CN110979341A - Driver driving behavior analysis method and analysis system - Google Patents
Driver driving behavior analysis method and analysis system Download PDFInfo
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- CN110979341A CN110979341A CN201910949480.9A CN201910949480A CN110979341A CN 110979341 A CN110979341 A CN 110979341A CN 201910949480 A CN201910949480 A CN 201910949480A CN 110979341 A CN110979341 A CN 110979341A
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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
- B60W40/09—Driving style or behaviour
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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
Abstract
The invention discloses a driver driving behavior analysis method, which is implemented according to the following steps: step 1, collecting and storing driving data of drivers to a server database every day; step 2, preprocessing all driving data of all drivers in one day acquired in the step 1 to obtain a data matrix; step 3, scoring each driving data according to a scoring standard of the driving behavior of the driver; step 4, solving the weight value of each driving data score by adopting an entropy weight method; step 5, solving the comprehensive score of the driving data of each driver according to the weight; step 6, processing the daily comprehensive scores of all drivers in a certain time into a matrix and then extracting features; and 7, carrying out cluster analysis on the feature matrix extracted in the step 6 to obtain the driving behavior of the driver. The invention aims to provide a driver driving behavior analysis method which can monitor and manage the driving behavior of a driver.
Description
Technical Field
The invention belongs to the technical field of driver driving behavior judgment, and particularly relates to a driver driving behavior analysis method and a driver driving behavior analysis system.
Background
In order to accurately judge whether dangerous driving behaviors, such as sudden speed change, sudden steering, sudden stop, overspeed, fatigue driving, overtime driving and the like, which are unfavorable for the service life of a vehicle and the safety of a driver exist in the driving process of the driver, the driving behaviors of the driver need to be monitored and managed, and the driving technology of the driver is generally analyzed by collecting driving data of the driver. The conventional method for driving the driver mainly comprises the steps of collecting daily driving data of the driver, including data of uniform speed, refueling frequency, steering frequency, collision and the like, objectively reflecting the daily driving habits of the driver through the data, displaying results to the driver, prompting the driver to correct bad driving habits, and reducing accident rate caused by artificial reasons at a later stage. However, this method often cannot accurately describe the driving behavior of the driver, and the driving behavior of the driver will be misjudged when the wrong driver data is acquired.
Disclosure of Invention
The invention aims to provide a driver driving behavior analysis method which can monitor and manage the driving behavior of a driver.
The invention adopts the technical scheme that a method for analyzing the driving behavior of a driver is implemented according to the following steps:
step 1, collecting and storing driving data of drivers to a server database every day;
step 2, preprocessing all driving data of all drivers in one day acquired in the step 1 to obtain a data matrix;
step 3, scoring each driving data according to a scoring standard of the driving behavior of the driver;
step 4, solving the weight value of each driving data score by adopting an entropy weight method;
step 5, solving the comprehensive score of the driving data of each driver according to the weight;
step 6, processing the daily comprehensive scores of all drivers in a certain time into a matrix and then extracting features;
and 7, carrying out cluster analysis on the feature matrix extracted in the step 6 to obtain the driving behavior of the driver.
The invention is also characterized in that:
in step 1, the driving data comprises time, speed, acceleration, throttle signal, left turn signal, right turn signal, hand brake signal, foot brake signal and driving mileage.
The specific process of step 2 is as follows:
b collected data points of a characteristic quantity reflecting the driving behaviors of c drivers are arranged from top to bottom in a matrix form according to a collection sequence to form an a x b x c data array M, each driver data array needs to be checked from the first line to the last line of the matrix when aiming at characteristic quantities with high time continuity such as vehicle speed, driving mileage and the like, whether data collection errors exist or not is judged, four continuous lines of data are detected each time, high time continuity characteristic quantity data in the four lines of data are compared, and whether the data are rapidly mutated to 0 or not is judged. If this is the case, the row is deleted to reduce the impact of data errors.
In step 3, the scoring standard of the driving behavior of the driver is as follows:
the specific grading criterion is that the average vehicle speed is 100 minutes when the average vehicle speed is less than 35km/h, 90 minutes when the average vehicle speed is 35-50km/h, 75 minutes when the average vehicle speed is 45-60km/h, 50 minutes when the average vehicle speed is 60-80km/h, and 20 minutes when the average vehicle speed is more than 80km/h, the highest driving speed of the urban area is 80km/h, and the highest driving speed of the expressway is 120 km/h. The driving time is 100 minutes after full driving, 85 minutes after overspeed behavior occurs for 1-2 times, 70 minutes after 3-4 times, 50 minutes after 5-6 times, 10 minutes after more than 6 times, 100 minutes within 30km every day, 90 minutes after 30-40km, 80 minutes after 40-70km, 65 minutes after 70-90km, 50 minutes after 90-120 km, 30 minutes after 120km, 10 minutes after 200km, 100 minutes after 2 hours and more than 2 hours each day, 85 minutes after driving for 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-6 hours, 25 minutes after 6-7 hours, 10 minutes after 7 hours, 100 minutes after 2 hours and less than 3 hours, 80 minutes after 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-5 hours, 10 minutes after 5 hours and 10 minutes after two hours of continuous driving, and one time after 15 minutes before rest is set as fatigue driving. 80 points are obtained after 1 time, 50 points are obtained after 2 times, 10 points are obtained after 3 times and more, and the judgment time of night driving is 20:00 to 0:00 and 0:00 to 06: 00. 100 minutes is obtained within 1 hour when the vehicle is driven at night, 85 minutes is obtained within 1-2 hours when the vehicle is driven at night, 60 minutes is obtained within 2-3 hours, 40 minutes is obtained within 3-5 hours, 10 minutes is obtained within 5 hours, 100 minutes is obtained when the average vehicle speed at night is less than 30km/h, 90 minutes is obtained within 30-40km/h, 75 minutes is obtained within 40-50km/h, 50 minutes is obtained within 50-60km/h, 10 minutes is obtained above 60km/h, and four quick behaviors comprise quick acceleration and quick deceleration, the specific scoring criteria are that the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, the sharp turning is performed for more than ten times and 20 minutes, the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, and the sharp turning is performed for more than ten times and 20 minutes.
The specific process of step 4 is as follows:
the driver scoring matrix according to the scoring criterion is N, each row represents different drivers, and each column represents different driver driving behavior characteristic quantities:
step 4.1, standardizing the scores of all driving data
The driving data score was normalized using the following formula:
wherein x isijData elements, X, represented as ith row, jth column of the matrixiA set of all data elements represented as row i;
step 4.2, calculating information entropy of each driving data score
According to the definition of the information entropy, the information entropy of each driving data score is obtained by adopting the following formula:
wherein the content of the first and second substances,
m denotes the number of drivers, if pijWhen 0, then
Step 4.3, calculating the weight of each driving data score
The following formula is adopted to obtain the weight of each driving data score:
where n is the number of characteristic quantities reflecting the driver's driving behavior.
The specific process of step 5 is as follows:
according to the parameters of the driving behavior feature quantities obtained in step 4, the comprehensive driving score of each driver can be represented as:
wherein S isiExpressed as the composite score for the ith driver.
The specific process of step 6 is as follows:
and (4) processing the driver comprehensive scores obtained in the step (5) into a matrix with m rows and n columns, wherein each row represents each driver, each column represents the driver comprehensive scores every day, then performing feature extraction on the matrix, and processing into m feature matrices, wherein each feature matrix comprises a full range, a four-quadrant interval, an average difference, a variance and a standard deviation.
The specific process of step 7 is as follows:
and (6) carrying out cluster analysis on the m feature matrixes obtained in the step (6) to obtain the driving behavior type of each driver.
The invention has the beneficial effects that:
the invention solves the problem that the driver driving behavior can be misjudged when the wrong driver data is acquired in the prior art, can describe the driver driving behavior more accurately and is beneficial to monitoring and managing the driver; thereby reducing the incidence of traffic accidents.
Drawings
Fig. 1 is a flow chart of a driver driving behavior analysis method of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the method for analyzing the driving behavior of the driver of the present invention is implemented specifically according to the following steps:
step 1, collecting and storing driving data of drivers to a server database every day;
in step 1, the driving data comprises time, speed, acceleration, throttle signal, left turn signal, right turn signal, hand brake signal, foot brake signal and driving mileage.
Step 2, preprocessing all driving data of all drivers in one day acquired in the step 1 to obtain a data matrix;
the specific process of step 2 is as follows:
b collected data points of a characteristic quantity reflecting the driving behaviors of c drivers are arranged from top to bottom in a matrix form according to a collection sequence to form an a x b x c data array M, each driver data array needs to be checked from the first line to the last line of the matrix when aiming at characteristic quantities with high time continuity such as vehicle speed, driving mileage and the like, whether data collection errors exist or not is judged, four continuous lines of data are detected each time, high time continuity characteristic quantity data in the four lines of data are compared, and whether the data are rapidly mutated to 0 or not is judged. If this is the case, the row is deleted to reduce the impact of data errors.
Step 3, scoring each driving data according to a scoring standard of the driving behavior of the driver;
in step 3, the scoring standard of the driving behavior of the driver is as follows:
the specific grading criterion is that the average vehicle speed is 100 minutes when the average vehicle speed is less than 35km/h, 90 minutes when the average vehicle speed is 35-50km/h, 75 minutes when the average vehicle speed is 45-60km/h, 50 minutes when the average vehicle speed is 60-80km/h, and 20 minutes when the average vehicle speed is more than 80km/h, the highest driving speed of the urban area is 80km/h, and the highest driving speed of the expressway is 120 km/h. The driving time is 100 minutes after full driving, 85 minutes after overspeed behavior occurs for 1-2 times, 70 minutes after 3-4 times, 50 minutes after 5-6 times, 10 minutes after more than 6 times, 100 minutes within 30km every day, 90 minutes after 30-40km, 80 minutes after 40-70km, 65 minutes after 70-90km, 50 minutes after 90-120 km, 30 minutes after 120km, 10 minutes after 200km, 100 minutes after 2 hours and more than 2 hours each day, 85 minutes after driving for 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-6 hours, 25 minutes after 6-7 hours, 10 minutes after 7 hours, 100 minutes after 2 hours and less than 3 hours, 80 minutes after 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-5 hours, 10 minutes after 5 hours and 10 minutes after two hours of continuous driving, and one time after 15 minutes before rest is set as fatigue driving. 80 points are obtained after 1 time, 50 points are obtained after 2 times, 10 points are obtained after 3 times and more, and the judgment time of night driving is 20:00 to 0:00 and 0:00 to 06: 00. 100 minutes is obtained within 1 hour when the vehicle is driven at night, 85 minutes is obtained within 1-2 hours when the vehicle is driven at night, 60 minutes is obtained within 2-3 hours, 40 minutes is obtained within 3-5 hours, 10 minutes is obtained within 5 hours, 100 minutes is obtained when the average vehicle speed at night is less than 30km/h, 90 minutes is obtained within 30-40km/h, 75 minutes is obtained within 40-50km/h, 50 minutes is obtained within 50-60km/h, 10 minutes is obtained above 60km/h, and four quick behaviors comprise quick acceleration and quick deceleration, the specific scoring criteria are that the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, the sharp turning is performed for more than ten times and 20 minutes, the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, and the sharp turning is performed for more than ten times and 20 minutes.
Step 4, solving the weight value of each driving data score by adopting an entropy weight method;
the specific process of step 4 is as follows:
the driver scoring matrix according to the scoring criterion is N, each row represents different drivers, and each column represents different driver driving behavior characteristic quantities:
step 4.1, standardizing the scores of all driving data
The driving data score was normalized using the following formula:
wherein x isijData elements, X, represented as ith row, jth column of the matrixiA set of all data elements represented as row i;
step 4.2, calculating information entropy of each driving data score
According to the definition of the information entropy, the information entropy of each driving data score is obtained by adopting the following formula:
wherein the content of the first and second substances,
m denotes the number of drivers, if pijWhen 0, then
Step 4.3, calculating the weight of each driving data score
The following formula is adopted to obtain the weight of each driving data score:
where n is the number of characteristic quantities reflecting the driver's driving behavior.
Step 5, solving the comprehensive score of the driving data of each driver according to the weight;
the specific process of step 5 is as follows:
according to the parameters of the driving behavior feature quantities obtained in step 4, the comprehensive driving score of each driver can be represented as:
wherein S isiExpressed as the composite score for the ith driver.
Step 6, processing the daily comprehensive scores of all drivers in a certain time into a matrix and then extracting features;
the specific process of step 6 is as follows:
and (4) processing the driver comprehensive scores obtained in the step (5) into a matrix with m rows and n columns, wherein each row represents each driver, each column represents the driver comprehensive scores every day, then performing feature extraction on the matrix, and processing into m feature matrices, wherein each feature matrix comprises a full range, a four-quadrant interval, an average difference, a variance and a standard deviation.
Step 7, performing cluster analysis on the feature matrix extracted in the step 6 to obtain the driving behavior of the driver;
and (6) carrying out cluster analysis on the m feature matrixes obtained in the step (6) to obtain the driving behavior type of each driver.
The method for analyzing the driving behavior of the driver solves the problem that misjudgment is caused to the driving behavior of the driver when wrong driver data is acquired in the prior art, can describe the driving behavior of the driver more accurately, and is beneficial to monitoring and managing the driver; thereby reducing the incidence of traffic accidents.
Claims (8)
1. A driver driving behavior analysis method is characterized by being implemented according to the following steps:
step 1, collecting and storing driving data of drivers to a server database every day;
step 2, preprocessing all driving data of all drivers in one day acquired in the step 1 to obtain a data matrix;
step 3, scoring each driving data according to a scoring standard of the driving behavior of the driver;
step 4, solving the weight value of each driving data score by adopting an entropy weight method;
step 5, solving the comprehensive score of the driving data of each driver according to the weight;
step 6, processing the daily comprehensive scores of all drivers in a certain time into a matrix and then extracting features;
and 7, carrying out cluster analysis on the feature matrix extracted in the step 6 to obtain the driving behavior of the driver.
2. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein in step 1, the driving data comprises time, speed, acceleration, throttle signal, left turn signal, right turn signal, hand brake signal, foot brake signal and mileage.
3. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein the specific process of step 2 is as follows:
b collected data points of a characteristic quantity reflecting the driving behaviors of c drivers are arranged from top to bottom in a matrix form according to a collection sequence to form an a x b x c data array M, each driver data array needs to be checked from the first line to the last line of the matrix when aiming at characteristic quantities with high time continuity such as vehicle speed, driving mileage and the like, whether data collection errors exist or not is judged, four continuous lines of data are detected each time, high time continuity characteristic quantity data in the four lines of data are compared, and whether the data are rapidly mutated to 0 or not is judged. If this is the case, the row is deleted to reduce the impact of data errors.
4. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein in the step 3, the scoring criteria of the driving behavior of the driver are as follows:
the specific grading criterion is that the average vehicle speed is 100 minutes when the average vehicle speed is less than 35km/h, 90 minutes when the average vehicle speed is 35-50km/h, 75 minutes when the average vehicle speed is 45-60km/h, 50 minutes when the average vehicle speed is 60-80km/h, and 20 minutes when the average vehicle speed is more than 80km/h, the highest driving speed of the urban area is 80km/h, and the highest driving speed of the expressway is 120 km/h. The driving time is 100 minutes after full driving, 85 minutes after overspeed behavior occurs for 1-2 times, 70 minutes after 3-4 times, 50 minutes after 5-6 times, 10 minutes after more than 6 times, 100 minutes within 30km every day, 90 minutes after 30-40km, 80 minutes after 40-70km, 65 minutes after 70-90km, 50 minutes after 90-120 km, 30 minutes after 120km, 10 minutes after 200km, 100 minutes after 2 hours and more than 2 hours each day, 85 minutes after driving for 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-6 hours, 25 minutes after 6-7 hours, 10 minutes after 7 hours, 100 minutes after 2 hours and less than 3 hours, 80 minutes after 2-3 hours, 70 minutes after 3-4 hours, 50 minutes after 4-5 hours, 10 minutes after 5 hours and 10 minutes after two hours of continuous driving, and one time after 15 minutes before rest is set as fatigue driving. 80 points are obtained after 1 time, 50 points are obtained after 2 times, 10 points are obtained after 3 times and more, and the judgment time of night driving is 20:00 to 0:00 and 0:00 to 06: 00. 100 minutes is obtained within 1 hour when the vehicle is driven at night, 85 minutes is obtained within 1-2 hours when the vehicle is driven at night, 60 minutes is obtained within 2-3 hours, 40 minutes is obtained within 3-5 hours, 10 minutes is obtained within 5 hours, 100 minutes is obtained when the average vehicle speed at night is less than 30km/h, 90 minutes is obtained within 30-40km/h, 75 minutes is obtained within 40-50km/h, 50 minutes is obtained within 50-60km/h, 10 minutes is obtained above 60km/h, and four quick behaviors comprise quick acceleration and quick deceleration, the specific scoring criteria are that the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, the sharp turning is performed for more than ten times and 20 minutes, the sharp turning is performed for 1 time and 95 minutes, the sharp turning is performed for 2-3 times and 85 minutes, the sharp turning is performed for 4-5 times and 65 minutes, the sharp turning is performed for 6-9 times and 40 minutes, and the sharp turning is performed for more than ten times and 20 minutes.
5. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein the specific process of step 4 is as follows:
the driver scoring matrix according to the scoring criterion is N, each row represents different drivers, and each column represents different driver driving behavior characteristic quantities:
step 4.1, standardizing the scores of all driving data
The driving data score was normalized using the following formula:
wherein x isijExpressed as a matrixData element of row i, column j, XiA set of all data elements represented as row i;
step 4.2, calculating information entropy of each driving data score
According to the definition of the information entropy, the information entropy of each driving data score is obtained by adopting the following formula:
wherein the content of the first and second substances,
m denotes the number of drivers, if pijWhen 0, then
Step 4.3, calculating the weight of each driving data score
The following formula is adopted to obtain the weight of each driving data score:
where n is the number of characteristic quantities reflecting the driver's driving behavior.
6. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein the specific process of step 5 is as follows:
according to the parameters of the driving behavior feature quantities obtained in step 4, the comprehensive driving score of each driver can be represented as:
wherein S isiExpressed as the composite score for the ith driver.
7. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein the specific process of step 6 is as follows:
and (4) processing the driver comprehensive scores obtained in the step (5) into a matrix with m rows and n columns, wherein each row represents each driver, each column represents the driver comprehensive scores every day, then performing feature extraction on the matrix, and processing into m feature matrices, wherein each feature matrix comprises a full range, a four-quadrant interval, an average difference, a variance and a standard deviation.
8. The method for analyzing the driving behavior of the driver as claimed in claim 1, wherein the specific process of step 7 is as follows:
and (6) carrying out cluster analysis on the m feature matrixes obtained in the step (6) to obtain the driving behavior type of each driver.
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Application publication date: 20200410 |