CN101598549B - Method for dynamic estimation of vehicle running gradient and relative height - Google Patents
Method for dynamic estimation of vehicle running gradient and relative height Download PDFInfo
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Abstract
The invention discloses a method for dynamic estimation of vehicle running gradient and relative height. In the method, based on a uniaxial accelerometer and an odometer hardware, an algorithm is used to calculate road grade and vehicle running relative height information, the uniaxial accelerometer is used to measure acceleration accy in the vehicle longitudinal axis direction; the odometer is used to accurately measure vehicle running speed, thus providing the vehicle with vehicle acceleration information a<vehicle>, and according to the geometric relationship between accy and a<vehicle>, gradient value and height change value can be obtained, then whether the vehicle runs upslope and downslope can be identified according to height change and restricting rules. In the process of concrete implementation, installation error of the uniaxial accelerometer, noise processing of acceleration data, alignment of data of two types of sensors and upslope and downslope identification rules are taken into consideration. The method features high measurement accuracy, improvement of stability and reliability of a system, realization of real-time measurement of vehicle running gradient and height, very high sensibility and response speed, small power dissipation, quick startup, simpleness and practicability.
Description
Technical field
The invention belongs to the vehicle mounted guidance technical field, be specifically related to detection of dynamic vehicle ' road grade and method highly in a kind of navigational system.
Background technology
In existing onboard navigation system, can obtain sea level elevation information by GPS, but because the gps system measurement characteristics is limit, the elevation information error is very big, therefore the elevation information of the direct output of GPS receiver can not satisfy the judgement of vehicle climb and fall, and the relative height of travelling changes the needs of estimating.
Current, the method for measuring inclination angle (being road grade) mainly contains two kinds: traditional bubble formula frame (bar formula) level meter: its detection method still is " bubble moves, and naked eyes are differentiated ".This original detection method has many shortcomings, as measured value because of the people easily, function singleness, measurement range be little etc.; Another is based on the inclinator of acceleration transducer: it compare with traditional bubble formula frame (bar formula) level meter have detection of electrons, measuring accuracy height, measurement range are big, use and characteristics such as easy to carry, but this method can not realize accelerated motion situation lower angle and measure, and uses and is subjected to bigger restriction.
Summary of the invention
The objective of the invention is in order to solve the problem of judgement vehicle climb and fall in the vehicle mounted guidance, thereby realize more accurate location, the method of a kind of detection of dynamic vehicle ' road grade and the relative height of travelling is provided, this method has adopted middle position value filtering and information fusion technology means, has reached real-time judge vehicle climb and fall technique effect.
The present invention utilizes the relations of distribution of acceleration to calculate the vehicle running gradient angle on single-axis accelerometer and mileage gauge metrical information basis.If the single-axis accelerometer sensitive direction is consistent with the vehicle y direction, the acceleration accy on this single-axis accelerometer sensor measurement vehicle y direction; Mileage gauge is the measuring vehicle travel speed accurately, and vehicle acceleration information a so just can be provided
Car, by acceleration accy vehicle acceleration and a
CarGeometric relationship between the two can obtain value of slope and height change value, judges the vehicle climb and fall by height change then.In the specific design process, to consider the judgment rule problem of the aligning and the climb and fall of the noise processed of single-axis accelerometer alignment error, acceleration information, two kinds of sensing datas.It is better that the method is measured inclination angle stability with respect to three, twin-axis accelerometer.
A kind of vehicle running gradient that the present invention proposes and relative height method for dynamic estimation are on the hardware device basis of existing onboard navigation system, detect the gradient of vehicle ' at first in real time, the range information in conjunction with vehicle ' obtains the relative height of travelling then.The quality of vehicle gradient result of calculation directly affects the accuracy of height, so the key of this method just is the calculating of the gradient.Concrete steps are:
The acquisition mode of equipment established angle has two kinds:
1. according to the established angle initialization established angle α that sets.
2. measure established angle α.During established angle information when can't obtain to set, adopt the single-axis accelerometer mode, promptly utilize the thick marking device established angle of quiescent conditions descending slope computing formula α, specific as follows:
The acceleration accy induction that single-axis accelerometer is measured be non-gravitational acceleration, promptly vehicle thrust F, holding power N and friction force f in the making a concerted effort of this durection component, promptly have respectively
accy＝(F×cosα+N×sinα-f×cosα)/m (1)
M is a vehicle mass in the formula.
Have according to the stress balance on the vehicle longitudinal axis and the X direction:
a
Car=(F-f)/m-G0 * sin θ (2)
N＝m×G0×cosθ
G0 is a vehicle acceleration of gravity in the formula, and θ is the gradient of vehicle position.
Obtain by formula (1), formula (2):
Accy=a
Car* cos α+G0 * sin θ * cos α+G0 * cos θ * sin α (3)
Accy=a
Car* cos α+G0 * sin (θ+α) (4)
Obtain the computing formula of the gradient by formula (4):
When α=0, by obtaining by formula (4):
Accy=a
Car+ G0 * sin θ (6)
The gradient θ computing formula of this moment is:
Work as stationary vehicle, when not considering the equipment established angle, promptly under the quiescent conditions: a
Car=0, α=0,
Accelerometer output data accy when collection vehicle is static guarantees vehicle at surface level as far as possible, and the gradient θ that measure this moment is exactly the established angle α of equipment:
For the compensation of equipment established angle in the following calculating gradient process provides foundation.Although can not guarantee stationary vehicle fully on surface level in this process, this evaluated error of established angle is little to the little time error of height change, does not influence to judge climb and fall trend.
It is that the middle position value filtering of n (generally getting 5) is handled with the data that obtain rejecting behind the wild point and carried out follow-up work again that accelerometer data accy is carried out length.The acceleration of the responsive vehicle y direction of single-axis accelerometer in this method, its data are designated as accy, therefore acceleration accy are carried out middle position value filtering and obtain filtered result and be designated as accy_filted;
Step 3, the information of acceierometer sensor and mileage gauge sensor transmissions is carried out time alignment.
Utilize information fusion method that the information of acceierometer sensor and mileage gauge sensor transmissions is carried out time alignment.Utilize the information of two kinds of sensors simultaneously, what guarantee these two is the most key synchronously, the consideration hardware cost need be selected the suitable data transmission frequency, the frequency acquisition of the accelerometer that quantity of information is bigger is made as n1 Hz, and the sample frequency of the less mileage gauge of quantity of information is made as n2 Hz, n2＜n1, get n1=50Hz herein, n2=1Hz, the umber of pulse that is mileage gauge output is that 1s once exports, and the data of single-axis accelerometer output are that 1s has 50 output, therefore unified two parts data under the principle of time alignment.If system has GPS, with gps time (also being the mileage gauge time) is benchmark, it is such that the acceleration output of corresponding GPS current time n second is handled: time of acceleration transducer (comprises the n data of second) between n and n+1 all acceleration informations are got average, this average as with corresponding acceleration information accy_filted_AVG gps time n second, so just realized the fusion of accelerometer and mileage gauge data under the time alignment principle; If system do not have GPS, then with the accelerometer times of collection as aiming at foundation with the mileage gauge pulse.
The final data form is that each second is to having an accekeration accy_filted_AVG and a mileage gauge umber of pulse odopulse.
The acceleration a of step 4, calculating vehicle
Car
Try to achieve distance D is_ODM=odopulse * K_od that the vehicle per second travels by the umber of pulse odopulse of mileage gauge sensor per second output and the scale factor K_od (packaged parameter) of mileage gauge, can calculate the acceleration a of vehicle per second then according to range difference
Car:
a
Car(t)=Dis_ODM (t)-Dis_ODM (t-1).
This method has merged the information of mileage gauge and inertial sensor, and the influence that brings with regard to the deficiency of having avoided one-sided information like this realizes detecting in real time the gradient.
With the acceleration information accy_filted_AVG substitution gradient computing formula (5) that obtains in the step 3:
Obtain vehicle gradient θ:
Wherein G0 is the acceleration of gravity of vehicle, and accy_filted_AVG is for merging the accekeration after aiming at.
Step 6, computed altitude change:
By distance D is_ODM and the per second gradient θ that the vehicle per second travels, calculate the difference in height high of per second:
high＝Dis_ODM×sin(θ) (10)
Step 7, judgement climb and fall.
Can obtain the height that the vehicle per second travels in real time by step 6, owing to be to carry out calculating in single second, even this difference in height can be very not big at climb and fall yet, wherein proportion will be bigger in this difference in height for some stochastic errors such as data error, the error of calculation, therefore judges that by the difference in height of per second climb and fall is just very unreliable.Can select the segmentation computed altitude poor, reduce the influence of error, get 3 seconds herein, carry out integration in promptly per 3 seconds, calculate a difference in height as a time period.This integral time be if oversizely then can produce the integration cumulative errors, if too short, then the height difference behind the integration is smaller is easy to generate erroneous judgement, and 3 seconds is to carry out the empirical value that statistical study is determined by many groups experimental data.Below with these height of lift differences as foundation, judge whether climb and fall of vehicle:
(1) segmentation computed altitude: it is 3 seconds that every period is set,, carried out one time integration in promptly per 3 seconds, calculated a difference in height high_step in per 3 seconds.
(2) the climb and fall height threshold is set: upward slope threshold value Delt_UP=0.4, descending threshold value Delt_DOWN=-0.4.Then think going up a slope when high_step＞Delt_UP, and note the moment of satisfying this condition, structure satisfies the moment sequence T1 of upward slope condition;
When high_step＜Delt_DOWN then thinks at descending, and note the moment of satisfying this condition, structure satisfies the moment sequence T2 of descending condition;
(3), otherwise then be not if time difference Δ t=T1 (n+1)-T1 (the n)≤6s between adjacent element T1 (n), T1 (n+1) thinks that then this time period [T1 (n), T1 (n+1)] is the section of going up a slope among the moment sequence T1.
If time difference Δ t=T2 (n+1)-T2 (the n)≤6s between adjacent element T2 (n), T2 (n+1) thinks that then this time period [T2 (n), T2 (n+1)] is a lower slope section among the sequence T2 constantly, otherwise then is not.
The invention has the advantages that:
(1) this method has been considered the equipment established angle and inertial data has been adopted denoising, has improved measuring accuracy greatly;
(2) this method utilizes single-axis accelerometer and mileage gauge information fusion to realize gradient identification, has avoided relying on fully the drawback of acceierometer sensor, has improved the stability and the reliability of system;
(3) this method has realized the real-time detection vehicle gradient and height, and higher sensitivity and response speed are arranged;
(4) power consumption is little, starts fast, simple.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method;
Fig. 2 is the force analysis synoptic diagram of vehicle ' on the gradient;
Fig. 3 is a vehicle running gradient calculating principle synoptic diagram among the present invention;
Fig. 4 judges the climb and fall analogous diagram;
Fig. 5 is emulation gradient figure as a result;
Fig. 6 is emulation height figure as a result.
Embodiment
The present invention is described in further detail below in conjunction with drawings and Examples.
This method has been considered the compensation of established angle, the denoising of acceleration information and the fusion treatment of information in order to improve the vehicle precision of gradient estimated result up and down, and method flow provided by the invention is specifically realized as shown in Figure 1 as follows:
Accelerometer is processed be installed on the vehicle after, have equipment established angle α usually.Equipment established angle α is meant comprehensive established angle, and the angle of inertia device mounting plane and surface level just must be demarcated this established angle α before calculating carrying out the gradient, so that when the gradient is calculated this angle is compensated.The acquisition mode of equipment established angle has two kinds:
(1) according to the established angle initialization established angle α that sets;
(2) measure established angle α; If mount message can't conveniently be provided, can adopt the single-axis accelerometer mode, promptly utilize the thick marking device established angle of quiescent conditions descending slope computing formula α, specific as follows:
The acceleration accy induction that single-axis accelerometer is measured be non-gravitational acceleration, promptly vehicle thrust F, holding power N and friction force f in the making a concerted effort of this durection component, promptly have respectively as shown in Figure 3:
accy＝(F×cosα+N×sinα-f×cosα)/m (1)
Stress balance according on the vehicle longitudinal axis and the X direction has as shown in Figure 2
a
Car=(F-f)/m-G0 * sin θ (2)
N＝m×G0×cosθ
G0 is an acceleration of gravity in the formula, and m is the quality of vehicle, and θ is the gradient of vehicle position.
Obtain by formula (1), formula (2):
Accy=a
Car* cos α+G0 * sin θ * cos α+G0 * cos θ * sin α (3)
Accy=a
Car* cos α+G0 * sin (θ+α) (4)
Obtain the computing formula of the gradient by formula (4):
When α=0, obtain by formula (4):
Accy=a
Car+ G0 * sin θ (6)
The gradient θ computing formula of this moment is:
Work as stationary vehicle, when not considering the equipment established angle, promptly under the quiescent conditions: a
Car=0, α=0,
Accelerometer output data accy when collection vehicle is static guarantees vehicle at surface level as far as possible, and the gradient θ that measure this moment is exactly the established angle α of equipment:
For the compensation of equipment established angle in the following calculating gradient process provides foundation.
Because the inertia device sensitivity of environment to external world causes its output very unstable, therefore among the present invention accelerometer data being carried out window width is that the middle position value filtering of n (n is a natural number, generally gets 5) is handled with the data that obtain rejecting behind the wild point and carried out follow-up work again.Therefore the acceleration accy of the responsive vehicle y direction of single-axis accelerometer in this method carries out middle position value filtering to accy and obtains filtered result and be designated as accy_filted.
Step 3, the information of acceierometer sensor and mileage gauge sensor transmissions is carried out time alignment.
The present invention carries out time alignment by the mode of data fusion.Data fusion is as a kind of data processing method, and its basic thought is the data of utilization system various aspects, extracts the effective information of relevant object or environment to greatest extent, to reach more accurate, more fully to be familiar with the purpose of object of observation or environment.The present invention combines the information of single-axis accelerometer and mileage gauge two aspects, utilize the information of two kinds of sensors simultaneously, what guarantee these two is the most key synchronously, the consideration hardware cost need be selected the suitable data transmission frequency, the frequency acquisition of the accelerometer that quantity of information is bigger is made as n1 Hz, and the sample frequency of the less mileage gauge of quantity of information is made as n2 Hz, n2＜n1, get n1=50Hz herein, n2=1Hz, the umber of pulse that is mileage gauge output is that 1s once exports, and the data of single-axis accelerometer output are that 1s has 50 output, therefore unified two parts data under the principle of time alignment.If system has GPS, with gps time (also being the mileage gauge time) is benchmark, it is such that the acceleration output of corresponding GPS current time n second is handled: time of acceleration transducer (comprises the n data of second) between n and n+1 all acceleration informations are got average, this average as with corresponding acceleration information accy_filted_AVG gps time n second, so just realized the fusion of accelerometer and mileage gauge data under the time alignment principle; If system does not have GPS, can the conduct of accelerometer times of collection aim at foundation with the mileage gauge pulse.
The final data form is that each second is to having an accekeration accy_filted_AVG and a mileage gauge umber of pulse odopulse.
The acceleration a of step 4, calculating vehicle
Car
Try to achieve distance D is_ODM=odopulse * K_od that the vehicle per second travels by the umber of pulse odopulse of mileage gauge sensor per second output and the scale factor K_od (packaged parameter) of mileage gauge, just can calculate the acceleration a of vehicle per second then
Car:
a
Car(t)=Dis_ODM (t)-Dis_ODM (t-1).
Calculate the gradient of per second, and established angle is compensated, it is as follows to obtain the gradient:
Wherein G0 is the acceleration of gravity of vehicle.
Step 6, computed altitude change.
The height value that distance D is_ODM that is travelled by the vehicle per second and per second gradient θ calculate per second:
high＝Dis_ODM×sin(θ)。
Step 7, judgement climb and fall.
Can obtain the height that the vehicle per second travels in real time by top, owing to be to carry out calculating in single second, even this difference in height can be very not big at climb and fall yet, wherein proportion will be bigger in this difference in height for some stochastic errors such as data error, the error of calculation, therefore judges that by the difference in height of per second climb and fall is just very unreliable.Can select the segmentation computed altitude poor, reduce the influence of error, get 3 seconds herein, carry out integration in promptly per 3 seconds, calculate a difference in height as a time period.This integral time be if oversizely then can produce the integration cumulative errors, if too short, then the height difference behind the integration is smaller is easy to generate erroneous judgement, and 3 seconds is to carry out the empirical value that statistical study is determined by many groups experimental data.The following step then with these height of lift differences as foundation, judge whether climb and fall of vehicle in conjunction with some restriction rules again.
(1) segmentation computed altitude: it is 3 seconds that every period is set, and carries out one time integration in promptly per 3 seconds, calculates a difference in height high_step in per 3 seconds.
(2) the climb and fall height threshold is set: upward slope threshold value Delt_UP=0.4, descending threshold value Delt_DOWN=-0.4.
When high_step＞Delt_UP, then think going up a slope, and note the moment sequence T1 that the moment structure that satisfies this condition satisfies the upward slope condition;
When high_step＜Delt_DOWN then thinks at descending, and note the moment sequence T2 that the moment structure that satisfies this condition satisfies the descending condition;
(3), otherwise then be not if time difference Δ t=T1 (n+1)-T1 (the n)≤6s between adjacent element T1 (n), T1 (n+1) thinks that then this time period [T1 (n), T1 (n+1)] is the section of going up a slope among the moment sequence T1.
If time difference Δ t=T2 (n+1)-T2 (the n)≤6s between adjacent element T2 (n), T2 (n+1) thinks that then this time period [T2 (n), T2 (n+1)] is a lower slope section among the sequence T2 constantly, otherwise then is not.
To be vehicle ' replace three-dimensional track figure on the road at a climb and fall to Fig. 4, and what the x axle was represented is the east orientation position, and what the y axle was represented is the north orientation position, and what the z axle was represented is height, dotted line ._: represent level road; The runic solid line-: representative is to go up a slope; Star line *: represent descending; The upward slope section of this section track and lower slope section have been marked among the figure down and the height of climb and fall, the result of climb and fall and actual coincideing as can be seen.
Emulation experiment generates two groups of data: the umber of pulse of the accekeration of single-axis accelerometer output and mileage gauge output, these two groups of data are muting, but owing to the data of accelerometer in the actual conditions and mileage gauge measurement all have noise, therefore when emulation, desirable accekeration and the value of vehicle acceleration that obtained by desirable umber of pulse are added 10% noise respectively, Fig. 5 and Fig. 6 are respectively the gradient that obtains by matlab emulation and height figure as a result: what wherein solid line was represented among Fig. 5 is desirable intensity gradient curve, dotted line is that noise data is used the intensity gradient curve that the inventive method obtains, what solid line was represented among Fig. 6 is desirable altitude curve, dotted line is that noise data is used the intensity gradient curve that the inventive method obtains, as can be seen from the figure value of slope that obtains by the inventive method and height value and muting ideal value are very approaching, gradient error is in 0.4934 °, height error has reached reasonable effect in 1.8692m.
Claims (1)
1. vehicle running gradient and relative height method for dynamic estimation is characterized in that:
Step 1, thick marking device established angle α;
The acquisition mode of equipment established angle α has following two kinds:
(1) according to the established angle initialization established angle α that sets;
(2) measure established angle α;
If mount message can't be provided, the established angle α of equipment obtains by following formula:
Wherein G0 is the acceleration of gravity of vehicle, and accy is the acceleration information of single-axis accelerometer induction, and θ is the gradient;
Step 2, the data accy that the individual axis acceleration flowmeter sensor is exported carry out the middle position value filtering processing;
Accelerometer data is carried out the middle position value filtering processing that window width is n, obtain filtered result and be designated as accy_filted, n is a natural number;
Step 3, the information of individual axis acceleration flowmeter sensor and mileage gauge sensor transmissions is carried out time alignment;
If system has GPS, with the gps time is benchmark, it is such that the acceleration output of corresponding GPS current time n second is handled: time all acceleration informations between n and n+1 to acceleration transducer are got average, this average as with corresponding acceleration information accy_filted_AVG gps time n second, so just realized the fusion of accelerometer and mileage gauge data under the time alignment principle; If system do not have GPS, with the accelerometer times of collection as aiming at foundation with the mileage gauge pulse;
The acceleration a of step 4, calculating vehicle
Car
Try to achieve distance D is_ODM=odopulse * K_od that the vehicle per second travels by the umber of pulse odopulse of mileage gauge sensor per second output and the scale factor K_od of mileage gauge, calculate the acceleration a of vehicle per second then
Car:
a
Car(t)=Dis_ODM (t)-Dis_ODM (t-1);
Step 5, calculating vehicle gradient θ;
Calculate the gradient of per second, and established angle is compensated, it is as follows to obtain the gradient:
Wherein G0 is the acceleration of gravity of vehicle;
Step 6, computed altitude change;
The difference in height high that distance D is_ODM that is travelled by the vehicle per second and per second gradient θ calculate per second:
high＝Dis_ODM×sin(θ)；
Step 7, climb and fall are judged;
(1) segmentation computed altitude: it is 3 seconds that every period is set, and carries out one time integration in promptly per 3 seconds, calculates a difference in height high_step in per 3 seconds;
(2) the climb and fall height threshold is set: upward slope threshold value Delt_UP=0.4, descending threshold value Delt_DOWN=-0.4;
Then think going up a slope when high_step＞Delt_UP, and note the moment of satisfying this condition, structure satisfies the moment sequence T1 of upward slope condition;
When high_step＜Delt_DOWN then thinks at descending, and note the moment of satisfying this condition, structure satisfies the moment sequence T2 of descending condition;
(3), otherwise then be not if time difference Δ t=T1 (n+1)-T1 (the n)≤6s between adjacent element T1 (n), T1 (n+1) thinks that then this time period [T1 (n), T1 (n+1)] is the section of going up a slope among the moment sequence T1;
If time difference Δ t=T2 (n+1)-T2 (the n)≤6s between adjacent element T2 (n), T2 (n+1) thinks that then this time period [T2 (n), T2 (n+1)] is a lower slope section among the sequence T2 constantly, otherwise then is not.
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| DE102004009417A1 (en) * | 2004-02-24 | 2005-09-08 | Continental Teves Ag & Co. Ohg | Vehicle inclination angle determination method in which the vehicle's height is measured in two positions, the separation between the positions is determined and the results combined to determine the longitudinal inclination |
| CN2709946Y (en) * | 2004-04-19 | 2005-07-13 | 成都航空仪表公司 | Combined meter for automatic measuring altitute and slope of vehicle running |
| CN101424535A (en) * | 2007-10-30 | 2009-05-06 | 武汉大学 | Vehicle positioning method and device |
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