Automobile cabin self-adaptive adjusting method and device

Abstract

The invention relates to an automobile cabin self-adaptive adjusting method and device, and the method comprises the steps: collecting and fusing the biological feature information, vehicle information and environment information of a driver in a current vehicle, so as to generate a driver digital portrait; based on the driver digital portrait, calculating an initial comfort parameter set corresponding to the biological characteristic information, analyzing the vehicle information and the environment information to obtain corresponding driving style analysis data, and when the driving style analysis data is greater than a preset threshold value, adjusting a plurality of comfort parameters to obtain a comfort update parameter set; and performing preset verification and correction operation on the comfort updating parameter set to generate a final execution instruction so as to control the current vehicle to execute corresponding cabin self-adaptive adjustment. Therefore, the problems that the memory function of an existing vehicle seat excessively depends on manual presetting, cannot adapt to a new user, is difficult to distinguish different body types under the same height, is single in adjustment dimension, and is insufficient in linkage with a vehicle safety system are solved.

Description

Technical Field

[0001] This application relates to the field of intelligent connected vehicle technology, and in particular to a method and device for adaptive adjustment of a car cabin. Background Technology

[0002] Existing vehicle seat memory functions only call up preset positions through facial recognition or combine height and weight to perform vehicle seat memory operations, but they do not solve the problem of body proportions, and neither involves the coordinated adjustment of dynamic behavior and environment.

[0003] Therefore, the existing vehicle seat memory function relies on manual presets, which cannot adapt to new users, and it only remembers position parameters, which cannot distinguish different body types (such as leg length and arm length) at the same height. In addition, the adjustment dimension of the existing technology is limited, ignoring the impact of driving style and ambient temperature on comfort, and its linkage with the vehicle safety system is insufficient, which urgently needs to be addressed. Summary of the Invention

[0004] This application provides an adaptive adjustment method and device for automotive cabins to solve the problems of existing vehicle seat memory functions relying too much on manual presets, being unable to adapt to new users, having difficulty distinguishing different body types of the same height, having a single adjustment dimension, and insufficient linkage with vehicle safety systems.

[0005] The first aspect of this application provides a method for adaptive adjustment of an automotive cabin, comprising the following steps: collecting biometric information of the driver, vehicle information, and environmental information in the current vehicle, and fusing the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver; based on the digital profile of the driver, calculating an initial comfort parameter set corresponding to the biometric information, and analyzing the vehicle information and environmental information to obtain corresponding driving style analysis data; and when the driving style analysis data is greater than a preset threshold, adjusting multiple comfort parameters of the initial comfort parameter set to obtain an updated comfort parameter set; performing preset verification and correction operations on the updated comfort parameter set to generate a final execution command, and controlling the current vehicle to perform corresponding adaptive cabin adjustment operations through the final execution command.

[0006] Optionally, in one embodiment of this application, the step of collecting the driver's biometric information, vehicle information, and environmental information in the current vehicle and fusing the biometric information, vehicle information, and environmental information to generate a corresponding driver digital profile includes: acquiring timestamp data from different sensors and buses in the current vehicle, and performing data alignment and preprocessing operations on the timestamp data for the biometric information, vehicle information, and environmental information to obtain the driver digital profile, which includes weight, total height, seated height, leg length, torso length, shoulder width, arm length, in-vehicle temperature, vehicle speed, steering angular velocity, and lateral acceleration.

[0007] Optionally, in one embodiment of this application, the step of calculating the initial comfort parameter set corresponding to the biometric information based on the driver's digital profile includes: acquiring the leg length, arm length, torso length, total height, and sitting height of the biometric information, and inputting the leg length, arm length, torso length, total height, and sitting height into a pre-constructed ergonomic model; determining the corresponding seat fore-and-aft position based on the ergonomic model and according to the leg length and a preset leg length coefficient, and determining the corresponding steering wheel extension based on the arm length, torso length, preset arm length coefficient, and torso length coefficient; determining the reference height corresponding to the total height by querying a preset height table, and calculating the height ratio between the sitting height and the total height, so as to adjust the reference height based on the height ratio and the torso length to obtain the corresponding seat height; and constructing and outputting the initial comfort parameter set based on the seat fore-and-aft position, the steering wheel extension, and the seat height.

[0008] Optionally, in one embodiment of this application, the step of analyzing the vehicle information and the environmental information to obtain corresponding driving style analysis data, and adjusting multiple comfort parameters of the initial comfort parameter set to obtain a comfort update parameter set when the driving style analysis data is greater than a preset threshold, includes: calculating the average steering angular velocity and the maximum lateral acceleration of the absolute value of steering angular velocity within a target time window every preset period, and comparing the average steering angular velocity with a first preset steering threshold and a second preset steering threshold, and comparing the maximum lateral acceleration with a first preset lateral threshold and a second preset lateral threshold, wherein the first preset steering threshold is less than the second preset steering threshold, and the first preset lateral threshold is less than the second preset lateral threshold. A lateral threshold is set; if the maximum lateral acceleration is less than the first preset lateral threshold and the average steering angular velocity is less than the first preset steering threshold, the current vehicle is determined to be in comfort mode; if the maximum lateral acceleration is between the first preset lateral threshold and the second preset lateral threshold, and the average steering angular velocity is between the first preset steering threshold and the second preset steering threshold, the current vehicle is determined to be in standard mode; if the maximum lateral acceleration is greater than the second preset lateral threshold and the average steering angular velocity is greater than the second preset steering threshold, the current vehicle is determined to be in sport mode; based on the comfort mode, the standard mode, or the sport mode, the initial comfort parameter set is dynamically adjusted to obtain the comfort update parameter set.

[0009] Optionally, in one embodiment of this application, the step of performing a preset verification and correction operation on the comfort update parameter set to generate a final execution instruction includes: simulating that the driver's knee joint angle is less than or equal to a preset angle threshold when the driver depresses the brake pedal at the position corresponding to the comfort update parameter set; moving the driver's seat forward until the knee joint angle is greater than the preset angle threshold; calculating the driver's eye position according to the comfort update parameter set and determining whether the eye position is within a preset eye ellipse, wherein if the eye position is not within the preset eye ellipse, adjusting the seat height and steering wheel height until the eye position is within the preset eye ellipse; calculating the distance between the driver's chest and the center of the steering wheel based on the comfort update parameter set, and in response to the distance being less than a preset distance threshold, moving the driver's seat backward or retracting the steering wheel until the distance is greater than or equal to the preset distance threshold; generating the final execution instruction when the knee joint angle is greater than the preset angle threshold, the eye position is within the preset eye ellipse, and the distance is greater than or equal to the preset distance threshold.

[0010] Optionally, in one embodiment of this application, after controlling the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution instruction, the method further includes: recording all manual operation data of the driver, and associating the manual operation data with the driver's digital profile, driving style and environmental data to obtain corresponding association information; updating the ergonomic model according to the association information, so as to readjust the comfort parameter set through the updated ergonomic model.

[0011] A second aspect of this application provides an adaptive adjustment device for an automotive cabin, comprising: a data acquisition module for acquiring biometric information of the driver, vehicle information, and environmental information within the current vehicle, and fusing the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver; an analysis module for calculating an initial comfort parameter set corresponding to the biometric information based on the digital profile of the driver, and analyzing the vehicle information and environmental information to obtain corresponding driving style analysis data, and adjusting multiple comfort parameters of the initial comfort parameter set to obtain an updated comfort parameter set when the driving style analysis data exceeds a preset threshold; and an adaptive adjustment module for performing preset verification and correction operations on the updated comfort parameter set to generate a final execution command, and controlling the current vehicle to perform corresponding adaptive cabin adjustment operations through the final execution command.

[0012] Optionally, in one embodiment of this application, the acquisition module includes: a first acquisition unit, configured to acquire timestamp data of different sensors and buses in the current vehicle, and perform data alignment and preprocessing operations on the timestamp data for the biometric information, the vehicle information and the environmental information to obtain the driver digital profile, the driver digital profile including weight, total height, seated height, leg length, torso length, shoulder width, arm length, in-vehicle temperature, vehicle speed, steering angular velocity and lateral acceleration.

[0013] Optionally, in one embodiment of this application, the analysis module includes: a second acquisition unit, configured to acquire the leg length, arm length, torso length, total height, and sitting height of the object feature information, and input the leg length, arm length, torso length, total height, and sitting height into a pre-constructed ergonomic model; a determination unit, configured to determine the corresponding seat fore-and-aft position based on the ergonomic model and according to the leg length and a preset leg length coefficient, and determine the corresponding steering wheel extension based on the arm length, torso length, preset arm length coefficient, and torso length coefficient; a query unit, configured to determine the reference height corresponding to the total height by querying a preset height table, and calculate the height ratio between the sitting height and the total height, so as to adjust the reference height based on the height ratio and the torso length to obtain the corresponding seat height; and a construction unit, configured to construct and output the initial comfort parameter set based on the seat fore-and-aft position, the steering wheel extension, and the seat height.

[0014] Optionally, in one embodiment of this application, the analysis module further includes: a comparison unit, configured to calculate the average steering angular velocity and the maximum lateral acceleration of the absolute value of steering angular velocity within a target time window at preset intervals, and compare the average steering angular velocity with a first preset steering threshold and a second preset steering threshold, and compare the maximum lateral acceleration with a first preset lateral threshold and a second preset lateral threshold, wherein the first preset steering threshold is less than the second preset steering threshold, and the first preset lateral threshold is less than the second preset lateral threshold; and a first determination unit, configured to determine if the maximum lateral acceleration is less than the first preset lateral threshold and the average steering angular velocity is less than the first preset lateral threshold. A first determination unit determines that the current vehicle is in comfort mode if a preset steering threshold is set; a second determination unit determines that the current vehicle is in standard mode if the maximum lateral acceleration is between the first preset lateral threshold and the second preset lateral threshold, and the average steering angular velocity is between the first preset steering threshold and the second preset steering threshold; a third determination unit determines that the current vehicle is in sport mode if the maximum lateral acceleration is greater than the second preset lateral threshold, and the average steering angular velocity is greater than the second preset steering threshold; a dynamic adjustment unit dynamically adjusts the initial comfort parameter set based on the comfort mode, the standard mode, or the sport mode to obtain the updated comfort parameter set.

[0015] Optionally, in one embodiment of this application, the adaptive adjustment module includes: a forward-moving unit, configured to, at the position corresponding to the comfort update parameter set, simulate and calculate that the driver's knee joint angle is less than or equal to a preset angle threshold when the driver depresses the brake pedal, and move the driver's seat forward until the knee joint angle is greater than the preset angle threshold; a judging unit, configured to calculate the driver's corresponding eye position according to the comfort update parameter set, and judge whether the eye position is within a preset eye ellipse, wherein, when the eye position is not within the preset eye ellipse, adjust the seat height and steering wheel height until the eye position is within the preset eye ellipse; a calculation unit, configured to, based on the comfort update parameter set, calculate the distance between the driver's chest and the center of the steering wheel, and, in response to the distance being less than a preset distance threshold, move the driver's seat backward or retract the steering wheel until the distance is greater than or equal to the preset distance threshold; and a generation unit, configured to, when the knee joint angle is greater than the preset angle threshold, the eye position is within the preset eye ellipse, and the distance is greater than or equal to the preset distance threshold, generate the final execution command.

[0016] Optionally, in one embodiment of this application, it further includes: a recording module, configured to record all manual operation data of the driver after controlling the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution instruction, and associate the manual operation data with the driver's digital profile, driving style and environmental data to obtain corresponding association information; and an iteration module, configured to update the ergonomic model according to the association information, so as to readjust the comfort parameter set through the updated ergonomic model.

[0017] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the adaptive adjustment method for the vehicle cabin as described in the above embodiments.

[0018] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described adaptive adjustment method for an automotive cockpit.

[0019] A fifth aspect of this application provides a computer program product, including a computer program that is executed to implement the above-described adaptive adjustment method for a car cabin.

[0020] Therefore, the embodiments of this application have the following beneficial effects:

[0021] This application's embodiments can collect the driver's biometric information, vehicle information, and environmental information within the current vehicle, and fuse these information to generate a corresponding digital driver profile. Based on the driver's digital profile, an initial comfort parameter set corresponding to the biometric information is calculated, and the vehicle and environmental information are analyzed to obtain corresponding driving style analysis data. If the driving style analysis data exceeds a preset threshold, multiple comfort parameters in the initial comfort parameter set are adjusted to obtain an updated comfort parameter set. The updated comfort parameter set undergoes preset verification and correction operations to generate a final execution command, which then controls the current vehicle to perform corresponding adaptive cabin adjustment operations. This application improves vehicle driving safety, comfort, and health by seamlessly collecting the driver's multi-dimensional biometrics (weight, height, sitting height, arm length), fusing real-time driving style and in-vehicle environmental data, and utilizing intelligent algorithm models to automatically and accurately adjust the cabin environment to the optimal state suitable for the driver. This solves the problems of existing vehicle seat memory functions relying too much on manual presets, being unable to adapt to new users, having difficulty distinguishing different body types of the same height, having limited adjustment dimensions, and lacking sufficient linkage with vehicle safety systems.

[0022] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0023] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an adaptive adjustment method for a car cabin provided according to an embodiment of this application; Figure 2 A schematic diagram of the logical architecture of a data awareness layer is provided for one embodiment of this application; Figure 3 A schematic diagram of the execution flow of a central intelligent decision-making layer is provided as an embodiment of this application; Figure 4 A schematic diagram illustrating the execution logic of an adaptive adjustment method for an automotive cockpit, provided as an embodiment of this application; Figure 5 This is an example diagram of an adaptive adjustment device for a car cabin according to an embodiment of this application; Figure 6 This is a schematic diagram of the vehicle structure provided in an embodiment of this application.

[0024] Among them, 10-Automotive cockpit adaptive adjustment device; 100-Acquisition module, 200-Analysis module, 300-Adaptive adjustment module; 601-Memory, 602-Processor, 603-Communication interface. Detailed Implementation

[0025] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0026] The following describes an embodiment of the adaptive adjustment method and apparatus for a car cabin according to the present application, with reference to the accompanying drawings. Addressing the problems mentioned in the background section, this application provides an adaptive adjustment method for a car cabin. In this method, the biometric information of the driver, vehicle information, and environmental information within the vehicle are collected and fused to generate a corresponding digital profile of the driver. Based on the digital profile, an initial comfort parameter set corresponding to the biometric information is calculated, and the vehicle and environmental information are analyzed to obtain corresponding driving style analysis data. If the driving style analysis data exceeds a preset threshold, multiple comfort parameters in the initial comfort parameter set are adjusted to obtain an updated comfort parameter set. The updated comfort parameter set undergoes preset verification and correction operations to generate a final execution command, which controls the current vehicle to perform corresponding adaptive cabin adjustment operations. This application, by seamlessly collecting multi-dimensional biometric characteristics of the driver (weight, height, sitting height, arm length) and fusing real-time driving style and in-vehicle environmental data, and utilizing an intelligent algorithm model, automatically and accurately adjusts the cabin environment to the optimal state adapted to the driver, improving the safety, comfort, and health of vehicle driving. This solves the problems of existing vehicle seat memory functions relying too much on manual presets, being unable to adapt to new users, having difficulty distinguishing different body types of the same height, having limited adjustment dimensions, and lacking sufficient linkage with vehicle safety systems.

[0027] Specifically, Figure 1 This is a flowchart of an adaptive adjustment method for a car cabin provided in an embodiment of this application.

[0028] like Figure 1 As shown, the adaptive adjustment method for the car cabin includes the following steps: In step S101, the driver's biometric information, vehicle information, and environmental information are collected and fused together to generate a corresponding digital profile of the driver.

[0029] The embodiments of this application first collect the driver's biometric information, vehicle operating status information, and driving environment information inside the vehicle, and then perform multi-dimensional fusion analysis on the three types of information to construct a driver digital profile that matches the driver.

[0030] Therefore, the embodiments of this application generate a digital profile of the driver by integrating three types of information: driver biometrics, vehicle information, and environmental information, thereby providing accurate data support for subsequent personalized driving services and safety interventions.

[0031] Optionally, in one embodiment of this application, the biometric information of the driver, vehicle information, and environmental information in the current vehicle are collected and fused to generate a corresponding digital profile of the driver. This includes: acquiring timestamp data from different sensors and buses in the current vehicle, and performing data alignment and preprocessing operations on the timestamp data for the biometric information, vehicle information, and environmental information to obtain a digital profile of the driver. The digital profile of the driver includes weight, total height, seated height, leg length, torso length, shoulder width, arm length, in-vehicle temperature, vehicle speed, steering angular velocity, and lateral acceleration.

[0032] It should be noted that, as Figure 2 As shown, this embodiment of the application first utilizes the global data perception layer to collect the driver's biometric information, vehicle information, and environmental information within the current vehicle, as specifically described below: 1. Biometric Sensing Functional Unit: This functional unit includes a high-precision pressure sensor array built into the seat (for obtaining weight and seated center of gravity) and in-cabin 3D vision sensors (such as depth cameras, for accurately extracting key body dimensions such as total height, seated height, leg length, shoulder width, and arm length).

[0033] 2. Behavior and Environmental Perception Functional Unit: This functional unit can be connected to the vehicle's CAN bus (for real-time acquisition of vehicle speed, lateral acceleration, steering angular velocity, etc. to determine driving style) and the in-vehicle temperature and humidity sensor (for acquiring environmental data).

[0034] Secondly, embodiments of this application can receive timestamp data from different sensors and buses through a central intelligent decision-making layer (domain controller), and perform data alignment and preprocessing to output a structured driver digital profile data structure. This structure includes, but is not limited to, the following fields: weight (kg), total height (mm), seated height (mm), leg length (mm), torso length (mm), shoulder width (mm), arm length (mm), in-vehicle temperature (°C), vehicle speed (km / h), steering angular velocity (rad / s), lateral acceleration (g), etc.

[0035] Therefore, the embodiments of this application effectively solve the adaptation problem of different body types with the same height by introducing dimensions such as sitting height, leg length, and arm length, and realize the customization for different drivers, providing reliable data support for subsequent adaptive adjustment of the cockpit.

[0036] In step S102, based on the driver's digital profile, an initial comfort parameter set corresponding to the biometric information is calculated, and vehicle information and environmental information are analyzed to obtain corresponding driving style analysis data. If the driving style analysis data is greater than a preset threshold, multiple comfort parameters of the initial comfort parameter set are adjusted to obtain a comfort update parameter set.

[0037] Furthermore, embodiments of this application also require calculating an initial comfort parameter set corresponding to the driver's biometric information based on the driver's digital profile; simultaneously, embodiments of this application can parse vehicle information and environmental information to generate driving style analysis data; when the analysis data exceeds a preset threshold, embodiments of this application can dynamically adjust the parameters in the initial comfort parameter set to obtain an updated comfort parameter set.

[0038] Therefore, the embodiments of this application dynamically adjust comfort parameters by combining driver digital profiles and driving style analysis, thereby achieving personalized cabin comfort parameter adaptation and improving the driving experience and cabin intelligence level.

[0039] Optionally, in one embodiment of this application, based on the driver's digital profile, an initial set of comfort parameters corresponding to biometric information is calculated, including: acquiring the leg length, arm length, torso length, total height, and sitting height of the biometric information, and inputting the leg length, arm length, torso length, total height, and sitting height into a pre-constructed ergonomic model; determining the corresponding seat fore-and-aft position based on the ergonomic model and according to the leg length and a preset leg length coefficient, and determining the corresponding steering wheel extension based on the arm length, torso length, preset arm length coefficient, and torso length coefficient; determining the reference height corresponding to the total height by querying a preset height table, and calculating the height ratio between the sitting height and the total height, so as to adjust the reference height based on the height ratio and torso length to obtain the corresponding seat height; and constructing and outputting the initial set of comfort parameters based on the seat fore-and-aft position, steering wheel extension, and seat height.

[0040] In actual implementation, such as Figure 3 As shown, embodiments of this application can execute corresponding decision-making operations based on a central intelligent decision-making layer (domain controller). Firstly, embodiments of this application can construct an advanced ergonomic model through the domain controller. This model is a hybrid model based on rules and data, the core of which is to establish a quantitative mapping relationship between driver biometrics and cockpit configuration parameters. The mapping relationship is implemented through predefined algorithms and lookup tables.

[0041] The model takes biometric data from a digital profile of the driver as input and outputs a preliminary set of comfort parameters (i.e., the initial comfort parameter set) P_comfort.

[0042] It should be noted that the quantification mapping relationship (i.e., the initial comfort parameter set) is as follows: 1. Seat fore-aft position S_x: The fore-aft position of the seat is strongly correlated with leg length, and the formula for calculation is: S_x = A leg_length + B. The coefficients A and B are determined through ergonomic experiments to ensure that the foot can naturally press down on the pedal for the entire distance with the heel on the floor, and the knee joint maintains a comfortable angle of approximately 110°-130°.

[0043] 2. Seat height S_z: The seat height is related to the total height, sitting height, and torso length. In this embodiment, a reference height can first be obtained by looking up a table based on the total height, and then fine-tuned according to the ratio of sitting height to total height. If the driver's torso is long (high ratio), the seat can be raised appropriately to ensure that he has sufficient vertical vision.

[0044] 3. Steering wheel extension amount W_x: The steering wheel extension is strongly correlated with arm length and torso length, and its calculation formula is: W_x = C arm_length - D The goal of torso_length + E is to ensure that when the driver holds the wheel in a "3 o'clock-9 o'clock" position, the elbow flexion angle is between 90° and 120°, and the coefficients C, D, and E are determined through regression analysis.

[0045] Therefore, this application embodiment constructs an advanced ergonomic model that combines rules and data to establish a quantitative mapping relationship between driver biometrics and cabin configuration parameters, outputting an adapted initial comfort parameter set, thereby achieving personalized and accurate matching of cabin parameters and improving driving comfort.

[0046] Optionally, in one embodiment of this application, vehicle information and environmental information are analyzed to obtain corresponding driving style analysis data. If the driving style analysis data exceeds a preset threshold, multiple comfort parameters of the initial comfort parameter set are adjusted to obtain a comfort update parameter set. This includes: calculating the average steering angular velocity and the maximum lateral acceleration within a target time window at preset intervals, comparing the average steering angular velocity with a first preset steering threshold and a second preset steering threshold, and comparing the maximum lateral acceleration with a first preset lateral threshold and a second preset lateral threshold, wherein the first preset steering threshold is less than the second preset steering threshold. If the preset lateral threshold is less than the second preset lateral threshold; if the maximum lateral acceleration is less than the first preset lateral threshold and the average steering angle is less than the first preset steering threshold, then the vehicle is determined to be in comfort mode; if the maximum lateral acceleration is between the first and second preset lateral thresholds and the average steering angle is between the first and second preset steering thresholds, then the vehicle is determined to be in standard mode; if the maximum lateral acceleration is greater than the second preset lateral threshold and the average steering angle is greater than the second preset steering threshold, then the vehicle is determined to be in sport mode; based on comfort mode, standard mode, or sport mode, the initial comfort parameter set is dynamically adjusted to obtain a comfort update parameter set.

[0047] Furthermore, such as Figure 4 As shown, embodiments of this application can classify driving styles by analyzing real-time vehicle dynamic data, as specifically described below: 1. Comfort Mode: This mode provides a smooth driving experience. Its quantitative standard is: average steering angle velocity <0.5 rad / s (i.e., the first preset steering threshold) and maximum lateral acceleration <0.2g (i.e., the first preset lateral threshold).

[0048] 2. Standard Mode: This mode is for normal daily driving. Its quantitative standards are: average steering angle velocity between 0.5 and 1.0 (i.e., the second preset steering threshold) rad / s, and maximum lateral acceleration between 0.2 and 0.4g (i.e., the second preset lateral threshold).

[0049] 3. Sport Mode: This mode is for aggressive driving, and its quantitative standard is: average steering angle speed >1.0 rad / s, or maximum lateral acceleration >0.4g.

[0050] Secondly, this application embodiment can maintain a data buffer with a time window (e.g., 60 seconds). Every 5 seconds, the average value of the absolute value of the steering angular velocity and the maximum value of the absolute value of the lateral acceleration within the window are calculated and compared with the above thresholds to perform mode determination.

[0051] Subsequently, in this embodiment of the application, the identified mode label can be used to dynamically adjust the output of the ergonomic model. For example, in sports mode, the system will instruct P_comfort.seat_bolster_pressure (seat side bolster pressure) and P_comfort.lumbar_support_strength (lumbar support strength) to increase by a preset increment from their base values.

[0052] Therefore, the embodiments of this application, for the first time, take driving style and ambient temperature as core adjustment parameters, so that the cabin environment can dynamically change with driving scenarios (high-speed cruising, mountain road cornering) and seasons (severe winter, hot summer), leaping from static adjustment to dynamic adaptation.

[0053] In step S103, the comfort update parameter set is preset, verified and corrected to generate the final execution command, and the current vehicle is controlled to perform the corresponding cabin adaptive adjustment operation through the final execution command.

[0054] Subsequently, embodiments of this application can perform a preset verification and correction process on the updated comfort parameter set to generate directly executable cabin adjustment commands, and drive the vehicle to complete the adaptive adjustment action of the cabin system according to the commands.

[0055] Therefore, the embodiments of this application can verify and correct the comfort update parameter set and generate execution instructions to drive the cockpit to adaptively adjust, thereby ensuring the accuracy and effectiveness of the parameters and improving the reliability of the cockpit adjustment and driving comfort.

[0056] Optionally, in one embodiment of this application, a preset verification and correction operation is performed on the comfort update parameter set to generate a final execution instruction, including: at the position corresponding to the comfort update parameter set, simulating that the driver's knee joint angle is less than or equal to a preset angle threshold when the driver depresses the brake pedal, moving the driver's seat forward until the knee joint angle is greater than the preset angle threshold; calculating the driver's corresponding eye position based on the comfort update parameter set, and determining whether the eye position is within a preset eye ellipse, wherein if the eye position is not within the preset eye ellipse, adjusting the seat height and steering wheel height until the eye position is within the preset eye ellipse; calculating the distance between the driver's chest and the center of the steering wheel based on the comfort update parameter set, and in response to the distance being less than a preset distance threshold, moving the driver's seat backward or retracting the steering wheel until the distance is greater than or equal to the preset distance threshold; generating a final execution instruction when the knee joint angle is greater than the preset angle threshold, the eye position is within the preset eye ellipse, and the distance is greater than or equal to the preset distance threshold.

[0057] As one possible approach, embodiments of this application utilize a set of non-negotiable hard rules, namely a proactive security rule base, to verify and correct P_comfort. The rules are executed in a "condition-action" format, as described below: 1. Rule R1 (Brake pedal accessibility): (1) Condition: Under the P_comfort position, simulate the knee joint angle ≤90° when the driver presses the brake.

[0058] (2) Action: Forcefully move the seat forward by Δx until the knee joint angle is >90°.

[0059] 2. Rule R2 (Driver's Visibility): (1) Condition: The driver’s eye position calculated by P_comfort is not within the 95% eye ellipse defined by the SAE J1517 standard.

[0060] (2) Action: Forcefully adjust the seat height Δz and steering wheel height Δw_z until the eyes enter the safe field of vision area.

[0061] 3. Rule R3 (Airbag Distance): (1) Condition: The calculated distance between the chest and the center of the steering wheel is less than 250 mm.

[0062] (2) Action: Forcefully move the seat back or retract the steering wheel until the distance is ≥ 250mm.

[0063] Subsequently, in this embodiment of the application, only the parameter set P_final (i.e. the final execution instruction) that has passed all security rule verifications (or been forcibly corrected) will be issued.

[0064] Furthermore, in this embodiment, the main actuator and comfort actuator of the collaborative execution layer can be controlled to perform corresponding adjustment operations according to the final execution command. The main actuator includes a seat motor, a steering wheel electric adjuster, and a rearview mirror motor; the comfort actuator includes a seat heating / ventilation / massage system, a steering wheel heating system, and an air conditioning system.

[0065] Therefore, the embodiments of this application, by adopting a dual decision-making mechanism of comfort calculation and safety verification, fundamentally eliminate the situation of sacrificing safety in pursuit of comfort, and through cabin domain collaborative control, realize the leap from single seat adjustment to multi-domain linkage of seat, steering wheel, rearview mirror, air conditioning, heating / ventilation system, providing a holistic cabin experience.

[0066] Optionally, in one embodiment of this application, after controlling the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution command, the method further includes: recording all manual operation data of the driver, and associating the manual operation data with the driver's digital profile, driving style and environmental data to obtain corresponding association information; updating the ergonomic model according to the association information, so as to readjust the comfort parameter set through the updated ergonomic model.

[0067] It should be noted that the embodiments of this application record all manual fine-tuning operations performed by the user through a self-learning algorithm and associate them with the current driver digital profile, driving style, and environmental data. By updating the coefficients in the above mapping relationship (such as fine-tuning A, B, C, etc.) or by looking up tables, the ergonomic model is continuously optimized. In addition, the embodiments of this application can also provide a UI interface through a personalized configuration interface, allowing users to set special preferences (such as health mode).

[0068] Therefore, this application embodiment uses a self-learning algorithm to associate user manual fine-tuning operations with profiles and operating condition data to continuously optimize ergonomic model parameters and provide a personalized configuration interface, thereby realizing dynamic iteration and customized adaptation of cabin comfort parameters.

[0069] The execution logic of the adaptive adjustment method for the automotive cabin of this application will be described below through a specific embodiment.

[0070] This application uses a specific embodiment of a driver with a total height of 175cm, a seated height of 96cm, and a relatively long torso entering a vehicle as an example for illustration, as detailed below: 1. Data perception and fusion: Pressure sensors determined its weight to be 72kg, and a 3D camera identified its body joints, calculating its total height to be 175cm, its seated height to be 96cm (indicating a relatively long torso), and its leg length to be 79cm. The feature fusion module generated a corresponding digital profile, in which the sitting_height / stature ratio was significantly higher than the average. CAN bus data showed that its recent driving style was stable, and the interior temperature was 5°C.

[0071] 2. Intelligent decision-making: (1) Ergonomic model calculation: Based on its long torso and relatively short legs, the model uses predefined formulas and tables to calculate the initial position P_comfort as follows: a higher seat height S_z (e.g., 15mm higher than the standard 175cm height) to optimize the field of vision, and a relatively forward seat position S_x (due to its shorter leg length), while shortening the steering wheel extension W_x to accommodate its arm length.

[0072] (2) Driving style recognition: The style recognition module calculates the current average steering angular velocity as 0.3 rad / s and the maximum lateral acceleration as 0.15g, and determines it to be in comfort mode based on the threshold.

[0073] (3) Dynamic adjustment of model parameters: Because it is in comfort mode, the system instructs the seat side bolsters and lumbar support to maintain standard support without enhancement.

[0074] (4) Security rule base verification: The safety rule base was used for simulation verification. Under the current seat height and steering wheel proximity scheme, the distance between driver C's chest and the steering wheel was only 230mm, triggering rule R3 (safe distance <250mm).

[0075] (5) Forced correction: The system rejected the original steering wheel position and forced it to retract 25mm, ultimately achieving a chest clearance of 255mm, meeting safety requirements. Subsequently, rule R1 (pedal accessibility) was automatically recalibrated, confirming that with the seat in its current position, driver C could still easily depress the brake pedal fully and maintain a knee angle of 115°, thus passing the rule.

[0076] 3. Execution and comfort are linked: The final instruction set P_final is issued. The seat moves to the calculated high and forward positions, and the steering wheel moves to the retracted position; at the same time, the environmental module automatically activates the seat heater to level 2 (driver C's historical preferred level) because the temperature is below 10°C.

[0077] 4. The driver felt that the headroom was slightly cramped after raising the seat, so he manually lowered the seat height by 5mm. The system recorded this adjustment and linked it to the digital profile of "(72kg, 175cm, seat height 96cm, comfort mode)". In the future, when the system adjusts the seat for a driver with similar characteristics, it can automatically subtract 5mm from the height calculated by the model.

[0078] According to the adaptive adjustment method for a car cabin proposed in this application, the driver's seating position is detected, activating the full-domain data perception layer to collect biometric, vehicle, and environmental data. The central decision-making layer fuses the data to generate a structured digital profile of the driver. The advanced ergonomic model calculates a preliminary comfort parameter set P_comfort based on the biometrics in the digital profile and through a predefined quantitative mapping relationship. The dynamic driving style recognition module analyzes the vehicle's dynamic data, identifies the current mode according to a preset threshold, and adjusts relevant parameters in P_comfort (such as side wing pressure) accordingly. The active safety rule base performs mandatory verification and correction on the adjusted parameter set, generating the final execution instruction P_final. The collaborative execution layer receives the P_final instruction and synchronously drives the main actuator and comfort actuator. A fine-tuning interface is provided, the system learns the user's fine-tuning operations, and updates the ergonomic model to achieve continuous optimization. This application collects multi-dimensional biometric data of the driver (weight, height, sitting height, arm length) without being noticed, integrates real-time driving style and in-vehicle environment data, and uses intelligent algorithm models to automatically and accurately adjust the cabin environment to the optimal state that is suitable for the driver, thereby improving the safety, comfort and health of driving.

[0079] Secondly, the adaptive adjustment device for a car cabin according to an embodiment of this application is described with reference to the accompanying drawings.

[0080] Figure 5 This is a block diagram of an adaptive adjustment device for a car cabin according to an embodiment of this application.

[0081] like Figure 5 As shown, the car cabin adaptive adjustment device 10 includes: a data acquisition module 100, an analysis module 200, and an adaptive adjustment module 300.

[0082] The acquisition module 100 is used to collect the driver's biometric information, vehicle information, and environmental information in the current vehicle, and to integrate the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver.

[0083] The analysis module 200 is used to calculate the initial comfort parameter set corresponding to the biometric information based on the driver's digital profile, and analyze vehicle information and environmental information to obtain the corresponding driving style analysis data. When the driving style analysis data is greater than a preset threshold, multiple comfort parameters of the initial comfort parameter set are adjusted to obtain the comfort update parameter set.

[0084] The adaptive adjustment module 300 is used to perform preset verification and correction operations on the comfort update parameter set to generate the final execution command, and control the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution command.

[0085] Optionally, in one embodiment of this application, the acquisition module 100 includes: a first acquisition unit, used to acquire timestamp data of different sensors and buses in the current vehicle, and to perform data alignment and preprocessing operations on the timestamp data to obtain a digital profile of the driver, the digital profile of the driver including weight, total height, seated height, leg length, torso length, shoulder width, arm length, in-vehicle temperature, vehicle speed, steering angular velocity and lateral acceleration.

[0086] Optionally, in one embodiment of this application, the analysis module 200 includes: a second acquisition unit, a determination unit, a query unit, and a construction unit.

[0087] The second acquisition unit is used to acquire the leg length, arm length, torso length, total height and sitting height of the object, and input the leg length, arm length, torso length, total height and sitting height into the pre-built ergonomic model.

[0088] The determining unit is used to determine the corresponding seat fore-and-aft position based on the ergonomic model and according to the leg length and the preset leg length coefficient, and to determine the corresponding steering wheel extension amount based on the arm length, torso length, preset arm length coefficient and torso length coefficient.

[0089] The query unit is used to determine the reference height corresponding to the total height by querying a preset height table, and to calculate the height ratio between the sitting height and the total height. Based on the height ratio and torso length, the reference height is adjusted to obtain the corresponding seat height.

[0090] The building unit is used to build and output an initial set of comfort parameters based on the seat fore-aft position, steering wheel extension, and seat height.

[0091] Optionally, in one embodiment of this application, the analysis module 200 further includes: a comparison unit, a first determination unit, a second determination unit, a third determination unit, and a dynamic adjustment unit.

[0092] The comparison unit is used to calculate the average steering angular velocity and the maximum lateral acceleration of the absolute value of the steering angular velocity within the target time window every preset period, and compare the average steering angular velocity with the first preset steering threshold and the second preset steering threshold, and compare the maximum lateral acceleration with the first preset lateral threshold and the second preset lateral threshold, wherein the first preset steering threshold is less than the second preset steering threshold, and the first preset lateral threshold is less than the second preset lateral threshold.

[0093] The first determination unit is used to determine that the current vehicle is in comfort mode if the maximum lateral acceleration is less than a first preset lateral threshold and the average steering angular velocity is less than a first preset steering threshold.

[0094] The second determination unit is used to determine that the current vehicle is in standard mode if the maximum lateral acceleration is between the first preset lateral threshold and the second preset lateral threshold, and the average steering angular velocity is between the first preset steering threshold and the second preset steering threshold.

[0095] The third determination unit is used to determine that the current vehicle is in motion mode if the maximum lateral acceleration is greater than the second preset lateral threshold and the average steering angular velocity is greater than the second preset steering threshold.

[0096] The dynamic adjustment unit is used to dynamically adjust the initial set of comfort parameters based on comfort mode, standard mode, or sport mode to obtain an updated set of comfort parameters.

[0097] Optionally, in one embodiment of this application, the adaptive adjustment module 300 includes: a forward shift unit, a judgment unit, a calculation unit, and a generation unit.

[0098] The forward-moving unit is used to simulate and calculate when the driver's knee joint angle is less than or equal to a preset angle threshold when the driver applies the brakes, and then moves the driver's seat forward until the knee joint angle is greater than the preset angle threshold, at the position corresponding to the comfort update parameter set.

[0099] The judgment unit is used to calculate the driver's eye position based on the comfort update parameter set and determine whether the eye position is within the preset eye ellipse. If the eye position is not within the preset eye ellipse, the seat height and steering wheel height are adjusted until the eye position is within the preset eye ellipse.

[0100] The calculation unit is used to calculate the distance between the driver's chest and the center of the steering wheel based on the comfort update parameter set, and in response to the distance being less than a preset distance threshold, to move the driver's seat back or retract the steering wheel until the distance is greater than or equal to the preset distance threshold.

[0101] The generation unit is used to generate the final execution instruction when the knee joint angle is greater than a preset angle threshold, the eye position is within a preset eye ellipse and the distance is greater than or equal to a preset distance threshold.

[0102] Optionally, in one embodiment of this application, the vehicle cabin adaptive adjustment device 10 of this application embodiment further includes: a recording module and an iteration module.

[0103] The recording module is used to record all manual operation data of the driver after controlling the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution command, and to associate the manual operation data with the driver's digital profile, driving style and environmental data to obtain the corresponding association information. The iteration module is used to update the ergonomic model based on the associated information, so as to readjust the set of comfort parameters using the updated ergonomic model.

[0104] It should be noted that the foregoing explanation of the embodiment of the adaptive adjustment method for the car cabin also applies to the adaptive adjustment device for the car cabin in this embodiment, and will not be repeated here.

[0105] The adaptive cabin adjustment device for automobiles proposed in this application includes a data acquisition module 100, used to acquire the driver's biometric information, vehicle information, and environmental information in the current vehicle, and to fuse the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver; an analysis module 200, used to calculate an initial comfort parameter set corresponding to the biometric information based on the driver's digital profile, and to analyze the vehicle information and environmental information to obtain corresponding driving style analysis data, and to adjust multiple comfort parameters of the initial comfort parameter set to obtain an updated comfort parameter set when the driving style analysis data exceeds a preset threshold; and an adaptive adjustment module 300, used to perform preset verification and correction operations on the updated comfort parameter set to generate a final execution command, and to control the current vehicle to perform corresponding adaptive cabin adjustment operations through the final execution command. This application improves the safety, comfort, and health of vehicle driving by seamlessly acquiring the driver's multi-dimensional biometric characteristics (weight, height, sitting height, arm length), fusing real-time driving style and in-vehicle environmental data, and using an intelligent algorithm model to automatically and accurately adjust the cabin environment to the optimal state adapted to the driver.

[0106] Figure 6 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0107] When the processor 602 executes the program, it implements the adaptive adjustment method for the car cabin provided in the above embodiments.

[0108] Furthermore, the vehicle also includes: Communication interface 603 is used for communication between memory 601 and processor 602.

[0109] The memory 601 is used to store computer programs that can run on the processor 602.

[0110] The memory 601 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0111] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0112] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0113] The processor 602 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0114] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described adaptive adjustment method for a car cabin.

[0115] This application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-described adaptive adjustment method for automotive cockpits.

[0116] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0117] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0118] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0120] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0121] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0122] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0123] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for adaptive adjustment of an automobile cabin, characterized in that, Includes the following steps: Collect the driver's biometric information, vehicle information, and environmental information in the current vehicle and fuse the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver; Based on the driver's digital profile, an initial comfort parameter set corresponding to the biometric information is calculated, and the vehicle information and the environmental information are analyzed to obtain corresponding driving style analysis data. If the driving style analysis data is greater than a preset threshold, multiple comfort parameters of the initial comfort parameter set are adjusted to obtain a comfort update parameter set. The comfort update parameter set is preset, verified, and corrected to generate a final execution command, which is then used to control the current vehicle to perform the corresponding cabin adaptive adjustment operation.

2. The method according to claim 1, characterized in that, The process of collecting and fusing the driver's biometric information, vehicle information, and environmental information within the vehicle to generate a corresponding digital driver profile includes: The timestamp data of different sensors and buses in the current vehicle are obtained, and the timestamp data is used to perform data alignment and preprocessing operations on the biometric information, the vehicle information and the environmental information to obtain the driver digital profile. The driver digital profile includes weight, total height, seated height, leg length, torso length, shoulder width, arm length, in-vehicle temperature, vehicle speed, steering angular velocity and lateral acceleration.

3. The method according to claim 2, characterized in that, The calculation of the initial comfort parameter set corresponding to the biometric information based on the driver's digital profile includes: The leg length, arm length, torso length, total height, and sitting height of the object are obtained, and the leg length, arm length, torso length, total height, and sitting height are input into a pre-constructed ergonomic model. Based on the ergonomic model, the corresponding seat fore-and-aft position is determined according to the leg length and the preset leg length coefficient, and the corresponding steering wheel extension amount is determined based on the arm length, the torso length, the preset arm length coefficient and torso length coefficient. The reference height corresponding to the total height is determined by querying a preset height table, and the height ratio between the sitting height and the total height is calculated. Based on the height ratio and the torso length, the reference height is adjusted to obtain the corresponding seat height. Based on the fore-and-aft position of the seat, the steering wheel extension, and the seat height, the initial set of comfort parameters is constructed and output.

4. The method according to claim 3, characterized in that, The process involves analyzing the vehicle information and the environmental information to obtain corresponding driving style analysis data. If the driving style analysis data exceeds a preset threshold, multiple comfort parameters in the initial comfort parameter set are adjusted to obtain a comfort update parameter set, including: The average steering angular velocity and the maximum lateral acceleration within the target time window are calculated once every preset period. The average steering angular velocity is compared with a first preset steering threshold and a second preset steering threshold, and the maximum lateral acceleration is compared with a first preset lateral threshold and a second preset lateral threshold, respectively. The first preset steering threshold is less than the second preset steering threshold, and the first preset lateral threshold is less than the second preset lateral threshold. If the maximum lateral acceleration is less than the first preset lateral threshold and the average steering angular velocity is less than the first preset steering threshold, then the current vehicle is determined to be in comfort mode. If the maximum lateral acceleration is between the first preset lateral threshold and the second preset lateral threshold, and the average steering angular velocity is between the first preset steering threshold and the second preset steering threshold, then the current vehicle is determined to be in standard mode. If the maximum lateral acceleration is greater than the second preset lateral threshold and the average steering angular velocity is greater than the second preset steering threshold, then the current vehicle is determined to be in motion mode. Based on the comfort mode, the standard mode, or the sports mode, the initial comfort parameter set is dynamically adjusted to obtain the updated comfort parameter set.

5. The method according to claim 4, characterized in that, The preset verification and correction operation on the comfort update parameter set to generate the final execution instruction includes: At the location corresponding to the comfort update parameter set, if the knee joint angle of the driver is less than or equal to a preset angle threshold when the driver applies the brakes, the driver's seat is moved forward until the knee joint angle is greater than the preset angle threshold. The driver's eye position is calculated based on the comfort update parameter set, and it is determined whether the eye position is within a preset eye ellipse. If the eye position is not within the preset eye ellipse, the seat height and steering wheel height are adjusted until the eye position is within the preset eye ellipse. Based on the comfort update parameter set, the distance between the driver's chest and the center of the steering wheel is calculated, and in response to the distance being less than a preset distance threshold, the driver's seat is moved back or the steering wheel is retracted until the distance is greater than or equal to the preset distance threshold. The final execution instruction is generated when the knee joint angle is greater than the preset angle threshold, the eye position is within the preset eye ellipse, and the distance is greater than or equal to the preset distance threshold.

6. The method according to claim 5, characterized in that, After controlling the current vehicle to perform the corresponding cabin adaptive adjustment operation through the final execution command, the method further includes: Record all manual operation data of the driver, and associate the manual operation data with the driver's digital profile, driving style and environmental data to obtain corresponding association information; The ergonomic model is updated based on the associated information, so that the comfort parameter set can be readjusted using the updated ergonomic model.

7. An adaptive adjustment device for an automobile cabin, characterized in that, include: The data acquisition module is used to collect the driver's biometric information, vehicle information, and environmental information in the current vehicle and fuse the biometric information, vehicle information, and environmental information to generate a corresponding digital profile of the driver. The analysis module is used to calculate the initial comfort parameter set corresponding to the biometric information based on the driver's digital profile, and analyze the vehicle information and the environmental information to obtain the corresponding driving style analysis data. When the driving style analysis data is greater than a preset threshold, multiple comfort parameters of the initial comfort parameter set are adjusted to obtain a comfort update parameter set. The adaptive adjustment module is used to perform preset verification and correction operations on the comfort update parameter set to generate a final execution command, and control the current vehicle to perform corresponding cabin adaptive adjustment operations through the final execution command.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the adaptive adjustment method for an automotive cabin as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the adaptive adjustment method for the vehicle cabin as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the adaptive adjustment method for a car cabin as described in any one of claims 1-6.

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