Driving fatigue monitoring method, device, computer equipment and storage medium

Abstract

The present invention relates to the field of intelligent automobiles. The present invention discloses a driving fatigue monitoring method, device, computer equipment and storage medium. The method includes: obtaining individual characteristics of the driver, driving status data and driving environment data; determining the driving fatigue characteristics of the driver based on the driving status data; determining the driving fatigue warning duration of the driver based on the individual characteristics of the driver and the driving environment data; obtaining the driving time of the driver; when the driving time is less than the driving fatigue warning duration, and the difference between the driving fatigue warning duration and the driving time is less than or equal to a preset time length, judging whether the driving fatigue characteristics meet the preset fatigue conditions; if the driving fatigue characteristics meet the preset fatigue conditions, issuing a first driving fatigue reminder. The present invention combines the monitoring of driving fatigue characteristics with the driving fatigue warning duration to greatly improve the accuracy of driving fatigue reminders.

Description

Technical Field

[0001] The present invention relates to the field of intelligent vehicles, and in particular to a driving fatigue monitoring method, device, computer equipment and storage medium. Background Art

[0002] Currently, driver fatigue monitoring is categorized into two types based on the monitoring mechanism: direct fatigue monitoring and monitoring fatigue monitoring. Direct fatigue monitoring primarily involves facial behavior recognition, such as using a camera to monitor facial features, eye movement signals, head movements, and other facial behaviors, followed by image analysis and processing to determine driver fatigue. Indirect fatigue monitoring can be divided into driver fatigue warnings based on vehicle trajectory and driver operating behavior.

[0003] These two forms of fatigue monitoring each have their own advantages and disadvantages. The disadvantage of direct fatigue monitoring is that it requires increased hardware costs, and the installation of monitoring cameras may affect the driving field of view and appearance. The advantage of direct fatigue monitoring is that the judgment of fatigue monitoring is relatively accurate, and there is no need to call the vehicle's own sensors, and the judgment logic is relatively simple. The advantage of indirect fatigue monitoring is that it does not require additional vehicle hardware. The disadvantage is that the accuracy of fatigue monitoring is low, and fatigue characteristics are difficult to extract. Summary of the Invention

[0004] Based on this, it is necessary to provide a driving fatigue monitoring method, device, computer equipment and storage medium to address the above technical problems, so as to reduce the difficulty of extracting fatigue characteristics while ensuring the accuracy of fatigue monitoring.

[0005] A driving fatigue monitoring method comprising:

[0006] Obtain driver individual characteristics, driving status data and driving environment data;

[0007] determining the driver's driving fatigue characteristics based on the driving state data; determining the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data;

[0008] Obtaining the driving time of the driver;

[0009] When the driving time is less than the driving fatigue warning time, and the difference between the driving fatigue warning time and the driving time is less than or equal to a preset time length, determining whether the driving fatigue characteristic meets a preset fatigue condition;

[0010] If the driving fatigue characteristic meets the preset fatigue condition, a first driving fatigue reminder is issued.

[0011] A driving fatigue monitoring device, comprising:

[0012] An acquisition module is used to obtain driver individual characteristics, driving status data and driving environment data;

[0013] A feature determination and warning duration module is used to determine the driver's driving fatigue features based on the driving state data; and to determine the driver's driving fatigue warning duration based on the driver's individual features and the driving environment data;

[0014] A driving time acquisition module is used to acquire the driving time of the driver;

[0015] a fatigue judgment module, configured to judge whether the driving fatigue characteristic satisfies a preset fatigue condition when the driving duration is less than the driving fatigue warning duration and the difference between the driving fatigue warning duration and the driving duration is less than or equal to a preset time length;

[0016] The first reminder module is configured to issue a first driving fatigue reminder if the driving fatigue characteristic meets a preset fatigue condition.

[0017] A computer device includes a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the above-mentioned driving fatigue monitoring method is implemented.

[0018] One or more readable storage media storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute the above-mentioned driving fatigue monitoring method.

[0019] The above-mentioned driving fatigue monitoring method, device, computer equipment and storage medium do not require additional hardware for fatigue monitoring. They only need to combine the driver's individual characteristics and driving environment data to accurately match the current driver's driving fatigue warning duration, thereby reducing the difficulty of extracting fatigue characteristics. In addition, by combining the monitoring of driving fatigue characteristics on the basis of the driving fatigue warning duration, the accuracy of driving fatigue reminders can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 This is a flow chart of a method for monitoring driver fatigue according to an embodiment of the present invention;

[0022] Figure 21 is a schematic structural diagram of a driving fatigue monitoring device according to an embodiment of the present invention;

[0023] Figure 3 FIG. 1 is a schematic diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] In one embodiment, if Figure 1 As shown, a driving fatigue monitoring method is provided, including the following steps S10-S50.

[0026] S10: Obtain driver individual characteristics, driving status data, and driving environment data.

[0027] It is understandable that the driving fatigue monitoring method provided in this embodiment can be applied to a vehicle-mounted system to monitor the driver's fatigue state and issue a driving fatigue reminder in a timely manner.

[0028] Here, the driver's individual characteristics include but are not limited to height, weight, gender, and driving experience (actual driving experience). The driver's individual characteristics can be personal information pre-entered by the driver. In one example, after the vehicle is started, a selection page for the driver's individual characteristics is displayed on the vehicle display screen, and the selection page displays the following options:

[0029] Option 1: Driver height range {1500mm~1599mm, 1600mm~1780mm, 1781mm~1830mm};

[0030] Option 2: Actual driving experience interval {<1 year, 1-3 years, >3 years};

[0031] Option 3: Gender {Male, Female}.

[0032] After the driver selects the above options, he / she selects confirm, and the vehicle system saves the driver's individual characteristics according to the driver's selection.

[0033] Driving status data can be measurement data monitored by onboard sensors. Driving status data may include, but is not limited to, driver image data captured by in-vehicle cameras, vehicle speed data collected by speed sensors, steering data collected by steering sensors, and pressure sensor data collected by pedal sensors.

[0034] Driving environment data includes but is not limited to environmental parameters that can affect the driver's vision, hearing, touch / feeling, and smell / breathing. For example, environmental parameters that affect vision include:

[0035] In-car vision: windshield, inside and outside rearview mirrors, etc.

[0036] In-vehicle operating parts: instruments, switches, control parts, etc.;

[0037] External environment: lighting, background, glare, etc.

[0038] Road traffic: pedestrians, vehicles, signs, etc.

[0039] Environmental parameters that affect hearing include: vehicle sounds (prompt sounds, alarms, in-car music, etc.), driving noise inside the car, and external environmental noise.

[0040] Environmental parameters that affect touch / feeling include:

[0041] Feeling of cold and warm: temperature, humidity, radiation;

[0042] Vehicle handling, seat contact, etc.;

[0043] Sense of direction and balance.

[0044] Environmental parameters that affect smell / breathing include: oxygen concentration, ambient odor inside the vehicle, and harmful gases outside the vehicle.

[0045] S20. Determine the driver's driving fatigue characteristics based on the driving state data; and determine the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data.

[0046] It is understandable that the driver's driving fatigue characteristics can be analyzed based on the collected driving status data. Driving fatigue characteristics include but are not limited to the driver's head posture, torso posture, hand posture and foot posture. Head posture may involve whether the head shakes with the vehicle, whether eye movements occur, whether the head adjusts up and down, whether the head turns to observe, and whether there is a small swing. Torso posture may involve whether the posture is maintained. Hand posture involves steering operation, switch operation and maintaining direction. Foot posture involves pedal operation, switch operation, and foot posture maintenance. These driving fatigue characteristics can be compared with preset fatigue conditions to determine whether the driver is fatigued.

[0047] The association data between the driving fatigue warning duration and the driver's individual characteristics and driving environment data (such as individual type-level-duration data) can be pre-configured. After obtaining the driver's individual characteristics and driving environment data, the corresponding driving fatigue warning duration is matched from the association data.

[0048] It should be noted that the driving fatigue warning duration can be the driver's overall warning duration (regardless of driving scenario), or the driver's warning duration in the current driving scenario.

[0049] S30: Obtain the driving time of the driver.

[0050] S40: When the driving duration is less than the driving fatigue warning duration, and the difference between the driving fatigue warning duration and the driving duration is less than or equal to a preset time length, determine whether the driving fatigue characteristic meets a preset fatigue condition.

[0051] S50: If the driving fatigue characteristic meets a preset fatigue condition, a first driving fatigue reminder is issued.

[0052] Understandably, the driver's driving time may be the driver's overall driving time (regardless of driving scenario), or it may refer to the driver's driving time in the current driving scenario (excluding the driving time in previous driving scenarios).

[0053] In order to provide more accurate warning of driving fatigue, when the driving time reaches the driving fatigue warning time (T warn ) before the preset time length T0 (such as 0.5h), the fatigue characteristics of the driving state are monitored to determine whether the driving fatigue characteristics meet the preset fatigue conditions. The preset fatigue conditions can be set according to actual needs.

[0054] In [T warn -T0,T warn If the driving fatigue characteristic satisfies the preset fatigue condition, a first driving fatigue reminder is issued; if the driving fatigue characteristic does not satisfy the preset fatigue condition, no reminder is issued. The first driving fatigue reminder may be a sound reminder and / or a light reminder.

[0055] This embodiment does not require additional hardware for fatigue monitoring. It only needs to combine the driver's individual characteristics and driving environment data to accurately match the current driver's driving fatigue warning duration. Based on the driving fatigue warning duration, combined with the monitoring of driving fatigue characteristics, the accuracy of driving fatigue reminders is greatly improved.

[0056] Optionally, after step S30, that is, after obtaining the driver's driving time, the method further includes:

[0057] S41: If the driving duration is greater than or equal to the driving fatigue warning duration, issue a second driving fatigue reminder.

[0058] Understandably, when the driving time is greater than or equal to the driving fatigue warning time, a second driving fatigue reminder is issued. The second driving fatigue reminder can be a sound reminder and / or a light reminder.

[0059] This embodiment provides a driving fatigue reminder based on the driving fatigue warning duration, which can improve the accuracy of the driving fatigue reminder.

[0060] Optionally, the driving environment data includes weather parameters, road parameters, and time parameters;

[0061] In step S20, determining the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data includes:

[0062] S201, determining a driving fatigue level based on the weather parameter, the road parameter, and the time parameter; and determining an individual type of the driver based on the individual characteristics of the driver;

[0063] S202: According to the individual type and the driving fatigue level, the driving fatigue warning duration is matched from the individual type-level-duration data.

[0064] Understandably, a combination of driving fatigue scenarios can be constructed based on weather parameters, road parameters, and time parameters. A driving distraction index system can be established by applying the analytic hierarchy process (AHP) to obtain the subjective rating of the degree of driving distraction based on each scenario factor. This allows for the calculation of a cumulative driving distraction comprehensive score for each scenario combination, and the classification of driving fatigue levels based on the driving distraction comprehensive score. Each driving scenario corresponds to a driving fatigue level.

[0065] In some examples, to simplify calculations, weather parameters are categorized into three types: rain and snow, fog and haze, and normal sunny days, labeled A, B, and C. Road parameters are categorized into urban roads, suburban roads, circular mountain roads, and expressways based on daily use, labeled D, E, F, and G. Time parameters are categorized into morning, afternoon, and nighttime hours, labeled H, L, and Q, based on the driver's physiological and metabolic status at different times.

[0066] The physical characteristics of drivers can be defined based on the national standard "Human Body Dimensions of Chinese Adults." The actual driving experience can be divided into three ranges: 1 year, 1 to 3 years, and more than 3 years, according to "Analysis of Cognitive Biases in Perception Ability of Novice Drivers of Different Driving Ages" ([J]. Liu Xuemei, Xiangwang. Heilongjiang Science, 2020, 11(22):162-164.). Driver types can be categorized based on their physical characteristics and actual driving experience.

[0067] Table 1 Individual types based on physical signs and actual driving experience

[0068]

[0069] As shown in Table 1, drivers can be divided into 12 individual types based on their physical characteristics and actual driving experience.

[0070] Based on the distribution patterns of adult percentiles and the impact of actual driving experience on driver perception, a driver monitoring sample can be developed that takes into account physical signs, gender, and actual driving experience. Based on a limited sample size, driving fatigue data is collected and processed to explore the relationship between the sample's physical signs, gender, actual driving experience, and driving fatigue. The influencing factors are reflected in the absolute duration of driving fatigue, forming individual type-level-duration data. This individual type-level-duration data includes the driving fatigue warning duration for various individual types at various driving fatigue levels.

[0071] After obtaining the individual type and driving fatigue level, the corresponding driving fatigue warning duration can be matched from the individual type-level-duration data.

[0072] In this embodiment, the driver fatigue level is determined based on driving environment data, the driver type is determined based on individual characteristics, and finally, the corresponding driver fatigue warning duration is matched based on the individual type, level, and duration data. This reduces the difficulty of obtaining driving environment data and individual driver characteristics while ensuring the accuracy of the driver fatigue warning duration.

[0073] Optionally, step S201, i.e., determining the driving fatigue level according to the weather parameter, the road parameter, and the time parameter, includes:

[0074] S2011. Determine a driving scenario based on the weather parameter, the road parameter, and the time parameter;

[0075] S2012: Obtain the driving fatigue level corresponding to the driving scene from scene fatigue level relationship data.

[0076] Understandably, if weather parameters are divided into X types, road parameters into Y types, and time parameters into Z types, then X*Y*Z driving scenarios can be constructed. In one example, weather parameters are divided into three types: rain and snow, fog and haze, and ordinary sunny days, denoted as A, B, and C. Road parameters are divided into urban roads, suburban roads, circular mountain roads, and highways based on daily use, denoted as D, E, F, and G. Time parameters are divided into morning, afternoon, and nighttime periods based on the driver's physiological state and metabolic status at different times, denoted as H, L, and Q. Thus, 36 driving scenarios can be constructed.

[0077] The scene fatigue level relationship data includes the driving fatigue level of each driving scene. The scene fatigue level relationship data may be an analysis result obtained by statistically analyzing historical driving data.

[0078] In this embodiment, since the scene fatigue level relationship data is pre-configured, the current driving fatigue level can be determined through simple matching.

[0079] Optionally, before step S2012, that is, before obtaining the driving fatigue level corresponding to the driving scene from the scene fatigue level relationship data, the method further includes:

[0080] S21. Construct multiple driving scenarios based on weather, road, and time dimensions;

[0081] S22. Obtaining subjective scoring data of factors associated with the driving scenario;

[0082] S23. Determine a cumulative driving distraction comprehensive score for the driving scenario based on the subjective scoring data of the factors;

[0083] S24: Divide the driving fatigue level of each driving scenario according to the accumulated driving distraction comprehensive score, and generate scenario fatigue level relationship data.

[0084] Understandably, a driving fatigue ergonomics model based on the three factors of human-machine-environment can be constructed to capture the three major factors influencing driving fatigue. Human factors include physical signs, physiological state, psychological state, digestion, and metabolism; machine (vehicle) factors include hand and foot control, driver's field of view, sitting posture, posture maintenance, and human-machine interaction; and environmental factors include weather, traffic, noise, climate, and air quality. For a given vehicle model, vehicle factors are immutable, while human and environmental factors are variable.

[0085] Multiple driving scenarios can be constructed based on weather, road, and time dimensions. A hierarchical analysis method can be used to establish a driving distraction index system to obtain the subjective score of the degree of driving distraction based on factors in each scenario. The cumulative driving distraction comprehensive score of each driving scenario can then be calculated, and the level of driving fatigue can be divided.

[0086] In the driving distraction indicator system, the cumulative driving distraction comprehensive score is the first-level indicator, and second-level indicators and third-level indicators can be defined under this first-level indicator. The first-level indicator can be the weighted sum of each second-level indicator, and the second-level indicator can be the weighted sum of all third-level indicators under the second-level indicator.

[0087] The subjective scoring data for factors associated with driving scenarios can refer to the impact scores of each scenario factor on the three-level driving distraction indicator. These impact scores can be subjectively assessed by experts. In one example, a score of 0 to 5 is used to represent the impact of a single scenario factor on a single three-level driving distraction indicator, with a maximum score of 5 and a score of 0 indicating no impact.

[0088] Table 2 Impact scores of various scenario factors on the three-level indicators of driving distraction

[0089]

[0090] In the example in Table 2, we obtain the impact scores of 10 scenario factors on the three-level driving distraction indicator. These impact scores can be used to calculate the cumulative driving distraction comprehensive scores for 36 driving scenarios. Based on these 36 comprehensive scores, driving scenarios can be divided into K levels. K can be set as needed, for example, 5. The higher the cumulative driving distraction comprehensive score, the higher the driving fatigue level of the corresponding driving scenario. The scenario fatigue level relationship data includes the driving fatigue level for each driving scenario.

[0091] This embodiment establishes a correlation between driving scenarios and driving fatigue levels through a driving distraction index system, thereby ensuring the accuracy of driving fatigue level assessment.

[0092] Optionally, the subjective scoring data of the factors include a visual distraction index and a cognitive distraction index;

[0093] Step S23, i.e., determining the cumulative driving distraction comprehensive score of the driving scene based on the subjective scoring data of the factors, comprises:

[0094] S231. Process the subjective scoring data of the factors using a driving distraction index model to generate the cumulative driving distraction comprehensive score. The driving distraction index model includes:

[0095]

[0096] Among them, P {A,D,H} The cumulative driving distraction comprehensive score;

[0097] A represents the weather parameter;

[0098] D represents the road parameter;

[0099] H represents the time parameter;

[0100] Indicates the secondary indicator weight of the visual distraction indicator; the visual distraction indicator belongs to the secondary indicator;

[0101] i is the serial number of the third-level indicator under the visual distraction indicator, and its maximum value is the total number of the third-level indicators under the visual distraction indicator;

[0102] represents the weight of the i-th third-level indicator under the visual distraction indicator;

[0103] a i represents the score of the weather parameter on the i-th third-level indicator under the visual distraction indicator;

[0104] d i represents the score of the road parameter on the i-th third-level indicator under the visual distraction indicator;

[0105] h i represents the score of the time parameter on the i-th third-level indicator under the visual distraction indicator;

[0106] Indicates the secondary indicator weight of the cognitive distraction indicator; the cognitive distraction indicator is a secondary indicator;

[0107] j is the serial number of the third-level indicator under the cognitive distraction indicator, and its maximum value is the total number of the third-level indicators under the cognitive distraction indicator;

[0108] represents the j-th third-level indicator weight under the cognitive distraction indicator;

[0109] a j represents the score of the weather parameter on the j-th third-level indicator under the cognitive distraction indicator;

[0110] d j represents the score of the road parameter on the j-th third-level indicator under the cognitive distraction indicator;

[0111] h j It represents the score of the time parameter on the j-th third-level indicator under the cognitive distraction indicator.

[0112] Understandably, the cumulative driving distraction comprehensive score is a first-level indicator. Therefore, the cumulative driving distraction comprehensive score is the weighted sum of each second-level indicator. A second-level indicator can be the weighted sum of all third-level indicators under the second-level indicator.

[0113] The visual distraction indicator includes at least one of a line of sight interference indicator, a visibility reduction indicator, a resolution reduction indicator, and a response time indicator. The cognitive distraction indicator includes at least one of a physiological impact indicator, a cognitive interference indicator, and a psychological stress indicator. The maximum value of the sequence number i for the third-level indicator under the visual distraction indicator can be 4. The maximum value of the sequence number j for the third-level indicator under the cognitive distraction indicator can be 3.

[0114] In this embodiment, the obtained cumulative driving distraction comprehensive score combines the evaluation scores of multiple indicators, which can better reflect the impact of the driving scene on driving distraction.

[0115] Optionally, the driver's individual characteristics include physical signs, gender, and driving experience;

[0116] Before step S202, that is, before determining the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level, the method further includes:

[0117] S221. Obtain driving fatigue road test data;

[0118] S222, determining fatigue duration data for each driving scenario based on the driving fatigue road test data;

[0119] S223, dividing the fatigue duration data according to driving fatigue levels to generate level-duration data;

[0120] S224: Divide the level-duration data by individual type to generate the individual type-level-duration data.

[0121] Understandably, individual characteristics of the driver include physical signs (such as height and weight), gender, and driving experience.

[0122] Road tests are conducted in various driving scenarios, and the time when the driver subjectively feels fatigued (or the vehicle is difficult to control) is recorded. At the same time, various driving status data (including the driver's driving status and the vehicle's control status) and driving environment data (weather, road and time) are collected to form driving fatigue road test data.

[0123] Fatigue duration data for various driving scenarios can be extracted from driving fatigue road test data. The fatigue duration for different driving scenarios at the same fatigue level can be averaged to obtain the driving duration for each fatigue level, which is the level-duration data. The level-duration data for different individual driver types can be stored, and the fatigue duration for different drivers of the same individual type and fatigue level can be averaged to obtain the individual type-level-duration data.

[0124] This embodiment can achieve accurate configuration of driving fatigue duration, establish a correlation between driving fatigue duration and individual type and driving fatigue level, and reduce the difficulty of predicting driving fatigue duration.

[0125] Optionally, step S202, i.e., matching the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level, includes:

[0126] S2021. Matching the absolute warning duration for each driving scenario from the individual type-level-duration data according to the individual type and the driving fatigue level, and obtaining the actual driving duration for each driving scenario;

[0127] S2022: Process the actual driving time and the absolute warning time using a warning time calculation model to generate the driving fatigue warning time. The warning time calculation model includes:

[0128]

[0129] Among them, T warn The driving fatigue warning duration;

[0130] T X is the absolute warning duration of the driver in the current driving scenario; X is the driving fatigue level of the current driving scenario;

[0131] k represents the sequence number of the current driving scene, and its value is a positive integer;

[0132] is the actual driving time of the driver in the lth driving scene;

[0133] T l is the absolute warning duration of the driver in the lth driving scenario;

[0134] T k is the absolute warning duration of the driver in the kth driving scenario; k>l.

[0135] Understandably, when k=1, the driver has only experienced one driving scenario. In this case, the driving fatigue warning duration is the absolute warning duration adapted to the driver in the driving scenario.

[0136] When k>1, it means that the driver has experienced more than one driving scene. In this case, the driving fatigue warning time needs to be subtracted from the time of the previous driving scene. The shorter the driving fatigue warning time, the faster the driver enters the fatigue state. It should be noted that here, warn The actual driving time for comparison refers to the driver's driving time in the current driving scenario, and does not include the driving time in previous driving scenarios.

[0137] This embodiment can accurately calculate the driving fatigue warning duration, thereby providing accurate fatigue reminders to the driver.

[0138] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0139] In one embodiment, a driving fatigue monitoring device is provided, which corresponds to the driving fatigue monitoring method in the above embodiment. Figure 2As shown, the driving fatigue monitoring device includes an acquisition module 10, a feature determination and warning duration module 20, a driving duration acquisition module 30, a fatigue judgment module 40, and a first reminder module 50. The functional modules are described in detail as follows:

[0140] An acquisition module 10 is used to acquire driver individual characteristics, driving status data, and driving environment data;

[0141] The characteristic determination and warning duration module 20 is used to determine the driver's driving fatigue characteristics based on the driving state data; and determine the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data;

[0142] A driving time acquisition module 30 is used to acquire the driving time of the driver;

[0143] a fatigue judgment module 40 configured to judge whether the driving fatigue characteristic satisfies a preset fatigue condition when the driving duration is less than the driving fatigue warning duration and the difference between the driving fatigue warning duration and the driving duration is less than or equal to a preset time length;

[0144] The first reminder module 50 is configured to issue a first driving fatigue reminder if the driving fatigue characteristic meets a preset fatigue condition.

[0145] Optionally, the driving fatigue monitoring device also includes:

[0146] The second reminder module is used to issue a second driving fatigue reminder if the driving time is greater than or equal to the driving fatigue warning time.

[0147] Optionally, the driving environment data includes weather parameters, road parameters, and time parameters;

[0148] The module 20 for determining characteristics and warning duration includes:

[0149] a level and type determination unit, configured to determine a driving fatigue level according to the weather parameter, the road parameter, and the time parameter; and determine an individual type of the driver according to the individual characteristics of the driver;

[0150] The warning duration matching unit is used to match the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level.

[0151] Optionally, the level and type determination unit includes:

[0152] a driving scene determination unit, configured to determine a driving scene according to the weather parameter, the road parameter, and the time parameter;

[0153] The fatigue level matching unit is used to obtain the driving fatigue level corresponding to the driving scene from the scene fatigue level relationship data.

[0154] Optionally, the feature determination and warning duration module 20 further includes:

[0155] A driving scenario construction unit is used to construct multiple driving scenarios based on weather, road, and time dimensions;

[0156] A subjective scoring unit is used to obtain subjective scoring data of factors associated with the driving scenario;

[0157] a scenario scoring unit, configured to determine a cumulative driving distraction comprehensive score for the driving scenario based on the subjective scoring data of the factors;

[0158] The scene fatigue level relationship data unit is used to divide the driving fatigue level of each driving scene according to the accumulated driving distraction comprehensive score and generate the scene fatigue level relationship data.

[0159] Optionally, the subjective scoring data of the factors include a visual distraction index and a cognitive distraction index;

[0160] The scene scoring unit includes:

[0161] A driving distraction index model scoring unit is configured to process the subjective scoring data of the factors using a driving distraction index model to generate the cumulative driving distraction comprehensive score. The driving distraction index model includes:

[0162]

[0163] Among them, P {A,D,H} The cumulative driving distraction comprehensive score;

[0164] A represents the weather parameter;

[0165] D represents the road parameter;

[0166] H represents the time parameter;

[0167] Indicates the secondary indicator weight of the visual distraction indicator; the visual distraction indicator belongs to the secondary indicator;

[0168] i is the serial number of the third-level indicator under the visual distraction indicator, and its maximum value is the total number of the third-level indicators under the visual distraction indicator;

[0169] represents the weight of the i-th third-level indicator under the visual distraction indicator;

[0170] a irepresents the score of the weather parameter on the i-th third-level indicator under the visual distraction indicator;

[0171] d i represents the score of the road parameter on the i-th third-level indicator under the visual distraction indicator;

[0172] h i represents the score of the time parameter on the i-th third-level indicator under the visual distraction indicator;

[0173] Indicates the secondary indicator weight of the cognitive distraction indicator; the cognitive distraction indicator is a secondary indicator;

[0174] j is the serial number of the third-level indicator under the cognitive distraction indicator, and its maximum value is the total number of the third-level indicators under the cognitive distraction indicator;

[0175] represents the j-th third-level indicator weight under the cognitive distraction indicator;

[0176] a j represents the score of the weather parameter on the j-th third-level indicator under the cognitive distraction indicator;

[0177] d j represents the score of the road parameter on the j-th third-level indicator under the cognitive distraction indicator;

[0178] h j It represents the score of the time parameter on the j-th third-level indicator under the cognitive distraction indicator.

[0179] Optionally, the visual distraction index includes at least one of a line of sight interference index, a visibility reduction index, a resolution reduction index, and a response time index; the cognitive distraction index includes at least one of a physiological impact index, a cognitive interference index, and a psychological stress index.

[0180] Optionally, the driver's individual characteristics include physical signs, gender, and driving experience;

[0181] The module 20 for determining characteristics and warning duration also includes:

[0182] Fatigue road test data acquisition unit, used to acquire driving fatigue road test data;

[0183] A scene warning duration determination unit, configured to determine fatigue duration data for each driving scene based on the driving fatigue road test data;

[0184] a level-duration data generating unit, configured to divide the fatigue duration data according to the driving fatigue level to generate level-duration data;

[0185] An individual type-level-duration data unit is generated, which is used to divide the level-duration data according to individual types to generate the individual type-level-duration data.

[0186] Optionally, the matching warning duration unit includes:

[0187] an absolute warning duration determination unit, configured to match the absolute warning duration of each driving scenario from the individual type-level-duration data according to the individual type and the driving fatigue level, and obtain the actual driving duration of each driving scenario;

[0188] A driving fatigue warning duration generating unit is configured to process the actual driving duration and the absolute warning duration using a warning duration calculation model to generate the driving fatigue warning duration. The warning duration calculation model includes:

[0189]

[0190] Among them, T warn The driving fatigue warning duration;

[0191] T X is the absolute warning duration of the driver in the current driving scenario; X is the driving fatigue level of the current driving scenario;

[0192] k represents the sequence number of the current driving scene, and its value is a positive integer;

[0193] is the actual driving time of the driver in the lth driving scene;

[0194] T l is the absolute warning duration of the driver in the lth driving scenario;

[0195] T k is the absolute warning duration of the driver in the kth driving scenario; k>l.

[0196] For the specific definition of the driving fatigue monitoring device, please refer to the definition of the driving fatigue monitoring method above, which will not be repeated here. The various modules in the above-mentioned driving fatigue monitoring device can be implemented in whole or in part by software, hardware, or a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0197] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer-readable instructions. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer-readable instructions are executed by the processor, a driving fatigue monitoring method is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0198] In one embodiment, a computer device is provided, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the following steps are implemented:

[0199] Obtain driver individual characteristics, driving status data and driving environment data;

[0200] determining the driver's driving fatigue characteristics based on the driving state data; determining the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data;

[0201] Obtaining the driving time of the driver;

[0202] When the driving time is less than the driving fatigue warning time, and the difference between the driving fatigue warning time and the driving time is less than or equal to a preset time length, determining whether the driving fatigue characteristic meets a preset fatigue condition;

[0203] If the driving fatigue characteristic meets the preset fatigue condition, a first driving fatigue reminder is issued.

[0204] In one embodiment, one or more computer-readable storage media storing computer-readable instructions are provided. The computer-readable storage media provided in this embodiment include non-volatile computer-readable storage media and volatile computer-readable storage media. The computer-readable storage media store computer-readable instructions that, when executed by one or more processors, implement the following steps:

[0205] Obtain driver individual characteristics, driving status data and driving environment data;

[0206] determining the driver's driving fatigue characteristics based on the driving state data; determining the driver's driving fatigue warning duration based on the driver's individual characteristics and the driving environment data;

[0207] Obtaining the driving time of the driver;

[0208] When the driving time is less than the driving fatigue warning time, and the difference between the driving fatigue warning time and the driving time is less than or equal to a preset time length, determining whether the driving fatigue characteristic meets a preset fatigue condition;

[0209] If the driving fatigue characteristic meets the preset fatigue condition, a first driving fatigue reminder is issued.

[0210] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions. The computer-readable instructions can be stored in a non-volatile readable storage medium or a volatile readable storage medium. When the computer-readable instructions are executed, they can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0211] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0212] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A driving fatigue monitoring method, characterized in that: include: Obtain driver individual characteristics, driving status data and driving environment data; determining a driver's driving fatigue characteristic based on the driving status data; determining the driver's driving fatigue warning duration according to the driver's individual characteristics and the driving environment data; Obtaining the driving time of the driver; When the driving time is less than the driving fatigue warning time, and the difference between the driving fatigue warning time and the driving time is less than or equal to a preset time length, determining whether the driving fatigue characteristic meets a preset fatigue condition; If the driving fatigue characteristic meets the preset fatigue condition, issuing a first driving fatigue reminder; Wherein, the driving environment data includes weather parameters, road parameters, and time parameters; The determining of the driver's driving fatigue warning duration according to the driver's individual characteristics and the driving environment data includes: determining a driving fatigue level according to the weather parameter, the road parameter, and the time parameter; and determining the individual type of the driver according to the individual characteristics of the driver; According to the individual type and the driving fatigue level, the driving fatigue warning duration is matched from the individual type-level-duration data.

2. The driving fatigue monitoring method according to claim 1, characterized in that: After obtaining the driving time of the driver, the method further includes: If the driving duration is greater than or equal to the driving fatigue warning duration, a second driving fatigue reminder is issued.

3. The driving fatigue monitoring method according to claim 1, wherein: The determining of the driving fatigue level according to the weather parameter, the road parameter, and the time parameter includes: determining a driving scenario according to the weather parameter, the road parameter, and the time parameter; The driving fatigue level corresponding to the driving scene is acquired from scene fatigue level relationship data.

4. The driving fatigue monitoring method according to claim 3, wherein: Before acquiring the driving fatigue level corresponding to the driving scenario from the scenario fatigue level relationship data, the method further includes: Construct multiple driving scenarios based on weather, road, and time dimensions; Obtaining subjective scoring data of factors associated with the driving scenario; Determining a cumulative driving distraction comprehensive score for the driving scenario based on the subjective scoring data of the factors; The driving fatigue level of each driving scene is divided according to the accumulated driving distraction comprehensive score, and the scene fatigue level relationship data is generated.

5. The driving fatigue monitoring method according to claim 4, characterized in that: The subjective rating data of the factors include visual distraction index and cognitive distraction index; Determining the cumulative driving distraction comprehensive score of the driving scenario based on the subjective scoring data of the factors includes: The subjective scoring data of the factors are processed by a driving distraction index model to generate the cumulative driving distraction comprehensive score, wherein the driving distraction index model includes: in, The cumulative driving distraction comprehensive score; A represents the weather parameter; D represents the road parameter; H represents the time parameter; Indicates the secondary indicator weight of the visual distraction indicator; the visual distraction indicator belongs to the secondary indicator; i is the serial number of the third-level indicator under the visual distraction indicator, and its maximum value is the total number of the third-level indicators under the visual distraction indicator; represents the weight of the i-th third-level indicator under the visual distraction indicator; represents the score of the weather parameter on the i-th third-level indicator under the visual distraction indicator; represents the score of the road parameter on the i-th third-level indicator under the visual distraction indicator; represents the score of the time parameter on the i-th third-level indicator under the visual distraction indicator; Indicates the secondary indicator weight of the cognitive distraction indicator; the cognitive distraction indicator is a secondary indicator; j is the serial number of the third-level indicator under the cognitive distraction indicator, and its maximum value is the total number of the third-level indicators under the cognitive distraction indicator; represents the j-th third-level indicator weight under the cognitive distraction indicator; represents the score of the weather parameter on the j-th third-level indicator under the cognitive distraction indicator; represents the score of the road parameter on the j-th third-level indicator under the cognitive distraction indicator; It represents the score of the time parameter on the j-th third-level indicator under the cognitive distraction indicator.

6. The driving fatigue monitoring method according to claim 5, characterized in that: The visual distraction index includes at least one of a line of sight interference index, a visibility reduction index, a resolution reduction index, and a reaction time index; the cognitive distraction index includes at least one of a physiological impact index, a cognitive interference index, and a psychological stress index.

7. The driving fatigue monitoring method according to claim 1, wherein: The individual characteristics of the driver include physical signs, gender, and driving experience; Before determining the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level, the method further includes: Obtain driving fatigue road test data; Determining fatigue duration data for each driving scenario based on the driving fatigue road test data; Dividing the fatigue duration data according to driving fatigue levels to generate level-duration data; The level-duration data is divided according to individual types to generate the individual type-level-duration data.

8. The driving fatigue monitoring method according to claim 3, wherein: The step of matching the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level includes: Matching the absolute warning duration of each driving scenario from the individual type-level-duration data according to the individual type and the driving fatigue level, and obtaining the actual driving duration of each driving scenario; The actual driving time and the absolute warning time are processed by a warning time calculation model to generate the driving fatigue warning time, wherein the warning time calculation model includes: in, The driving fatigue warning duration; is the absolute warning duration of the driver in the current driving scenario; X is the driving fatigue level of the current driving scenario; k represents the sequence number of the current driving scene, and its value is a positive integer; For the driver in l The actual driving time of each driving scenario; For the driver in l The absolute warning duration for each driving scenario; is the absolute warning duration of the driver in the kth driving scenario; k> l .

9. A driving fatigue monitoring device, characterized in that: include: An acquisition module is used to obtain driver individual characteristics, driving status data and driving environment data; A feature determination and warning duration module is used to determine the driver's driving fatigue features based on the driving state data; and to determine the driver's driving fatigue warning duration based on the driver's individual features and the driving environment data; A driving time acquisition module is used to acquire the driving time of the driver; a fatigue judgment module, configured to judge whether the driving fatigue characteristic satisfies a preset fatigue condition when the driving duration is less than the driving fatigue warning duration and the difference between the driving fatigue warning duration and the driving duration is less than or equal to a preset time length; a first reminder module, configured to issue a first driving fatigue reminder if the driving fatigue characteristic meets a preset fatigue condition; Wherein, the driving environment data includes weather parameters, road parameters, and time parameters; The module for determining characteristics and warning duration includes: a level and type determination unit, configured to determine a driving fatigue level according to the weather parameter, the road parameter, and the time parameter; and determine an individual type of the driver according to the individual characteristics of the driver; The warning duration matching unit is used to match the driving fatigue warning duration from the individual type-level-duration data according to the individual type and the driving fatigue level.

10. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein: When the processor executes the computer-readable instructions, the driving fatigue monitoring method according to any one of claims 1 to 8 is implemented.

11. One or more readable storage media storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute the driving fatigue monitoring method according to any one of claims 1 to 8.

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