parameters; and / or,

[0117] Provide multimedia entertainment content to users according to multimedia service parameters.

[0118] Optionally, the device further includes:

[0119] The historical data acquisition module is used to acquire historical behavioral data of users regarding the smart cockpit;

[0120] The preference prediction module is used to analyze users' historical behavior data to obtain user preference information;

[0121] The personalized service parameter generation module is used to generate personalized service parameters for vehicles in commuting mode based on user preference information.

[0122] Optionally, the preference prediction module is specifically used to: input historical behavior data into a preset preference model and output user preference information;

[0123] The preference model is obtained through the following training method:

[0124] Acquire control data from different users regarding the smart cockpit to form a user sample set; the control data includes: user settings for in-vehicle devices and / or user-vehicle interaction data.

[0125] Preprocess the control data in the user sample set to obtain feature input data;

[0126] Based on the feature input data, a neural network model is trained to obtain a preset preference model. The preference model is used to determine the user's corresponding preference label based on the control data of different users. The preference label includes in-vehicle device setting label and / or interaction label.

[0127] Optionally, the device further includes:

[0128] The real-time data acquisition module is used to acquire user behavior data related to the smart cockpit;

[0129] The update module is used to update the personalized service parameters of the vehicle in commuting mode based on the user's behavior data.

[0130] Optionally, the device further includes:

[0131] The manual settings module is used to generate personalized service parameters for the vehicle in commuting mode in response to user input.

[0132] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0133] Thirdly, based on the same inventive concept, embodiments of this application provide a storage medium storing machine-executable instructions, which, when executed by a processor, implement the vehicle mode control method proposed in the first aspect of this application.

[0134] It should be noted that the specific implementation of the storage medium in this application embodiment refers to the specific implementation of the vehicle mode control method proposed in the first aspect of the above-mentioned application embodiment, and will not be repeated here.

[0135] Fourthly, based on the same inventive concept, referring to Figure 5This application provides a vehicle 500, including a processor 501 and a memory 502; the memory 502 stores machine-executable instructions that can be executed by the processor 501, and the processor 501 executes the machine-executable instructions to implement the vehicle mode control method proposed in the first aspect of this application.

[0136] It should be noted that the specific implementation of the vehicle 500 in this application embodiment refers to the specific implementation of the vehicle mode control method proposed in the first aspect of the above-mentioned application embodiment, and will not be repeated here.

[0137] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0138] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0139] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0140] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0141] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0142] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0143] Finally, 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. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. 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 terminal device that includes said element.

[0144] The present application provides a detailed description of a vehicle mode control method, device, medium, and vehicle. Specific examples have been used to illustrate the principles and implementation methods of the present application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present application. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present application. Therefore, the content of this specification should not be construed as a limitation of the present application.

Claims

1. A vehicle mode control method, characterized in that, The method includes: In response to the vehicle start signal, obtain current environmental information; If the current environmental information meets the preset commuting conditions, the vehicle is controlled to enter commuting mode; The vehicle's smart cockpit is controlled based on the personalized service parameters in the commuting mode.

2. The method according to claim 1, characterized in that, The current environmental information includes current time information, vehicle location information, and / or navigation destination; Before controlling the vehicle to enter commuting mode when the current environmental information meets preset commuting conditions, the method further includes: If the time information is detected to be within a preset time range and / or the vehicle location information is within a preset area range and / or the navigation destination is a preset target location, the current environmental parameters are determined to meet preset commuting conditions.

3. The method according to claim 1, characterized in that, The personalized service parameters include vehicle setting parameters, intelligent voice service parameters, navigation service parameters, and / or multimedia service parameters; The control of the vehicle's smart cockpit based on the personalized service parameters includes: Adjust multiple in-vehicle devices according to the vehicle setting parameters; and / or, Provide dialogue services to the user according to the intelligent voice service parameters; and / or, Provide navigation display information to the user according to the navigation service parameters; and / or, Multimedia entertainment content is provided to users in accordance with the multimedia service parameters.

4. The method according to claim 1, characterized in that, Before controlling the vehicle's smart cockpit based on personalized service parameters under the commuting mode, the method further includes: Acquire historical behavioral data of the user regarding the smart cockpit; The user's historical behavior data is analyzed to obtain user preference information; Based on the user preference information, personalized service parameters for the vehicle in commuting mode are generated.

5. The method according to claim 4, characterized in that, Analyzing the user's historical behavior data yields user preference information, including: The historical behavior data is input into a preset preference model, and the user preference information is output. The preference model is obtained through the following training method: Acquire control data from different users regarding the smart cockpit to form a user sample set; the control data includes: user settings for in-vehicle devices and / or user-vehicle interaction data. The control data in the user sample set is preprocessed to obtain feature input data; Based on the feature input data, a neural network model is trained to obtain the preset preference model; wherein, the preference model is used to determine the user's corresponding preference label according to the control data of different users, and the preference label includes in-vehicle device setting label and / or interaction label.

6. The method according to claim 1, characterized in that, After controlling the vehicle to enter commuter mode, the method further includes: Acquire user behavior data related to the smart cockpit; Based on the user's behavior data, update the personalized service parameters of the vehicle in commuting mode.

7. The method according to claim 1, characterized in that, The method further includes: In response to user input, personalized service parameters for the vehicle in commuting mode are generated.

8. A vehicle mode control device, characterized in that, The device includes: The information acquisition module is used to obtain current environmental information in response to the vehicle start signal; The condition judgment module is used to control the vehicle to enter commuting mode when the current environmental information meets the preset commuting conditions; A personalized control module is used to control the vehicle's smart cockpit based on personalized service parameters in the commuting mode.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the vehicle mode control method as described in any one of claims 1 to 7.

10. A vehicle, characterized in that, It includes a processor and a memory; the memory stores machine-executable instructions that can be executed by the processor, and the processor is used to execute the machine-executable instructions to implement the vehicle mode control method as described in any one of claims 1 to 7.

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