Method and device for controlling a vehicle cockpit, device, vehicle and program

Abstract

A method and apparatus for vehicle cockpit control, an apparatus, a vehicle and a product.SOLUTION: The method includes determining candidate configurations for a plurality of components in a vehicle cockpit based on a current scene of the vehicle. The method further includes adjusting the candidate setting based on safety limitation information of the vehicle and input information of the user. The method further includes controlling a corresponding component in the vehicle cockpit based on the adjusted candidate configuration. In this way, it is possible to provide the user with personalized and unified control of the vehicle cockpit while ensuring safety.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to the field of vehicle technology, and more particularly to methods and apparatus for vehicle cockpit control, devices, vehicles, and products. [Background technology]

[0002] With the continuous development of smart vehicles, people's needs for vehicles are no longer limited to simple driving and travel functions, but also include the pursuit of a more intelligent and humanized riding experience. For example, users can rest, sing, or work in the car according to their needs. Since vehicles incorporate various smart devices and various smart devices provide different functions to users, vehicles can be considered as "comfortable spaces" that can meet the different usage needs of users. Summary of the Invention [Problem to be solved by the invention]

[0003] Embodiments of the present disclosure provide methods and apparatus for vehicle cockpit control, equipment, vehicles, and products. [Means for solving the problem]

[0004] In a first aspect of the present disclosure, there is provided a method for vehicle cockpit control, the method including determining candidate configurations for a plurality of components in the vehicle cockpit based on a current scene of the vehicle, the method further including adjusting the candidate configurations based on vehicle safety limit information and user input information, and controlling corresponding components in the vehicle cockpit based on the adjusted candidate configurations.

[0005] In a second aspect of the present disclosure, there is provided an apparatus for vehicle cockpit control, the apparatus including a candidate configuration determination unit configured to determine candidate configurations for a plurality of components in the vehicle cockpit based on a current scene of the vehicle, the apparatus further including an adjustment unit configured to adjust the candidate configurations based on vehicle safety limit information and user input information, and a control unit configured to control corresponding components in the vehicle cockpit based on the adjusted candidate configurations.

[0006] In a third aspect of the present disclosure, there is provided an electronic device including one or more processors and a storage device storing one or more programs, the one or more programs, when executed by the one or more processors, causing the one or more processors to implement a method according to the first aspect of the present disclosure.

[0007] In a fourth aspect of the present disclosure, there is provided a vehicle including a plurality of sound sensors and an electronic device according to the third aspect of the present disclosure.

[0008] In a fifth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement the method according to the first aspect of the present disclosure.

[0009] In a sixth aspect of the present disclosure, there is provided a computer program product tangibly stored on a computer-readable medium and including machine-executable instructions that, when executed, implement a method according to the first aspect of the present disclosure.

[0010] It should be noted that the contents described in the Summary of the Invention are not intended to limit the essential or important features of the embodiments of the present disclosure, and do not limit the scope of the present disclosure. Other features of the present disclosure will be easily understood from the following description. [Brief explanation of the drawings]

[0011] These and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent with reference to the drawings and the following detailed description, in which the same or similar reference numerals indicate the same or similar elements. [Figure 1] 1 shows a schematic diagram of an exemplary environment in which several embodiments of the present disclosure can be implemented. [Figure 2] FIG. 1 illustrates a schematic diagram for vehicle cockpit control according to some embodiments of the present disclosure. [Figure 3] 1 illustrates a flowchart of a method for vehicle cockpit control according to some embodiments of the present disclosure. [Figure 4] 1 shows a schematic diagram of an interaction interface displaying candidate settings according to some embodiments of the present disclosure. [Figure 5] FIG. 1 shows a schematic diagram for generating a configuration according to some embodiments of the present disclosure. [Figure 6] FIG. 1 illustrates a block diagram of an apparatus for vehicle cockpit control according to some embodiments of the present disclosure. [Figure 7] 1 shows a block diagram of a device capable of implementing several embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments of the present disclosure will be described in more detail with reference to the drawings. Although several embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be realized in various forms and should not be understood as being limited to the embodiments described herein. On the contrary, these embodiments are provided for a clearer and more complete understanding of the present disclosure. It should also be understood that the drawings and embodiments of the present disclosure are merely illustrative and do not limit the scope of protection of the present disclosure.

[0013] In describing embodiments of the present disclosure, the term "comprising" and similar terms should be understood as an open-ended inclusion, i.e., "including, but not limited to." The term "based on" should be understood as "based at least in part on." The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment." The terms "first," "second," etc. may refer to different objects or the same object. Other explicit and implicit definitions may also be included below.

[0014] As described above, users have different vehicle needs for different application scenarios. However, the various components in a vehicle cockpit are not efficiently integrated, and intelligent control has not been realized. As a result, users have no way to simultaneously control multiple components with a single command or a single interface. For example, if a user wants to take a nap in the car, they need to adjust the seat position and angle, the air conditioning temperature, or open or close the windows. During this process, the user must manually operate each component, which is time-consuming and results in a poor user experience.

[0015] In related art, simple control logic can be established according to user control to trigger a certain action when a certain condition is met. For example, a user can turn on an air conditioner when making a gesture. However, the logic of such a trigger chain is relatively simple, and it cannot meet the various application needs of users in various scenarios, resulting in a poor user experience.

[0016] Therefore, an embodiment of the present disclosure provides an aspect for controlling a vehicle cockpit. In the embodiment of the present disclosure, the method includes determining candidate settings for a plurality of components in the vehicle cockpit based on a current scene of the vehicle. The method further includes adjusting the candidate settings based on vehicle safety limit information and user input information. The method also includes controlling corresponding components in the vehicle cockpit based on the adjusted candidate settings. In this manner, settings for controlling a plurality of components in the vehicle cockpit that meet requirements can be determined according to a scene corresponding to the vehicle. In this process, by combining user feedback and vehicle-related information, locations that may pose safety risks or cause discomfort to the user can be adjusted, ensuring that the settings of each component in the vehicle cockpit do not affect driving safety or inconvenience the user, improving the user's riding experience, and ensuring safety and comfort during the driving process.

[0017] FIG. 1 illustrates a schematic diagram of an exemplary environment 100 in which several embodiments of the present disclosure can be implemented. As illustrated in FIG. 1, the environment 100 includes a vehicle 102 and a processor 104 communicatively coupled to the vehicle. The vehicle 102 refers to any type of mobile, motorized or non-motorized, vehicle capable of transporting people and / or objects. As illustrated in FIG. 1, the vehicle 102 is illustrated as an automobile. While the vehicle 102 is illustrated as an automobile in FIG. 1, it should be understood that this is for illustrative purposes only and is not limiting, and examples may include passenger cars, trucks, motorcycles, electric vehicles, etc. It should also be understood that embodiments of the present disclosure are applicable to transportation systems other than vehicles. The processor 108 may be implemented as various devices having computing capabilities (e.g., a service terminal, a user terminal, etc.).

[0018] According to an embodiment of the present disclosure, the vehicle 102 may be equipped with multiple types of sensors for collecting data of multiple modalities. For example, the vehicle 102 may include multiple sensors distributed at various locations on the vehicle 102 (e.g., interior, body, exterior). For example, the sensors may include visual sensors (also referred to as optical or video cameras), audio sensors, a Global Positioning System (GPS), etc. The visual sensors capture multiple images of the vehicle 102's surrounding environment, such as a front image, a rear image, a left image, and a right image of the vehicle 102. The Global Positioning System can estimate the geographic location of the vehicle 102.

[0019] In some embodiments of the present disclosure, the audio sensor may collect audio data within the vehicle cockpit and audio data from the environment surrounding the vehicle 103. For example, the audio data may include the sounds of conversations between occupants in the vehicle cockpit, the horns of other vehicles, the engine sounds of the vehicle, etc. In some embodiments of the present disclosure, the multimodal data may further include data from a vehicle control unit and vehicle driving data. Note that the multimodal data may also include data transmitted by a roadside unit or other device received by the vehicle 102. In some embodiments of the present disclosure, the data collected by the various sensors may include information such as the vehicle's 102 driving path, speed, and acceleration. The multimodal data typically includes data from various vehicle sensors (e.g., cameras, radar, speed sensors, etc.) and data from user interactions (e.g., touchscreens, voice control, mobile phone applications, etc.). In other embodiments, the multimodal data may further include environmental data in which the vehicle is located, such as weather, air humidity, and season, providing the system with comprehensive information about the vehicle cockpit environment.

[0020] In some embodiments of the present disclosure, the current scene of the vehicle 102 can be determined based on the multimodal data. The current scene refers to the scene in which the vehicle 102 is located. The scene here is not limited to the vehicle's normal driving scene (e.g., objects or weather in the surrounding environment in which the vehicle 102 is located) but can also reflect the user's needs and status to some extent. For example, the current scene of the vehicle 102 may include an acceleration scene, an entertainment scene, a parking and rest scene, a work scene, etc. In some embodiments of the present disclosure, the current scene in which the vehicle 102 is located can be determined based on the multimodal scene data according to a preset model or algorithm. In one example, if the vehicle speed continues to increase and acceleration is high, the vehicle 102 may be in an acceleration scene; if the user frequently interacts with the entertainment system, the vehicle 102 may be in an entertainment scene; and if the vehicle has been parked in a certain location for a long time and an in-vehicle sensor detects that the user has not moved for a long time, the vehicle 102 may be in a parking and rest scene.

[0021] In some embodiments of the present disclosure, after determining a current scene of the vehicle 102, the processor 104 can determine a corresponding candidate setting 106 based on the current scene. The candidate setting 106 may include a set of parameters, which may include operation parameters for multiple components in the vehicle cockpit. It can be understood that multiple sets of settings (e.g., multiple sets of preset parameters) can be pre-established to collectively control the components in the vehicle cockpit and provide a user with an intelligent vehicle cockpit atmosphere. The vehicle cockpit atmosphere provided by each set of settings can be adapted to different scenes or different user needs. One set of settings includes parameters for multiple components in the vehicle cockpit (e.g., operation parameters for the air conditioner (cooling mode, temperature 26 degrees), operation parameters for the seats (e.g., seat angle, seat position), and operation parameters for the windows (e.g., whether to open the windows, window opening degree)). In some embodiments of the present disclosure, a set of settings corresponding to the preset scene and the user's behavior pattern can be established according to a preset vehicle scene and a user's behavior pattern. The multiple sets of settings may include a setting for an acceleration scene, a setting for a parking and rest scene, and a setting for an entertainment scene. In some embodiments of the present disclosure, the settings may include multiple operating parameters for multiple components in a vehicle cockpit, and are a parameter group consisting of the multiple operating parameters.

[0022] In some embodiments of the present disclosure, the processor 104 can adjust the candidate settings 106 according to the vehicle's safety limit parameters and the user's input information, adjusting the locations where there may be safety risks or where it may cause discomfort to the user, ensuring that the settings of each component in the vehicle cockpit do not affect driving safety, and the finally determined candidate settings 106 are more in line with the user's preferences while satisfying safety and comfort.

[0023] In this manner, the settings for controlling each component in the vehicle cockpit can be determined according to the current scene of the vehicle, and the environment in the vehicle cockpit can be adjusted in real time to provide the user with a more comfortable and convenient riding experience. In addition, by adjusting the settings according to the relevant information of the vehicle or the user's feedback, it is possible to avoid the inclusion of unsafe factors or factors that are not preferred by the user in the settings, so that the finally adjusted atmosphere in the vehicle cockpit is safer while meeting the needs of the user.

[0024] 1, a schematic diagram of an exemplary environment 100 in which embodiments of the present disclosure may be implemented has been described. It should be understood that the environment 100 is merely exemplary and is not intended to limit the scope of the present disclosure. The environment 100 may include more components than those shown in FIG. 1, and each component in the environment 100 may be implemented in a different manner.

[0025] Hereinafter, a schematic diagram for controlling a vehicle cockpit according to an embodiment of the present disclosure will be described with reference to FIG. 2. As shown in FIG. 2, blocks 202 to 224 may be a process for generating preset settings for controlling multiple components in a vehicle cockpit based on multimodal data 202. This process may be understood to involve pre-establishing multiple sets of preset settings using the flow shown in FIG. 2 based on historical multimodal data and storing them in the vehicle or another cloud server. The corresponding settings are invoked according to the vehicle's scene. In another embodiment of the present disclosure, blocks 202 to 224 may be a process for generating settings in real time according to the vehicle's current scene. For example, after the vehicle collects multimodal data 202, the multimodal generative model 216 is used to directly generate settings for controlling multiple components. Hereinafter, the direct generation of settings for controlling multiple components will be described as an example.

[0026] 2, multimodal data 202 may include data of multiple modalities, such as, but not limited to, audio content 202-1, vehicle data and vehicle control state 202-2, environmental data 202-3, and image data 202-4. Audio content 202-1 may include conversational content of occupants in the vehicle cockpit, multimedia audio, sounds emitted by the vehicle control units themselves, such as the air conditioner, and audio data of the surrounding environment, such as sounds emitted by other vehicles (horns, tire noise), pedestrian sounds, animal sounds, water sounds, rain sounds, etc. In some embodiments of the present disclosure, vehicle data and vehicle control state 202-2 is signal data related to the vehicle and vehicle control units, and may include, for example, current vehicle sensor data, control parameters, and related vehicle cockpit state data. In one example, vehicle data and vehicle control state 202-2 may include, but is not limited to, vehicle data (e.g., vehicle speed, acceleration, steering angle, etc.), vehicle control units (e.g., window, door, seat sensors, thermometer, window sensors, etc.).

[0027] In some embodiments of the present disclosure, the environmental data 202-3 may include data about the vehicle's surrounding environment, such as weather, season, temperature, humidity, wind speed, etc. The image data 202-4 is image data collected from inside and outside the vehicle using a vehicle's visual sensor, such as an on-board camera, to provide visual information about the number of occupants inside the vehicle, their operating postures, and the outside environment. In some embodiments of the present disclosure, the image data may be keyframe image data in a collected image sequence or video data.

[0028] In some embodiments of the present disclosure, the multimodal data 202 can be sent to an instruction generation model 204. The instruction generation model 204 may include a predefined artificial instruction library and determine instructions 208 associated with the multimodal data 202 according to search rules (e.g., keyword-based search, scene tag-based search, etc.). It can be understood that the artificial instruction library may include multiple instruction templates for different scenes and user needs. In other embodiments of the present disclosure, the instruction generation model 204 may further include a generative model, which can generate corresponding instructions 208 according to the multimodal data 202.

[0029] In one example, the instruction 208 may be: "As a smart vehicle assistant, it can generate an intelligent vehicle interior atmosphere according to real-time input data. In the first step, it receives various input tables (including information such as user voice needs, vehicle interior surveillance video, vehicle status, and environmental sensing). In the second step, it understands the current vehicle scene and generates an optimal vehicle cockpit atmosphere to provide the most comfortable, safe, and personalized vehicle interior environment. In the third step, it returns the corresponding result according to the data format of each effector. The content of the input information table is: <xxxxxxx>The instructions 208 can be summarized as follows. The instructions 208 can be understood to be essentially trigger instructions that can issue instructions on behalf of the user. That is, the vehicle can generate instructions by itself to control the vehicle cockpit, and can provide the user with an intelligent vehicle cockpit atmosphere even when the user does not issue instructions. In some embodiments, the instructions can provide clear semantic direction to the multimodal generative model 216 and guide the multimodal generative model 216 to generate clear results.

[0030] In some embodiments of the present disclosure, the instruction generation module 204 can generate a corresponding instruction 208, and then input the instruction 208 to the prompt engineering module 206, which can generate prompt information (e.g., prompt words) for the multimodal generative model 216 based on the instruction and other information, accurately understand the user's intention and the current vehicle scene, and generate a final result according to the prompt information. In some embodiments of the present disclosure, the prompt engineering module 206 can generate the corresponding prompt words in combination with the instruction 208, the context information 210, and the dialogue history 212.

[0031] In some embodiments of the present disclosure, the context information 210 may be previously determined settings, audio content, environmental data, image data, etc., based on which settings were preset, or may be multimodal data collected in a previous cycle. It can be understood that using the context information as a basis for generating prompt words allows the multimodal generative model to more accurately understand the current scene of the vehicle. The interaction history 212 may be a user's historical input (e.g., content input by voice or other means), such as previously specified adjustment content or previously specified generation intent. In some embodiments of the present disclosure, the interaction history 212 may be obtained from an interaction history database 214, which may be stored in the vehicle or in a cloud server communicatively connected to the vehicle.

[0032] Prompt words are text input to the multimodal generative model 216 that can be used to present or guide the multimodal generative model 216 to produce output results that match expectations. In some embodiments of the present disclosure, the prompt engineering module 206 may be provided with generative pre-trained models. Generative pre-trained models are machine learning or deep learning models that can be trained with data to generate text. In some embodiments, the generative pre-trained models can be fine-tuned to complete various natural language processing tasks, such as prompt word generation, text generation, etc.

[0033] In some embodiments of the present disclosure, after receiving a prompt word, the multimodal generative model 216 can generate corresponding settings based on the prompt word. The settings include multiple operating parameters for multiple components in the vehicle cockpit (e.g., air conditioning, windows, doors, entertainment system, ambient lighting, etc.). For example, the temperature in the vehicle cockpit can be determined based on weather data. In hot summer, the system can lower the air conditioning temperature and increase the air speed, and in cold winter, the system can increase the air conditioning temperature and turn on the seat heating function. By combining the weather data and seasonal data, it can determine whether the air humidity needs to be adjusted in the current environment and determine the air conditioning parameters. For example, in dry winter, the system can increase the air humidity to improve occupant comfort.

[0034] In some embodiments of the present disclosure, a post-generation checking module 218 may be used to check the results generated by the multimodal generative model 216 to ensure that the results are more accurate and consistent with user preferences and to avoid safety risks in the results. For example, the results may be checked using a pre-defined checking policy. In some embodiments of the present disclosure, the checking policy may include various aspects of checking policies, such as a vehicle signal policy determined based on vehicle engineering and safety, and pre-defined limit parameters and parameter ranges according to user needs. In other embodiments, the results may be fed back to the user to check the results according to the user's acceptance and opinions (e.g., user input information). To avoid inconvenience or risks caused by directly applying candidate setting controls, the user may have the control authority to approve or reject the final generated results.

[0035] In some embodiments of the present disclosure, the checked candidate configurations may be sent to the multimodal effector 220 in a format accepted by the components in the vehicle cockpit. The multimodal effector 220 is a key module connected to the control units of each component in the vehicle cockpit, executing the checked parameter set and converting it into actual control operations. The multimodal effector 220 can adjust components in the vehicle cockpit, such as vehicle ambient lights, air conditioning, seats, screen background images, music, vehicle fragrance, or other vehicle control effectors. The hardware of each control system in the vehicle cockpit is connected to each other and executes parameter instructions in the checked parameters via an in-vehicle bus or corresponding communication protocol to realize intelligent adjustment of the atmosphere in the vehicle cockpit. For example, in one example, a control system corresponding to an ambient light can receive the atmosphere parameters included in the checked configuration and adjust the operating parameters of the ambient light according to the atmosphere parameters, such as adjusting the brightness, color, and dynamic changes of the ambient light. In addition, the control system corresponding to the air conditioner can receive the air conditioner parameters included in the checked parameters and adjust the temperature, wind speed, wind direction, mode, etc. of the air conditioner according to the air conditioner parameters.

[0036] It can be understood that after controlling the parameters of each component in the vehicle cockpit according to the settings, the user can decide whether to adjust the parameters of one or more components in the settings according to their needs. For example, if the user feels uncomfortable after adjusting the seat angle according to the settings, they can input information via voice input, gesture input, or other means to provide feedback on their adjustment information 224. For example, the input adjustment information may be, "The seat angle is uncomfortable, please readjust it." The multimodal generative model 216 can adjust the generated results or regenerate the settings according to the adjustment information. For example, the adjustment information 224, as part of the multimodal data 202, can provide a reference for the multimodal generative model 216 to adjust or generate settings for controlling multiple components.

[0037] In this manner, settings for controlling each component in the vehicle cockpit that meets the requirements can be generated according to the current vehicle scene and the user's needs, and the generated settings can be checked. The vehicle cockpit atmosphere created by controlling each component according to the generated settings is safe and more in line with the user's preferences, improving the user's riding experience.

[0038] 3 illustrates a flowchart of a method 300 for vehicle cockpit control according to some embodiments of the present disclosure. The method 300 may be executed by the processor 104 shown in FIG. 1. Furthermore, with the continuous development and innovative upgrade of smart cars, more and more vehicles (e.g., smart cars) are equipped with computing capabilities. That is, the method 300 according to embodiments of the present disclosure is not limited to being executed by the processor 104, but may also be executed by the vehicle 102, and the present disclosure is not limited thereto.

[0039] In block 302, the method 300 includes determining candidate configurations for a plurality of components within the vehicle cockpit based on a current scene of the vehicle. The current scene refers to a scene in which the vehicle is located, and may include a scene of the vehicle itself and the behavioral patterns of occupants within the vehicle. In some embodiments of the present disclosure, the current scene may be determined by multimodal data collected or acquired by the vehicle. The multimodal data may be collected using various types of sensors (e.g., audio sensors, visual sensors, speed sensors, etc.) provided in the vehicle and may include data of various modalities, such as, but not limited to, audio data, image data, and vehicle and vehicle control state data. In some embodiments of the present disclosure, the audio data may include audio data such as audio data outside the vehicle (e.g., ambient environmental sounds), audio data from the vehicle body (e.g., engine sounds), and audio data inside the vehicle (e.g., voice content within the cockpit). The image data may include images inside the vehicle cockpit (e.g., images including occupants) and image data of the vehicle's surrounding environment (e.g., image data of the front or rear of the vehicle). Vehicle and vehicle control status data may be signal data related to the vehicle and vehicle control unit, and may include, for example, but not limited to, vehicle speed, acceleration, cockpit sensor data, door information, window sensor data, etc.

[0040] In some embodiments of the present disclosure, the scene of the vehicle itself may include an external environment scene and an internal environment scene. The external environment scene may include, but is not limited to, weather (e.g., sunny, rainy, snowy, etc.), temperature, humidity, traffic conditions (e.g., presence or absence of traffic jams, traffic jam duration, traffic jam sections, etc.), road type, etc. The internal environment scene of the vehicle may include the vehicle state (e.g., vehicle speed, acceleration, steering angle, braking state, door and window open / closed states, etc.) and the occupant state (occupant behavior, physiological state, and emotion, etc.). In some embodiments of the present disclosure, the occupant state may be determined by a recognition result obtained by performing image recognition on collected images including the driver. In some embodiments of the present disclosure, the occupant's (e.g., driver or passenger) physiological state, such as heart rate and respiratory rate, detected by a sensor inside the vehicle can indirectly estimate the occupant's emotion or comfort. It can be understood that by analyzing the occupant's voice commands, facial expressions, movements, etc., it can be determined whether the user is tired, distracted, or happy.

[0041] In some embodiments of the present disclosure, the current scene may include multiple types of scenes, such as a driving scene plus occupant fatigue, a vehicle non-driving scene plus occupant rest, a driving scene plus occupant work, or a driving scene plus occupant entertainment. In different scenes, the user's needs for each component in the vehicle cockpit may be different. In some embodiments of the present disclosure, scene data may be used to pre-establish preset settings corresponding to different scenes. The preset settings may include operating parameters for each component in the vehicle cockpit. Each component in the vehicle cockpit can be used to create an atmosphere in the cockpit and provide the occupants with a comfortable riding experience. For example, the pre-established current scene may include operating parameters for an air conditioner, an ambient light, an entertainment system, etc.

[0042] In some embodiments of the present disclosure, after establishing preset settings according to different scenes, a correspondence relationship between the preset settings and the preset scenes may be established, and the correspondence relationship may be expressed in multiple ways, such as a table, a function, or a model. In some embodiments of the present disclosure, the preset settings and the correspondence relationship between the preset settings and the preset scenes may be stored in the vehicle or in a cloud server communicatively connected to the vehicle. After determining the current scene of the vehicle, a candidate setting corresponding to the current scene may be determined from multiple preset settings based on the correspondence relationship. It can be understood that a preset scene that satisfies the similarity between the current scene and a preset scene and a preset setting corresponding to the preset scene may be determined, and the candidate setting may be set as a candidate setting.

[0043] In block 304, the method 300 includes adjusting the candidate settings based on vehicle safety limit information and user input information. The vehicle safety limit information may include vehicle signal protection rules set based on vehicle dynamics (e.g., vehicle dynamics performance, such as acceleration, deceleration, steering, and braking) or safety requirements. For example, the safety limit information may include requiring that windows not be opened when driving at high speeds and that the seat angle have a corresponding adjustment range. For example, the safety limit information may include requiring that, when adjusting parameters such as the seat position and angle, parameter changes can accommodate drivers of different heights and maintain good comfort. Furthermore, the safety limit information may include requiring that, when adjusting parameters related to acceleration and braking, parameter changes do not affect the vehicle's acceleration and braking distances. In some embodiments of the present disclosure, the user input information may include user feedback information for the candidate settings, such as whether or not to accept them, the degree of acceptance, parameters that need to be adjusted, etc. For example, the user input information may include requiring that the air conditioner temperature not exceed 29 degrees and that the seat angle not exceed 150 degrees. In some embodiments of the present disclosure, parameters in the candidate settings that do not comply with the check policy may be adjusted according to a preset check policy. For example, if the candidate settings include a parameter indicating that the air conditioner temperature is 30 degrees, the temperature can be adjusted to 29 degrees or any other temperature less than 29 degrees. The pre-set check policy may include a check policy established based on ergonomically set component parameters, user setting information, etc.

[0044] In block 306, the method 300 includes controlling a corresponding component in the vehicle cockpit based on the adjusted candidate configuration. The operating parameters of the component in the vehicle cockpit are adjusted according to the operating parameters included in the configuration. For example, the adjusted candidate configuration is sent to a control system of the corresponding component, which then adjusts the operating parameters of the component. In one example, the operating parameters for a seat included in the configuration are sent to a seat adjustment system to move the seat back five centimeters.

[0045] In this manner, the setting information for controlling each component in the vehicle cockpit can be determined according to the vehicle scene, the environment in the vehicle cockpit can be adjusted in real time, and the user can have a more comfortable and convenient riding experience. In addition, by adjusting the setting according to the vehicle related information or the user's feedback, it is possible to avoid the inclusion of unsafe factors or factors that are not preferred by the user in the candidate setting, so that the finally adjusted atmosphere in the vehicle cockpit is safer while meeting the user's needs.

[0046] In some embodiments of the present disclosure, to ensure that the cockpit atmosphere ultimately provided to the user by the controlled components is favorable to the user and to avoid the user having a bad experience after controlling the vehicle cockpit, after a candidate setting is determined, the user's acceptance of the candidate setting and the content to be adjusted can be determined in a proactive manner by inquiring of the user. The user can select whether or not to adjust parameters in the candidate setting according to their needs. In some embodiments of the present disclosure, the user's adjustment intention can be determined based on information input by the user. The adjustment intention refers to the parameters and parameter values ​​that the user wants to adjust for the determined candidate setting. For example, the user can input information by voice input, touchscreen input, gesture recognition, etc. In one example, the user can input information through an in-vehicle voice assistant, for example, the user can say, "Adjust the seat further back." In some embodiments of the present disclosure, after the user determines the parameters that they want to adjust and the corresponding adjustment parameter values, the parameter values ​​for the parameters in the candidate setting can be adjusted to the corresponding adjustment parameter values. In some embodiments of the present disclosure, when adjusting parameters according to a user's adjustment intention, a feedback mechanism can be established, so that clear indications and suggestions can be given to the user in a timely manner when the user attempts to perform an operation that does not meet the user's requirements.

[0047] In this manner, the user can be provided with the ability to edit the candidate configuration, so that the adjusted candidate configuration is more in line with the user's needs and preferences, ensuring that the final cockpit environment provided meets the user's needs and preferences.

[0048] In some embodiments of the present disclosure, the user's intention to adjust may be to modify an operating parameter for a component in the candidate configuration. If the configuration does not meet the user's needs or is not preferred by the user after multiple adjustments, the user's intention to adjust may be to regenerate the configuration. In the process of regenerating the configuration, a multimodal generative model (e.g., multimodal generative model 216 shown in FIG. 2 ) can generate multiple parameters corresponding to multiple components according to the method described in the above embodiments. For example, the multimodal generative model can generate corresponding configurations based on a vehicle scene. After generating the configuration, it is also necessary to check the generated configuration using vehicle-related information and user feedback information to determine whether the configuration meets the requirements.

[0049] In some embodiments of the present disclosure, the plurality of preset configurations may be generated by a multimodal generative model based on multimodal data collected by the vehicle. For example, the multimodal generative model may generate a set of preset parameters including a plurality of parameters based on the multimodal data, contextual information, or other information. Different preset configurations correspond to different vehicle scenes. In some embodiments of the present disclosure, a user may customize operating parameters of components in a vehicle cockpit to determine the preset configurations. For example, the user may determine operating parameters of components in a vehicle cockpit based on the vehicle's usage scene and historical driving trajectory to generate the preset configurations.

[0050] In some embodiments of the present disclosure, to allow a user to be more involved in the vehicle cockpit control process, a function for setting a preset setting or a candidate setting may be provided to the user through an interaction interface or other means. For example, the user may input setting information according to their needs to assist the multimodal generative model in generating a preset setting that more closely matches their requirements. In one example, a user suffering from a lumbar spine disorder may have special requirements for the seat angle, desiring that the seat angle be adjustable from 95 degrees to 105 degrees to relieve intradiscal pressure. The user may input their needs through voice input or text input. A command word (e.g., "Set the seat angle range to 95-105 degrees") may be generated according to the information input by the user. The multimodal generative model may then generate parameters corresponding to the seat component that matches the requirements according to the prompt word.

[0051] FIG. 4 is a schematic diagram of an interaction interface displaying candidate configurations according to some embodiments of the present disclosure. As shown in FIG. 4, the interactive interface 402 includes multiple display components 404, a selection button 406, and a decision control 408. The multiple display components 404 are used to display multiple components in the candidate configuration and parameters corresponding to the multiple components. A user can view the parameters included in the candidate configuration and select the parameters of the components that need to be adjusted according to their needs. In some embodiments of the present disclosure, after completing the selection, the user can choose whether to trigger the selection button 406 according to their needs. In response to triggering the selection button 406, the user can be provided with an option to set the parameters of the components. The user can adjust the parameters of the components according to their needs and choose whether to trigger the decision control 408. The decision control 408 can be triggered by a trigger event to operate and complete a response. The trigger event may include a mouse click, a tap by the user's finger, etc. The decision control 408 may be in the form of a button control, and the button control may include a box button with text, a button with only an icon, etc. In response to a user trigger operation, the parameters modified by the user and the corresponding parameter values ​​can be obtained to determine candidate settings after adjustment.

[0052] In some embodiments of the present disclosure, a vehicle may have some operations that are not permitted to be performed in different scenarios. For example, if the vehicle speed is too high, seat adjustment may be prohibited to ensure the driver's safety, or if it is raining or snowing, opening the sunroof may be prohibited. Based on this, to ensure the safety of the parameters included in the candidate configurations, the determined candidate configurations may be checked to determine whether the parameters included in the candidate configurations meet the safety performance requirements and whether they are permitted to be performed. In some embodiments of the present disclosure, the vehicle's environmental sensor data and data from other sensors, such as a speed sensor, may be used to determine whether an operation corresponding to a parameter in a candidate configuration is permitted to be performed. If the operation is not permitted, the parameter in the candidate configuration may be directly deleted. By integrating the vehicle's environmental sensor data and other sensor data into the parameter safety judgment, it is possible to more accurately determine whether the parameters in the candidate configurations meet the safety performance requirements, and, if necessary, take corresponding measures to ensure the safe driving of the vehicle.

[0053] In some embodiments of the present disclosure, if the operating parameters of some components exceed their normal values, driving safety may be affected. For example, if the seat angle is too tilted or the audio volume is too loud, driving safety may be affected. Based on this, a safety limit value may be set for one or more components in the vehicle cockpit according to the safety performance of the vehicle. The safety limit value may be set by a user by comprehensively considering various aspects such as vehicle design, ergonomics, and safety regulations. In some embodiments of the present disclosure, if it is determined that a parameter in a candidate setting exceeds a safety limit, the parameter may be adjusted. The adjustment method may be direct adjustment. For example, the safety limit value of the seat is 150 degrees and the audio volume is 70%. If it is determined that the seat angle parameter included in the candidate setting is 155 degrees or the audio volume is 80%, the parameters of the seat component or audio component in the candidate setting may be adjusted according to the corresponding safety limit value. For example, the seat angle parameter may be adjusted to an angle of 150 degrees or less than 150 degrees, or the audio volume may be adjusted to 70% or less than 70%. The specific adjustment can be comprehensively adjusted by combining other factors, such as the user's intention. For example, if the user wants to tilt the seat further to have a better resting experience, but the adjusted angle exceeds the safety limit, the system can control the seat as close to the user's desired angle as possible while ensuring safety. For example, the system can adjust the seat angle to a safe value close to 150 degrees.

[0054] This method ultimately ensures that the parameters for controlling the components in the vehicle cockpit meet safety requirements, avoids causing unexpected losses to the user, and improves the user's riding experience while ensuring vehicle safety.

[0055] In some embodiments of the present disclosure, the real-time state of the vehicle influences the setting of some parameters. For example, if the vehicle speed is too high, opening a window or sunroof may affect driving safety. Based on this, a signal protection rule for the vehicle can be determined according to the real-time state of the vehicle, and parameters and corresponding parameter values ​​that need to be adjusted can also be determined based on the signal protection rule. For example, if it is detected that the vehicle speed exceeds a certain threshold, opening a window or sunroof may increase air resistance and affect driving stability, so a signal protection rule can be triggered. For example, the opening degree or opening angle of the window or sunroof can be limited. The signal protection rule can be customized according to different vehicle models, different settings, and even different driver habits.

[0056] In some embodiments of the present disclosure, the candidate settings can also be checked according to user setting information. For example, a user can preset some parameters according to his or her preferences. A user checking policy can be formed according to the user setting information. One or more parameters in the candidate settings can be adjusted according to the user checking policy to ensure that the parameters in the candidate settings do not violate the user's intention. For example, if a user presets the maximum volume of audio to 60%, if the volume parameter for audio in the candidate settings is 80%, adjustment is required (e.g., for the volume parameter, the volume is lowered). In this way, the rationality and safety of the parameter adjustment in the candidate settings can be ensured.

[0057] In some embodiments of the present disclosure, a candidate configuration can be adjusted using pre-set check policies. The types and contents of the check policies vary. When adjusting parameters in the candidate configuration, the check results of each check policy must be taken into consideration. If any check result does not meet the requirements, the operation corresponding to the parameter in the candidate configuration cannot be performed. When adjusting one or more parameters in the candidate configuration, all check policies must be taken into consideration to avoid one or more parameters in the adjusted candidate configuration not satisfying a certain check policy.

[0058] In this manner, the basis for adjusting the candidate configuration can be determined from various aspects, and the adjusted candidate configuration will be safer and more in line with the user's preferences.

[0059] In some embodiments of the present disclosure, if multiple adjustments do not meet the requirements (e.g., the user's acceptance of all candidate settings is lower than normal), the multimodal generative model can regenerate settings for controlling multiple components using multimodal data. For example, the multimodal generative model can generate settings for controlling multiple components in a vehicle cockpit using vehicle scene data and user feature data, so that the resulting vehicle cockpit ambiance better meets the user's needs.

[0060] FIG. 5 illustrates a schematic diagram for generating a configuration according to some embodiments of the present disclosure. As shown in FIG. 5, a multimodal data input module 502 can collect and obtain multimodal data in response to vehicle commands or user commands. The multimodal data may include audio content, environmental data, image data, etc. The multimodal data may be input to an instruction generation module 504, which can generate instruction words using a generative pre-training model or select instruction words corresponding to the multimodal data from a predefined artificial instruction library. The instruction words provide a clear semantic direction for the multimodal generative model, guiding it to generate clear results. The instruction words may be input to a prompt engineering module 506, which can integrate various input data and generate corresponding prompt information based on the instruction words, context information, and dialogue history, ensuring that the multimodal generative model can accurately understand the user's needs and then provide data support for flexible and personalized adjustment of each component in the vehicle cockpit.

[0061] In one embodiment of the present disclosure, the prompt information may be input to the multimodal generative model 508, which can collectively generate adjustment parameters for each component in the vehicle cockpit according to the prompt information. The generated adjustment parameters are input to the multimodal effector module 512 in the cockpit space according to a set signal format and communication protocol. The multimodal effector module 512 can be understood to be connected to the control units of each component in the cockpit, execute the adjustment parameters output from the multimodal generative model 508, and convert them into actual control operations. For example, the ambient light can receive the ambient light parameters output from the multimodal generative model 508 and adjust the brightness, color, and dynamic changes of the ambient light in the vehicle cockpit. The seat can receive the adjustment parameters (e.g., temperature, wind speed, wind direction, mode, etc.) output from the multimodal generative model 508 and input the adjustment parameters to the air conditioning control system to make corresponding adjustments.

[0062] To ensure that the adjustment parameters generated by the multimodal generative model meet safety requirements and user needs, the adjustment parameters can be adjusted by a checking module 510 to determine the adjusted adjustment parameters. The checking module 510 may be configured with multiple checking policies, such as a safety policy based on vehicle dynamics and a user checking policy based on user settings. In some embodiments of the present disclosure, a proactive inquiry method can be used to determine whether the user is satisfied with the adjustment parameters, and the adjustment parameters can be adjusted according to the user's adjustment intention.

[0063] This method can realize intelligent and unified control of each component in the vehicle cockpit, and users can transform the vehicle into a true "intelligent change space" that is voice-adjusted simply by expressing their needs through voice input or other methods, providing users with an immersive cockpit atmosphere experience.

[0064] In some embodiments of the present disclosure, a vehicle display may display a wallpaper image, and different users may have different preferences for the wallpaper image. Users often have a strong desire for the wallpaper image to change in conjunction with the vehicle's driving scene. Based on this, the candidate settings may include image instructions for the wallpaper image. These instructions are used to generate a wallpaper image according to the current scene and do not include a specific wallpaper image. In some embodiments of the present disclosure, the image instructions may include multiple parameters (e.g., which may include a theme, a color, a dynamic effect, etc.) for guiding the wallpaper image generation process.

[0065] In some embodiments of the present disclosure, wallpaper images are divided into dynamically changing live wallpapers and static wallpaper images. The state of the wallpaper image can be determined by the current scene of the vehicle. For example, when the vehicle is in a driving scene, the vehicle's surrounding environment (e.g., objects in the surrounding environment, weather, time, etc.) is constantly changing. To provide the user with a corresponding real-time visual experience, the wallpaper image can be dynamically generated and displayed according to the vehicle's scene data. For example, changes in the surrounding environment affect the color, brightness, content, etc. of the wallpaper image. In one example, when the vehicle is driving and the exterior of the vehicle is a sunset scene, the content of the wallpaper image may be adjusted to include a beautiful sunset scene that matches the change in the vehicle's surrounding environment. If it suddenly rains while the vehicle is driving, the wallpaper image may become darker or have a raindrop effect. These live wallpaper images can be displayed on displays inside the vehicle (e.g., a central control screen, a screen near the dashboard, etc.) to provide real-time visual enjoyment to the occupants.

[0066] To improve driving safety, wallpaper images should not be adjusted to distract the driver's attention or gaze. Therefore, when designing and adjusting wallpaper images, the impact on driving safety must be considered, and wallpaper images must not interfere with the driver's judgment or operation in critical situations (such as emergency braking or lane changes).

[0067] In some embodiments of the present disclosure, the current scene of the vehicle is determined by multimodal data, which contains a wealth of information. Therefore, materials for generating a wallpaper image can be determined based on the current scene. The materials can be text materials, graphic / image materials, video materials, etc. Text materials are characters with a specific format, i.e., characters with effects such as font, font size, style, and color. Image materials are materials in image format, including illustrations, stickers, icons, etc. For example, if a vehicle is traveling on an overpass planted with roses, the roses can be used as the material for generating a wallpaper image, and the generated wallpaper image can include roses. It can be understood that the attributes and parameters of the materials can be dynamically adjusted according to real-time changes in the current scene, thereby achieving dynamic changes in the wallpaper image. In some embodiments of the present disclosure, a material library can be predefined based on user needs or other factors, and the material library can include multiple types of materials (e.g., icons, text, dynamic effects, etc.). Based on the current scene, materials corresponding to the scene can be selected from the material library.

[0068] 6 shows a block diagram of an apparatus 600 for vehicle cockpit control according to some embodiments of the present disclosure. As shown in FIG. 6, the apparatus 600 includes a candidate setting determination unit 602 configured to determine candidate settings for a plurality of components in the vehicle cockpit based on a current scene of the vehicle. The apparatus 600 further includes an adjustment unit 604 configured to adjust the candidate settings based on vehicle safety limit information and user input information. The apparatus 600 further includes a control unit 606 configured to control corresponding components in the vehicle cockpit based on the adjusted candidate settings.

[0069] In some embodiments, the adjustment unit 604 is further configured to determine a user's adjustment intention based on the user's input information, and adjust the candidate settings based on the safety limit information and the user's adjustment intention.

[0070] In some embodiments, the adjustment unit 604 is further configured to determine components to be adjusted and operating parameters of the components to be adjusted included in the adjustment intention, and adjust parameters for the components to be adjusted in the safety limit information based on the operating parameters.

[0071] In some embodiments, the adjustment unit 604 is further configured to determine a safety limit value for the component to be adjusted included in the safety limit information, and in response to the operating parameter for the component to be adjusted included in the candidate configuration being greater than the safety limit value, adjust the operating parameter for the component to be adjusted in the candidate configuration to make the operating parameter less than the safety limit value.

[0072] In some embodiments, the apparatus 600 further includes a preset setting generation unit configured to acquire multimodal data collected by the vehicle in the preset scene, and output, based on the multimodal data of the vehicle, a plurality of parameters for controlling a plurality of components through a multimodal generative model, and encapsulate the plurality of parameters to determine a preset setting matching the preset scene.

[0073] In some embodiments, the preset setting generation unit is further configured to obtain user historical input information and historical adjustment information, and output, based on the historical input information and the historical adjustment information, a plurality of parameters for controlling the plurality of components based on the multimodal data by the multimodal generative model.

[0074] In some embodiments, the adjustment unit 604 is further configured to acquire environmental sensor data of the vehicle and a driving state of the vehicle, determine whether execution of the plurality of parameters included in the candidate configuration is permitted based on the environmental sensor data and the driving state, and adjust the parameters in the candidate configuration in response to the execution of the plurality of parameters being not permitted.

[0075] In some embodiments, the candidate settings include image instructions for generating wallpaper images, and the device 600 further includes a wallpaper image determination unit configured to determine, in response to the image parameters, a dynamic status of the wallpaper images and a number of wallpaper images based on a current scene.

[0076] In some embodiments, the wallpaper image determination unit is further configured to determine whether the vehicle is moving based on a current scene or a driving state of the vehicle, and in response to the vehicle being moving, set the generated wallpaper image to dynamically change according to the current scene.

[0077] In some embodiments, the wallpaper image determination unit is further configured to determine material for generating the wallpaper image based on the current scene, and generate and display the wallpaper image based on the material in response to the image command.

[0078] In some embodiments, the apparatus 600 further includes a scene determination unit configured to obtain ambient environment information of the vehicle, a driving state of the vehicle, and a driving operation of a user, and determine a current scene of the vehicle based on the ambient environment information, the driving state, and the driving operation.

[0079] It can be appreciated that the apparatus 600 of the present disclosure can be utilized to achieve at least one of the many advantages that can be achieved by such a method or process.

[0080] 7 shows a schematic block diagram of an exemplary device 700 suitable for implementing embodiments of the present disclosure. As shown, device 700 includes a computing unit 701 that can perform various appropriate operations and processes in accordance with computer program instructions stored in a read-only memory (ROM) 702 or loaded from a storage unit 708 into a random access memory (RAM) 703. RAM 703 may further store various programs and data necessary for the operation of device 700. The computing unit 701, ROM 702, and RAM 703 are connected to one another by a bus 704. An input / output (I / O) interface 705 is also connected to bus 704.

[0081] Several components in device 700 are connected to I / O interface 705, including input unit 706 such as a keyboard, mouse, etc., output unit 707 such as various types of displays, speakers, etc., storage unit 708 such as a magnetic disk, optical disk, etc., and communication unit 709 such as a network card, modem, wireless communication transceiver, etc. The communication unit 709 allows device 700 to exchange information / data with other devices via computer networks such as the Internet and / or various telecommunication networks.

[0082] The computing unit 701 may be various general-purpose and / or special-purpose processing components having processing and computing capabilities. Examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, computing units that execute various machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs each of the methods and processes described above, such as method 300. For example, in some embodiments, method 300 may be implemented as a computer software program tangibly embodied in a machine-readable medium such as the storage unit 708. In some embodiments, some or all of the computer program may be loaded and / or installed into the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, it may perform one or more steps of the method 300 described above. Preferably, in alternative embodiments, the computing unit 701 may be configured to perform the method 300 in any other suitable manner (eg, by firmware).

[0083] The functionality described herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard parts (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), etc.

[0084] Program code implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are performed. The program code may be executed entirely on a machine, partially on a machine, partially on a machine as a separate software package and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of this disclosure, a machine-readable medium may be a tangible medium that contains or can store a program for use by or in connection with an instruction execution system, device, or apparatus. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination thereof. More specific examples of machine-readable storage media include one or more wire-based electrical connections, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a convenient compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. Additionally, although operations are described in a particular order, this should not be understood as requiring such operations to be performed in the particular order or sequence shown, or that all illustrated operations be performed. In some cases, multitasking and parallel processing may be advantageous. Similarly, although the above discussion includes details of specific implementations, these should not be construed as limiting the scope of the present disclosure. Certain features described in the context of a single embodiment may also be implemented in combination in one embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments alone or in any suitable subcombination.

[0086] Although the present subject matter has been described in language specific to structural features and / or logical operations of methods, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or operations described above. Rather, the specific features and operations described above are merely example forms of implementing the claims.< / xxxxxxx>

Claims

1. 1. A method for vehicle cockpit control, comprising: determining candidate configurations for a plurality of components within the vehicle cockpit based on a current scene of the vehicle; adjusting the candidate configuration based on the vehicle safety limit information and user input information; and controlling corresponding components in the vehicle cockpit based on the adjusted candidate configuration.

2. adjusting the candidate configuration based on the vehicle safety limit information and user input information, determining an adjustment intention of the user based on the input information of the user; and adjusting the candidate settings based on the safety limit information and the user's adjustment intention.

3. The step of adjusting the candidate configuration includes: determining a component to be adjusted included in the adjustment intention and setting information of the component to be adjusted; and adjusting a setting for the component to be adjusted in the candidate configuration based on the setting information.

4. The step of adjusting the candidate configuration includes: determining a safety limit value for the component to be adjusted, which is included in the safety limit information; 3. The method of claim 2, further comprising: in response to a setting for a component to be adjusted included in the candidate configuration being greater than the safety limit value, adjusting the setting for the component to be adjusted so that the adjusted setting is less than the safety limit value.

5. acquiring multimodal data collected by the vehicle in a preset scene; outputting a plurality of parameters for controlling a plurality of components using a multimodal generative model based on the multimodal data of the vehicle; The method of claim 1 , further comprising: determining a preset setting that matches the preset scene by encapsulating the plurality of parameters.

6. The step of outputting a plurality of parameters for controlling a plurality of the components includes: acquiring user history input information and history adjustment information; and outputting, by the multimodal generative model based on the historical input information and the historical adjustment information, a plurality of parameters for controlling a plurality of components based on the multimodal data.

7. The step of adjusting the candidate configuration includes: acquiring environmental sensor data of the vehicle and a driving state of the vehicle; determining whether execution of a plurality of parameters included in the candidate configuration is permitted based on the environmental sensor data and the driving state; and adjusting the candidate configuration in response to the plurality of parameters not being permitted to execute.

8. The candidate settings include image instructions for generating a wallpaper image, and the method further comprises: The method of claim 1 , further comprising the step of determining, in response to the image command, a dynamic status of the wallpaper image and a number of the wallpaper images based on the current scene.

9. determining a dynamic state of the wallpaper image based on the current scene, determining whether the vehicle is moving based on the current scene or a driving state of the vehicle; 9. The method of claim 8, further comprising: in response to the vehicle being in motion, setting the generated wallpaper image to dynamically change according to the current scene.

10. determining material for generating the wallpaper image based on the current scene; The method of claim 8 , further comprising the step of: generating and displaying the wallpaper image based on the material in response to the image command.

11. acquiring surrounding environment information of the vehicle, a running state of the vehicle, and a driving operation of a user; The method of claim 1 , further comprising: determining a current scene of the vehicle based on the ambient environment information, the driving state, and the driving maneuver.

12. 1. An apparatus for vehicle cockpit control, comprising: a candidate configuration determination unit configured to determine candidate configurations for a plurality of components in the vehicle cockpit based on a current scene of the vehicle; an adjustment unit configured to adjust the candidate settings based on the vehicle safety limit information and user input information; a control unit configured to control corresponding components in the vehicle cockpit based on the adjusted candidate configuration.

13. An electronic device, at least one processor; and a memory coupled to said at least one processor and storing instructions that, when executed by said at least one processor, cause said device to perform the method of any one of claims 1 to 11.

14. A vehicle comprising a plurality of sensors and the electronic device of claim 13.

15. 12. A computer program tangibly stored on a computer readable medium and comprising machine executable instructions that, when executed, perform the method of any one of claims 1 to 11.

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