CN105185038A - Safety driving system based on Android smart phone - Google Patents
Safety driving system based on Android smart phone Download PDFInfo
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- CN105185038A CN105185038A CN201510677997.9A CN201510677997A CN105185038A CN 105185038 A CN105185038 A CN 105185038A CN 201510677997 A CN201510677997 A CN 201510677997A CN 105185038 A CN105185038 A CN 105185038A
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
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Abstract
The invention discloses a safety driving system based on an Android smart phone; the safety driving system comprises the Android smart phone, a smart wristband, a driver fatigue state monitoring module, a real time road condition analysis module, and an early warning module; a preposition camera of the Android smart phone collects a face image of the driver, and can monitor the driver fatigue state according to a PERCLOS index. Present driving scene images collected by a postposition camera of the Android smart phone and present driving road information obtained by an Android smart phone GPS module and navigation software commonly form the real time road condition information. The safety driving system combines driver fatigue state with the real time road condition information so as to prepare early warning measures with more accuracy and efficiency, thus providing more reliability for safety driving.
Description
Technical field
The present invention relates to a kind of automotive safety driving aid, be specially a kind of safe driving system based on Android intelligent.
Background technology
In recent years, along with the development of highway communication, traffic hazard presents increasing trend, and has become one of serious problem of society today that countries in the world face.According to " report of world's prevention risk factors for road injury " prediction that the World Health Organization (WHO) issues, the casualties caused due to road traffic accident to the year two thousand thirty will become the one of the main reasons of human death, and fatigue driving then will become one of main factor causing traffic hazard.According to relevant department's data, exceed traffic hazard more than half relevant with driver tired driving, and the accident caused due to driver fatigue accounts for 20% in all traffic hazards, account for 40% even more in major traffic accidents.Therefore, development one can carry out real-time follow-up, monitoring to driver fatigue state, and sends in time and to remind and the safe driving system of warning is quite necessary there is the fatigue driving initial stage.
Existing fatigue detecting scheme mainly contains three classes.First kind method is the detection method based on physiological driver's parameter, and the method, mainly through the change of the monitoring driver physiological signal such as EEG signals, electrocardiosignal or electromyographic signal when driving, carrys out the tired situation of monitoring driving person with this.The judgment accuracy of the method to fatigue is higher, but the acquisition of most of physiological signal needs additionally to add sensor in human body carrys out collection signal, easily cause the discomfort of driver, comparatively large to the degree of dependence of individual, be therefore not too applicable to the practical application being applied to driver tired driving detection.
Equations of The Second Kind method is the detection method based on vehicle behavioural characteristic.These class methods, mainly through the travel route of vision sensor, speed pickup, rotating of steering wheel angular transducer control measurement, are analyzed and are judged
The state of mind such as whether driver diverts one's attention, sleepy.This scheme, because will obtain the running data of vehicle, so will to carry out to a certain degree integrated with automotive system, more difficultly to realize on independently equipment.
3rd class methods are the fatigue detection method based on physiological driver's response feature, and it is mainly by detecting some position of health, and the fatigue state of physiological characteristic Changing Pattern to driver as head, eye, mouth judges.By being combined with machine vision technique, the detection method based on physiological driver's response parameter is made to have non-contacting advantage.In addition, the features such as its accuracy is good, Parameters variation is consistent also make the method have good actual application value.PERCLOS(PercentEyelidClosureoverthePupilTime) be a kind of conventional fatigue detection method based on physiological driver's response feature, the percent that the method accounts for special time according to the eyes closed time judges degree of fatigue, at present, the method is considered to judge the most effective evaluate parameter of fatigue driving.
Current, vary with each individual because fatigue characterizes, therefore still there is Evaluation threshold based on the driver fatigue monitor system of PRCLOS method and be difficult to determine, and accurately cannot judge the shortcoming in the moment entering fatigue driving state.
In addition, existing driver fatigue monitor system is directly corresponding with early warning degree by driver's fatigue degree, and actual driving scene advanced warning grade is not merely relevant with degree of fatigue, be difficult to carry out accurately, effectively instructing to driver with degree of fatigue determination advanced warning grade therefore merely
Nowadays, Android intelligent is by due to widespread use, and be mostly all integrated with camera, acceleration transducer and gravity sensor, have the internal memory of CPU and 512MB of at least 1Ghz, this type of hardware condition is enough to meet an operation safe driving system on smart mobile phone simultaneously.Based on Android intelligent safe driving system by effectively solve current driver fatigue monitor system because of expensive, inconvenience and the shortcoming that can not popularize very well are installed.
Summary of the invention
The object of this invention is to provide a kind of safe driving system based on Android intelligent, the traffic hazard solving fatigue driving initiation is day by day serious, and current driver fatigue monitor system due to expensive, install inconvenience and can not popularize very well, and fatigue evaluation threshold value is difficult to determine, degree of fatigue and advanced warning grade not yet objective quantification, the single and problem not accurately of Forewarning Measures.
Based on a safe driving system for Android intelligent, comprising: Android intelligent, Intelligent bracelet, driver fatigue state monitoring modular, real-time road analysis module and warning module.Android intelligent comprises preposition and rearmounted two cameras, GPS module, Intelligent bracelet is connected with Android intelligent by communication, driver fatigue state monitoring modular, real-time road analysis module and warning module are arranged in Android intelligent, and warning module is worked in coordination with by Android intelligent and Intelligent bracelet and formed.
Based on a safe driving system for Android intelligent, described driver fatigue state monitoring modular comprises driver's facial image acquisition, Face datection, face tracking, human eye location, tired identification, forecasting fatigue and tired grade classification 7 parts.Wherein, driver's facial image acquisition is realized by the front-facing camera of Android intelligent, the Adaboost algorithm that Face datection and human eye are located through based on Haar-Like feature, LBP feature realizes, face tracking adopts Kalman filter to realize, tired identification is number percent, upper eyelid curvature comprehensive descision eyes closed state by pupil aperture and eyes size, based on PERCLOS index, judge driver fatigue state, and according to the adjustment fatigue threshold of different driver's system self-adaptions.
Based on a safe driving system for Android intelligent, described real-time road analysis module comprises current line parking lot scape, pedestrian, vehicle and the collection of roadblock frame, driving scene image analysis and current carriage way information, category of roads, the degree of crowding and obtains 3 parts.Wherein, current line parking lot scape image acquisition is realized by the post-positioned pick-up head of Android intelligent, current carriage way acquisition of information coordinates navigation software to realize by the GPS module in Android intelligent, current line car scene image and current carriage way information structure real-time road.
Based on a safe driving system for Android intelligent, described warning module comprises advanced warning grade and divides and preventive intervention procedure 2 parts.Wherein, it is that the result of forecasting fatigue in comprehensive driver fatigue state monitoring modular and the security situation of result to the driving environment residing for driver of real-time road analysis module are assessed that advanced warning grade divides, and is then divided into advanced warning grade without early warning, slight early warning, moderate early warning and Deep Early Warning according to security situation.Preventive intervention procedure is the intervening measure realizing in various degree by Android intelligent and Intelligent bracelet according to advanced warning grade, to remind driver to take a good rest, thus prevents fatigue driving.
Beneficial effect of the present invention:
1, use Android intelligent as the carrier of safe driving system, reduce the cost of system, add the dirigibility of system;
2, use Intelligent bracelet as a part for preventive intervention procedure measure, use Intelligent bracelet vibrating function to be used as early warning means, improve the validity of giving fatigue pre-warning;
3, for different driver, different driving environment, adopt adaptive algorithm determination fatigue threshold, improve the system universality of system;
4, the fatigue state of look-ahead driver, makes early warning according to prediction case to driver, and potential danger is prevented trouble before it happens, and improves the reliability of system;
5, comprehensive driver fatigue predicts the outcome and the security situation of real-time road to the driving environment residing for driver is assessed, and according to security situation, advanced warning grade is divided, tired grade and advanced warning grade are made a distinction, is conducive to the accurate intervention of Forewarning Measures.
Accompanying drawing explanation
Fig. 1 be according to the safe driving system based on Android intelligent of the present invention structural representation.
Reference numeral: Android intelligent 1, Intelligent bracelet 2, driver fatigue state monitoring modular 3, real-time road analysis module 4 and warning module 5.
Fig. 2 is the process flow diagram of the embodiment according to the safe driving system based on Android intelligent of the present invention.
Embodiment
In order to make the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with embodiment and accompanying drawing, the present invention is described in detail.Should be appreciated that specific embodiment described herein only in order to explain the present invention, but not as a limitation of the invention.
Embodiment:
Based on a safe driving system for Android intelligent, comprising: Android intelligent 1, Intelligent bracelet 2, driver fatigue state monitoring modular 3, real-time road analysis module 4 and warning module 5.Android intelligent 1 comprises preposition and rearmounted two cameras, GPS module, Intelligent bracelet 2 is connected with Android intelligent 1 by communication, driver fatigue state monitoring modular 3, real-time road analysis module 4 and warning module 5 are arranged in Android intelligent 1, and warning module 5 is worked in coordination with by Android intelligent 1 and Intelligent bracelet 2 and formed.
Fig. 2 is the corresponding driver fatigue state monitoring modular of the working-flow figure according to the present embodiment, step S1, the corresponding real-time road analysis module of step S2, the corresponding warning module of step S3, and specific works flow process comprises following steps:
Step S101: Android intelligent camera is switched to front-facing camera, takes driver's face-image.
Step S102: carry out Initialize installation to face motion tracker parameter, in the preferred embodiment, face motion tracker is realized by Kalman filter.
Step S103: the face-image being gathered a frame driver by the front-facing camera of Android intelligent 1.
Step S104: carry out illumination compensation to image by pre-service, improves picture quality.
Step S105: in order to improve image processing speed, carries out the suitable size of scaling by image.
Step S106: face motion tracker is estimated face location, to reduce the hunting zone of Face datection.
Step S107: cut image according to the position of estimating of face tracking device to face, the image after cutting send subsequent treatment.
Step S108: carry out Face datection, in the preferred embodiment, Face datection algorithm adopts Haar-Like, LBP feature, is realized by Adaboost algorithm.
Step S109: the result of Face datection is upgraded as parameter face motion tracker.
Step S110: according to the regularity of distribution in face " three five, front yards ", the facial image detected is segmented again, the region of search of human eye detection is reduced further, to improve detection efficiency.
Step S111: human eye detection.
Step S112: the possible outcome behind human eye location comprises eyebrow, will have an impact to tired recognition effect, therefore needs to carry out the process that iris is separated with eyebrow.
Step S113: for adapting to the different eye states of different driver in different situations, system adopts the method for self-adaptation fatigue threshold, the eyes opening degree under driver's normal condition is determined by the driver's eyes folding degree of 100 frames before the driving initial stage, using 80% of this eyes opening degree as fatigue threshold, when eyes closed degree exceedes this threshold value, think that driver's eyes state is closed, otherwise, for opening.
Step S114: Eye states recognition.
Step S115: adopt PERCLOS index to weigh the degree of fatigue of driver.
Step S116: predict according to the fatigue state of driver fatigue forecast model to driver's after this 15s moment, in the preferred embodiment, driver fatigue forecast model is the grey forecasting model adopting PERCLOS Index Establishment.
Step S117: grade classification is carried out to forecasting fatigue result.
Step S118: judge whether driver fatigue driving occurs, if occurred, invocation step S2, analyzes real-time road.
Step S201: Android intelligent 1 camera is switched to post-positioned pick-up head.
Step S202: coordinate navigation software to obtain current carriage way information, category of roads, the degree of crowding by the GPS module in Android intelligent 1.
Step S203: gather current line car scene image by the post-positioned pick-up head of Android intelligent 1.
Step S204: carry out pre-service to the current line car scene image collected, improves picture quality.
Step S205: pedestrian detection is carried out to the current line car scene image collected.
Step S206: vehicle detection is carried out to the current line car scene image collected.
In the present embodiment, the detection algorithm of pedestrian and vehicle realizes based on the target detection principle of Viola-Jones.
By the detection to pedestrian and vehicle, thus realize the judgement to present road pedestrian, vehicle congested conditions.
Step S3: in combining step S1 in the result of forecasting fatigue and step S2 to the analysis of current carriage way information and current line parking lot scape, the security situation of the driving environment residing for driver is assessed, then according to security situation, advanced warning grade is divided into without early warning, slight early warning, moderate early warning and Deep Early Warning, and by intervening measure that Android intelligent 1 and Intelligent bracelet 2 are taked in various degree accurately, to remind driver to take a good rest, thus prevent fatigue driving.
Claims (7)
1. based on a safe driving system for Android intelligent, it is characterized in that: comprise Android intelligent (1), Intelligent bracelet (2), driver fatigue state monitoring modular (3), real-time road analysis module (4) and warning module (5); Android intelligent (1) comprises preposition and rearmounted two cameras, GPS module, Intelligent bracelet (2) is connected with Android intelligent (1) by communication, driver fatigue state monitoring modular (3), real-time road analysis module (4) and warning module (5) are arranged in Android intelligent (1), and warning module (5) is worked in coordination with by Android intelligent (1) and Intelligent bracelet (2) and formed.
2. a kind of safe driving system based on Android intelligent according to claim 1, is characterized in that: described driver fatigue state monitoring modular (3) comprises driver's facial image acquisition, Face datection, face tracking, human eye location, tired identification, forecasting fatigue and tired grade classification 7 parts.
3. a kind of safe driving system based on Android intelligent according to claim 2, it is characterized in that: wherein driver's facial image acquisition is realized by the front-facing camera of Android intelligent (1), Face datection and human eye are located through based on Haar-Like feature, the Adaboost algorithm of LBP feature realizes, face tracking adopts Kalman filter to realize, tired identification is the number percent by pupil aperture and eyes size, upper eyelid curvature comprehensive descision eyes closed state, based on PERCLOS index, judge driver fatigue state, and adjust fatigue threshold according to different driver's system self-adaptions, forecasting fatigue sets up forecasting fatigue model, driver fatigue variable condition is predicted.
4. a kind of safe driving system based on Android intelligent according to claim 1, is characterized in that: described real-time road analysis module (4) comprises current line parking lot scape, pedestrian, vehicle and the collection of roadblock frame, driving scene image analysis and current carriage way information, category of roads, the degree of crowding and obtains 3 parts.
5. a kind of safe driving system based on Android intelligent according to claim 4, it is characterized in that: wherein scape image acquisition in current line parking lot is realized by the post-positioned pick-up head of Android intelligent (1), current carriage way acquisition of information coordinates navigation software to realize by the GPS module in Android intelligent (1), current line car scene image and current carriage way information structure real-time road condition information.
6. a kind of safe driving system based on Android intelligent according to claim 1, is characterized in that: described warning module (5) comprises advanced warning grade and divides and preventive intervention procedure 2 parts.
7. a kind of safe driving system based on Android intelligent according to claim 6, it is characterized in that: wherein advanced warning grade divides is that in comprehensive driver fatigue state monitoring modular (3), the result of forecasting fatigue and the security situation of result to the driving environment residing for driver of real-time road analysis module (5) are assessed, then according to security situation, advanced warning grade is divided into without early warning, slight early warning, moderate early warning and Deep Early Warning, preventive intervention procedure is the intervening measure realizing in various degree by Android intelligent (1) and Intelligent bracelet (2) according to advanced warning grade, take a good rest to remind driver, thus prevent fatigue driving.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105564436A (en) * | 2016-02-24 | 2016-05-11 | 深圳市中天安驰有限责任公司 | Advanced driver assistance system |
| CN105825625A (en) * | 2016-04-29 | 2016-08-03 | 大连楼兰科技股份有限公司 | Internet of vehicles platform used for vehicle fatigue driving reminding |
| CN105825624A (en) * | 2016-04-29 | 2016-08-03 | 大连楼兰科技股份有限公司 | Internet of vehicles fatigue driving reminding method |
| CN105825632A (en) * | 2016-04-29 | 2016-08-03 | 大连楼兰科技股份有限公司 | Fatigue driving reminding system with double reminding of intelligent mobile phone and intelligent watch based on Internet of vehicles |
| CN106205055A (en) * | 2016-09-13 | 2016-12-07 | 珠海格力电器股份有限公司 | The control method of a kind of intelligent terminal and device |
| CN107832721A (en) * | 2017-11-16 | 2018-03-23 | 百度在线网络技术（北京）有限公司 | Method and apparatus for output information |
| CN108021875A (en) * | 2017-11-27 | 2018-05-11 | 上海灵至科技有限公司 | A kind of vehicle driver's personalization fatigue monitoring and method for early warning |
| CN109308467A (en) * | 2018-09-18 | 2019-02-05 | 陕西科技大学 | Traffic accident prior-warning device and method for early warning based on machine learning |
| CN112183466A (en) * | 2020-10-26 | 2021-01-05 | 同济大学 | Distracted driving identification method based on road scene identification |
| CN113520397A (en) * | 2021-07-23 | 2021-10-22 | 吉林大学 | Driving distraction behavior identification method based on wearable inertial measurement unit |
| CN114049794A (en) * | 2021-12-02 | 2022-02-15 | 华中科技大学 | Vehicle-mounted safety auxiliary driving system based on smart phone |
| CN114141039A (en) * | 2021-11-26 | 2022-03-04 | 东南大学 | Vehicle safe driving early warning system and method based on Android mobile phone high-precision positioning |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112183466B (en) * | 2020-10-26 | 2022-12-16 | 同济大学 | Distracted driving identification method based on road scene identification |
| CN113520397A (en) * | 2021-07-23 | 2021-10-22 | 吉林大学 | Driving distraction behavior identification method based on wearable inertial measurement unit |
| CN114141039A (en) * | 2021-11-26 | 2022-03-04 | 东南大学 | Vehicle safe driving early warning system and method based on Android mobile phone high-precision positioning |
| CN114049794A (en) * | 2021-12-02 | 2022-02-15 | 华中科技大学 | Vehicle-mounted safety auxiliary driving system based on smart phone |
| CN114049794B (en) * | 2021-12-02 | 2023-12-29 | 华中科技大学 | Vehicle-mounted safety auxiliary driving system based on smart phone |
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