CN115590482B - Method, device, equipment and storage medium for predicting abnormal behavior of driver - Google Patents
Method, device, equipment and storage medium for predicting abnormal behavior of driver Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a storage medium for predicting abnormal behaviors of a driver, which are used for collecting biological information of the driver, wherein the biological information of the driver comprises sound volume information, current somatosensory information and a plurality of historical somatosensory information; determining a somatosensory information standard value according to a plurality of historical somatosensory information, and determining a somatosensory information fluctuation index based on the somatosensory information standard value and the current somatosensory information; determining a driver identity of the driver according to the sound volume information; determining a historical abnormal behavior state of the driver based on the driver identity and a preset identity-preset historical abnormal behavior state library; based on the somatosensory information fluctuation index and the historical abnormal behavior state, a behavior abnormality index of the driver is determined to predict abnormal behavior of the driver. The method is characterized in that the behavior of the driver is predicted by combining multidimensional information of the body feeling information, the identity information and the vehicle state information of the driver.
Description
Technical Field
The present invention relates to the field of vehicles, and in particular, to a method, apparatus, device, and storage medium for predicting abnormal behavior of a driver.
Background
In recent years, the automobile industry in China is rapidly developed, the popularization rate of household sedans is higher and higher, and based on the fact that the number of vehicles on a road is increased rapidly, the occurrence of vehicle accidents is also increased gradually, so that the behavior actions of drivers are monitored, the abnormal behavior trends of the drivers are prejudged, the abnormal behaviors are found and stopped in time, and the method has important significance for maintaining the harmony and stability of society.
According to the use property of the car, the car is simply divided into two major types of operation vehicles and private cars, the supervision force on the operation vehicles in the market is relatively large at present, the car is provided with a safety control system for immediately alarming if potential safety hazards exist in the network car through a human body gesture recognition technology and a voice recognition and face recognition technology, and the car is provided with a safety monitoring platform for supervising the situation that passengers use taxis or the network car through an artificial intelligent security mode, timely alarming and stopping or alarming the bad behaviors of the passengers or the car owners, recording and automatically judging the words of the passengers and the car owners and timely and accurately alarming and alarming. However, these existing risk control systems and monitoring platforms are directed to operating vehicles, and the driving safety of the vehicles is monitored based on the interaction and sensitive words between the driver and the passengers, and in fact, in real traffic accidents, private vehicles also occupy an important proportion, and in some events, there is often no relative relationship between the driver and the passengers, but only one driver. Therefore, in the process of preventing traffic accidents, the supervision of private cars is also important, and the existing supervision systems on the market cannot achieve the purpose.
Disclosure of Invention
In view of the drawbacks of the prior art, the present invention provides a method, apparatus, device and storage medium for predicting abnormal behavior of a driver, so as to solve the above technical problems.
The invention provides a method for predicting abnormal behavior of a driver, which comprises the following steps: collecting biological information of a driver, wherein the biological information of the driver comprises sound volume information, current somatosensory information and a plurality of historical somatosensory information; determining a somatosensory information standard value according to a plurality of historical somatosensory information, and determining a somatosensory information fluctuation index based on the somatosensory information standard value and the current somatosensory information; determining a driver identity of the driver according to the sound volume information; determining a historical abnormal behavior state of the driver based on the driver identity and a preset identity-preset historical abnormal behavior state library; based on the somatosensory information fluctuation index and the abnormal behavior state, a behavior abnormality index of the driver is determined to predict abnormal behavior of the driver.
According to an aspect of the embodiment of the present application, the somatosensory information includes at least one of heartbeat pulse information, palm temperature and humidity information, blood flow velocity information, electrocardiogram information, and hand pressure information, and determining the somatosensory information fluctuation index includes: comparing the current heartbeat pulse with the standard heartbeat pulse to obtain a heartbeat pulse difference value; comparing the current palm temperature and humidity with the standard palm temperature and humidity to obtain a palm temperature and humidity difference value; comparing the current blood flow velocity with the standard blood flow velocity to obtain a blood flow velocity difference value; comparing the current electrocardiogram with a standard electrocardiogram to obtain an electrocardiogram difference value; comparing the current hand holding pressure with the standard hand holding pressure to obtain a hand holding pressure difference value; and determining the somatosensory information fluctuation index of the driver based on at least one of the heartbeat pulse difference value, the palm temperature and humidity difference value, the blood flow speed difference value, the electrocardiogram difference value and the hand pressure difference value and the weight of each difference value.
According to an aspect of the embodiment of the present application, the historical somatosensory information includes at least one of historical heart beat pulse information, historical palm temperature and humidity information, historical blood flow velocity information, historical electrocardiogram information and historical hand pressure information, and determining the somatosensory information standard value according to the plurality of historical somatosensory information includes: adding the plurality of historical heartbeat pulses to obtain an average value of the historical heartbeat pulses, wherein the average value is used as a heartbeat pulse standard value; adding and processing the plurality of historical palm humiture to obtain a historical palm humiture average value, wherein the historical palm humiture average value is used as a palm humiture pulse standard value; adding the plurality of historical blood flow speeds to obtain an average value of the historical blood flow speeds, and taking the average value of the historical blood flow speeds as a blood flow speed standard value; adding the historical electrocardiograms to obtain an average value of the historical electrocardiograms, and taking the average value of the historical electrocardiograms as an electrocardiogram standard value; adding the plurality of historical hand-holding pressures to obtain an average value of the historical hand-holding pressures, and taking the average value as a hand-holding pressure standard value; and determining a somatosensory information standard value based on at least one of a historical heartbeat pulse standard value, a historical palm temperature and humidity standard value, a historical blood flow speed standard value, a historical electrocardiogram standard value and a historical hand-held pressure standard value.
According to one aspect of an embodiment of the present application, the means for obtaining a plurality of historical somatosensory information includes: acquiring historical somatosensory information from a preset driver somatosensory information database based on the driver identity; and taking the plurality of pieces of somatosensory information acquired in a preset time period before the current acquisition time as a plurality of pieces of historical somatosensory information, wherein the current acquisition time is the acquisition time of the current somatosensory information.
According to one aspect of an embodiment of the present application, predicting abnormal behavior of a driver includes: obtaining a behavior abnormality index of the driver based on the somatosensory information fluctuation index, the abnormal behavior state and the weight of the somatosensory information fluctuation index and the abnormal behavior state; comparing the behavioral abnormality index with a preset safety threshold, and if the behavioral abnormality index is larger than the safety threshold, judging that the driver has abnormal behavioral intention or abnormal behavioral action.
According to an aspect of the embodiment of the present application, predicting abnormal behavior of the driver further includes: acquiring running state information of a vehicle, wherein the running state information comprises collision information of the vehicle; if no collision occurs, judging that the driver has abnormal behavior intention; if a collision occurs, it is determined that there is an abnormal behavior of the driver.
According to an aspect of the embodiment of the present application, after determining that the driver has an abnormal behavioral intention or an abnormal behavioral action, the method further includes: after judging that the driver has abnormal behavior intention, executing intervention actions and discouraging actions, wherein the intervention actions comprise forced deceleration, the discouraging actions comprise playing discouraging videos, and the discouraging videos comprise videos preset in a vehicle-end memory; after the abnormal behavior action of the driver is judged, the alarming action is executed, wherein the alarming action comprises voice alarming or text alarming.
According to an aspect of an embodiment of the present application, there is provided a driver abnormal behavior prediction apparatus including: the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring driver biological information of a driver, and the driver biological information comprises sound volume information, current somatosensory information and a plurality of historical somatosensory information; the standard value and index determining module is used for determining a body feeling information standard value according to a plurality of historical body feeling information and determining a body feeling information fluctuation index based on the body feeling information standard value and the current body feeling information; the identity identification module is used for determining the identity identification of the driver according to the volume information; the historical abnormal behavior state determining module is used for determining the historical abnormal behavior state of the driver based on the driver identity and a preset identity-preset historical abnormal behavior state library; the abnormal behavior prediction module is used for determining the behavior abnormal index of the driver based on the somatosensory information fluctuation index and the abnormal behavior state so as to predict the abnormal behavior of the driver.
The present invention provides a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the processing method of any of the above.
The present invention provides an electronic device including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the processing method of any of the above.
The method collects biological information of the driver and vehicle state information, confirms identity information of the driver based on volume information in the biological information of the driver, obtains an abnormal behavior history record of the driver, establishes a body feeling information standard value of the driver based on body feeling information in the biological information of the driver, obtains a body feeling information fluctuation index of the driver at each moment through current monitoring, combines the abnormal behavior history record and the body feeling information fluctuation index of the driver to obtain a behavior abnormality index of the driver, and predicts the abnormal behavior state of the driver by combining collision conditions in the vehicle state information, namely, predicts the behavior of the driver by combining the body feeling information, the identity information and multidimensional information of the vehicle state information of the driver. All the information required by the vehicle is obtained from the driver and the vehicle, so that the driving safety monitoring of the vehicle in the passenger-free environment is realized.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is evident that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 is a schematic diagram of a driver abnormal behavior prediction system architecture shown in an exemplary embodiment of the present application;
FIG. 2 is a flowchart illustrating a method of predicting driver abnormal behavior according to an exemplary embodiment of the present application;
FIG. 3 is a flow chart illustrating driver abnormal behavioral intent or action determination according to an exemplary embodiment of the application;
FIG. 4 is a flowchart illustrating an overall process of a method for predicting driver abnormal behavior according to an exemplary embodiment of the present application;
FIG. 5 is a schematic block diagram of a driver abnormal behavior prediction apparatus shown in an embodiment of the present application;
FIG. 6 is a block diagram of a driver abnormal behavior prediction apparatus shown in an exemplary embodiment of the present application;
FIG. 7 is a schematic diagram of a computer system according to an exemplary embodiment of the application.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Fig. 1 is a schematic diagram of a smart cockpit system architecture shown in an exemplary embodiment of the present application.
Referring to fig. 1, a system architecture may include a vehicle device 101, a cloud 102, and a computer device 103. Wherein the computer device 103 may be at least one of a desktop graphics processor (Graphic Processing Unit, GPU) computer, a GPU computing cluster, a neural network computer, or the like. The related technician can use the computer device 103 to process the acquired data, and according to the acquired biological information of the driver and the vehicle state information, combine the historical abnormal behavior record in the cloud database to predict the abnormal behavior of the target driver. The vehicle device 101 is configured to collect biological information of a driver and vehicle state information, and in this embodiment, the vehicle device 101 collects the biological information of the driver and the vehicle state information by using a camera, a microphone, a temperature and humidity sensor and a pressure sensor, and uploads the biological information and the vehicle state information to the cloud end for providing the biological information and the vehicle state information to the computer device 103 for processing.
Illustratively, after acquiring the biological information and the vehicle state information of the driver of the image vehicle device 101, the computer device 103 identifies the identity of the driver based on the volume information thereof, obtains the body feeling information fluctuation index of the driver based on the body feeling information thereof, and combines the historical abnormal behavior recording result of the driver to obtain the abnormal behavior index of the driver so as to judge the abnormal behavior state of the driver.
The implementation details of the technical scheme of the embodiment of the application are described in detail below:
Fig. 2 is a flowchart illustrating a driver abnormal behavior prediction method according to an exemplary embodiment of the present application.
It should be understood that step S210 and step S250 may or may not be performed simultaneously, and thus the following steps are merely exemplary and illustrative, and are not limiting of the present application.
As shown in fig. 2, in an exemplary embodiment, the driver abnormal behavior prediction method at least includes steps S210 to S250, which are described in detail as follows:
In step S210, biological information of the driver is collected, where the biological information of the driver includes sound volume information, current somatosensory information and a plurality of historical somatosensory information.
In one embodiment of the invention, the cockpit includes an information acquisition unit with multiple sensors including, but not limited to, microphones, cameras, temperature and humidity sensors, and pressure sensors. Through the microphone of placing in the car cabin, gather and obtain driver's pronunciation information, through the camera, gather and obtain driver's portrait information, through temperature and humidity sensor, gather and obtain driver's body temperature, palm temperature and palm humidity, through pressure sensor, gather and obtain driver's heartbeat, pulse, blood velocity, electrocardiogram and hold pressure, wherein temperature and humidity sensor and pressure sensor all have 3 at least, set up respectively in steering wheel, shelves handle and seat.
Step S220, a body feeling information standard value is determined according to the plurality of historical body feeling information, and a body feeling information fluctuation index is determined based on the body feeling information standard value and the current body feeling information.
The historical somatosensory information comprises at least one of historical heart beat pulse information, historical palm temperature and humidity information, historical blood flow velocity information, historical electrocardiogram information and historical hand pressure information, and determining the somatosensory information standard value according to the historical somatosensory information comprises:
adding the plurality of historical heartbeat pulses to obtain an average value of the historical heartbeat pulses, wherein the average value is used as a heartbeat pulse standard value;
Adding and processing the plurality of historical palm humiture to obtain a historical palm humiture average value, wherein the historical palm humiture average value is used as a palm humiture pulse standard value;
adding the plurality of historical blood flow speeds to obtain an average value of the historical blood flow speeds, and taking the average value of the historical blood flow speeds as a blood flow speed standard value;
adding the historical electrocardiograms to obtain an average value of the historical electrocardiograms, and taking the average value of the historical electrocardiograms as an electrocardiogram standard value;
adding the plurality of historical hand-holding pressures to obtain an average value of the historical hand-holding pressures, and taking the average value as a hand-holding pressure standard value;
and determining a somatosensory information standard value based on at least one of a historical heartbeat pulse standard value, a historical palm temperature and humidity standard value, a historical blood flow speed standard value, a historical electrocardiogram standard value and a historical hand-held pressure standard value.
In one embodiment of the present invention, the obtained 100 historical heart beat pulse values are averaged to be used as the standard value of the heart beat pulse information. And similarly, obtaining a standard value of palm temperature and humidity, a standard value of blood flow speed, a standard value of electrocardiogram and a standard value of hand holding pressure, and combining the standard values to obtain a body feeling information standard value.
The method for acquiring the plurality of historical somatosensory information comprises the following steps: acquiring historical somatosensory information from a preset driver somatosensory information database based on the driver identity; and taking the plurality of pieces of somatosensory information acquired in a preset time period before the current acquisition time as a plurality of pieces of historical somatosensory information, wherein the current acquisition time is the acquisition time of the current somatosensory information.
In one embodiment of the invention, the collected historical somatosensory information of the driver is stored in a cloud database, and the identity characteristic information of the driver is used as an access keyword, so that a plurality of historical somatosensory information can be extracted from the database according to the identity characteristic information of the driver.
In one embodiment of the present invention, an information acquisition period is preset before the current time, and somatosensory information acquired in the information acquisition period is used as historical somatosensory information.
Determining a somatosensory information fluctuation index includes:
comparing the current heartbeat pulse with the standard heartbeat pulse to obtain a heartbeat pulse difference value;
Comparing the current palm temperature and humidity with the standard palm temperature and humidity to obtain a palm temperature and humidity difference value;
comparing the current blood flow velocity with the standard blood flow velocity to obtain a blood flow velocity difference value;
Comparing the current electrocardiogram with a standard electrocardiogram to obtain an electrocardiogram difference value;
Comparing the current hand holding pressure with the standard hand holding pressure to obtain a hand holding pressure difference value;
And determining the somatosensory information fluctuation index of the driver based on at least one of the heartbeat pulse difference value, the palm temperature and humidity difference value, the blood flow speed difference value, the electrocardiogram difference value and the hand pressure difference value and the weight of each difference value.
In one embodiment of the invention, the body feeling information of the driver at the current moment is collected, and the obtained body feeling information of the driver at each current moment is compared with the corresponding body feeling information standard value, for example, the palm temperature and humidity at the current moment are compared with the palm temperature and humidity standard value, so as to obtain the difference value of the palm temperature and humidity at the current moment and the palm temperature and humidity standard value. And similarly obtaining the difference value of the current palm temperature and humidity and the standard value of the palm temperature and humidity, the difference value of the current blood flow speed and the standard value of the blood flow speed, the difference value of the current electrocardiogram and the standard value of the electrocardiogram and the difference value of the current hand-held pressure and the standard value of the hand-held pressure, and carrying out de-weighting on the obtained data according to different weights of various data to obtain the somatosensory information fluctuation index of the driver.
Step S230, determining the driver identity of the driver according to the volume information.
In one embodiment of the invention, a microphone collects voice information of a driver, a camera collects image information of the driver, corresponding voiceprint features are obtained according to the voice information, face features are obtained according to the image information, and identity identification of the driver is confirmed according to at least one of the voiceprint features or the face features.
Step S240, determining the historical abnormal behavior state of the driver based on the driver identity and the preset identity-preset historical abnormal behavior state library.
In one embodiment of the invention, the driver's identity, the historical abnormal behavior state, and the mapping relationship between the driver's identity and the historical abnormal behavior state are preset in the driver identity and the preset identity-preset historical abnormal behavior state library. The historical abnormal behavior record state of the driver can be obtained according to the identity information of the driver.
Step S250, determining a behavior abnormality index of the driver based on the somatosensory information fluctuation index and the historical abnormal behavior state to predict abnormal behavior of the driver.
In one embodiment of the invention, after the somatosensory information fluctuation index and the abnormal behavior state of the driver are obtained, weighting is carried out on the somatosensory information fluctuation index and the abnormal behavior state to obtain the behavior abnormality index of the driver, and the abnormal behavior state of the driver is predicted according to the behavior abnormality index of the driver. If the abnormal behavior index is larger than a preset safety threshold, judging that the driver has abnormal behavior intention or abnormal behavior action.
FIG. 3 is a flow chart illustrating driver abnormal behavior intent or action determination in accordance with an exemplary embodiment of the present application.
As shown in fig. 3, the behavior abnormality index of the driver is obtained based on the body feeling information fluctuation index of the driver and the past abnormality behavior record of the driver and the weights of the fluctuation index and the abnormality behavior history record; comparing the behavioral abnormality index with a preset safety threshold, and if the behavioral abnormality index is larger than the safety threshold, judging that the driver has abnormal behavioral intention or abnormal behavioral action.
In one embodiment of the invention, the body feeling information fluctuation index and the abnormal behavior history of the driver are weighted and calculated to obtain the abnormal behavior index of the driver, and when the abnormal behavior index is larger than a safety threshold value, the abnormal behavior index is evaluated by combining with the state information of the vehicle.
If no collision occurs, judging that the driver has abnormal behavior intention; if a collision occurs, it is determined that there is an abnormal behavior of the driver.
In one embodiment of the invention, if the vehicle is not crashed, the driver is regarded as having abnormal behavioral consciousness, and intervention and dissuading actions are required to be executed immediately; if the vehicle collides, the driver is considered to have abnormal behavior action, and the alarming action needs to be executed immediately.
After judging that the driver has abnormal behavioral intention, executing intervention and dissuading actions; the intervening actions comprise but are not limited to forced deceleration, and the discouraging actions comprise playing discouraging videos, wherein the discouraging videos comprise but are not limited to videos preset in a vehicle-end memory; after determining that the driver has abnormal behavior actions, executing alarm actions.
In one embodiment of the invention, a section of video of the driver family is implanted in the vehicle-mounted system in advance, the video comprises the concern of the family about the driver and exhort, and when the intelligent cabin judges that the driver has abnormal behavior intention, the video is played so as to wake up the good and good of the driver's mind, thereby avoiding the abnormal behavior action of the driver.
In one embodiment of the invention, a forced deceleration function module is implanted in the vehicle-mounted system in advance, and when the intelligent cabin judges that the abnormal behavior intention of the driver exists, the forced deceleration action is executed, so that the influence of the abnormal behavior action of the driver is reduced.
In one embodiment of the invention, a 4G/5G module connected with a cloud is arranged in the vehicle-mounted system, and when the intelligent cabin judges that the driver has abnormal behavior actions, the intelligent cabin gives an alarm to related departments through a cloud call.
As shown in fig. 4, driver biological information including sound volume information and body feeling information and vehicle state information including collision information are collected; based on the somatosensory information of the driver, establishing a somatosensory information standard value of the driver; collecting current somatosensory information of a driver, and obtaining a somatosensory information fluctuation index of the driver based on the current somatosensory information of the driver and a somatosensory information standard value; based on the volume information, identifying the identity of the driver to obtain an abnormal behavior history record of the driver; obtaining a behavioral abnormality index of the driver based on the somatosensory information fluctuation index and the abnormal behavior record; based on the behavior abnormality index and the collision information, an abnormal behavior state of the driver is predicted.
In one embodiment of the invention, biological information of a driver and state information of a vehicle are collected according to a sensor in an automobile cabin, a body feeling information standard value is generated according to body feeling information in the collected biological information, the identity of the driver is determined according to the sound volume information in the collected biological information, and whether the vehicle collides or not is judged according to the collected state information of the vehicle. And generating a somatosensory information standard value according to a plurality of historical somatosensory information of the driver, collecting the current somatosensory information of the driver after generating the somatosensory information standard value, comparing each item of information with the corresponding standard value to obtain a difference value between each item of information and the standard value, and carrying out weighted calculation on each difference value to obtain a somatosensory information fluctuation index. And performing feature recognition processing on the acquired volume information to obtain corresponding face features and tone features, determining the identity of the driver according to the face features and tone features, and searching in a cloud database based on the identity information of the driver to obtain an abnormal behavior history record of the driver. And carrying out weighted calculation on the somatosensory information fluctuation index and the abnormal behavior history record of the driver to obtain an abnormal behavior index of the driver, and when the abnormal behavior index is greater than a safety threshold value, combining the collision condition of the vehicle to obtain the abnormal behavior intention or abnormal behavior action of the driver, and executing corresponding safety corresponding action.
Fig. 5 is a schematic block diagram of a driver abnormal behavior prediction apparatus according to an embodiment of the present application.
As shown in fig. 5, the driver abnormal behavior prediction apparatus includes an acquisition module 510, a standard value and index determination module 520, an identification determination module 530, a historical abnormal behavior state determination module 540, and an abnormal behavior prediction module 550, and the following explanation is given in detail for each module:
The acquisition module 510 is configured to acquire driver biological information of a driver, where the driver biological information includes sound volume information, current somatosensory information, and a plurality of historical somatosensory information;
The standard value and index determining module 520 is configured to determine a body feeling information standard value according to a plurality of historical body feeling information, and determine a body feeling information fluctuation index based on the body feeling information standard value and the current body feeling information;
The identity determination module 530 is configured to determine a driver identity of the driver according to the volume information;
a historical abnormal behavior state determining module 540, configured to determine a historical abnormal behavior state of the driver based on the driver identity and a preset identity-preset historical abnormal behavior state library;
An abnormal behavior prediction module 550 for determining a behavior abnormality index of the driver based on the somatosensory information fluctuation index and the abnormal behavior state to predict an abnormal behavior of the driver.
It should be noted that, the device for predicting abnormal driver behavior provided in the above embodiment and the method for predicting abnormal driver behavior provided in fig. 2 in the above embodiment belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiment, which is not repeated here. In practical application, the device for predicting abnormal behavior of driver provided in the above embodiment may allocate the functions to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.
In addition, it should be noted that the device can be integrated into a vehicle-end system in a seven-component production process, so that no matter the vehicle is used as an operation vehicle or a private vehicle in the later period, the device can complete corresponding prediction of abnormal behavior of the driver, and no extra hardware cost is generated.
Fig. 6 is a block diagram of a driver abnormal behavior prediction apparatus shown in an exemplary embodiment of the present application.
As shown in fig. 6, the driver abnormal behavior prediction apparatus includes a hardware module 6100, a processing module 6200, and an execution module 6300; the hardware module comprises an information acquisition unit 6110 and an information transmission unit 6120, and the processing module comprises an information matching unit 6210, a self-learning unit 6220, a data analysis unit 6230 and an arbitration unit 6240.
It should be noted that, the information collecting unit 6110 includes the collecting module 510 of fig. 5, and the processing module 6200 includes the standard value and index determining module 520, the identity determining module 530, the historical abnormal behavior state determining module 540, and the abnormal behavior predicting module 550 of fig. 5.
An information acquisition unit 6110 for acquiring biological information of the driver and vehicle state information; the information transmission unit 6120 is used for completing information transmission between the hardware module and a preset new information base and between the hardware module and the software module and executing alarm action of the module; an information matching unit 6210 for identifying the identity of the driver based on the portrait information and the voiceprint information to obtain a record of the past abnormal behavior of the driver; a self-learning unit 6220 for establishing a criterion value of biological information of a driver based on the biological information of the driver; a data analysis unit 6230 for obtaining a biological information fluctuation index of the driver based on the current biological information and a biological information standard value of the driver, and obtaining a behavioral abnormality index of the driver based on the biological information fluctuation index and an abnormal behavioral record; 6240 an arbitration unit for obtaining an abnormal behavior state of the driver based on the behavior abnormality index and the collision information.
The execution module 6300 is configured to execute a safety corresponding action according to the abnormal behavior state of the driver.
The information collection unit 6100 includes multiple sensors, including but not limited to a microphone 6111, a camera 6112, a temperature and humidity sensor 6113, and a pressure sensor 6114.
In one embodiment of the invention, voice information of a driver is acquired through a microphone 6111 arranged in a vehicle cabin, portrait information of the driver is acquired through a camera 6112, body temperature, palm temperature and palm humidity of the driver are acquired through a temperature and humidity sensor 6113, and heartbeat, pulse, blood flow speed, electrocardiogram and hand holding pressure of the driver are acquired through a pressure sensor 6114, wherein at least 3 temperature and humidity sensors and at least 3 pressure sensors are respectively arranged in a steering wheel, a gear handle and a seat.
Obtaining face features and voiceprint features based on the face information and the voice information, and confirming identity information of a driver based on the face features and the voiceprint features; based on the identity information of the driver, obtaining an abnormal behavior history record of the driver from a preset information base.
In one embodiment of the present invention, the image information and the voice information of the driver are acquired through the microphone 6111 and the camera 6112, the corresponding face feature information and voice print feature information are obtained after processing, the relevant feature information is transmitted to the software module 6200 through the 4G/5G module 4121, the identity information corresponding to the relevant feature information is found in the cloud database through the information matching unit 6210, namely the identity information of the driver, and the abnormal behavior history record of the driver is obtained through the identity information of the driver.
In one embodiment of the invention, the hardware comprises a camera module, a heartbeat and pulse acquisition module, a body temperature acquisition module, a palm humidity acquisition module, a pressure acquisition module, a respiration rate acquisition module, a blood flow rate acquisition module, an electrocardiogram acquisition module and a 4G/5G communication module; the camera module is arranged in the vehicle center console and used for collecting the body temperature, facial expression, behavior and gesture of the person; the heartbeat pulse acquisition module is arranged at the neck positions of the steering wheel, the stop handle and the vehicle seat; the body temperature acquisition module is integrated to the camera module; the palm center humidity acquisition module is integrated to the steering wheel and the gear handle; the pressure acquisition module is integrated to the steering wheel and the gear handle; the respiration rate acquisition sensor is integrated to the steering wheel; the blood flow rate acquisition module is integrated to the neck positions of the steering wheel, the gear handle and the vehicle seat; the electrocardiogram acquisition module is integrated to the neck positions of the steering wheel, the gear handle and the vehicle seat; the 4G/5G module is integrated to the vehicle machine, transmitted to the vehicle machine through the CAN, and finally uploaded to the cloud. The hardware module collects the data, the physical value is converted into an AD value through the data conversion module of the processing module, all sensor data are synchronized through the data synchronization module, then the data analysis module and the self-learning module provide a combination of conditions, then an instruction is sent through the arbitration module, and finally the execution module executes the corresponding instruction.
FIG. 7 is a schematic diagram of a computer system according to an exemplary embodiment of the application.
Fig. 7 shows a schematic diagram of a computer system suitable for use in implementing an embodiment of the application. It should be noted that, the computer system 700 of the electronic device shown in fig. 7 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
In one embodiment of the present invention, as shown in fig. 7, the computer system 700 includes a central processing unit (Central Processing Unit, CPU) 701, which can perform various appropriate actions and processes, such as performing the method in the above embodiment, according to a program stored in a Read-Only Memory (ROM) 702 or a program loaded from the storage portion 508 into a random access Memory (Random Access Memory, RAM) 703. In the RAM 703, various programs and data required for the system operation are also stored. The CPU 701, ROM 702, and RAM 703 are connected to each other through a bus 704. An Input/Output (I/O) interface 505 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input section 706 including a keyboard, a mouse, and the like; an output portion 707 including a Cathode Ray Tube (CRT), a Liquid crystal display (Liquid CRYSTAL DISPLAY, LCD), and a speaker, etc.; a storage section 708 including a hard disk or the like; and a communication section 709 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 710 as needed, so that a computer program read out therefrom is installed into the storage section 708 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 709, and/or installed from the removable medium 711. When executed by a Central Processing Unit (CPU) 701, performs the various functions defined in the system of the present application.
Another aspect of the application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the foregoing data analysis and driver abnormal behavior intent prediction. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), a flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may comprise a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing. The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Another aspect of the application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions so that the computer device performs the data analysis and the driver abnormal behavior intention prediction provided in the above-described respective embodiments.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
The units involved in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functions of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It should be understood that the foregoing is only illustrative of the preferred embodiments of the present application and is not intended to limit the embodiments of the present application, and that various changes and modifications can be made herein without departing from the spirit and scope of the application as defined by the appended claims.
Claims (8)
1. A driver abnormal behavior prediction method, characterized by comprising:
Collecting biological information of a driver, wherein the biological information of the driver comprises sound volume information, current somatosensory information and a plurality of historical somatosensory information;
Determining a somatosensory information standard value according to a plurality of historical somatosensory information, wherein the historical somatosensory information comprises at least one of historical heart beat pulse information, historical palm temperature and humidity information, historical blood flow velocity information, historical electrocardiogram information and historical hand pressure information, and determining a somatosensory information fluctuation index based on the somatosensory information standard value and the current somatosensory information, wherein the current somatosensory information comprises at least one of current heart beat pulse information, current palm temperature and humidity information, current blood flow velocity information, current electrocardiogram information and current hand pressure information, and the determining the somatosensory information fluctuation index comprises: comparing the current heartbeat pulse with the standard heartbeat pulse to obtain a heartbeat pulse difference value; comparing the current palm temperature and humidity with the standard palm temperature and humidity to obtain a palm temperature and humidity difference value; comparing the current blood flow velocity with the standard blood flow velocity to obtain a blood flow velocity difference value; comparing the current electrocardiogram with a standard electrocardiogram to obtain an electrocardiogram difference value; comparing the current hand holding pressure with the standard hand holding pressure to obtain a hand holding pressure difference value; determining a somatosensory information fluctuation index of the driver based on the weight of at least one of the heartbeat pulse difference value, the palm temperature and humidity difference value, the blood flow velocity difference value, the electrocardiogram difference value and the hand pressure difference value and each difference value;
Determining a driver identity of the driver according to the sound volume information;
Determining the historical abnormal behavior state of the driver based on the mapping relation between the driver identity and a preset identity-preset historical abnormal behavior state library;
Determining a behavioral abnormality index of the driver based on the somatosensory information fluctuation index and the historical abnormal behavior state to predict abnormal behavior of the driver, including obtaining a behavioral abnormality index of the driver based on the somatosensory information fluctuation index, the historical abnormal behavior state, and weights of the somatosensory information fluctuation index and the historical abnormal behavior state; and comparing the behavioral abnormality index with a preset safety threshold, and if the behavioral abnormality index is larger than the safety threshold, judging that the driver has abnormal behavioral intention or abnormal behavioral action.
2. The driver abnormal behavior prediction method according to claim 1, wherein determining a somatosensory information standard value from a plurality of pieces of historic somatosensory information comprises:
adding the plurality of historical heartbeat pulses to obtain an average value of the historical heartbeat pulses, wherein the average value is used as a heartbeat pulse standard value;
Adding and processing the plurality of historical palm humiture to obtain a historical palm humiture average value, wherein the historical palm humiture average value is used as a palm humiture pulse standard value;
adding the plurality of historical blood flow speeds to obtain an average value of the historical blood flow speeds, and taking the average value of the historical blood flow speeds as a blood flow speed standard value;
adding the historical electrocardiograms to obtain an average value of the historical electrocardiograms, and taking the average value of the historical electrocardiograms as an electrocardiogram standard value;
adding the plurality of historical hand-holding pressures to obtain an average value of the historical hand-holding pressures, and taking the average value as a hand-holding pressure standard value;
And determining a somatosensory information standard value based on at least one of the historical heartbeat pulse standard value, the historical palm temperature and humidity standard value, the historical blood flow speed standard value, the historical electrocardiogram standard value and the historical hand-held pressure standard value.
3. The driver abnormal behavior prediction method according to claim 2, wherein the manner of acquiring the plurality of pieces of history somatosensory information includes:
Acquiring the historical somatosensory information from a preset driver somatosensory information database based on the driver identity;
And taking a plurality of pieces of somatosensory information acquired in a preset time period before the current acquisition time as the plurality of pieces of historical somatosensory information, wherein the current acquisition time is the acquisition time of the current somatosensory information.
4. The driver abnormal behavior prediction method according to claim 1, characterized in that predicting the abnormal behavior of the driver further comprises:
acquiring driving state information of a vehicle, wherein the driving state information comprises collision information of the vehicle;
If no collision occurs, judging that the driver has abnormal behavior intention;
if a collision occurs, it is determined that there is an abnormal behavior action of the driver.
5. The driver abnormal behavior prediction method according to any one of claims 1 to 4, characterized by comprising:
After judging that the driver has abnormal behavior intention, executing intervention action and dissuading action;
The intervention action comprises forced deceleration, the discouraging action comprises playing discouraging videos, and the discouraging videos comprise videos preset in a vehicle-end memory;
After the abnormal behavior action of the driver is judged, the alarm action is executed, wherein the alarm action comprises a voice alarm or a text alarm.
6. A driver abnormal behavior prediction apparatus, characterized by comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring driver biological information of a driver, and the driver biological information comprises sound volume information, current somatosensory information and a plurality of historical somatosensory information;
The standard value and the index determining module are used for determining a body feeling information standard value according to a plurality of historical body feeling information, the historical body feeling information comprises at least one of historical heartbeat pulse information, historical palm temperature and humidity information, historical blood flow speed information, historical electrocardiogram information and historical hand holding pressure information, the body feeling information fluctuation index is determined based on the body feeling information standard value and the current body feeling information, the current body feeling information comprises at least one of current heartbeat pulse information, current palm temperature and humidity information, current blood flow speed information, current electrocardiogram information and current hand holding pressure information, and the body feeling information fluctuation index is determined to comprise: comparing the current heartbeat pulse with the standard heartbeat pulse to obtain a heartbeat pulse difference value; comparing the current palm temperature and humidity with the standard palm temperature and humidity to obtain a palm temperature and humidity difference value; comparing the current blood flow velocity with the standard blood flow velocity to obtain a blood flow velocity difference value; comparing the current electrocardiogram with a standard electrocardiogram to obtain an electrocardiogram difference value; comparing the current hand holding pressure with the standard hand holding pressure to obtain a hand holding pressure difference value; determining a somatosensory information fluctuation index of the driver based on the weight of at least one of the heartbeat pulse difference value, the palm temperature and humidity difference value, the blood flow velocity difference value, the electrocardiogram difference value and the hand pressure difference value and each difference value;
The identity identification module is used for determining the identity identification of the driver according to the volume information;
The historical abnormal behavior state determining module is used for determining the historical abnormal behavior state of the driver based on the mapping relation between the driver identity and the preset historical abnormal behavior state library;
The abnormal behavior prediction module is used for determining a behavior abnormality index of the driver based on the somatosensory information fluctuation index and the abnormal behavior state to predict the abnormal behavior of the driver, and comprises the steps of obtaining a behavior abnormality index of the driver based on the somatosensory information fluctuation index, the historical abnormal behavior state, the weight of the somatosensory information fluctuation index and the historical abnormal behavior state; and comparing the behavioral abnormality index with a preset safety threshold, and if the behavioral abnormality index is larger than the safety threshold, judging that the driver has abnormal behavioral intention or abnormal behavioral action.
7. A computer-readable storage medium having stored thereon computer-readable instructions that, when executed by a processor of a computer, cause the computer to perform the driver abnormal behavior prediction method according to any one of claims 1 to 5.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the driver abnormal behavior prediction method according to any one of claims 1 to 5.
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