CN106339692B - A kind of fatigue driving state information determines method and system - Google Patents
A kind of fatigue driving state information determines method and system Download PDFInfo
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- CN106339692B CN106339692B CN201610807443.0A CN201610807443A CN106339692B CN 106339692 B CN106339692 B CN 106339692B CN 201610807443 A CN201610807443 A CN 201610807443A CN 106339692 B CN106339692 B CN 106339692B
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
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- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- G07C5/0841—Registering performance data
- G07C5/085—Registering performance data using electronic data carriers
- G07C5/0866—Registering performance data using electronic data carriers the electronic data carrier being a digital video recorder in combination with video camera
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- G08B21/02—Alarms for ensuring the safety of persons
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Abstract
The present invention disclose it is a kind of based on path deviations detection fatigue driving state information determine method and system.Method includes: the vehicle-state for acquiring for the first moment；The vehicle-state at the first moment is handled, the driving status feature at the first moment is obtained；The driving status feature at the first moment is inputted into driving habit model；Signal is reconstructed, the prediction driving condition for the second moment is obtained；Obtain the driving status feature at the second moment；Operation is carried out according to driving status feature of the prediction driving condition at the second moment to the second moment, driving behavior offset target is obtained, fatigue state is determined according to driving behavior offset target.System includes: data acquisition module, processing module, fatigue driving judgment module.This method and system improve the reliability and accuracy of fatigue detecting by machine learning and improved multi-information merging technology.
Description
Technical field
The present invention relates to safe driving fields, determine method and system more particularly to a kind of fatigue driving state information.
Background technique
Mainly there are detection (electromyogram, electroencephalogram, eye electricity based on physiological driver's parameter to the research of fatigue driving detection
Figure, sound frequency, breathing, pulse etc.), the detection (frequency of wink, pupil size etc.) based on Characteristics of drivers' behavior and be based on
The various ways methods such as the detection (travel speed, deviation, throttle and braking forces measuring) of vehicle behavior feature.Wherein, it examines
The angle for surveying lane shift is that the Road information of vehicle front is acquired by being placed in the camera of vehicle window front upper part, passes through calculation
Method detects angle when run-off-road.
The above measurement method be all directly status information is detected and is obtained whether the conclusion of fatigue driving.However,
Since the information such as personal driving habit, reaction speed, physiological status vary with each individual, cannot seek unity of standard, therefore " normal " and
Jeopardize safe " abnormal " characteristic parameter to be difficult to quantify to define, the judgement conclusion of fatigue state is inaccurate.
Summary of the invention
Method is determined the object of the present invention is to provide a kind of fatigue driving state information based on path deviations detection and is
System.
To achieve the above object, the present invention provides following schemes:
It is a kind of based on path deviations detection fatigue driving state information determine method, comprising:
Acquire the vehicle-state at the first moment；
The vehicle-state at first moment is handled, the driving status feature at the first moment is obtained；
The driving status feature at first moment is inputted into driving habit model；
Signal is reconstructed, the prediction driving condition for the second moment is obtained；Second moment is later than described
One moment；
Obtain the driving status feature at the second moment；
Operation is carried out according to driving status feature of the prediction driving condition at second moment to second moment, is obtained
Driving behavior offset target is obtained, the fatigue state of driver is determined according to the driving behavior offset target.
Optionally, before the vehicle-state at the first moment of the acquisition, further includes:
Acquire the vehicle-state before the first moment；
Vehicle-state before first moment is handled, driving status feature is obtained；
The driving status feature is modeled, driving habit model is obtained.
Optionally, the vehicle-state before the first moment of the acquisition, specifically includes:
The vehicle-state before the first moment is acquired during normal vehicle operation.
Optionally, the acquisition vehicle-state, specifically includes:
Shoot the path deviations state of lane line and barrier acquisition vehicle when driving；
Detect acceleration, deceleration and the shift state of the acceleration acquisition vehicle of vehicle；
Temperature and humidity in collecting vehicle；
Sound in collecting vehicle.
Optionally, the driving status feature specifically includes: time of time, vehicle shift lane line that vehicle drives at a constant speed
Number, the reaction time corrected behind vehicle shift lane, camera monitor the reaction that barrier rear vehicle is slowed down or hidden
Time, vehicle interior temperature, vehicle humidity, interior noise are the time within the scope of single-frequency, the machine learning by driving habit
Technology, driver encounter barrier and avoid or the average time of speed-down action.
The invention also discloses a kind of fatigue driving state information determining systems based on path deviations detection, including data
Acquisition module, processing module, fatigue driving judgment module；
The data acquisition module is for acquiring vehicle-state；
The processing module obtains driving status feature for handling the vehicle-state；By the traveling shape
State feature inputs driving habit model；
Signal is reconstructed in the fatigue driving judgment module, obtains prediction driving condition；It is driven according to the prediction
State carries out operation to the driving status feature, driving behavior offset target is obtained, according to the driving behavior offset target
Determine the fatigue state of driver.
Optionally, the data acquisition module specifically includes: camera, acceleration transducer, Temperature Humidity Sensor and wheat
Gram wind；
The camera is used to acquire the path deviations state of vehicle when driving by shooting lane line and barrier；
The acceleration transducer is used to acquire the acceleration, deceleration and offset shape of vehicle by the acceleration of detection vehicle
State；
The Temperature Humidity Sensor is for temperature and humidity in collecting vehicle；
The microphone is for sound in collecting vehicle.
Optionally, the driving status feature specifically includes: time of time, vehicle shift lane line that vehicle drives at a constant speed
Number, the reaction time corrected behind vehicle shift lane, camera monitor the reaction that barrier rear vehicle is slowed down or hidden
Time, vehicle interior temperature, vehicle humidity, interior noise are the time within the scope of single-frequency, the machine learning by driving habit
Technology, driver encounter barrier and avoid or the average time of speed-down action.
Optionally, the fatigue driving judgment module also models the driving status feature, obtains driving habit
Model.
Optionally, the fatigue driving judgment module models the driving status feature, specifically includes:
The fatigue driving judgment module models the driving status feature during normal vehicle operation.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: the present invention utilizes engineering
It practises and multi-information merging technology, the detection of study and interior external environment to the driving habit of driver is realized tired to driver
The quantization of labor driving condition substantially increases the accuracy and reliability of fatigue driving state detection.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is that the present invention is based on the fatigue driving state information that path deviations detect to determine fatigue driving in embodiment of the method
Status information determines flow chart；
Fig. 2 is that the present invention is based on the fatigue driving state information that path deviations detect to determine driving habit in embodiment of the method
The building flow chart of model；
Fig. 3 is the knot for the fatigue driving state information determining system embodiment that the embodiment of the present invention is detected based on path deviations
Composition.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Method is determined the object of the present invention is to provide a kind of fatigue driving state information based on path deviations detection and is
System.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Fig. 1 is that the present invention is based on the fatigue driving state information that path deviations detect to determine fatigue driving in embodiment of the method
Status information determines flow chart.
Referring to Fig. 1, the fatigue driving state information based on path deviations detection determines method, comprising:
Step 101, the vehicle-state at the first moment is acquired；
Step 102, the vehicle-state at first moment is handled, obtains the driving status feature at the first moment；
Step 103, the driving status feature at first moment is inputted into driving habit model；
Step 104, signal is reconstructed, obtains the prediction driving condition for the second moment；The evening at second moment
In first moment；
Step 105, the driving status feature at the second moment is obtained；
Step 106, according to the prediction driving condition at second moment to the driving status feature at second moment into
Row operation obtains driving behavior offset target, the fatigue state of driver is determined according to the driving behavior offset target.
Above-mentioned steps are completed during carrying out fatigue driving detection to vehicle.
Figure Fig. 2 is to determine to drive in embodiment of the method the present invention is based on the fatigue driving state information that path deviations detect to practise
The building flow chart of used model.As shown in Fig. 2, the process includes:
Step 201, vehicle-state is acquired；Vehicle-state is acquired in real time using multi-information merging technology, including collecting vehicle
Interior ambient condition, vehicle external environment state and vehicle running state.
Step 202, collected vehicle-state is handled, obtains driving status feature；The driving status feature
For the data for meeting autoencoder network format；
Step 203, the driving status feature is modeled, obtains driving habit model；The driving habit model
It can be realized and vehicle driving state is predicted, obtain prediction driving condition.The driving habit model is self-editing in stack
It is constructed in code network.
Above-mentioned steps are to pass through machine learning during vehicle keeps normally travel in the state that driver is awake
It completes, belongs to the training stage of driving model.
Training stage of the invention is to construct driving habit model by machine learning method, to the driving habit of driver
It records and learns in real time, and interior external environment is added in the factor to driver tired driving detection.Use of the invention
Stage is to carry out various real time information detections to the fatigue driving state of driver by the technology of multi-information fusion, will be examined
It can be realized the quantization to driver tired driving state in the real time information input model of survey.This fatigue driving detection side
Method is compared by real time running state and normal driving status, can more accurately quantify the driving shape of driver
State substantially increases the reliability of fatigue driving state monitoring.
Optionally, the acquisition vehicle-state, specifically includes:
Shoot the path deviations state of lane line and barrier acquisition vehicle when driving；
Detect acceleration, deceleration and the shift state of the acceleration acquisition vehicle of vehicle；
Temperature and humidity in collecting vehicle；
Sound in collecting vehicle.
Optionally, described that vehicle-state is handled, driving status feature is obtained, is specifically included:
Video image is pre-processed, greyscale transformation, Threshold segmentation (change of both images or gradient image are then carried out
The two), it realizes and recognition and tracking is carried out to road, and obtain obstacle information；It, can by the analysis to road and barrier
To obtain the shape and extending direction of road, the relative position of position and other vehicle of this vehicle in lane；
The acceleration, deceleration and shift state of vehicle are handled, the velocity information of vehicle is obtained；
According to video image and velocity information, calculate the time that vehicle drives at a constant speed, the number of vehicle shift lane line,
Reaction time for being corrected behind vehicle shift lane, camera monitor barrier rear vehicle slowed down or hide reaction when
Between；
Collected temperature, humidity information and interior acoustic information are handled, it is wet that vehicle interior temperature, car is calculated
Degree and interior noise are the time within the scope of single-frequency；
The information in driving habit model is extracted in real time, calculates the machine learning techniques by driving habit, and driver meets
The average time of evacuation or speed-down action is carried out to barrier.
Optionally, the training stage, the mileage travelled of training stage will guarantee at 200 kilometers or more before service stage.
Optionally, carrying out modeling to the driving status feature is carried out by stack autoencoder network.
Optionally, the driving status feature include: vehicle drive at a constant speed time T1, vehicle shift lane line number
Reaction time T2, the camera corrected behind R1, vehicle shift lane monitor that barrier rear vehicle is slowed down or hidden anti-
T3, vehicle interior temperature N1, vehicle humidity N2, interior noise are time T4 within the scope of single-frequency, pass through driving habit between seasonable
Machine learning techniques, driver encounters barrier and avoids or the average time T5 of speed-down action.
The calculation formula of the driving behavior offset target S are as follows:
S=a × T1 × R1+b × T2+c × T3+d × N1+f × N2+g × T4+h × T5
Driving condition is divided into three kinds of states, respectively normal driving state, fatigue driving state and between normal driving shape
Improper driving condition between state and fatigue driving state.Driving behavior offset target is set into two benchmark: normal driving
Benchmark and fatigue driving benchmark.Normal driving benchmark is between normal driving state and improper driving condition, fatigue driving
Benchmark is between improper driving condition and fatigue driving state.When the real-time calculated result of driving behavior offset target S is small
When normal driving benchmark, judge driving condition for normal driving state；When the real-time calculated result of driving behavior offset target S
When greater than normal driving benchmark and being less than fatigue driving benchmark, judge driving condition for improper driving condition；Work as driving behavior
When the real-time calculated result of offset target S is greater than fatigue driving benchmark, judge driving condition for fatigue driving state.
Optionally, when judging driving condition for improper driving condition and fatigue driving state, system can issue in real time
Driver is reminded in alarm.
Optionally, the driving habit of different people is not identical, and the result after training is also different.The driving habit
Model has the function of storing multiple and different training results.Different people use when fatigue driving state detection corresponding
Driving habit model.
Fig. 3 is the structure chart of the fatigue driving state information determining system embodiment detected the present invention is based on path deviations.
Referring to Fig. 3, the invention also discloses a kind of fatigue driving state information determining system based on path deviations detection,
Including data acquisition module 301, processing module 302, fatigue driving judgment module 303；
The data acquisition module 301 is for acquiring vehicle-state；
The processing module 302 obtains driving status feature for handling the vehicle-state；By the traveling
State feature inputs driving habit model；
Signal is reconstructed in the fatigue driving judgment module 303, obtains prediction driving condition；It is driven according to the prediction
It sails state and operation is carried out to the driving status feature, obtain driving behavior offset target, referred to according to driving behavior offset
Mark the fatigue state for determining driver.
Optionally, the data acquisition module 301 specifically includes: camera 304, acceleration transducer 305, temperature and humidity pass
Sensor 306 and microphone 307；
The camera 304 is used to acquire the path deviations state of vehicle when driving by shooting lane line and barrier；
The acceleration transducer 305 is used to acquire acceleration, deceleration and the offset of vehicle by the acceleration of detection vehicle
State；
The Temperature Humidity Sensor 306 is for temperature and humidity in collecting vehicle；
The microphone 307 is for sound in collecting vehicle.
Optionally, the driving status feature specifically includes: time T1 that vehicle drives at a constant speed, vehicle shift lane line
Reaction time T2, the camera corrected behind number R1, vehicle shift lane monitor that barrier rear vehicle is slowed down or hidden
Reaction time T3, vehicle interior temperature N1, vehicle humidity N2, interior noise be time T4 within the scope of single-frequency, pass through driving
The machine learning techniques of habit, driver encounter barrier and avoid or the average time T5 of speed-down action.
Optionally, the fatigue driving judgment module 303 also models the driving status feature, obtains driving and practise
Used model.
Optionally, the fatigue driving judgment module 303 models the driving status feature, specifically includes:
The fatigue driving judgment module 303 builds the driving status feature during normal vehicle operation
Mould, the fatigue driving judgment module 303 models the driving status feature and to complete before fatigue detecting.Institute
Stating modeling process will guarantee VMT Vehicle-Miles of Travel at 200 kilometers or more.
Optionally, the calculation formula of the driving behavior offset target S are as follows:
S=a × T1 × R1+b × T2+c × T3+d × N1+f × N2+g × T4+h × T5
Optionally, a kind of fatigue driving state information determining system based on path deviations detection further includes alarm mould
Block 308 can be sounded an alarm when fatigue driving judgment module 303 judges that driving condition is abnormal.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (8)
1. a kind of fatigue driving state information based on path deviations detection determines method characterized by comprising
Acquire the vehicle-state at the first moment；
The vehicle-state at first moment is handled, the driving status feature at the first moment is obtained；
The driving status feature at first moment is inputted into driving habit model；
Signal is reconstructed, the prediction driving condition for the second moment is obtained；When second moment is later than described first
It carves；
Obtain the driving status feature at the second moment；
Operation is carried out according to driving status feature of the prediction driving condition at second moment to second moment, is driven
Behavior offset target is sailed, the fatigue state of driver is determined according to the driving behavior offset target；
The driving status feature specifically includes: number, the vehicle shift of time, vehicle shift lane line that vehicle drives at a constant speed
Reaction time for correcting behind lane, camera monitor the reaction time that barrier rear vehicle slowed down or hidden, interior temperature
Degree, vehicle humidity, interior noise are the time within the scope of single-frequency, the machine learning techniques by driving habit, driver
Encounter the average time that barrier carries out evacuation or speed-down action.
2. a kind of fatigue driving state information based on path deviations detection according to claim 1 determines method, special
Sign is, before the vehicle-state at the first moment of the acquisition, further includes:
Acquire the vehicle-state before the first moment；
Vehicle-state before first moment is handled, driving status feature is obtained；
The driving status feature is modeled, driving habit model is obtained.
3. a kind of fatigue driving state information based on path deviations detection according to claim 2 determines method, special
Vehicle-state before sign is the first moment of the acquisition specifically includes:
The vehicle-state before the first moment is acquired during normal vehicle operation.
4. a kind of fatigue driving state information based on path deviations detection according to claim 1 or 2 determines method,
It is characterized in that, the acquisition vehicle-state specifically includes:
Shoot the path deviations state of lane line and barrier acquisition vehicle when driving；
Detect acceleration, deceleration and the shift state of the acceleration acquisition vehicle of vehicle；
Temperature and humidity in collecting vehicle；
Sound in collecting vehicle.
5. a kind of fatigue driving state information determining system based on path deviations detection, which is characterized in that acquired including data
Module, processing module, fatigue driving judgment module；
The data acquisition module is used to acquire the vehicle-state at the first moment；
For the processing module for handling the vehicle-state at first moment, the driving status for obtaining for the first moment is special
Sign；The driving status feature at first moment is inputted into driving habit model；
Signal is reconstructed in the fatigue driving judgment module, obtains the prediction driving condition for the second moment；Obtain the
The driving status feature at two moment；It is special according to driving status of the prediction driving condition at second moment to second moment
Sign carries out operation, obtains driving behavior offset target, the fatigue state of driver is determined according to the driving behavior offset target；
Second moment is later than first moment；
The driving status feature specifically includes: number, the vehicle shift of time, vehicle shift lane line that vehicle drives at a constant speed
Reaction time for correcting behind lane, camera monitor the reaction time that barrier rear vehicle slowed down or hidden, interior temperature
Degree, vehicle humidity, interior noise are the time within the scope of single-frequency, the machine learning techniques by driving habit, driver
Encounter the average time that barrier carries out evacuation or speed-down action.
6. a kind of fatigue driving state information determining system based on path deviations detection according to claim 5, special
Sign is that the data acquisition module specifically includes: camera, acceleration transducer, Temperature Humidity Sensor and microphone；
The camera is used to acquire the path deviations state of vehicle when driving by shooting lane line and barrier；
The acceleration transducer is used to acquire acceleration, deceleration and the shift state of vehicle by the acceleration of detection vehicle；
The Temperature Humidity Sensor is for temperature and humidity in collecting vehicle；
The microphone is for sound in collecting vehicle.
7. a kind of fatigue driving state information determining system based on path deviations detection according to claim 5, special
Sign is that the fatigue driving judgment module also models the driving status feature, obtains driving habit model.
8. a kind of fatigue driving state information determining system based on path deviations detection according to claim 7, special
Sign is that the fatigue driving judgment module models the driving status feature, specifically includes:
The fatigue driving judgment module models the driving status feature during normal vehicle operation.
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| CN107958601A (en) * | 2017-11-22 | 2018-04-24 | 华南理工大学 | A kind of fatigue driving detecting system and method |
| CN108482380B (en) * | 2018-03-06 | 2019-09-06 | 知行汽车科技（苏州）有限公司 | The driving monitoring system of automatic adjusument sample frequency |
| CN109299784B (en) * | 2018-08-01 | 2022-02-18 | 广州大学 | Auxiliary driving method and device based on neural network and readable storage medium |
| CN110638473A (en) * | 2019-09-10 | 2020-01-03 | 中国平安财产保险股份有限公司 | Method, device, electronic device and storage medium for determining fatigue driving |
| CN110733508A (en) * | 2019-10-29 | 2020-01-31 | 深圳联安通达科技有限公司 | fatigue driving detection method and device |
| CN112949015A (en) * | 2019-12-10 | 2021-06-11 | 奥迪股份公司 | Modeling apparatus, assistance system, vehicle, method, and storage medium |
| CN112687076A (en) * | 2020-12-15 | 2021-04-20 | 南通路远科技信息有限公司 | Method and device for preventing fatigue driving and bracelet |
| CN113460060B (en) * | 2021-08-20 | 2022-12-09 | 武汉霖汐科技有限公司 | Driver fatigue degree evaluation system, control method, and storage medium |
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