CN108492527B - Fatigue driving monitoring method based on overtaking behavior characteristics - Google Patents
Fatigue driving monitoring method based on overtaking behavior characteristics Download PDFInfo
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- CN108492527B CN108492527B CN201810481127.8A CN201810481127A CN108492527B CN 108492527 B CN108492527 B CN 108492527B CN 201810481127 A CN201810481127 A CN 201810481127A CN 108492527 B CN108492527 B CN 108492527B
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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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- 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
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- 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
Abstract
The invention discloses a fatigue driving monitoring method based on overtaking behavior characteristics, which comprises the following steps: (1) whether the driver overtakes or not is judged by monitoring the steering lamp, the steering wheel angle and the vehicle speed; (2) obtaining fatigue characteristics of a vehicle in a process of overtaking through devices such as a sensor, wherein the fatigue characteristics comprise: the system comprises a vehicle speed parameter, a lane position parameter and an overtaking duration parameter; (3) and taking the fatigue characteristics as a fatigue judgment index set, classifying the fatigue characteristics based on an SVM classifier trained by using fatigue driving experimental data performed in advance, and judging the fatigue state of the driver. Through the mode, the method can analyze the characteristic change of the overtaking behavior of the driver in the fatigue state, further improve the comprehensive fatigue monitoring precision of the driver, reduce the phenomena of alarm leakage and frequent false alarm caused by low algorithm precision, and prevent the occurrence of fatigue accidents.
Description
Technical Field
The invention relates to an automobile driving assisting technology, in particular to a fatigue driving monitoring method based on overtaking behavior characteristics.
Background
Among road traffic accidents occurring in China, fatigue driving is one of the main reasons for death of people in single accidents. When a driver is in a fatigue state, the reaction time is prolonged, the action is slowed down, and the states of cognition, judgment and misoperation and microsleep are all direct causes of traffic accidents. According to the conservative estimation of the national road traffic safety administration: at least 10 million recorded fatigue driven traffic accidents occur annually in the united states, resulting in the death of 1550 and the injury of 71000, resulting in an economic loss of $ 125 billion. Meanwhile, the research results of the American automobile society show that in every 6 traffic accidents, the traffic accidents are caused by drowsiness together. In traffic accidents causing casualties on germany highways, about 25% are also caused by fatigue driving. The cause analysis result of road traffic accidents in 2011 of China shows that: although fatigue driving is not the cause of the largest number of fatalities, the number of fatalities in a single accident is the second of all the main causes, and the average 1 traffic participant will die after every two fatigability-cause traffic accidents are only carried out after illegal loading. Meanwhile, in 2004-2011, the results of deep analysis of the causes of death super-huge traffic accidents of more than 10 people in China show that 1 cause is caused by fatigue driving every year on average, and analysis further shows that the fatigue driving is one of the main causes of the malignant driving accidents.
Currently, monitoring of fatigue driving is mainly divided into monitoring of a driver and monitoring of a vehicle. The monitoring of the driver mainly comprises eye feature recognition, facial feature recognition, driver operation behavior monitoring and physiological response monitoring, and the research on fatigue mechanism and characterization is also facilitated by detecting human body peripheral physiological signals by using a contact sensor. The method has high cost, complex structure and poor expandability, and is easily influenced by light and individual factors of drivers. Vehicle monitoring is often studied based on vehicle state information such as driving time, driving speed, driving route, steering wheel angle, relative road offset, etc. The method is influenced by external factors such as vehicle types, road conditions, weather and the like, the accuracy of fatigue detection is not high, and the anti-interference performance and the adaptability are poor.
In summary, since the research on fatigue driving in China is started late, most of the research is carried out in the environment of simulation experiments, and a certain gap exists between the research and the driving in the environment of real vehicles, many problems still need to be solved. In the process of researching the fatigue state, the method for calibrating the fatigue state is single, further breakthrough is difficult to obtain, and most detection methods have the problems of high result error, long monitoring time, poor real-time performance, low sensitivity, poor reliability, high cost and the like.
Disclosure of Invention
The invention aims to solve the technical problem of providing a fatigue driving monitoring method based on overtaking behavior characteristics aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a fatigue driving monitoring method based on overtaking behavior characteristics comprises the following steps:
a fatigue driving monitoring method based on overtaking behavior characteristics comprises the following steps:
1) whether the driver overtakes or not is judged according to the monitoring result by monitoring the steering lamp, the steering wheel corner and the vehicle acceleration;
the method comprises the following specific steps: whether a left steering lamp is turned on or not is confirmed according to the monitoring result, whether the steering wheel angle is continuously larger than 6 degrees or not is judged, and whether the vehicle is in an acceleration state or not is judged; when the three conditions are simultaneously met, judging that overtaking behaviors are carried out;
2) acquiring overtaking data through a sensor, the overtaking data comprising: vehicle speed recording data of the moment of judging the overtaking behavior, lane position information of the moment of judging the overtaking behavior, and the starting moment and the overtaking ending moment of the overtaking behavior; the method for judging the overtaking behavior end comprises the following steps: and judging that the overtaking behavior is ended when the right turn light of the vehicle is turned off.
3) Obtaining fatigue characteristics of the overtaking process of the vehicle according to the acquired overtaking data, wherein the fatigue characteristics comprise: the system comprises a vehicle speed parameter, a lane position parameter and an overtaking duration parameter;
the vehicle speed parameter is obtained by the following method:
obtaining the maximum value V of overtaking speedmax: the maximum value of the vehicle speed in one overtaking sample;
obtaining the minimum value V of overtaking speedmin: namely the minimum value of the vehicle speed in one overtaking sample;
acquiring the passing speed range Vrange: i.e. the difference between the maximum and minimum vehicle speed values in an overtaking sample: vrange＝Vmax-Vmin；
Obtaining the average overtaking speed Vmean: namely the average value of the vehicle speed in one overtaking sample;
the lane position parameter is obtained by the following method:
acquiring a lane departure standard deviation SDLP, wherein the lane departure standard deviation describes the current operation state of a driver;
wherein d isavgThe average value of the lane positions in the sampling period is obtained; diThe position value of the lane in the sampling period is obtained; n is the number of lane position samples in the analysis sampling period; the sampling period is the time from the beginning to the end of one overtaking behavior.
The overtaking duration parameter is obtained by the following method:
obtaining the starting time T of overtaking behaviorstart: namely oneThe time of starting overtaking in the overtaking sample;
obtaining the overtaking behavior ending time Tend: namely the overtaking ending time in an overtaking sample;
acquiring the overtaking behavior duration T: i.e. the difference between the overtaking ending time and the overtaking starting time in one overtaking sample: t ═ Tend-Tstar；
4) And taking the fatigue characteristics as a fatigue judgment index set, classifying the fatigue characteristics based on an SVM classifier trained by using fatigue driving experimental data performed in advance, and judging the fatigue state of the driver.
According to the vehicle speed parameter, the lane position parameter and the overtaking duration parameter extracted from the fatigue data in the step 3), as a fatigue judgment index set, classifying the vehicle based on an SVM classifier trained by using fatigue driving experimental data which is performed in advance, and judging the fatigue state of the driver;
the fatigue state of the driver is divided into three levels, namely waking, fatigue and very fatigue, and the output of the classifier is one of the three fatigue levels.
The invention has the following beneficial effects: the invention provides a fatigue driving monitoring method based on overtaking behavior characteristics, which can further improve the comprehensive fatigue monitoring precision of a driver by analyzing the characteristic change of the overtaking behavior of the driver in a fatigue state aiming at the overtaking behavior which is frequent on an expressway and has serious accident consequences, reduce the phenomena of alarm leakage and frequent false alarm caused by lower algorithm precision and prevent the occurrence of fatigue accidents.
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The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of a method of an embodiment of the present invention;
fig. 2 is a flowchart of a passing behavior determination method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, a fatigue driving monitoring method based on overtaking behavior characteristics specifically includes:
the method comprises the following steps: and monitoring the behavior of the driver and judging whether to carry out overtaking behavior or not.
The comprehensive judgment is carried out by three indexes of a steering lamp, a steering wheel corner and the vehicle speed, the comprehensive judgment comprises the steps of monitoring whether the left steering lamp is turned on or not, monitoring whether the steering wheel corner is continuously larger than 6 degrees or not, and monitoring whether the vehicle is accelerated or not. When more than two conditions are satisfied from among the three conditions, it is determined that the overtaking behavior is performed, and a specific flow is shown in fig. 2.
Step two: overtaking data is acquired by utilizing equipment such as a sensor.
Vehicle speed data in the overtaking process is obtained through the output of a vehicle CAN; the lane position information is collected by the Mobiley device, the lane position information comprises a left lane line distance and a right lane line distance, the center of the vehicle is used as the position of the vehicle, the lane line distance is 1.8m, the vehicle is in the middle of a lane, and the default value is 1.8m when the image recognition algorithm cannot detect the lane line. (ii) a The start of the overtaking behavior is the overtaking starting time, and the overtaking ending returns to the original lane to be the overtaking ending time.
Step three: and extracting fatigue characteristics. Firstly, data processing is carried out, the first step is synchronization processing, and asynchronous data at the beginning stage are deleted to ensure that the acquisition time of each index is synchronous; the next step is data standardization, and due to the fact that the collecting frequency and the like of each sensor are different, experimental index data are processed through the sampling frequency or the time sequence. And because the sampling frequency of the sensor is high, the data volume in each second is large, for the convenience of analysis, one second is taken as the minimum timing unit, and the average value of the data in each second is considered as the data value of the second. For each fatigue data sample, when the minimum vehicle speed during overtaking is less than 80km/h, the sample is considered invalid, because the traffic accidents caused by fatigue driving are commonly found in the case of freeway or urban expressway free stream, and the traffic accidents caused by fatigue of drivers rarely occur in crowded urban roads, so in order to reduce the complexity of calculation and improve the accuracy of the evaluation result, the sample with the minimum value less than 80km/h is ignored in the embodiment of the invention.
Then extracting the speed parameter to obtain the maximum value V of the overtaking speedmax: the maximum value of the vehicle speed in one overtaking sample; obtaining the maximum value V of overtaking speedmin: namely the minimum value of the vehicle speed in one overtaking sample; acquiring the passing speed range Vrange: i.e. the difference between the maximum vehicle speed and the minimum vehicle speed in one overtaking sample: vrange＝Vmax-Vmin(ii) a Obtaining the average overtaking speed Vmean: i.e. the average value of the vehicle speed in one overtaking sample.
Extracting lane position parameters, and calculating lane departure Standard Deviation (SDLP) according to the lane line spacing:
wherein d isavgThe average value of the lane positions in the sampling period is 1.8m in the embodiment of the invention; diIn the embodiment of the invention, the left lane line spacing value is adopted for the lane position value in the sampling period; n is the number of lane position samples in the analysis sampling period.
Extracting overtaking duration parameters to obtain overtaking behavior starting time Tstart: namely the overtaking starting time in an overtaking sample; obtaining the overtaking behavior ending time Tend: namely the overtaking ending time in an overtaking sample; acquiring the time length T used for overtaking: i.e. the difference between the overtaking ending time and the overtaking starting time in one overtaking sample: t ═ Tend-Tstar. The judgment method for the overtaking behavior end is as follows: and judging that the overtaking behavior is ended when the right turn light of the vehicle is turned off.
Step four: and (5) judging the fatigue state.
And (3) classifying the vehicle speed parameter, the lane position parameter and the overtaking duration parameter extracted from the fatigue data in the third step as a fatigue judgment index set based on an SVM classifier trained by using fatigue driving experimental data performed in advance, and judging the fatigue state of the driver.
The classifier adopted by the invention is an SVM classifier, and the fatigue state of a driver is divided into three grades, namely waking, fatigue and very fatigue. The SVM classifier has been trained by using the data of the fatigue driving experiment performed in advance, and the fatigue characteristics of an effective fatigue data sample are input.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (3)
1. A fatigue driving monitoring method based on overtaking behavior characteristics is characterized by comprising the following steps:
1) judging whether the driver overtakes or not;
2) acquiring overtaking data through a sensor, the overtaking data comprising: vehicle speed record data of the start of the moment of the overtaking behavior, lane position information of the start of the moment of the overtaking behavior, the start moment of the overtaking behavior and the end moment of the overtaking behavior;
3) obtaining fatigue characteristics of the overtaking process of the vehicle according to the acquired overtaking data, wherein the fatigue characteristics comprise: the system comprises a vehicle speed parameter, a lane position parameter and an overtaking duration parameter;
the vehicle speed parameter is obtained by the following method:
obtaining the maximum value V of overtaking speedmax: the maximum value of the vehicle speed in one overtaking sample;
obtaining the minimum value V of overtaking speedmin: namely the minimum value of the vehicle speed in one overtaking sample;
acquiring the passing speed range Vrange: i.e. the difference between the maximum and minimum vehicle speed values in an overtaking sample: vrange＝Vmax-Vmin；
Obtaining the average overtaking speed Vmean: i.e. speed in one overtaking sampleAverage value of (d);
the lane position parameter is obtained by the following method:
acquiring a lane departure standard deviation SDLP, wherein the lane departure standard deviation describes the current operation state of a driver;
wherein d isavgThe average value of the lane positions in the sampling period is obtained; diThe position value of the lane in the sampling period is obtained; n is the number of lane position samples in the analysis sampling period; the sampling period is the time period from the beginning to the end of the one-time overtaking behavior;
the overtaking duration parameter is obtained by the following method:
obtaining the starting time T of overtaking behaviorstart: namely the overtaking starting time in an overtaking sample;
obtaining the overtaking behavior ending time Tend: namely the overtaking ending time in an overtaking sample;
acquiring the overtaking behavior duration T: i.e. the difference between the overtaking ending time and the overtaking starting time in one overtaking sample: t ═ Tend-Tstar；
4) According to the vehicle speed parameter, the lane position parameter and the overtaking duration parameter extracted from the overtaking data in the step 3), as a fatigue judgment index set, classifying the overtaking duration parameter based on an SVM classifier trained by using fatigue driving experimental data which is performed in advance, and judging the fatigue state of a driver;
the fatigue state of the driver is divided into three levels, namely waking, fatigue and very fatigue, and the output of the classifier is one of the three fatigue levels.
2. The fatigue driving monitoring method based on the overtaking behavior feature as claimed in claim 1, wherein the step 1) is to determine whether the driver overtakes specifically as follows: whether the driver overtakes or not is judged according to the monitoring result by monitoring the steering lamp, the steering wheel corner and the vehicle acceleration; whether a left steering lamp is turned on or not is confirmed according to the monitoring result, whether the steering wheel angle is continuously larger than 6 degrees or not is judged, and whether the vehicle is in an acceleration state or not is judged; and when the three conditions are simultaneously met, judging that the overtaking action is performed.
3. The fatigue driving monitoring method based on overtaking behavior characteristics as claimed in claim 1, wherein the method for judging the overtaking behavior ending in step 2) is as follows: and judging that the overtaking behavior is ended when the right turn light of the vehicle is turned off.
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| DE102019210979A1 (en) * | 2019-07-24 | 2021-01-28 | Volkswagen Aktiengesellschaft | Emergency assistance system with acoustic outside warning |
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| CN104207791B (en) * | 2014-08-26 | 2017-02-15 | 江南大学 | Fatigue driving detection method |
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