Method, device and controller for warning of fatigue driving behavior

Abstract

The application relates to a fatigue driving behavior early warning method, device, controller, storage medium and computer program product. The method comprises the following steps: acquiring real-time characteristic parameter values of various driving characteristic parameters in a current time period during vehicle driving; determining a target driving characteristic parameter meeting a preset fatigue early warning condition in the vehicle driving process according to the real-time characteristic parameter values of the driving characteristic parameters in the current time period; determining a target early warning mode matched with the target driving characteristic parameter based on an early warning level of the target driving characteristic parameter, and performing fatigue early warning based on the matched target early warning mode. The method can improve the safety of the vehicle driving process.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and in particular to a method, device, controller, storage medium, and computer program product for warning of fatigued driving behavior. Background Technology

[0002] With the continuous development of automotive technology, vehicle safety technology has also made great strides. Among these advancements, driver fatigue detection is a technology that analyzes the relationship between indicators such as driver physiological signals, facial dynamic and static features, body posture, driving behavior characteristics, and vehicle driving status, and extracts these key feature indicators to characterize fatigue status, thereby determining the fatigue detection results during the driving process.

[0003] Currently, fatigue driving detection generally ends once the fatigue test result is determined. During vehicle operation, fatigue test results alone are insufficient to provide auxiliary assistance to the driving process. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, device, controller, computer-readable storage medium, and computer program product for warning of fatigued driving behavior that can improve the safety of vehicle operation, in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a method for early warning of fatigued driving behavior, the method comprising:

[0006] Obtain the real-time feature parameter values ​​of each driving feature parameter of the vehicle during the current time period;

[0007] Based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period, determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation.

[0008] Based on the warning level of the target driving characteristic parameters, a target warning method matching the target driving characteristic parameters is determined, and fatigue warning is given based on the matching target warning method.

[0009] In one embodiment, determining the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period includes:

[0010] Based on the current time period, multiple consecutive time windows are determined;

[0011] For any driving feature parameter, based on the real-time feature parameter value of the driving feature parameter in each consecutive time window, the index parameter of the driving feature parameter in each consecutive time window is determined. The index parameter includes at least one of the feature parameter mean, feature parameter standard deviation, or feature parameter sample entropy.

[0012] Fatigue driving is determined based on the index parameters of each time window, and it is determined whether the driving characteristic parameters in each time window are judged to be in a state of fatigue driving.

[0013] If a driving characteristic parameter is determined to be in a state of fatigued driving within at least one time window, then the driving characteristic parameter is determined to be a target driving characteristic parameter that meets the preset fatigue warning conditions.

[0014] In one embodiment, the method further includes:

[0015] The vehicle’s historical driving parameters are obtained, including the driver’s brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within a historical time period.

[0016] Based on the driver's EEG signals and historical characteristic parameter values ​​of each driving characteristic parameter during the historical time period, fatigue correlation parameters of each driving characteristic parameter are determined. The fatigue correlation parameters are parameters used to characterize the degree of correlation between each driving characteristic parameter and the driver's fatigue state.

[0017] Based on the fatigue correlation parameters of each driving characteristic parameter, the warning level of each driving characteristic parameter is determined.

[0018] Based on the warning level of each driving characteristic parameter, determine the warning method that matches the corresponding driving characteristic parameter.

[0019] In one embodiment, determining the warning level of each driving characteristic parameter based on the fatigue correlation parameter of each driving characteristic parameter includes:

[0020] If the fatigue correlation parameter of the driving characteristic parameter is within the range of the first warning threshold, then the warning level of the driving characteristic parameter is determined to be the first warning level, and the first warning level indicates that the driving characteristic parameter is correlated with fatigued driving to the first degree.

[0021] If the fatigue correlation parameter of the driving characteristic parameter is within the range of the second warning threshold, then the warning level of the driving characteristic parameter is determined to be the second warning level, which indicates that the driving characteristic parameter is correlated with fatigued driving to a second degree.

[0022] If the fatigue correlation parameter of the driving characteristic parameter is within the range of the third warning threshold, then the warning level of the driving characteristic parameter is determined to be the third warning level. The third warning level indicates that the driving characteristic parameter and fatigue driving are correlated to the third degree, where the first degree of correlation is greater than the second degree of correlation, and the second degree of correlation is greater than the third degree of correlation.

[0023] In one embodiment, the warning levels of the driving characteristic parameters include a first warning level, a second warning level, and a third warning level; determining the warning method matching the driving characteristic parameters based on the warning levels of each driving characteristic parameter includes:

[0024] When the warning level of the driving characteristic parameter is the first warning level, the warning methods matched with the driving characteristic parameter include the first warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method;

[0025] When the warning level of the driving characteristic parameter is the second warning level, the warning methods matching the driving characteristic parameter include the second warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method;

[0026] When the warning level of the driving characteristic parameter is the third warning level, the warning method matching the driving characteristic parameter includes the third warning method; the warning danger level of the first cumulative warning method is higher than that of the second cumulative warning method, the warning danger level of the second cumulative warning method is higher than that of the third cumulative warning method, the warning danger level of the third cumulative warning method is higher than that of the first warning method, the warning danger level of the first warning method is higher than that of the second warning method, and the warning danger level of the second warning method is higher than that of the third warning method.

[0027] In one embodiment, the target driving characteristic parameter carries the determination results within each consecutive time window; the warning level of the target driving characteristic parameter includes a first warning level, a second warning level, and a third warning level; the step of determining a target warning method matching the target driving characteristic parameter based on the warning level of the target driving characteristic parameter, and performing fatigue warning based on the matched target warning method, includes:

[0028] If the warning level of the target driving feature parameter is the first warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the first warning method, and fatigue warning is given based on the matched first warning method.

[0029] If the warning level of the target driving feature parameter is the second warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the second warning method, and fatigue warning is given based on the matched second warning method.

[0030] If the warning level of the target driving characteristic parameter is the third warning level, and the determination result is that the driver is judged to be in a state of fatigue driving once within each consecutive time window, then the target warning method is the third warning method, and fatigue warning is given based on the matched third warning method.

[0031] In one embodiment, the target driving characteristic parameter carries the determination results within each consecutive time window; the warning level of the target driving characteristic parameter includes a first warning level and a second warning level; the step of determining a target warning method matching the target driving characteristic parameter based on the warning level of the target driving characteristic parameter, and performing fatigue warning based on the matched target warning method, further includes:

[0032] The cumulative analysis results are obtained by performing cumulative analysis on the determination results of the target driving characteristic parameters at the first and second warning levels within each continuous time window.

[0033] If, among the target driving characteristic parameters of the first and second warning levels, the cumulative analysis results show that at least one target driving characteristic parameter is determined to be in a state of fatigued driving in each consecutive time window, then a fatigue warning is issued based on the first cumulative warning method that matches the target driving characteristic parameter.

[0034] If the cumulative analysis result is a target driving characteristic parameter of the first warning level and the second warning level, and the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigue driving in each consecutive time window reaches the first cumulative number threshold, then a fatigue warning is issued based on the second cumulative warning method that matches the target driving characteristic parameter.

[0035] If the cumulative analysis result is that among the target driving characteristic parameters of the first warning level and the second warning level, the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigued driving in each consecutive time window reaches the second cumulative number threshold, then a fatigue warning is issued based on the third cumulative warning method that matches the target driving characteristic parameter, and the first cumulative number threshold is greater than the second cumulative number threshold.

[0036] Secondly, this application also provides a warning device for fatigued driving behavior, the device comprising:

[0037] The data acquisition module is used to acquire the real-time characteristic parameter values ​​of various driving characteristic parameters of the vehicle during the current time period.

[0038] The data processing module is used to determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period.

[0039] The early warning module is used to determine the target early warning method that matches the target driving characteristic parameters based on the early warning level of the target driving characteristic parameters, and to provide fatigue early warning based on the matched target early warning method.

[0040] Thirdly, this application also provides a controller. The controller includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the above-described method for warning of fatigued driving behavior.

[0041] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned method for warning of fatigued driving behavior.

[0042] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps of the aforementioned method for warning of fatigued driving behavior.

[0043] The aforementioned methods, devices, controllers, storage media, and computer programs for warning of fatigued driving behavior acquire real-time characteristic parameter values ​​of various driving characteristic parameters within the current time period during vehicle operation. Based on these real-time characteristic parameter values, they determine target driving characteristic parameters that meet preset fatigue warning conditions during vehicle operation. Based on the warning level of the target driving characteristic parameters, they determine a target warning method that matches the target driving characteristic parameters and issue a fatigue warning based on the matched target warning method. By processing the real-time characteristic parameter values ​​of various driving characteristic parameters during operation, target driving characteristic parameters that meet fatigue warning conditions can be promptly identified from the driving characteristic parameters. Further combining this with the target warning method matched to the target driving characteristic parameters provides targeted reminders to the user, improving the safety and reliability of the driving process. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a method for early warning of fatigued driving behavior in one embodiment;

[0045] Figure 2 This is a schematic diagram of the structure of a method for warning of fatigued driving behavior in one embodiment;

[0046] Figure 3Here is a flowchart of a method for early warning of fatigued driving behavior in another embodiment;

[0047] Figure 4 This is a structural block diagram of a fatigue driving behavior warning device in one embodiment;

[0048] Figure 5 This is a diagram of the internal structure of the controller in one embodiment. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0050] The fatigue driving behavior warning method provided in this application embodiment can be applied to a controller, which can be a vehicle controller. In one embodiment, the controller acquires the real-time feature parameter values ​​of each driving feature parameter during the current time period while the vehicle is driving; based on the real-time feature parameter values ​​of the driving feature parameters during the current time period, it determines the target driving feature parameter that meets the preset fatigue warning conditions during the vehicle's driving process; based on the warning level of the target driving feature parameter, it determines the target warning method that matches the target driving feature parameter, and performs fatigue warning based on the matched target warning method.

[0051] In one embodiment, such as Figure 1 As shown, a method for early warning of fatigued driving behavior is provided. Taking the application of this method to a controller as an example, the method includes the following steps:

[0052] Step 102: Obtain the real-time feature parameter values ​​of each driving feature parameter during the current time period while the vehicle is in motion.

[0053] The current time period refers to the duration of the vehicle's journey from one point in time to another during its current driving process. This time period can be any length, such as 15 seconds or 1 minute. Driving characteristic parameters refer to driving information during the driving process. Specifically, these parameters can include driving behavior information and vehicle motion information. Driving behavior information can include steering wheel angle, steering wheel angle angular velocity, accelerator pedal opening, accelerator pedal frequency, brake pedal opening, and brake pedal frequency. Vehicle motion information can include vehicle speed, acceleration, braking deceleration, yaw angle, pitch angle, roll angle, and yaw rate.

[0054] The real-time feature parameter value can refer to the specific value of each driving feature parameter within the current time period. For example, when the driving feature parameter is vehicle speed and the current time period is 15 seconds, the real-time feature parameter value can refer to the actual value of vehicle speed collected at each time point within 15 seconds, such as every second or every 5 seconds. If the vehicle speed is constant, the real-time feature parameter value of vehicle speed may be the same within the current time period. If the vehicle speed changes, the real-time feature parameter value of vehicle speed may include multiple different values ​​within the current time period.

[0055] Step 104: Based on the real-time characteristic parameter values ​​of the driving characteristic parameters within the current time period, determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation.

[0056] The preset fatigue warning conditions are used to determine whether a fatigue warning can be issued. The target driving characteristic parameters are selected from driving characteristic parameters. When the driving characteristic parameters meet the preset fatigue warning conditions, the driving characteristic parameters are determined as the target driving characteristic parameters. The preset fatigue warning conditions can be set based on factors such as the correlation between driving characteristic parameters and fatigued driving.

[0057] Step 106: Based on the warning level of the target driving characteristic parameters, determine the target warning method that matches the target driving characteristic parameters, and conduct fatigue warning based on the matched target warning method.

[0058] The warning level can be used to characterize the degree of warning. The higher the warning level, the higher the degree of driving danger during the current driving process. For different target driving characteristic parameters, the corresponding warning level can be different or the same. The specific warning level of the target driving characteristic parameter can be determined according to the correlation between the target driving characteristic parameter and fatigue driving.

[0059] The aforementioned method for warning of fatigued driving behavior involves acquiring real-time characteristic parameter values ​​of various driving characteristics during the current time period while the vehicle is in motion; determining target driving characteristic parameters that meet preset fatigue warning conditions based on these real-time characteristic parameter values; determining a target warning method that matches the target driving characteristic parameter based on its warning level; and issuing a fatigue warning based on the matched target warning method. By processing the real-time characteristic parameter values ​​of various driving characteristics during driving, target driving characteristic parameters that meet fatigue warning conditions can be identified promptly. Further combining this with the matched target warning method provides targeted alerts to the user, improving the safety and reliability of the driving process.

[0060] In one embodiment, determining the target driving characteristic parameter that meets the preset fatigue warning condition during vehicle driving based on the real-time characteristic parameter value of the driving characteristic parameter within the current time period includes: determining multiple consecutive time windows based on the current time period; for any driving characteristic parameter, determining the index parameter of the targeted driving characteristic parameter in each consecutive time window based on the real-time characteristic parameter value of the targeted driving characteristic parameter in each consecutive time window, wherein the index parameter includes at least one of the characteristic parameter mean, characteristic parameter standard deviation, or characteristic parameter sample entropy; performing fatigue driving judgment based on the index parameter of each time window, determining whether the driving characteristic parameter in each time window is judged to be in a fatigue driving state; if there is a driving characteristic parameter judged to be in a fatigue driving state in at least one time window, then the driving characteristic parameter is determined to be the target driving characteristic parameter that meets the preset fatigue driving condition.

[0061] The time window refers to the division of the current time period. In order to conduct a more detailed analysis of the real-time feature parameter values, the real-time feature parameter values ​​can be segmented. For example, when the current time period is 15 seconds, it can be divided into three consecutive time windows with a 5-second interval: 0-5 seconds, 5-10 seconds, and 10-15 seconds.

[0062] Specifically, for each time window, the controller can calculate the index parameter for the real-time feature parameter value, thereby determining the numerical change of the driving feature parameter within the time window. Based on the numerical change, it can determine whether the driver is currently in a state of fatigue. Since the mean, standard deviation, and entropy (a value indicating the degree of disorder in the data sequence) can reflect the changes in the data from multiple perspectives, the index parameter can include at least one of the following: the mean of the changing feature parameter, the standard deviation of the feature parameter, and the sample entropy of the feature parameter.

[0063] Specifically, the controller can determine the real-time characteristic parameter value of each driving characteristic parameter within a continuous time window. Further, based on the real-time characteristic parameter value within each time window, it determines the index parameter of each time window. By analyzing the index parameter of each time window, it determines whether the driving characteristic parameter is judged to be in a state of fatigued driving in each time window. If any driving characteristic parameter is judged to be in a state of fatigued driving in at least one time window, then the driving characteristic parameter is determined to be the target driving characteristic parameter that meets the preset fatigue warning conditions.

[0064] In the above embodiments, the controller divides the current time period into multiple consecutive time windows, further processes the real-time feature parameter values ​​within the consecutive time windows to obtain index parameters, and analyzes the index parameters to accurately determine whether there is a state of fatigued driving in the current time period.

[0065] In one embodiment, the method further includes: acquiring historical driving parameters of the vehicle, including driver brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within a historical time period; determining fatigue correlation parameters for each driving characteristic parameter based on the driver brainwave signals and historical characteristic parameter values ​​within the historical time period, wherein the fatigue correlation parameters are parameters used to characterize the degree of correlation between each driving characteristic parameter and the driver's fatigue state; determining the warning level of each driving characteristic parameter based on the fatigue correlation parameters; and determining a warning method matching the corresponding driving characteristic parameter based on the warning level of each driving characteristic parameter.

[0066] Among these, historical driving parameters can be parameters generated during vehicle operation that the controller acquires from historical time periods. Driver brainwave signals refer to the postsynaptic potential signals of neurons in the driver's cerebral cortex. Electrode caps can be used to collect these brainwave signals, and the collection location can be... Figure 2 The positions O1, O2, A1, and A2 in the diagram, among which, Figure 2 Different letters indicate the location of different electrodes on the head. The brainwave signal potential is very low, less than 100μV, making it susceptible to interference from surrounding electromagnetic radiation, including power frequency, and internal electronic noise within the instrument during signal collection. Data acquisition can be performed outside of an electromagnetic barrier room, but the instrument should be grounded as much as possible and kept away from strong electrostatic and electromagnetic fields. A quiet testing environment should also be maintained to prevent unnecessary noise from entering during data acquisition.

[0067] In one embodiment, as shown in Table 1, the fatigue correlation parameters are calculated by the controller based on the EEG signal and historical feature parameter values. The fatigue correlation parameters can characterize the degree of correlation between each driving feature parameter and the driver's fatigue state. Generally, the larger the value of the fatigue correlation parameter, the stronger the correlation between its driving feature parameter and the driver's fatigue state.

[0068] Table 1

[0069]

[0070] As shown in Table 1, the controller can calculate the fatigue correlation value, i.e., the fatigue correlation parameter, by combining the driver's EEG signal with the driving behavior information and vehicle motion information. For example, the fatigue correlation parameter calculated for vehicle speed is 0.55, and the fatigue correlation parameter calculated for acceleration is 0.45. The closer the fatigue correlation parameter is to 1, the greater the correlation with the driver's fatigue state. Correspondingly, the higher the fatigue correlation parameter of the driving characteristic parameter, the higher the warning level. Different driving characteristic parameters can be assigned corresponding warning levels.

[0071] Specifically, when calculating fatigue-related parameters, the controller first analyzes the EEG signal to obtain four types of segment filters: α, β, θ, and δ, and determines the frequency band energy proportion of any one of these segment filters. Further, based on the historical characteristic parameter values ​​of driving characteristics, the controller determines the historical mean, standard deviation, and sample entropy of each driving characteristic parameter, such as the mean, standard deviation, and sample entropy of the steering wheel angle, the yaw rate, the vehicle speed, and the acceleration. The controller then calculates the energy proportions with the mean, standard deviation, and sample entropy to determine the fatigue-related parameters.

[0072] In this embodiment, the controller calculates the fatigue correlation parameter by combining the brainwave signal and the historical characteristic parameter values ​​of the driving characteristic parameters. Since the fatigue correlation parameter can reflect the degree of correlation between each driving characteristic parameter and the driver's fatigue state, the controller can set the warning level of each driving characteristic parameter according to the degree of fatigue correlation, thereby making the fatigue warning more reliable.

[0073] In one embodiment, the warning level of each driving characteristic parameter is determined based on the fatigue correlation parameter of each driving characteristic parameter, including: if the fatigue correlation parameter of the driving characteristic parameter is within a first warning threshold range, then the warning level of the driving characteristic parameter is determined to be a first warning level, whereby the first warning level indicates that the driving characteristic parameter and fatigued driving have a first degree of correlation; if the fatigue correlation parameter of the driving characteristic parameter is within a second warning threshold range, then the warning level of the driving characteristic parameter is determined to be a second warning level, whereby the second warning level indicates that the driving characteristic parameter and fatigued driving have a second degree of correlation; if the fatigue correlation parameter of the driving characteristic parameter is within a third warning threshold range, then the warning level of the driving characteristic parameter is determined to be a third warning level, whereby the first degree of correlation is greater than the second degree of correlation, and the second degree of correlation is greater than the third degree of correlation.

[0074] The first, second, and third warning threshold ranges are defined as threshold ranges used to divide the warning levels of driving characteristic parameters. Each different warning threshold range corresponds to a specific warning level. The first warning threshold range corresponds to the first warning level, the second warning threshold range corresponds to the second warning level, and the third warning threshold range corresponds to the third warning level. Each warning level represents the correlation between driving characteristic parameters and fatigued driving. The first warning level represents a higher degree of correlation than the second warning level, and the second warning level represents a higher degree of correlation than the third warning level. If a fatigue-related parameter falls within a certain warning threshold range, the driving characteristic parameter corresponding to that fatigue-related parameter is the warning level corresponding to that warning threshold range. The specific ranges of the first, second, and third warning threshold ranges can be adaptively adjusted according to actual application scenarios.

[0075] Specifically, the first warning threshold range can be 0.6-1. According to Table 1, if the fatigue correlation parameters of steering wheel angle and steering wheel angle angular velocity are within the first warning threshold range, then the warning level for steering wheel angle and steering wheel angle angular velocity is the first warning level. The second warning threshold range can be 0.5-0.6. If the fatigue correlation parameters of brake pedal opening and vehicle speed are within the second warning threshold range, then the warning level for brake pedal opening and vehicle speed is the second warning level. The third warning threshold range can be 0.3-0.5. If the fatigue correlation parameters of acceleration and accelerator pedal opening are within the third warning threshold range, then the warning level for acceleration and accelerator pedal opening is the third warning level. For other fatigue correlation parameters that do not fall within the first, second, and third warning threshold ranges, their corresponding driving characteristic parameters can be temporarily left without a warning level.

[0076] In this embodiment, the controller can accurately determine the warning level of each driving characteristic parameter by setting a warning threshold range and combining the fatigue correlation parameters of each driving characteristic parameter.

[0077] In one embodiment, the warning levels of the driving characteristic parameters include a first warning level, a second warning level, and a third warning level. Based on the warning level of each driving characteristic parameter, a warning method matching the driving characteristic parameter is determined, including: when the warning level of the driving characteristic parameter is the first warning level, the warning method matching the driving characteristic parameter includes a first warning method, a first cumulative warning method, a second cumulative warning method, and a third cumulative warning method; when the warning level of the driving characteristic parameter is the second warning level, the warning method matching the driving characteristic parameter includes a second warning method, a first cumulative warning method, a second cumulative warning method, and a third cumulative warning method; when the warning level of the driving characteristic parameter is the third warning level, the warning method matching the driving characteristic parameter includes a third warning method; the warning danger level of the first cumulative warning method is higher than that of the second cumulative warning method, the warning danger level of the second cumulative warning method is higher than that of the third cumulative warning method, the warning danger level of the third cumulative warning method is higher than that of the first warning method, the warning danger level of the first warning method is higher than that of the second warning method, and the warning danger level of the second warning method is higher than that of the third warning method.

[0078] Specifically, for the first warning level, the corresponding warning methods include the first warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method. For the second warning level, the corresponding warning methods include the second warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method. For the third warning level, the corresponding warning method includes the third warning method. Different warning methods can be presented through the flashing of instrument lights in the vehicle, the sound of the sound unit, the vibration generated by the built-in motors on both sides of the driver's seat, and the vibration of the seat belt.

[0079] The different warning methods manifest in different ways. For example, the first cumulative warning method can be accompanied by rapid flashing of the instrument indicator light, a rapid and continuous beeping sound, and simultaneous vibration of the seat belt and the built-in motors on both sides of the seat. The second cumulative warning method can be accompanied by rapid flashing of the instrument indicator light, a rapid and continuous beeping sound, and vibration of the seat belt. The third cumulative warning method can be accompanied by rapid flashing of the instrument indicator light and a rapid and continuous beeping sound. The first cumulative warning method can be accompanied by rapid flashing of the instrument indicator light and a brief beeping sound. The second cumulative warning method can be accompanied by rapid flashing of the instrument indicator light. The third cumulative warning method can be accompanied by the instrument indicator light being on for 5 seconds and then disappearing.

[0080] In this embodiment, different warning levels for driving characteristic parameters correspond to different warning methods, and each warning method is presented in a different form, thereby enabling warnings at the corresponding warning levels and improving the safety and reliability of the driving process.

[0081] In one embodiment, the target driving feature parameter carries the determination result within each consecutive time window; the warning level of the target driving feature parameter includes a first warning level, a second warning level, and a third warning level; based on the warning level of the target driving feature parameter, a target warning method matching the target driving feature parameter is determined, and fatigue warning is performed based on the matched target warning method, including: if the warning level of the target driving feature parameter is the first warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the first warning method, and fatigue warning is performed based on the matched first warning method; if the warning level of the target driving feature parameter is the second warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the second warning method, and fatigue warning is performed based on the matched second warning method; if the warning level of the target driving feature parameter is the third warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the third warning method, and fatigue warning is performed based on the matched third warning method.

[0082] Specifically, the controller uses corresponding warning methods to issue warnings for target driving characteristic parameters at different warning levels. For example, if a target driving characteristic parameter at the first warning level is determined to be fatigued driving once within consecutive time windows, the first warning method will be used to issue a fatigue warning. If a target driving characteristic parameter at the second warning level is determined to be fatigued driving once within consecutive time windows, the second warning method will be used to issue a fatigue warning. If a target driving characteristic parameter at the third warning level is determined to be fatigued driving once within consecutive time windows, the third warning method will be used to issue a fatigue warning.

[0083] In this embodiment, there are corresponding warning methods for the target driving characteristic parameters of different warning levels. At each warning level, when the judgment result meets the corresponding conditions, the corresponding method will be used to carry out fatigue warning, thereby realizing the warning of the corresponding warning level and improving the safety and reliability of the driving process.

[0084] In one embodiment, the target driving feature parameters carry the determination results within each consecutive time window; the warning levels of the target driving feature parameters include a first warning level and a second warning level; based on the warning levels of the target driving feature parameters, a target warning method matching the target driving feature parameters is determined, and fatigue warning is performed based on the matching target warning method, further including: performing cumulative analysis based on the determination results of the target driving feature parameters at the first warning level and the second warning level within each consecutive time window to obtain a cumulative analysis result; if the cumulative analysis result shows that among the target driving feature parameters at the first warning level and the second warning level, at least one target driving feature parameter is determined to be in a fatigued driving state within each consecutive time window, then based on the determination results of the target driving feature parameters at the first warning level and the second warning level, fatigue warning is performed. Fatigue warning is issued based on a first cumulative warning method that matches the target driving feature parameters. If the cumulative analysis result is at the first warning level and the second warning level, and the cumulative number of times the target driving feature parameters are determined to be in a state of fatigued driving within consecutive time windows reaches the first cumulative number threshold, then fatigue warning is issued based on a second cumulative warning method that matches the target driving feature parameters. If the cumulative analysis result is at the first warning level and the second warning level, and the cumulative number of times the target driving feature parameters are determined to be in a state of fatigued driving within consecutive time windows reaches the second cumulative number threshold, then fatigue warning is issued based on a third cumulative warning method that matches the target driving feature parameters, where the first cumulative number threshold is greater than the second cumulative number threshold.

[0085] Specifically, for target driving characteristic parameters at the first and second warning levels, the judgment results of each target driving characteristic parameter within a time window can be combined for analysis to determine the warning method. For example, the target driving characteristic parameters at the first warning level can be steering wheel angle and steering wheel angle angular velocity, while the target driving characteristic parameters at the second warning level can be brake pedal opening, brake pedal frequency, and vehicle speed. If any one of the target driving characteristic parameters at the first and second warning levels—namely, steering wheel angle, steering wheel angle angular velocity, brake pedal opening, brake pedal frequency, and vehicle speed—is judged as fatigue driving within a continuous time window, then a fatigue warning is issued using the first cumulative warning method. If the sum of the number of times each of the steering wheel angle, steering wheel angle angular velocity, brake pedal opening, brake pedal frequency, and vehicle speed is judged as fatigue driving within a continuous time window reaches the first cumulative threshold, then a fatigue warning is issued using the second cumulative warning method. When the sum of the number of times that are judged as fatigue driving within a continuous time window, including steering wheel angle, steering wheel angle angular velocity, brake pedal opening, brake pedal frequency, and vehicle speed, reaches the second cumulative threshold, a fatigue warning will be issued using the third cumulative warning method, wherein the first cumulative threshold is greater than the second cumulative threshold.

[0086] In this embodiment, the controller combines the target driving characteristic parameters of the first and second warning levels and analyzes the judgment results in each consecutive time window, thereby enabling the implementation of multiple warning levels as comprehensively as possible and improving the safety and reliability of the driving process.

[0087] In one embodiment, such as Figure 3 The diagram shown is a flowchart illustrating a method for early warning of fatigued driving behavior in one embodiment:

[0088] This embodiment includes an EEG signal acquisition module, an EEG signal processing module, a driver behavior information acquisition module, a driver behavior information processing module, a multi-source information correlation analysis module, and a dangerous driving behavior early warning module.

[0089] The EEG signal acquisition module utilizes the principles of postsynaptic potentials and electric field effects (an action potential generates a current, which in turn generates a magnetic field, which then influences the action potentials on nearby neurons). Both dry and wet electrode caps can be used. The EEG signal processing module includes hardware and software for resampling, removing power line interference, removing electrooculogram artifacts, and filtering. It can process the acquired raw, complex signals (which can be processed through…) Figure 2 The module performs resampling, preprocessing, interference removal, and filtering on the O1, O2, A1, and A2 locations to produce ideal results. This module can significantly improve the reliability of subsequent fatigue detection applications of the acquired complex signals. While removing interference from other electrical signals, it decomposes and reconstructs four typical section filters (α, β, θ, and δ) for fatigue detection, facilitating subsequent calculations of frequency band energy ratio or fuzzy entropy indices.

[0090] The driver behavior information acquisition module consists of inertial measurement units (IMUs) located in the center of the steering wheel and inertial measurement units located at the edges of the treads for the brake and accelerator pedals, the in-vehicle navigation system, and occupant status monitoring cameras. It is primarily used to integrate two methods: directly acquiring driver behavior information and indirectly inferring driver behavior information by acquiring vehicle motion information. This assists in the subsequent reliable numerical indexing of dangerous driving behaviors.

[0091] The driver behavior information processing module includes hardware and algorithms. It provides parameter information that can be filtered and characterized to reflect the driver's fatigue state, such as vehicle speed, accelerator pedal frequency and acceleration, brake pedal frequency and acceleration, and steering wheel angle changes, which can be used for subsequent correlation analysis. On the other hand, this module can also perform preliminary index calculations (mean, standard deviation, variance, sample entropy, fuzzy entropy, etc.) on driver behavior data to facilitate subsequent correlation analysis.

[0092] The multi-source information correlation analysis module performs correlation analysis on the raw complex signals collected by the EEG acquisition module and the vehicle motion data collected by the driving behavior information acquisition module, such as vehicle speed, accelerator pedal frequency and acceleration, brake pedal frequency and acceleration, and steering wheel angle. Based on the EEG fatigue detection results, it seeks to find the difference between driving behavior under fatigued driving conditions and normal driving conditions, and reflects this through data. In this way, it can judge high-risk driving behaviors induced by fatigue through driving operation and vehicle motion data.

[0093] The dangerous driving behavior warning module uses instrument lights, sounds, and vibrations generated by built-in motors on both sides of the driver's seat and seatbelts to achieve a six-level warning system (instrument light illuminates for 5 seconds and then disappears; instrument light flashes rapidly; instrument light flashes rapidly and is accompanied by a brief beep; instrument light flashes rapidly and is accompanied by a rapid, continuous beep; instrument light flashes rapidly and is accompanied by a rapid, continuous beep, along with seatbelt vibration; instrument light flashes rapidly and is accompanied by a rapid, continuous beep, along with simultaneous vibrations of the seatbelt and built-in motors on both sides of the seat). This system reminds the driver to drive properly, rest, or change drivers after safely parking.

[0094] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0095] Based on the same inventive concept, this application also provides a fatigue driving behavior warning device for implementing the fatigue driving behavior warning method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more fatigue driving behavior warning device embodiments provided below can be found in the limitations of the fatigue driving behavior warning method described above, and will not be repeated here.

[0096] In one embodiment, such as Figure 4 As shown, a fatigue driving behavior warning device 400 is provided, including: a data acquisition module 402, a data processing module 404, and a warning module 406, wherein:

[0097] The data acquisition module 402 is used to acquire the real-time characteristic parameter values ​​of various driving characteristic parameters of the vehicle during the current time period.

[0098] The data processing module 404 is used to determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation based on the real-time characteristic parameter values ​​of the driving characteristic parameters within the current time period.

[0099] The early warning module 406 is used to determine the target early warning method that matches the target driving characteristic parameters based on the early warning level of the target driving characteristic parameters, and to provide fatigue early warning based on the matched target early warning method.

[0100] In one embodiment, the data processing module is further configured to: determine multiple consecutive time windows based on the current time period; for any driving characteristic parameter, determine the index parameter of the driving characteristic parameter in each consecutive time window based on the real-time characteristic parameter value of the driving characteristic parameter in each consecutive time window, wherein the index parameter includes at least one of the characteristic parameter mean, characteristic parameter standard deviation, or characteristic parameter sample entropy; perform fatigue driving judgment based on the index parameter of each time window, and determine whether the driving characteristic parameter in each time window is judged to be in a fatigue driving state; if there is a driving characteristic parameter judged to be in a fatigue driving state in at least one time window, then determine the driving characteristic parameter as a target driving characteristic parameter that meets the preset fatigue warning conditions.

[0101] In one embodiment, the device further includes a warning method determination module;

[0102] The warning method determination module is used to acquire the vehicle's historical driving parameters, including the driver's brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within a historical time period. Based on the driver's brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within the historical time period, the module determines the fatigue correlation parameter of each driving characteristic parameter, which is a parameter used to characterize the degree of correlation between each driving characteristic parameter and the driver's fatigue state. Based on the fatigue correlation parameter of each driving characteristic parameter, the module determines the warning level of each driving characteristic parameter. Based on the warning level of each driving characteristic parameter, the module determines the warning method that matches the corresponding driving characteristic parameter.

[0103] In one embodiment, the aforementioned warning method determination module is further configured to: if the fatigue correlation parameter of the driving characteristic parameter is within a first warning threshold range, determine the warning level of the driving characteristic parameter as a first warning level, where the first warning level indicates that the driving characteristic parameter and fatigued driving have a first degree of correlation; if the fatigue correlation parameter of the driving characteristic parameter is within a second warning threshold range, determine the warning level of the driving characteristic parameter as a second warning level, where the second warning level indicates that the driving characteristic parameter and fatigued driving have a second degree of correlation; if the fatigue correlation parameter of the driving characteristic parameter is within a third warning threshold range, determine the warning level of the driving characteristic parameter as a third warning level, where the first degree of correlation is greater than the second degree of correlation, and the second degree of correlation is greater than the third degree of correlation.

[0104] In one embodiment, the aforementioned warning method determination module is further configured to: when the warning level of the driving characteristic parameter is a first warning level, the warning methods matching the driving characteristic parameter include a first warning method, a first cumulative warning method, a second cumulative warning method, and a third cumulative warning method; when the warning level of the driving characteristic parameter is a second warning level, the warning methods matching the driving characteristic parameter include a second warning method, a first cumulative warning method, a second cumulative warning method, and a third cumulative warning method; when the warning level of the driving characteristic parameter is a third warning level, the warning methods matching the driving characteristic parameter include a third warning method; the warning danger level of the first cumulative warning method is higher than that of the second cumulative warning method, the warning danger level of the second cumulative warning method is higher than that of the third cumulative warning method, the warning danger level of the third cumulative warning method is higher than that of the first warning method, the warning danger level of the first warning method is higher than that of the second warning method, and the warning danger level of the second warning method is higher than that of the third warning method.

[0105] In one embodiment, the aforementioned warning module is further configured to: if the warning level of the target driving feature parameter is a first warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within consecutive time windows, then the target warning method is the first warning method, and fatigue warning is performed based on the matched first warning method; if the warning level of the target driving feature parameter is a second warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within consecutive time windows, then the target warning method is the second warning method, and fatigue warning is performed based on the matched second warning method; if the warning level of the target driving feature parameter is a third warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within consecutive time windows, then the target warning method is the third warning method, and fatigue warning is performed based on the matched third warning method.

[0106] In one embodiment, the aforementioned warning module is further configured to perform cumulative analysis based on the determination results of the target driving feature parameters at the first warning level and the second warning level within each consecutive time window to obtain cumulative analysis results; if the cumulative analysis results show that at least one target driving feature parameter at the first warning level and the second warning level is determined to be in a state of fatigued driving within each consecutive time window, then a fatigue warning is issued based on a first cumulative warning method matching the target driving feature parameter; if the cumulative analysis results show that the cumulative number of times the target driving feature parameter at the first warning level and the second warning level is determined to be in a state of fatigued driving within each consecutive time window reaches a first cumulative number threshold, then a fatigue warning is issued based on a second cumulative warning method matching the target driving feature parameter; if the cumulative analysis results show that the cumulative number of times the target driving feature parameter at the first warning level and the second warning level is determined to be in a state of fatigued driving within each consecutive time window reaches a second cumulative number threshold, then a fatigue warning is issued based on a third cumulative warning method matching the target driving feature parameter, wherein the first cumulative number threshold is greater than the second cumulative number threshold.

[0107] The modules in the aforementioned fatigue driving warning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the controller in hardware form or independent of it, or stored in the memory of the controller in software form, so that the processor can call and execute the corresponding operations of each module.

[0108] In one embodiment, a controller is provided, which may be a vehicle controller on a vehicle, and its internal structure diagram may be as follows: Figure 5 As shown, the controller includes a processor, memory, and input / output interfaces. The memory is connected to the processor, and the processor is connected to the input / output interfaces. The processor provides computational and control capabilities. The controller's memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The processor's input / output interfaces are used for exchanging information between the processor and other controllers. When the computer program is executed by the processor, it implements a method for warning of driver fatigue.

[0109] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the controller to which the present application is applied. A specific controller may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0110] In one embodiment, a controller is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the above-described method for warning of fatigued driving behavior.

[0111] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned method for warning of fatigued driving behavior. In another embodiment, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of the aforementioned method for warning of fatigued driving behavior.

[0112] 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, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for early warning of fatigued driving behavior, characterized in that, The method includes: Obtain the real-time feature parameter values ​​of each driving feature parameter of the vehicle during the current time period; Based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period, determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation. Based on the warning level of the target driving characteristic parameters, a target warning method matching the target driving characteristic parameters is determined, and fatigue warning is given based on the matching target warning method. The method further includes: The vehicle’s historical driving parameters are obtained, including the driver’s brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within a historical time period. Based on the driver's EEG signals and historical characteristic parameter values ​​of each driving characteristic parameter during the historical time period, fatigue correlation parameters of each driving characteristic parameter are determined. The fatigue correlation parameters are parameters used to characterize the degree of correlation between each driving characteristic parameter and the driver's fatigue state. Based on the fatigue correlation parameters of each driving characteristic parameter, the warning level of each driving characteristic parameter is determined. Based on the warning level of each driving characteristic parameter, determine the warning method that matches the corresponding driving characteristic parameter; The target driving characteristic parameter carries the judgment results within each consecutive time window; the warning level of the target driving characteristic parameter includes a first warning level and a second warning level; the step of determining a target warning method matching the target driving characteristic parameter based on the warning level of the target driving characteristic parameter, and performing fatigue warning based on the matched target warning method, further includes: The cumulative analysis results are obtained by performing cumulative analysis on the determination results of the target driving characteristic parameters at the first and second warning levels within each continuous time window. If, among the target driving characteristic parameters of the first and second warning levels, the cumulative analysis results show that at least one target driving characteristic parameter is determined to be in a state of fatigued driving in each consecutive time window, then a fatigue warning is issued based on the first cumulative warning method that matches the target driving characteristic parameter. If the cumulative analysis result is a target driving characteristic parameter of the first warning level and the second warning level, and the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigue driving in each consecutive time window reaches the first cumulative number threshold, then a fatigue warning is issued based on the second cumulative warning method that matches the target driving characteristic parameter. If the cumulative analysis result is that among the target driving characteristic parameters of the first warning level and the second warning level, the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigued driving in each consecutive time window reaches the second cumulative number threshold, then a fatigue warning is issued based on the third cumulative warning method that matches the target driving characteristic parameter, and the first cumulative number threshold is greater than the second cumulative number threshold.

2. The method according to claim 1, characterized in that, The step of determining the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period includes: Based on the current time period, multiple consecutive time windows are determined; For any driving feature parameter, based on the real-time feature parameter value of the driving feature parameter in each consecutive time window, the index parameter of the driving feature parameter in each consecutive time window is determined. The index parameter includes at least one of the feature parameter mean, feature parameter standard deviation, or feature parameter sample entropy. Fatigue driving is determined based on the index parameters of each time window, and it is determined whether the driving characteristic parameters in each time window are judged to be in a state of fatigue driving. If a driving characteristic parameter is determined to be in a state of fatigued driving within at least one time window, then the driving characteristic parameter is determined to be a target driving characteristic parameter that meets the preset fatigue warning conditions.

3. The method according to claim 1, characterized in that, The step of determining the warning level of each driving characteristic parameter based on the fatigue correlation parameter of each driving characteristic parameter includes: If the fatigue correlation parameter of the driving characteristic parameter is within the range of the first warning threshold, then the warning level of the driving characteristic parameter is determined to be the first warning level, and the first warning level indicates that the driving characteristic parameter is correlated with fatigued driving to the first degree. If the fatigue correlation parameter of the driving characteristic parameter is within the range of the second warning threshold, then the warning level of the driving characteristic parameter is determined to be the second warning level, which indicates that the driving characteristic parameter is correlated with fatigued driving to a second degree. If the fatigue correlation parameter of the driving characteristic parameter is within the range of the third warning threshold, then the warning level of the driving characteristic parameter is determined to be the third warning level. The third warning level indicates that the driving characteristic parameter is third-degree correlated with fatigued driving, where the first degree of correlation is greater than the second degree of correlation, and the second degree of correlation is greater than the third degree of correlation.

4. The method according to claim 1, characterized in that, The warning levels of the driving characteristic parameters include a first warning level, a second warning level, and a third warning level; determining the warning method matching the driving characteristic parameter based on the warning level of each driving characteristic parameter includes: When the warning level of the driving characteristic parameter is the first warning level, the warning methods matched with the driving characteristic parameter include the first warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method; When the warning level of the driving characteristic parameter is the second warning level, the warning methods matched with the driving characteristic parameter include the second warning method, the first cumulative warning method, the second cumulative warning method, and the third cumulative warning method; When the warning level of the driving characteristic parameter is the third warning level, the warning method matching the driving characteristic parameter includes the third warning method; the warning danger level of the first cumulative warning method is higher than that of the second cumulative warning method, the warning danger level of the second cumulative warning method is higher than that of the third cumulative warning method, the warning danger level of the third cumulative warning method is higher than that of the first warning method, the warning danger level of the first warning method is higher than that of the second warning method, and the warning danger level of the second warning method is higher than that of the third warning method.

5. The method according to claim 1, characterized in that, The target driving characteristic parameter carries the judgment results within each consecutive time window; the warning level of the target driving characteristic parameter includes a first warning level, a second warning level, and a third warning level; the step of determining a target warning method matching the target driving characteristic parameter based on the warning level of the target driving characteristic parameter, and performing fatigue warning based on the matched target warning method, includes: If the warning level of the target driving feature parameter is the first warning level, and the determination result is that the driver is determined to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the first warning method, and fatigue warning is given based on the matched first warning method. If the warning level of the target driving feature parameter is the second warning level, and the determination result is that the driver is judged to be in a state of fatigued driving once within each consecutive time window, then the target warning method is the second warning method, and fatigue warning is given based on the matched second warning method. If the warning level of the target driving characteristic parameter is the third warning level, and the determination result is that the driver is judged to be in a state of fatigue driving once within each consecutive time window, then the target warning method is the third warning method, and fatigue warning is given based on the matched third warning method.

6. A warning device for fatigued driving behavior, characterized in that, The device includes: The data acquisition module is used to acquire the real-time characteristic parameter values ​​of various driving characteristic parameters of the vehicle during the current time period. The data processing module is used to determine the target driving characteristic parameters that meet the preset fatigue warning conditions during vehicle operation based on the real-time characteristic parameter values ​​of driving characteristic parameters within the current time period. The early warning module is used to determine the target early warning method that matches the target driving characteristic parameters based on the early warning level of the target driving characteristic parameters, and to provide fatigue early warning based on the matched target early warning method; The warning method determination module is used to acquire the vehicle's historical driving parameters, including the driver's brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within a historical time period; based on the driver's brainwave signals and historical characteristic parameter values ​​of each driving characteristic parameter within the historical time period, it determines the fatigue correlation parameter of each driving characteristic parameter, which is a parameter used to characterize the degree of correlation between each driving characteristic parameter and the driver's fatigue state; based on the fatigue correlation parameter of each driving characteristic parameter, it determines the warning level of each driving characteristic parameter; and based on the warning level of each driving characteristic parameter, it determines the warning method matching the corresponding driving characteristic parameter. The early warning module is further configured to perform cumulative analysis based on the determination results of the target driving characteristic parameters at the first and second early warning levels within each consecutive time window, and obtain cumulative analysis results; if, among the target driving characteristic parameters at the first and second early warning levels, at least one of the target driving characteristic parameters is determined to be in a state of fatigued driving within each consecutive time window, then a fatigue warning is issued based on a first cumulative early warning method matching the target driving characteristic parameter; if, among the target driving characteristic parameters at the first and second early warning levels, the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigued driving within each consecutive time window reaches a first cumulative number threshold, then a fatigue warning is issued based on a second cumulative early warning method matching the target driving characteristic parameter; if, among the target driving characteristic parameters at the first and second early warning levels, the cumulative number of times the target driving characteristic parameter is determined to be in a state of fatigued driving within each consecutive time window reaches a second cumulative number threshold, then a fatigue warning is issued based on a third cumulative early warning method matching the target driving characteristic parameter, wherein the first cumulative number threshold is greater than the second cumulative number threshold.

7. A controller comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

Images