Method and apparatus for monitoring driving of a vehicle

Abstract

The application discloses a kind of driving monitoring method and device of vehicle.Therein, the method includes: in response to the physiological data, behavior data and vehicle driving data of user during vehicle driving process, wherein, physiological data includes user physiological monitoring data and historical pathological data;At least one predetermined behavior mode of user is obtained by behavior pattern analysis to physiological data and behavior data, wherein, predetermined behavior mode is used to represent the behavior mode of user under the action of physiological data and behavior data;Driving state of user driving vehicle is obtained by driving state analysis to at least one predetermined behavior mode and vehicle driving data, and warning response is carried out to driving state, wherein, driving state is used to represent the dangerous driving behavior of user.This application solves the technical problem that the safety of driving monitoring of vehicle in related technology is not high.

Description

Technical Field

[0001] This invention relates to the field of vehicle networking, and more specifically, to a method and apparatus for monitoring vehicle driving. Background Technology

[0002] During driving, drivers may encounter different external environments and various external pressures, causing their physiological indicators to fluctuate. Therefore, drivers are prone to sudden illnesses while driving, which poses a significant safety hazard to the vehicle's driving condition.

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

[0004] This invention provides a method and apparatus for monitoring vehicle driving, which at least solves the technical problem of low safety in vehicle driving monitoring in related technologies.

[0005] According to one aspect of the present invention, a vehicle driving monitoring method is provided, comprising: acquiring user physiological data, behavioral data, and vehicle driving data in response to the vehicle driving process, wherein the physiological data includes user physiological monitoring data and historical pathological data; obtaining at least one predetermined behavioral pattern of the user by performing behavioral pattern analysis on the physiological data and behavioral data, wherein the predetermined behavioral pattern is used to characterize the behavioral pattern of the user under the influence of the physiological data and behavioral data; obtaining the driving state of the user driving the vehicle by performing driving state analysis on the at least one predetermined behavioral pattern and vehicle driving data, and providing a warning response to the driving state, wherein the driving state is used to characterize the user's dangerous driving behavior.

[0006] Furthermore, by performing behavioral pattern analysis on physiological and behavioral data, at least one predetermined behavioral pattern of the user is obtained, including: conducting a health assessment of the user based on physiological data to obtain the user's health assessment result, wherein the health assessment result is used to characterize the user's health status while driving a vehicle; and by performing behavioral pattern analysis on the health assessment result and behavioral data, at least one predetermined behavioral pattern of the user is obtained.

[0007] Furthermore, a health assessment is conducted on the user based on physiological data to obtain the user's health assessment results, including: obtaining the user's target pathological data by performing feature analysis on historical pathological data, wherein the target pathological data is used to characterize the pathological data with the highest frequency of onset in the historical case data; and conducting a health assessment on the user based on the user's physiological monitoring data and the target pathological data to obtain the health assessment results.

[0008] Furthermore, by performing behavioral pattern analysis on the health assessment results and behavioral data, at least one predetermined behavioral pattern of the user is obtained, including: in response to the health assessment result indicating that the user is currently driving an abnormality, determining the user's current abnormality information; by performing accompanying behavior analysis on the current abnormality information, determining the accompanying behavior information corresponding to the current abnormality information, wherein the accompanying behavior information is used to characterize the user's behavior accompanying the current abnormality information; and based on the behavioral data and accompanying behavior information, evaluating the user's behavioral pattern while driving the vehicle, and determining at least one predetermined behavioral pattern of the user currently driving the vehicle.

[0009] Furthermore, after obtaining at least one predetermined behavior pattern of the user, the method further includes: prioritizing the at least one predetermined behavior pattern based on the behavior data to obtain a priority sequence of at least one predetermined behavior pattern; and determining a target behavior pattern based on the priority sequence, wherein the target behavior pattern is used to characterize a preset number of behavior patterns that rank high in the priority sequence.

[0010] Furthermore, by analyzing the driving state of at least one predetermined behavior pattern and vehicle driving data, the driving state of the user's vehicle is obtained, including: determining the preset driving data of the vehicle corresponding to the target behavior pattern; obtaining a data matching result by matching the preset driving data and the vehicle driving data, wherein the data matching result is used to characterize the matching degree between the vehicle driving data and the preset driving data; and determining the driving state of the user's vehicle based on the target behavior pattern and the vehicle driving data in response to the data matching result satisfying the preset matching degree.

[0011] Furthermore, the system provides early warning responses to driving conditions, including: generating a warning signal based on the driving condition, wherein the warning signal is used to warn that the vehicle is in a dangerous driving condition; identifying a target vehicle from vehicles within a preset range of the vehicle, wherein both the target vehicle and the vehicle are covered by the Internet of Things; and sending the warning signal to the target vehicle and / or the target terminal.

[0012] According to another aspect of the present invention, a vehicle driving monitoring device is also provided, comprising: a data acquisition module, configured to acquire user physiological data, behavioral data, and vehicle driving data in response to vehicle driving, wherein the physiological data includes user physiological monitoring data and historical pathological data; a behavior analysis module, configured to obtain at least one predetermined behavior pattern of the user by performing behavior pattern analysis on the physiological data and behavioral data, wherein the predetermined behavior pattern is used to characterize the behavior pattern of the user under the influence of the physiological data and behavioral data; and a state analysis module, configured to obtain the driving state of the user driving the vehicle by performing driving state analysis on at least one predetermined behavior pattern and vehicle driving data, and to provide a warning response to the driving state, wherein the driving state is used to characterize the user's dangerous driving behavior.

[0013] According to a third aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is executed, the above-described vehicle driving monitoring method is executed in the processor of the device.

[0014] According to a fourth aspect of the present invention, a vehicle is also provided, comprising: one or more processors; a storage device for storing one or more programs; wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the above-described vehicle driving monitoring method.

[0015] In this embodiment of the invention, by acquiring the user's physiological data, behavioral data, and vehicle driving data during vehicle driving, and by performing behavioral pattern analysis on the physiological and behavioral data, at least one predetermined behavioral pattern of the user is obtained. By analyzing the driving state of the user's vehicle based on the at least one predetermined behavioral pattern and the vehicle driving data, the driving state of the vehicle is obtained, and a warning response is provided based on the driving state. It is readily apparent that the user's physiological and behavioral data influence the user's predetermined behavioral pattern, and simultaneously, the user's predetermined behavioral pattern and vehicle driving data influence the vehicle's driving state. By performing layer-by-layer analysis of physiological data, behavioral data, and vehicle driving data, a relatively safe driving monitoring system is achieved, preventing dangerous driving behaviors. This solves the technical problem of low safety in vehicle driving monitoring in related technologies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 This is a flowchart of a vehicle driving monitoring method according to an embodiment of the present invention;

[0018] Figure 2 This is a structural block diagram of a vehicle driving monitoring module according to an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of data analysis and processing according to an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of a vehicle driving monitoring device according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0023] Example 1

[0024] According to an embodiment of the present invention, an embodiment of a driving monitoring method for a vehicle is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] Figure 1 This is a flowchart of a vehicle driving monitoring method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0026] Step S102, in response to acquiring the user's physiological data, behavioral data and vehicle driving data during vehicle driving, wherein the physiological data includes user physiological monitoring data and historical pathological data;

[0027] Specifically, the aforementioned user physiological monitoring data can be used to represent the driver's physiological indicators, including at least physiological indicators such as blood pressure, heart rate, and body temperature.

[0028] In the process of acquiring user physiological monitoring data, sensors can be used to collect data on the driver's physiological indicators. Specifically, photoelectric sensors can be installed on the vehicle steering wheel to measure the driver's blood pressure by measuring changes in blood flow in the human blood vessels using photoelectric means. The blood pressure monitoring scheme is photoplethysmography (a non-invasive detection method that uses photoelectric means to detect changes in blood volume in living tissue. The principle is that when a light beam of a certain wavelength shines on the skin surface of the fingertip, the light beam will be transmitted to the photoelectric receiver through transmission or reflection).

[0029] The absorption of light by skin, muscles, and tissues remains constant throughout the bloodstream. When the heart contracts, peripheral blood volume is at its maximum, light absorption is also at its highest, and the detected light intensity is at its lowest. Conversely, during diastole, peripheral blood volume is at its lowest, and the detected light intensity is at its highest. This causes the light intensity detected by the photodetector to fluctuate pulsatiably. This change in light intensity signal is then converted into an electrical signal. After amplification, this electrical signal can be used to obtain changes in pulse flow and blood pressure data, allowing for photoelectric measurements of blood vessels on the face. In addition, temperature and heart rate sensors can be installed on seat belts. When the driver enters the vehicle and fastens the seat belt, the sensors collect the driver's temperature and heart rate signals.

[0030] In addition to collecting physiological data such as the driver's blood pressure, heart rate, and body temperature, the system can also access public healthcare databases to assess the collected data and determine potential illnesses in the driver. For example, if the driver's blood pressure exceeds the normal blood pressure threshold, it can be determined whether the driver has experienced a sudden onset of hypertension; if the driver's heart rate exceeds the normal heart rate threshold, it can be determined whether the driver has experienced a sudden onset of heart disease.

[0031] The aforementioned historical pathology data can be used to represent the driver's historical pathology case data, including at least frequently occurring pathological cases and genetic history. During the acquisition of historical pathology data, with user authorization, the driver's historical medical records and pathology reports can be collected through the health management platform.

[0032] The aforementioned behavioral data can be used to represent the driver's behavior while driving the vehicle, including at least the driver's eye angle, blinking frequency, head rotation, and mouth position.

[0033] Specifically, when collecting blink frequency data, sensors can be used to collect the proportion of time the eyes are closed within a certain period, similar to the concept of duty cycle. Generally, an 80% standard can be used for detection, meaning the percentage of time the eyes are closed for more than 80% of the total time. This data can reflect the driver's fatigue level and mental state, but this time proportion is not set exclusively. When collecting the gaze angle, the line connecting the center of the eyeball and the bright spot on the surface of the eyeball can be defined as the driver's gaze direction. Under normal conditions, the driver looks directly ahead of the moving vehicle, and the speed of the gaze direction is relatively fast. However, when the driver suddenly falls ill, the speed of the driver's gaze direction will slow down, showing a dullness, and the gaze axis will deviate from the normal position.

[0034] The aforementioned vehicle driving data can be used to represent driving data during vehicle operation, including at least vehicle speed, wheel angle, and lane departure.

[0035] In one optional embodiment, by analyzing the driver's physiological and behavioral data during vehicle operation, possible behavioral patterns of the driver can be determined. Furthermore, by analyzing the behavioral patterns and vehicle operation data, the driver's current driving state can be determined, enabling a warning response to the current driving state when it is deemed to be dangerous driving.

[0036] Step S104: By performing behavioral pattern analysis on physiological data and behavioral data, at least one predetermined behavioral pattern of the user is obtained, wherein the predetermined behavioral pattern is used to characterize the behavioral pattern of the user under the influence of physiological data and behavioral data.

[0037] Specifically, the aforementioned predetermined behavior patterns can be used to represent possible user behaviors obtained through analysis of the driver's physiological and behavioral data, including but not limited to the user's current behavior patterns such as loss of steering wheel control or loss of accelerator and brake control while driving the vehicle.

[0038] In one optional embodiment, predictive analysis of the driver's physiological and behavioral data can be performed using a behavioral pattern model. For example, if physiological data indicates that the driver may be experiencing a sudden heart attack, and behavioral data shows a shift in the driver's gaze angle, using a behavioral pattern model to predict these two input variables—the sudden heart attack and the gaze angle shift—can yield a prediction of the user's potential sudden steering wheel jerk.

[0039] In another alternative embodiment, the aforementioned behavior pattern model can be a pre-trained model. Based on big data, massive amounts of historical driving accident data can be collected, specifically including the physiological data of the drivers involved in the accidents and the actual behavior of the drivers under the influence of their behavioral data at the time. By training the initial behavior pattern model with the collected historical data, a final behavior pattern model can be obtained, enabling the prediction of driver user behavior based on the trained behavior pattern model.

[0040] Step S106: By analyzing the driving state of at least one predetermined behavior pattern and vehicle driving data, the driving state of the user driving the vehicle is obtained, and a warning response is given to the driving state. The driving state is used to characterize the user's dangerous driving behavior.

[0041] Specifically, the aforementioned driving status can be used to indicate that the driver is currently engaging in dangerous driving behavior, including but not limited to dangerous driving behaviors such as the vehicle deviating from the planned driving route, the vehicle accelerating in speed-limited sections, and the vehicle crossing the lane lines.

[0042] In one optional embodiment, a state analysis model can be used to analyze the driver's predetermined behavior pattern and vehicle driving data to determine the driving state. For example, if the predetermined behavior pattern indicates that the driver may be prone to sudden steering wheel movements, and vehicle driving data shows that the vehicle's wheel angles are significantly deflected and the vehicle is deviating from its predetermined driving trajectory, the state analysis model can be used to predict these two input variables: sudden steering wheel movements and lane deviation. The prediction result indicates that the driver's current driving state involves dangerous driving behavior.

[0043] The aforementioned warning response can be used to alert users to dangerous driving behaviors. Specifically, when the driver's health is in a dangerous state, the system will promptly issue a reminder. If the driver suddenly falls ill and is unable to drive or becomes unconscious, the system will control the vehicle to stop in a safe area and activate the hazard warning lights. Simultaneously, the system will send a request for assistance to nearby vehicles and the nearest hospital via the in-vehicle Internet of Things (IoT) and upload the driver's physiological data in real time.

[0044] Figure 2 This is a structural block diagram of a vehicle driving monitoring module according to an embodiment of the present invention. Figure 2As shown, the vehicle health monitoring module includes user behavior pattern monitoring (for monitoring the driver's behavior patterns), user physiological indicator monitoring (for monitoring the driver's physiological indicators), vehicle driving data collection (for collecting vehicle driving data), data processing and analysis module (for analyzing the above-mentioned behavioral data, physiological data and vehicle driving data), and wireless transmission module (for transmitting data).

[0045] Specifically, after collecting the aforementioned user and vehicle data, it needs to be displayed on the in-vehicle central control screen and stored in the cloud. If it is determined that there are abnormalities in the user's health data, the data can be transmitted to other vehicles and nearby hospitals to enable emergency rescue for the user.

[0046] By acquiring user physiological data, behavioral data, and vehicle driving data during vehicle operation, and through behavioral pattern analysis of the physiological and behavioral data, at least one predetermined behavioral pattern of the user is obtained. Furthermore, by analyzing the driving state of this predetermined behavioral pattern and the vehicle driving data, the driving state of the user is determined, and a warning response is provided based on this driving state. It is readily apparent that the user's physiological and behavioral data influence their predetermined behavioral patterns, and conversely, these patterns, along with the vehicle driving data, affect the vehicle's driving state. Through this layered analysis of physiological, behavioral, and driving data, a relatively safe driving monitoring system is achieved, preventing dangerous driving behaviors and thus addressing the technical problem of insufficient safety in vehicle driving monitoring in related technologies.

[0047] Optionally, by performing behavioral pattern analysis on physiological and behavioral data, at least one predetermined behavioral pattern of the user is obtained, including: performing a health assessment on the user based on physiological data to obtain a health assessment result of the user, wherein the health assessment result is used to characterize the user's health status while driving a vehicle; and performing behavioral pattern analysis on the health assessment result and behavioral data to obtain at least one predetermined behavioral pattern of the user.

[0048] Specifically, in one optional embodiment, during the behavioral pattern analysis of physiological and behavioral data, a health assessment of the physiological data is first required. This assessment can be achieved using a human health model. Specifically, this involves accessing a health management platform to retrieve the driver's medical history and pathology information. By performing sensitivity analysis on the retrieved medical history and pathology information, frequently occurring pathological information of the driver can be identified, and subsequently, the corresponding physiological characteristics can be determined.

[0049] For example, if the driver's frequent pathological information is multiple heart diseases, the corresponding physiological characteristics would be heart rate values, etc. Therefore, after determining the physiological characteristics, it is necessary to extract the data corresponding to those characteristics from the collected physiological monitoring data, applying preset frequencies and preset thresholds. In other words, if the driver suffers from multiple heart diseases, it is necessary to extract the driver's heart rate values ​​using preset thresholds and preset frequencies to determine the driver's health assessment results. The aforementioned preset threshold can represent a pre-set normal heart rate threshold, and the aforementioned preset frequency can represent a pre-set frequency threshold for extracting heart rate values.

[0050] In another optional embodiment, after obtaining the user's health assessment results, at least one predetermined behavior pattern of the user can be obtained by performing behavior pattern analysis on the health assessment results and behavior data. Specifically, the behavior pattern analysis on the health assessment results and behavior data can be performed using the aforementioned behavior pattern model to obtain at least one predetermined behavior pattern. For example, the predetermined behavior pattern includes at least the following behavior patterns: sharp turning of the steering wheel, sudden acceleration, and sudden change of vehicle lane.

[0051] By conducting health assessments on users' physiological data, the results can be determined. Then, based on the health assessment results and behavioral data, behavioral pattern analysis can be performed on drivers to achieve the goal of accurately assessing the possible behaviors of drivers.

[0052] Optionally, a health assessment is performed on the user based on physiological data to obtain the user's health assessment results, including: obtaining the user's target pathological data by performing feature analysis on historical pathological data, wherein the target pathological data is used to characterize the pathological data with the highest frequency of onset in the historical case data; and performing a health assessment on the user based on the user's physiological monitoring data and the target pathological data to obtain the health assessment results.

[0053] Specifically, the aforementioned feature analysis can be used to represent the analysis of multiple features in a user's historical pathological data, or it can be used to represent the analysis of genetic features in a user's historical pathological data. No single feature is uniquely limited here.

[0054] In one optional embodiment, the user's historical pathological data includes both common pathological data, such as the common cold, and multifocal case data, such as the user having multiple heart diseases. By filtering the incidence frequency of each pathological data in the historical pathological data, the pathological data with the highest frequency can be identified and identified as the target pathological data.

[0055] In another optional embodiment, after obtaining the user's target pathological data, a health assessment can be performed on the user based on the user's physiological monitoring data and the target pathological data. Specifically, the user's physiological monitoring data collected by the sensors can be preferentially filtered through the target pathological data. In other words, if the target pathological data shows that the user has multiple heart diseases, the heart rate data corresponding to the multiple heart diseases can be preferentially determined from multiple user physiological monitoring data, thereby determining the user's current health assessment result.

[0056] By analyzing historical pathological data and user physiological monitoring data, it is possible to quickly and accurately determine the user's current health assessment results.

[0057] Optionally, by performing behavioral pattern analysis on health assessment results and behavioral data, at least one predetermined behavioral pattern of the user is obtained, including: in response to the health assessment result indicating that the user is currently driving an abnormality, determining the user's current abnormality information; by performing accompanying behavior analysis on the current abnormality information, determining the accompanying behavior information corresponding to the current abnormality information, wherein the accompanying behavior information is used to characterize the user's accompanying behavior under the current abnormality information; and based on the behavioral data and accompanying behavior information, evaluating the user's behavioral pattern while driving the vehicle, and determining at least one predetermined behavioral pattern of the user currently driving the vehicle.

[0058] Specifically, the aforementioned current abnormal information can be used to indicate that the driver's physical health is abnormal while driving the vehicle.

[0059] The aforementioned accompanying behavior analysis can be used to describe the process of analyzing the accompanying behaviors that may exist in the current abnormal information. For example, if the current abnormal information shows that the driver suddenly suffers from multiple heart diseases while driving the vehicle, then the driver may exhibit accompanying behaviors such as blurred vision and limb tremors.

[0060] In one optional embodiment, when analyzing a user's behavioral patterns using health assessment results and behavioral data, the analysis can be based on accompanying behavioral information corresponding to the health assessment results and the behavioral data. For example, if the accompanying behavioral information corresponds to behaviors such as blurred vision and limb tremors that occur when a user experiences a sudden cardiac event, and the behavioral data shows a shift in the user's gaze angle and a large head rotation angle, then by evaluating the accompanying behaviors and behavioral data, it can be determined that the user may exhibit behavioral patterns such as loss of steering wheel control or loss of accelerator and brake control while driving the vehicle.

[0061] By analyzing the accompanying behavior of users' current abnormal information, and by analyzing the accompanying behavior information and the behavior data obtained from actual monitoring, the goal of accurately determining the user's behavior patterns while driving a vehicle can be achieved.

[0062] Optionally, after obtaining at least one predetermined behavior pattern of the user, the method further includes: prioritizing the at least one predetermined behavior pattern based on the behavior data to obtain a priority sequence of the at least one predetermined behavior pattern; and determining a target behavior pattern based on the priority sequence, wherein the target behavior pattern is used to characterize a preset number of behavior patterns that rank high in the priority sequence.

[0063] Specifically, the priority sequence described above can be used to represent the priority order in which at least one predetermined behavior pattern occurs.

[0064] The aforementioned preset quantity can be used to represent the number of pre-defined top-ranking behavioral patterns, but it is not limited to this and can be determined according to the actual situation.

[0065] In one optional embodiment, since there is more than one of the aforementioned predetermined behavioral patterns, in order to further determine the most likely behavioral pattern, it is necessary to prioritize at least one predetermined behavioral pattern. Specifically, if the aforementioned at least one predetermined behavioral pattern includes behavioral patterns such as loss of steering wheel control or loss of accelerator and brake control, it is necessary to prioritize them, that is, to determine whether the user is most likely to suddenly turn the steering wheel, slam on the brakes, or slam on the accelerator when experiencing a sudden cardiac event.

[0066] In another optional embodiment, when prioritizing at least one predetermined behavior pattern, the most influential behavior data can be determined from the driver's behavior data, and then the corresponding behavior pattern can be determined. For example, when a driver suffers a sudden cardiac event, if the most influential behavior data among the driver's multiple behavior data is limb tremors, then the driver may be prone to sudden steering wheel jerk behavior. Therefore, this sudden steering wheel jerk behavior can be determined as the target behavior pattern.

[0067] By prioritizing at least one predetermined behavior pattern using a priority sequence, the target behavior pattern can be determined, enabling the user to quickly and accurately identify the target behavior pattern when there is an abnormality in the vehicle they are driving.

[0068] Optionally, the driving state of the user's vehicle is obtained by analyzing the driving state of at least one predetermined behavior pattern and vehicle driving data, including: determining the preset driving data of the vehicle corresponding to the target behavior pattern; obtaining a data matching result by matching the preset driving data and the vehicle driving data, wherein the data matching result is used to characterize the matching degree between the vehicle driving data and the preset driving data; and determining the driving state of the user's vehicle based on the target behavior pattern and the vehicle driving data in response to the data matching result satisfying the preset matching degree.

[0069] Specifically, the aforementioned preset driving data can be used to represent the driving data of a vehicle under a pre-defined target behavior pattern.

[0070] The aforementioned preset matching degree can be used to represent the matching degree between pre-set vehicle driving data and preset driving data.

[0071] In one optional embodiment, if the target behavior pattern indicates that the driver suddenly turns the steering wheel when experiencing a sudden cardiac event, the corresponding preset driving data can be the vehicle deviating from a predetermined driving path. Specifically, this deviation manifests as steering wheel deviation, significant wheel angle deflection, and significant lane departure from lane lines. Simultaneously, the vehicle driving data includes the vehicle's current speed, wheel angle, and lane departure data collected by sensors. By matching the preset driving data with the vehicle driving data, it can be determined whether the current matching degree meets the preset matching degree. In other words, it is determined whether the currently collected driving data matches the preset driving data. If they match, meaning the currently collected driving data also includes steering wheel deviation, significant wheel angle deflection, and significant lane departure from lane lines, then the data matching result meets the preset matching degree. Through the target behavior pattern and the vehicle driving data, the user's driving state is determined; that is, the aforementioned driving state can be used to indicate that the current user is engaging in dangerous driving behavior.

[0072] Optionally, the driving status warning response includes: generating a warning signal based on the driving status, wherein the warning signal is used to warn that the vehicle is in a dangerous driving state; identifying a target vehicle from vehicles within a preset range of the vehicle, wherein both the target vehicle and the vehicle are covered by the Internet of Things; and sending the warning signal to the target vehicle and / or the target terminal.

[0073] Specifically, the aforementioned preset range can be used to represent a pre-defined coverage area centered on the current vehicle and with a preset distance as its circumference.

[0074] The aforementioned target vehicles can be used to represent a set of vehicles within a preset range that covers the Internet of Things.

[0075] The aforementioned target terminal can be used to represent a terminal providing emergency assistance to the current vehicle, and can be a traffic management emergency department or a nearby hospital, etc.

[0076] In one optional embodiment, by issuing a warning about dangerous driving, a warning signal can be sent to target vehicles covered by the Internet of Things within a preset range for emergency rescue of the driver. Simultaneously, a warning signal can also be sent to nearby hospitals or traffic emergency management departments to provide emergency assistance to the current driver.

[0077] Figure 3 This is a schematic diagram of data analysis and processing according to an embodiment of the present invention. Figure 3 As shown, the signals collected by the vehicle health monitoring module may contain glitches and noise due to changes in the external environment. Therefore, the data analysis and processing module needs to process the collected signals. The processing steps include filtering, noise reduction, and deduplication. Next, the data is converted and processed, and the results are analyzed. Finally, the system will draw conclusions based on the driver's health status.

[0078] Example 2

[0079] According to an embodiment of the present invention, a vehicle driving monitoring device is also provided. This device can execute a vehicle driving monitoring method provided in Embodiment 1 above. The specific implementation method and preferred application scenario are the same as those in Embodiment 1 above, and will not be repeated here.

[0080] Figure 4 This is a schematic diagram of a vehicle driving monitoring device according to an embodiment of the present invention, such as... Figure 4 As shown, the device includes:

[0081] The data acquisition module 402 is used to acquire the user's physiological data, behavioral data and vehicle driving data in response to the vehicle driving process. The physiological data includes user physiological monitoring data and historical pathological data.

[0082] The behavior analysis module 404 is used to obtain at least one predetermined behavior pattern of the user by performing behavior pattern analysis on physiological data and behavior data, wherein the predetermined behavior pattern is used to characterize the behavior pattern of the user under the influence of physiological data and behavior data.

[0083] The state analysis module 406 is used to obtain the driving state of the user's vehicle by analyzing at least one predetermined behavior pattern and vehicle driving data, and to provide early warning response to the driving state, wherein the driving state is used to characterize the user's dangerous driving behavior.

[0084] Optionally, the behavior analysis module 404 includes: a health assessment module, used to perform a health assessment on the user based on physiological data to obtain the user's health assessment result, wherein the health assessment result is used to characterize the user's health status while driving a vehicle; and a behavior pattern analysis module, used to perform behavior pattern analysis on the health assessment result and behavior data to obtain at least one predetermined behavior pattern of the user.

[0085] Optionally, the health assessment module includes: a feature analysis module, used to obtain the user's target pathological data by performing feature analysis on historical pathological data, wherein the target pathological data is used to characterize the pathological data with the highest frequency of incidence in the historical case data; and an assessment result acquisition module, used to perform a health assessment on the user based on the user's physiological monitoring data and the target pathological data, and obtain a health assessment result.

[0086] Optionally, the behavior pattern analysis module includes: an anomaly information determination module, used to determine the user's current anomaly information in response to a health assessment result indicating an anomaly in the user's current driving behavior; an accompanying behavior analysis module, used to determine the accompanying behavior information corresponding to the current anomaly information by performing accompanying behavior analysis on the current anomaly information, wherein the accompanying behavior information is used to characterize the user's accompanying behavior under the current anomaly information; and a behavior pattern evaluation module, used to evaluate the user's behavior pattern while driving the vehicle based on behavior data and accompanying behavior information, and determine at least one predetermined behavior pattern of the user currently driving the vehicle.

[0087] Optionally, the behavior pattern evaluation module includes: a sorting module, used to prioritize at least one predetermined behavior pattern based on behavior data to obtain a priority sequence of at least one predetermined behavior pattern; and a target behavior pattern determination module, used to determine a target behavior pattern based on the priority sequence, wherein the target behavior pattern is used to characterize a preset number of behavior patterns that rank high in the priority sequence.

[0088] Optionally, the state analysis module 406 includes: a preset driving data determination module, used to determine the preset driving data of the vehicle corresponding to the target behavior pattern; a data matching module, used to obtain a data matching result by matching the preset driving data and the vehicle driving data, wherein the data matching result is used to characterize the matching degree between the vehicle driving data and the preset driving data; and a driving state determination module, used to determine the driving state of the user driving the vehicle by means of the target behavior pattern and the vehicle driving data in response to the data matching result satisfying the preset matching degree.

[0089] Optionally, the state analysis module 406 further includes: a warning signal generation module for generating a warning signal based on the driving state, wherein the warning signal is used to warn that the vehicle is in a dangerous driving state; a target vehicle determination module for determining a target vehicle from vehicles within a preset range of the vehicle, wherein both the target vehicle and the vehicle are covered by the Internet of Things; and a warning signal sending module for sending a warning signal to the target vehicle and / or the target terminal.

[0090] Example 3

[0091] According to an embodiment of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, it controls the execution of the above-described vehicle driving monitoring method in the processor of the device.

[0092] Example 4

[0093] According to an embodiment of the present invention, a vehicle is also provided, comprising: one or more processors; a storage device for storing one or more programs; wherein when one or more programs are executed by one or more processors, the one or more processors perform the above-described vehicle driving monitoring method.

[0094] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0095] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0097] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0100] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A driving monitoring method of a vehicle, characterized by, include: In response to acquiring user physiological data, behavioral data, and vehicle driving data during vehicle driving, wherein the physiological data includes user physiological monitoring data and historical pathological data; By performing behavioral pattern analysis on the physiological data and the behavioral data, at least one predetermined behavioral pattern of the user is obtained. The predetermined behavioral pattern characterizes the user's behavioral patterns under the influence of the physiological data and the behavioral data. Obtaining at least one predetermined behavioral pattern of the user by performing behavioral pattern analysis on the physiological data and the behavioral data includes: obtaining the user's target pathological data by performing feature analysis on the historical pathological data, wherein the target pathological data characterizes the pathological data with the highest frequency of occurrence in the historical pathological data; determining the physiological manifestation characteristics corresponding to the target pathological data; extracting data corresponding to the physiological manifestation characteristics from the user's physiological monitoring data; determining the user's health assessment result based on the data corresponding to the physiological manifestation characteristics, wherein the health assessment result characterizes the user's health status while driving; and obtaining at least one predetermined behavioral pattern of the user by performing behavioral pattern analysis on the health assessment result and the behavioral data. By analyzing the driving status of the user's vehicle using the at least one predetermined behavior pattern and the vehicle driving data, the driving status of the user's vehicle is obtained, and a warning response is given to the driving status, wherein the driving status is used to characterize the user's dangerous driving behavior.

2. The driving monitoring method of a vehicle according to claim 1, characterized by, By performing behavioral pattern analysis on the health assessment results and the behavioral data, at least one predetermined behavioral pattern of the user is obtained, including: In response to the health assessment result indicating that the user is currently driving an abnormality, the user's current abnormality information is determined; By performing accompanying behavior analysis on the current abnormal information, the accompanying behavior information corresponding to the current abnormal information is determined, wherein the accompanying behavior information is used to characterize the user's behavior under the current abnormal information; Based on the behavioral data and the accompanying behavioral information, the user's behavior patterns while driving the vehicle are evaluated to determine at least one predetermined behavior pattern of the user currently driving the vehicle.

3. The driving monitoring method of a vehicle according to claim 1, characterized by, After obtaining at least one predetermined behavioral pattern of the user, the method further includes: Based on the behavioral data, the at least one predetermined behavioral pattern is prioritized to obtain a priority sequence of the at least one predetermined behavioral pattern; Based on the priority sequence, a target behavior pattern is determined, wherein the target behavior pattern is used to characterize a preset number of behavior patterns that rank high in the priority sequence.

4. The driving monitoring method of a vehicle according to claim 3, characterized by, By analyzing the driving state of the user-driven vehicle through the at least one predetermined behavior pattern and the vehicle driving data, the driving state of the user-driven vehicle is obtained, including: Determine the preset driving data of the vehicle corresponding to the target behavior pattern; By matching the preset driving data and the vehicle driving data, a data matching result is obtained, wherein the data matching result is used to characterize the degree of matching between the vehicle driving data and the preset driving data; In response to the data matching result satisfying a preset matching degree, the driving status of the user's vehicle is determined by the target behavior pattern and the vehicle driving data.

5. The driving monitoring method of a vehicle according to claim 1, characterized by, The warning response to the driving state includes: Based on the driving state, a warning signal is generated, wherein the warning signal is used to warn that the vehicle is in a dangerous driving state; From vehicles within a preset range of the vehicle, a target vehicle is determined, wherein both the target vehicle and the vehicle are covered by the Internet of Things (IoT). The warning signal is sent to the target vehicle and / or target terminal.

6. A driving monitoring apparatus of a vehicle characterized by comprising: include: The data acquisition module is used to acquire the user's physiological data, behavioral data, and vehicle driving data in response to the vehicle driving process. The physiological data includes user physiological monitoring data and historical pathological data. The behavior analysis module is used to obtain at least one predetermined behavior pattern of the user by performing behavior pattern analysis on the physiological data and the behavior data, wherein the predetermined behavior pattern is used to characterize the behavior pattern of the user under the influence of the physiological data and the behavior data. The status analysis module is used to analyze the driving status of the user driving the vehicle by analyzing the at least one predetermined behavior pattern and the vehicle driving data, and to provide a warning response to the driving status, wherein the driving status is used to characterize the user's dangerous driving behavior. The behavior analysis module is further configured to obtain at least one predetermined behavior pattern of the user by performing behavior pattern analysis on the physiological data and the behavior data, including: obtaining the user's target pathological data by performing feature analysis on the historical pathological data, wherein the target pathological data is used to characterize the pathological data with the highest frequency of incidence in the historical pathological data; determining the physiological manifestation characteristics corresponding to the target pathological data; extracting data corresponding to the physiological manifestation characteristics from the user's physiological monitoring data; determining the user's health assessment result based on the data corresponding to the physiological manifestation characteristics, wherein the health assessment result is used to characterize the user's health status while driving; and obtaining at least one predetermined behavior pattern of the user by performing behavior pattern analysis on the health assessment result and the behavior data.

7. A non-volatile storage medium, characterized by The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the execution of the driving monitoring method for the vehicle according to any one of claims 1-5 in the processor of the device.

8. A vehicle characterized by comprising: include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors perform the driving monitoring method for a vehicle as described in any one of claims 1 to 5.

Images