CN101872171A - Driver fatigue state recognition method and system based on information fusion - Google Patents

Driver fatigue state recognition method and system based on information fusion Download PDF

Info

Publication number
CN101872171A
CN101872171A CN200910082566A CN200910082566A CN101872171A CN 101872171 A CN101872171 A CN 101872171A CN 200910082566 A CN200910082566 A CN 200910082566A CN 200910082566 A CN200910082566 A CN 200910082566A CN 101872171 A CN101872171 A CN 101872171A
Authority
CN
China
Prior art keywords
confidence level
tired
driver
parameter
bearing circle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN200910082566A
Other languages
Chinese (zh)
Other versions
CN101872171B (en
Inventor
宋正河
朱忠祥
谢斌
毛恩荣
张俊
成波
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Agricultural University
Original Assignee
China Agricultural University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Agricultural University filed Critical China Agricultural University
Priority to CN2009100825662A priority Critical patent/CN101872171B/en
Publication of CN101872171A publication Critical patent/CN101872171A/en
Application granted granted Critical
Publication of CN101872171B publication Critical patent/CN101872171B/en
Expired - Fee Related legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

The invention discloses driver fatigue state recognition method and system based on information fusion, wherein the method comprises the steps of: acquiring eye state information of a driver, operating state information of a steering wheel and track driving state information of a vehicle; processing the eye state information of the driver, the operating state information of the steering wheel and the track driving state information of the vehicle to obtain fatigue expression information of the driver; and carrying out weighting fusion processing on the fatigue expression information to obtain a fatigue membership degree corresponding to a drive fatigue grade, and obtaining the fatigue degree of the driver according to the fatigue membership degree. The invention improves the reliability and the accuracy of monitoring the driver fatigue state, can reduce the quantity of traffic accidents caused by the fatigue driving of the driver, ensures the road traffic safety, and has strong practicability because the acquisition of multi-source information does not cause the interference on the driving of the driver.

Description

Driver fatigue state recognition method and system based on information fusion
Technical field
The present invention relates to traffic engineering, particularly a kind of driver fatigue state recognition method and system based on information fusion.
Background technology
Rapid increase along with expanding economy and automobile quantity, the incidence of road traffic accident also increases gradually, and driver tired driving is a key factor that causes road traffic accident, therefore, the fatigue conditions of scientificlly and effectively monitoring the driver has great significance to guaranteeing traffic safety.
Monitoring to driver fatigue state in the prior art mainly is to adopt single source information monitoring, for example, judges by monitoring driver physiological situation whether the driver is in fatigue driving state.For a long time, EEG measuring or electrocardio measurement are described as " golden standard " of monitoring driver fatigue state.The ripple of the different frequency range in the brain wave has corresponding variation at the different times of driver fatigue, when tired in the early stage, slow wave delta, alpha can change, and the belta ripple can increase when mid-term was tired, and delta, theta and alpha ripple have increase by a relatively large margin when extremely tired.The radio-frequency component HF and the low-frequency component LF of the HRV in the electrocardio index can change when fatigue.
But, the method of the single source information monitoring of this employing driver fatigue state remains in following technological deficiency in the prior art: on the one hand, there is the not high defective of accuracy, stability and reliability that information is single, discern in single source information, if should list source information inaccurately will make above-mentioned driver fatigue state recognition method lose efficacy, then be difficult to monitor the driving fatigue situation that obtains the driver, thereby make driver's driving have very big potential safety hazard; On the other hand, single source information measuring equipment (as the equipment of measuring the brain electricity or measure cardiac electrical equipment) can produce bigger interference to driver's driving, is not suitable for the monitoring of actual driver fatigue state, poor practicability.
Summary of the invention
The purpose of this invention is to provide a kind of driver fatigue state recognition method and system, effectively overcome prior art and be not suitable for that practical application, information are single, the accuracy and the not high defective of reliability of identification based on information fusion.
The invention provides a kind of driver fatigue state recognition method, comprising based on information fusion:
Step 1, collection driver's eye status information, bearing circle operation state information and track of vehicle transport condition information;
Step 2, the eye status information to described driver, bearing circle operation state information and track of vehicle transport condition information are handled, obtain driver's tired performance information, described tired performance information comprises that on average degree of opening, the longest time of closing one's eyes, bearing circle keep motionless time scale, angle variable quantity, the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that described transversal displacement is in the safe distance scope that bearing circle rotates suddenly to interior eyes closed degree of unit interval above 80% shared time scale, eyes;
Step 3, described tired performance information is weighted fusion treatment obtains the tired degree of membership corresponding, and draw driver's tired grade according to described tired degree of membership with the driver fatigue grade.
The invention provides a kind of driver fatigue state recognition system, comprising based on information fusion:
Acquisition module is used to gather driver's eye status information, bearing circle operation state information and track of vehicle transport condition information;
Processing module, the eye status information, bearing circle operation state information and the track of vehicle transport condition information that are used for described driver are handled, obtain driver's tired performance information, described tired performance information comprises that on average degree of opening, the longest time of closing one's eyes, bearing circle keep motionless time scale, angle variable quantity, the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that described transversal displacement is in the safe distance scope that bearing circle rotates suddenly to interior eyes closed degree of unit interval above 80% shared time scale, eyes;
Fusion Module is used for that described tired performance information is weighted fusion treatment and obtains the tired degree of membership corresponding with the driver fatigue grade, and draws driver's tired grade according to described tired degree of membership.
The invention provides a kind of driver fatigue state recognition method and system based on information fusion, at first gather driver's eye status information, bearing circle operation state information and track of vehicle transport condition information, afterwards to described driver's eye status information, bearing circle operation state information and track of vehicle transport condition information are handled, obtain driver's tired performance information, at last described tired performance information is weighted fusion treatment and obtains the tired degree of membership corresponding, and draw driver's tired grade according to described tired degree of membership with the driver fatigue grade.Because the present invention integrates the driving fatigue situation of estimating the driver by the multi-source information to reflection driver driving fatigue situation, therefore the reliability and the accuracy of driver fatigue state identification have been improved, can effectively reduce the traffic hazard quantity that driver tired driving causes, guarantee traffic safety.Compared with prior art, the present invention is based on the driver fatigue state recognition method of information fusion and the enforcement of system and can not cause interference driver's driving, have practical, be convenient to advantages such as enforcement, be with a wide range of applications.
Description of drawings
Fig. 1 is the process flow diagram that the present invention is based on driver fatigue state recognition method first embodiment of information fusion;
Fig. 2 is the process flow diagram that the present invention is based on driver fatigue state recognition method second embodiment of information fusion;
Fig. 3 is the structural representation that the present invention is based on driver fatigue state recognition system first embodiment of information fusion;
Fig. 4 is the structural representation that the present invention is based on driver fatigue state recognition system second embodiment of information fusion.
Embodiment
The inventor finds in realizing process of the present invention, driver's fatigue conditions can be reflected in multiple aspect, physiological status, manipulative behavior and vehicle-state and driver fatigue state all have certain correlativity, such as, the eyelid movement in the facial state, head movement, eye sight line and facial expression all obvious variation can occur when fatigue state takes place the driver; Aspect trailer reversing, the lateral excursion of pedaling the power of stepping on and track of vehicle of the grip of the time domain of steering wheel angle and frequency-domain index, bearing circle, angular velocity, the speed of a motor vehicle, pedal etc. all have certain correlativity with driver's fatigue state.Based on this, the external symptom that the present invention grasps the key link when monitoring driver fatigue state, the information of eye state, bearing circle manipulation and track of vehicle three aspects is merged, be applied in the driver fatigue state monitoring, integrate the fatigue conditions of estimating the driver, can when the supplemental characteristic fiduciary level is lower, reject it, utilize the higher information of other fiduciary levels, thereby can remedy the deficiency of single source information monitoring, improve the accuracy rate stability of driver fatigue state monitoring.
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Fig. 1 is the process flow diagram that the present invention is based on driver fatigue state recognition method first embodiment of information fusion, and as shown in Figure 1, present embodiment comprises:
Step 101, collection driver's eye status information, bearing circle operation state information and track of vehicle transport condition information.
The eye status information of collection driver in this step can adopt the multiple image acquisition mode of prior art, for example photography or shooting.Photography is meant the eyes image of gathering a driver every a setting-up time (as 1 minute), after shooting is meant continuous acquisition driver's eyes image, extracts rest image by a setting-up time (as 1 minute).The bearing circle operation state information of gathering the driver can adopt the multiple sensors mode of prior art, and scrambler or angular transducer for example are set on bearing circle.Collection vehicle track transport condition information can adopt the multiple metering system of prior art, for example adopts the lateral excursion distance of position-measurement device measuring vehicle apart from center line of road.Driver's eye parameter in the present embodiment can be to carry out statistical treatment by the camera collection eyes image to obtain; The bearing circle parameter can be that direction of passage dish scrambler is gathered instantaneous direction dish corner and carried out statistical treatment and obtain; The track of vehicle parameter can be to obtain apart from the process statistical treatment by the lateral excursion of Flame Image Process collection vehicle track apart from center line of road.
Step 102, the eye status information to the driver, bearing circle operation state information and track of vehicle transport condition information are handled, and obtain driver's tired performance information.
The eye status information, bearing circle operation state information and the track of vehicle transport condition information that collect are carried out statistical treatment, obtain driver's tired performance information.This fatigue performance information comprises that on average degree of opening, the longest time of closing one's eyes, bearing circle keep motionless time scale, angle variable quantity, the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that described transversal displacement is in the safe distance scope that bearing circle rotates suddenly to interior eyes closed degree of unit interval above 80% shared time scale, eyes.
Step 103, tired performance information is weighted fusion treatment obtains the tired degree of membership corresponding, and draw driver's tired grade according to tired degree of membership with the driver fatigue grade.
Present embodiment integrates the driving fatigue situation of estimating the driver by the multi-source information to reflection driver driving fatigue situation, driver fatigue state monitoring reliability and accuracy have been improved, can reduce the traffic hazard quantity that driver tired driving causes, guarantee traffic safety; And the driving of obtaining not the driver of this multi-source information causes interference, and is practical.
Fig. 2 is the process flow diagram that the present invention is based on driver fatigue state recognition method second embodiment of information fusion, and as shown in Figure 2, present embodiment comprises:
Step 201, obtain eye status information, bearing circle operation state information and the track of vehicle transport condition information of reflection driver fatigue state, and obtain tired performance information through handling;
In the present embodiment technical scheme, at first adopt the method for expert's video scoring, video recording to the facial state of driver in the whole driving procedure is cut into per 1 minute independent video, and several experts are carried out the fatigue scoring that effective statistical treatment obtains whole driving procedure to per 1 minute scoring.Tired grade is divided into 4 grades here, promptly clear-headed, slight tired, moderate is tired and severe is tired.With above-mentioned expert's scoring is standard, determines and obtain the information type of needed reflection driver fatigue state, comprises eye status information, bearing circle operation state information and track of vehicle transport condition information.On this basis, use statistical methods such as correlation analysis, variance analysis to carry out statistical treatment by driver's eye status information, bearing circle operation state information and the track of vehicle transport condition information of sensor acquisition, extract with driver fatigue and estimate the strongest tired performance information of correlativity.For example, driver's eye parameter is to carry out statistical treatment by the single frames degree of closing one's eyes that the eyes image processing of gathering is obtained to obtain, in the unit interval, this driver's eye parameter comprises on average degree of opening (can represent with ratio value) of the PERCLOS80 the strongest with tired correlativity (the unit interval eyes closed surpasses 80% shared time scale), the longest closing one's eyes the time (closed promptly being judged as above 80% closed one's eyes) and eyes; The bearing circle parameter is to carry out statistical treatment by the instantaneous direction dish corner to the collection of bearing circle scrambler to obtain, and this bearing circle parameter comprises that described bearing circle keeps motionless time scale and the unexpected angle variable quantity that rotates of bearing circle in the unit interval; The track of vehicle parameter be track of vehicle by vehicle being exercised sensor acquisition apart from the lateral excursion of center line of road apart from obtaining through statistical treatment, this track of vehicle parameter comprises the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that transversal displacement is in the safe distance scope, and this safe distance is that transversal displacement is less than 100mm/s.
Step 202, the tired performance information input artificial neural network that will obtain obtain the elementary tired degree of membership in the different measuring cycle in corresponding different parameters space.
Eye state parameter in the tired performance information, bearing circle operation state parameter and track of vehicle transport condition parameter are imported the neural network model that trains respectively, obtain elementary evaluation result, promptly belong to the elementary degree of membership of each tired grade.Present embodiment can adopt three layers of BP feedforward network, the following formula of the model formation of these three layers of BP feedforward networks (1):
y k＝f 2(ω (2)，f 1(ω (1)，x k，b (1)，b (2)) (1)
Wherein, f 1(), f 2() is respectively neural network hidden layer and the neuronic transport function of output layer; ω (1), ω (2)Be respectively and connect input layer and the neuronic weight vector of hidden layer, hidden layer and output layer; b (1), b (2)Be respectively the neuronic offset vector of hidden layer and output layer; x kBe the parameter in different parameters space, such as eye state parameter, bearing circle operation state parameter or track of vehicle transport condition parameter, y kElementary degree of membership for corresponding each tired grade.Concrete output layer y kBe expressed as follows shown in the table 1:
The tired grade statement of table 1
Figure B2009100825662D0000061
For example, as above first in the table 1 gone, 0.9,0.1,0.1,0.1 identify clear-headed to tired grade, slight tired, moderate is tired, severe is tired degree of membership respectively, when the clear-headed degree of membership of tired grade 0.9 was the highest, this output tired grade of correspondence as a result was " regaining consciousness ".
Neural network in the present embodiment is classified according to driver's physiological characteristic and operating feature in advance, utilize dissimilar drivers' training sample that the BP neural network of setting up is carried out off-line training, weight and biasing are stored into notepad, when carrying out real-time judge, call in stored parameters, according to the thought of template matches, use corresponding driver's type training parameter to carry out online judgement; So parameter comprises the dissimilar parameters of mating different drivers respectively in this neural network model.
Step 203, will carry out multicycle D_S evidence theory through the tired grade degree of membership in different measuring cycle of neural network output and merge, draw the basic confidence level in corresponding different parameters space.
The measurement of sensor comprises a plurality of measuring periods, the degree of membership in different measuring cycle is carried out single-sensor multicycle D_S evidence theory merge, at first the elementary tired degree of membership process normalization of a plurality of measuring periods that neural network is exported is converted into the basic reliability distribution parameter as the important evidence of D_S evidence theory; Utilize following formula (2) that the basic reliability distribution parameter of a plurality of measuring periods is merged then, just can draw the basic confidence level in different parameters space.This fusion formula of D_S evidence theory that merges a plurality of measuring periods is as follows:
M ( A i ) = c - 1 Σ I A j = A i Π 1 ≤ s ≤ n M s ( A i ) , i = 1,2 , . . . , k - - - ( 2 )
Wherein
c = 1 - Σ I A i = Φ Π 1 ≤ s ≤ n M s ( A i ) = Σ I A = φ Π 1 ≤ s ≤ n M s ( A i )
To be sensor distribute formula according to the cumulative measurement of n measuring period to the fusion posteriority confidence level of k proposition to following formula, wherein, establishes M 1(A i), M 2(A i) ... M n(A i) be sensor in each measuring period, distribute the posteriority confidence level distribution that obtains by continuous target situation and changeless priori confidence level, i=1,2 ..., k, M j(A i) be illustrated in j cycle proposition A iConfidence level distribute.Can draw the basic confidence level in different parameters space by above-mentioned formula 2, i.e. eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level.
The fusion that neural network in process step 202～203 and D_S evidence theory have been finished the subsystem irrespective of size continues execution in step 204～step 205, carries out system-level fusion.
Step 204, definite fusion weight that tired performance information is weighted fusion.
Eye state parameter, bearing circle operation state parameter and track of vehicle transport condition parameter and driver's fatigue state all has certain correlativity, but relevant degree is different, therefore also should be different with the corresponding fusion weight of above-mentioned three class parameters.This fusion weight is to determine by the parameter confidence level that obtains in the system-level fusion of Chang Quanchong zygote.
At first, Chang Quanchong utilizes that the normalized result of class separability index determines in the pattern-recognition, and the class separability of big more this index of explanation of class separability index in the pattern-recognition is good more.Utilize the parameter in different parameters space that the separating capacity of fatigue is determined Chang Quanchong at system-level level.This Chang Quanchong represents that with J it determines that method is to utilize following formula (3):
J = Σ i M Σ j ≠ i M ( μ i - μ j ) 2 σ i 2 + σ j 2 , - - - ( 3 )
Wherein, but J represents the classification capacity of parameter, but represents the classification capacity of eye state parameter such as the Chang Quanchong J of eye state parameter.μ i, μ jThe average of representing adjacent class respectively, σ i, σ jBe the variance of adjacent class, M represents classification number altogether.By the J value is carried out normalized as Chang Quanchong.Adjacent class is meant adjacent tired grade, is two adjacent classes such as slight fatigue and moderate fatigue.Calculate the average or the variance of parameter corresponding with adjacent class in three parameter spaces respectively, can obtain μ i, μ j and σ i, σ j.Tired grade in the present embodiment is 4 grades, so M equals 4.Can obtain the Chang Quanchong separately of three types of corresponding parameters, i.e. eye state parameter Chang Quanchong, bearing circle operation state parameter Chang Quanchong and track of vehicle transport condition parameter Chang Quanchong by above-mentioned formula 3.
Then, determine to merge weight by the parameter confidence level that obtains in the system-level fusion of Chang Quanchong zygote.Can preestablish one with reference to the confidence level upper limit with reference to the confidence level lower limit, will be set at 0.8 with reference to the confidence level upper limit in the present embodiment, will be 0.5 with reference to the confidence level lower limit set.The parameter and the parameter confidence level of each parameter space are reported to fusion center together.When parameter confidence level＜0.5, merging weight is zero, does not promptly merge this parameter space, and the parameter of this parameter space is rejected; When parameter confidence level＞0.8, do not become power this moment, use Chang Quanchong to merge; When the parameter confidence level is between the 0.5-0.8, calculate the fusion weight according to formula (4)-(9).This moment the parameter confidence level with merge the linear proportional relationship of weight, Chang Quan likens to and is scale-up factor, the relation between the weight as shown in the formula:
W 1 W 3 = w 13 × r 1 r 3 - - - ( 4 )
W 2 W 3 = w 23 × r 2 r 3 - - - ( 5 )
W 1+W 2+W 3＝1 (6)
W 1 = r 1 w 12 r 1 w 12 + r 2 + r 3 w 32 - - - ( 7 )
W 2 = r 2 r 1 w 12 + r 2 + w 32 r 3 - - - ( 8 )
W 3 = r 3 r 1 w 13 + r 2 w 23 + r 3 - - - ( 9 )
Wherein, W 1, W 2, W 3Represent that respectively the eye state parameter merges weight, bearing circle operation state parameter merges weight and track of vehicle transport condition parameter merges weight; r 1, r 2, r 3Represent eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level respectively; w 12, w 23, w 13Represent the ratio, eye state parameter Chang Quanchong of the ratio, bearing circle operation state parameter Chang Quanchong of eye state parameter Chang Quanchong and bearing circle operation state parameter Chang Quanchong and track of vehicle transport condition parameter Chang Quanchong and the ratio of track of vehicle transport condition parameter Chang Quanchong respectively.
Step 205, self D_S that tired performance information is carried out variable weight merge, and obtain the tired degree of membership corresponding with the driver fatigue grade, and draw driver's tired grade according to described tired degree of membership.
Self D_S that eye state parameter, bearing circle operation state parameter and track of vehicle transport condition parameter is carried out weight according to the fusion weight of the three class different parameters that obtain in the step 204 merges, promptly merge at the system-level multisensor D_S evidence theory that carries out the different parameters space, obtain the tired degree of membership corresponding, and draw driver's tired grade according to this fatigue degree of membership with the driver fatigue grade.Formula (2) is still adopted in multisensor D_S evidence theory fusion in this step, just the data fusion of a plurality of measuring periods is changed into the data fusion of a plurality of parameter spaces.
Present embodiment integrates the driving fatigue situation of estimating the driver by the multi-source information to reflection driver driving fatigue situation, tripartite surface information can more comprehensively be estimated fatigue, if losing efficacy, one-sided information can not make evaluation system paralyse yet, still can obtain fatigue state, thereby driver fatigue state monitoring reliability and accuracy have been improved, can reduce the traffic hazard quantity that driver tired driving causes, guarantee traffic safety; And the driving of obtaining not the driver of this multi-source information causes interference, and is practical.
Fig. 3 is the structural representation that the present invention is based on driver fatigue state recognition system first embodiment of information fusion, as shown in Figure 3, present embodiment driver fatigue monitoring system can comprise acquisition module 301, processing module 302 and Fusion Module 303, wherein, acquisition module 301 is used to gather driver's eye status information, bearing circle operation state information and track of vehicle transport condition information; Eye status information, bearing circle operation state information and track of vehicle transport condition information that processing module 302 is used for described driver are handled, and obtain driver's tired performance information; Fusion Module 303 is used for that described tired performance information is weighted fusion treatment and obtains the tired degree of membership corresponding with the driver fatigue grade, and draws driver's tired grade according to described tired degree of membership.
Concrete, the eye status information that acquisition module 301 is gathered the reflection driver fatigue state, bearing circle operation state information and track of vehicle transport condition information, processing module 302 is carried out analyzing and processing with the above-mentioned information that acquisition module 301 obtains, obtain driver's tired performance information, this fatigue performance information comprises that the eyes closed degree surpasses 80% shared time scale in the unit interval, on average close one's eyes the time, the longest time of closing one's eyes, bearing circle keeps motionless time scale, the angle variable quantity that bearing circle rotates suddenly, the transversal displacement standard deviation of the snakelike transport condition of vehicle and described transversal displacement are in the time scale of safe distance scope.Fusion Module 303 can obtain the comprehensive tired degree of membership corresponding with tired grade after above-mentioned tired performance information is weighted fusion treatment, and draws driver's tired grade according to this comprehensive tired degree of membership.
Present embodiment driver fatigue monitoring system integrates the driving fatigue situation of estimating the driver by the multi-source information to reflection driver driving fatigue situation, driver fatigue state monitoring reliability and accuracy have been improved, can reduce the traffic hazard quantity that driver tired driving causes, guarantee traffic safety; And the driving of obtaining not the driver of this multi-source information causes interference, and is practical.
Fig. 4 is the structural representation that the present invention is based on driver fatigue state recognition system second embodiment of information fusion, as shown in Figure 4, present embodiment is on the basis of first embodiment, Fusion Module 303 can comprise subsystem irrespective of size integrated unit 3031 and system-level integrated unit 3032, wherein, subsystem irrespective of size integrated unit 3031 is used for the eye status information with described tired performance information, bearing circle operation state information and track of vehicle transport condition information are weighted fusion treatment respectively, obtain eye status information confidence level, bearing circle operation state information credibility and track of vehicle transport condition information credibility; System-level integrated unit 3032 is according to described eye status information confidence level, bearing circle operation state information credibility and track of vehicle transport condition information credibility determine that respectively the eye status information merges weight, bearing circle operation state information fusion weight and track of vehicle transport condition information fusion weight, and according to described eye status information fusion weight, bearing circle operation state information fusion weight and track of vehicle transport condition information fusion weight are to described eye status information, bearing circle operation state information and track of vehicle transport condition information are weighted fusion treatment, obtain driver's tired grade.
Further, subsystem irrespective of size integrated unit 3031 may further include first subelement, second subelement, wherein, first subelement is used for described eye status information, bearing circle operation state information and track of vehicle transport condition information are weighted the elementary tired degree of membership that fusion obtains corresponding driver fatigue grade according to the neural network parameter qualified relation, and described elementary tired degree of membership comprises corresponding a plurality of described eye status informations, bearing circle operation state information and the elementary tired degree of membership in track of vehicle transport condition information measurement cycle; Second subelement is used for that described elementary tired degree of membership is carried out normalized and is converted into the basic reliability distribution parameter, and the described a plurality of described eye status informations of correspondence, bearing circle operation state information and the basic reliability distribution parameter in track of vehicle transport condition information measurement cycle are carried out the D_S evidence theory merge, obtain eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level respectively.System-level integrated unit 3032 comprises that weight determines subelement, is used for determining respectively that according to described eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level the eye state parameter merges weight, bearing circle operation state parameter merges weight and track of vehicle transport condition parameter merges weight.
Concrete, on the basis of first embodiment, the process that 303 pairs of above-mentioned tired performance informations of Fusion Module are weighted fusion treatment can be divided into the subsystem irrespective of size and merge and two steps of system-level fusion, and neural network merges and the D_S evidence theory merges two steps and the fusion of subsystem irrespective of size can be divided into.The artificial neural network that first subelement in the subsystem irrespective of size integrated unit 3031 has trained eye state parameter, bearing circle operation state parameter and the input of track of vehicle transport condition parameter earlier, can obtain the elementary tired degree of membership that belongs to the different fatigue grade of these three kinds of parameter space correspondences respectively, and be the elementary tired degree of membership in different measuring cycle.Second subelement carries out the basic reliability distribution parameter that normalized is converted into the corresponding different measuring cycle with above-mentioned elementary tired degree of membership, utilize the D_S evidence theory that the basic reliability distribution parameter of single space many measuring periods is merged then, just can obtain the basic confidence level of every class parameter space, i.e. eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level.When 3032 pairs three class parameters of system-level integrated unit are weighted fusion, weight wherein determines that subelement needs to determine earlier the fusion weight of three class parameters, this fusion weight is to change according to the basic confidence level that second subelement draws, be to determine comprehensively that in conjunction with the confidence level of all kinds of parameters concrete definite method can be referring to method embodiment by Chang Quanchong.After the fusion weight was determined, 3032 of system-level integrated units can merge weight according to this three class parameters are weighted fusion and the fusion of self variable weight D_S evidence theory, obtain tired grade result.
Present embodiment driver fatigue monitoring system integrates the driving fatigue situation of estimating the driver by the multi-source information to reflection driver driving fatigue situation, driver fatigue state monitoring reliability and accuracy have been improved, can reduce the traffic hazard quantity that driver tired driving causes, guarantee traffic safety; And the driving of obtaining not the driver of this multi-source information causes interference, and is practical.
It should be noted that at last: above embodiment is only in order to technical scheme of the present invention to be described but not limit it, although the present invention is had been described in detail with reference to preferred embodiment, those of ordinary skill in the art is to be understood that: it still can make amendment or be equal to replacement technical scheme of the present invention, and these modifications or be equal to replacement and also can not make amended technical scheme break away from the spirit and scope of technical solution of the present invention.

Claims (8)

1. the driver fatigue state recognition method based on information fusion is characterized in that, comprising:
Step 1, collection driver's eye status information, bearing circle operation state information and track of vehicle transport condition information;
Step 2, the eye status information to described driver, bearing circle operation state information and track of vehicle transport condition information are handled, obtain driver's tired performance information, described tired performance information comprises that on average degree of opening, the longest time of closing one's eyes, bearing circle keep motionless time scale, angle variable quantity, the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that described transversal displacement is in the safe distance scope that bearing circle rotates suddenly to interior eyes closed degree of unit interval above 80% shared time scale, eyes;
Step 3, described tired performance information is weighted fusion treatment obtains the tired degree of membership corresponding, and draw driver's tired grade according to described tired degree of membership with the driver fatigue grade.
2. method according to claim 1 is characterized in that, described step 3 comprises:
Step 31, eyes closed degree in the described unit interval is surpassed 80% shared time scale, eyes, and on average degree of opening and the longest time of closing one's eyes merge and obtain eye state parameter confidence level, the angle variable quantity that keeps motionless time scale and bearing circle to rotate suddenly described bearing circle merges and obtains bearing circle operation state parameter confidence level, and the time scale that the transversal displacement standard deviation and the described transversal displacement of the snakelike transport condition of described vehicle is in the safe distance scope merges and obtains track of vehicle transport condition parameter confidence level;
Step 32, determine that according to described eye state parameter confidence level the eye state parameter merges weight, determine that according to described bearing circle operation state parameter confidence level bearing circle operation state parameter merges weight, obtain track of vehicle transport condition parameter according to described track of vehicle transport condition parameter confidence level and merge weight; According to described eye state parameter fusion weight, bearing circle operation state parameter fusion weight and track of vehicle transport condition parameter fusion weight described tired performance information is weighted fusion treatment and obtains the tired degree of membership corresponding, and draw driver's tired grade according to described tired degree of membership with the driver fatigue grade.
3. method according to claim 2 is characterized in that, described step 31 specifically comprises:
Step 311, described tired performance information is merged according to neural network model, obtain the elementary tired degree of membership of eye state parameter, the elementary tired degree of membership of bearing circle operation state parameter and the elementary tired degree of membership of track of vehicle transport condition parameter in the different measuring cycle of corresponding tired performance information respectively;
Step 312, the elementary tired degree of membership in different measuring cycle is converted into the basic reliability distribution parameter, and the basic reliability distribution parameter in described different measuring cycle merged according to D-S evidence theory model obtains eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level respectively.
4. method according to claim 2 is characterized in that, the parameter confidence level comprises described eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level, and described step 32 comprises:
Step 321, extract predefined with reference to the confidence level upper limit with reference to the confidence level lower limit;
Step 322, with described parameter confidence level with compare with reference to the confidence level upper limit with reference to the confidence level lower limit, if described parameter confidence level less than described with reference to the confidence level lower limit, then merging weight with described parameter confidence level corresponding parameters is zero; If with reference to the confidence level upper limit, then merge weight with described parameter confidence level corresponding parameters is to be used for the Chang Quanchong of identification parameter to the separating capacity of fatigue to described parameter confidence level greater than described; If described parameter confidence level, is then melted worm with described parameter confidence level corresponding parameters and is closed weight and the linear proportional relationship of described parameter confidence level with reference to the confidence level lower limit with between with reference to the confidence level upper limit described.
5. the driver fatigue state recognition system based on information fusion is characterized in that, comprising:
Acquisition module is used to gather driver's eye status information, bearing circle operation state information and track of vehicle transport condition information;
Processing module, the eye status information, bearing circle operation state information and the track of vehicle transport condition information that are used for described driver are handled, obtain driver's tired performance information, described tired performance information comprises that on average degree of opening, the longest time of closing one's eyes, bearing circle keep motionless time scale, angle variable quantity, the transversal displacement standard deviation of the snakelike transport condition of vehicle and the time scale that described transversal displacement is in the safe distance scope that bearing circle rotates suddenly to interior eyes closed degree of unit interval above 80% shared time scale, eyes;
Fusion Module is used for that described tired performance information is weighted fusion treatment and obtains the tired degree of membership corresponding with the driver fatigue grade, and draws driver's tired grade according to described tired degree of membership.
6. system according to claim 5 is characterized in that, described Fusion Module comprises:
Subsystem irrespective of size integrated unit, on average degree of opening and the longest time of closing one's eyes merge and obtain eye state parameter confidence level to be used for that eyes closed degree in the described unit interval is surpassed 80% shared time scale, eyes, the angle variable quantity that keeps motionless time scale and bearing circle to rotate suddenly described bearing circle merges and obtains bearing circle operation state parameter confidence level, and the time scale that the transversal displacement standard deviation and the described transversal displacement of the snakelike transport condition of described vehicle is in the safe distance scope merges and obtains track of vehicle transport condition parameter confidence level;
System-level integrated unit, be used for merging weight, bearing circle operation state parameter and merge weight and track of vehicle transport condition parameter fusion weight and described tired performance information is weighted fusion treatment obtains the tired degree of membership corresponding, and draw driver's tired grade according to described tired degree of membership with the driver fatigue grade according to the eye state parameter relevant with track of vehicle transport condition parameter confidence level with described eye state parameter confidence level, bearing circle operation state parameter confidence level.
7. system according to claim 6 is characterized in that, described subsystem irrespective of size integrated unit comprises:
First subelement, be used for described tired performance information is merged according to neural network model, obtain the elementary tired degree of membership of eye state parameter, the elementary tired degree of membership of bearing circle operation state parameter and the elementary tired degree of membership of track of vehicle transport condition parameter in the different measuring cycle of corresponding tired performance information respectively;
Second subelement, be used for the elementary tired degree of membership in different measuring cycle is converted into the basic reliability distribution parameter, and the basic reliability distribution parameter in described different measuring cycle merged according to D-S evidence theory model obtain eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level respectively.
8. system according to claim 6 is characterized in that, described system-level integrated unit comprises:
Weight is determined subelement, is used for determining respectively that according to described eye state parameter confidence level, bearing circle operation state parameter confidence level and track of vehicle transport condition parameter confidence level the eye state parameter merges weight, bearing circle operation state parameter merges weight and track of vehicle transport condition parameter merges weight.
CN2009100825662A 2009-04-24 2009-04-24 Driver fatigue state recognition method and system based on information fusion Expired - Fee Related CN101872171B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN2009100825662A CN101872171B (en) 2009-04-24 2009-04-24 Driver fatigue state recognition method and system based on information fusion

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN2009100825662A CN101872171B (en) 2009-04-24 2009-04-24 Driver fatigue state recognition method and system based on information fusion

Publications (2)

Publication Number Publication Date
CN101872171A true CN101872171A (en) 2010-10-27
CN101872171B CN101872171B (en) 2012-06-27

Family

ID=42997091

Family Applications (1)

Application Number Title Priority Date Filing Date
CN2009100825662A Expired - Fee Related CN101872171B (en) 2009-04-24 2009-04-24 Driver fatigue state recognition method and system based on information fusion

Country Status (1)

Country Link
CN (1) CN101872171B (en)

Cited By (36)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103473890A (en) * 2013-09-12 2013-12-25 合肥工业大学 Driver fatigue real-time monitoring system and monitoring method based on multi-information
CN103578227A (en) * 2013-09-23 2014-02-12 吉林大学 Fatigue driving detection method based on GPS positioning information
CN103989471A (en) * 2014-05-08 2014-08-20 东北大学 Fatigue driving detection system and method based on EEG identification
CN104246850A (en) * 2012-04-25 2014-12-24 丰田自动车株式会社 Drift-assessment device
CN104391569A (en) * 2014-10-15 2015-03-04 东南大学 Brain-machine interface system based on cognition and emotional state multi-mode perception
CN104952209A (en) * 2015-04-30 2015-09-30 广州视声光电有限公司 Driving prewarning method and device
CN105139070A (en) * 2015-08-27 2015-12-09 南京信息工程大学 Fatigue driving evaluation method based on artificial nerve network and evidence theory
CN105160818A (en) * 2015-08-26 2015-12-16 厦门雅迅网络股份有限公司 Forward collision risk-based detecting method and detecting device for detecting fatigue driving
CN105261153A (en) * 2015-11-03 2016-01-20 北京奇虎科技有限公司 Vehicle running monitoring method and device
CN105279900A (en) * 2014-07-22 2016-01-27 罗伯特·博世有限公司 Method and device for ascertaining a fatigue degree of a driver of a vehicle, and vehicle
CN105303829A (en) * 2015-09-11 2016-02-03 深圳市乐驰互联技术有限公司 Vehicle driver emotion recognition method and device
CN105320966A (en) * 2015-10-30 2016-02-10 东软集团股份有限公司 Vehicle driving state recognition method and apparatus
CN105740847A (en) * 2016-03-02 2016-07-06 同济大学 Fatigue grade discrimination algorithm based on driver eye portion identification and vehicle driving track
CN106108922A (en) * 2015-05-07 2016-11-16 铃木株式会社 Sleepy detection device
CN106251583A (en) * 2016-09-30 2016-12-21 防城港市港口区高创信息技术有限公司 Fatigue driving discrimination method based on driving behavior Yu eye movement characteristics
CN106236047A (en) * 2016-09-05 2016-12-21 合肥飞鸟信息技术有限公司 The control method of driver fatigue monitoring system
CN106355837A (en) * 2016-09-09 2017-01-25 常州大学 Fatigue driving monitoring method on basis of mobile phone
CN106580349A (en) * 2016-12-07 2017-04-26 中国民用航空总局第二研究所 Controller fatigue detection method and device and controller fatigue responding method and device
CN107233103A (en) * 2017-05-27 2017-10-10 西南交通大学 High ferro dispatcher's fatigue state assessment method and system
CN107379967A (en) * 2017-07-14 2017-11-24 南京信息工程大学 A kind of Drunken driving measure and control device based on multi-sensor information fusion
CN107844777A (en) * 2017-11-16 2018-03-27 百度在线网络技术（北京）有限公司 Method and apparatus for generating information
CN108108766A (en) * 2017-12-28 2018-06-01 东南大学 Driving behavior recognition methods and system based on Fusion
CN108369768A (en) * 2015-12-14 2018-08-03 罗伯特·博世有限公司 The eyes of at least one eye eyeball of passenger used for vehicles open the method and apparatus that data are classified and sleepy and/or micro-sleep the method and apparatus of the passenger for detecting vehicle
EP3357423A4 (en) * 2015-09-28 2018-10-17 Delta Kogyo Co., Ltd. Biological state estimation device, biological state estimation method, and computer program
CN109584507A (en) * 2018-11-12 2019-04-05 深圳佑驾创新科技有限公司 Driver behavior modeling method, apparatus, system, the vehicles and storage medium
CN110143202A (en) * 2019-04-09 2019-08-20 南京交通职业技术学院 A kind of dangerous driving identification and method for early warning and system
CN110177299A (en) * 2018-08-30 2019-08-27 永康市胜时电机有限公司 Video playing speed adjust platform
CN110371133A (en) * 2019-06-28 2019-10-25 神龙汽车有限公司 Fatigue of automobile driver detection system and method
CN110641467A (en) * 2018-06-25 2020-01-03 广州汽车集团股份有限公司 Vehicle distance control method and device of adaptive cruise system
CN111273634A (en) * 2018-12-05 2020-06-12 大众汽车有限公司 Arrangement of an at least partially automatic control system of a motor vehicle
CN112041907A (en) * 2018-04-25 2020-12-04 株式会社日立物流 Management assistance system
CN112233276A (en) * 2020-10-13 2021-01-15 重庆科技学院 Steering wheel corner statistical characteristic fusion method for fatigue state recognition
CN114820692A (en) * 2022-06-29 2022-07-29 珠海视熙科技有限公司 State analysis method and device of tracking target, storage medium and terminal
CN115366909A (en) * 2022-10-21 2022-11-22 四川省公路规划勘察设计研究院有限公司 Dynamic early warning method and device for driver accidents in long and large longitudinal slope section and electronic equipment
CN116682264A (en) * 2023-07-11 2023-09-01 营口港信科技有限公司 Active safety prevention and control system for port vehicle
CN117351648A (en) * 2023-10-08 2024-01-05 海南大学 Driver fatigue monitoring and early warning method and system

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE10247662A1 (en) * 2002-10-11 2004-04-29 Audi Ag motor vehicle
CN1830389A (en) * 2006-04-21 2006-09-13 太原理工大学 Device for monitoring fatigue driving state and its method
CN100489908C (en) * 2006-06-14 2009-05-20 吴玉沈 Active automobile driver fatigue driving alarming device and method

Cited By (52)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104246850A (en) * 2012-04-25 2014-12-24 丰田自动车株式会社 Drift-assessment device
CN104246850B (en) * 2012-04-25 2016-11-09 丰田自动车株式会社 Crawl decision maker
CN103473890A (en) * 2013-09-12 2013-12-25 合肥工业大学 Driver fatigue real-time monitoring system and monitoring method based on multi-information
CN103578227B (en) * 2013-09-23 2015-10-07 吉林大学 Based on the method for detecting fatigue driving of GPS locating information
CN103578227A (en) * 2013-09-23 2014-02-12 吉林大学 Fatigue driving detection method based on GPS positioning information
CN103989471B (en) * 2014-05-08 2015-11-04 东北大学 A kind of method for detecting fatigue driving based on electroencephalogram identification
CN103989471A (en) * 2014-05-08 2014-08-20 东北大学 Fatigue driving detection system and method based on EEG identification
CN105279900A (en) * 2014-07-22 2016-01-27 罗伯特·博世有限公司 Method and device for ascertaining a fatigue degree of a driver of a vehicle, and vehicle
CN104391569A (en) * 2014-10-15 2015-03-04 东南大学 Brain-machine interface system based on cognition and emotional state multi-mode perception
CN104952209A (en) * 2015-04-30 2015-09-30 广州视声光电有限公司 Driving prewarning method and device
CN106108922A (en) * 2015-05-07 2016-11-16 铃木株式会社 Sleepy detection device
CN106108922B (en) * 2015-05-07 2019-04-05 铃木株式会社 Drowsiness detection device
CN105160818B (en) * 2015-08-26 2019-10-29 厦门雅迅网络股份有限公司 It is a kind of based on preceding method for detecting fatigue driving and device to risk of collision
CN105160818A (en) * 2015-08-26 2015-12-16 厦门雅迅网络股份有限公司 Forward collision risk-based detecting method and detecting device for detecting fatigue driving
CN105139070A (en) * 2015-08-27 2015-12-09 南京信息工程大学 Fatigue driving evaluation method based on artificial nerve network and evidence theory
CN105139070B (en) * 2015-08-27 2018-02-02 南京信息工程大学 fatigue driving evaluation method based on artificial neural network and evidence theory
CN105303829A (en) * 2015-09-11 2016-02-03 深圳市乐驰互联技术有限公司 Vehicle driver emotion recognition method and device
EP3357423A4 (en) * 2015-09-28 2018-10-17 Delta Kogyo Co., Ltd. Biological state estimation device, biological state estimation method, and computer program
CN105320966A (en) * 2015-10-30 2016-02-10 东软集团股份有限公司 Vehicle driving state recognition method and apparatus
CN105261153A (en) * 2015-11-03 2016-01-20 北京奇虎科技有限公司 Vehicle running monitoring method and device
CN108369768B (en) * 2015-12-14 2021-04-20 罗伯特·博世有限公司 Method and device for detecting drowsiness and/or microsleep of a vehicle occupant
CN108369768A (en) * 2015-12-14 2018-08-03 罗伯特·博世有限公司 The eyes of at least one eye eyeball of passenger used for vehicles open the method and apparatus that data are classified and sleepy and/or micro-sleep the method and apparatus of the passenger for detecting vehicle
CN105740847A (en) * 2016-03-02 2016-07-06 同济大学 Fatigue grade discrimination algorithm based on driver eye portion identification and vehicle driving track
CN106236047A (en) * 2016-09-05 2016-12-21 合肥飞鸟信息技术有限公司 The control method of driver fatigue monitoring system
CN106355837A (en) * 2016-09-09 2017-01-25 常州大学 Fatigue driving monitoring method on basis of mobile phone
CN106251583B (en) * 2016-09-30 2018-09-25 江苏筑磊电子科技有限公司 Fatigue driving discrimination method based on driving behavior and eye movement characteristics
CN106251583A (en) * 2016-09-30 2016-12-21 防城港市港口区高创信息技术有限公司 Fatigue driving discrimination method based on driving behavior Yu eye movement characteristics
CN106580349A (en) * 2016-12-07 2017-04-26 中国民用航空总局第二研究所 Controller fatigue detection method and device and controller fatigue responding method and device
CN106580349B (en) * 2016-12-07 2020-01-21 中国民用航空总局第二研究所 Controller fatigue detection method and device and controller fatigue response method and device
CN107233103A (en) * 2017-05-27 2017-10-10 西南交通大学 High ferro dispatcher's fatigue state assessment method and system
CN107379967A (en) * 2017-07-14 2017-11-24 南京信息工程大学 A kind of Drunken driving measure and control device based on multi-sensor information fusion
CN107844777A (en) * 2017-11-16 2018-03-27 百度在线网络技术（北京）有限公司 Method and apparatus for generating information
CN107844777B (en) * 2017-11-16 2021-06-11 百度在线网络技术（北京）有限公司 Method and apparatus for generating information
CN108108766B (en) * 2017-12-28 2021-10-29 东南大学 Driving behavior identification method and system based on multi-sensor data fusion
CN108108766A (en) * 2017-12-28 2018-06-01 东南大学 Driving behavior recognition methods and system based on Fusion
CN112041907A (en) * 2018-04-25 2020-12-04 株式会社日立物流 Management assistance system
CN112041907B (en) * 2018-04-25 2021-10-15 株式会社日立物流 Management assistance system
CN110641467A (en) * 2018-06-25 2020-01-03 广州汽车集团股份有限公司 Vehicle distance control method and device of adaptive cruise system
CN110177299A (en) * 2018-08-30 2019-08-27 永康市胜时电机有限公司 Video playing speed adjust platform
CN110177299B (en) * 2018-08-30 2021-04-30 深圳市野生动物园有限公司 Video playing speed adjusting platform
CN109584507A (en) * 2018-11-12 2019-04-05 深圳佑驾创新科技有限公司 Driver behavior modeling method, apparatus, system, the vehicles and storage medium
CN109584507B (en) * 2018-11-12 2020-11-13 深圳佑驾创新科技有限公司 Driving behavior monitoring method, device, system, vehicle and storage medium
CN111273634A (en) * 2018-12-05 2020-06-12 大众汽车有限公司 Arrangement of an at least partially automatic control system of a motor vehicle
US11500382B2 (en) 2018-12-05 2022-11-15 Volkswagen Aktiengesellschaft Configuration of a control system for an at least partially autonomous transportation vehicle
CN110143202A (en) * 2019-04-09 2019-08-20 南京交通职业技术学院 A kind of dangerous driving identification and method for early warning and system
CN110371133A (en) * 2019-06-28 2019-10-25 神龙汽车有限公司 Fatigue of automobile driver detection system and method
CN112233276A (en) * 2020-10-13 2021-01-15 重庆科技学院 Steering wheel corner statistical characteristic fusion method for fatigue state recognition
CN114820692A (en) * 2022-06-29 2022-07-29 珠海视熙科技有限公司 State analysis method and device of tracking target, storage medium and terminal
CN115366909A (en) * 2022-10-21 2022-11-22 四川省公路规划勘察设计研究院有限公司 Dynamic early warning method and device for driver accidents in long and large longitudinal slope section and electronic equipment
CN116682264A (en) * 2023-07-11 2023-09-01 营口港信科技有限公司 Active safety prevention and control system for port vehicle
CN116682264B (en) * 2023-07-11 2023-12-26 营口港信科技有限公司 Active safety prevention and control system for port vehicle
CN117351648A (en) * 2023-10-08 2024-01-05 海南大学 Driver fatigue monitoring and early warning method and system

Also Published As

Publication number Publication date
CN101872171B (en) 2012-06-27

Similar Documents

Publication Publication Date Title
CN101872171B (en) Driver fatigue state recognition method and system based on information fusion
US7389178B2 (en) System and method for vehicle driver behavior analysis and evaluation
Zhenhai et al. Driver drowsiness detection based on time series analysis of steering wheel angular velocity
CN103824420B (en) Fatigue driving identification system based on heart rate variability non-contact measurement
CN109147279B (en) Driver fatigue driving monitoring and early warning method and system based on Internet of vehicles
CN109000940A (en) A kind of rolling stock exception axis temperature diagnostic method and system
Das et al. Differentiating alcohol-induced driving behavior using steering wheel signals
CN105139070A (en) Fatigue driving evaluation method based on artificial nerve network and evidence theory
CN104794855A (en) Driver's attention comprehensive assessment system
CN103340637A (en) System and method for driver alertness intelligent monitoring based on fusion of eye movement and brain waves
CN110509983A (en) A kind of steering-by-wire road feel feedback device suitable for different drive demands
CN105261151A (en) High-grade highway driver fatigue state detection method based on operation behavior characteristics
CN113743471B (en) Driving evaluation method and system
CN101987017A (en) Electroencephalo-graph (EEG) signal identification and detection method for measuring alertness of driver
CN109229108A (en) A kind of driving behavior safe evaluation method based on driving fingerprint
CN109740477A (en) Study in Driver Fatigue State Surveillance System and its fatigue detection method
CN112991685A (en) Traffic system risk assessment and early warning method considering fatigue state influence of driver
Liu et al. A review of driver fatigue detection: Progress and prospect
Mase et al. Capturing uncertainty in heavy goods vehicles driving behaviour
Yang et al. Real-time driver cognitive workload recognition: Attention-enabled learning with multimodal information fusion
CN101566848A (en) Fault diagnostic method of floatation process based on statistic distribution of foam size
CN108537161A (en) A kind of driving of view-based access control model characteristic is divert one's attention detection method
CN113793074B (en) Social cooperation evaluation method for automatic driving vehicle
CN110459034A (en) A kind of fatigue driving method for early warning and system
CN111775948B (en) Driving behavior analysis method and device

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
C14 Grant of patent or utility model
GR01 Patent grant
C17 Cessation of patent right
CF01 Termination of patent right due to non-payment of annual fee

Granted publication date: 20120627

Termination date: 20130424