In-vehicle immersive scene generation method and device, electronic equipment, medium and vehicle

Abstract

The embodiment of the invention relates to the technical field of vehicles, and discloses an in-vehicle immersive scene generation method and device, electronic equipment, a medium and a vehicle. The method comprises the following steps: acquiring state perception data, environment perception data and user preference data; determining a user state, a vehicle state, an environmental state, and a preference state based on the state awareness data, the environmental awareness data, and the user preference data; determining a search keyword group based on the user state, the vehicle state, the environmental state, and the preference state; determining an optimal scene template based on the preference state, the retrieval keyword group and the scene database; obtaining a user input instruction, and adjusting the optimal scene template based on the user input instruction to obtain a standard scene template; and disassembling the standard scene template into at least one in-vehicle machine control instruction so as to start a corresponding in-vehicle machine function at least based on all in-vehicle machine control instructions, and generating an in-vehicle immersive scene. Interaction scenes of the intelligent cabin of the automobile are enriched, and the automobile use experience of the user is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to an in-vehicle immersive scene generation method and device, electronic equipment, medium and vehicle. BACKGROUND

[0002] With the development of automobile intelligence and networking, the automobile cabin is gradually evolving into the third living space in addition to the living space and working space. Users' demands for in-vehicle experience are no longer limited to driving functions and basic entertainment, but are pursuing more comfortable, personalized and immersive experiences. The existing technology has the following limitations:

[0003] Single experience: the vehicle infotainment system mainly provides traditional media playback functions such as audio and video, lacking depth of immersion.

[0004] Interaction fragmentation: visual, auditory, tactile, olfactory and other sensory stimuli are independent and cannot effectively integrate to form a unified scene.

[0005] Lack of scene awareness: the system cannot dynamically adapt scenes according to the state of the occupants, the state of the vehicle, the external environment and user preferences.

[0006] Insufficient personalization: the preset scenes are limited and cannot meet the diverse needs of different users, lacking autonomous learning and adaptive capabilities.

[0007] Environmental control isolation: air conditioning, seat, light and other environmental adjustment functions are not well linked with the entertainment system, and cannot provide overall environmental support for immersive experiences.

[0008] Therefore, there is an urgent need for an in-vehicle immersive scene generation method that can integrate multi-sensory interaction to provide personalized and immersive in-vehicle experiences. SUMMARY

[0009] The present application aims to provide an in-vehicle immersive scene generation method, device, electronic equipment, medium and vehicle to at least solve the problems of single experience, interaction fragmentation and insufficient personalization of automobile intelligent cabin, and is beneficial to enriching automobile intelligent cabin interaction scenes and improving user vehicle experience.

[0010] To solve the above technical problems, in a first aspect, the present application provides an in-vehicle immersive scene generation method, at least comprising:

[0011] acquiring state perception data, environment perception data and user preference data;

[0012] determining user state, vehicle state, environment state and preference state based on the state perception data, the environment perception data and the user preference data;

[0013] The search keyword group is determined based on the user status, the vehicle status, the environment status, and the preference status;

[0014] The optimal scene template is determined based on the preference state, the search keyword group, and the scene database;

[0015] Obtain user input instructions and adjust the optimal scene template based on the user input instructions to obtain a standard scene template;

[0016] The standard scene template is decomposed into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all of the vehicle control commands and generate an immersive in-vehicle scene.

[0017] Optionally, determining the user state, vehicle state, environmental state, and preference state based on the state perception data, the environment perception data, and the user preference data specifically includes:

[0018] The user's state is determined based on the state-aware data;

[0019] The vehicle state and the environmental state are determined based on the environmental perception data.

[0020] The preference state is determined based on the user preference data.

[0021] Optionally, determining the optimal scene template based on the preference state, the search keyword group, and the scene database specifically includes:

[0022] The main keyword in the search keyword group is determined based on the preference state;

[0023] The scenario database is searched based on the main keyword to obtain the first scenario template group;

[0024] Calculate the similarity between each scene template in the first scene template group and the search keyword group in turn;

[0025] Scene group similarity is determined based on all the aforementioned similarities and preset weights;

[0026] If the similarity of the scene group is greater than the preset similarity, then the scene template with the highest similarity is defined as the optimal scene template.

[0027] Optionally, after determining the scene group similarity based on all the aforementioned similarities and preset weights, the method further includes:

[0028] If the similarity of the scene group is not greater than the preset similarity, then the AI ​​retrieval tool is invoked based on the main keyword to search the relevant scene library to obtain the second scene template group;

[0029] Calculate the similarity between each scene template in the second scene template group and the search keyword group in sequence;

[0030] The scene template with the highest similarity is defined as the optimal scene template.

[0031] Optionally, after defining the scene template with the highest similarity as the optimal scene template, the method further includes:

[0032] If the scene template with the highest similarity is any scene template in the second scene template group, then the scene template with the highest similarity is added to the scene database.

[0033] Optionally, the step of obtaining user input instructions and adjusting the optimal scene template based on the user input instructions to obtain a standard scene template specifically includes:

[0034] The user input instruction is obtained and decomposed into user feedback data;

[0035] The adjustment strategy is determined based on the user feedback data;

[0036] The optimal scene template is adjusted according to the adjustment strategy to obtain the standard scene template.

[0037] Secondly, the present invention also provides an in-vehicle immersive scene generation device, comprising at least:

[0038] The data acquisition module is used to acquire state-aware data, environment-aware data, and user preference data.

[0039] The status acquisition module is used to determine the user status, vehicle status, environment status, and preference status based on the status perception data, the environment perception data, and the user preference data.

[0040] The phrase acquisition module is used to determine the search keyword phrases based on the user status, the vehicle status, the environment status, and the preference status.

[0041] The optimal acquisition module is used to determine the optimal scene template based on the preference state, the search keyword group, and the scene database.

[0042] A standard acquisition module is used to acquire user input instructions and adjust the optimal scene template based on the user input instructions to obtain a standard scene template;

[0043] The scene generation module is used to decompose the standard scene template into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all of the vehicle control commands and generate an immersive in-vehicle scene.

[0044] Thirdly, the present invention also provides an electronic device, including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps of the in-vehicle immersive scene generation method according to any one of the first aspects.

[0045] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the in-vehicle immersive scene generation method according to any one of the first aspects.

[0046] Fifthly, the present invention also provides a vehicle that integrates at least the in-vehicle immersive scene generation device as described in the second aspect.

[0047] The technical solution provided by this invention first acquires state perception data, environment perception data, and user preference data; second, it determines user state, vehicle state, environment state, and preference state based on the state perception data, environment perception data, and user preference data; third, it determines search keyword groups based on user state, vehicle state, environment state, and preference state; fourth, it determines an optimal scene template based on preference state, search keyword groups, and a scene database; then, it acquires user input commands and adjusts the optimal scene template based on the user input commands to obtain a standard scene template; finally, it decomposes the standard scene template into at least one vehicle control command to enable the corresponding vehicle function based on at least all vehicle control commands, thereby generating an immersive in-vehicle scene.

[0048] Therefore, this invention, on the one hand, by fusing state perception data, environmental perception data, and user preference data, can comprehensively consider multiple influencing factors when generating immersive in-vehicle scenes, making the generated immersive scenes more in line with the user's actual needs, providing a deep sense of immersion, and thus improving the user's driving experience. On the other hand, this invention, by determining the optimal scene template based on preference states, search keyword groups, and a scene database, can select the optimal scene template for the user's current driving scenario and fine-tune the optimal scene template based on user input commands, further ensuring that the optimal scene template can meet the user's personalized needs to the greatest extent and improve the user's driving experience. Attached Figure Description

[0049] Figure 1 This is a flowchart of a method for generating an immersive in-vehicle scene according to an embodiment of the present invention;

[0050] Figure 2 This is a flowchart of another in-vehicle immersive scene generation method provided by an embodiment of the present invention;

[0051] Figure 3This is a schematic diagram of the structure of an in-vehicle immersive scene generation device provided in an embodiment of the present invention;

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0055] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0056] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0057] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0058] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0059] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0060] Figure 1 This is a flowchart of a method for generating an immersive in-vehicle scene according to an embodiment of the present invention. This embodiment is applicable to the generation of immersive scenes in various types of vehicle intelligent cockpits. This method for generating an immersive in-vehicle scene can be, but is not limited to, executed by the immersive in-vehicle scene generation device described in this embodiment of the present invention. This execution entity can be implemented using software and / or hardware. For example... Figure 1 As shown, the method for generating an immersive in-vehicle scene includes at least the following steps:

[0061] S1. Acquire state-aware data, environment-aware data, and user preference data.

[0062] The status perception data can include user emotion data, user stress data, user heart rate data, user data (i.e., user distribution, etc.), and user gaze data. User emotion and gaze data can be obtained through in-vehicle camera image information, analyzing facial expressions, gaze, and gestures for confirmation. Alternatively, user emotion and fatigue data can be directly obtained through the Driver Monitoring System (DMS). When necessary, user voice information can be collected via microphone, and analyzing tone of voice can assist in obtaining user emotion data. User stress and heart rate data can be obtained through heart rate and skin conductance sensors integrated into the seat or steering wheel. User data can be obtained through seat pressure sensors to determine the presence of a user in the seat. Environmental perception data includes vehicle speed, gear position, turn signal data, GPS data, ambient temperature, ambient humidity, weather data, and road condition data. Vehicle speed, gear position, turn signal, and GPS data can all be obtained through the corresponding vehicle bus interface. Ambient temperature and humidity data can be obtained through the corresponding temperature and humidity sensors on the vehicle's infotainment system. Weather data can be obtained through third-party weather service interfaces. Traffic data can be acquired through the vehicle's external cameras / in-vehicle sensors. User preference data can include user settings and feedback regarding touchscreens, voice assistants, physical knobs / buttons, and mobile application programming interfaces.

[0063] S2. Determine the user status, vehicle status, environment status, and preference status based on status perception data, environment perception data, and user preference data.

[0064] Among these, user status can include emotional state, attention level, fatigue level, etc. Vehicle status can include driving status, parked status, etc. Environmental status can include time, location, and whether the environment is crowded, etc. Preference status can include explicit user preferences and implicit user preferences.

[0065] S3. Determine the search keyword group based on user status, vehicle status, environment status, and preference status.

[0066] The expression for the search keyword group can be {user status, vehicle status, environment status, preference status}. The search keyword group can also be represented using a multi-dimensional vector.

[0067] S4. Determine the optimal scene template based on preference status, search keyword groups, and scene database.

[0068] The scenario database contains several scenario templates, and users can also upload their own frequently used driving environments as scenario templates to the scenario database for others to use.

[0069] S5. Obtain user input instructions and adjust the optimal scene template based on the user input instructions to obtain a standard scene template.

[0070] Among them, user input commands enable users to fine-tune the optimal scene template through voice, touch screen or physical control so that the optimal scene template meets the user's personalized needs.

[0071] S6. Decompose the standard scene template into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all vehicle control commands and generate an immersive in-vehicle scene.

[0072] Among these features, the vehicle control commands can be used to drive various hardware functions within the vehicle, such as turning on the screen, turning on the audio system, adjusting the seat, and turning off the air conditioning. An immersive in-car experience can be the optimal configuration for the user's vehicle usage.

[0073] The technical solution provided in this embodiment first acquires state perception data, environment perception data, and user preference data; second, it determines the user state, vehicle state, environment state, and preference state based on the state perception data, environment perception data, and user preference data; third, it determines search keyword groups based on the user state, vehicle state, environment state, and preference state; fourth, it determines the optimal scene template based on the preference state, search keyword groups, and scene database; then, it acquires user input commands and adjusts the optimal scene template based on the user input commands to obtain a standard scene template; finally, it decomposes the standard scene template into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all vehicle control commands, thereby generating an immersive in-vehicle scene.

[0074] Therefore, this embodiment demonstrates that, on the one hand, by integrating state perception data, environmental perception data, and user preference data, it can comprehensively consider multiple influencing factors when generating immersive in-vehicle scenes. This results in immersive scenes that better meet the actual needs of users, providing them with a deep sense of immersion and thus enhancing the user's driving experience. On the other hand, this embodiment determines the optimal scene template based on preference states, search keyword groups, and a scene database. It can select the optimal scene template for the user's current driving scenario and fine-tune the optimal scene template based on user input commands, further ensuring that the optimal scene template can meet the user's personalized needs to the greatest extent and enhance the user's driving experience.

[0075] Based on the above embodiments or implementation methods Figure 2 This is a flowchart of another in-vehicle immersive scene generation method provided by an embodiment of the present invention. This embodiment is based on the above embodiment with additions. Figure 2 As shown, the method for generating an immersive in-vehicle scene includes at least the following steps:

[0076] S1. Acquire state-aware data, environment-aware data, and user preference data.

[0077] S2. Determine the user status, vehicle status, environment status, and preference status based on status perception data, environment perception data, and user preference data.

[0078] In one specific implementation, step S2 may optionally include:

[0079] (2-1) Determine user status based on status-aware data.

[0080] Among the state-aware data, user emotion data can be used to determine the user's emotional state, user gaze data can be used to determine the user's attention state, and user heart rate data and user stress data can be used to determine the user's fatigue level. User data is used to represent the current distribution location of users inside the vehicle.

[0081] (2-2) Determine vehicle status and environmental status based on environmental perception data.

[0082] The vehicle status can be determined based on vehicle speed, turn signal, and gear position data from the environmental perception data. The environmental status can be obtained based on weather, road condition, GPS, ambient temperature, and ambient humidity data from the environmental perception data.

[0083] (2-3) Determine the preference status based on user preference data.

[0084] Among them, the preference state can be defined as the explicit preference state, which is the commonly used parameter settings and preference feedback, and the implicit preference state, which is the user behavior in a specific scenario / or with a low setting frequency.

[0085] S3. Determine the search keyword group based on user status, vehicle status, environment status, and preference status.

[0086] For example, the search keyword group could be {(driver, happy, good attention, medium heart rate, low stress, low fatigue), (vehicle speed 30km / h, turn signal not used, gear 3), (cloudy day, certain highway, temperature 18°C, humidity 60%), (air conditioning set to 24 degrees, seat heating turned on, central control volume reduced, etc.)}. It should be noted that this search keyword group is only an example, and in specific applications, the search keyword group can be represented in a multi-dimensional vector format.

[0087] S41. Determine the main keyword in the search keyword group based on preference status.

[0088] The purpose of determining the main keywords in the search keyword group based on preference status is to treat one or more explicit preferences of the user as mandatory user needs, so that the scenario template group includes the user's most important operating habits, in order to ensure the user's car use experience.

[0089] S42. Search the scenario database based on the main keyword to obtain the first scenario template group.

[0090] The first scenario template group contains multiple scenario templates, each of which contains at least a main keyword. The scenario database can be a vector database, where each scenario template can be a multi-dimensional vector, with each dimension of the multi-dimensional vector representing a vehicle infotainment hardware function setting.

[0091] S43. Calculate the similarity between each scene template in the first scene template group and the search keyword group in turn.

[0092] Calculating the similarity between the scene template and the search keyword group can be understood as calculating the similarity between two multi-dimensional vectors. For example, the similarity calculation method can be determined by the following formula:

[0093] ;

[0094] In the formula, S can represent the similarity, and C i w can represent the value of the i-th dimension. i P can represent the weight of the i-th dimension. j It can represent the value of the j-th penalty factor.

[0095] S44. Determine the scene group similarity based on the total similarity and the preset weight.

[0096] The purpose of step S44 is to prevent a situation where the scene templates in the first scene template group do not match the search keyword group. If the scene template with the highest similarity in the first scene template group is still defined as the optimal scene template, it may give users a bad experience. The preset weights can be flexibly adjusted according to actual needs. Generally, the preset weights can be determined according to the search order of the scene templates. For example, when searching the scene database based on the main keyword, the first output scene template has the highest weight, and the last output scene template has the lowest weight.

[0097] In another specific implementation, step S44 is followed by:

[0098] (44-1) If the similarity of the scene group is not greater than the preset similarity, then the AI ​​search engine is called based on the main keyword to search the relevant scene library to obtain the second scene template group.

[0099] The AI ​​retrieval tool can be any general-purpose large model, such as DeepSeek or Doubao, and this embodiment does not limit it. The relevant scene library can be a driving scene database on the Internet or a driving scene database stored in the vehicle's cloud.

[0100] (44-2) Calculate the similarity between each scene template in the second scene template group and the search keyword group in turn.

[0101] (44-3) Define the scene template with the highest similarity as the optimal scene template.

[0102] Among them, the scene template with the highest similarity is selected by comprehensively considering each scene template in the first scene template group and the second scene template group, and then selecting the optimal scene template.

[0103] (44-5) If the scene template with the highest similarity is any scene template in the second scene template group, then add the scene template with the highest similarity to the scene database.

[0104] In this embodiment, the settings, such as step (44-5), can continuously enrich the scene database so that the scene database can be continuously improved and fit the user's usage habits.

[0105] S45. If the scene group similarity is greater than the preset similarity, then the scene template with the highest similarity is defined as the optimal scene template.

[0106] S51. Obtain user input instructions and decompose the user input instructions into user feedback data.

[0107] Each user feedback data point corresponds to a user's adjustment request, such as raising the air conditioning slightly, adjusting the seat back, or activating the seat heating.

[0108] S52. Determine adjustment strategies based on user feedback data.

[0109] S53. Adjust the optimal scene template according to the adjustment strategy to obtain the standard scene template.

[0110] At this point, the standard scenario template has been adjusted to meet the user's needs through user input instructions.

[0111] S6. Decompose the standard scene template into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all vehicle control commands and generate an immersive in-vehicle scene.

[0112] The vehicle control commands provided in this embodiment are not limited to controlling the main display screen / instrument panel, AR-HUD (augmented reality head-up display), ambient lighting system, windows / sunroof, audio system, directional speakers / bone conduction, in-vehicle noise reduction system, multi-functional seats, steering wheel / armrest haptic feedback, air conditioning, fragrance system, etc.

[0113] Alternatively, this embodiment can further introduce a preference mechanism. This involves recording user interaction data for different scenarios, such as selections, adjustments, dwell time, physiological reactions, and proactive feedback. This data is then combined and a reward function is used to calculate the reward value of the interaction to determine its quality. Through continuous feedback of reward values, the vehicle system can continuously adjust its strategy, thereby optimizing scenario recommendations and achieving continuous evolution of personalized experiences. For example, when a user exhibits physiological or behavioral reactions to a scenario, vehicle sensors continuously collect this data, and then automatically calculate a reward value R based on this data using a reward function. t The reward function is set as follows:

[0114] ;

[0115] In the formula, R t R can represent the total reward at time step t. ω1, ω2, ω3, and ω4 can all be represented as weighting coefficients used to balance the importance of different reward items. λ can be represented as the penalty coefficient. f This can be represented as explicit user feedback, R p This can be represented as implicit physiological feedback. R i This can be represented as behavioral interaction feedback. R s This can be represented as safety and comfort feedback. C can be represented as a change penalty term, used to prevent policy oscillation.

[0116] Furthermore, if the reward value is too small (e.g., less than the preset value), it means that the current in-car immersive scene does not meet the user's requirements. It needs to be adjusted in time according to the user's physiological or behavioral response, and the reward value needs to be recalculated. If the reward value is too large, it means that the current in-car immersive scene meets the user's requirements. At this time, the vehicle system will record the current in-car immersive scene as historical data so that when the reward value is too small in the future, the vehicle system can adjust the in-car immersive scene in time based on the historical data, thereby optimizing scene recommendations and realizing the continuous evolution of personalized experience.

[0117] Therefore, this embodiment demonstrates that, on the one hand, by integrating state perception data, environmental perception data, and user preference data, it can comprehensively consider multiple influencing factors when generating immersive in-vehicle scenes. This results in immersive scenes that better meet the actual needs of users, providing them with a deep sense of immersion and thus enhancing the user's driving experience. On the other hand, this embodiment determines the optimal scene template based on preference states, search keyword groups, and a scene database. It can select the optimal scene template for the user's current driving scenario and fine-tune the optimal scene template based on user input commands, further ensuring that the optimal scene template can meet the user's personalized needs to the greatest extent and enhance the user's driving experience.

[0118] Figure 3 This is a schematic diagram of the structure of an in-vehicle immersive scene generation device provided in an embodiment of the present invention. This embodiment generates at least various types of intelligent cockpit immersive scenes for vehicles. This in-vehicle immersive scene generation device can be implemented using software and / or hardware. Figure 3 As shown, the in-vehicle immersive scene generation device includes at least:

[0119] The data acquisition module 110 is used to acquire state-aware data, environment-aware data, and user preference data.

[0120] The status acquisition module 120 is used to determine the user status, vehicle status, environment status and preference status based on status perception data, environment perception data and user preference data.

[0121] The phrase acquisition module 130 is used to determine the search keyword phrases based on user status, vehicle status, environment status, and preference status.

[0122] The optimal acquisition module 140 is used to determine the optimal scene template based on preference status, search keyword groups, and scene database.

[0123] The standard acquisition module 150 is used to acquire user input instructions and adjust the optimal scene template based on the user input instructions to obtain a standard scene template.

[0124] The scene generation module 160 is used to decompose the standard scene template into at least one vehicle control command, so as to enable the corresponding vehicle function based on at least all vehicle control commands and generate an immersive in-vehicle scene.

[0125] Optionally, the status acquisition module 120 is specifically used for:

[0126] The user's state is determined based on state-aware data; the vehicle's state and the environment's state are determined based on environment-aware data; and the user's preference state is determined based on user preference data.

[0127] Optionally, the optimal acquisition module 140 is specifically used for:

[0128] The process involves: determining the main keyword in the search keyword group based on the preference state; searching the scenario database based on the main keyword to obtain the first scenario template group; calculating the similarity between each scenario template in the first scenario template group and the search keyword group in turn; determining the scenario group similarity based on the total similarity and the preset weight; and defining the scenario template with the highest similarity as the optimal scenario template when the scenario group similarity is greater than the preset similarity.

[0129] Optionally, the optimal acquisition module 140 is also specifically used for:

[0130] When the similarity of the scene group is not greater than the preset similarity, the AI ​​retrieval tool is called based on the main keyword to search the relevant scene library to obtain the second scene template group; and the similarity between each scene template in the second scene template group and the search keyword group is calculated in turn; and the scene template with the highest similarity is defined as the optimal scene template.

[0131] Optionally, it also includes:

[0132] The template addition module 170 is used to add the scene template with the highest similarity to the scene database when the scene template with the highest similarity is any scene template in the second scene template group.

[0133] Optionally, the standard acquisition module 150 is specifically used for:

[0134] The system acquires user input commands and decomposes them into user feedback data; determines adjustment strategies based on the user feedback data; and adjusts the optimal scenario template according to the adjustment strategies to obtain a standard scenario template.

[0135] The technical solution provided in this embodiment first acquires state perception data, environment perception data, and user preference data through a data acquisition module. Further, the state acquisition module determines the user state, vehicle state, environment state, and preference state based on the state perception data, environment perception data, and user preference data. Further, the phrase acquisition module determines search keyword phrases based on the user state, vehicle state, environment state, and preference state. Further, the optimal acquisition module determines an optimal scene template based on the preference state, search keyword phrases, and a scene database. Further, the standard acquisition module acquires user input commands and adjusts the optimal scene template based on the user input commands to obtain a standard scene template. Finally, the scene generation module decomposes the standard scene template into at least one vehicle control command, enabling the corresponding vehicle functions based on at least all vehicle control commands to generate an immersive in-vehicle scene.

[0136] Therefore, this embodiment demonstrates that, on the one hand, by integrating state perception data, environmental perception data, and user preference data, it can comprehensively consider multiple influencing factors when generating immersive in-vehicle scenes. This results in immersive scenes that better meet the actual needs of users, providing them with a deep sense of immersion and thus enhancing the user's driving experience. On the other hand, this embodiment determines the optimal scene template based on preference states, search keyword groups, and a scene database. It can select the optimal scene template for the user's current driving scenario and fine-tune the optimal scene template based on user input commands, further ensuring that the optimal scene template can meet the user's personalized needs to the greatest extent and enhance the user's driving experience.

[0137] This embodiment provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 4 The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the above-mentioned in-vehicle immersive scene generation methods are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not shown). The memory 1002 stores a computer program that can be executed by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to execute the in-vehicle immersive scene generation method in any optional implementation of the above embodiments, so as to achieve at least the following functions: acquiring state perception data, environment perception data, and user preference data; determining user state, vehicle state, environment state, and preference state based on the state perception data, environment perception data, and user preference data; determining search keyword groups based on user state, vehicle state, environment state, and preference state; determining the optimal scene template based on preference state, search keyword groups, and scene database; acquiring user input instructions and adjusting the optimal scene template based on user input instructions to obtain a standard scene template; decomposing the standard scene template into at least one vehicle control instruction to enable the corresponding vehicle function based on at least all vehicle control instructions, thereby generating an in-vehicle immersive scene.

[0138] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the in-vehicle immersive scene generation method provided in all embodiments of this application: acquiring state perception data, environmental perception data, and user preference data; determining user state, vehicle state, environmental state, and preference state based on the state perception data, environmental perception data, and user preference data; determining search keyword groups based on user state, vehicle state, environmental state, and preference state; determining an optimal scene template based on preference state, search keyword groups, and a scene database; acquiring user input instructions and adjusting the optimal scene template based on the user input instructions to obtain a standard scene template; and decomposing the standard scene template into at least one vehicle control instruction to enable the corresponding vehicle function based on at least all vehicle control instructions, thereby generating an in-vehicle immersive scene.

[0139] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0140] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0141] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0142] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] This embodiment provides a vehicle that integrates at least one in-vehicle immersive scene generation device provided in the above embodiment.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An in-vehicle immersive scene generation method, characterized by, At least comprising: acquiring state awareness data, environment awareness data and user preference data; determining user state, vehicle state, environment state and preference state based on the state awareness data, the environment awareness data and the user preference data; determining retrieval keywords based on the user state, the vehicle state, the environment state and the preference state; determining the optimal scene template based on the preference state, the retrieval keywords and the scene database; acquiring user input instructions and adjusting the optimal scene template based on the user input instructions to obtain a standard scene template; dissolving the standard scene template into at least one car machine control instruction to enable corresponding car machine functions based on all the car machine control instructions to generate an immersive scene in the car.

2. The in-vehicle immersive scene generation method according to claim 1, characterized by, The determination of the user state, the vehicle state, the environment state and the preference state based on the state awareness data, the environment awareness data and the user preference data specifically includes: determining the user state based on the state awareness data; determining the vehicle state and the environment state based on the environment awareness data; determining the preference state based on the user preference data.

3. The in-vehicle immersive scene generation method of claim 1, wherein, The determination of the optimal scene template based on the preference state, the retrieval keywords and the scene database specifically includes: determining the main keywords in the retrieval keywords based on the preference state; retrieving the first scene template group from the scene database based on the main keywords; sequentially calculating the similarity of each scene template in the first scene template group with the retrieval keywords; determining the scene group similarity based on all the similarities and preset weights; if the scene group similarity is greater than a preset similarity, defining the scene template with the highest similarity as the optimal scene template.

4. The in-vehicle immersive scene generation method according to claim 3, characterized by, After determining the scene group similarity based on all the similarities and preset weights, it further includes: if the scene group similarity is not greater than the preset similarity, calling an artificial intelligence (AI) retriever to search the related scene library based on the main keywords to obtain a second scene template group; sequentially calculating the similarity of each scene template in the second scene template group with the retrieval keywords; defining the scene template with the highest similarity as the optimal scene template.

5. The in-vehicle immersive scene generation method according to claim 3, characterized by, After defining the scene template with the highest similarity as the optimal scene template, it further includes: if the scene template with the highest similarity is any scene template in the second scene template group, adding the scene template with the highest similarity to the scene database.

6. The in-vehicle immersive scene generation method of claim 3, wherein, The acquisition of the user input instructions and the adjustment of the optimal scene template based on the user input instructions to obtain a standard scene template specifically includes: acquiring the user input instructions and decomposing the user input instructions into user feedback data; determining the adjustment strategy based on the user feedback data; adjusting the optimal scene template according to the adjustment strategy to obtain the standard scene template.

7. An in-vehicle immersive scene generation apparatus characterized by comprising: At least comprising: a data acquisition module for acquiring state awareness data, environment awareness data and user preference data; a state acquisition module configured to determine a user state, a vehicle state, an environment state, and a preference state based on the state-aware data, the environment-aware data, and the user preference data; a phrase acquisition module configured to determine a search keyword phrase based on the user state, the vehicle state, the environment state, and the preference state; an optimal acquisition module configured to determine an optimal scene template based on the preference state, the search keyword phrase, and a scene database; a standard acquisition module configured to acquire a user input instruction, and adjust the optimal scene template based on the user input instruction to obtain a standard scene template; a scene generation module configured to disassemble the standard scene template into at least one vehicle-machine control instruction, and enable a corresponding vehicle-machine function based on all the vehicle-machine control instructions to generate an in-vehicle immersive scene.

8. An electronic device comprising a memory and a processor, said memory storing a computer program operable on said processor, characterized in that, The processor executes the program to implement the steps in the in-vehicle immersive scene generation method of any one of claims 1 to 6.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps in the in-vehicle immersive scene generation method of any one of claims 1 to 6.

10. A vehicle characterized by comprising: The vehicle is at least integrated with the in-vehicle immersive scene generation apparatus of claim 7.

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