CN106060258B - Driver driving style analysis method based on smart phone - Google Patents

Driver driving style analysis method based on smart phone Download PDF

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CN106060258B
CN106060258B CN201610417783.2A CN201610417783A CN106060258B CN 106060258 B CN106060258 B CN 106060258B CN 201610417783 A CN201610417783 A CN 201610417783A CN 106060258 B CN106060258 B CN 106060258B
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speed
driver
acceleration
driving style
data
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CN106060258A (en
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丁建勋
李棒
钟业文
郑杨边牧
张梦婷
唐飞
陈一锴
龙建成
石琴
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Hefei University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00—Measuring or testing not otherwise provided for
    • G01D21/02—Measuring two or more variables by means not covered by a single other subclass
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position

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Abstract

The invention provides a driver driving character analysis system and method based on a smart phone, wherein the system comprises: the device comprises a data acquisition unit, a data storage unit and a data analysis unit; acquiring acceleration data of a driver driving a vehicle at different speeds by acquiring triaxial accelerometer data and GPS positioning data of the smart phone; analyzing skewness and kurtosis of acceleration data under different speeds to obtain acceleration and deceleration characteristics of a driver when the driver drives a vehicle, and finally obtaining the driving style of the driver, wherein the driving style comprises an aggressive type, a stable type and a conservative type; the method can conveniently, quickly and economically acquire the acceleration data of the vehicle under different speeds, and provides a quantitative and quick method for analyzing the driving style.

Description

Driver driving style analysis method based on smart phone
Technical Field
The invention belongs to the field of data processing and application of mobile phone sensors (GPS sensors and three-axis accelerometer sensors), and particularly relates to a system and a method for analyzing the driving style of a driver in urban traffic.
Background
There are a lot of studies on the relationship between the driving style of drivers and traffic accidents at home and abroad. From the results of the research, most of the viewpoints are that the traffic accidents have a certain relationship with the driving style of the driver; the driver does not mean that the driver is of the aggressive type when driving a journey at a time, but the aggressive driver is easy to drive the aggressive behavior; although the driving style of the driver is difficult to change, the driver has certain plasticity, and the driver can improve the driving behavior of the driver by accurately recognizing the driving style of the driver, so that the influence of the poor driving style on the traffic can be reduced.
In the research of urban traffic, the driving style is a crucial factor; drivers with different driving styles bring different influences on urban traffic safety and efficiency; the existing method for identifying the driving style comprises the following steps: method for investigating questionnaires, interviews and the like, method for identifying specific aggressive driving behaviour and method for mining acceleration and speed data of driving processes and the like
With the rapid development of the smart phone, the smart phone can simply and rapidly acquire the GPS positioning and acceleration and deceleration of the real-time running of the vehicle, and a rapid technical means is provided for the data acquisition of the running state of the vehicle.
The style recognition of the prior art front driving style has the following disadvantages:
1. in the past, the driving style mostly adopts investigation methods such as questionnaires, interviews and the like, so that time and labor are wasted, and the recognition of the driving style lacks quantitative concepts.
2. The existing driving style identification needs data acquisition, which mostly depends on different systems and equipment to acquire data in the driving process, and has the disadvantages of complex data acquisition, huge data volume and higher identification difficulty.
3. The driving style is embodied by a section of overall driving style, most of the existing driving styles are recognized by recognizing specific driving behaviors in the driving process, and the whole driving process cannot be analyzed integrally, so that the analysis result is inaccurate or imperfect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a driver driving style analysis method based on a smart phone, so that the acceleration data of a vehicle under different speeds can be conveniently, quickly and economically obtained, the driving style of the driver can be obtained by analyzing the skewness and the kurtosis of statistical data according to the skewness and the kurtosis of the acceleration data of the vehicle, a quantitative and quick method is provided for analyzing the driving style, and the driver can conveniently and correctly know the driving style.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a driver driving style analysis system based on a smart phone, which is characterized by comprising the following steps: the device comprises a data acquisition unit, a data storage unit and a data analysis unit;
the data acquisition unit acquires triaxial accelerometer data and GPS positioning data on the mobile phone and stores the triaxial accelerometer data and the GPS positioning data in the data storage unit;
the data analysis unit is used for preprocessing, classifying and calculating data of the triaxial accelerometer and GPS positioning data so as to obtain skewness coefficients and kurtosis coefficients of acceleration; and determining the driving style of the driver according to the skewness coefficient and the kurtosis coefficient and storing the driving style in the data storage unit, wherein the driving style comprises an aggressive type, a stable type and a conservative type.
The invention relates to a driver driving style analysis method based on a smart phone, which is characterized by comprising the following steps of:
step 1, fixing a smart phone on a vehicle driven by a driver, acquiring triaxial accelerometer data and GPS positioning data at fixed time intervals by using the smart phone, and respectively storing the triaxial accelerometer data and the GPS positioning data in multidimensional arrays Acce and Place;
step 2, acquiring the acceleration of each acquisition point in the process of the advancing direction of the vehicle according to the data of the three-axis accelerometer; calculating the speed of each acquisition point according to the GPS positioning data;
step 3, classifying the speed of each acquisition point, namely low speed, medium speed and high speed in the running process of the vehicle; respectively counting the acceleration of each acquisition point at low speed, medium speed and high speed;
step 4, calculating and obtaining a skewness coefficient SK of the acceleration of the vehicle at three speeds, wherein SK is { SK1,SK2,SK3And kurtosis coefficient PK ═ PK1,PK2,PK3}；SK1,SK2,SK3Skewness coefficients respectively representing the acceleration of the vehicle at low speed, medium speed and high speed; PK1,PK2,PK3Kurtosis coefficients respectively representing acceleration of the vehicle at low speed, medium speed, and high speed;
and 5, performing hypothesis test on the skewness coefficient SK and the kurtosis coefficient PK of the acceleration to evaluate the driving style of the driver, so as to obtain the driving style of the driver at low speed, medium speed and high speed, wherein the driving style comprises an aggressive type, a stable type and a conservative type.
The method for analyzing the driving style of the driver based on the smart phone is also characterized in that the step 5 is carried out according to the following process:
step 5.1, assuming a stable weight type skewness coefficient SK at the ith speediIs pi(ii) a Kurtosis coefficient PK of steady weight at ith speediIs qi；i∈{1,2,3}；
Step 5.2, determining critical value mu of skewness coefficient of steady weight at ith speediCritical value gamma of kurtosis coefficient of steady weighti；μi,γi＞0；
Step 5.3, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (1) is satisfied, if yes, executing a step 5.4; otherwise, executing step 5.5;
|SKi-pi|≤μi(1)
step 5.4, judging the skewness coefficient PK of the acceleration at the ith speediWhether the formula (2) is satisfied or not, if so, the driving style of the driver at the ith speed is steady; otherwise, executing step 5.6;
|PKi-qi|≤γi(2)
step 5.5, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (3) is satisfied or not, if so, indicating that the driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative;
SKi-pi＞μi(3)
step 5.6, judging the skewness coefficient PK of the acceleration at the ith speediWhether the formula (4) is satisfied or not, if so, indicating that the driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative:
PKi-qi＞γi(4)。
compared with the prior art, the beneficial technical effects of the invention are as follows:
1. the invention skillfully associates the driving style with the distribution situation of the driving acceleration data of the driver, integrally grasps the distribution situation of the acceleration of the vehicle driven by the driver, and creatively corresponds the right deviation, the left deviation, the peak and the low peak with different driving styles (aggressive type, steady type and conservative type) of the driver;
2. the invention applies the skewness and the kurtosis of the data to belong to the digital characteristics, not only can carry out digital quantification on different driving style characteristics, but also expands the utilization of the driving data of the driver, and is convenient for academic research and application in other aspects, thereby ensuring the sufficiency, effectiveness and objectivity of the data.
3. According to the invention, the data is collected and analyzed by adopting the smart phone, so that the data can be conveniently and quickly obtained and analyzed, and compared with questionnaire survey, the work load is greatly reduced; meanwhile, the invention adopts real-time data for whole-process identification, rather than self-evaluation by the driver after driving is finished, thus overcoming the limitation and subjectivity of self-recognition and being more scientific and reasonable;
4. the system adopted by the invention not only can be attached to a mobile phone, but also is an integrated analysis system, has high efficiency, can be conveniently integrated into the existing assistant driving system and is used as a component of the assistant driving system, so that a driver can conveniently and correctly know the driving style of the driver, and the driving behavior of the driver is improved.
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FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a flow chart of the inspection steps of the present invention;
FIG. 4 is a graph of acceleration profiles for different skewness in accordance with the present invention;
FIG. 5 is a graph of acceleration profiles of different kurtosis according to the present invention.
Detailed Description
In this embodiment, a driver driving style analysis system based on a smart phone includes: the device comprises a data acquisition unit, a data storage unit and a data analysis unit;
the data acquisition unit acquires triaxial accelerometer data and GPS positioning data on the mobile phone and stores the triaxial accelerometer data and the GPS positioning data in the data storage unit; the data analysis unit is used for preprocessing, classifying and calculating data of the triaxial accelerometer and GPS positioning data so as to obtain skewness coefficients and kurtosis coefficients of acceleration; and determining the driving style of the driver according to the skewness coefficient and the kurtosis coefficient, and storing the driving style in the data storage unit, wherein the driving style comprises an aggressive type, a stable type and a conservative type. The skewness is the measurement of the distribution skewness direction and degree of the statistical data, and is the digital characteristic of the asymmetric degree of the statistical data distribution, the skewness is more than zero and is right-skewed, the skewness indicates that the distribution is skewed to the right side of the mean value, the skewness is less than zero and is left-skewed, and the distribution is skewed to the left of the mean value; the kurtosis is a digital characteristic that statistical data are distributed at the peak value height of the average value, the larger the kurtosis coefficient is, the more extreme values are distributed, and the smaller the kurtosis coefficient is, the more the distribution is near the average value; when the acquired acceleration data is deviated to the right, the acceleration of vehicle acceleration is larger than the deceleration of vehicle deceleration when the driver drives the vehicle, and the vehicle is represented as a glancing; when the acquired acceleration data is deviated to the left, the acceleration of the vehicle during acceleration is smaller than the deceleration of the vehicle during deceleration, and the behavior is conservative; when the acquired acceleration data is low peak, the acceleration and the deceleration are large when the vehicle is driven by a driver, and the vehicle appears as a glancing; when the acquired acceleration data is in a peak, the acceleration and the deceleration are small when the vehicle is driven by a driver, and the acceleration and the deceleration are conservative;
fig. 1 is a schematic structural diagram of the system, and the system mainly realizes acquisition and processing of triaxial accelerometer data and GPS positioning data on a mobile phone by using a smart phone as a carrier, so as to obtain a driving style of a driver.
As shown in fig. 2, a method for analyzing a driving style of a driver based on a smart phone is performed according to the following steps:
step 1, fixing a smart phone on a vehicle driven by a driver, acquiring triaxial accelerometer data and GPS positioning data by using the smart phone at a fixed time interval t, and respectively storing the triaxial accelerometer data and the GPS positioning data in multidimensional arrays Acce and Place; the storage form of the data is as follows:
triaxial accelerometer data Acce ═ a [ ("ax1,ay1,az1),(ax2,ay2,az2),(ax3,ay3,az3)...(axN,ayN,azN)]GPS positioning data Place [ [ (long)1,lati1),(long2,lati2),(long3,lati3)...(longN,latiN)]。
Wherein (a)xN,ayN,azN) The acceleration value of a triaxial accelerometer on the smart phone at the Nth acquisition point is represented, x, y and z represent three coordinate axes of the smart phone, the x axis is the vertical direction on the screen surface of the smart phone, the y axis is the vertical direction perpendicular to the screen surface of the smart phone, and the z axis is the direction perpendicular to the front surface of the smart phone and facing outwards. (Long)N,latiN) Anchor point representing Nth acquisition point, longNRepresenting longitude coordinates, latiNRepresenting latitude coordinates.
Step 2, acquiring the acceleration of each acquisition point in the process of the advancing direction of the vehicle according to the data of the triaxial accelerometer; calculating the speed of each acquisition point according to the GPS positioning data;
because the coordinates of the smart phone are different from those of the vehicle, three-axis acceleration data need to be converted, wherein an X coordinate axis represents the forward direction of the vehicle, a Y coordinate axis represents the transverse direction of the vehicle, and a Z coordinate axis represents the upward direction vertical to the plane of the vehicle chassis, included angles between corresponding vehicle coordinate axes (X, Y, Z) and vehicle coordinate axes (X, Y, Z) are α, β and gamma respectively, and the acceleration value in the direction of the vehicle X, Y, Z coordinate axis is as follows:
Figure GDA0002243324940000051
the acceleration of the vehicle is
According to the positioning coordinates of the acquisition points and the speed as the displacement generated in unit time, the speed of the vehicle at the acquisition points is obtained
Step 3, classifying the speed of each acquisition point, namely low speed, medium speed and high speed in the running process of the vehicle; respectively counting the acceleration corresponding to each acquisition point at low speed, medium speed and high speed; i ∈ {1,2,3} represents low speed, medium speed, and high speed, respectively. The first table shows different speed ranges corresponding to the low speed, the medium speed and the high speed.
Watch 1
0～20km/h 30～60km/h 60～+∞km/h
Low speed Medium speed High speed
Counting the acceleration of the vehicle at the ith speed
Figure GDA0002243324940000054
i∈{1,2,3}；
Step 4, calculating and obtaining a skewness coefficient SK of the acceleration of the vehicle at three speeds, wherein SK is { SK1,SK2,SK3And kurtosis coefficient PK ═ PK1,PK2,PK3}；SK1,SK2,SK3Skewness coefficients respectively representing the acceleration of the vehicle at low speed, medium speed and high speed; PK1,PK2,PK3Kurtosis coefficients respectively representing acceleration of the vehicle at low speed, medium speed, and high speed;
coefficient of acceleration kurtosis
Figure GDA0002243324940000061
Coefficient of acceleration kurtosis
Figure GDA0002243324940000062
Wherein
Figure GDA0002243324940000063
Is the average value of the acceleration of the vehicle at the i-th speed,
Figure GDA0002243324940000064
and 5, performing hypothesis test on the skewness coefficient SK and the kurtosis coefficient PK of the acceleration, and evaluating the driving style of the driver to obtain the driving style of the driver at low speed, medium speed and high speed, wherein the driving style comprises an aggressive type, a stable type and a conservative type.
As shown in fig. 3, the specific process is as follows:
step 5.1, assuming a stable weight type skewness coefficient SK at the ith speediIs pi(ii) a Kurtosis coefficient PK of steady weight at ith speediIs qi；i∈{1,2,3}；
Step 5.2, determining the skewness coefficient of the steady type at the ith speedLimit value muiCritical value gamma of kurtosis coefficient of steady weighti；μi,γi＞0；
Step 5.3, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (1) is satisfied, if yes, executing a step 5.4; otherwise, executing step 5.5;
|SKi-pi|≤μi(1)
step 5.4, judging the skewness coefficient PK of the acceleration at the ith speediWhether the formula (2) is satisfied or not, if so, the driving style of the driver at the ith speed is steady; otherwise, executing step 5.6;
|PKi-qi|≤γi(2)
step 5.5, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (3) is satisfied or not, if so, indicating that the driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative;
SKi-pi＞μi(3)
step 5.6, judging the skewness coefficient PK of the acceleration at the ith speediWhether the formula (4) is satisfied or not, if so, indicating that the driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative:
PKi-qi＞γi(4)。
as shown in table two, different skewness and kurtosis coefficients correspond to different driving styles at the ith speed.
Watch two
Figure GDA0002243324940000071
As shown in FIG. 4, the kurtosis coefficients of the three curves in the graph all satisfy PKi-qi≤γiThe skewness coefficients are a, b and c respectively, and a, b and c satisfy | a-pi|≤μi、b-pi＞μi、c-pi＜-μiAcceleration profile of (a); at the ith speed, if the distribution characteristics of the statistical acceleration data meet the curve 1, the driver is shown to be a steady type; if the distribution characteristics of the statistical acceleration data meet the curve 2, the driver is represented as a glancing type; if the distribution characteristics of the statistical acceleration data satisfy the curve 3, it indicates that the driver is conservative.
As shown in FIG. 5, the skewness coefficients of three curves in the graph satisfy SKi-pi≤μiThe kurtosis coefficients are e, d and f respectively, and the e, d and f satisfy | e-qi|≤γi、d-qi＜-γi、f-qi＞γiAcceleration profile of (a); at the ith speed, if the distribution characteristic of the statistical acceleration data meets a curve 4, the driver is shown to be a steady type; if the distribution characteristics of the statistical acceleration data meet the curve 5, the driver is indicated to be conservative; if the distribution characteristic of the statistical acceleration data satisfies the curve 6, it indicates that the driver is of the aggressive type.

Claims (1)

1. A driver driving style analysis method based on a smart phone is characterized by comprising the following steps:
step 1, fixing a smart phone on a vehicle driven by a driver, acquiring triaxial accelerometer data and GPS positioning data at fixed time intervals by using the smart phone, and respectively storing the triaxial accelerometer data and the GPS positioning data in multidimensional arrays Acce and Place;
step 2, acquiring the acceleration of each acquisition point in the process of the advancing direction of the vehicle according to the data of the three-axis accelerometer; calculating the speed of each acquisition point according to the GPS positioning data;
step 3, classifying the speed of each acquisition point, namely low speed, medium speed and high speed in the running process of the vehicle; respectively counting the acceleration of each acquisition point at low speed, medium speed and high speed;
step 4, calculating and obtaining a skewness coefficient SK of the acceleration of the vehicle at three speeds, wherein SK is { SK1,SK2,SK3And kurtosis coefficient PK ═ PK1,PK2,PK3}；SK1,SK2,SK3Skewness coefficients respectively representing the acceleration of the vehicle at low speed, medium speed and high speed; PK1,PK2,PK3Kurtosis coefficients respectively representing acceleration of the vehicle at low speed, medium speed, and high speed;
step 5, performing hypothesis test on the skewness coefficient SK and the kurtosis coefficient PK of the acceleration so as to evaluate the driving style of the driver, and obtaining the driving style of the driver at low speed, medium speed and high speed, wherein the driving style comprises an aggressive type, a stable type and a conservative type;
step 5.1, assuming a stable weight type skewness coefficient SK at the ith speediIs pi(ii) a Kurtosis coefficient PK of steady weight at ith speediIs qi；i∈{1,2,3}；
Step 5.2, determining critical value mu of skewness coefficient of steady weight at ith speediCritical value gamma of kurtosis coefficient of steady weighti；μi,γi＞0；
Step 5.3, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (1) is satisfied, if yes, executing a step 5.4; otherwise, executing step 5.5;
|SKi-pi|≤μi(1)
step 5.4, judging the skewness coefficient PK of the acceleration at the ith speediWhether the formula (2) is satisfied or not, if so, the driving style of the driver at the ith speed is steady; otherwise, executing step 5.6;
|PKi-qi|≤γi(2)
step 5.5, judging the skewness coefficient SK of the acceleration under the ith speediWhether the formula (3) is satisfied or not, if so, indicating that the driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative;
SKi-pi＞μi(3)
step 5.6, judging the skewness coefficient PK of the acceleration at the ith speediWhether or not the formula (4) is satisfied, and if so, it indicates thatThe driving style of the driver at the ith speed is an aggressive type; otherwise, the driving style of the driver at the ith speed is conservative:
PKi-qi＞γi(4)。
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