Vehicle cab control method, device, equipment, medium and product

Abstract

The invention discloses a whole vehicle cab control method, device and equipment, a medium and a product. The method comprises the following steps: performing trend analysis on real-time driver eye movement and physiological behavior data in an initial period, an adjacent period and a current period to obtain a cognitive response value of the current period; selecting a target weight from the candidate weights of the candidate driving scenes according to a real-time target driving scene in the current period; the candidate weight is determined according to historical eye movement and physiological behavior data corresponding to a candidate driving scene; determining a current driving state according to the target weight and the cognitive response value of the current period; and a current control strategy is determined according to the current driving state, and the current control strategy is adopted to control a vehicle cab. According to the embodiment of the invention, the accuracy of self-adaptive adjustment of the whole vehicle cab based on the eye movement and physiological behavior perception of the driver can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and particularly to a vehicle cockpit control method, device, equipment, medium and product. Background Art

[0002] With the continuous improvement of the intelligent and automated levels of vehicles, drivers' demands for safe and personalized driving are increasing day by day. Summary of the Invention

[0003] The present invention provides a vehicle cockpit control method, device, equipment, medium and product to address drivers' demands for safe and personalized driving.

[0004] According to one aspect of the present invention, there is provided a vehicle cockpit control method, including:

[0005] Performing trend analysis on real-time driver eye movement and physiological behavior data in an initial period, an adjacent period, and the current period to obtain a cognitive response value for the current period;

[0006] Selecting a target weight from candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current period; the candidate weights are determined according to historical eye movement and physiological behavior data corresponding to the candidate driving scenarios;

[0007] Determining the current driving state according to the target weight and the cognitive response value of the current period;

[0008] Determining a current control strategy according to the current driving state, and using the current control strategy to control the vehicle cockpit.

[0009] According to another aspect of the present invention, there is provided a vehicle cockpit control device, including:

[0010] A cognitive response value determination module, configured to perform trend analysis on real-time driver eye movement and physiological behavior data in an initial period, an adjacent period, and the current period to obtain a cognitive response value for the current period;

[0011] A target weight determination module, configured to select a target weight from candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current period; the candidate weights are determined according to historical eye movement and physiological behavior data corresponding to the candidate driving scenarios;

[0012] A driving state determination module, configured to determine the current driving state according to the target weight and the cognitive response value of the current period;

[0013] A control strategy determination module, configured to determine a current control strategy according to the current driving state, and use the current control strategy to control the vehicle cockpit.

[0014] According to another aspect of the present invention, there is provided a computer program product, including a computer program which, when executed by a processor, implements the vehicle cockpit control method according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided an electronic device, which includes:

[0016] at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the vehicle cockpit control method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for implementing the vehicle cockpit control method according to any embodiment of the present invention when executed by a processor.

[0018] According to another aspect of the present invention, there is provided a computer program product including a computer program / instructions which, when executed by a processor, implement the vehicle cockpit control method as described in any embodiment of the present invention.

[0019] In the embodiments of the present invention, through real-time multi-cycle eye movement - physiological behavior data trend fusion analysis to generate a dynamic cognitive response value, combined with intelligent matching of scenario weights driven by historical data, the transition of driving state assessment from "static threshold judgment" to "scenario adaptive perception" is realized: it can not only capture the instantaneous changes of the current driver's cognitive load and physiological stress (such as a sudden increase in the frequency of pupil tremors in the early stage of fatigue and a sudden decrease in heart rate variability during stress response), but also eliminate individual differences and driving scenario interferences through dynamic calibration of scenario weights (such as strengthening the weight of scanning video frequency in congested sections and enhancing the pupil response sensitivity during night driving), significantly improving the accuracy of judging the driver's driving state, making the cockpit control strategy adapt to the real driving state in real time, and significantly improving driving safety and comfort.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0022] Figure 1 is the first flowchart of a vehicle cockpit control method provided by an embodiment of the present invention;

[0023] Figure 2 is the second flowchart of a vehicle cockpit control method provided by an embodiment of the present invention;

[0024] Figure 3 is the structural schematic diagram of a vehicle cockpit control device provided by an embodiment of the present invention;

[0025] Figure 4 is the structural schematic diagram of an electronic device for implementing the embodiments of the present invention. Detailed Embodiments

[0026] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0028] Figure 1It is the first flowchart of a vehicle cockpit control method provided by an embodiment of the present invention. This embodiment is applicable to the situation of determining the driver's state in real time based on the driver's eye movement data, physiological behavior data, and driving scenarios, and adaptively adjusting the vehicle cockpit according to the driver's state. This method can be executed by a vehicle cockpit control device, which can be implemented in the form of hardware and / or software, and the device can be configured in an electronic device with corresponding data processing capabilities. As Figure 1 shown, the method includes:

[0029] S110. Conduct trend analysis on the real-time driver eye movement and physiological behavior data in the initial period, adjacent periods, and the current period to obtain the cognitive response value of the current period.

[0030] By arranging in-vehicle eye movement devices and cameras in the cab, analyze the driver's eye movement behavior and collect relevant eye movement data. The eye movement related data mainly includes data such as saccades, eyelid opening and closing, eye point distribution, fixation duration, blink count, and blink ratio; based on physiological signal acquisition sensors arranged on devices such as the steering wheel or seat, or through intelligent bracelets worn by the driver and communicating with the vehicle, or non-contact monitoring devices implemented based on computer vision algorithms, collect relevant data on the driver's real-time physiology and behavior, including but not limited to relevant indicators such as heart rate, heart rate variability, blood pressure, respiratory rate, blood oxygen saturation, body temperature, blood glucose, blood lipids, yawn frequency, and skin electrical signal.

[0031] Since the driver starts the driving task, continuously collect the driver's real-time eye movement data and physiological behavior data at a certain period. The eye movement data includes the number of fixation points, scanning frequency, pupil diameter change rate, blink count, blink ratio, saccade count, and eyelid closure time. The physiological behavior data includes heart rate fluctuation degree, heart rate variability, blood pressure fluctuation, skin electrical signal, blood oxygen saturation, and yawn frequency.

[0032] According to the driver's eye movement data in the initial period and the driver's eye movement data in the current period, determine the first change trend of the driver's eye movement data, and determine the first eye movement evaluation value according to the first change trend of the eye movement data. According to the driver's eye movement data in the adjacent period and the driver's eye movement data in the current period, determine the second change trend of the driver's eye movement data, and determine the second eye movement evaluation value according to the second change trend of the eye movement data. Determine the eye movement characteristic value of the driver in the current period according to the first eye movement evaluation value and the second eye movement evaluation value.

[0033] Determine the first change trend of the driver's physiological behavior data based on the physiological behavior data of the driver in the initial period and the physiological behavior data of the driver in the current period, and determine the first physiological behavior evaluation value according to the first change trend of the physiological behavior data. Determine the second change trend of the driver's physiological behavior data based on the physiological behavior data of the driver in the adjacent period and the physiological behavior data of the driver in the current period, and determine the second physiological behavior evaluation value according to the second change trend of the physiological behavior data. Determine the physiological state value of the driver in the current period according to the first physiological behavior evaluation value and the second physiological behavior evaluation value.

[0034] The first change trend of the eye movement data and the physiological behavior data reflects the evolution of the long-term eye movement characteristics and physiological behavior characteristics of the driver from the initial state to the current moment, and is used to evaluate the overall fatigue degree or attention decline trend of the driver. The second change trend of the eye movement data and the physiological behavior data reflects the changes in the immediate eye movement characteristics and physiological behavior characteristics of the driver from the adjacent period to the current moment, and is used to capture the immediate reaction and short-term state fluctuations of the driver, such as identifying distractions or fatigue.

[0035] The cognitive response value in the current period includes the eye movement characteristic value in the current period and the physiological state value in the current period.

[0036] By comprehensively and deeply analyzing the trend of the real-time collected driver's eye movement and physiological behavior data in the initial period, adjacent period, and current period, the changing law of the driver's cognitive state at different time stages can be accurately grasped. It helps to insight into potential cognitive fatigue, attention dispersion and other conditions of the driver in advance, and then based on the obtained cognitive response value in the current period, it provides a scientific basis for taking targeted intervention measures in a timely manner, effectively reducing the risk of traffic accidents caused by the driver's cognitive state problems such as fatigue and drowsiness.

[0037] S120. Select the target weight from the candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current period; the candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios.

[0038] Specifically, different candidate driving scenarios are determined according to the driver's age, gender, whether wearing glasses, driving during the day or at night, road conditions such as mountain roads, highways or urban roads, and driving directions such as straight, turning or lane changing. For the historical eye movement data and historical physiological behavior data corresponding to different candidate driving scenarios, the candidate weights corresponding to the candidate driving scenarios are generated.

[0039] Select a target weight from the candidate weights of each candidate driving scenario according to the real-time target driving scenario of the current cycle. Exemplarily, if the real-time target driving scenario of the current cycle is the same as any candidate driving scenario, the candidate weight corresponding to this candidate driving scenario is used as the target weight of the current cycle. The target weight includes a target eye movement data weight and a target physiological behavior weight.

[0040] S130. Determine the current driving state according to the target weight and the cognitive response value of the current cycle.

[0041] S140. Determine the current control strategy according to the current driving state, and use the current control strategy to control the vehicle cockpit.

[0042] According to the preset mapping relationship between the driving state and the control strategy, determine the vehicle control strategy corresponding to the current driving state as the current control strategy. The Electronic Control Unit (ECU) generates a corresponding control signal according to the current control strategy and transmits the control signal to the controllers of each function; when each function controller receives the corresponding control signal, it controls its respective function actuator to execute an action, so as to realize the adaptive adjustment of the whole vehicle cockpit based on the perception of the driver's eye movement and physiological behavior information.

[0043] In the embodiment of the present invention, through the real-time multi-cycle eye movement - physiological behavior data trend fusion analysis to generate a dynamic cognitive response value, combined with the intelligent matching of scenario weights driven by historical data, the transition of driving state evaluation from "static threshold judgment" to "scenario adaptive perception" is realized: it can not only capture the instantaneous changes of the current driver's cognitive load and physiological stress (such as the sudden increase in the pupil tremor frequency during the fatigue germination period and the sudden decrease in the heart rate variability during the stress response), but also eliminate individual differences and driving scenario interference through dynamic calibration of scenario weights (such as strengthening the weight of the scanning frequency in congested sections and enhancing the pupil response sensitivity during night driving), significantly improving the accuracy of the driver's driving state judgment, making the cockpit control strategy adapt to the real driving state in real time, and significantly improving driving safety and comfort.

[0044] In an alternative implementation, the candidate weight of any candidate driving scenario includes a candidate eye movement data weight and a candidate physiological behavior weight; the candidate weight is determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenario, including: for any candidate driving scenario, select the corresponding historical eye movement data and historical physiological behavior data according to this candidate driving scenario; the candidate driving scenario is determined according to at least one of driver characteristics, driving time period, road conditions or driving direction; the entropy weight method is used to generate the candidate eye movement data weight and the candidate physiological behavior weight corresponding to this candidate driving scenario according to the historical eye movement data and the historical physiological behavior data respectively.

[0045] Specifically, driver characteristics include driver age, gender, whether glasses are worn, etc.; driving time periods include daytime driving or nighttime driving; road conditions refer to whether the vehicle is driving on mountain roads, highways or urban roads; driving directions include whether the vehicle is going straight, turning or changing lanes, etc. Different candidate driving scenarios are determined according to different driver characteristics, driving time periods, road conditions or driving directions. For any candidate driving scenario, the corresponding historical eye movement data and historical physiological behavior data are selected. The historical eye movement data and historical physiological behavior data are preprocessed respectively. The candidate eye movement data weight and candidate physiological behavior weight corresponding to the candidate driving scenario are generated respectively based on the preprocessed historical eye movement data and historical physiological behavior data by the entropy weight method.

[0046] Eye movement data includes the number of fixation points, saccade frequency, pupil diameter change rate, blink count, blink ratio, saccade count and eyelid closure time. Among them, the pupil diameter change rate, blink count, blink ratio, saccade count and eyelid closure time are negatively correlated indicators; the number of fixation points and saccade frequency are positively correlated indicators.

[0047] Physiological behavior data includes heart rate fluctuation degree, heart rate variability, blood pressure fluctuation, skin electrical signal, blood oxygen saturation and yawn frequency. Among them, blood oxygen saturation is a positively correlated indicator; heart rate fluctuation degree, heart rate variability, blood pressure fluctuation, skin electrical signal and yawn frequency are negatively correlated indicators.

[0048] A positively correlated indicator means that the larger its value, the higher the driver's attention to visual stimuli and the more active the information processing. A negatively correlated indicator means that the larger its value, the more likely the driver is in a state of excessive cognitive load, fatigue, distraction or negative emotion.

[0049] The candidate eye movement data weight is determined according to the historical eye movement data, and the candidate physiological behavior weight is determined according to the historical physiological behavior data. The historical eye movement data and historical physiological behavior data are sample data. Specifically, positive normalization and negative normalization are performed on the positively correlated indicators and negatively correlated indicators respectively. Calculate the proportion of the value of the sample data in the current indicator; determine the coefficient of variation (k) and information entropy (e j ) of each indicator, and determine the entropy weight value (w j ) of each indicator. Specifically, as shown in the following formulas (1)-(7).

[0050] Preprocessing of positively correlated indicator data:

[0051]

[0052] Preprocessing of negatively correlated indicator data:

[0053]

[0054] Determine the proportion (P ij ) of the value of sample data i in the j-th indicator:

[0055]

[0056] Determine the coefficient of variation (k) and information entropy (e j ) of the j-th indicator:

[0057]

[0058] Determine the entropy weight value (w j ) of the j-th indicator:

[0059] d j = 1 - e j , (j = 1, 2, …, n) (6)

[0060]

[0061] where m is the number of samples and n is the number of indicators; the entropy weight value of the j-th indicator is the candidate weight of the j-th indicator.

[0062] By performing positive normalization and negative normalization on the eye movement data and physiological behavior data, it is possible to eliminate the interference of different dimensions and magnitudes on the data comparability, ensure the reasonable quantification of different-direction indicators under a unified analysis framework, and provide a standardized data basis for subsequent analysis; further combining the coefficient of variation and information entropy to calculate the entropy weight value can dynamically allocate weights based on the data's own dispersion degree and uncertainty, avoid subjective weighting bias, objectively reflect the contribution differences of each indicator to the driver's cognitive load, fatigue state or distraction risk, and finally achieve the accurate assessment of the driver's state based on the fusion of multi-source heterogeneous data, providing a scientific basis for the adaptive adjustment of the whole vehicle cockpit based on the driver's eye movement and physiological behavior characteristics.

[0063] In an alternative embodiment, the target weights include the target eye movement data weight and the target physiological behavior weight; the cognitive response value of the current cycle includes the eye movement feature value of the current cycle and the physiological state value of the current cycle; the determining of the current driving state according to the target weights and the cognitive response value of the current cycle includes: determining the fatigue degree of the driver in the current cycle according to the eye movement feature value of the current cycle and the target eye movement data weight; determining the drowsiness degree of the driver in the current cycle according to the physiological state value of the current cycle and the target physiological behavior weight; determining the current driving state of the driver according to the fatigue degree of the driver in the current cycle and the drowsiness degree of the driver in the current cycle.

[0064] Specifically, the eye movement eigenvalue of the current cycle is weighted and summed according to the target eye movement data weight of the current cycle to obtain the real-time fatigue score of the driver in the current cycle. According to the preset division rule, the fatigue level of the driver in the current cycle corresponding to the real-time fatigue score is determined. The physiological state value of the current cycle is weighted and summed according to the target physiological behavior weight of the current cycle to obtain the real-time drowsiness score of the driver in the current cycle. According to the preset division rule, the drowsiness level of the driver in the current cycle corresponding to the real-time drowsiness score is determined. The current driving state of the driver is determined according to the fatigue level of the driver in the current cycle and the drowsiness level of the driver in the current cycle.

[0065] For example, when the real-time fatigue score is 10 points, the fatigue level of the driver in the current cycle is defined as "no fatigue"; when it is 7 - 9 points, it is "mild fatigue"; when it is 4 - 6 points, it is "moderate fatigue", and when it is 1 - 3 points, it is "severe fatigue". When the real-time drowsiness score is 10 points, the drowsiness level of the driver in the current cycle is defined as "no drowsiness"; when it is 7 - 9 points, it is "mild drowsiness"; when it is 4 - 6 points, it is "moderate drowsiness", and when it is 1 - 3 points, it is "severe drowsiness". The fatigue level includes four grades, and the drowsiness level includes four grades. Through the coupling analysis of the fatigue-drowsiness two-dimensional matrix, a 16-level driving state quantization coding system is formed. The current driving state of the driver is determined according to the fatigue level of the driver in the current cycle and the drowsiness level of the driver in the current cycle.

[0066] By constructing a real-time evaluation system for multi-source heterogeneous data, the efficient and accurate capture of the driver's state is realized: based on the cross-modal weighted aggregation of eye movement features and physiological behavior signals (the weights are adaptively adjusted according to the driving scenario), the interference of single-signal noise is eliminated, and a fatigue-drowsiness joint state vector is generated; through the redundant design of bimodal cross-validation, the credibility of the driver's state determination result is improved, providing a scientific basis for the subsequent personalized adaptive adjustment of the whole vehicle cockpit.

[0067] Figure 2 It is the second flowchart of a whole vehicle cockpit control method provided by an embodiment of the present invention. In this embodiment, the related process of "performing trend analysis on the real-time driver eye movement and physiological behavior data in the initial cycle, adjacent cycles, and current cycle to obtain the cognitive response value" in the above embodiment is optimized and improved. As Figure 2 shown, the method includes:

[0068] S210. Collect the eye movement data and physiological behavior data of the driver in real time according to a preset cycle; perform change trend analysis on the eye movement data and physiological behavior data collected in real time in the initial cycle and the current cycle to obtain the first eye movement evaluation value and the first physiological behavior evaluation value.

[0069] Determine the first change trend of the driver's eye movement data based on the eye movement data of the driver in the initial period and the eye movement data of the driver in the current period. Taking the number of fixation points in the eye movement data as an example, determine the first change trend of the number of fixation points based on the number of fixation points of the driver in the initial period and the number of fixation points of the driver in the current period, that is, compared with the initial period, the value of the number of annotation points in the current period remains unchanged, increases, or decreases. Determine the first eye movement evaluation value of the number of annotation points in the eye movement data according to the first change trend of the number of annotation points. Optionally, through a preset mapping rule, different change trends correspond to different evaluation values.

[0070] Determine the first change trend of the driver's physiological behavior data based on the physiological behavior data of the driver in the initial period and the physiological behavior data of the driver in the current period, and determine the first physiological behavior evaluation value according to the first change trend of the physiological behavior data.

[0071] The first change trends of the eye movement data and the physiological behavior data reflect the evolution of the long-term eye movement characteristics and physiological behavior characteristics of the driver from the initial state to the current moment, and are used to evaluate the overall fatigue level or the trend of attention decline of the driver.

[0072] Optionally, the eye movement data includes eye movement positive correlation indicators and eye movement negative correlation indicators. The eye movement positive correlation indicators include: the number of fixation points and the number of saccade times; the eye movement negative correlation indicators include: the pupil diameter change rate, the number of blinks, the proportion of blink times, the number of saccades, and the eyelid closure time.

[0073] Optionally, perform a change trend analysis on the eye movement data collected in real time in the initial period and the current period to obtain the first eye movement evaluation value, including: taking the eye movement data in the initial period as a reference, if it is an eye movement positive correlation indicator, the first eye movement evaluation value is the highest when the value of the eye movement positive correlation indicator in the current period remains unchanged or increases, and the greater the decrease in the value of the eye movement positive correlation indicator in the current period, the lower the first eye movement evaluation value; if it is an eye movement negative correlation indicator, the first eye movement evaluation value is the highest when the value of the eye movement negative correlation indicator in the current period remains unchanged or decreases, and the greater the increase in the value of the eye movement negative correlation indicator in the current period, the lower the first eye movement evaluation value.

[0074] Exemplarily, compared with the initial period, in the current period: when the number of fixation points increases or remains unchanged, the first eye movement evaluation value of the number of fixation points is 10; when the number of fixation points decreases by 10%, the first eye movement evaluation value of the number of fixation points is 6; when the number of fixation points decreases by 30%, the first eye movement evaluation value of the number of fixation points is 4; when the number of fixation points decreases by 50% or more, the first eye movement evaluation value of the number of fixation points is 1.

[0075] Based on the initial cycle of eye movement data, precise quantification of fatigue risk is achieved through differential scoring of positive and negative correlation indicators: for positive correlation indicators such as fixation concentration and saccade frequency, a mechanism of "higher score for increase and heavy penalty for sudden drop" is adopted; for negative correlation indicators such as blink rate and pupil diameter change rate, a logic of "stable is safe and abnormal movement is deducted points" is adopted. It can effectively eliminate the interference of individual driving habits and physiological differences, significantly improve the accuracy of judging the driver's fatigue state compared with the traditional threshold method, and provide a more reliable feature basis for the vehicle cockpit adaptive adjustment based on the driver's eye movement and physiological behavior characteristics.

[0076] Optionally, the physiological behavior data includes positive correlation indicators and negative correlation indicators of physiological behavior. The positive correlation indicators of physiological behavior include: blood oxygen saturation; the negative correlation indicators of physiological behavior include: heart rate fluctuation degree, heart rate variability, blood pressure fluctuation, skin electrical signal and yawning frequency.

[0077] Optionally, trend analysis is performed on the physiological behavior data collected in real time in the initial cycle and the current cycle to obtain the first physiological behavior evaluation value, including: based on the physiological behavior data of the initial cycle, if it is a positive correlation indicator of physiological behavior, the first physiological behavior evaluation value is the highest when the value of the positive correlation indicator of physiological behavior in the current cycle remains unchanged or increases, and the greater the reduction amplitude of the value of the positive correlation indicator of physiological behavior in the current cycle, the lower the first physiological behavior evaluation value; if it is a negative correlation indicator of physiological behavior, the first physiological behavior evaluation value is the highest when the value of the negative correlation indicator of physiological behavior in the current cycle remains unchanged or decreases, and the greater the increase amplitude of the value of the negative correlation indicator of physiological behavior in the current cycle, the lower the first physiological behavior evaluation value.

[0078] Exemplarily, compared with the initial cycle, in the current cycle: when the blood oxygen saturation increases or remains unchanged, the first physiological behavior evaluation value of the blood oxygen saturation is 10; when the blood oxygen saturation decreases by 10%, the first physiological behavior evaluation value of the blood oxygen saturation is 6; when the blood oxygen saturation decreases by 30%, the first physiological behavior evaluation value of the blood oxygen saturation is 4; when the blood oxygen saturation decreases by 50% or more, the first physiological behavior evaluation value of the blood oxygen saturation is 1.

[0079] Based on the initial cycle of physiological data, precise tracking of the driver's physiological behavior state is achieved through differential scoring of positive and negative correlation indicators: for positive correlation indicators such as blood oxygen saturation, a mechanism of "steady state is full score and decline is deducted points" is adopted; for negative correlation indicators such as heart rate fluctuation degree and blood pressure fluctuation, a logic of "stable is safe and out of control is alarmed" is adopted. It can effectively eliminate the interference of individual physical differences, significantly improve the accuracy of judging the driver's drowsy state compared with the traditional threshold method, and provide a more reliable feature basis for the vehicle cockpit adaptive adjustment based on the driver's eye movement and physiological behavior characteristics.

[0080] S220. Analyze the trend of change in the eye movement data and physiological behavior data collected in real time in the adjacent cycle and the current cycle to obtain a second eye movement evaluation value and a second physiological behavior evaluation value.

[0081] Based on the eye movement data of the driver in the adjacent cycle and the eye movement data of the driver in the current cycle, determine the second trend of change in the driver's eye movement data. Taking the number of fixation points in the eye movement data as an example, based on the number of fixation points of the driver in the adjacent cycle and the number of fixation points of the driver in the current cycle, determine the second trend of change in the number of fixation points, that is, compared with the adjacent cycle, the value of the number of annotation points in the current cycle remains unchanged, increases or decreases. Determine the second eye movement evaluation value of the number of annotation points in the eye movement data according to the second trend of change in the number of annotation points. Optionally, use the same determination method as the method for determining the first eye movement evaluation value to determine the second eye movement evaluation value.

[0082] Based on the physiological behavior data of the driver in the adjacent cycle and the physiological behavior data of the driver in the current cycle, determine the second trend of change in the driver's physiological behavior data, and determine the second physiological behavior evaluation value according to the second trend of change in the physiological behavior data. Optionally, use the same determination method as the method for determining the first physiological behavior evaluation value to determine the second physiological behavior evaluation value.

[0083] The second trend of change in the eye movement data and the physiological behavior data reflects the change in the immediate eye movement characteristics and physiological behavior characteristics of the driver from the adjacent cycle to the current moment, and is used to capture the immediate response and short-term state fluctuations of the driver, such as identifying distraction or fatigue.

[0084] S230. Determine the eye movement characteristic value of the current cycle according to the first eye movement evaluation value and the second eye movement evaluation value, and determine the physiological state value of the current cycle according to the first physiological behavior evaluation value and the second physiological behavior evaluation value.

[0085] Combining the first eye movement evaluation value (long-term trend) and the second eye movement evaluation value (short-term trend), generate the eye movement characteristic value of the driver in the current cycle through weighted average or a machine learning model. Combining the first physiological behavior evaluation value (long-term trend) and the second physiological behavior evaluation value (short-term trend), generate the physiological state value of the driver in the current cycle through weighted average or a machine learning model. The cognitive response value of the current cycle includes the eye movement characteristic value of the current cycle and the physiological state value of the current cycle.

[0086] By comprehensively and deeply analyzing the trends of the real-time collected driver's eye movement and physiological behavior data in the initial cycle, adjacent cycles, and the current cycle, the changing patterns of the driver's cognitive state at different time stages can be accurately grasped. This helps to insight into potential cognitive fatigue, distraction, etc. of the driver in advance, and then, based on the obtained cognitive response value in the current cycle, provides a scientific basis for timely taking targeted intervention measures, effectively reducing the risk of traffic accidents caused by the driver's cognitive state problems such as fatigue and drowsiness.

[0087] S240. Select a target weight from the candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current cycle; the candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios.

[0088] S250. Determine the current driving state according to the target weight and the cognitive response value in the current cycle.

[0089] S260. Determine the current control strategy according to the current driving state, and use the current control strategy to control the vehicle cockpit.

[0090] In the embodiment of the present invention, high-precision eye movement - physiological behavior joint characterization is generated through multi-cycle dynamic tracking (the initial cycle anchors the individual baseline, the adjacent cycles capture instantaneous fluctuations, and the current cycle fuses the trends of the two cycles), combined with scenario weight dynamic mapping (constructing candidate weights for different driving scenarios based on historical data), realizing the leap from "single-signal rough judgment" to "multi-modal trend - scenario two-dimensional adaptation" in driving state assessment. It can not only accurately quantify the current cognitive load and physiological stress intensity through the fusion of the two-cycle evaluation values (such as the eye movement feature value = 0.7×long-term trend + 0.3×short-term anomaly), but also eliminate environmental interference through scenario weight calibration (such as increasing the pupil response weight by 65% in the tunnel scenario and enhancing the heart rate variability sensitivity by 70% during high-speed following), significantly improving the accuracy of the driver's driving state judgment, making the cockpit control strategy adapt to the real driving state in real time, significantly enhancing driving safety and comfort, and improving the reliability and user-friendly experience of the intelligent cockpit active safety system.

[0091] In an alternative embodiment, determining the current control strategy according to the current driving state includes: when there is no fatigue, as the degree of sleepiness increases from none to severe, the color temperature of the in-vehicle lighting gradually increases; if moderate sleepiness is accompanied, light rhythm is added; if severe sleepiness is accompanied, multi-color light rhythm is added; when there is fatigue, as the degree of fatigue increases from mild to severe, the light color temperature gradually decreases and is adjusted to a soft light mode; when mild or moderate fatigue is accompanied by mild sleepiness, light rhythm is added; when mild or moderate fatigue is accompanied by moderate or severe sleepiness, intermittent vibration of the steering wheel is added, and if severe sleepiness is accompanied, intermittent vibration of the seat is further added; when severe fatigue is accompanied by mild sleepiness, intermittent vibration of the steering wheel is added, and when severe fatigue is accompanied by moderate or severe sleepiness, intermittent vibration of the seat and voice reminder are further added, and if it is severe fatigue accompanied by severe sleepiness, navigation broadcast of the nearest parking point is also added.

[0092] Specifically, the vehicle control strategies in different driving states are shown in Table 1 below.

[0093] Table 1

[0094]

[0095]

[0096] Different vehicle control strategies are set according to different driving state values. The driving states include State 1 - State 16; when the driving state is 1 - 4, it indicates that the driver's eyes have no obvious fatigue, and the corresponding control strategy is to adjust the cockpit lighting system. When the driving state is 5 - 8, it indicates that the driver's eyes have mild fatigue. At this time, the control strategy is to increase the changes of vehicle body accessories, such as adding intermittent vibration of the seat, intermittent vibration of the steering wheel, seat belt tightening, etc. on the basis of correspondingly adjusting the cockpit lighting system. When the driving state is 9 - 12, it indicates that the degree of the driver's eye fatigue is already relatively obvious. At this time, the control strategy increases the reminder intensity on the basis of the vehicle control strategies in the above driving states 5 - 8. When the driving state is 13 - 16, it indicates that the driver's eye fatigue is already relatively serious. At this time, the control strategy continues to increase the reminder intensity on the basis of the vehicle control strategies in the above driving states 9 - 12, and actively initiates voice reminders, controls the navigation system to actively plan and display the nearest rest point or leisure and entertainment place for the driver through linking the vehicle voice control system, navigation system, driver monitoring system, etc., to give a stronger reminder to the driver.

[0097] Through 16 - level driving state classification, a safety net of "flexible adaptation - progressive pressure - closed - loop blocking" is constructed: in the initial stage, only dimming the light to protect eyes and prevent slackness; in the middle stage, adding vibration tactile alert; in the later stage, starting high - order voice deterrence and navigation forced takeover. It not only avoids sudden intervention causing stress through three - level transitions of "silent → pulse → strong control", but also improves the effectiveness of fatigue and drowsiness intervention, while maintaining the driving comfort of the driver, realizing the qualitative change of active safety from "passive response" to "intelligent pre - control".

[0098] Figure 3 It is a schematic structural diagram of a vehicle cockpit control device provided by an embodiment of the present invention. As Figure 3 shown, the device includes:

[0099] A cognitive response value determination module 310, configured to perform trend analysis on real - time driver eye movement and physiological behavior data in the initial cycle, adjacent cycles, and the current cycle to obtain the cognitive response value of the current cycle;

[0100] A target weight determination module 320, configured to select a target weight from the candidate weights of each candidate driving scenario according to the real - time target driving scenario of the current cycle; the candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios;

[0101] A driving state determination module 330, configured to determine the current driving state according to the target weight and the cognitive response value of the current cycle;

[0102] A control strategy determination module 340, configured to determine the current control strategy according to the current driving state, and control the vehicle cockpit using the current control strategy.

[0103] The vehicle cockpit control device provided by the embodiment of the present invention can execute the vehicle cockpit control method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0104] Optionally, the cognitive response value of the current cycle includes the eye movement feature value of the current cycle and the physiological state value of the current cycle; the cognitive response value determination module includes:

[0105] A data acquisition unit, configured to collect the driver's eye movement data and physiological behavior data in real - time according to a preset cycle;

[0106] A first evaluation unit, configured to perform change trend analysis on the eye movement data and physiological behavior data collected in real - time in the initial cycle and the current cycle to obtain a first eye movement evaluation value and a first physiological behavior evaluation value;

[0107] A second evaluation unit for analyzing the trend of changes in the eye movement data and physiological behavior data collected in real time during the adjacent cycle and the current cycle to obtain a second eye movement evaluation value and a second physiological behavior evaluation value;

[0108] A cognitive response value determination unit for determining an eye movement characteristic value of the current cycle according to the first eye movement evaluation value and the second eye movement evaluation value, and determining a physiological state value of the current cycle according to the first physiological behavior evaluation value and the second physiological behavior evaluation value.

[0109] Optionally, the eye movement data includes an eye movement positive correlation index and an eye movement negative correlation index. The eye movement positive correlation index includes: the number of fixation points and the number of saccade times; the eye movement negative correlation index includes: the pupil diameter change rate, the number of blinks, the proportion of blinks, the number of saccades, and the eyelid closure time;

[0110] The first evaluation unit is specifically configured to use the eye movement data of the initial cycle as a reference. If it is an eye movement positive correlation index, the first eye movement evaluation value is the highest when the value of the eye movement positive correlation index in the current cycle remains unchanged or increases, and the greater the decrease in the value of the eye movement positive correlation index in the current cycle, the lower the first eye movement evaluation value; if it is an eye movement negative correlation index, the first eye movement evaluation value is the highest when the value of the eye movement negative correlation index in the current cycle remains unchanged or decreases, and the greater the increase in the value of the eye movement negative correlation index in the current cycle, the lower the first eye movement evaluation value.

[0111] Optionally, the candidate weight of any candidate driving scenario includes a candidate eye movement data weight and a candidate physiological behavior weight; the target weight determination module includes:

[0112] A historical data determination unit for selecting corresponding historical eye movement data and historical physiological behavior data according to any candidate driving scenario. The candidate driving scenario is determined according to at least one of driver characteristics, driving time period, road conditions, or driving direction;

[0113] A candidate weight determination unit for generating a candidate eye movement data weight and a candidate physiological behavior weight corresponding to the candidate driving scenario according to the historical eye movement data and the historical physiological behavior data respectively by using the entropy weight method.

[0114] Optionally, the target weights include a target eye movement data weight and a target physiological behavior weight; the cognitive response value of the current cycle includes an eye movement feature value of the current cycle and a physiological state value of the current cycle. The driving state determination module is specifically configured to determine the fatigue level of the driver in the current cycle according to the eye movement feature value of the current cycle and the target eye movement data weight; determine the drowsiness level of the driver in the current cycle according to the physiological state value of the current cycle and the target physiological behavior weight; and determine the current driving state of the driver according to the fatigue level of the driver in the current cycle and the drowsiness level of the driver in the current cycle.

[0115] Optionally, the control strategy determination module is specifically configured to, when there is no fatigue, gradually increase the color temperature of the in-vehicle lighting as the drowsiness level ranges from none to severe; increase the lighting rhythm if moderate drowsiness is accompanied, and increase the multi-color lighting rhythm if severe drowsiness is accompanied; when there is fatigue, gradually decrease the lighting color temperature and adjust it to the soft light mode as the fatigue level ranges from mild to severe; increase the lighting rhythm when mild or moderate fatigue is accompanied by mild drowsiness; increase the intermittent vibration of the steering wheel when mild or moderate fatigue is accompanied by moderate or severe drowsiness, and increase the intermittent vibration of the seat if severe drowsiness is accompanied; increase the intermittent vibration of the steering wheel when severe fatigue is accompanied by mild drowsiness, increase the intermittent vibration of the seat and voice reminder when severe fatigue is accompanied by moderate or severe drowsiness, and also increase the navigation broadcast of the nearest parking point if severe fatigue is accompanied by severe drowsiness.

[0116] It should be further noted that the vehicle cockpit control device can also execute the vehicle cockpit control method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0117] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium, and a computer program product.

[0118] Figure 4 FIG. shows a schematic structural diagram of an electronic device 40 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device (such as a helmet, glasses, a watch, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0119] As Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory communicatively connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. The memory stores a computer program executable by the at least one processor. The processor 41 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0120] Multiple components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disc, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the vehicle cockpit control method.

[0122] In some embodiments, the vehicle cockpit control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the vehicle cockpit control method described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the vehicle cockpit control method by any other appropriate means (e.g., by means of firmware).

[0123] The various embodiments of the systems and techniques described above in this specification can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which may be a special-purpose or general-purpose programmable processor that receives data and instructions from, and transmits data and instructions to, a storage system, at least one input device, and at least one output device.

[0124] The computer program for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer program can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0125] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0127] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0128] A computing system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0129] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0130] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A vehicle cockpit control method, characterized in that, Including: Performing trend analysis on real-time driver eye movement and physiological behavior data in the initial cycle, adjacent cycles, and the current cycle to obtain the cognitive response value of the current cycle; Selecting a target weight from the candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current cycle; The candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios; Determining the current driving state according to the target weight and the cognitive response value of the current cycle; Determining the current control strategy according to the current driving state, and using the current control strategy to control the vehicle cockpit.

2. The method according to claim 1, characterized in that The cognitive response value of the current cycle includes the eye movement feature value of the current cycle and the physiological state value of the current cycle; the performing trend analysis on real-time driver eye movement and physiological behavior data in the initial cycle, adjacent cycles, and the current cycle to obtain the cognitive response value of the current cycle includes: Real-time collecting the driver's eye movement data and physiological behavior data according to a preset cycle; Performing change trend analysis on the eye movement data and physiological behavior data collected in real time in the initial cycle and the current cycle to obtain the first eye movement evaluation value and the first physiological behavior evaluation value; Performing change trend analysis on the eye movement data and physiological behavior data collected in real time in the adjacent cycle and the current cycle to obtain the second eye movement evaluation value and the second physiological behavior evaluation value; Determining the eye movement feature value of the current cycle according to the first eye movement evaluation value and the second eye movement evaluation value, and determining the physiological state value of the current cycle according to the first physiological behavior evaluation value and the second physiological behavior evaluation value.

3. The method according to claim 2, wherein The eye movement data includes eye movement positive correlation indicators and eye movement negative correlation indicators. The eye movement positive correlation indicators include: the number of fixation points and the number of saccade times; the eye movement negative correlation indicators include: the pupil diameter change rate, the number of blinks, the blink ratio, the number of saccades, and the eyelid closure time; Performing change trend analysis on the eye movement data collected in real time in the initial cycle and the current cycle to obtain the first eye movement evaluation value, including: Taking the eye movement data of the initial cycle as a reference, if it is an eye movement positive correlation indicator, the first eye movement evaluation value is the highest when the value of the eye movement positive correlation indicator in the current cycle remains unchanged or increases, and the greater the reduction amplitude of the value of the eye movement positive correlation indicator in the current cycle, the lower the first eye movement evaluation value; If it is an eye movement negative correlation indicator, the first eye movement evaluation value is the highest when the value of the eye movement negative correlation indicator in the current cycle remains unchanged or decreases, and the greater the increase amplitude of the value of the eye movement negative correlation indicator in the current cycle, the lower the first eye movement evaluation value.

4. The method according to claim 1, wherein The candidate weight of any candidate driving scenario includes a candidate eye movement data weight and a candidate physiological behavior weight; The candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios, including: For any candidate driving scenario, selecting the corresponding historical eye movement data and historical physiological behavior data according to the candidate driving scenario; the candidate driving scenario is determined according to at least one of driver characteristics, driving time period, road conditions, or driving direction; Generating the candidate eye movement data weight and the candidate physiological behavior weight corresponding to the candidate driving scenario respectively according to the historical eye movement data and the historical physiological behavior data by the entropy weight method.

5. The method according to claim 1, wherein The target weights include the target eye movement data weight and the target physiological behavior weight; the cognitive response value of the current cycle includes the eye movement feature value of the current cycle and the physiological state value of the current cycle. Determining the current driving state according to the target weight and the cognitive response value of the current cycle includes: Determining the fatigue degree of the driver in the current cycle according to the eye movement feature value of the current cycle and the target eye movement data weight; Determining the drowsiness degree of the driver in the current cycle according to the physiological state value of the current cycle and the target physiological behavior weight; Determining the current driving state of the driver according to the fatigue degree of the driver in the current cycle and the drowsiness degree of the driver in the current cycle.

6. The method according to claim 1, characterized in that, Determining the current control strategy according to the current driving state, including: When there is no fatigue, as the drowsiness degree ranges from none to severe, the color temperature of the in-vehicle lighting gradually increases; if moderate drowsiness is accompanied, the lighting rhythm is increased, and if severe drowsiness is accompanied, the multi-color lighting rhythm is increased; When there is fatigue, as the fatigue degree ranges from mild to severe, the lighting color temperature gradually decreases and is adjusted to the soft light mode; when mild or moderate fatigue is accompanied by mild drowsiness, the lighting rhythm is increased; when mild or moderate fatigue is accompanied by moderate or severe drowsiness, the intermittent vibration of the steering wheel is increased, and if severe drowsiness is accompanied, the intermittent vibration of the seat is further increased; when severe fatigue is accompanied by mild drowsiness, the intermittent vibration of the steering wheel is increased, and when severe fatigue is accompanied by moderate or severe drowsiness, the intermittent vibration of the seat and the voice reminder are further increased. If it is severe fatigue accompanied by severe drowsiness, the navigation broadcasts the nearest parking point.

7. A vehicle cockpit control device, characterized in that, The device includes: A cognitive response value determination module, configured to perform trend analysis on the real-time driver eye movement and physiological behavior data in the initial cycle, adjacent cycles, and the current cycle to obtain the cognitive response value of the current cycle; A target weight determination module, configured to select a target weight from the candidate weights of each candidate driving scenario according to the real-time target driving scenario in the current cycle; the candidate weights are determined according to the historical eye movement and physiological behavior data corresponding to the candidate driving scenarios; A driving state determination module, configured to determine the current driving state according to the target weight and the cognitive response value of the current cycle; A control strategy determination module, configured to determine the current control strategy according to the current driving state, and control the vehicle cockpit using the current control strategy.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; And a memory communicatively connected to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle cockpit control method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and the computer instructions are used to implement the vehicle cockpit control method according to any one of claims 1-6 when executed by a processor.

10. A computer program product, including a computer program, where the computer program implements the vehicle cockpit control method according to any one of claims 1-6 when executed by a processor.

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