CN105975757A - Urgent speed reduction behavior recognition method based on vehicle driving data - Google Patents

Urgent speed reduction behavior recognition method based on vehicle driving data Download PDF

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CN105975757A
CN105975757A CN201610280484.9A CN201610280484A CN105975757A CN 105975757 A CN105975757 A CN 105975757A CN 201610280484 A CN201610280484 A CN 201610280484A CN 105975757 A CN105975757 A CN 105975757A
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main constituent
speed change
change factor
data
contribution ratio
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CN105975757B (en
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黄亮
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Rainbow Radio (beijing) New Technology Co Ltd
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Rainbow Radio (beijing) New Technology Co Ltd
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Abstract

The invention belongs to the technical field of a vehicle, and specifically relates to an urgent speed reduction behavior recognition method based on vehicle driving data. The method comprises the steps of (1), collecting the vehicle driving data, wherein the vehicle driving data comprises instantaneous fuel consumption, instantaneous acceleration, variation quantity per second of an instantaneous speed and the rotation speed of an engine; (2), obtaining a speed change factor through adoption of a principal component analysis method; (3), providing the threshold value of the speed change factor; and (4), recognizing whether the behavior at a moment is an urgent speed reduction behavior or not by comparing the score of the speed change factor at the certain moment with the threshold value, wherein the behavior at the moment is the urgent speed reduction behavior when the score of the speed change factor is less than or equal to the threshold value. According to the method provided by the invention, the defect of the prior art is overcome, and the recognition accuracy and effectiveness of the urgent speed reduction behavior can be remarkably improved.

Description

A kind of urgency deceleration Activity recognition method based on vehicle operation data
Technical field
The invention belongs to technical field of vehicle, be specifically related to a kind of urgency deceleration Activity recognition side based on vehicle operation data Method.
Background technology
Showing according to road traffic accident statistics, dangerous driving behavior is one of major reason causing vehicle accident, its Middle anxious deceleration is the dangerous driving behavior that accident is occurred frequently.For automobile manufacturing enterprise, if it is possible to accurate evaluation driver Performance in driving behavior the most suddenly deceleration behavior, just can increase car for driving behavior more reasonable design vehicle The suitability, improve safety coefficient.
Along with the development of car networking, vehicle driving trace (such as: longitude, dimension) and vehicle physical feature is (such as: instantaneous Speed, acceleration, steering wheel angle) record and preservation be possibly realized, this makes research worker can utilize abundant vehicle row Sail data to assess driving behavior.
When driving, anxious deceleration is one of dangerous driving behavior, and it often means that now driver is likely encountered Emergency, is one of signal of being in danger.Therefore the identification of anxious deceleration behavior is critically important.Except prior art uses acceleration Lower threshold judge anxious deceleration behavior, GB7258-1997 " motor vehicles operation safety condition " also specifies, for seating capacity The passenger vehicle of≤9, the braking deceleration >=2.9m/s2 of road examination inspection driving emergency brake, but, only judge according to braking deceleration Result be sometimes inaccurate, there is defect unilateral, that judging nicety rate is low.
Summary of the invention
It is an object of the invention to for the deficiencies in the prior art, it is provided that a kind of high based on PCA, accuracy rate Urgency deceleration Activity recognition method based on vehicle operation data.
The present invention solves the technical scheme of problem: urgency deceleration Activity recognition method based on vehicle operation data, including Following steps:
(1) collection vehicle running data: described vehicle operation data includes instantaneous oil consumption, instantaneous acceleration, instantaneous velocity Variable quantity per second, engine speed；
(2) the speed change factor is obtained by PCA: by described instantaneous oil consumption, instantaneous acceleration, instantaneous velocity Variable quantity per second, engine speed are as 4 original index, by the main constituent of PCA synthesis equal number, so After choose first three main constituent that accumulative variance contribution ratio is more than 85%, then first three main constituent described in choosing is with each Variance contribution ratio proportion in the variance contribution ratio of all selected main constituents carry out linear combination as weight, formed and become The speed factor；
(3) the speed change factor is taken threshold value；
(4) by comparing the speed change factor identifies in the described moment to be whether anxious in the score in certain moment and the size of threshold value Deceleration behavior: when the score of the speed change factor is less than or equal to threshold value, the described moment is anxious deceleration the moment.
Further, in described step (2), the step being obtained the speed change factor by PCA is included:
(2.1) according to the vehicle operation data gathered, data matrix X '=(x ' is set upij)n×p, wherein, n is record number, p For index number, xi′jRepresent the data of the i-th row jth row, and i≤n, j≤p；By instantaneous oil consumption, instantaneous acceleration, instantaneous velocity These 4 original index of variable quantity per second, engine speed, as the original variable of principal component analysis, take p=4；
(2.2) each achievement data is standardized, in order to eliminate indices in dimension and the difference of the order of magnitude, standard The method changed is by each data xi′jFirst deduct the average (i.e. the column mean of data matrix) of jth index, then divided by jth The standard deviation (i.e. the row standard deviation of data matrix) of index, obtains data x after standardizationij, and then obtain standardized data square Battle array；The average of each index is 0, and variance is 1；
(2.3) set up covariance matrix R, covariance matrix R according to standardized data matrix can reflect between each index Dependency, each element R of covariance matrix RijRepresenting the covariance of i, j variable, computing formula is:
R i j = Σ k = 1 n x i k * x j k ,
Wherein k is integer, represents the kth value of i, j variable；
(2.4) eigenvalue and the characteristic vector of covariance matrix R are solved: obtain p by solving characteristic equation | λ E-R |=0 Eigenvalue λi, i=1,2 ... p, wherein E is unit matrix, eigenvalue λiThe variance of size each original main constituent just, energy The reflection acceleration information amount that comprised of main constituent number, and obtain respectively corresponding to eigenvalue λiCharacteristic vector, solve Process is to be decomposed by covariance matrix R, and formula is:
R = Σ i = 1 p λ i e i e i T ,
Wherein, λiIt is the eigenvalue of covariance matrix R, eiIt is the characteristic vector of a length of p, ei TIt is eiTransposed vector；
Again by eigenvalue λiArrange according to order from big to small, obtain:
λ1>λ2>…>λp；
(2.5) variance contribution ratio of each main constituent and accumulative variance contribution ratio are calculated, and true according to accumulative variance contribution ratio Fixed final selected main constituent number:
The computing formula of variance contribution ratio is:
The computing formula of accumulative variance contribution ratio isI.e. ask accumulative for the variance contribution ratio of i before ranking With；
Choose front m the main constituent that accumulative variance contribution ratio is more than 85%, take m=3；
(2.6) main constituent calculating formula is write out by loading matrix:
Loading matrix is the matrix representing main constituent with original variable linear transformation relation, and the coefficient of loading matrix is the most every The value of individual main constituent characteristic of correspondence vector, writes out i-th main constituent f accordinglyiComputing formula:
fi=e1i*x1+e2i*x2+…+epi*xp,
Wherein, epiIt is the i-th component of pth characteristic vector, xpIt it is pth index；It is a n-dimensional vector, therefore The main constituent obtained also is a n-dimensional vector；
(2.7) score of the speed change factor of every record, the change of i-th record is calculated according to m the main constituent selected Speed factor siThe computing formula of score be:
s i = w ~ 1 f 1 i + w ~ 2 f 2 i + ... + w ~ k f k i + ... + w ~ m f m i , 1≤k≤m；
Wherein any one fkiRepresent the i-th component of kth main constituent, coefficientComputing formula be:
w ~ k = w k Σ j = 1 m w j .
Further, in described step (3), the threshold value of the described speed change factor is-4.
Further, in described step (2.5), choose first three main constituent more than 85% of accumulative variance contribution ratio.
Further, in described step (2.5), the variance contribution ratio of first three main constituent selected is respectively 53.5%, 21.8%, 14.5%, the coefficient of first three main constituent described then calculated in step (2.7) is respectively 0.596,0.243,0.161, therefore speed change factor s of i-th recordiThe computing formula of score be:
si=0.596*f1i+0.243*f2i+0.161*f3i。
Further, described in the accumulation contribution rate of first three main constituent that selects can reach more than 89.8%, this is described Three main constituents can represent the velocity variations information of 89.8%.
The invention have the benefit that the present invention utilizes the dimensionality reduction thought of PCA, multi objective is converted into comprehensive Close index, reduce the dimension of observation space, obtain topmost information, by by the several variablees main one-tenth relevant to anxious deceleration Point analytic process carries out comprehensively, significantly improves the anxious accuracy of deceleration Activity recognition, effectiveness.
Accompanying drawing explanation
Fig. 1 is the flow chart of urgency deceleration Activity recognition method based on vehicle operation data of the present invention；
Fig. 2 is the contrast broken line of instantaneous velocity in application the method for the invention the speed change factor obtained and experiment of slowing down Figure.
Detailed description of the invention
Below in conjunction with the accompanying drawings and specific embodiment, the present invention is further illustrated.
As it is shown in figure 1, urgency deceleration Activity recognition method based on vehicle operation data, comprise the steps:
(1) collection vehicle running data: described vehicle operation data includes instantaneous oil consumption, instantaneous acceleration, instantaneous velocity Variable quantity per second, engine speed；
(2) the speed change factor is obtained by PCA: by described instantaneous oil consumption, instantaneous acceleration, instantaneous velocity Variable quantity per second, engine speed are as 4 original index, by the main constituent of PCA synthesis equal number, so After choose first three main constituent that accumulative variance contribution ratio is more than 85%, then first three main constituent described in choosing is with each Variance contribution ratio proportion in the variance contribution ratio of all selected main constituents carry out linear combination as weight, formed and become The speed factor；
(3) the speed change factor is taken threshold value；
(4) by comparing the speed change factor identifies in the described moment to be whether anxious in the score in certain moment and the size of threshold value Deceleration behavior: when the score of the speed change factor is less than or equal to threshold value, the described moment is anxious deceleration the moment.
In described step (2), the step being obtained the speed change factor by PCA is included:
(2.1) according to the vehicle operation data gathered, data matrix X '=(x is set upi′j)n×p, wherein, n is record number, p For index number, x 'ijRepresent the data of the i-th row jth row, and i≤n, j≤p；By instantaneous oil consumption, instantaneous acceleration, instantaneous velocity These 4 original index of variable quantity per second, engine speed, as the original variable of principal component analysis, take p=4；
(2.2) each achievement data is standardized, in order to eliminate indices in dimension and the difference of the order of magnitude, standard The method changed is by each data xi′jFirst deduct the average (i.e. the column mean of data matrix) of jth index, then divided by jth The standard deviation (i.e. the row standard deviation of data matrix) of index, obtains data x after standardizationij, and then obtain standardized data square Battle array；The average of each index is 0, and variance is 1；
(2.3) set up covariance matrix R, covariance matrix R according to standardized data matrix can reflect between each index Dependency, each element R of covariance matrix RijRepresenting the covariance of i, j variable, computing formula is:
R i j = Σ k = 1 n x i k * x j k ,
Wherein k is integer, represents the kth value of i, j variable；
(2.4) eigenvalue and the characteristic vector of covariance matrix R are solved: obtain p by solving characteristic equation | λ E-R |=0 Eigenvalue λi, i=1,2 ... p, wherein E is unit matrix, eigenvalue λiThe variance of size each original main constituent just, energy The reflection acceleration information amount that comprised of main constituent number, and obtain respectively corresponding to eigenvalue λiCharacteristic vector, solve Process is to be decomposed by covariance matrix R, and formula is:
R = Σ i = 1 p λ i e i e i T ,
Wherein, λiIt is the eigenvalue of covariance matrix R, eiIt is the characteristic vector of a length of p, ei TIt is eiTransposed vector；
Again by eigenvalue λiArrange according to order from big to small, obtain:
λ1>λ2>…>λp；
(2.5) variance contribution ratio of each main constituent and accumulative variance contribution ratio are calculated, and true according to accumulative variance contribution ratio Fixed final selected main constituent number:
The computing formula of variance contribution ratio is:
The computing formula of accumulative variance contribution ratio isI.e. ask accumulative for the variance contribution ratio of i before ranking With；
Choose front m the main constituent that accumulative variance contribution ratio is more than 85%, take m=3；
(2.6) main constituent calculating formula is write out by loading matrix:
Loading matrix is the matrix representing main constituent with original variable linear transformation relation, and the coefficient of loading matrix is the most every The value of individual main constituent characteristic of correspondence vector, writes out i-th main constituent f accordinglyiComputing formula:
fi=e1i*x1+e2i*x2+…+epi*xp,
Wherein, epiIt is the i-th component of pth characteristic vector, xpIt it is pth index；It is a n-dimensional vector, therefore The main constituent obtained also is a n-dimensional vector；
(2.7) score of the speed change factor of every record, the change of i-th record is calculated according to m the main constituent selected Speed factor siThe computing formula of score be:
s i = w ~ 1 f 1 i + w ~ 2 f 2 i + ... + w ~ k f k i + ... + w ~ m f m i , 1≤k≤m；
Wherein any one fkiRepresent the i-th component of kth main constituent, coefficientComputing formula be:
w ~ k = w k Σ j = 1 m w j .
In described step (3), the threshold value of the described speed change factor is-4.
In described step (2.5), choose first three main constituent more than 85% of accumulative variance contribution ratio.
In described step (2.5), the variance contribution ratio of first three main constituent selected is respectively 53.5%, and 21.8%, 14.5%, the coefficient of first three main constituent described then calculated in step (2.7) is respectively 0.596, and 0.243, 0.161, therefore speed change factor s of i-th recordiThe computing formula of score be:
si=0.596*f1i+0.243*f2i+0.161*f3i。
The accumulation contribution rate of described first three selected main constituent can reach more than 89.8%, and these three main constituent is described The velocity variations information of 89.8% can be represented.
Using the method for the invention to test, specific experiment process is:
Automobile after start-up, first slowly accelerates a period of time, brings to a halt when reaching higher speed per hour, makes automobile anxious suddenly Slow down, the most again speed is slowly promoted, when speed reaches high value, unclamp throttle, allow car speed slowly reduce, until Stop.The speed data of experimental record as in figure 2 it is shown, fine rule represents the change of the instantaneous velocity of traveling, thick line represent speed change because of The fluctuation of sub-score, abscissa express time, the vertical coordinate on the left side represents instantaneous velocity, the vertical coordinate on the right represent speed change because of Son.Present invention determine that and identify that the anxious standard slowing down the moment is: when certain moment speed change factor score≤-4, this moment is anxious subtracting The speed moment.
As in figure 2 it is shown, the method for the invention is identifying anxious deceleration the moment, distinguish slow moderating process aspect the most effective. With the data instance in Fig. 2, at 43s, speed change factor score is-7.64, less than threshold value-4, when being therefore identified as anxious deceleration Carving, just can see in conjunction with instantaneous velocity change curve, after this moment of 43s, in 4s, instantaneous velocity is reduced to by 57.75km/h 0.3km/h, the anxious decelerating phase in the most corresponding experiment, therefore this identification visible is effective, and equally, this method of discrimination is to slowly Moderating process also have differentiation, as after 80s, automobile just reaches about 57km/h in the range of decrease of more than 13s speed, just corresponding The slow decelerating phase in experiment, and the speed change factor is the highest this moment, and it is not recognized as anxious deceleration, therefore with practical situation phase Symbol.By carrying out the experiment of other groups, it is also demonstrated that the effectiveness of anxious deceleration Activity recognition method of the present invention.
The ultimate principle of the present invention is:
The present invention is by means of PCA, and PCA is the statistical method of a kind of Data Dimensionality Reduction, it by In an orthogonal transformation, become to summarize a few index of original most information by original multiple index comprehensives, do not damage On the premise of losing important information, reduce the latitude of observation space.
In practical problem is studied, for comprehensively and systematically problem analysis, it is necessary to consider numerous influence factor.These relate to And factor be commonly referred to as index or variable.Because each index reflects some studied a question to varying degrees There is certain dependency between information, and index each other, thus the information of the statistical data reflection of gained is to a certain extent There is overlap.When with study of statistical methods Multivariable, variable can increase amount of calculation too much and increase the complexity of problem analysis Property, it is desirable to during carrying out quantitative analysis, the variable related to is less, and the quantity of information obtained is more.Principal component analysis Adapt to what this requirement produced just, be the ideal tools of such issues that solve.
On the whole, principal component analysis is intended to utilize the thought of dimensionality reduction, and multi objective is converted into a few aggregative indicator, Reduce the dimension of observation space, to obtain topmost information.Assume there be p index, the most at most have p aggregative indicator (main Composition).Not increasing due to population variance and do not subtract, the variance of front several aggregative indicatores is relatively big, and the variance of the most several aggregative indicatores is less. Strictly, only front several aggregative indicatores just deserve to be called " leading " composition, rear several aggregative indicatores actually " secondary " composition.In practice always It is that reservation is front several, several after ignoring.Retain how many main constituents and depend on that the cumulative variance of member-retaining portion is in variance summation Percentage.
Four original index are formed four main constituents through combination by the present invention, then choose accumulation contribution rate be 85% with On first three main constituent, then first three main constituent is carried out linear combination using its variance contribution ratio ratio as weight, finally Synthesizing an aggregative indicator, i.e. the speed change factor, then having judged the urgency deceleration behavior of automobile by the speed change factor being taken threshold value.
The present invention is not limited to above-mentioned embodiment, in the case of without departing substantially from flesh and blood of the present invention, and art technology Personnel it is contemplated that any deformation, improve, replace and each fall within protection scope of the present invention.

Claims (6)

1. a urgency deceleration Activity recognition method based on vehicle operation data, it is characterised in that comprise the steps:
(1) collection vehicle running data: described vehicle operation data include instantaneous oil consumption, instantaneous acceleration, instantaneous velocity every Second variable quantity, engine speed；
(2) obtain the speed change factor by PCA: by described instantaneous oil consumption, instantaneous acceleration, instantaneous velocity per second Variable quantity, engine speed, as 4 original index, by the main constituent of PCA synthesis equal number, are then selected Take first three main constituent that accumulative variance contribution ratio is more than 85%, then first three main constituent described in choosing is with respective side Difference contribution rate proportion in the variance contribution ratio of all selected main constituents carries out linear combination as weight, formed speed change because of Son；
(3) the speed change factor is taken threshold value；
(4) identify whether the described moment is anxious deceleration in the score in certain moment and the size of threshold value by comparing the speed change factor Behavior: when the score of the speed change factor is less than or equal to threshold value, the described moment is anxious deceleration the moment.
Urgency deceleration Activity recognition method based on vehicle operation data the most according to claim 1, it is characterised in that described In step (2), the step being obtained the speed change factor by PCA is included:
(2.1) according to the vehicle operation data gathered, data matrix X '=(x ' is set upij)n×p, wherein, n is record number, and p is for referring to Mark number, x 'ijRepresent the data of the i-th row jth row, and i≤n, j≤p；By instantaneous oil consumption, instantaneous acceleration, instantaneous velocity per second These 4 original index of variable quantity, engine speed, as the original variable of principal component analysis, take p=4；
(2.2) being standardized each achievement data, standardized method is by each data x 'ijFirst deduct jth index Average, then the standard deviation divided by jth index, obtain data x after standardizationij, and then obtain standardized data matrix；
(2.3) each element R of covariance matrix R, covariance matrix R is set up according to standardized data matrixijRepresent i, j variable Covariance, computing formula is:
R i j = Σ k = 1 n x i k * x j k ,
Wherein k is integer；
(2.4) eigenvalue and the characteristic vector of covariance matrix R are solved: obtain p feature by solving characteristic equation | λ E-R |=0 Value λi, i=1,2 ... p, wherein E is unit matrix；And obtain respectively corresponding to eigenvalue λiCharacteristic vector, solution procedure be by Covariance matrix R decomposes, and formula is:
R = Σ i = 1 p λ i e i e i T ,
Wherein, λiIt is the eigenvalue of covariance matrix R, eiIt is the characteristic vector of a length of p, ei TIt is eiTransposed vector；
Again by eigenvalue λiArrange according to order from big to small, obtain:
λ1>λ2>…>λp；
(2.5) calculate the variance contribution ratio of each main constituent and accumulative variance contribution ratio, and determine according to accumulative variance contribution ratio The most selected main constituent number:
The computing formula of variance contribution ratio is:
The computing formula of accumulative variance contribution ratio is
Choose front m the main constituent that accumulative variance contribution ratio is more than 85%, take m=3；
(2.6) main constituent calculating formula is write out by loading matrix:
The value of coefficient the most each main constituent characteristic of correspondence vector of loading matrix, writes out i-th main constituent f accordinglyiCalculating Formula:
fi=e1i*x1+e2i*x2+…+epi*xp,
Wherein, epiIt is the i-th component of pth characteristic vector, xpIt it is pth index；
(2.7) calculate the score of the speed change factor of every record according to m main constituent selecting, i-th speed change recorded because of Sub-siThe computing formula of score be:
s i = w ~ 1 f 1 i + w ~ 2 f 2 i + ... + w ~ k f k i + ... + w ~ m f m i , 1 ≤ k ≤ m ;
Wherein any one fkiRepresent the i-th component of kth main constituent, coefficientComputing formula be:
w ~ k = w k Σ j = 1 m w j .
Urgency deceleration Activity recognition method based on vehicle operation data the most according to claim 1, it is characterised in that described In step (3), the threshold value of the described speed change factor is-4.
Urgency deceleration Activity recognition method based on vehicle operation data the most according to claim 2, it is characterised in that described In step (2.5), choose first three main constituent more than 85% of accumulative variance contribution ratio.
Urgency deceleration Activity recognition method based on vehicle operation data the most according to claim 2, it is characterised in that described In step (2.5), the variance contribution ratio of first three main constituent selected is respectively 53.5%, and 21.8%, 14.5%, then exist The coefficient of first three main constituent described calculated in step (2.7) is respectively 0.596, and 0.243,0.161, therefore i-th note Speed change factor s of recordiThe computing formula of score be:
si=0.596*f1i+0.243*f2i+0.161*f3i。
Urgency deceleration Activity recognition method based on vehicle operation data the most according to claim 4, it is characterised in that described The accumulation contribution rate of first three main constituent selected can reach more than 89.8%.
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CN114462857A (en) * 2022-02-09 2022-05-10 中国汽车工程研究院股份有限公司 High-risk vehicle screening method for new energy automobile and storage medium
CN114821858A (en) * 2022-04-29 2022-07-29 东风商用车有限公司 Vehicle index abnormity graphic representation method, device, equipment and storage medium

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