CN105151049B - The early warning system detected based on driver's face feature and deviation - Google Patents
The early warning system detected based on driver's face feature and deviation Download PDFInfo
- Publication number
- CN105151049B CN105151049B CN201510553569.5A CN201510553569A CN105151049B CN 105151049 B CN105151049 B CN 105151049B CN 201510553569 A CN201510553569 A CN 201510553569A CN 105151049 B CN105151049 B CN 105151049B
- Authority
- CN
- China
- Prior art keywords
- driver
- fatigue
- warning system
- image
- deviation
- 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.)
- Expired - Fee Related
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W2040/0818—Inactivity or incapacity of driver
- B60W2040/0827—Inactivity or incapacity of driver due to sleepiness
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Emergency Alarm Devices (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses the early warning system based on driver's face feature and deviation detection, camera one including the information for gathering driver's eyes open and-shut mode and driver's rotary head state and the camera two for gathering road image, image storage module, the information gathered for storing described camera one and camera two, image analysis system, for analyzing the information stored in image storage module, and warning system, the warning system send corresponding alarm sound according to the result that image analysis system judges.The present invention can judge the fatigue state residing for driver in good time by the face image and road image of driver, and send corresponding alarm sound according to the fatigue state, and alarm is timely, positive effect.
Description
Technical field
The present invention relates to fatigue driving alarm technique, and in particular to is detected based on driver's face feature and deviation
Early warning system.
Background technology
With growing and automobile consumption the increasingly rise of traffic, the fatigue of the especially professional driver of driver is driven
The problem of sailing starts to attract attention, because fatigue driving is one of the main reason for causing pernicious traffic accident.According to investigations, full generation
In the etesian traffic accident in boundary, 19% is relevant with fatigue driving.Especially during long-distance and scorch, fatigue is driven
Direct collision accident, accounts for the 25% of total number of accident, its caused death toll, more occupies disaster accident caused by sailing
41%.
The important hidden danger that driver tired driving occurs as traffic accident, has caused the extensive concern of people.At present
There is the detection means of many fatigue drivings to judge whether driver is in fatigue driving state, but these detection dresses
Put and the defects of different degrees of all be present.Such as the SAM fatigue drivings alarm of Digital Installations companies of U.S. exploitation
System, the corner of steering wheel is detected using the magnetic stripe being placed on below steering wheel, if can't detect steering wheel in a period of time
Any corrective action is carried out, then system just will be considered that driver tired driving and alarm, but the place wrong report more than bend can be non-
Chang Yanchong.Such as：The posture of driver head is detected by hardware device to carry out the judgement of fatigue driving, detection means need with
Driver head contacts, and influences the notice of driver, accelerates the speed of driver fatigue, adds fatigue driving generation
Probability, reduce the security of driving.Software aspects, some softwares are by monitoring driver's countenance feature in a period of time
Inside whether change to judge whether to be in fatigue state, but because facial expression is changeable and interpersonal face-image is poor
It is different too big, so effect is also all bad.
The content of the invention
The present invention in view of the above-mentioned problems, provide based on driver's face feature and deviation detection early warning system,
Using the face image of multiple cameras shooting driver and the road image of driving, and captured image is made accordingly
Analysis calculate after compared with corresponding threshold value, determine fatigue state, warning system is sent accordingly according to fatigue state
Voice message, play in good time early warning effect.
In order to solve the above technical problems, one aspect of the present invention is：Based on driver's face feature and car
The early warning system of road deviation detection, including,
Camera one, for gathering the information of driver's eyes open and-shut mode and driver's rotary head state,
Image storage module, the information gathered for storing described camera one,
Image analysis system, for analyzing the information stored in image storage module, calculate driver's eye closing number and account for
Percentage, eye closing duration and the driver's rotary head duration of total detection number are as a result and by the result with presetting
Fatigue threshold compare, judge the fatigue state residing for the driver, and
Warning system, the fatigue state judged according to image analysis system, warning system send corresponding alarm
Sound；
Also include being used for the camera two for gathering road image, the road image of the camera two collection, which is stored to image, to be deposited
Storing up in module, image analysis system analysis road image simultaneously compares analysis result with deviation threshold value set in advance,
Warning system sends corresponding alarm sound；
The process of image analysis system analysis road image includes determining the trapezoidal region of interest of gathered road image
Domain, based on structured road it is assumed that road image is carried out into region segmentation, it is divided into near-sighted field and far visual field, with LPF
Deng image pre-processing method denoising, rim detection is carried out based on gray level threshold segmentation, determines track edge feature candidate point, so as to
Left-lane line, right-lane line are determined, image analysis system calculates the interior angle angular bisector of left-lane line and right-lane line with being somebody's turn to do
The angle of the picture vertical line of road image and by the angle compared with predetermined value to judge whether track deviates, statistics deviates number
Account for the percentage of total detection number, deviate duration and compared with predetermined threshold value, judge the fatigue state of driver, alarm
System sends corresponding alarm sound according to the fatigue state；
Also include level of fatigue assessment method：
1) with 3 minutes for a time quantum, " eye closing " number that statistics detects in 3 minutes accounts for the hundred of total detection number
Point ratio, as PERCLOS values, the PERCLOS values of 3 minutes, selected threshold 12.5%, 25% before real-time statistics；
2) with 1 minute for a chronomere, " deviation " number that statistics detects in one minute accounts for total detection time
Several percentage, as deviation value Y, it is 66%, 33% to choose fatigue threshold；
3) PERCLOS and Y, which intersects, judges, i.e.,：
During a.PERCLOS≤12.5%, and Y≤33% and 33% ＜ Y≤66% are judged as slight fatigue, and Y is more than 66%
For severe fatigue；
B.12.5% during ＜ PERCLOS≤25%, and Y≤33% and 33% ＜ Y≤66% are judged as moderate fatigue, and Y surpasses
66% is crossed as severe fatigue；
During c.PERCLOS ＞ 25%, Y is that arbitrary value is judged as severe fatigue.
Also include the information module for obtaining this driving duration and current vehicle speed and according in the information module
Information calculates the current sensitivity coefficient of driver, and the sensitivity coefficient compares after being multiplied with result with fatigue threshold, report
Alert system sends corresponding alarm sound.
When the eye closing duration of driver reaches 5S, warning system sends corresponding alarm sound.
When speed is higher than 20km/h and driver rotary head state for the left or when right-hand rotation head duration reaches 6S, image
Analysis system judges that driver is in rotary head state too long, and warning system sends corresponding alarm sound.
Wherein, when angle reaches 4S not less than 30 degree and duration, warning system sends corresponding alarm sound.
The beneficial effects of the invention are as follows：The present invention can judge to drive in good time by the face image and road image of driver
Fatigue state residing for the person of sailing, and corresponding alarm sound is sent according to the fatigue state, alarm is timely, positive effect.
Brief description of the drawings
Fig. 1 is face image analysis process figure；
Fig. 2 is the analysis process figure of road image；
Fig. 3 is the calculating schematic diagram of angle α；
Fig. 4 is level of fatigue evaluation form.
Embodiment
With reference to embodiment, the embodiment of the present invention is further described.
The early warning system detected based on driver's face feature and deviation, including camera one, camera two, shooting
First, camera two is infrared camera, for gathering the letter of driver's eyes open and-shut mode and driver's rotary head state
Breath, camera two are used to gather road image.
Image storage module, the information gathered for storing described camera one and camera two.
Image analysis system, for analyzing the information stored in image storage module.
Warning system, the judgement made according to image analysis system send corresponding alarm sound, and alarm sound is excellent
Choosing uses voice message.
Embodiment one,
PERCLOS is defined as in the unit interval time scale shared by eyes closed.It was verified that driver's eyes close
Time is longer, and degree of fatigue is more serious, and the length by measuring the eyes closed time just can determine the degree of fatigue driving.
For 40ms once, with 3 minutes for a time quantum, statistics detects the frequency of shooting face image in 3 minutes
" eye closing " number account for the percentage of total detection number, as PERCLOS values.The PERCLOS of 3 minutes before system real-time statistics
Value.
In driving conditions, camera one gathers driver's face image and stored to image storage module, image analysis system
The image (concrete analysis process is as shown in Figure 1) in image storage module is analyzed, passes through the facial face of driver's face image
Characteristic point judges the rotary head state of the driver and use ELBP eye image template matching methods judge the open and-shut mode of eyes,
Corresponding statistics is made, statistical result is compared with fatigue threshold for 25%, i.e., when eye closing number ratio is more than 25%
When (PERCLOS ＞ 25%), it is determined as that obvious fatigue characteristic occurs in driver, warning system sends corresponding voice message；When
During 12.5% ＜ PERCLOS≤25%, judge that slight fatigue characteristic occurs in driver, warning system sends corresponding voice and carried
Show；As PERCLOS≤12.5%, judge that driver does not occur fatigue characteristic, warning system send corresponding voice message or
It is Jing Yin.
If driver's eye closing duration reaches 0.5S, warning system sends alarm sound at once.
If in addition, when counting driver's left or right rotary head duration up to 6 seconds, judge driver for " rotary head is too long " shape
State, warning system send corresponding alarm sound.
Embodiment two,
On the basis of embodiment one, the system increase, which is used to obtaining this accordingly, drives duration and current vehicle speed
Information module, the sensitivity according to possessed by the data obtained in the information module calculate current driver's, different sensitivity
With different sensitivity coefficients, sensitivity coefficient setting is as follows：
(sensitivity b) sensitivity coefficients Kb is 1 to medium sensitivity, muting sensitivity (sensitivity a) Ka=1.18, high sensitivity
(sensitivity c) Kc=0.95.
By the sensitivity coefficient with described in embodiment one statistical result be multiplied after compared with fatigue threshold, judge driver
Instantly fatigue state simultaneously sends corresponding alarm sound.
Wherein, if driver's eye closing duration reaches 0.5S, warning system sends alarm sound at once.
In addition, in the case where speed is higher than 20km/h, if counting driver's left or right rotary head duration up to 6 seconds
When, driver is judged for " rotary head is too long " state, and warning system sends corresponding alarm sound, if speed is less than
20km/h, then rotary head state is not detected.
Embodiment three,
On the basis of embodiment two, increase the camera two for catching road image, road image in the present system
Store to image storage module, image analysis system analysis road image and determine lane line, it is as shown in Figure 2 to make a concrete analysis of process：
The trapezoidal area-of-interest of road image is determined, based on structured road it is assumed that road image is carried out into region segmentation, is divided near
Visual field and far visual field, with the image pre-processing method denoising such as LPF, rim detection is carried out based on gray level threshold segmentation, really
Track edge feature candidate point is determined, so that it is determined that left-lane line l1, right-lane line l2.
As shown in figure 3, image analysis system calculates left-lane line and the right side according to the left-lane line and right-lane line of determination
The interior angle angular bisector of lane line with the picture vertical line of the road image angle α and by the angle α compared with predetermined value to sentence
Whether disconnected track deviates, if angle α >=30 °, judges that the vehicle is in wide-angle and deviates state, wide-angle deviates state and continued
During Shi Changda 4S, warning system sends corresponding alarm at once.
The frequency of the seizure road image of camera two once, with 1 minute for a chronomere, counts one minute for 40ms
" deviation " number inside detected accounts for the percentage of total detection number, as deviation value Y, i.e.,：
Deviation probable value Y=deviates number/always and detects number (in 1 minute), the Y value of 1 minute before real-time statistics.
Such as Fig. 4, in addition to level of fatigue assessment method：
1) with 3 minutes for a time quantum, " eye closing " number that statistics detects in 3 minutes accounts for the hundred of total detection number
Point ratio, as PERCLOS values, the PERCLOS values of 3 minutes, selected threshold 12.5%, 25% before real-time statistics；
2) with 1 minute for a chronomere, " deviation " number that statistics detects in one minute accounts for total detection time
Several percentage, as deviation value Y, it is 66%, 33% to choose fatigue threshold；
3) PERCLOS and Y, which intersects, judges, i.e.,：
During a.PERCLOS≤12.5%, and Y≤33% and 33% ＜ Y≤66% are judged as slight fatigue, and Y is more than 66%
For severe fatigue；
B.12.5% during ＜ PERCLOS≤25%, and Y≤33% and 33% ＜ Y≤66% are judged as moderate fatigue, and Y surpasses
66% is crossed as severe fatigue；
During c.PERCLOS ＞ 25%, Y is that arbitrary value is judged as severe fatigue.
For those skilled in the art, the technical scheme described in foregoing embodiments can still be repaiied
Change, or equivalent substitution is carried out to which part technical characteristic, within the spirit and principles of the invention, that is made any repaiies
Change, equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (5)
1. the early warning system detected based on driver's face feature and deviation, it is characterised in that including,
Camera one, for gathering the information of driver's eyes open and-shut mode and driver's rotary head state,
Image storage module, the information gathered for storing described camera one,
Image analysis system, for analyzing the information stored in image storage module, calculate driver's eye closing number and account for total inspection
Survey number percentage, eye closing duration and driver's rotary head duration as a result and by the result with it is default tired
Labor threshold value is compared, and judges the fatigue state residing for the driver, and
Warning system, the fatigue state judged according to image analysis system, warning system send corresponding alarm sound；
Also include being used for the camera two for gathering road image, the road image of the camera two collection, which is stored to image, stores mould
In block, image analysis system analysis road image simultaneously compares analysis result with deviation threshold value set in advance, alarms
System sends corresponding alarm sound；
The process of image analysis system analysis road image includes determining the trapezoidal area-of-interest of gathered road image, base
In structured road it is assumed that road image is carried out into region segmentation, it is divided into near-sighted field and far visual field, with LPF denoising,
Rim detection is carried out based on gray level threshold segmentation, track edge feature candidate point is determined, so that it is determined that left-lane line, right lane
Line, image analysis system calculate the interior angle angular bisector and the picture vertical line of the road image of left-lane line and right-lane line
Angle and by the angle compared with predetermined value to judge whether track deviates, statistics deviates the percentage that number accounts for total detection number
Than, deviate duration and compared with predetermined threshold value, judge the fatigue state of driver, warning system is according to the fatigue state
Send corresponding alarm sound；
Also include level of fatigue assessment method：
1) with 3 minutes for a time quantum, " eye closing " number that statistics detects in 3 minutes accounts for the percentage of total detection number
Than, as PERCLOS values, the PERCLOS values of 3 minutes, selected threshold 12.5%, 25% before real-time statistics；
2) with 1 minute for a chronomere, " deviation " number that statistics detects in one minute accounts for total detection number
Percentage, as deviation value Y, it is 66%, 33% to choose fatigue threshold；
3) PERCLOS and Y, which intersects, judges, i.e.,：
During a.PERCLOS≤12.5%, and Y≤33% and 33% ＜ Y≤66% are judged as slight fatigue, and Y attaches most importance to more than 66%
Degree fatigue；
B.12.5% during ＜ PERCLOS≤25%, and Y≤33% and 33% ＜ Y≤66% are judged as moderate fatigue, and Y exceedes
66% is severe fatigue；
During c.PERCLOS ＞ 25%, Y is that arbitrary value is judged as severe fatigue.
2. the early warning system detected as claimed in claim 1 based on driver's face feature and deviation, it is characterised in that
Also include the information module for obtaining this driving duration and current vehicle speed and calculated according to the information in the information module
Go out the current sensitivity coefficient of driver, the sensitivity coefficient compares after being multiplied with result with fatigue threshold, warning system hair
Go out corresponding alarm sound.
3. the early warning system detected as claimed in claim 1 or 2 based on driver's face feature and deviation, its feature are existed
In when the eye closing duration of driver reaches 5S, warning system sends corresponding alarm sound.
4. the early warning system detected as claimed in claim 2 based on driver's face feature and deviation, it is characterised in that
When speed is higher than 20km/h and driver rotary head state for the left or when right-hand rotation head duration reaches 6S, image analysis system
Judge that driver is in rotary head state too long, warning system sends corresponding alarm sound.
5. the early warning system detected as claimed in claim 1 based on driver's face feature and deviation, it is characterised in that
When angle reaches 4S not less than 30 degree and duration, warning system sends corresponding alarm sound.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510553569.5A CN105151049B (en) | 2015-08-27 | 2015-08-27 | The early warning system detected based on driver's face feature and deviation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510553569.5A CN105151049B (en) | 2015-08-27 | 2015-08-27 | The early warning system detected based on driver's face feature and deviation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105151049A CN105151049A (en) | 2015-12-16 |
| CN105151049B true CN105151049B (en) | 2018-02-02 |
Family
ID=54792214
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510553569.5A Expired - Fee Related CN105151049B (en) | 2015-08-27 | 2015-08-27 | The early warning system detected based on driver's face feature and deviation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105151049B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108846308A (en) * | 2018-04-24 | 2018-11-20 | 浙江吉利控股集团有限公司 | A kind of method for detecting fatigue driving and device |
Families Citing this family (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107635845A (en) * | 2016-05-08 | 2018-01-26 | 深圳市欸阿技术有限公司 | The method and device of vehicle traveling control |
| CN105931430A (en) * | 2016-05-25 | 2016-09-07 | 惠州华阳通用电子有限公司 | Alarm sensitivity detection method and apparatus for driver state early warning system |
| CN106096575A (en) * | 2016-06-24 | 2016-11-09 | 苏州大学 | A kind of driving states monitoring method and system |
| CN106080388A (en) * | 2016-08-29 | 2016-11-09 | 天津森宇科技发展有限公司 | Monitoring driver drives the intelligent apparatus of state |
| CN107085924A (en) * | 2017-05-14 | 2017-08-22 | 成都才盖科技有限公司 | A kind of rapid vehicle skew method for early warning and system based on solar energy |
| CN107491722A (en) * | 2017-06-16 | 2017-12-19 | 南京栎树交通互联科技有限公司 | One kind realizes that driver fatigue sentences method for distinguishing based on lane line image procossing |
| TWI646001B (en) * | 2017-08-18 | 2019-01-01 | 神達電腦股份有限公司 | Lane departure warning method and system |
| CN109515313A (en) * | 2017-09-18 | 2019-03-26 | 佛山市顺德区顺达电脑厂有限公司 | Lane departure warning method and its system |
| CN109747529A (en) * | 2017-11-02 | 2019-05-14 | 郭宇铮 | A kind of lane line prior-warning device |
| CN108382396B (en) * | 2018-02-02 | 2020-08-07 | 上海灏领科技有限公司 | Driver driving state recognition system and application method thereof |
| CN108437999B (en) * | 2018-03-20 | 2020-07-28 | 中国计量大学 | Attention auxiliary system |
| CN108928294B (en) * | 2018-06-04 | 2021-02-12 | Oppo（重庆）智能科技有限公司 | Driving danger reminding method and device, terminal and computer readable storage medium |
| CN108960183B (en) * | 2018-07-19 | 2020-06-02 | 北京航空航天大学 | Curve target identification system and method based on multi-sensor fusion |
| CN109271959A (en) * | 2018-09-30 | 2019-01-25 | 京东方科技集团股份有限公司 | A kind of vehicle shift method for early warning and device |
| CN109934189B (en) * | 2019-03-19 | 2021-08-13 | 山东同云网络科技有限公司 | Driving early warning method, system and terminal |
| CN110077414B (en) * | 2019-04-04 | 2020-12-01 | 合肥思艾汽车科技有限公司 | Vehicle driving safety guarantee method and system based on driver state monitoring |
| CN110077398B (en) * | 2019-05-20 | 2021-05-07 | 上海域驾智能科技有限公司 | Risk handling method for intelligent driving |
| CN110901650B (en) * | 2019-11-02 | 2021-11-26 | 芜湖职业技术学院 | Vehicle compaction line self-adjusting system and method |
| CN111508232A (en) * | 2020-04-16 | 2020-08-07 | 盛视科技股份有限公司 | Road safety snapshot all-in-one |
| CN112738364A (en) * | 2020-12-07 | 2021-04-30 | 浙江合众新能源汽车有限公司 | User monitoring device and method based on facial expression and voice recognition |
| CN112616227B (en) * | 2020-12-17 | 2023-05-16 | 内蒙古农业大学 | Tunnel lamplight adjusting system and method based on pupil change characteristics of driver |
| CN112590801B (en) * | 2020-12-24 | 2021-11-09 | 吉林大学 | Front collision early warning control method based on fatigue degree of driver |
| CN112998675B (en) * | 2021-02-22 | 2024-04-05 | 深圳市众鸿科技股份有限公司 | Vehicle-mounted intelligent cabin safe driving system based on big data |
| CN113173168A (en) * | 2021-04-30 | 2021-07-27 | 重庆工程职业技术学院 | Intelligent driving control system and method for vehicle |
| CN113744499B (en) * | 2021-08-12 | 2023-05-30 | 科大讯飞股份有限公司 | Fatigue early warning method, glasses, system and computer readable storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001138767A (en) * | 1999-11-17 | 2001-05-22 | Nissan Motor Co Ltd | Alarm device for vehicle |
| JP3861600B2 (en) * | 2001-01-29 | 2006-12-20 | 日産自動車株式会社 | Armpit driving and dozing operation alarm device |
| US7202793B2 (en) * | 2002-10-11 | 2007-04-10 | Attention Technologies, Inc. | Apparatus and method of monitoring a subject and providing feedback thereto |
| CN1830389A (en) * | 2006-04-21 | 2006-09-13 | 太原理工大学 | Device for monitoring fatigue driving state and its method |
| CN101746269B (en) * | 2010-01-08 | 2013-04-03 | 东南大学 | Fatigue driving fusion detection method based on soft computing |
-
2015
- 2015-08-27 CN CN201510553569.5A patent/CN105151049B/en not_active Expired - Fee Related
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108846308A (en) * | 2018-04-24 | 2018-11-20 | 浙江吉利控股集团有限公司 | A kind of method for detecting fatigue driving and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105151049A (en) | 2015-12-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105151049B (en) | The early warning system detected based on driver's face feature and deviation | |
| CN102509418B (en) | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion | |
| CN102436715B (en) | Detection method for fatigue driving | |
| CN107697069B (en) | Intelligent control method for fatigue driving of automobile driver | |
| CN105303830A (en) | Driving behavior analysis system and analysis method | |
| CN104408878A (en) | Vehicle fleet fatigue driving early warning monitoring system and method | |
| CN102054163B (en) | Method for testing driver fatigue based on monocular vision | |
| CN110532976A (en) | Method for detecting fatigue driving and system based on machine learning and multiple features fusion | |
| CN110136447A (en) | Lane change of driving a vehicle detects and method for distinguishing is known in illegal lane change | |
| CN107972671A (en) | A kind of driving behavior analysis system | |
| CN104794855A (en) | Driver's attention comprehensive assessment system | |
| CN104794856A (en) | Driver's attention comprehensive evaluation system | |
| CN104809887B (en) | A kind of retrograde detection method of vehicle on expressway and autoalarm | |
| CN110766982B (en) | Vehicle collision detection system based on vehicle-mounted sensor | |
| CN106652445A (en) | Road traffic accident judging method and device | |
| CN108230619A (en) | Method for detecting fatigue driving based on multi-feature fusion | |
| CN108197575A (en) | A kind of abnormal behaviour recognition methods detected based on target detection and bone point and device | |
| CN104021370A (en) | Driver state monitoring method based on vision information fusion and driver state monitoring system based on vision information fusion | |
| CN102201148A (en) | Driver fatigue detecting method and system based on vision | |
| CN103400111A (en) | Method for detecting fire accident on expressway or in tunnel based on video detection technology | |
| CN104269028A (en) | Fatigue driving detection method and system | |
| CN107844783A (en) | A kind of commerial vehicle abnormal driving behavioral value method and system | |
| CN106408877A (en) | Rail traffic driver fatigue state monitoring method | |
| CN103700220A (en) | Fatigue driving monitoring device | |
| CN104966059A (en) | Method for detecting phoning behavior of driver during driving based on intelligent monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180202 Termination date: 20180827 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |