CN107600073B - A kind of vehicle centroid side drift angle estimating system and method based on Multi-source Information Fusion - Google Patents
A kind of vehicle centroid side drift angle estimating system and method based on Multi-source Information Fusion Download PDFInfo
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Abstract
The present invention relates to a kind of vehicle centroid side drift angle estimating system and method based on Multi-source Information Fusion, which includes: multi-source information acquiring unit, Vehicular yaw angle observation device and vehicle centroid side drift angle observer；The multi-source information acquiring unit is separately connected Vehicular yaw angle observation device and vehicle centroid side drift angle observer, the Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer；Vehicular yaw angle observation device obtains Vehicular yaw angle and yaw velocity deviation and the information input as vehicle centroid side drift angle observer, vehicle centroid side drift angle observer obtains vehicle centroid side drift angle and the input information as Vehicular yaw angle observation device, and vehicle centroid lateral deviation angular estimation is completed in Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer.Compared with prior art, the present invention does not depend on that vehicle dynamic model, estimated accuracy are high, calculation amount is small, can be widely applied.
Description
Technical field
The present invention relates to a kind of vehicle centroid side drift angle estimating system and methods, are based on multi-source information more particularly, to one kind
The vehicle centroid side drift angle estimating system and method for fusion.
Background technique
Side slip angle is the key state scalar of intelligent automobile motion tracking control.Slip angle estimation is by road surface
It is one of state variable that vehicle is most difficult to estimation with the influence of the uncertain factors such as Tire nonlinearity, therefore how estimates intelligence
Automobile side slip angle becomes Recent study emphasis, difficult point.
Slip angle estimation method mainly has both at home and abroad at present: 1, based on the method for kinematics model, robustness is good, estimates
Meter result is hardly influenced by model parameter, but more demanding to the precision of information of sensor.2, based on dynamic model
Observer, the requirement to sensor is relatively low, but higher to the susceptibility of model parameter.It is at full speed with automobile " intelligence "
Development, the estimation for being loaded as car status information of the sensors such as camera, radar, GPS and IMU (inertial sensor) provide
More information sources.GPS can be provided about information such as vehicle location and speed, and error range is controllable, and camera is available
Location information between vehicle and lane line, IMU can obtain yaw velocity, longitudinal acceleration of the vehicle and vehicle and laterally accelerate
Degree.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on multi-source information
The vehicle centroid side drift angle estimating system and method for fusion.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion, which includes: multi-source information acquiring
Unit, Vehicular yaw angle observation device and vehicle centroid side drift angle observer；
The multi-source information acquiring unit is separately connected Vehicular yaw angle observation device and vehicle centroid side drift angle observer,
The Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer；
Vehicular yaw angle observation device obtains Vehicular yaw angle and yaw velocity deviation and sees as vehicle centroid side drift angle
The information input of device is surveyed, vehicle centroid side drift angle observer obtains vehicle centroid side drift angle and as Vehicular yaw angle observation device
Information is inputted, vehicle centroid lateral deviation angular estimation is completed in Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer.
The multi-source information acquiring unit includes:
GPS: for providing vehicle course angle and obtaining vehicle in the vertical side velocity in northeast day direction；
Inertial sensor: for obtaining longitudinal acceleration of the vehicle, side acceleration and yaw velocity；
Camera: for obtaining the lateral distance of vehicle and lane line.
The GPS is mounted on right above vehicle centroid, and the inertial sensor is mounted at vehicle centroid.
The state equation of the Vehicular yaw angle observation device are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,For vehicle to be estimated
The derivative of yaw angle,For the derivative of yaw velocity deviation to be estimated, rmThe yaw angle speed obtained for inertial sensor
Measured value is spent,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation.
Vehicular yaw angle ψ to be estimated and yaw velocity deviation r to be estimatedbiasEstimated by kalman filter method
Meter obtains.
Vehicle centroid side drift angle observer are as follows:
It indicatesDerivative,Indicate estimating for y vector
Evaluation,Indicate the estimated value of the lateral distance of vehicle and lane line,Indicate longitudinal speed estimated value,Indicate lateral vehicle
Fast estimated value, yfThe lateral distance of the vehicle and lane line that are obtained for camera,WithThe vehicle that respectively GPS is obtained
Longitudinally and laterally speed in northeast day direction, K is feedback gain matrix,ψ is the Vehicular yaw angle that Vehicular yaw angle observation device is estimated,rmFor the yaw velocity measured value that inertial sensor obtains, rbiasFor the estimation of Vehicular yaw angle observation device
Obtained yaw velocity deviation, lcfTake aim at the distance for a little arriving vehicle centroid, a in advance for cameraxmAnd aymRespectively inertial sensor
The longitudinal acceleration of the vehicle and side acceleration of acquisition, bxAnd byRespectively indicate longitudinal acceleration and the side of inertial sensor acquisition
To the measured deviation of acceleration, β is vehicle centroid side drift angle to be estimated.
A kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion, this method comprises the following steps:
(1) acquire multi-source information, including vehicle course angle, vehicle northeast day direction vertical side velocity, longitudinal direction of car
The lateral distance of acceleration, side acceleration, yaw velocity and vehicle and lane line；
(2) Vehicular yaw angle observation device and vehicle centroid side drift angle observer are established, by vehicle course angle and yaw angle
Input information of the speed as Vehicular yaw angle observation device, vertical side velocity, longitudinal direction of car by vehicle in northeast day direction add
The lateral distance of speed, side acceleration and vehicle and lane line is as the input information of side slip angle observer, together
When Vehicular yaw angle observation device and vehicle centroid side drift angle observer are interconnected；
(3) Vehicular yaw angle observation device is estimated to obtain Vehicular yaw angle and yaw velocity deviation and as vehicle centroid side
The input information of drift angle observer, vehicle centroid side drift angle observer estimate to obtain vehicle centroid side drift angle as Vehicular yaw angle
The input information of observer, and then complete vehicle centroid lateral deviation angular estimation.
Vehicle course angle and vehicle are in the vertical side velocity in northeast day direction by being mounted on vehicle centroid in step (1)
The GPS of surface is obtained, and longitudinal acceleration of the vehicle, side acceleration and yaw velocity are by being mounted at vehicle centroid
Inertial sensor obtains, and the lateral distance of vehicle and lane line passes through the camera being installed on vehicle and obtains.
The state equation of Vehicular yaw angle observation device in step (2) are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,For vehicle to be estimated
The derivative of yaw angle,For the derivative of yaw velocity deviation to be estimated, rmThe yaw angle speed obtained for inertial sensor
Measured value is spent,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation；
In turn, estimate to obtain Vehicular yaw angle ψ to be estimated and yaw angle to be estimated by kalman filter method
Velocity deviation rbias。
Vehicle centroid side drift angle observer in step (2) are as follows:
It indicatesDerivative,Indicate estimating for y vector
Evaluation,Indicate the estimated value of the lateral distance of vehicle and lane line,Indicate longitudinal speed estimated value,Indicate lateral vehicle
Fast estimated value, yfThe lateral distance of the vehicle and lane line that are obtained for camera,WithThe vehicle that respectively GPS is obtained
Longitudinally and laterally speed in northeast day direction, K is feedback gain matrix,ψ is the Vehicular yaw angle that Vehicular yaw angle observation device is estimated,rmFor the yaw velocity measured value that inertial sensor obtains, rbiasFor the estimation of Vehicular yaw angle observation device
Obtained yaw velocity deviation, lcfTake aim at the distance for a little arriving vehicle centroid, a in advance for cameraxmAnd aymRespectively inertial sensor
The longitudinal acceleration of the vehicle and side acceleration of acquisition, bxAnd byRespectively indicate longitudinal acceleration and the side of inertial sensor acquisition
To the measured deviation of acceleration, β is vehicle centroid side drift angle to be estimated.
Compared with prior art, the present invention has the advantage that
(1) present invention is merged using the much information that camera, GPS and inertial sensor acquire, while by vehicle
The observation of vehicle centroid side drift angle is completed in yaw angle observer and the interconnection of vehicle centroid side drift angle observer, does not depend on vehicle power
Model is learned, robustness and reliability of the algorithm for estimating in real vehicle use process are effectively improved；
(2) vehicle centroid side drift angle estimation method estimated accuracy of the present invention is high, calculation amount is small, can be widely applied.
Detailed description of the invention
Fig. 1 is vehicle centroid lateral deviation angular estimation structural block diagram of the present invention；
Fig. 2 is the calculation process block diagram of Kalman filtering algorithm.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
As shown in Figure 1, a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion, which includes: more
Source information acquisition unit, Vehicular yaw angle observation device and vehicle centroid side drift angle observer；
Multi-source information acquiring unit is separately connected Vehicular yaw angle observation device and vehicle centroid side drift angle observer, and vehicle is horizontal
Pivot angle observer and the interconnection of vehicle centroid side drift angle observer；
Vehicular yaw angle observation device obtains Vehicular yaw angle and yaw velocity deviation and sees as vehicle centroid side drift angle
The information input of device is surveyed, vehicle centroid side drift angle observer obtains vehicle centroid side drift angle and as Vehicular yaw angle observation device
Information is inputted, vehicle centroid lateral deviation angular estimation is completed in Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer.
Multi-source information acquiring unit includes: GPS: for providing vehicle course angle and obtaining vehicle in northeast day direction
Vertical side velocity；Inertial sensor (IMU): for obtaining longitudinal acceleration of the vehicle, side acceleration and yaw velocity；Camera shooting
Head: for obtaining the lateral distance of vehicle and lane line.Wherein GPS is mounted on right above vehicle centroid, inertial sensor installation
At vehicle centroid.
The building of above-mentioned Vehicular yaw angle observation device and vehicle centroid side drift angle observer is as follows:
1) Vehicular yaw angle observation device
In order to avoid directlying adopt yaw-rate sensor integral bring measured deviation, estimated using GPS signal horizontal
Pivot angle speed deviation.The state equation of Vehicular yaw angle observation device are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,For vehicle to be estimated
The derivative of yaw angle,For the derivative of yaw velocity deviation to be estimated, rmThe yaw angle speed obtained for inertial sensor
Measured value is spent,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation.
By above-mentioned state equation row sliding-model control:
xk+1=Φk+1,k·xk+H·uk+wk,
yk=Cxk+vk,
In formula, With C=[1 0] be discrete system sytem matrix and input matrix,
Wherein state variablewk,vkRespectively system noise and measurement noise, irrelevant and mean value are 0, it may be assumed that
In formula, E represents mathematic expectaion, and Q and R are system noise and the covariance for measuring noise.
The recurrence equation of Kalman filter can be expressed as follows:
Pk+1|k+1=(I-Kk+1C)Pk+1|k,
Filter initial value: Indicate the priori value of k moment state variable x,Indicate table
Show the predicted value of k+1 moment state variable x,Indicate the correction value of k+1 moment state variable x, PkTo indicate the k+1 moment
The priori value of error co-variance matrix, Pk+1|kIndicate the predicted value of k+1 moment error co-variance matrix, Pk+1|k+1When indicating k+1
Carve the correction value of error co-variance matrix, Kk+1For k+1 moment Kalman feedback oscillator, I is unit battle array.With C=[1 0].The calculation process block diagram of Kalman filtering algorithm is as shown in Figure 2.
2) vehicle centroid side drift angle observer
It is assumed that inertial sensor IMU is mounted at vehicle centroid, for measuring longitudinal acceleration of the vehicle axm, side acceleration
aymWith yaw velocity rm, longitudinal acceleration of the vehicle axmWith side acceleration aymIt can indicate are as follows:
B in above formulaxAnd byThe longitudinal acceleration of inertial sensor acquisition and the measured deviation of side acceleration are respectively indicated,
vxFor longitudinal speed, vyFor lateral speed,For longitudinal speed derivative,For lateral speed derivative,rmIt is used
Property sensor obtain yaw velocity measured value, rbiasThe yaw velocity estimated for Vehicular yaw angle observation device is inclined
Difference.
The lateral distance of vehicle and lane line is set as yf, it meets following relational expression:
yf=yCOG+lcfSin ψ,
yCOGIndicate distance of the vehicle centroid to lane line, lcfIndicate that camera takes aim at the distance y for a little arriving vehicle centroid in advanceCOGIt can
It indicates are as follows:
It is hereby achieved that:
In above formulaIndicate the derivative of the lateral distance of vehicle and lane line, lcfIndicate that camera is taken aim at a little in advance to vehicle centroid
Distance.
It is assumed that not considering the inclination and weaving of vehicle, GPS is located at right above vehicle centroid for measuring vehicle in east
The speed in northern day directionWith
So as to construct following state space form:
It is enabled in above formula: X=[yf vx vy]T。
Therefore, building vehicle centroid side drift angle observer is as follows:
It indicatesDerivative,Indicate the estimated value of y vector,Indicate the estimation of the lateral distance of vehicle and lane line
Value,Indicate longitudinal speed estimated value,Indicate lateral speed estimated value, yfThe cross of the vehicle and lane line that are obtained for camera
To distance,WithThe vehicle that respectively GPS is obtained northeast day direction longitudinally and laterally speed, K
For feedback gain matrix,ψ is that the vehicle that Vehicular yaw angle observation device is estimated is horizontal
Pivot angle, For yaw rate estimated value, rmThe yaw velocity measurement obtained for inertial sensor
Value, rbiasFor the yaw velocity deviation that Vehicular yaw angle observation device is estimated, lcfIt is taken aim in advance for camera a little to vehicle centroid
Distance, axmAnd aymThe respectively longitudinal acceleration of the vehicle and side acceleration of inertial sensor acquisition, bxAnd byIt respectively indicates
The measured deviation of longitudinal acceleration and side acceleration that inertial sensor obtains, β are vehicle centroid side drift angle to be estimated.
In Fig. 1,For the Vehicular yaw angular estimation value that Vehicular yaw angle observation device is estimated,For the sight of Vehicular yaw angle
The yaw velocity estimation of deviation value that device is estimated is surveyed,For yaw rate estimated value, and then willWithAs defeated
Enter information input to vehicle centroid side drift angle observer, the ψ in vehicle centroid side drift angle observer is Vehicular yaw angle observation device
Estimate obtained Vehicular yaw angle,For yaw rate estimated value, i.e., the ψ in vehicle centroid side drift angle observer is used
Instead of yaw rate estimated valueWithInstead of.
A kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion, this method comprises the following steps:
(1) acquire multi-source information, including vehicle course angle, vehicle northeast day direction vertical side velocity, longitudinal direction of car
The lateral distance of acceleration, side acceleration, yaw velocity and vehicle and lane line；
(2) Vehicular yaw angle observation device and vehicle centroid side drift angle observer are established, by vehicle course angle and yaw angle
Input information of the speed as Vehicular yaw angle observation device, vertical side velocity, longitudinal direction of car by vehicle in northeast day direction add
The lateral distance of speed, side acceleration and vehicle and lane line is as the input information of side slip angle observer, together
When Vehicular yaw angle observation device and vehicle centroid side drift angle observer are interconnected；
(3) Vehicular yaw angle observation device is estimated to obtain Vehicular yaw angle and yaw velocity deviation and as vehicle centroid side
The input information of drift angle observer, vehicle centroid side drift angle observer estimate to obtain vehicle centroid side drift angle as Vehicular yaw angle
The input information of observer, and then complete vehicle centroid lateral deviation angular estimation.
Vehicle course angle and vehicle are in the vertical side velocity in northeast day direction by being mounted on vehicle centroid in step (1)
The GPS of surface is obtained, and longitudinal acceleration of the vehicle, side acceleration and yaw velocity are by being mounted at vehicle centroid
Inertial sensor obtains, and the lateral distance of vehicle and lane line passes through the camera being installed on vehicle and obtains.
The state equation of Vehicular yaw angle observation device in step (2) are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,For vehicle to be estimated
The derivative of yaw angle,For the derivative of yaw velocity deviation to be estimated, rmThe yaw angle speed obtained for inertial sensor
Measured value is spent,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation；
In turn, estimate to obtain Vehicular yaw angle ψ to be estimated and yaw angle to be estimated by kalman filter method
Velocity deviation rbias。
Vehicle centroid side drift angle observer in step (2) are as follows:
It indicatesDerivative,Indicate estimating for y vector
Evaluation,Indicate the estimated value of the lateral distance of vehicle and lane line,Indicate longitudinal speed estimated value,Indicate lateral speed
Estimated value, yfThe lateral distance of the vehicle and lane line that are obtained for camera,WithThe vehicle that respectively GPS is obtained exists
The longitudinally and laterally speed in northeast day direction, K is feedback gain matrix,ψ is the Vehicular yaw angle that Vehicular yaw angle observation device is estimated,rmFor the yaw velocity measured value that inertial sensor obtains, rbiasFor the estimation of Vehicular yaw angle observation device
Obtained yaw velocity deviation, lcfTake aim at the distance for a little arriving vehicle centroid, a in advance for cameraxmAnd aymRespectively inertial sensor
The longitudinal acceleration of the vehicle and side acceleration of acquisition, bxAnd byRespectively indicate longitudinal acceleration and the side of inertial sensor acquisition
To the measured deviation of acceleration, β is vehicle centroid side drift angle to be estimated.
Claims (10)
1. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion, which is characterized in that the system includes: multi-source
Information acquisition unit, Vehicular yaw angle observation device and vehicle centroid side drift angle observer；
The multi-source information acquiring unit is separately connected Vehicular yaw angle observation device and vehicle centroid side drift angle observer, described
Vehicular yaw angle observation device and vehicle centroid side drift angle observer interconnection；
Vehicular yaw angle observation device obtains Vehicular yaw angle and yaw velocity deviation and as vehicle centroid side drift angle observer
Information input, vehicle centroid side drift angle observer obtains vehicle centroid side drift angle and the input as Vehicular yaw angle observation device
Vehicle centroid lateral deviation angular estimation is completed in information, Vehicular yaw angle observation device and the interconnection of vehicle centroid side drift angle observer.
2. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion according to claim 1, feature
It is, the multi-source information acquiring unit includes:
GPS: for providing vehicle course angle and obtaining vehicle in the vertical side velocity in northeast day direction；
Inertial sensor: for obtaining longitudinal acceleration of the vehicle, side acceleration and yaw velocity；
Camera: for obtaining the lateral distance of vehicle and lane line.
3. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion according to claim 2, feature
It is, the GPS is mounted on right above vehicle centroid, and the inertial sensor is mounted at vehicle centroid.
4. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion according to claim 1, feature
It is, the state equation of the Vehicular yaw angle observation device are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,It is horizontal for vehicle to be estimated
The derivative of pivot angle,For the derivative of yaw velocity deviation to be estimated, rmIt is surveyed for the yaw velocity that inertial sensor obtains
Magnitude,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation.
5. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion according to claim 4, feature
It is, Vehicular yaw angle ψ to be estimated and yaw velocity deviation r to be estimatedbiasEstimated by kalman filter method
It obtains.
6. a kind of vehicle centroid side drift angle estimating system based on Multi-source Information Fusion according to claim 2, feature
It is, vehicle centroid side drift angle observer are as follows:
It indicatesDerivative,Indicate the estimated value of y vector,Indicate the estimated value of the lateral distance of vehicle and lane line,Indicate longitudinal speed estimated value,Indicate lateral speed estimation
Value, yfThe lateral distance of the vehicle and lane line that are obtained for camera,WithThe vehicle that respectively GPS is obtained is in northeast
The longitudinally and laterally speed in its direction, K is feedback gain matrix,ψ is Vehicular yaw angle to be estimated,rmIt is obtained for inertial sensor
The yaw velocity measured value taken, rbiasFor yaw velocity deviation to be estimated, lcfIt is taken aim in advance for camera a little to vehicle centroid
Distance, axmAnd aymThe respectively longitudinal acceleration of the vehicle and side acceleration of inertial sensor acquisition, bxAnd byIt respectively indicates
The measured deviation of longitudinal acceleration and side acceleration that inertial sensor obtains, β are the vehicle of multi-source information acquiring unit estimation
Side slip angle, k11、k13It is parameter to be designed in feedback gain matrix K with p.
7. a kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion, which is characterized in that this method includes as follows
Step:
(1) multi-source information is acquired, vertical side velocity, the longitudinal direction of car including vehicle course angle, vehicle in northeast day direction accelerate
The lateral distance of degree, side acceleration, yaw velocity and vehicle and lane line；
(2) Vehicular yaw angle observation device and vehicle centroid side drift angle observer are established, by vehicle course angle and yaw velocity
As the input information of Vehicular yaw angle observation device, by vehicle the vertical side velocity in northeast day direction, longitudinal acceleration of the vehicle,
The input information of side acceleration and the lateral distance of vehicle and lane line as vehicle centroid side drift angle observer simultaneously will
Vehicular yaw angle observation device and vehicle centroid side drift angle observer are interconnected；
(3) Vehicular yaw angle observation device is estimated to obtain Vehicular yaw angle and yaw velocity deviation and as vehicle centroid side drift angle
The input information of observer, vehicle centroid side drift angle observer estimate to obtain vehicle centroid side drift angle as Vehicular yaw angle observation
The input information of device, and then complete vehicle centroid lateral deviation angular estimation.
8. a kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion according to claim 7, feature
It is, vehicle course angle and vehicle are in the vertical side velocity in northeast day direction by being mounted on vehicle centroid just in step (1)
The GPS of top is obtained, and longitudinal acceleration of the vehicle, side acceleration and yaw velocity are used at vehicle centroid by being mounted on
Property sensor obtain, the lateral distance of vehicle and lane line passes through the camera being installed on vehicle and obtains.
9. a kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion according to claim 7, feature
It is, the state equation of Vehicular yaw angle observation device in step (2) are as follows:
Wherein, ψ is Vehicular yaw angle to be estimated, rbiasFor yaw velocity deviation to be estimated,It is horizontal for vehicle to be estimated
The derivative of pivot angle,For the derivative of yaw velocity deviation to be estimated, rmIt is surveyed for the yaw velocity that inertial sensor obtains
Magnitude,For the vehicle course angle that GPS is provided, β is the vehicle centroid side drift angle of multi-source information acquiring unit estimation；
In turn, estimate to obtain Vehicular yaw angle ψ to be estimated and yaw velocity to be estimated by kalman filter method
Deviation rbias。
10. a kind of vehicle centroid side drift angle estimation method based on Multi-source Information Fusion according to claim 7, feature
It is, vehicle centroid side drift angle observer in step (2) are as follows:
It indicatesDerivative,Indicate the estimated value of y vector,Indicate the estimated value of the lateral distance of vehicle and lane line,Indicate longitudinal speed estimated value,Indicate lateral speed estimation
Value, yfThe lateral distance of the vehicle and lane line that are obtained for camera,WithThe vehicle that respectively GPS is obtained is in northeast
The longitudinally and laterally speed in its direction, K is feedback gain matrix,ψ is Vehicular yaw angle to be estimated,rmIt is obtained for inertial sensor
The yaw velocity measured value taken, rbiasFor yaw velocity deviation to be estimated, lcfIt is taken aim in advance for camera a little to vehicle centroid
Distance, axmAnd aymThe respectively longitudinal acceleration of the vehicle and side acceleration of inertial sensor acquisition, bxAnd byIt respectively indicates
The measured deviation of longitudinal acceleration and side acceleration that inertial sensor obtains, β are the vehicle of multi-source information acquiring unit estimation
Side slip angle, k11、k13It is parameter to be designed in feedback gain matrix K with p.
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| US10775494B2 (en) * | 2018-03-07 | 2020-09-15 | Aptiv Technologies Limited | Method and system for determining the pointing angle of a moving object |
| CN108545081B (en) * | 2018-03-20 | 2020-04-28 | 北京理工大学 | Centroid slip angle estimation method and system based on robust unscented Kalman filtering |
| CN108873043B (en) * | 2018-05-09 | 2021-01-15 | 华南农业大学 | Method and device for calculating vehicle sideslip angle |
| CN108801286B (en) * | 2018-08-01 | 2021-11-30 | 奇瑞汽车股份有限公司 | Method and device for determining a driving trajectory |
| WO2022000127A1 (en) * | 2020-06-28 | 2022-01-06 | 华为技术有限公司 | Target tracking method and device therefor |
| CN112874529B (en) * | 2021-02-05 | 2022-07-08 | 北京理工大学 | Vehicle mass center slip angle estimation method and system based on event trigger state estimation |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008012354A1 (en) * | 2008-03-03 | 2009-09-10 | Robert Bosch Gmbh | Method for determining sideslip angle in driving state-interacting control system in vehicle, involves determining longitudinal and lateral acceleration of longitudinal speed and greed rate of vehicle |
| CN102009653A (en) * | 2010-11-10 | 2011-04-13 | 清华大学 | Wheel barycenter distortion angle observation method integrated with Kalman filtering and acceleration integral |
| CN104890674A (en) * | 2015-06-10 | 2015-09-09 | 山东理工大学 | Automobile mass center side slip angle measurement device and automobile mass center side slip angle calculation method |
| CN104977933A (en) * | 2015-07-01 | 2015-10-14 | 吉林大学 | Regional path tracking control method for autonomous land vehicle |
| CN105151047A (en) * | 2015-09-08 | 2015-12-16 | 吉林大学 | Automobile gravity center slip angle measuring method |
| WO2016020834A1 (en) * | 2014-08-04 | 2016-02-11 | Modelway S.R.L. | A method for estimating variables affecting the vehicle dynamics and corresponding virtual sensor |
-
2017
- 2017-08-10 CN CN201710680628.4A patent/CN107600073B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102008012354A1 (en) * | 2008-03-03 | 2009-09-10 | Robert Bosch Gmbh | Method for determining sideslip angle in driving state-interacting control system in vehicle, involves determining longitudinal and lateral acceleration of longitudinal speed and greed rate of vehicle |
| CN102009653A (en) * | 2010-11-10 | 2011-04-13 | 清华大学 | Wheel barycenter distortion angle observation method integrated with Kalman filtering and acceleration integral |
| WO2016020834A1 (en) * | 2014-08-04 | 2016-02-11 | Modelway S.R.L. | A method for estimating variables affecting the vehicle dynamics and corresponding virtual sensor |
| CN104890674A (en) * | 2015-06-10 | 2015-09-09 | 山东理工大学 | Automobile mass center side slip angle measurement device and automobile mass center side slip angle calculation method |
| CN104977933A (en) * | 2015-07-01 | 2015-10-14 | 吉林大学 | Regional path tracking control method for autonomous land vehicle |
| CN105151047A (en) * | 2015-09-08 | 2015-12-16 | 吉林大学 | Automobile gravity center slip angle measuring method |
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