Control method of a cabin system in a vehicle, vehicle and readable storage medium

Abstract

This application provides a control method for a vehicle cockpit system, a vehicle, and a readable storage medium. The method includes: acquiring vehicle load data, wherein the load data represents factors affecting the normal driving of passengers in the vehicle under the current driving scenario; determining the load level to which the load data belongs, wherein the load level represents the level of driving load faced by passengers in the current driving scenario; adjusting the interaction strategy between passengers and the vehicle's cockpit system based on the load level, wherein the adjusted interaction strategy represents rules for adjusting cockpit information in the cockpit system; and controlling the cockpit system to adjust the cockpit information based on the adjusted interaction strategy. This application solves the technical problem of ineffective control of the vehicle cockpit system.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and more specifically, to a control method for a cockpit system in a vehicle, a vehicle, and a readable storage medium. Background Technology

[0002] Currently, intelligent cockpit interaction methods in vehicles often employ fixed configurations or adjust based solely on a single factor (such as vehicle speed). This makes it difficult to accurately reflect the actual cognitive load of passengers (such as the driver) under different driving scenarios. Consequently, in high-load driving scenarios, the cockpit system still pushes a large amount of redundant information or complex interaction options, increasing the cognitive burden and operational interference for passengers. On the other hand, in low-load scenarios, interactive functions are limited. Therefore, there remains a technical challenge in effectively controlling the cockpit system in vehicles.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a control method for a vehicle cockpit system, a vehicle, and a readable storage medium to at least solve the technical problem of the inability to effectively control a vehicle cockpit system.

[0005] According to one aspect of the embodiments of this application, a control method for a vehicle cockpit system is provided. The method may include: acquiring vehicle load data, wherein the load data represents factors affecting the normal driving of a passenger in the vehicle under the current driving scenario; determining the load level to which the load data belongs, wherein the load level represents the level of driving load faced by the passenger in the current driving scenario; adjusting the interaction strategy between the passenger and the vehicle cockpit system based on the load level, wherein the adjusted interaction strategy represents the rules for adjusting cockpit information in the cockpit system; and controlling the cockpit system to adjust the cockpit information based on the adjusted interaction strategy.

[0006] Furthermore, the vehicle's load data is obtained, including: obtaining the vehicle's operating status data; and determining the load data based on the operating status data.

[0007] Furthermore, based on the operational status data, load data is determined, including: determining the vehicle's operational load parameters, driving load parameters, and environmental load parameters based on the operational status data. The operational load parameters represent the operational load reflected by the driver and passengers per unit time, the driving load parameters represent the driving load caused by the vehicle's driving scenario, and the environmental load parameters represent the environmental load caused by the surrounding traffic environment of the vehicle. Using the weights of the operational load parameters, driving load parameters, and environmental load parameters, a weighted sum is performed on the operational load parameters, driving load parameters, and environmental load parameters to obtain the load data.

[0008] Furthermore, the operational status data includes at least one of the following vehicle status data: steering wheel angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations. Based on the operational status data, the vehicle's operational load parameters are determined, including: determining the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations based on the vehicle's model and / or driving mode; and using the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations, a weighted sum is performed on the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations to obtain the operational load parameters.

[0009] Furthermore, the operational status data includes at least one of the following vehicle status data: vehicle driving scenario type, road curvature, and road width. Based on the operational status data, the vehicle's driving load parameters are determined, including: determining the weights of road width, road curvature, and the scenario coefficient corresponding to the driving scenario type based on the vehicle's road type; and using the weights of road width, road curvature, and the scenario coefficient corresponding to the driving scenario type, performing a weighted summation of road width, road curvature, and scenario coefficient to obtain the driving load parameters.

[0010] Furthermore, the operational status data includes at least one of the following vehicle status data: the number of traffic participants, weather conditions, and illumination conditions. Based on the operational status data, the environmental load parameters of the vehicle are determined, including: determining the weights of the number of traffic participants, weather conditions, and illumination conditions based on the vehicle's driving scenario type and / or road type; and using the weights of the number of traffic participants, weather conditions, and illumination conditions, a weighted sum is performed on the number of traffic participants, weather conditions, and illumination conditions to obtain the environmental load parameters.

[0011] Further, determining the load level to which the load data belongs includes: determining the load level as a first load level in response to the load data being less than a first load threshold in the load level range; determining the load level as a second load level in response to the load data being greater than the first load threshold and less than a second load threshold in the load level range, wherein the second load level is higher than the first load level; and determining the load level as a third load level in response to the load data being greater than the second load threshold, wherein the third load level is higher than the second load level.

[0012] Furthermore, the first load level is used to represent a driving scenario where the driving operation is below the operation threshold, the vehicle's operating state is stable, and the complexity of the vehicle's driving environment is below the complexity threshold; the second load level is used to represent a driving scenario where the driving operation frequency is within the target range, the vehicle's operating state is within the operating change range, and the complexity is within the complexity range; the third load level is used to represent a driving scenario where at least one of the following is true: the driving operation is above the operation threshold, the change in the operating state is above the change threshold, and the complexity is above the complexity threshold.

[0013] Furthermore, based on the load level, the interaction strategy between the driver / passenger and the vehicle's cockpit system is adjusted, including: determining the adjustment rules for the interaction constraint parameters of cockpit information according to the load level, wherein the interaction constraint parameters include at least one of the following: display density, interaction input method, interaction response frequency, and task priority; and adjusting the interaction strategy according to the adjustment rules to obtain the adjusted interaction strategy.

[0014] Furthermore, based on the adjusted interaction strategy, the control cockpit system adjusts the cockpit information, including: in response to detecting a change in load level, generating a state switching command based on the adjusted interaction strategy; and based on the state switching command, controlling the cockpit system to adjust the cockpit information according to the task priority sequence within the switching time window.

[0015] According to another aspect of the embodiments of this application, a control device for a vehicle cockpit system is also provided. The device includes: an acquisition unit for acquiring vehicle load data, wherein the load data represents factors affecting the normal driving of the vehicle's occupants in the current driving scenario; a determination unit for determining the load level to which the load data belongs, wherein the load level represents the level of driving load faced by the occupants in the current driving scenario; an adjustment unit for adjusting the interaction strategy between the occupants and the vehicle's cockpit system based on the load level, wherein the adjusted interaction strategy represents the rules for adjusting cockpit information in the cockpit system; and a control unit for controlling the cockpit system to adjust the cockpit information based on the adjusted interaction strategy.

[0016] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0017] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program, which, when executed by a processor, implements the methods in various embodiments of this application.

[0020] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0021] In this embodiment, by acquiring load data of factors affecting the driver and passengers in a vehicle during normal driving in the current driving scenario, the actual cognitive load of the driver and passengers can be accurately quantified. Subsequently, the load level of the load data can be determined, and the interaction strategy between the driver and passengers and the vehicle's cockpit system can be adjusted based on the load level. This overcomes the limitations of related technologies that rely solely on single factors such as vehicle speed or use fixed configurations, leading to an inability to accurately determine the comprehensive cognitive load in complex driving scenarios. Based on the adjusted interaction strategy, the cockpit system is controlled to adjust cockpit information, solving the technical problem of ineffective control of the vehicle's cockpit system and achieving the technical effect of effectively controlling the vehicle's cockpit system. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0023] Figure 1 This is a flowchart of a control method for a vehicle cabin system according to an embodiment of this application;

[0024] Figure 2 This is a schematic diagram of an intelligent cockpit interaction control system based on driving load according to an embodiment of this application;

[0025] Figure 3 This is a schematic diagram of the data acquisition process of a running status acquisition module according to an embodiment of this application;

[0026] Figure 4 This is a schematic diagram of a load parameter according to an embodiment of this application;

[0027] Figure 5 This is a schematic diagram of a load level according to an embodiment of this application;

[0028] Figure 6 This is a schematic diagram of an interactive module according to an embodiment of this application;

[0029] Figure 7 This is a flowchart of a time-based progressive adjustment method according to an embodiment of this application;

[0030] Figure 8 This is a flowchart of a priority-based progressive adjustment method according to an embodiment of this application;

[0031] Figure 9 This is a schematic diagram of a control device for a vehicle cabin system according to an embodiment of this application. Detailed Implementation

[0032] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] According to an embodiment of this application, an embodiment of a control method for a cockpit system in a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0035] This embodiment provides a control method for a vehicle cockpit system. Figure 1 This is a flowchart of a control method for a vehicle cabin system according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps.

[0036] Step S102: Obtain vehicle load data.

[0037] In the technical solution provided in step S102 of this application, the load data can be used to represent the factors affecting the normal driving of the vehicle's passengers in the current driving scenario.

[0038] In this embodiment, the aforementioned load data refers to a set of technical parameters that comprehensively reflect the cognitive and operational load of the driver / passenger during normal driving in the current driving scenario. Normal driving refers to internal and external variables that occupy the driver / passenger's attention, increase operational complexity, or enhance psychological stress.

[0039] Optionally, the aforementioned passengers can be the driver or other occupants who need to pay attention to the vehicle's status; this is merely an example and no specific restrictions are imposed here.

[0040] Optionally, the aforementioned load data can be determined from operational load data, driving load parameters, and environmental load parameters. Operational load parameters reflect the frequency and intensity of the driver's own operational behavior, and may include the frequency of steering wheel angle changes, braking frequency, accelerator pedal operation amplitude, and the total number of operations per unit time. Driving load parameters reflect the impact of the road environment on the difficulty of driving operations, and may include the driving scenario type (highway / urban / rural), road curvature, and road width. Environmental load parameters reflect the uncertainties brought about by the external traffic environment and natural conditions, and may include the number of surrounding traffic participants, weather condition coefficient, and illumination condition coefficient.

[0041] In this embodiment of the application, by obtaining the vehicle's load data, the abstract driving difficulty can be transformed into a calculable value, which solves the limitation of judging the load based solely on vehicle speed in related technologies.

[0042] Step S104: Determine the load level to which the load data belongs.

[0043] In the technical solution provided in step S104 of this application, the load level can be used to represent the level of driving load faced by the driver and passenger in the current driving scenario.

[0044] In this embodiment, the load levels may include low load level (corresponding to low load), medium load level (corresponding to medium load) and high load level (corresponding to high load).

[0045] Optionally, a low load level corresponds to scenarios with few driving operations, stable vehicle operation, and low driving environment complexity. In this case, the driver and passengers have ample cognitive resources available for processing non-driving tasks. A medium load level corresponds to scenarios with moderate driving operation frequency, some changes in vehicle status, and moderate environmental complexity. In this case, the driver and passengers need to focus on some driving information, and interference from non-driving tasks is somewhat limited. A high load level corresponds to scenarios with frequent driving operations, drastic changes in vehicle status, or high environmental complexity. In this case, the driver and passengers' cognitive resources are almost entirely occupied, and information interference needs to be minimized.

[0046] In this embodiment, by dividing the load level, a customized interactive experience can be provided for driving scenarios with different risk levels. For example, entertainment information can be automatically blocked under high load level, while rich services can be provided under low load level, thus realizing intelligent interaction.

[0047] Step S106: Adjust the interaction strategy between the driver / passenger and the vehicle's cockpit system based on the load level.

[0048] In the technical solution provided by step S106 of this application, the adjusted interaction strategy is used to represent the rules for adjusting cockpit information in the cockpit system.

[0049] In this embodiment, the interaction strategy refers to a set of rules that the cockpit system dynamically manages interface display, input response, and information presentation based on the current driving load. The adjusted interaction strategy refers to a dynamic interaction constraint scheme that is regenerated and adapted to the current driving scenario after a change in load level is detected.

[0050] Optionally, the adjusted interaction strategy can match the complexity of cockpit interaction to the cognitive load of the driver and passengers by changing the way cockpit information is presented and the interaction logic. The adjusted interaction strategy may include display density rules, interaction input method rules, interaction response frequency rules, task priority rules, and progressive adjustment rules, etc.

[0051] Optionally, the display density rules can limit the number and hierarchy of information elements displayed simultaneously in the cockpit interface. For example, under high load, the amount of information can be reduced, retaining only core safety information; under low load, the information richness can be increased.

[0052] Optionally, the interaction input method rules can limit the currently available input channels and constraints. For example, under high load, complex touch can be disabled, retaining only physical buttons or simple voice commands; under low load, multiple input methods such as touch, gestures, and voice can be enabled.

[0053] Optionally, the interaction response frequency rules can limit the cockpit interface refresh rate, animation playback rate, and prompt tone trigger frequency. Under high load, the user interface (UI) refresh rate and animation complexity are reduced to minimize visual interference; under low load, a smooth high refresh rate and rich animations are maintained.

[0054] Optionally, task priority rules can define the execution order and blocking strategies for different categories of interactive tasks. Tasks are categorized into safety critical (P0), driving necessary (P1), driving assistance (P2), and non-driving interaction (P3), and the rules determine which tasks are disabled, delayed, or reduced in frequency based on the load level.

[0055] Optionally, the progressive adjustment rules can stipulate that the process of switching interaction strategies is not completed instantaneously, but is gradually adjusted through a smooth transition within a time window or according to a priority sequence to avoid interface jumps.

[0056] In this embodiment, the interaction strategy between the driver / passenger and the vehicle's cockpit system is adjusted based on the load level. Instead of passively responding to interaction commands, the system actively senses the state of the driver / passenger and adjusts its own behavior, demonstrating the contextual awareness capability of the intelligent cockpit.

[0057] Step S108: Based on the adjusted interaction strategy, control the cockpit system to adjust the cockpit information.

[0058] In the technical solution provided by step S108 of this application, adjusting the cockpit information by controlling the cockpit system is to transform the adjusted interaction strategy into specific execution actions at the physical or software level.

[0059] In this embodiment, the aforementioned cabin information may include carriers that transmit information to the driver and passengers, such as the instrument panel display content, the central control screen UI interface, the voice broadcast content, the light prompts, and the air conditioning status display.

[0060] Optionally, based on the adjusted interaction strategy, the cockpit system can adjust the cockpit information, such as adjusting the visibility and layout of interface elements, enabling and disabling interaction channels, adjusting rendering performance and feedback rhythm, and scheduling and blocking task queues.

[0061] Optionally, the visibility and layout adjustment of interface elements can be dynamically hidden or shown based on display density rules, such as navigation lists, music album art, and vehicle status icons. For example, under high load, the music list and recommendation pop-ups can be hidden, leaving only vehicle speed and core navigation guidance.

[0062] Optionally, enabling or disabling the interaction channel can be done at the software level by disabling or enabling specific touch areas, gesture recognition functions, or voice command sets based on input method rules. For example, complex multi-finger touch gestures can be disabled under high load, and only specific keywords can be enabled for wake-up.

[0063] Alternatively, adjustments to rendering performance and feedback cadence can be made by modifying the parameters of the graphics rendering engine according to response frequency rules. For example, reducing the UI refresh rate (e.g., from 60Hz to 30Hz), simplifying animation effects (e.g., switching a complex 3D rotating map to a 2D planar arrow), and reducing the frequency of unnecessary sound prompts.

[0064] Optionally, task queue scheduling and blocking can be based on task priority rules, rescheduling background tasks. For example, pausing background downloads, postponing non-urgent push notifications, and reducing the refresh frequency of P2 / P3 tasks to ensure that P0 / P1 security tasks are processed first.

[0065] In the embodiments of this application, during the process of adjusting the cockpit information by the control cockpit system, the adjustment can be gradual in the time dimension (smooth transition) or gradual in the priority dimension (orderly stripping). By performing the adjustment gradually, the shock or confusion caused by page jumps or sudden failures of functions is avoided, and the continuity of the interaction is maintained.

[0066] Steps S102 to S108 described above acquire load data of factors affecting the driver and passengers' normal driving in the current driving scenario, thereby accurately quantifying the driver and passengers' actual cognitive load. Subsequently, the load level of the load data can be determined, and the interaction strategy between the driver and passengers and the vehicle's cockpit system can be adjusted based on this load level. This overcomes the limitations of related technologies that rely solely on single factors such as vehicle speed or use fixed configurations, leading to an inability to accurately determine the comprehensive cognitive load in complex driving scenarios. Based on the adjusted interaction strategy, the cockpit system is controlled to adjust cockpit information, solving the technical problem of ineffective control of the vehicle's cockpit system and achieving the technical effect of effective control of the vehicle's cockpit system.

[0067] The above-mentioned method of this application will be further described below.

[0068] As an optional implementation, step S102, obtaining vehicle load data, includes: obtaining vehicle operating status data; and determining load data based on the operating status data.

[0069] In this embodiment, the vehicle's operating status data can be raw physical signals or basic state parameters directly collected by the vehicle's sensors and control system. For example, the operating status data can be vehicle speed information, steering wheel operation information, braking operation information, vehicle driving environment information, driver operation frequency information, etc.

[0070] Optionally, after obtaining the operating status data, the weights of the vehicle's operating load parameters, the vehicle's driving load parameters, and the vehicle's environmental load parameters can be determined. Then, using the weights of the operating load parameters, driving load parameters, and environmental load parameters, the operating load parameters, driving load parameters, and environmental load parameters can be weighted and summed to obtain the load data.

[0071] In this embodiment, by comprehensively considering three dimensions—operation (corresponding to operational load parameters), driving (corresponding to driving load parameters), and environment (corresponding to environmental load parameters)—and introducing weights, the true load state of the driver and passengers can be more accurately reflected. Accurately calculated load data forms the basis for subsequent load level determination and interaction strategy adjustment, ensuring the effectiveness of intelligent cockpit interaction control, thereby better protecting driving safety and improving user experience.

[0072] As an optional implementation, load data is determined based on operational status data, including: determining vehicle operational load parameters, vehicle driving load parameters, and vehicle environmental load parameters based on operational status data. The operational load parameters represent the operational load reflected by the driver / passenger within a unit of time, the driving load parameters represent the driving load caused by the vehicle's driving scenario, and the environmental load parameters represent the environmental load caused by the surrounding traffic environment of the vehicle. The operational load parameters, driving load parameters, and environmental load parameters are then weighted and summed using their respective weights to obtain the load data.

[0073] In this embodiment, load data (i.e., total load data L) is determined based on operational status data. total During the process, the vehicle's operational load parameters, driving load parameters, and environmental load parameters can be determined based on the operational status data. Then, using the weights of the operational load parameters, driving load parameters, and environmental load parameters, a weighted sum can be performed on these parameters to obtain the load data.

[0074] Optionally, the weights of the vehicle's operating load parameters can be determined by w op The weights of the vehicle's driving load parameters can be represented by w. drv The weights of the vehicle's environmental load parameters can be represented by w. env This can be represented. Operating load parameters can be expressed through L. op The driving load parameters can be represented by L. drv The environmental load parameters can be represented by L. env To express.

[0075] Optionally, total load data L total It can be determined using the following formula:

[0076] L total =w op ·L op +w drv ·L drv +w env ·L env

[0077] Among them, w op +w drv +w env =1.

[0078] Optionally, the weights of operational load parameters, driving load parameters, and environmental load parameters can be adjusted based on driving mode, vehicle speed range, or user preferences. A general configuration principle is to assign higher weights to parameters that have a more direct impact on "driving attention expenditure."

[0079] Optionally, w op It can directly reflect how busy the driver and passengers are; drv It can directly reflect the difficulty of operation; w env It can directly reflect uncertainty and risk.

[0080] Optionally, during manual driving, the weight of the operational load parameters in the total load parameters can be increased, and the weighting relationship can then be w. op >w drv >w env For example, w op =0.45, w drv =0.35, w env =0.20.

[0081] Alternatively, during assisted driving, the vehicle's control system takes over the operation, and environmental uncertainty becomes the main source of risk. In this case, the weighting relationship can be w. env >=w drv >w op For example, w op=0.25, w drv =0.35, w env =0.40.

[0082] In this embodiment, the complex information from the three dimensions of operation, road, and environment is fused into a unified scalar value, facilitating subsequent threshold determination. The weights can be dynamically adjusted to adapt to different driving scenarios. For example, in manual driving, more attention is paid to the driver's and passengers' operational fatigue, while in assisted driving, more attention is paid to environmental risks, thus more accurately reflecting the driver's and passengers' true cognitive load.

[0083] As an optional implementation, the operating status data includes at least one of the following vehicle status data: steering wheel angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations. Based on the operating status data, the vehicle's operating load parameters are determined, including: determining the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations based on the vehicle's model and / or driving mode; and using the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations, performing a weighted summation of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations to obtain the operating load parameters.

[0084] In this embodiment, the steering wheel angle change frequency can be the steering wheel angle change frequency, which can be expressed as f. steer This can be represented by f. The braking operation frequency can be expressed as f. brake This can be represented. The accelerator pedal operation range can be expressed via a. acc The number of driving operations can be represented as the number of driving operations per unit time, which can be expressed as n. op The weights for the frequency of angle changes can be represented by λ1. The weight for the frequency of braking operations can be represented by λ2. The weight for the magnitude of accelerator pedal operation can be represented by λ3. The weight for the number of driving operations can be represented by λ4.

[0085] Alternatively, the operating load parameters can be determined using the following formula:

[0086] L op =λ1f steer +λ2f brake +λ3a acc +λ4n op

[0087] Optionally, the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and driving operation number can be configured according to the vehicle model (e.g., vehicle type) and / or driving mode.

[0088] For example, if the vehicle type is a family car, primarily driven manually with frequent lateral maneuvers, then braking while following other vehicles is crucial. In this case, λ1 > λ2 ≈ λ3 > λ4. For instance, λ1 (steering) = 0.35, λ2 (braking) = 0.25, λ3 (acceleration) = 0.25, λ4 (number of times) = 0.15. If the vehicle type is a commercial vehicle / large vehicle, with a larger body and greater inertia, the braking workload is higher, and the operating rhythm is relatively stable. In this case, λ2 > λ1 > λ3 > λ4. For instance, λ1 (steering) = 0.30, λ2 (braking) = 0.35, λ3 (acceleration) = 0.20, λ4 (number of times) = 0.15.

[0089] For another example, weights can be configured based on the driving mode. If the driving mode is Comfort / Standard, with smooth operation and no emphasis on extreme control, then λ1≈λ2≈λ3>λ4. For instance, λ1 (steering) = 0.30, λ2 (braking) = 0.30, λ3 (acceleration) = 0.25, λ4 (number of times) = 0.15. If the driving mode is Sport, with frequent steering and acceleration operations and high operational intensity, then λ1+λ2>λ3+λ4. For instance, λ1 (steering) = 0.35, λ2 (braking) = 0.20, λ3 (acceleration) = 0.30, λ4 (number of times) = 0.15.

[0090] In this embodiment of the application, by combining the weights of the frequency of angle changes in vehicle model and driving mode, the weight of braking operation frequency, the weight of accelerator pedal operation amplitude, and the weight of driving operation number, the differences in operation characteristics under different driving scenarios can be captured more accurately, thereby obtaining an operation load value that is more in line with the actual experience of the driver and passengers.

[0091] As an optional implementation, the operational status data includes at least one of the following vehicle status data: vehicle driving scenario type, road curvature, and road width. Based on the operational status data, the vehicle's driving load parameters are determined, including: determining the weights of road width, road curvature, and the scenario coefficient corresponding to the driving scenario type based on the vehicle's road type; and using the weights of road width, road curvature, and the scenario coefficient corresponding to the driving scenario type, performing a weighted summation of road width, road curvature, and scenario coefficient to obtain the driving load parameters.

[0092] In this embodiment, the driving scenario type can be highway, urban road, or rural road. Road curvature can be determined by C. road The road width can be represented by W. road This is represented. The scenario coefficients corresponding to the driving scenario type can be represented by S. scene To express.

[0093] Alternatively, the driving load parameters can be determined using the following formula:

[0094] L drv =U1S scene +U2C road +U3 / W road

[0095] Among them, S scene These can be used to represent scenario coefficients (highway < city road < rural road). Road width is taken as the reciprocal to reflect the higher load on narrow roads. U1 can be used to represent the weight of the scenario coefficient corresponding to the driving scenario type. U2 can be used to represent the weight of road curvature. U3 can be used to represent the weight of road width.

[0096] Optionally, U1, U2, and U3 can be configured according to road type. If the road type is a highway, the scenario is simple, vehicle speed is high but curvature is small, the road is wide, and lanes are clearly defined, then U1 > U2 > U3. For example, U1 = 0.45, U2 = 0.35, U3 = 0.20. If the road type is an urban road, the scenario is heavy, with frequent turns, many intersections, and large variations in road width, then U2 ≈ U3 > U1. For example, U1 = 0.30, U2 = 0.35, U3 = 0.35. If the road type is a rural / mountain road, with many curves and narrow roads, then U2 > U3 > U1. For example, U1 = 0.20, U2 = 0.45, U3 = 0.35.

[0097] In this embodiment, the main factors causing driving difficulties differ under different road types. Highways emphasize the overall environment and speed, urban areas emphasize local road conditions (curves, narrow roads), and rural areas emphasize curves. By determining the weights of road width, road curvature, and scene coefficients corresponding to different road types, the assessment is made to better reflect actual road characteristics.

[0098] As an optional implementation, the operational status data includes at least one of the following vehicle status data: the number of traffic participants, weather condition information, and illumination condition information. Based on the operational status data, the environmental load parameters of the vehicle are determined, including: determining the weights of the number of traffic participants, weather condition information, and illumination condition information based on the vehicle's driving scenario type and / or road type; and using the weights of the number of traffic participants, weather condition information, and illumination condition information, performing a weighted summation of the number of traffic participants, weather condition information, and illumination condition information to obtain the environmental load parameters.

[0099] In this embodiment, the number of traffic participants can be the number of surrounding traffic participants, which can be expressed as N. obj This can be represented as weather state coefficients, which can be expressed using S. weatherThis is represented. Illumination state information can be expressed as illumination state coefficients, which can be expressed through S... light To express.

[0100] Alternatively, the environmental load parameters can be determined using the following formula:

[0101] L env =V1N obj +V2S weather +V3S light

[0102] Optionally, V1 can be used to represent the weight of the number of traffic participants. V2 can be used to represent the weight of weather condition information. V3 can be used to represent the weight of lighting condition information.

[0103] Optionally, if the vehicle's driving scenario and / or road type is an urban road / congested scenario, with dense traffic participants and relatively minor impacts from weather and lighting changes, then V1 > V2 >= V3. For example, V1 = 0.50, V2 = 0.30, V3 = 0.20. If the vehicle's driving scenario and / or road type is a highway / smooth traffic scenario, with relatively few targets and significant impacts from weather on safety, then V2 >= V1 > V3. For example, V1 = 0.35, V2 = 0.40, V3 = 0.25. If the vehicle's driving scenario and / or road type is a nighttime / low-light scenario, with visual information attenuation and increased perceptual uncertainty, then V3 > V1 >= V2. For example, V1 = 0.35, V2 = 0.25, V3 = 0.40.

[0104] In this embodiment of the application, by determining the weights of the number of traffic participants, the weights of weather status information, and the weights of illumination status information, an accurate basis is provided for determining environmental load parameters.

[0105] As an optional implementation, step S104, determining the load level to which the load data belongs, includes: determining the load level as a first load level in response to the load data being less than a first load threshold in the load level range; determining the load level as a second load level in response to the load data being greater than the first load threshold and less than a second load threshold in the load level range, wherein the second load level is higher than the first load level; and determining the load level as a third load level in response to the load data being greater than the second load threshold, wherein the third load level is higher than the second load level.

[0106] In this embodiment, the load level can be a driving load level, which can be used to classify and describe the overall driving burden of the driver and passengers in the current driving scenario.

[0107] Optionally, the first load level can be a low load level. The second load level can be a medium load level. The third load level can be a high load level.

[0108] Optionally, if the load data is less than the first load threshold (θ1) in the load level range, the load level can be determined as the first load level. If the load data is greater than the first load threshold and less than the second load threshold (θ2) in the load level range, the load level can be determined as the second load level. If the load data is greater than the second load threshold, the load level can be determined as the third load level. Here, θ1 and θ2 can be preset values.

[0109] In this embodiment of the application, accurate level determination is the basis for subsequent adjustment of interaction strategy, ensuring that the timing of interaction strategy switching is appropriate and avoiding frequent state oscillations caused by small fluctuations near the load critical point.

[0110] As an optional implementation, the first load level is used to represent a driving scenario where the driving operation is below the operation threshold, the vehicle's operating state is stable, and the complexity of the vehicle's driving environment is below the complexity threshold; the second load level is used to represent a driving scenario where the driving operation frequency is within the target range, the vehicle's operating state is within the operating change range, and the complexity is within the complexity range; the third load level is used to represent at least one of the following: a driving scenario where the driving operation is above the operation threshold, the change in the operating state is above the change threshold, and the complexity is above the complexity threshold.

[0111] In this embodiment, low load level corresponds to driving scenarios with fewer driving operations, relatively stable vehicle operation, and low driving environment complexity; medium load level corresponds to driving scenarios with moderate driving operation frequency, some changes in vehicle operation, and moderate driving environment complexity; high load level corresponds to driving scenarios with frequent driving operations, drastic changes in vehicle operation, or high driving environment complexity.

[0112] Optionally, the driving load level can be determined based on the correspondence between driving load parameters and preset load level ranges.

[0113] As an optional implementation, step S106 involves adjusting the interaction strategy between the driver / passenger and the vehicle's cockpit system based on the load level. This includes: determining adjustment rules for the interaction constraint parameters of the cockpit information according to the load level, wherein the interaction constraint parameters include at least one of the following: display density, interaction input method, interaction response frequency, and task priority; and adjusting the interaction strategy according to the adjustment rules to obtain the adjusted interaction strategy.

[0114] In this embodiment, interaction constraint parameters refer to physical or logical parameters used to define or regulate the rules and performance indicators of cockpit information exchange between the cockpit system and the passengers. These may include display density, interaction input method, interaction response frequency, and task priority.

[0115] Optionally, display density refers to the number and size of information elements displayed per unit screen area or visual focus. Density needs to be reduced under high load and increased under high load.

[0116] Optionally, the interactive input method refers to the way the driver or passenger sends commands to the cockpit system, such as touch, voice, gestures, physical buttons, etc. Different methods require different levels of driver attention.

[0117] Optionally, the interaction response frequency refers to the frequency of the cockpit system UI refresh rate, animation playback frame rate, and triggering of non-critical prompts (such as advertisements and recommendations).

[0118] Optionally, task priority refers to the order and importance of different interactive tasks in the cockpit, which can be divided into levels such as safety critical, driving necessary, driving assistance and non-driving interaction.

[0119] Optionally, the adjustment rule refers to predefined mapping logic used to transform the load level state variable into the value or state of specific interaction constraint parameters. The adjustment rule defines how each interaction constraint parameter should change when the load level changes (e.g., high load -> reduced display density, disabled gesture input, reduced UI refresh rate, blocked entertainment tasks).

[0120] Optionally, the adjusted interaction strategy refers to the set of specific execution instructions generated after the adjustment rules are calculated, which is used to guide the cockpit hardware (screen, speaker, microphone) and software (UI interface, background service) to perform actual state switching or parameter modification.

[0121] Optionally, the interaction module can be used to adjust the interaction strategy between the driver / passenger and the vehicle's cockpit system based on the load level. The interaction module may include an interactive information display control module, an interactive input method control module, an interactive response frequency control module, and an interactive task priority control module.

[0122] Optionally, the interactive information display control module is used to adjust the display density and display hierarchy of interactive information according to the load level. For example, high load -> fewer information, larger font size, the central control screen only displays "50m ahead left turn" and vehicle speed. Medium load -> cancel split screen, hide secondary information, the central control screen simultaneously displays navigation, music playlist and vehicle status information. Low load -> more information, rich entry points, the central control screen simultaneously displays navigation, music playlist and vehicle status information.

[0123] Optionally, the interactive input method control module is used to select or switch input methods according to the load level. For example, under high load levels, input methods that cause less interference with driving are prioritized. High load -> Only physical steering wheel buttons are retained, voice wake-up is supported (only short commands such as navigation cancellation and air conditioning temperature adjustment are supported). Medium load -> Split screen is canceled, secondary information is hidden, voice interaction is prioritized, and touch operation is enhanced with an anti-accidental touch mechanism. Low load -> Touch, voice, gesture, and physical buttons.

[0124] Optionally, the interaction response frequency control module is used to adjust the frequency or rhythm of the interaction response according to the driving level. Adjustments include, but are not limited to: UI refresh frame rate, animation frame rate, and prompt trigger frequency. For example, high load -> UI refresh frame rate set to low, animation frame rate set to low, and prompt trigger frequency set to low. Medium load -> UI refresh frame rate set to medium, animation frame rate set to medium, and prompt trigger frequency set to medium. Low load -> UI refresh frame rate set to high, animation frame rate set to high, and prompt trigger frequency set to high.

[0125] Optionally, the interaction task priority control module is used to dynamically adjust the priority of different interaction tasks according to the driving load level. Safety-related > Non-safety-related. Current task > Recommended task.

[0126] In this embodiment of the application, by adjusting the interaction constraints, the amount of information and the complexity of interaction provided by the cockpit system are matched with the current cognitive ability of the driver and passengers, so as to avoid information overload or insufficient interaction.

[0127] As an optional implementation, step S108 involves controlling the cockpit system to adjust cockpit information based on the adjusted interaction strategy, including: in response to detecting a change in load level, generating a state switching command based on the adjusted interaction strategy; and controlling the cockpit system to adjust cockpit information according to task priority sequence within the switching time window based on the state switching command.

[0128] In this embodiment, the state transition command refers to a control signal generated by the control system to instruct the cockpit system to transition from the current interactive state to the target interactive state. This state transition command may include the source state (the currently displayed content and mode), the target state (the displayed content and mode determined according to the new load level), and transition parameters (such as transition duration, animation effects, etc.).

[0129] Optionally, the switching time window refers to the time period allowed for the cockpit system to fully transition from its current state to the target state. For example, switching from rich information mode to minimalist mode may take 5 or 10 seconds. Setting a time window is to avoid sudden jumps in the UI interface, thereby reducing the cognitive abruptness for drivers and passengers.

[0130] Optionally, the task priority sequence refers to the ranking of all interactive tasks within the cockpit (such as navigation display, music playback, message push, vehicle status monitoring, etc.) according to their importance to driving safety. This can be categorized into levels such as safety critical, driving necessary, driving assistance, and non-driving / entertainment. During state transitions, the status of each task is hidden, shown, or adjusted sequentially according to this sequence.

[0131] Optionally, cockpit information adjustments can be gradual, meaning that instead of changing all interface elements at once within the switching window, the changes are executed in stages and steps. For example, the least important P3 task can be hidden first, then the P2 task can be adjusted, and finally the P1 task display can be optimized. This approach achieves a smooth interface transition.

[0132] Optionally, when the load status changes, the interaction strategy does not switch all at once when the driving load level changes, but adjusts in stages and intensities according to preset adjustment rules. If a direct switch would cause a UI jump, it would affect driving concentration to some extent. Gradual adjustment achieves a smooth page transition and reduces cognitive load.

[0133] Optionally, the incremental adjustment may include time-based incremental adjustment and priority-based incremental adjustment.

[0134] Optionally, based on a gradual adjustment over time, the interaction strategy is gradually adjusted to match the target driving load level within a preset time period. For example, under low load, there are 10 interface elements. When switching to high load, the interface needs to display 3 elements. The switch is preset to be completed in 10 seconds, and it is completed gradually within these 10 seconds. At the 2nd second, 8 elements are displayed, at the 4th second, 6 elements are displayed, at the 7th second, 4 elements are displayed, and at the 10th second, 3 elements are displayed.

[0135] Optionally, based on priority-based progressive adjustment, the interaction strategy is adjusted sequentially according to the interaction priority, and the interactions are categorized by type. For example, P0: safety-critical interactions (collision warning, emergency braking prompts, etc.); P1: driving-necessary interactions (navigation steering, vehicle speed, lane information); P2: driving assistance interactions (energy consumption prompts, driving suggestions); P3: non-driving interactions (entertainment, messages, recommended content). Low load: easy driving, music playback + navigation + recommendation pop-ups exist simultaneously; medium load: limit the lowest priority P3 adjustment method: disable pop-ups, recommendations only keep background P2, P1, and P0 are normal; high load: disable P3, reduce the frequency and delay P2, P1 and P2 are normal; high load: disable P3 and P2, P1 is displayed in a minimalist way, for example, only intersection turning information is kept, P0 is displayed more strongly.

[0136] The vehicle cockpit system control method of this application embodiment acquires load data of factors affecting the driver and passengers during normal driving in the current driving scenario, thereby accurately quantifying the actual cognitive load of the driver and passengers. Then, the load level of the load data can be determined, and the interaction strategy between the driver and passengers and the vehicle's cockpit system can be adjusted based on the load level. This overcomes the limitations of related technologies that rely solely on single factors such as vehicle speed or use fixed configurations, leading to an inability to accurately determine the comprehensive cognitive load under complex driving scenarios. Based on the adjusted interaction strategy, the cockpit system is controlled to adjust cockpit information, solving the technical problem of ineffective control of the vehicle cockpit system and achieving the technical effect of effective control of the vehicle cockpit system.

[0137] The above technical solutions of the embodiments of this application will be further illustrated below with reference to preferred embodiments.

[0138] Currently, with the continuous enrichment of smart cockpit functions, in-vehicle displays, voice interaction, and multimedia services, while improving the user experience, also increase the cognitive burden on drivers to some extent.

[0139] In related technologies, cockpit interaction methods are mostly fixed configurations or adjusted based on a single factor (such as vehicle speed), which makes it difficult to accurately reflect the driver's real load status in different driving scenarios. This can easily cause information interference in high-load driving scenarios, thereby affecting driving safety.

[0140] In summary, the relevant technologies have the following defects and shortcomings: The page information is too complex and simplistic, affecting driving safety. Drivers under high load are easily distracted by information, thus affecting driving safety. Scene transitions are too abrupt, with page jumps that can easily distract the driver.

[0141] To address the aforementioned issues, this application provides a smart cockpit interaction control method and system based on driving load. By constructing a driving load level and dynamically adjusting the cockpit interaction strategy according to the driving load level, the user interaction experience is improved while ensuring driving safety.

[0142] Figure 2 This is a schematic diagram of an intelligent cockpit interaction control system based on driving load according to an embodiment of this application, as shown below. Figure 2 As shown, the intelligent cockpit interaction control system includes: an operation status acquisition module 201, a load parameter generation module 202, a load level determination module 203, and a cockpit interaction update module 204.

[0143] The operation status acquisition module 201 is used to acquire the vehicle's operation status data.

[0144] The load parameter generation module 202 is used to generate load data based on the running status data.

[0145] The load level determination module 203 is used to determine the load level to which the load data belongs.

[0146] The cockpit interaction module 204 can be updated to be an interaction module.

[0147] Figure 3 This is a schematic diagram of the data acquisition process of a running status acquisition module according to an embodiment of this application, as shown below. Figure 3 As shown, this operating status acquisition module can collect vehicle speed information, steering wheel operation information, braking operation information, vehicle driving environment information, and driver operation frequency information.

[0148] Figure 4 This is a schematic diagram of a load parameter according to an embodiment of this application, such as... Figure 4 As shown, the load parameters may include operational load parameters, driving load parameters, and environmental load parameters. The contents of the operational load parameters, driving load parameters, and environmental load parameters have been described in the preceding sections and will not be repeated here.

[0149] Figure 5 This is a schematic diagram of a load level according to an embodiment of this application, such as... Figure 5 As shown, the load level can include low load, medium load, and high load. The details of low load, medium load, and high load have been described in the preceding sections and will not be repeated here.

[0150] Figure 6 This is a schematic diagram of an interactive module according to an embodiment of this application, such as... Figure 6 As shown, the interaction module 600 may include an interaction information display control module 601, an interaction input mode control module 602, an interaction response frequency control module 603, and an interaction task priority control module 604.

[0151] The interactive information display control module 601 is used to adjust the display density and display level of interactive information according to the load level. The interactive input method control module 602 is used to select or switch the input method according to the load level. The interactive response frequency control module 603 is used to adjust the frequency or rhythm of the interactive response according to the driving level. The interactive task priority control module 604 is used to dynamically adjust the priority of different interactive tasks according to the driving load level.

[0152] Figure 7 This is a flowchart of a time-based progressive adjustment method according to an embodiment of this application, such as... Figure 7 As shown, the method may include the following steps.

[0153] Step S701: Obtain the target interaction strategy corresponding to the load level.

[0154] Step S702: Obtain the current interaction strategy.

[0155] In this embodiment, after obtaining the target interaction policy corresponding to the load level, the current interaction policy can be obtained.

[0156] Step S703: Within a preset time period, the current interaction strategy is gradually adjusted to the target interaction strategy according to the time progress.

[0157] In this embodiment, the current interaction strategy is gradually and smoothly transitioned to the target interaction strategy according to the time progress. This process avoids the instantaneous jump or hiding of UI elements, and achieves a smooth visual transition through continuous changes on the timeline (such as gradual changes in transparency, removal / addition of elements one by one, slowing down the animation rhythm, etc.).

[0158] Step S704: At the end of the preset time period, make the interaction strategy reach the target state corresponding to the load level.

[0159] In this embodiment, when the preset time period ends, the interaction strategy is completely switched to the target state corresponding to the current load level, thus completing the interaction adaptation after the load level change.

[0160] In this embodiment, the interaction strategy is gradually adjusted over a preset time period to match the target driving load level. For example, under low load, there are 10 interface elements. When switching to high load, the interface needs to display 3 elements. The switch is scheduled to be completed in 10 seconds, and is completed gradually within these 10 seconds. At the 2nd second, 8 elements are displayed; at the 4th second, 6 elements are displayed; at the 7th second, 4 elements are displayed; and at the 10th second, 3 elements are displayed.

[0161] Figure 8 This is a flowchart of a priority-based progressive adjustment method according to an embodiment of this application, such as... Figure 8 As shown, the method may include the following steps.

[0162] Step S801: When a change in driving load level is detected, the interaction strategy adjustment is triggered.

[0163] In this embodiment, an adjustment process can be triggered when a change in driving load level is detected.

[0164] Step S802: Obtain multiple interaction objects corresponding to the current interaction strategy.

[0165] In this embodiment, all interactive objects under the current interaction strategy (such as navigation prompts, music playback, vehicle status, message notifications, etc.) can be obtained.

[0166] Step S803: Determine the corresponding priority for the interactive object.

[0167] In this embodiment, priority can be assigned to each interactive object according to preset rules (e.g., P0 safety critical > P1 driving necessary > P2 driving assistance > P3 non-driving interaction).

[0168] Step S804: Sort the interactive objects according to their priority.

[0169] In this embodiment, the interactive objects can be adjusted step by step according to the order of priority from high to low or from low to high (for example, processing low priority objects first to release resources, or ensuring high priority objects first to establish a safety baseline).

[0170] Step S805: Adjust the interaction strategies corresponding to the interactive objects level by level according to the sorted priority order.

[0171] In this embodiment, the interaction strategy corresponding to the interaction object is adjusted step by step, which can prioritize the state locking or optimization of high-priority objects (such as enhancing vehicle speed and navigation arrows).

[0172] Step S806: After adjusting the high-priority interaction objects, continue to adjust the low-priority interaction objects until the target interaction strategy is completed.

[0173] In this embodiment, after the state locking or optimization of high-priority objects (such as enhancing vehicle speed and navigation arrows) is completed first, low-priority objects (such as hiding entertainment pop-ups and reducing the refresh rate of minor information) can be adjusted in sequence until all interactive objects are adjusted according to the requirements of the target load level, and the target interaction strategy is finally achieved.

[0174] In this embodiment, based on a priority-based progressive adjustment, the interaction strategy is adjusted sequentially according to the interaction priority. Interactions can be categorized by type, for example: P0: Safety-critical interactions (collision warning, emergency braking alerts, etc.); P1: Driving-necessary interactions (navigation, speed, lane information); P2: Driving assistance interactions (energy consumption alerts, driving suggestions); P3: Non-driving interactions (entertainment, messages, recommended content). Low load: Easy driving, music playback + navigation + recommendation pop-ups exist simultaneously; Medium load: Limit the lowest priority P3 adjustment method: close pop-ups, recommendations only remain in the background, P2, P1, and P0 are all normal; High load: Disable P3, reduce the frequency and delay P2, P1 and P1 are normal; High load: Disable P3 and P2, P1 is displayed in a minimalist manner, for example, only intersection turning information is retained, P0 is enhanced.

[0175] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0176] According to an embodiment of this application, a control device for a vehicle cockpit system is provided. It should be noted that the control device for the vehicle cockpit system can be used to execute the above-described control method for the vehicle cockpit system.

[0177] Figure 9 This is a schematic diagram of a control device for a vehicle cabin system according to an embodiment of this application, such as... Figure 9 As shown, the control device 900 of the cockpit system in the vehicle may include: an acquisition unit 902, a determination unit 904, an adjustment unit 906, and a control unit 908.

[0178] The acquisition unit 902 is used to acquire the vehicle's load data, wherein the load data represents the factors that affect the normal driving of the occupants in the vehicle under the current driving scenario.

[0179] The determination unit 904 is used to determine the load level to which the load data belongs, wherein the load level is used to represent the level of driving load faced by the driver and passenger in the current driving scenario.

[0180] The adjustment unit 906 is used to adjust the interaction strategy between the driver / passenger and the vehicle's cockpit system based on the load level, wherein the adjusted interaction strategy is used to represent the rules for adjusting cockpit information in the cockpit system.

[0181] Control unit 908 is used to control the cockpit system to adjust cockpit information based on the adjusted interaction strategy.

[0182] Optionally, the acquisition unit 902 includes: an acquisition subunit for acquiring vehicle operating status data; and a determination subunit for determining load data based on the operating status data.

[0183] Optionally, the determining subunit includes: a first determining subunit, used to determine the vehicle's operational load parameters, driving load parameters, and environmental load parameters based on the operating status data, wherein the operational load parameters represent the operational load reflected by the driver and passengers per unit time, the driving load parameters represent the driving load caused by the vehicle's driving scenario, and the environmental load parameters represent the environmental load caused by the surrounding traffic environment of the vehicle; and a first summing subunit, used to perform a weighted summation of the operational load parameters, driving load parameters, and environmental load parameters using the weights of the operational load parameters, the driving load parameters, and the environmental load parameters to obtain load data.

[0184] Optionally, the operating status data includes at least one of the following vehicle status data: steering wheel angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations. The first determining subunit includes: a second determining subunit, used to determine the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations based on the vehicle model and / or the vehicle's driving mode; and a second summing subunit, used to perform a weighted summation of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations using the weights of the angle change frequency, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations to obtain the operating load parameters.

[0185] Optionally, the operating status data includes at least one of the following vehicle status data: vehicle driving scenario type, road curvature, and road width. The first determining subunit includes: a third determining subunit, used to determine the weights of road width, road curvature, and scenario coefficients corresponding to the driving scenario type based on the vehicle's road type; and a third summing subunit, used to perform weighted summation of road width, road curvature, and scenario coefficients using the weights of road width, road curvature, and scenario coefficients corresponding to the driving scenario type to obtain driving load parameters.

[0186] Optionally, the operational status data includes at least one of the following vehicle status data: the number of traffic participants, weather status information, and illumination status information. The first determining subunit includes: a fourth determining subunit, used to determine the weights of the number of traffic participants, the weights of the weather status information, and the weights of the illumination status information based on the vehicle's driving scenario type and / or road type; and a fourth summing subunit, used to perform a weighted summation of the number of traffic participants, the weather status information, and the illumination status information using the weights of the number of traffic participants, the weather status information, and the illumination status information to obtain environmental load parameters.

[0187] Optionally, the determining unit 904 includes: a fifth determining subunit, configured to determine the load level as a first load level in response to the load data being less than a first load threshold in the load level range; a sixth determining subunit, configured to determine the load level as a second load level in response to the load data being greater than the first load threshold and less than a second load threshold in the load level range, wherein the second load level is higher than the first load level; and a seventh determining subunit, configured to determine the load level as a third load level in response to the load data being greater than the second load threshold, wherein the third load level is higher than the second load level.

[0188] Optionally, the first load level is used to represent a driving scenario where the driving operation is below the operation threshold, the vehicle's operating state is stable, and the complexity of the vehicle's driving environment is below the complexity threshold; the second load level is used to represent a driving scenario where the driving operation frequency is within the target range, the vehicle's operating state is within the operating change range, and the complexity is within the complexity range; the third load level is used to represent at least one of the following: a driving scenario where the driving operation is above the operation threshold, the change in the operating state is above the change threshold, and the complexity is above the complexity threshold.

[0189] Optionally, the adjustment unit 906 includes: an eighth determining subunit, used to determine the adjustment rules for the interaction constraint parameters of the cockpit information according to the load level, wherein the interaction constraint parameters include at least one of the following: display density, interaction input method, interaction response frequency and task priority; and a first adjustment subunit, used to adjust the interaction strategy according to the adjustment rules to obtain the adjusted interaction strategy.

[0190] Optionally, the control unit 908 includes: a generation subunit, used to generate a state switching command based on an adjusted interaction strategy in response to a detected change in load level; and a second adjustment subunit, used to control the cockpit system to adjust cockpit information according to a task priority sequence within a switching time window based on the state switching command.

[0191] In the control device of the vehicle's cockpit system in this embodiment, the acquisition unit 902 acquires the vehicle's load data, which represents the factors affecting the normal driving of the occupants in the current driving scenario; the determination unit 904 determines the load level to which the load data belongs, which represents the level of driving load faced by the occupants in the current driving scenario; the adjustment unit 906 adjusts the interaction strategy between the occupants and the vehicle's cockpit system based on the load level, whereby the adjusted interaction strategy represents the rules for adjusting cockpit information in the cockpit system; and the control unit 908 controls the cockpit system to adjust the cockpit information based on the adjusted interaction strategy, thereby solving the technical problem of ineffective control of the vehicle's cockpit system and achieving the technical effect of effective control of the vehicle's cockpit system.

[0192] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0193] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0194] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0195] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0196] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0197] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0198] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0199] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0200] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0201] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0202] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A control method of a cabin system in a vehicle, characterized by, include: Obtain the load data of the vehicle, wherein the load data is used to represent the factors affecting the normal driving of the occupants in the vehicle under the current driving scenario; Determine the load level to which the load data belongs, wherein the load level is used to represent the level of driving load faced by the driver / passenger in the current driving scenario; Based on the load level, the interaction strategy between the driver / passenger and the vehicle's cockpit system is adjusted, wherein the adjusted interaction strategy is used to represent the rules for adjusting cockpit information in the cockpit system. Based on the adjusted interaction strategy, the cockpit system is controlled to adjust the cockpit information.

2. The method of claim 1, wherein, Obtaining the vehicle's load data includes: Obtain the vehicle's operating status data; The load data is determined based on the operational status data.

3. The method of claim 2, wherein, Based on the operational status data, the load data is determined, including: Based on the operational status data, the vehicle's operational load parameters, driving load parameters, and environmental load parameters are determined. The operational load parameters represent the operational load reflected by the driver and passengers per unit time. The driving load parameters represent the driving load caused by the vehicle's driving scenario. The environmental load parameters represent the environmental load caused by the surrounding traffic environment of the vehicle. The load data is obtained by weighting and summing the operating load parameter, the driving load parameter, and the environmental load parameter using their respective weights.

4. The method of claim 3, wherein, The operating status data includes at least one of the following vehicle status data: the frequency of steering wheel angle changes, braking operation frequency, accelerator pedal operation amplitude, and number of driving operations. Based on the operating status data, the operating load parameters of the vehicle are determined, including: Based on the vehicle model and / or the vehicle's driving mode, determine the weights of the angle change frequency, the braking operation frequency, the accelerator pedal operation amplitude, and the number of driving operations. The operating load parameters are obtained by weighting the angle change frequency, the braking operation frequency, the accelerator pedal operation amplitude, and the number of driving operations using the weights of the angle change frequency, the braking operation frequency, the accelerator pedal operation amplitude, and the number of driving operations.

5. The method of claim 3, wherein, The operational status data includes at least one of the following vehicle status data: the vehicle's driving scenario type, road curvature, and road width. Based on the operational status data, the vehicle's driving load parameters are determined, including: Based on the road type of the vehicle, determine the weights of the road width, the road curvature, and the scene coefficients corresponding to the driving scenario type; The driving load parameters are obtained by weighting the road width, the road curvature, and the scene coefficient corresponding to the driving scenario type using the weights of the road width, the road curvature, and the scene coefficient.

6. The method of claim 3, wherein, The operational status data includes at least one of the following vehicle status data: the number of traffic participants, weather conditions, and illumination conditions. Based on the operational status data, the environmental load parameters of the vehicle are determined, including: Based on the vehicle's driving scenario type and / or road type, determine the weights of the number of traffic participants, the weights of the weather state information, and the weights of the lighting state information; The environmental load parameter is obtained by weighting the number of traffic participants, the weather condition information, and the illumination condition information using the weights of the number of traffic participants, the weather condition information, and the illumination condition information.

7. The method of claim 1, wherein, Determining the load level to which the load data belongs includes: In response to the load data being less than a first load threshold in the load level range, the load level is determined to be a first load level; In response to the load data being greater than the first load threshold and less than the second load threshold in the load level range, the load level is determined to be a second load level, wherein the second load level is higher than the first load level; In response to the load data being greater than the second load threshold, the load level is determined to be a third load level, wherein the third load level is higher than the second load level.

8. The method of claim 7, wherein, The first load level is used to represent a driving scenario where the driving operation is below the operation threshold, the vehicle's operating state is stable, and the complexity of the vehicle's driving environment is below the complexity threshold; the second load level is used to represent a driving scenario where the driving operation frequency is within the target range, the vehicle's operating state is within the operating change range, and the complexity is within the complexity range; the third load level is used to represent a driving scenario where at least one of the following is true: the driving operation is above the operation threshold, the change in the operating state is above the change threshold, and the complexity is above the complexity threshold.

9. The method of claim 1, wherein, Based on the load level, the interaction strategy between the driver / passenger and the vehicle's cockpit system is adjusted, including: Based on the load level, the adjustment rules for the interaction constraint parameters of the cockpit information are determined, wherein the interaction constraint parameters include at least one of the following: display density, interaction input method, interaction response frequency, and task priority; The interaction strategy is adjusted according to the adjustment rules to obtain the adjusted interaction strategy.

10. The method according to any one of claims 1 to 9, characterized in that, Based on the adjusted interaction strategy, the cockpit system is controlled to adjust the cockpit information, including: In response to detecting a change in the load level, a state switching instruction is generated based on the adjusted interaction strategy; Based on the state switching command, the cockpit system is controlled to adjust the cockpit information according to the task priority sequence within the switching time window.

11. A vehicle characterized by comprising: include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 10.

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