CN111038519B - Real-time estimation method for gradient of vehicle-mounted road - Google Patents
Real-time estimation method for gradient of vehicle-mounted road Download PDFInfo
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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
- B60W40/02—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 ambient conditions
- B60W40/06—Road conditions
- B60W40/076—Slope angle of the road
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
- B60W2520/105—Longitudinal acceleration
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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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/16—Pitch
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Abstract
The invention discloses a real-time estimation method for gradient of a vehicle-mounted road, which utilizes an acceleration sensor and an angular velocity sensor to respectively acquire longitudinal acceleration and pitch angle velocity of the vehicle and aims to overcome the defect that the traditional method directly processes signals in a time domain and is seriously interfered by acceleration and deceleration of an automobile. The method comprises the following steps: firstly, time domain filtering is carried out on the acceleration and angular velocity signals; secondly, performing signal Fourier transform, and converting the time domain signal into a frequency domain signal; thirdly, signal normalization, namely normalizing the signals of the acceleration and the angular velocity in the overlapped frequency domain range; fourthly, inverse Fourier transform is carried out, and the signals are converted from the frequency domain to the time domain; fifthly, reverse normalization processing; and sixthly, weighting and fusing the signals to obtain the final estimated road gradient.
Description
Technical Field
The invention relates to a slope identification method, in particular to a method for estimating road slope in real time by combining time domain and frequency domain transformation technologies.
Background
The accurate environment perception technology is an important component for constructing a modern automobile intelligent transportation system, can quickly and accurately estimate road gradient information under complex and changeable road conditions, and has important practical significance in aspects of automobile stability control, fuel economy improvement, automatic transmission smooth gear shifting control and the like.
Generally, a method based on kinematics is to perform data fusion by using acceleration and angular velocity acquired by an IMU sensor, but the vehicle-mounted longitudinal acceleration is greatly changed under the operation conditions of a complex and changeable road and a driver, and the acquired acceleration value often contains a lot of noises and the longitudinal acceleration of an automobile, so that the optimal estimated gradient value of a ramp is influenced. Whereas the dynamics-based method adopts the longitudinal balance equation of the automobile and designs a state observer to estimate the road gradient, the method involves parameters such as air resistance and rolling resistance, and the estimation effect depends on the given of a large number of coefficients.
In the existing patent, for example, chinese patent publication No. CN 103661393a, publication date 3/26/2014, the invention is named as "kinematic road slope estimation", and the invention estimates the road slope based on an extended kalman filter by combining the longitudinal acceleration and the acceleration offset, and provides a road slope estimation method based on kinematics in the time domain; the Chinese patent publication No. CN 105599768A, the publication date is 2016, 5, 25 and the name of the invention is 'vehicle control including dynamic vehicle mass and road slope estimation during vehicle running', the invention combines the identified mass and utilizes a Kalman filter to estimate the slope in real time;
in summary, most of the existing ramp recognition methods estimate the road gradient in a time domain by a signal weighting fusion method, and most of the existing ramp recognition methods use acceleration and angular velocity signals; a method for processing road gradient estimation in a frequency domain by transforming a time domain signal has not been found so far. Therefore, it is necessary to provide a real-time estimation method of the gradient of the road on the vehicle to make up for the deficiencies of the prior art.
Disclosure of Invention
The invention provides a method for estimating road gradient in real time by combining time domain and frequency domain transformation technologies, aiming at the problem of improper processing of directly weighting and fusing signals in a time domain, namely directly weighting signals with different frequencies and the like.
In order to solve the problems, the invention adopts the following technical scheme:
a real-time estimation method for the gradient of a vehicle-mounted road comprises the following steps of respectively collecting vehicle-mounted longitudinal acceleration ax and pitch angle gy by using an acceleration sensor and an angular velocity sensor, and estimating the gradient of the road in real time by a method of transforming and fusing time domain and frequency domain, wherein the specific method comprises the following steps:
step one, time domain filtering, namely filtering the ax and gy acquired by the sensor respectively as IIR input quantities, and performing filtering calculation according to the formula (1).
Wherein M and N are control coefficients of IIR filter
akCoefficient of IIR filter with respect to y (n-k)
bkCoefficient of IIR filter with respect to x (n-k)
x (k) -th input quantity of IIR filter
y (k) -th output of IIR filter
And step two, signal Fourier transform, namely, Fourier transform is carried out on the time domain signals to convert the time domain signals into frequency signals, namely, Fourier transform is carried out on the acceleration signals acquired by the acceleration sensor and the angular velocity signals acquired by the gyroscope, Z sampling signal points closest to the current moment are taken to form an array, fast discrete Fourier transform is carried out on the array, and the transform method is calculated according to the formula (2).
In which x (n) -Fourier transformed time domain signal input
X (k) -Fourier transformed frequency domain signal output
Z-number of signal points for Fourier transform
k-frequency represented by k
j-unit imaginary number
And step three, signal normalization, namely normalizing the frequency domain signals obtained in the step two, and normalizing the signals of the acceleration and the angular velocity in the overlapped frequency domain range. The normalization is performed in a z-score manner, and the normalization weight function is a frequency index and is specifically calculated according to the formula (3).
Where v is the input of a normalized signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Output of Y-normalized signal
And step four, performing inverse Fourier transform on the normalized signal, converting the signal from a frequency domain to a time domain, and specifically calculating according to the formula (4).
Wherein, x (m) -m time domain array signal after inverse Fourier transform
f (k) -the k frequency domain signal after inverse Fourier transform
N-number of signal points to be inverse Fourier transformed
And fifthly, performing inverse normalization processing, namely performing inverse normalization on the signals transformed to the time domain, and calculating the acceleration and the angular velocity respectively. The specific calculation formula is performed according to the formula (5).
v＝Yσ+μ (5)
Where v-output of denormalised signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Input of Y-inverse normalized signal
And step six, signal weighting and fusion, namely calculating all the acceleration signals subjected to inverse normalization according to the formula (6) to obtain a road inclination angle calculated from the acceleration signals.
za(k)＝arcsin(ax(k)) (6)
Wherein ax (k) -the k-th longitudinal acceleration signal after inverse Fourier transform
za (k) -road inclination obtained by using only the k-th longitudinal acceleration signal
And (4) calculating all the angular speed signals after inverse normalization according to the formula (7) to obtain a road inclination angle calculated by the angular speed signals.
A(k)＝gy(k)·dT+θ(k-1) (7)
Wherein A (k) -th predicted road inclination angle using angular velocity
Theta (k-1) -the road inclination angle output by the fusion of the k-1 th angular velocity and acceleration data is initialized to zero
And (4) calculating according to the formula (8) by combining za (k) and A (k) calculated by the formulas (6) and (7) to obtain a final road inclination angle after weighted fusion, and outputting the last element of the theta (k) array as the road inclination angle estimated at this time.
θ(k)＝K·za(k)+(1-K)·A(k) (8)
In the formula, theta (k) -the k-th angular velocity and acceleration data are fused to output the road inclination angle
K-weighting factor
Compared with the prior art, the invention has the beneficial effects that:
1. the influence of serious interference of an estimation result when the automobile is accelerated and decelerated suddenly is solved by a time domain and frequency domain conversion processing method;
2. by utilizing the idea of data fusion in the acceleration and angular velocity frequency domain, the problem that the gyroscope cannot stably output the road gradient for a long time due to the self zero drift error is solved;
drawings
FIG. 1 is a general flow chart of a method for real-time estimation of a vehicle-mounted road grade according to the present invention;
FIG. 2 is a signal normalization process according to the present invention;
FIG. 3 is a signal weighting fusion process according to the present invention;
Detailed Description
The invention is described in detail below with reference to the attached drawing figures:
referring to fig. 1, the signal is sequentially subjected to time domain processing 1, frequency domain processing and time domain processing 2, wherein the time domain processing 1 includes digital filtering of the acceleration and angular velocity signals, that is, filtering processing is performed on ax and gy acquired by a sensor respectively serving as IIR input quantities, and filtering calculation is performed according to equation (9).
Wherein M and N are control coefficients of IIR filter
akCoefficient of IIR filter with respect to y (n-k)
bkCoefficient of IIR filter with respect to x (n-k)
x (k) -th input quantity of IIR filter
y (k) -th output of IIR filter
And then, converting the time domain signals from the time domain processing 1 to the frequency domain processing, converting the time domain signals into frequency signals through Fourier transformation, namely performing Fourier transformation on the acceleration signals acquired by the acceleration sensor and the angular velocity signals acquired by the gyroscope, combining Z sampling signal points closest to the current moment into an array, performing fast discrete Fourier transformation on the array, and calculating by using a transformation method according to the formula (10).
In which x (n) -Fourier transformed time domain signal input
X (k) -Fourier transformed frequency domain signal output
Z-number of signal points for Fourier transform
k-frequency represented by k
j-unit imaginary number
Normalizing the obtained frequency domain signals, and normalizing the signals of the acceleration and the angular velocity in the overlapped frequency domain range. And (3) performing inverse Fourier transform on the normalized signal, converting the signal from a frequency domain to a time domain, and specifically calculating according to the formula (11).
Wherein, x (m) -m time domain array signal after inverse Fourier transform
f (k) -the k frequency domain signal after inverse Fourier transform
N-number of signal points to be inverse Fourier transformed
And (3) converting from frequency domain processing to time domain processing 2, performing inverse normalization on the signals transformed into the time domain, and calculating the acceleration and the angular velocity respectively. The specific calculation formula is performed according to equation (12). And then weighting and fusing the signals, and calculating to obtain the final road inclination angle.
v＝Yσ+μ (12)
Where v-output of denormalised signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Input of Y-inverse normalized signal
Referring to FIG. 2, the signals are normalized by first normalizing the acceleration signal F in the frequency domaina1,Fa2,...,FanAnd angular velocity Fω1,Fω2,...,FωnAnd (4) performing joint processing to find out the overlapped components of the two signal frequencies, then selecting the part of the signals, and performing normalization, namely normalizing the signals of the acceleration and the angular velocity in the overlapped frequency domain range. The normalization is performed in a z-score manner, and the normalization weight function is a frequency index and is specifically calculated according to the formula (13).
Where v is the input of a normalized signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Output of Y-normalized signal
Referring to fig. 3, signal weighted fusion, the acceleration signal in the time domain is calculated according to equation (14), and a road inclination angle calculated from the acceleration signal is obtained.
za(k)＝arcsin(ax(k)) (14)
Wherein ax (k) -the k-th longitudinal acceleration signal after inverse Fourier transform
za (k) -road inclination obtained by using only the k-th longitudinal acceleration signal
And (3) short-time integrating the angular speed signal in the time domain, namely calculating according to an equation (15) to obtain a road inclination angle calculated from the angular speed signal.
A(k)＝gy(k)·dT+θ(k-1) (15)
Wherein A (k) -th predicted road inclination angle using angular velocity
Theta (k-1) -the road inclination angle output by the fusion of the k-1 th angular velocity and acceleration data is initialized to zero
And (5) calculating the weighted and fused final road inclination angle according to the formula (16) by combining the za (k) and the A (k) calculated by the formulas (14) and (15).
θ(k)＝K·za(k)+(1-K)·A(k) (16)
In the formula, theta (k) -the k-th road inclination angle output by the fusion of angular velocity and acceleration data;
k is a weighting factor.
Claims (1)
1. A real-time estimation method for the gradient of a vehicle-mounted road is characterized by comprising the following steps:
step one, time domain filtering, namely filtering the acceleration signal and the angular velocity signal which are respectively used as input quantities of a filter, and carrying out filtering calculation according to a formula (1);
in the formula, MF and NF are control coefficients of IIR filter
Sequence numbering of m-filter input and output signals
akThe coefficients of the filter with respect to y (m-k)
bkThe coefficients of the filter with respect to x (m-k)
x (k) -th input quantity of filter
y (k) -th output quantity of filter
Performing signal Fourier transform, namely performing Fourier transform on the acceleration signal and the angular velocity signal, taking Z sampling signal points closest to the current moment to form an array, performing fast discrete Fourier transform on the array, and calculating the transform method according to the formula (2);
wherein Z (n) -the nth signal in the sampling signal array composed of Z sampling signal points
X (k) -Fourier transformed frequency domain signal output
Z-number of signal points for Fourier transform
k-frequency represented by k
j-unit imaginary number
Step three, signal normalization, namely finding out a frequency overlapping interval of the acceleration and the angular velocity in a frequency domain, and carrying out normalization calculation on the signals in the interval range according to a formula (3);
where v is the input of a normalized signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Output of Y-normalized signal
Step four, inverse Fourier transform, namely converting the signal from a frequency domain to a time domain, and specifically calculating according to the formula (4);
wherein, p (m) -m time domain array signal after inverse Fourier transform
f (k) -the k-th frequency domain signal before inverse Fourier transform
M-number of signal points for inverse Fourier transform
Fifthly, performing inverse normalization processing, namely calculating the acceleration signal and the angular velocity signal according to a formula (5);
v＝Yσ+μ (5)
where v-output of denormalised signal
Mu-mean of all signals to be normalized
Sigma-all standard deviations of the signal that need to be normalized
Input of Y-inverse normalized signal
Step six, signal weighting fusion, namely calculating all acceleration signals subjected to inverse normalization according to a formula (6) to obtain a road inclination angle calculated by the acceleration signals;
za(k)＝arcsin(ax(k)) (6)
wherein ax (k) -the k-th longitudinal acceleration signal after inverse Fourier transform
za (k) -road inclination obtained by using only the k-th longitudinal acceleration signal
Calculating all angular velocity signals after inverse normalization according to the formula (7) to obtain a road inclination angle calculated by the angular velocity signals;
A(k)＝gy(k)·dT+θ(k-1) (7)
wherein A (k) -th predicted road inclination angle using angular velocity
Theta (k-1) -the road inclination angle output by the fusion of the k-1 th angular velocity and acceleration data is initialized to zero
gy (k) -th angular velocity signal
dT-sample time interval
Calculating a final road inclination angle after weighted fusion according to the formula (8) by combining za (k) and A (k) calculated by the formulas (6) and (7); outputting the last element of the theta (k) array as the road inclination angle estimated this time;
θ(k)＝K·za(k)+(1-K)·A(k) (8)
in the formula, theta (k) -the k-th angular velocity and acceleration data are fused to output the road inclination angle
K is a weighting factor.
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