CN109927730B - Real-time fatigue driving behavior scoring system and method based on DMS system - Google Patents
Real-time fatigue driving behavior scoring system and method based on DMS system Download PDFInfo
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Abstract
The invention provides a real-time fatigue driving behavior scoring system based on a DMS system, which comprises DMS equipment, a server side and a client side; a real-time fatigue driving behavior scoring method based on a DMS system is used for comprehensively evaluating long-term behavior habits of drivers and realizing risk control of the drivers. The method has the advantages of simple data acquisition process, low cost and reasonable scoring system, and has better popularization and application prospects; according to the invention, the server side shares data to the client side, so that the driver is prompted to improve safety awareness and standardize driving behaviors, thereby reducing the social traffic accident rate and improving the safety level of public traffic.
Description
Technical Field
The invention belongs to the technical field of long-term monitoring of fatigue driving behaviors, and particularly relates to a real-time fatigue driving behavior scoring system and method based on a DMS (distributed management system).
Background
Along with the development of social economy and automobile industry, the number of motor vehicles is greatly increased, the convenience is brought to the life of people, and the life quality is improved. However, at the same time, the traffic accident rate is also increasing year by year, and fatigue driving has been considered as one of the main causes of traffic accidents, and must be paid high attention.
Dms (driver Monitoring system), i.e. a fatigue driving early warning system, is used to monitor the driving behavior of the driver in the whole driving process, and whether a fatigue state occurs. When the situations of CLOSED _ EYE, YAWNING, DOWN _ HEAD, ok _ about, PHONING, SMOKING and other wrong driving states of the driver are monitored, the early warning system can analyze the behaviors in time and give voice prompt to warn the driver and correct the wrong driving behaviors.
The fatigue driving monitoring system used in the industry at present is mainly used for monitoring and early warning, has no unified standard for driver management and risk control, and cannot comprehensively evaluate the long-term behavior habits of drivers. It is therefore highly desirable to provide a long-term assessment method.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the real-time fatigue driving behavior scoring system and method based on the DMS system are used for comprehensively evaluating long-term behavior habits of drivers and realizing risk control of the drivers.
The technical scheme adopted by the invention for solving the technical problems is as follows: a real-time fatigue driving behavior scoring system based on a DMS system comprises a DMS device, a server side and a client side; the DMS device is used for collecting driving behavior information and detecting specific fatigue driving behaviors; the server end comprises a fatigue driving scoring module, and the signal input end of the fatigue driving scoring module is connected with the signal output end of the DMS device through a wireless communication network; the fatigue driving scoring module is used for calculating the received fatigue driving behavior information in real time according to a large number of driving behavior data samples and a data model established by the large number of fatigue behavior data samples and giving a driving behavior score; the signal input end of the client is connected with the signal output end of the fatigue driving scoring module through an information sharing interface; for sharing information of driving behavior scores.
According to the scheme, the DMS device comprises a camera and a fatigue detection module, wherein the signal output end of the camera is connected with the signal input end of the fatigue detection module; the camera is used for collecting driving behavior information, and the fatigue detection module is used for detecting specific fatigue driving behaviors.
A real-time fatigue driving behavior scoring method based on a DMS system comprises the following steps:
step S1: starting the vehicle, and powering on and starting the DMS device;
step S2: the DMS device collects facial feature information of a driver in real time through a camera in the vehicle running process and sends the facial feature information to the fatigue detection module;
step S3: the fatigue detection module judges whether the driver has fatigue driving behavior by calculating the information received in the step S2; if the action duration of the driver is less than or equal to the set value, judging that no fatigue driving behavior exists, and circulating from the step S2; if the action duration of the driver is longer than a set value, judging that fatigue driving behaviors exist, and sending fatigue driving behavior information to a server side through a wireless communication network by the DMS device;
step S4: the server side calculates fatigue driving behavior scores for the information obtained in the step S3 through a fatigue driving behavior scoring module, and shares scoring information to the client side through an information sharing interface;
step S5: judging whether the vehicle reaches the destination, if not, starting to execute the loop from the step S2; if so, stopping driving, extinguishing the transmitter, and automatically shutting down and powering off the DMS device.
Further, the fatigue driving behaviors described in step S3 include eye closure, yawning, lowering head, looking left behind, making a call, and smoking.
Further, in step S4, the specific steps include:
step S41: setting the highest score of the fatigue driving behavior as 100 scores and the lowest score as 0 score;
step S42: calculating the risk coefficient of the single fatigue behavior n according to a data model established by a plurality of driving behavior data samples obtained in the step S3, namely the average value of the risk coefficients of the single driving behavior i under different vehicle speeds:
the larger the value is, the more dangerous the fatigue behavior is in the same vehicle speed range is;
step S43: calculating the number of times of the single fatigue behavior n according to the formula obtained in the step S42:
step S44: calculating n standard coefficients of the single fatigue behaviors according to a data model established by a large number of fatigue behavior data samples, namely the average value of n times of the single fatigue behaviors within a certain mileage range:
the larger the numerical value is, the fewer the occurrence times of the single fatigue behavior n in a certain mileage range is;
step S45: judging whether (1-n standard coefficient of the single fatigue behavior x n times of the single fatigue behavior/mileage) <0 is true, if yes, the score of the single fatigue behavior n is 0; if the single fatigue behavior is not satisfied, the single fatigue behavior n is divided into (1-the standard coefficient of the single fatigue behavior n is multiplied by the number of times of the single fatigue behavior n/mileage);
step S46: calculating a fatigue driving behavior score:
step S47: and sharing the scoring information to the client through the information sharing interface.
The invention has the beneficial effects that:
1. the real-time fatigue driving behavior scoring system and method based on the DMS system are used for comprehensively evaluating the long-term behavior habits of drivers and realizing risk control of the drivers.
2. The vehicle-mounted DMS device is adopted to collect the facial feature information of the driver in real time to realize accurate collection and real-time calculation, and the method has the advantages of simple data collection process and low cost, and has better popularization and application prospects.
3. According to the invention, the driving face characteristic information of the driver is transmitted to the cloud server by adopting a wireless network, so that the fatigue driving behavior is scored, monitored and shared in real time; the server side shares data to the client side, so that a driver is prompted to improve safety awareness and standardize driving behaviors, the social traffic accident rate is reduced, and the public traffic safety level is improved.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Fig. 2 is a flow chart of calculating a fatigue driving behavior score in an embodiment of the present invention.
Fig. 3 is a functional block diagram of an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 3, a real-time fatigue driving behavior scoring system based on a DMS system includes a DMS device, a server, and a client. The DMS device comprises a camera and a fatigue detection module, wherein the signal output end of the camera is connected with the signal input end of the fatigue detection module; the camera is used for collecting driving behavior information, and the fatigue detection module is used for detecting specific fatigue driving behaviors; the server end comprises a fatigue driving scoring module, and the signal input end of the fatigue driving scoring module is connected with the signal output end of a fatigue detection module in the DMS device through a wireless communication network; the fatigue driving scoring module is used for calculating the received fatigue driving behavior information in real time according to a large number of driving behavior data samples and a data model established by the large number of fatigue behavior data samples and giving a driving behavior score; the signal input end of the client is connected with the signal output end of the fatigue driving scoring module through an information sharing interface; for sharing information of driving behavior scores.
The one-time travel of the vehicle is a complete grading period, and the starting of the one-time travel is started by starting the vehicle and powering on the DMS device; in the running process of the vehicle, the DMS device analyzes and reports fatigue driving behaviors of a driver to the cloud server in real time, and calculates the fatigue driving score from the beginning of a travel to the current time point in real time; and (4) flameout when the vehicle arrives at the destination, automatically shutting down the DMS device, and ending the real-time scoring period. Referring to fig. 1 and 2, a real-time fatigue driving behavior scoring method based on a DMS system of the present invention includes the steps of:
step S1: and starting the vehicle, and powering on and starting the DMS device.
Step S2: the DMS device collects facial feature information of a driver in real time through the camera in the vehicle driving process and sends the facial feature information to the fatigue detection module.
Step S3: the fatigue detection module calculates the information received in the step S2 and judges whether the driver has fatigue driving behavior; looping from step S2 if the conclusion is that there is no fatigue driving behavior; and if the obtained conclusion is that the fatigue driving behavior exists, the DMS device sends the fatigue driving behavior information to the server side through the wireless communication network.
Step S31: the fatigue detection module calculates the information received in the step S2, and if the duration of closing the eyes of the driver is more than 1.5 seconds, the driver is judged to be eye-closed; if the mouth opening duration of the driver is more than 1.3 seconds, judging that the yawning of the driver is performed; if the driver head-lowering duration is more than 1.5 seconds, judging that the driver heads down; if the time for the driver to lean to the left or the right is more than 1.8 seconds, judging that the driver is in anticipation of the right; if the duration of the left-hand phone or the right-hand phone of the driver is more than 3 seconds, judging that the driver makes a call; if the duration of the cigarette holding time on the mouth of the driver is more than 2 seconds, judging that the driver smokes; if the action duration time of the driver is less than or equal to the requirement, judging that fatigue driving behaviors do not exist;
step S32: if the conclusion is that there is no fatigue driving behavior, repeating the step S2; and if the obtained conclusion is that the fatigue driving behavior exists, the DMS device sends the fatigue driving behavior information to the server side through the wireless communication network.
Step S4: the server side calculates fatigue driving behavior scores for the information obtained in the step S3 through a fatigue driving behavior scoring module, and shares scoring information to the client side through an information sharing interface:
step S41: setting the highest score of the fatigue driving behavior as 100 scores and the lowest score as 0 score;
step S42: calculating the risk coefficient of the single fatigue behavior n according to a data model established by a plurality of driving behavior data samples obtained in the step S3, namely the average value of the risk coefficients of the single driving behavior i under different vehicle speeds:
as shown in the following table, a larger value indicates a higher risk level of the fatigue behavior in the same vehicle speed range;
TABLE 1
| Vehicle speed | Eye closure | Yawning | Lowering head | Left look ahead | Telephone | Smoke extraction device |
| 60～80 | 0.3 | 0.1 | 0.4 | 0.2 | 0.5 | 0.2 |
| 80～120 | 0.5 | 0.3 | 0.6 | 0.4 | 0.7 | 0.7 |
| ＞120 | 1.2 | 0.8 | 1.3 | 1.0 | 1.3 | 1.1 |
Step S43: calculating the number of times of the single fatigue behavior n according to the formula obtained in the step S42:
step S44: calculating n standard coefficients of the single fatigue behaviors according to a data model established by a large number of fatigue behavior data samples, namely the average value of n times of the single fatigue behaviors within a certain mileage range:
as shown in the following table, the larger the numerical value is, the smaller the number of occurrences of the single fatigue behavior n in a certain mileage range is;
TABLE 2
| Eye closure | Yawning | Lowering head | Left look ahead | Telephone | Smoke extraction device |
| 0.83 | 0.45 | 0.65 | 0.24 | 0.13 | 0.16 |
Step S45: judging whether (1-n standard coefficient of the single fatigue behavior x n times of the single fatigue behavior/mileage) <0 is true, if yes, the score of the single fatigue behavior n is 0; if the single fatigue behavior is not satisfied, the single fatigue behavior n is divided into (1-the standard coefficient of the single fatigue behavior n is multiplied by the number of times of the single fatigue behavior n/mileage);
step S46: calculating a fatigue driving behavior score:
step S47: and sharing the scoring information to the client through the information sharing interface.
Step S5: judging whether the vehicle reaches the destination, if not, starting to execute the loop from the step S2; if so, stopping driving, extinguishing the transmitter, and automatically shutting down and powering off the DMS device.
In conclusion, the real-time fatigue driving behavior scoring system and method based on the DMS system are used for comprehensively evaluating the long-term behavior habits of the driver and realizing risk control on the driver. The method has the advantages of simple data acquisition process, low cost and reasonable scoring system, and has better popularization and application prospects; according to the invention, the server side shares data to the client side, so that the driver is prompted to improve safety awareness and standardize driving behaviors, thereby reducing the social traffic accident rate and improving the safety level of public traffic.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (3)
1. A real-time fatigue driving behavior scoring method based on a DMS system is characterized in that: the method comprises the following steps:
step S1: starting the vehicle, and powering on and starting the DMS device;
step S2: the DMS device collects facial feature information of a driver in real time through a camera in the vehicle running process and sends the facial feature information to the fatigue detection module;
step S3: the fatigue detection module judges whether the driver has fatigue driving behavior by calculating the information received in the step S2; if the action duration of the driver is less than or equal to the set value, judging that no fatigue driving behavior exists, and circulating from the step S2; if the action duration of the driver is longer than a set value, judging that fatigue driving behaviors exist, and sending fatigue driving behavior information to a server side through a wireless communication network by the DMS device;
the fatigue driving behaviors in the step S3 include eye closing, yawning, head lowering, right-looking-at-left, calling and smoking; setting a single fatigue behavior n as any one fatigue driving behavior of eye closure, yawning, head lowering, right expectation left, calling and smoking;
step S4: the server side calculates fatigue driving behavior scores for the information obtained in the step S3 through a fatigue driving behavior scoring module, and shares scoring information to the client side through an information sharing interface;
in the step S4, the specific steps are as follows:
step S41: setting the highest score of the fatigue driving behavior as 100 scores and the lowest score as 0 score;
step S42: calculating the risk coefficient of the single fatigue behavior n according to a data model established by a plurality of driving behavior data samples obtained in the step S3, namely the average value of the risk coefficients of the single driving behavior i under different vehicle speeds:
the risk coefficients of the single fatigue behaviors n when the vehicle speed is more than or equal to 60 and less than or equal to 80, the vehicle speed is more than 80 and less than or equal to 120 and the vehicle speed is more than 120 are respectively the risk coefficients of the single driving behavior i:
when the vehicle speed is not less than 60 and not more than 80, the eye closing risk coefficient is 0.3, the yawning risk coefficient is 0.1, the head lowering risk coefficient is 0.4, the left-hand expectation risk coefficient is 0.2, the calling risk coefficient is 0.5, and the smoking risk coefficient is 0.2;
when the vehicle speed is more than 80 and less than or equal to 120, the eye closing risk coefficient is 0.5, the yawning risk coefficient is 0.3, the head lowering risk coefficient is 0.6, the right-pan risk coefficient is 0.4, the calling risk coefficient is 0.7, and the smoking risk coefficient is 0.7;
when the vehicle speed is more than 120, the eye closing risk coefficient is 1.2, the yawning risk coefficient is 0.8, the head lowering risk coefficient is 1.3, the right-anticipated risk coefficient is 1.0, the calling risk coefficient is 1.3, and the smoking risk coefficient is 1.1;
the larger the value is, the more dangerous the fatigue behavior is in the same vehicle speed range is;
step S43: calculating the number of times of the single fatigue behavior n according to the formula obtained in the step S42:
step S44: calculating n standard coefficients of the single fatigue behaviors according to a data model established by a large number of fatigue behavior data samples, namely the average value of n times of the single fatigue behaviors within a certain mileage range:
the eye closing standard coefficient is 0.83, the yawning standard coefficient is 0.45, the head lowering standard coefficient is 0.65, the left-eye trypan standard coefficient is 0.24, the calling standard coefficient is 0.13, and the smoking standard coefficient is 0.16;
the larger the numerical value is, the fewer the occurrence times of the single fatigue behavior n in a certain mileage range is;
step S45: judging whether (1-n standard coefficient of the single fatigue behavior x n times of the single fatigue behavior/mileage) <0 is true, if yes, the score of the single fatigue behavior n is 0; if the single fatigue behavior is not satisfied, the single fatigue behavior n is divided into (1-the standard coefficient of the single fatigue behavior n is multiplied by the number of times of the single fatigue behavior n/mileage);
step S46: calculating a fatigue driving behavior score:
step S47: sharing the scoring information to the client through the information sharing interface;
step S5: judging whether the vehicle reaches the destination, if not, starting to execute the loop from the step S2; if so, stopping driving, extinguishing the transmitter, and automatically shutting down and powering off the DMS device.
2. A real-time fatigue driving behavior scoring system based on a DMS system is characterized in that: the system comprises DMS equipment, a server side and a client side; the DMS device is used for collecting driving behavior information and detecting specific fatigue driving behaviors; the server end comprises a fatigue driving scoring module, and the signal input end of the fatigue driving scoring module is connected with the signal output end of the DMS device through a wireless communication network; the fatigue driving scoring module is used for calculating the received fatigue driving behavior information in real time according to a large number of driving behavior data samples and a data model established by the large number of fatigue behavior data samples and giving a driving behavior score; the signal input end of the client is connected with the signal output end of the fatigue driving scoring module through an information sharing interface; for sharing information of driving behavior scores.
3. The DMS system based real-time fatigue driving behavior scoring system according to claim 2, wherein: the DMS device comprises a camera and a fatigue detection module, wherein the signal output end of the camera is connected with the signal input end of the fatigue detection module; the camera is used for collecting driving behavior information, and the fatigue detection module is used for detecting specific fatigue driving behaviors.
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