CN105799709B - The recognition methods of vehicle zig zag and device - Google Patents
The recognition methods of vehicle zig zag and device Download PDFInfo
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- CN105799709B CN105799709B CN201610139142.5A CN201610139142A CN105799709B CN 105799709 B CN105799709 B CN 105799709B CN 201610139142 A CN201610139142 A CN 201610139142A CN 105799709 B CN105799709 B CN 105799709B
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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
- B60W40/10—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 vehicle motion
- B60W40/109—Lateral acceleration
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Abstract
The invention discloses a kind of recognition methods of vehicle zig zag, the recognition methods of the vehicle zig zag includes the following steps：Obtain the monitoring data that the acceleration transducer of vehicle loading returns；The side acceleration for obtaining the vehicle is calculated by preset algorithm according to the monitoring data, the direction of the side acceleration is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form；Judge whether the vehicle takes a sudden turn according to the side acceleration.The invention also discloses a kind of identification devices of vehicle zig zag.The present invention obtains side acceleration using the monitoring data that acceleration transducer returns, and vehicle zig zag is identified according to side acceleration, makes that the identification taken a sudden turn to vehicle is more accurate and cost is lower.
Description
Technical field
The recognition methods taken a sudden turn the present invention relates to automobile technical field more particularly to vehicle and device.
Background technology
In intelligent transportation system, auxiliary drives, driving behavior analysis is very important a part.To driving behavior
Automatically analyze and help to find and prevent to influence traffic safety, cause the several factors of traffic accident.Wherein, zig zag is driven
Identification be the pith analyzed the driving behavior of driver, this analysis result can remind driver to note
The problems such as meaning, avoids fatigue driving, and energy is not concentrated, while many insurance companies are also by driving the driving behaviors such as zig zag
Analysis foundation insure model.
It is one kind in driving behavior to drive zig zag, and the behavior does not provide explicitly in traffic law, usually drives
It refers to being more than that defined speed is turned in the sharp turn of road in driving procedure to sail zig zag, and this linear velocity may be led
Cause the uncomfortable feeling of driver and passenger of automobile.The harm for driving zig zag most serious is that vehicle lateral acceleration is excessive, is caused
The traffic accidents such as the sideslip of vehicle even overturning.
According to existing gyroscope and acceleration transducer detection angular speed variation come identify zig zag be current detection and
Identify the effective means of zig zag, Judging index：
1, angular acceleration：Moment angular speed >=9 °/s；
2, differential seat angle：Orientation angle difference absolute value is travelled in five seconds is more than 45 °；
3, three seconds inside turn angles between 30-45 ° (including 30 °, do not include 45 °), and speed reduction is less than in three seconds
30%, and average speed is more than 50mph；
4, three seconds inside turn angles between 45-60 ° (including 45 °, do not include 60 °), and speed reduction is less than in three seconds
50%, and average speed is more than 45mph；
5, three seconds inside turn angles between 60-90 ° (including 60), and speed is reduced less than 75% in three seconds, and it is average
Speed is more than 30mph.
But this method has the following defects：
1, gyroscope is expensive, and for the embedded product of low-power consumption, and gyroscope chip expends larger work(
Rate causes fever and resource consumption serious；
2, gyroscope has more serious in long time integration error, can judge that zig zag has shadow to changing according to angular speed
It rings；
3, according to being completely dependent on standard as angular speed, without the subjective sensation in view of real driver and passenger,
Not from the factor for directly causing to drive peace --- the angle of transverse acceleration drives zig zag to define, and direct consequence is just
It is judgement of the direct interference due to driving caused safety problem severity of taking a sudden turn.For example, at home and abroad some highways
Turning area increases the superelevation part on road surface for safety and comfortable consideration, is allowed than flat when these bends are turned
Bent road surface has with high angular speed (linear velocity in other words)；For another example, when 36mph three seconds inside turn angles at 90 °
Cross force only do 1/4 or so of same turning (90 °) with the speed of 144mph.In other words if from transverse acceleration
From the point of view of, under the speed of 144mph in fact three seconds inside turn angles only need to turn 22.5 ° just have it is same laterally plus
Speed, if this illustrates from the point of view of security standpoint (ensureing that automobile is not turned on one's side, do not break away) and driver and passenger's comfort aspect, with
There are serious shortcomings for the definition of upper zig zag.
Invention content
The main purpose of the present invention is to provide a kind of recognition methods of vehicle zig zag and devices, it is intended to solve to use top
Spiral shell instrument takes a sudden turn error greatly by detecting angular speed variation identification vehicle, and the higher technical problem of cost.
To achieve the above object, the present invention provides a kind of recognition methods of vehicle zig zag, the knowledge of the vehicle zig zag
Other method includes the following steps：
Obtain the monitoring data that the acceleration transducer of vehicle loading returns；
The side acceleration for obtaining the vehicle, the lateral acceleration are calculated by preset algorithm according to the monitoring data
The direction of degree is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form；
Judge whether the vehicle takes a sudden turn according to the side acceleration.
Preferably, described to judge that the step of whether vehicle takes a sudden turn includes according to the side acceleration：
Judge whether the side acceleration is more than pre-set threshold value, if so, judge that the vehicle takes a sudden turn, it is no
Then, judge that the vehicle does not take a sudden turn；
Or judge whether the increment of the side acceleration is more than preset increments threshold values according to the side acceleration, if
It is then to judge that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
Preferably, the side acceleration that obtains the vehicle of being calculated by preset algorithm according to the monitoring data
Step includes：
Obtain the plane parameter of the gravity direction and direction of advance composition plane of the vehicle；
The lateral acceleration for obtaining the vehicle is calculated by preset algorithm according to the plane parameter and the monitoring data
Degree.
Preferably, the step of gravity direction for obtaining the vehicle and direction of advance form the plane parameter of plane is wrapped
It includes：
Three axis component data of the acceleration transducer after noise reduction are obtained according to the monitoring data；
According to three axis component data of the acceleration transducer after the noise reduction, asked by multiple linear regression approximating method
Go out the plane parameter of the gravity direction and direction of advance composition plane of the vehicle.
Preferably, the step of gravity direction for obtaining the vehicle and direction of advance form the plane parameter of plane is wrapped
It includes：
Gravity direction and the advance side of the preset vehicle are obtained from server or locally according to the information of the vehicle
To the plane parameter of composition plane.
In addition, to achieve the above object, the present invention also provides a kind of identification device of vehicle zig zag, the vehicle racings
Curved identification device includes：
Data acquisition module, the monitoring data that the acceleration transducer for obtaining vehicle loading returns；
Side acceleration acquisition module obtains the vehicle for being calculated by preset algorithm according to the monitoring data
Side acceleration, the normal direction in the direction of the side acceleration and the plane of gravity direction and the direction of advance composition of the vehicle
It is consistent to measure direction；
Judgment module, for judging whether the vehicle takes a sudden turn according to the side acceleration.
Preferably, the judgment module includes：
Side acceleration judgment module, for judging whether the side acceleration is more than pre-set threshold value, if so, judgement
The vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn；
It is additionally operable to judge whether the increment of the side acceleration is more than preset increments threshold values according to the side acceleration,
If so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
Preferably, the side acceleration acquisition module includes：
Plane parameter acquiring unit, the plane ginseng of gravity direction and direction of advance composition plane for obtaining the vehicle
Number；
Side acceleration obtaining unit, for being calculated by preset algorithm according to the plane parameter and the monitoring data
Obtain the side acceleration of the vehicle.
Preferably, the plane parameter acquiring unit includes：
Component data acquiring unit, for three axis point according to the acceleration transducer after monitoring data acquisition noise reduction
Measure data；
Plane parameter unit is solved, for the three axis component data according to the acceleration transducer after the noise reduction, is passed through
Multiple linear regression approximating method finds out the plane parameter of the gravity direction and direction of advance composition plane of the vehicle.
Preferably, the plane parameter acquiring unit includes：
Preset plane parameter acquiring unit, for obtaining preset institute from server or locally according to the information of the vehicle
State the plane parameter of the gravity direction and direction of advance composition plane of vehicle.
A kind of recognition methods for vehicle zig zag that the embodiment of the present invention proposes and device, are returned by acceleration transducer
Monitoring data, calculate the side acceleration for obtaining vehicle, while side acceleration judged, judge whether to be more than default
Threshold values realizes so that it is determined that the method whether vehicle takes a sudden turn to reducing vehicle cost, and when vehicle occurs suddenly
Can be accurately and timely when turning judge that the vehicle has occurred and that zig zag.
Description of the drawings
Fig. 1 is the flow diagram of the recognition methods first embodiment of vehicle of the present invention zig zag；
Fig. 2 is the flow diagram of the recognition methods second embodiment of vehicle of the present invention zig zag；
Fig. 3 is the high-level schematic functional block diagram of the identification device first embodiment of vehicle of the present invention zig zag；
Fig. 4 is the high-level schematic functional block diagram of the identification device second embodiment of vehicle of the present invention zig zag；
Fig. 5 is the high-level schematic functional block diagram of the identification device 3rd embodiment of vehicle of the present invention zig zag；
Fig. 6 is the high-level schematic functional block diagram of the identification device fourth embodiment of vehicle of the present invention zig zag.
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific implementation mode
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
The primary solutions of the embodiment of the present invention are：Obtain the monitoring number that the acceleration transducer of vehicle loading returns
According to；The side acceleration for obtaining the vehicle is calculated by preset algorithm according to the monitoring data, the side acceleration
Direction is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form；According to the lateral acceleration
Degree judges whether the vehicle takes a sudden turn.
Since the prior art judges whether vehicle occurs racing bending method, cost in such a way that gyroscope detects angular speed
It is high and monitoring data error after prolonged use is deposited as inertia device due to gyroscope become larger, exist to whether vehicle occurs
Zig zag judges inaccurate problem.
The present invention provides a solution, the method for monitoring vehicle side acceleration using acceleration transducer so that
The identification whether taken a sudden turn to vehicle is more accurate, while cost is lower.
It should be noted that the method that vehicle zig zag is identified in the monitoring data returned using acceleration transducer,
There are the following problems：
1, vehicle axis system vector how is obtained；
2, sensor chip coordinate system vector how is obtained；
3, because of influence of the weight component to each axis on slope, the various external force of consideration removal (mainly gravity and frictional force,
It is exactly actual vehicle acceleration of motion to eliminate gravity and frictional force factor remaining acceleration) to each number of axle according to effect, consider real
Border vehicle movement acceleration (data that acceleration transducer is shown might not react state of motion of vehicle)；
4, consider that acceleration transducer instantaneous value noise is larger, need to be filtered；
5, side acceleration judgment threshold is it is determined that how consideration turning radius, the relationship of speed, determine judgment threshold；
6, sensor chip coordinate system projects accuracy problem to vehicle axis system；
7, the side acceleration accuracy test problem that side is separated；
8, zig zag mode input data time duration problems of value.
As a kind of method preferably, for the problem described in above-mentioned 1 and 2, the embodiment of the present invention is according to directly asking side
To the method in vector acceleration direction, the plane equation vertical with side acceleration component vector can be found out, that is, is found out resonable
Think under situation, the plane equation of gravity direction and vehicle forward direction composition, planar process vector direction and side acceleration side
It, can be to avoid the problem described in above-mentioned 1 and 2 according to this scheme to consistent；
For the problem described in above-mentioned 3-8, because gravity can be reflected in inside acceleration information on the slope, and frictional force
It will not be reflected in inside acceleration information, so the embodiment of the present invention before projection, removes the influence of gravimetric data；
For the problem described in above-mentioned 4, the embodiment of the present invention solves the wink of sensor signal by being fitted the method solved
When noise problem；
In view of the problem described in above-mentioned 5, when measuring acceleration rate threshold, it is contemplated that the influence of car speed and radius
(a=v*v/r), it may be considered that side acceleration threshold value is measured under the same radius；
The problem of being measured to vehicle axis system projection accuracy for the sensing data described in 6 and 7, the embodiment of the present invention
It is projected according to space geometry principle, therefore there is no the problems described in 6 and 7；
The problem of zig zag mode input data time duration values being directed to described in 8, is calculating lateral accelerate
When spending, the duration is longer, should be bigger in turning this period value, therefore the embodiment of the present invention passes through that test of many times is default to close
The duration values of reason.
In an ideal case, we can isolate a specific plane and the normal vector based on this plane.Here
Using the direction of plane normal vector as the direction of side acceleration, and before planar direction is respectively directed to gravity direction and vehicle
Into direction, the plane equation under based on sensor coordinate system is first determined by calibrating, it is possible to consider that separating plane is found out
The method of side acceleration vector.
According to the problem described in above-mentioned 4, plane equation is found out in a manner of a kind of fitting.The data of fitting can directly from
Sensing data obtains, in an ideal case (plane is enough " flat ", and vertical with gravity), can obtain and accelerate about in gravity
Data in the case of degree and straight line acceleration of motion, for being fitted the side of gravity direction and straight ahead direction place plane
Journey.
The general plane equation of plane where setting gravity direction and straight ahead direction as：
Y=β0+β1x+β2z
Wherein x, y, z respectively represents three axis components of acceleration transducer.It is clear that relationship meets two between x, y, z
First linear regression relation.Consider that multiple linear regression approximating method finds out plane parameter (i.e. plane normal vector) namely polynary line
Property regression parameter estimation.Most common method is least square method (OLS) to multiple linear regression parameter Estimation at present.
Utilize plane where can obtaining gravity direction and direction of advance based on least square method multiple linear regression analysis method
Parameter, that is, the direction vector of side acceleration and plane parameter group at vector be to overlap.This plane parameter vector
It is fixed, whether not with being gradient plane and change, it has weighed the plane equation vertical with ground where running car
The vector of parameter, this equation parameter composition is exactly side acceleration direction vector.
Under sensor chip coordinate system, it is known that side acceleration direction vector and sensor output data, according to one
To another projecting method, the projection value that can calculate sensing data in side acceleration direction vector namely detaches vector
Side acceleration values out.
It is fixed then can to carry out judgment method in the side acceleration values of the separation on any road surface to zig zag for known automobile
Justice.In theory, side acceleration reflects the size of centripetal force, according to rigid motion formula：
It can be seen that in the case where turning radius is certain, side acceleration is bigger, and angular speed is bigger, and angular speed reflects
Unit interval angle of turn can set one threshold value of side acceleration to determine whether zig zag.In practical applications,
Turning radius is arranged to a stable constant value, that is, positive linear relationships, i.e., the side acceleration within the unit interval is presented in a and w*w
Size reflects angle of turn degree, so as to be used as setting side acceleration threshold value, big in real time according to side acceleration
It is small to judge to take a sudden turn with threshold value comparison.
Referring to Fig.1, it is the first embodiment for the recognition methods that vehicle of the present invention takes a sudden turn, the identification of the vehicle zig zag
Method includes：
Step S100 obtains the monitoring data that the acceleration transducer of vehicle loading returns.
Vehicle when driving, obtains the monitoring data that vehicle acceleration transducer mounted returns, and the vehicle can be taken
Multiple acceleration transducers are carried, the monitoring data by obtaining multiple acceleration transducers returns obtain more after being weighted averagely
For accurate monitoring data.
Step S200 calculates the side acceleration for obtaining the vehicle according to the monitoring data by preset algorithm.
It is transformed under vehicle axis system by space according to the monitoring data, isolates the lateral vector acceleration of vehicle,
The direction of the side acceleration is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form.
Step S300 judges whether the vehicle takes a sudden turn according to the side acceleration.
Judged whether vehicle occurs zig zag according to the vehicle side acceleration, it is anticipated that, Yi Zhongke
Can embodiment be：Judge whether the side acceleration of the vehicle is more than pre-set threshold value, if so, judging the vehicle hair
Raw zig zag, otherwise, it is determined that the vehicle does not take a sudden turn；Alternatively possible embodiment is：Laterally added according to described
Whether the increment of side acceleration described in velocity estimated is more than preset increments threshold values, if so, judging that racing occurs for the vehicle
It is curved, otherwise, it is determined that the vehicle does not take a sudden turn.
In the present embodiment, the monitoring data returned by obtaining vehicle acceleration transducer mounted, calculate and obtain
The side acceleration of the vehicle is identified according to the side acceleration to whether the vehicle occurs zig zag so that
It is more accurate and cost is lower to the identification of vehicle zig zag while anxious to vehicle in terms of driver and passenger's direct feel
Turning is identified so that the scope of application is wider.
Further, it is the second embodiment for the recognition methods that vehicle of the present invention takes a sudden turn, is based on above-mentioned reality shown in FIG. 1
Example is applied, the step S300 judges whether the vehicle occurs zig zag and include according to the side acceleration：
Step S301, judges whether the side acceleration is more than pre-set threshold value, if so, it is anxious to judge that the vehicle occurs
Turning, otherwise, it is determined that the vehicle does not take a sudden turn.
It is compared with pre-set threshold value according to the side acceleration of the vehicle of acquisition, judges that the side acceleration is
It is no to be more than pre-set threshold value, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn；Tool
It when body is implemented, is tested and is determined by test of many times, the pre-set threshold value value range is more suitable between 3.5G-4.5G.
Step S302 judges whether the increment of the side acceleration is more than preset increments valve according to the side acceleration
Value, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
Be compared with pre-set threshold value according to the increment of the side acceleration of the vehicle of acquisition, judge it is described lateral plus
Whether the increment of speed is more than preset increments threshold values, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle
It does not take a sudden turn；It is determined when it is implemented, being tested by test of many times, it is more suitable that the pre-set threshold value chooses 0.8G.
For this step, when it is implemented, the vehicle on the slope when driving, according to acceleration chip characteristics, accelerates
Degrees of data contains the effect of gravity, but the acceleration of vehicle actual travel should be gravity, frictional force, power work on the slope
The sum of with, and acceleration transducer can not reflect the effect (frictional force acts on vehicle rather than chip) of frictional force, that is,
It says when calculating side acceleration, gravity has included but friction force effect is not taken into account, so examining
Consider two scenes：
1, vehicle descending takes a sudden turn suddenly：Centripetal component-the gravity of centripetal acceleration actual data value=frictional force point at this time
Amount+powertrain components, but centripetal acceleration sensor output value=powertrain components-weight component.Namely use sensor output value
The centripetal acceleration separated in real time is (being less than in the case of plane) less than normal；
2, vehicle driving up takes a sudden turn suddenly：Centripetal component+the gravity of centripetal acceleration actual data value=frictional force point at this time
Amount+powertrain components, but centripetal acceleration sensor output value=powertrain components+weight component.Namely use sensor output value
The centripetal acceleration separated in real time is (being less than in the case of plane) still less than normal.
So the threshold value measured on slope should be less than the acceleration rate threshold measured in the plane.Because A=(x, y, z) is real
Border includes gravity, but there is no the effect comprising friction, the effect of frictional force plays positive interaction；For plane, A
Become smaller, the side acceleration calculated in real time on slope is less than normal, and (practical small gcos (c) * u, it is oblique that u, which generally takes 0.6, c,
Angle of slope, angle is bigger, and gap is smaller), that is to say, that on the slope a fixed threshold is given according to what is measured in plane
Value must have can just be judged to taking a sudden turn than actual larger acceleration, but actually take a sudden turn already
.So to judge to cause, there is a situation where take a sudden turn but do not report according to only plane threshold.In consideration of it, it is contemplated that increasing
Add one within the unit interval side acceleration delta threshold come judge to take a sudden turn on the slope judgement situation.This delta threshold
Not related with the gradient, thus its threshold design should peaceful areal acceleration delta threshold it is consistent.The setting of this threshold value
And measured according to test of many times.Additional conditions of this threshold value as decision condition in the plane, can be further
Increase the accuracy judged.
In the present embodiment, compared or passed through institute by the side acceleration and pre-set threshold value of the vehicle of acquisition
The increment and preset increments threshold values for stating the side acceleration of vehicle are compared, and are judged whether the vehicle takes a sudden turn, are made
The identification that take a sudden turn for the vehicle is more acurrate, and the vehicle on the slope when driving, still can be to the vehicle
Zig zag whether occurs and makes accurate judgement.
Further, with reference to Fig. 2 above-mentioned Fig. 1 is based on for the 3rd embodiment of the recognition methods of vehicle of the present invention zig zag
Shown in embodiment, the step S200 passes through preset algorithm according to the monitoring data and calculates and obtain the lateral of the vehicle
Acceleration includes：
Step S210 obtains the plane parameter of the gravity direction and direction of advance composition plane of the vehicle.
The three axis component data that the acceleration transducer after noise reduction is obtained according to the monitoring data, after the noise reduction
Acceleration transducer three axis component data, by multiple linear regression approximating method find out the vehicle gravity direction and
Direction of advance forms the plane parameter of plane.
Step S220 is calculated by preset algorithm according to the plane parameter and the monitoring data and is obtained the vehicle
Side acceleration.
It is calculated and is obtained by preset multiple linear regression model algorithm according to the plane parameter and the monitoring data
The side acceleration of the vehicle.
The present embodiment is when it is implemented, using multiple linear regression model using common least square method (OLS) to parameter
When being estimated, there is following hypothesis：
1：Zero-mean assumes：E(μi)=0, i=1,2 ..., n；
2：Assume (variance of μ is same constant) with variance
3：Without autocorrelation：Cov(μi, μj)=E (μiμj)=0, (i ≠ j, i, j=1,2 ..., n)；
4：Stochastic error μ (this assumes automatic set up) uncorrelated to explanatory variable X：Cov(Xji, μi)=0, (j=1,
2 ..., k, i=1,2 ..., n)；
5：It is zero that stochastic error μ, which obeys mean value, variance σ2Normal distribution：
6：Multicollinearity is not present between explanatory variable：Rank (X)=k+1≤n；
According to above-mentioned six it is assumed that in the design plane equation y=β0+β1x+β2Z analyzes it and assumes to six now
Meet situation：
(1) according to assuming 1, stochastic error is not considering sensor mechanism problem condition when sample is enough
Under, according to the chip information that chip manufacturer provides, random error should meet standard gaussian model, it is expected that meeting：E(μi)=
0, i=1,2 ..., n, n takes 1；
(2) according to assuming 2, when sample is enough, in the case where not considering sensor mechanism problem condition, according to chip
The chip information that manufacturer provides, random error should meet standard gaussian model, and variance meets：
(3) according to assuming 3, when sample is enough, in the case where not considering sensor mechanism problem condition, according to chip
The chip information that manufacturer provides is from uncorrelated between random error；
(4) assume that 4 set up automatically；
(5) it when sample is enough, in the case where not considering sensor mechanism problem condition, is provided according to chip manufacturer
Chip information, random error should meet standard gaussian model；
(6) x and z is orthogonal between variable, and Problems of Multiple Synteny is also just not present.
Judged according to the hypothesis of above-mentioned multiple linear regression model, is suitable based on least square method multiple linear regression model
For seeking the plane of regression equation of gravity and direction of advance.Least square method multiple linear regression is derived below：
For the multiple linear regression model containing k explanatory variable：
Yi=β0+β1X1i+β2X2i+…+βkXki+μi(i=1,2 ..., n),
IfRespectively as parameter beta0, β1..., βkEstimator, obtaining regression equation is：
Observation YiWith regressand valueResidual error eiFor：
From least square methodIt should make whole observation YiWith regressand valueResidual error eiQuadratic sum
Minimum, even if：
Formula 1
Obtain minimum value.According to the extremum principle of the function of many variables, Q is right respectivelySingle order local derviation is sought, and enables it
Equal to zero, i.e.,：
I.e.：
Abbreviation obtains following equations group：
Above-mentioned (k+1) a equation is known as normal equation, and matrix form is：
Because：
IfFor estimated value vector, regression modelBoth sides are same to multiply sample observations matrix X's
Transposed matrix X ', then have：Obtain normal equation group：
By hypothesis (6), R (X)=k+1X ' X are (k+1) rank square formation, so X ' X full ranks, the inverse matrix (X ' X) of X ' X-1It deposits
.Thus：
Formula 2
It is then the OLS estimators of vector β.
According to the multiple linear regression model of least square method, parameter is x and z, and y is side acceleration direction, meets two
First linear regression model (LRM).According to deriving above, the expression formula of the OLS estimators of two variable linear regression is exported.It is obtained by formula 1
Two variable linear regression is：Yi=β0+β1X1i+β2X2i+μi；For the convenience of calculating, first by model center：
Lpq=∑ xpixqi, (p, q=1,2)
LjY=∑ xjiyi, (j=1,2)
IfThen bivariate regression model is rewritten as centralization model：Yi=α0+β1xk+β2x2i+
μiNote：
By Lpq=∑ xpixqi, (p, q=1,2) is substituted into：
BecauseThen：
It is obtained by formula 2：
Formula 3
Wherein：
From formula 3：
：
VectorThe as plane parameter of gravity and place plane of advancing, while being also plane normal vector,
Side acceleration direction vector and vectorDirection is consistent.According under same coordinate system, sensing data is in a upslides
Image method obtains the value of side acceleration.
Assuming that acceleration transducer real time data A=(x, y, z), plane parameter vectorIt is then lateral
Acceleration
In the present embodiment, by three axis point for obtaining the monitoring data that vehicle acceleration transducer mounted returns
Data are measured, and calculate the gravity direction and direction of advance composition plane for finding out the vehicle by multiple linear regression approximating method
Plane parameter, and according to the plane parameter and the monitoring data pass through preset algorithm and calculate and obtain the lateral of the vehicle
Acceleration provides the value of vehicle side acceleration, enabling accurately identify institute to identify whether the vehicle takes a sudden turn
State whether vehicle takes a sudden turn.
Further, the fourth embodiment of the recognition methods of vehicle zig zag of the present invention, based on the implementation described in above-mentioned Fig. 3
, the plane parameter of the step S210, the gravity direction and direction of advance composition plane that obtain the vehicle include：
Step S211 obtains the gravity direction of the preset vehicle according to the information of the vehicle from server or locally
The plane parameter of plane is formed with direction of advance.
The plane parameter of gravity direction and direction of advance the composition plane of the preset vehicle, the preset plane is joined
Number is corresponding with the information of vehicles to be stored in server or vehicle local, is needing in use, according to the vehicle
Information the plane parameter of plane is formed from server or the local gravity direction for obtaining the preset vehicle and direction of advance.
When it is implemented, a kind of possible specific implementation mode is to be tested vehicle by automobile production manufacturer, and will
It is local that the plane parameter measured is saved in server or the vehicle, automatic to read and the vehicle when vehicle launch
The plane parameter of information matches.
Further, described to include to vehicle progress testing procedure by automobile production manufacturer：
1,5 seconds data of vehicle stationary are acquired；
2, the 5-10 second datas of automobile straight ahead are acquired；
3, the library of least square plane parameter Estimation is called directly to calculate plane parameter and preserve.
In the present embodiment, by shifting to an earlier date the corresponding plane parameter of the preset vehicle, and the plane parameter is preserved
In server or local, needs to directly acquire plane parameter corresponding with the information of vehicles when using, increase vehicle urgency
Turning identification applicability avoids user from carrying out obtaining plane parameter operation.
With reference to Fig. 3, for the first embodiment of the identification device of vehicle of the present invention zig zag, the identification of the vehicle zig zag
Device includes：
Data acquisition module 100, the monitoring data that the acceleration transducer for obtaining vehicle loading returns.
Vehicle when driving, obtains the monitoring data that vehicle acceleration transducer mounted returns, and the vehicle can be taken
Multiple acceleration transducers are carried, the monitoring data by obtaining multiple acceleration transducers returns obtain more after being weighted averagely
For accurate monitoring data.
Side acceleration acquisition module 200 obtains the vehicle for being calculated by preset algorithm according to the monitoring data
Side acceleration.
It is transformed under vehicle axis system by space according to the monitoring data, isolates the lateral vector acceleration of vehicle,
The direction of the side acceleration is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form.
Judgment module 300, for judging whether the vehicle takes a sudden turn according to the side acceleration.
Judged whether vehicle occurs zig zag according to the vehicle side acceleration, it is anticipated that, Yi Zhongke
Can embodiment be：Judge whether the side acceleration of the vehicle is more than pre-set threshold value, if so, judging the vehicle hair
Raw zig zag, otherwise, it is determined that the vehicle does not take a sudden turn；Alternatively possible embodiment is：Laterally added according to described
Whether the increment of side acceleration described in velocity estimated is more than preset increments threshold values, if so, judging that racing occurs for the vehicle
It is curved, otherwise, it is determined that the vehicle does not take a sudden turn.
In the present embodiment, the monitoring data returned by obtaining vehicle acceleration transducer mounted, calculate and obtain
The side acceleration of the vehicle is identified according to the side acceleration to whether the vehicle occurs zig zag so that
It is more accurate and cost is lower to the identification of vehicle zig zag while anxious to vehicle in terms of driver and passenger's direct feel
Turning is identified so that the scope of application is wider.
Further, with reference to Fig. 4 above-mentioned Fig. 3 is based on for the second embodiment of the identification device of vehicle of the present invention zig zag
Shown in embodiment, the judgment module 300 includes：
Side acceleration judgment module 301, for judging whether the side acceleration is more than pre-set threshold value, if so,
Judge that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
It is compared with pre-set threshold value according to the side acceleration of the vehicle of acquisition, judges that the side acceleration is
It is no to be more than pre-set threshold value, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn；Tool
It when body is implemented, is tested and is determined by test of many times, the pre-set threshold value value range is more suitable between 3.5G-4.5G.
Side acceleration judgment module 301 is additionally operable to judge the increment of the side acceleration according to the side acceleration
Whether preset increments threshold values is more than, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that urgency does not occur for the vehicle
Turning.
Be compared with pre-set threshold value according to the increment of the side acceleration of the vehicle of acquisition, judge it is described lateral plus
Whether the increment of speed is more than preset increments threshold values, if so, judging that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle
It does not take a sudden turn；It is determined when it is implemented, being tested by test of many times, it is more suitable that the pre-set threshold value chooses 0.8G.
For this module, when it is implemented, the vehicle on the slope when driving, according to acceleration chip characteristics, accelerates
Degrees of data contains the effect of gravity, but the acceleration of vehicle actual travel should be gravity, frictional force, power work on the slope
The sum of with, and acceleration transducer can not reflect the effect (frictional force acts on vehicle rather than chip) of frictional force, that is,
It says when calculating side acceleration, gravity has included but friction force effect is not taken into account, so examining
Consider two scenes：
1, vehicle descending takes a sudden turn suddenly：Centripetal component-the gravity of centripetal acceleration actual data value=frictional force point at this time
Amount+powertrain components, but centripetal acceleration sensor output value=powertrain components-weight component.Namely use sensor output value
The centripetal acceleration separated in real time is (being less than in the case of plane) less than normal；
2, vehicle driving up takes a sudden turn suddenly：Centripetal component+the gravity of centripetal acceleration actual data value=frictional force point at this time
Amount+powertrain components, but centripetal acceleration sensor output value=powertrain components+weight component.Namely use sensor output value
The centripetal acceleration separated in real time is (being less than in the case of plane) still less than normal.
So the threshold value measured on slope should be less than the acceleration rate threshold measured in the plane.Because A=(x, y, z) is real
Border includes gravity, but there is no the effect comprising friction, the effect of frictional force plays positive interaction；For plane, A
Become smaller, the side acceleration calculated in real time on slope is less than normal, and (practical small gcos (c) * u, it is oblique that u, which generally takes 0.6, c,
Angle of slope, angle is bigger, and gap is smaller), that is to say, that on the slope a fixed threshold is given according to what is measured in plane
Value must have can just be judged to taking a sudden turn than actual larger acceleration, but actually take a sudden turn already
.So to judge to cause, there is a situation where take a sudden turn but do not report according to only plane threshold.In consideration of it, it is contemplated that increasing
Add one within the unit interval side acceleration delta threshold come judge to take a sudden turn on the slope judgement situation.This delta threshold
Not related with the gradient, thus its threshold design should peaceful areal acceleration delta threshold it is consistent.The setting of this threshold value
And measured according to test of many times.Additional conditions of this threshold value as decision condition in the plane, can be further
Increase the accuracy judged.
In the present embodiment, compared or passed through institute by the side acceleration and pre-set threshold value of the vehicle of acquisition
The increment and preset increments threshold values for stating the side acceleration of vehicle are compared, and are judged whether the vehicle takes a sudden turn, are made
The identification that take a sudden turn for the vehicle is more acurrate, and the vehicle on the slope when driving, still can be to the vehicle
Zig zag whether occurs and makes accurate judgement.
Further, with reference to Fig. 5 above-mentioned Fig. 3 is based on for the 3rd embodiment of the identification device of vehicle of the present invention zig zag
Shown in embodiment, the side acceleration acquisition module 200 includes：
Plane parameter acquiring unit 210, gravity direction and direction of advance for obtaining the vehicle form the flat of plane
Face parameter.
The three axis component data that the acceleration transducer after noise reduction is obtained according to the monitoring data, after the noise reduction
Acceleration transducer three axis component data, by multiple linear regression approximating method find out the vehicle gravity direction and
Direction of advance forms the plane parameter of plane.
Side acceleration obtaining unit 220, for passing through preset algorithm according to the plane parameter and the monitoring data
Calculate the side acceleration for obtaining the vehicle.
It is calculated and is obtained by preset multiple linear regression model algorithm according to the plane parameter and the monitoring data
The side acceleration of the vehicle.
The present embodiment is when it is implemented, using multiple linear regression model using common least square method (OLS) to parameter
When being estimated, there is following hypothesis：
1：Zero-mean assumes：E(μi)=0, i=1,2 ..., n；
2：Assume (variance of μ is same constant) with variance
3：Without autocorrelation：Cov(μi, μj)=E (μiμj)=0, (i ≠ j, i, j=1,2 ..., n)；
4：Stochastic error μ (this assumes automatic set up) uncorrelated to explanatory variable X：Cov(Xji, μi)=0, (j=1,
2 ..., k, i=1,2 ..., n)；
5：It is zero that stochastic error μ, which obeys mean value, variance σ2Normal distribution：
6：Multicollinearity is not present between explanatory variable：Rank (X)=k+1≤n；
According to above-mentioned six it is assumed that in the design plane equation y=β0+β1x+β2Z analyzes it and assumes to six now
Meet situation：
(1) according to assuming 1, stochastic error is not considering sensor mechanism problem condition when sample is enough
Under, according to the chip information that chip manufacturer provides, random error should meet standard gaussian model, it is expected that meeting：E(μi)=
0, i=1,2 ..., n, n takes 1；
(2) according to assuming 2, when sample is enough, in the case where not considering sensor mechanism problem condition, according to chip
The chip information that manufacturer provides, random error should meet standard gaussian model, and variance meets：
(3) according to assuming 3, when sample is enough, in the case where not considering sensor mechanism problem condition, according to chip
The chip information that manufacturer provides is from uncorrelated between random error；
(4) assume that 4 set up automatically；
(5) it when sample is enough, in the case where not considering sensor mechanism problem condition, is provided according to chip manufacturer
Chip information, random error should meet standard gaussian model；
(6) x and z is orthogonal between variable, and Problems of Multiple Synteny is also just not present.
Judged according to the hypothesis of above-mentioned multiple linear regression model, is suitable based on least square method multiple linear regression model
For seeking the plane of regression equation of gravity and direction of advance.Least square method multiple linear regression is derived below：
For the multiple linear regression model containing k explanatory variable：
Yi=β0+β1X1i+β2X2i+…+βkXki+μi(i=1,2 ..., n),
IfRespectively as parameter beta0, β1..., βkEstimator, obtaining regression equation is：
Observation YiWith regressand valueResidual error eiFor：
From least square methodIt should make whole observation YiWith regressand valueResidual error eiQuadratic sum
Minimum, even if：
Formula 1
Obtain minimum value.According to the extremum principle of the function of many variables, Q is right respectivelySingle order local derviation is sought, and enables it
Equal to zero, i.e.,：
I.e.：
Abbreviation obtains following equations group：
Above-mentioned (k+1) a equation is known as normal equation, and matrix form is：
Because：
IfFor estimated value vector, regression modelBoth sides are same to multiply sample observations matrix X's
Transposed matrix X ', then have：Obtain normal equation group：
By assuming 6, R (X)=k+1, X ' X are (k+1) rank square formation, so X ' X full ranks, the inverse matrix (X ' X) of X ' X-1In the presence of.
Thus：
Formula 2
It is then the OLS estimators of vector β.
According to the multiple linear regression model of least square method, parameter is x and z, and y is side acceleration direction, meets two
First linear regression model (LRM).According to deriving above, the expression formula of the OLS estimators of two variable linear regression is exported.It is obtained by formula 1
Two variable linear regression is：Yi=β0+β1X1i+β2X2i+μi；For the convenience of calculating, first by model center：
Lpq=∑ xpixqi, (p, q=1,2)
LjY=∑ xjiyi, (j=1,2)
IfThen bivariate regression model is rewritten as centralization model：Yi=α0+β1x1i+β2x2i
+μi, note：
By Lpq=∑ xpixqi, (p, q=1,2) is substituted into：
BecauseThen：
It is obtained by formula 2：
Formula 3
Wherein：
From formula 3：
：
VectorThe as plane parameter of gravity and place plane of advancing, while being also plane normal vector,
Side acceleration direction vector and vectorDirection is consistent.According under same coordinate system, sensing data is in a upslides
Image method obtains the value of side acceleration.
Assuming that acceleration transducer real time data A=(x, y, z), plane parameter vectorIt is then lateral
Acceleration
In the present embodiment, by three axis point for obtaining the monitoring data that vehicle acceleration transducer mounted returns
Data are measured, and calculate the gravity direction and direction of advance composition plane for finding out the vehicle by multiple linear regression approximating method
Plane parameter, and according to the plane parameter and the monitoring data pass through preset algorithm and calculate and obtain the lateral of the vehicle
Acceleration provides the value of vehicle side acceleration, enabling accurately identify institute to identify whether the vehicle takes a sudden turn
State whether vehicle takes a sudden turn.
Further, with reference to Fig. 6 above-mentioned Fig. 5 is based on for the fourth embodiment of the identification device of vehicle of the present invention zig zag
The embodiment described, the plane parameter acquiring unit 210 include：
Preset plane parameter acquiring unit 211, for preset from server or local acquisition according to the information of the vehicle
The vehicle gravity direction and direction of advance composition plane plane parameter.
The plane parameter of gravity direction and direction of advance the composition plane of the preset vehicle, the preset plane is joined
Number is corresponding with the information of vehicles to be stored in server or vehicle local, is needing in use, according to the vehicle
Information the plane parameter of plane is formed from server or the local gravity direction for obtaining the preset vehicle and direction of advance.
When it is implemented, a kind of possible specific implementation mode is to be tested vehicle by automobile production manufacturer, and will
It is local that the plane parameter measured is saved in server or the vehicle, automatic to read and the vehicle when vehicle launch
The plane parameter of information matches.
Further, described to include to vehicle progress testing procedure by automobile production manufacturer：
1,5 seconds data of vehicle stationary are acquired；
2, the 5-10 second datas of automobile straight ahead are acquired；
3, the library of least square plane parameter Estimation is called directly to calculate plane parameter and preserve.
In the present embodiment, by shifting to an earlier date the corresponding plane parameter of the preset vehicle, and the plane parameter is preserved
In server or local, needs to directly acquire plane parameter corresponding with the information of vehicles when using, increase vehicle urgency
Turning identification applicability avoids user from carrying out obtaining plane parameter operation.
It these are only the preferred embodiment of the present invention, be not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (8)
1. a kind of recognition methods of vehicle zig zag, which is characterized in that the recognition methods of the vehicle zig zag includes following step
Suddenly：
Obtain the monitoring data that the acceleration transducer of vehicle loading returns；
The side acceleration for obtaining the vehicle is calculated by preset algorithm according to the monitoring data, the side acceleration
Direction is consistent with the normal vector direction of plane that the gravity direction of the vehicle and direction of advance form；
Judge whether the vehicle takes a sudden turn according to the side acceleration；
Described the step of calculating the side acceleration for obtaining the vehicle by preset algorithm according to the monitoring data includes：
The plane for obtaining and being made of plane the gravity direction and direction of advance of vehicle is calculated according to multiple linear regression approximating method
Parameter；
The side acceleration for obtaining the vehicle is calculated by preset algorithm according to the plane parameter and the monitoring data.
2. the method as described in claim 1, which is characterized in that described whether to judge the vehicle according to the side acceleration
The step of taking a sudden turn include：
Judge otherwise whether the side acceleration, is sentenced more than pre-set threshold value if so, judging that the vehicle takes a sudden turn
The fixed vehicle does not take a sudden turn；
Or judge whether the increment of the side acceleration is more than preset increments threshold values according to the side acceleration, if so,
Judge that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
3. the method as described in claim 1, which is characterized in that it is described according to multiple linear regression approximating method calculate obtain by
Vehicle gravity direction and direction of advance composition plane plane parameter the step of include：
Three axis component data of the acceleration transducer after noise reduction are obtained according to the monitoring data；
According to three axis component data of the acceleration transducer after the noise reduction, institute is found out by multiple linear regression approximating method
State the plane parameter of the gravity direction and direction of advance composition plane of vehicle.
4. the method as described in claim 1, which is characterized in that the gravity direction and direction of advance group for obtaining the vehicle
At plane plane parameter the step of include：
The gravity direction and direction of advance group of the preset vehicle are obtained from server or locally according to the information of the vehicle
At the plane parameter of plane.
5. a kind of identification device of vehicle zig zag, which is characterized in that the identification device of the vehicle zig zag includes：
Data acquisition module, the monitoring data that the acceleration transducer for obtaining vehicle loading returns；
Side acceleration acquisition module obtains the lateral of the vehicle for being calculated by preset algorithm according to the monitoring data
Acceleration, the normal vector side in the direction of the side acceleration and the plane of gravity direction and the direction of advance composition of the vehicle
To consistent；
Judgment module, for judging whether the vehicle takes a sudden turn according to the side acceleration；
The side acceleration acquisition module includes：
Plane parameter acquiring unit obtains the gravity direction by vehicle with before for being calculated according to multiple linear regression approximating method
The plane parameter of plane is formed into direction；
Side acceleration obtaining unit is obtained for being calculated by preset algorithm according to the plane parameter and the monitoring data
The side acceleration of the vehicle.
6. device as claimed in claim 5, which is characterized in that the judgment module includes：
Side acceleration judgment module, for judging whether the side acceleration is more than pre-set threshold value, if so, described in judgement
Vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn；
It is additionally operable to judge whether the increment of the side acceleration is more than preset increments threshold values according to the side acceleration, if
It is then to judge that the vehicle takes a sudden turn, otherwise, it is determined that the vehicle does not take a sudden turn.
7. device as claimed in claim 5, which is characterized in that the plane parameter acquiring unit includes：
Component data acquiring unit, for the three axis component numbers according to the acceleration transducer after monitoring data acquisition noise reduction
According to；
Plane parameter unit is solved, for the three axis component data according to the acceleration transducer after the noise reduction, by polynary
Linear regression fit method finds out the plane parameter of the gravity direction and direction of advance composition plane of the vehicle.
8. device as claimed in claim 5, which is characterized in that the plane parameter acquiring unit includes：
Preset plane parameter acquiring unit, for obtaining the preset vehicle from server or locally according to the information of the vehicle
Gravity direction and direction of advance composition plane plane parameter.
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| CN107909678A (en) * | 2017-11-29 | 2018-04-13 | 思建科技有限公司 | One kind driving risk evaluating method and system |
| CN108068823A (en) * | 2017-12-06 | 2018-05-25 | 上海评驾科技有限公司 | A kind of vehicle drive behavioral value method |
| CN110143200A (en) * | 2019-04-01 | 2019-08-20 | 深圳市元征科技股份有限公司 | A kind of vehicle status data acquisition methods, device, mobile unit and storage medium |
| CN110733510B (en) * | 2019-11-22 | 2020-09-29 | 辽宁工业大学 | Detection method for identifying sharp turn of vehicle |
| CN111310125B (en) * | 2020-02-14 | 2023-06-09 | 上海本安仪表系统有限公司 | Method for judging rapid acceleration, rapid deceleration and rapid turning of vehicle |
| CN112776714A (en) * | 2021-01-27 | 2021-05-11 | 吉林云帆智能工程有限公司 | Turning monitoring and early warning method for railway vehicle |
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