CN110588778B - Method and system for adjusting steering angle of vehicle steering wheel and vehicle - Google Patents
Method and system for adjusting steering angle of vehicle steering wheel and vehicle Download PDFInfo
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- CN110588778B CN110588778B CN201910824153.0A CN201910824153A CN110588778B CN 110588778 B CN110588778 B CN 110588778B CN 201910824153 A CN201910824153 A CN 201910824153A CN 110588778 B CN110588778 B CN 110588778B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/021—Determination of steering angle
- B62D15/024—Other means for determination of steering angle without directly measuring it, e.g. deriving from wheel speeds on different sides of the car
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
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Abstract
The embodiment of the invention relates to the technical field of automatic driving, and discloses a method and a system for adjusting a steering angle of a steering wheel of a vehicle and the vehicle, wherein the method comprises the following steps: when a vehicle runs on a curve, acquiring the real-time speed of the vehicle and the real-time track curvature of the vehicle, substituting the real-time speed, the real-time track curvature and the real-time factor coefficient of the vehicle into a preset polynomial model to obtain a target steering wheel corner of the vehicle, namely adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel corner; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel angle sample data, vehicle speed sample data and track curvature sample data of the vehicle. By implementing the embodiment of the invention, the steering wheel angle can be self-adaptively adjusted, and the complicated manual debugging steps are reduced.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a method and a system for adjusting a steering angle of a steering wheel of a vehicle and the vehicle.
Background
Under the scene of automatic driving, the intelligent automobile usually can autonomously adjust the steering wheel angle when driving to a curve area, so that the intelligent automobile is controlled to drive according to a pre-planned path track, and the curve driving safety is ensured. The steering wheel turning angle is dependent on the over-turning speed and the path track curvature, and the forward and backward directions are also related to vehicle individual parameters (such as steering wheel zero offset, centroid position and the like), and the vehicle individual parameters are mainly determined through artificial static debugging and calibration. However, in practice, it is found that since the individual vehicle parameters are changed due to the influence of a plurality of factors, such as the service time of the automobile and the mechanical wear condition of the automobile, the individual vehicle parameters need to be updated in time before the steering wheel angle is adjusted in the conventional method, so that complicated manual debugging work is added, and the operation is extremely inconvenient.
Disclosure of Invention
The embodiment of the invention discloses a method and a system for adjusting a steering wheel angle of a vehicle and the vehicle, which can adaptively adjust the steering wheel angle and reduce complicated manual debugging steps.
The embodiment of the invention discloses a method for adjusting the steering angle of a vehicle steering wheel in a first aspect, which comprises the following steps:
when the vehicle runs on a curve, acquiring the real-time speed of the vehicle and the real-time track curvature of the vehicle;
substituting the real-time speed, the real-time track curvature and the real-time factor coefficient of the vehicle into a preset polynomial model to obtain a target steering wheel corner of the vehicle; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle;
and adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel angle.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, before the substituting the real-time vehicle speed, the real-time trajectory curvature, and the real-time factor coefficient of the vehicle into a preset polynomial model, the method further includes:
collecting steering wheel corner sample data, vehicle speed sample data and track curvature sample data of a vehicle in the driving process of the vehicle, and storing the steering wheel corner sample data, the vehicle speed sample data and the track curvature sample data of the vehicle as a group of sample data groups to a preset database;
judging whether a sufficient number of sample data sets are stored in the preset database;
if not, continuing to execute the step of collecting steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle in the running process of the vehicle;
if so, reading a specified number of sample data groups from the preset database, and performing polynomial regression processing on a preset polynomial model by using the specified number of sample data groups to obtain the real-time factor coefficient of the vehicle.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the real-time factor coefficient of the vehicle includes a first factor coefficient, a second factor coefficient, and a third factor coefficient;
the performing polynomial regression processing on a preset polynomial model by using the sample data group with the specified number to obtain the real-time factor coefficient of the vehicle comprises:
performing polynomial regression processing on the preset polynomial model by using the sample data group with the specified number to solve a parameter theta in the polynomial model0Corresponding first factor coefficient, parameter theta1Corresponding second factor coefficient and parameter theta2A corresponding third factor coefficient, i.e.;
θ＝θ0+θ1ρ+θ2(v2ρ)；
the parameter theta is used for substituting steering wheel corner sample data included in each group of sample data groups, the parameter rho is used for substituting trajectory curvature sample data included in each group of sample data groups, and the parameter v is used for substituting vehicle speed sample data included in each group of sample data groups.
As an alternative implementation, in the first aspect of the embodiment of the present invention, the substituting the real-time vehicle speed, the real-time trajectory curvature, and the real-time factor coefficient of the vehicle into a preset polynomial model to obtain the target steering wheel angle of the vehicle includes:
determining a fitting polynomial according to the first factor coefficient, the second factor coefficient, the third factor coefficient and a preset polynomial model:
substituting the real-time vehicle speed and the real-time track curvature into the fitting polynomial to obtain a target steering wheel corner of the vehicle; wherein the fitting polynomial is:
θ'＝θ0'+θ1'ρ'+θ2'(v'2ρ')；
wherein θ' is a target steering wheel angle of the vehicle, and θ0' is the first factor coefficient, the theta1' is the second factor coefficient, the theta2' is the third factor coefficient, ρ ' is the real-time trajectory curvature, and v ' is the real-time vehicle speed.
As an alternative implementation, in the first aspect of the embodiment of the present invention, after the obtaining of the target steering wheel angle of the vehicle and before the adjusting of the steering wheel angle of the vehicle according to the target steering wheel angle, the method further includes:
judging whether the vehicle meets a low-speed large-turning driving condition or not based on the real-time vehicle speed and the real-time track curvature;
if not, executing the step of adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel angle;
if so, correcting the target steering wheel corner by using a correction factor corresponding to a low-speed large-turning scene to obtain a corrected steering wheel corner; and adjusting the steering angle of the steering wheel of the vehicle according to the corrected steering wheel angle.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, before the correcting the target steering wheel angle by using the correction factor corresponding to the low-speed large-turning scene, the method further includes:
selecting a target data group meeting the low-speed large-turning driving condition from the specified number of sample data groups;
substituting the real-time factor coefficient, the vehicle speed sample data included by the target data group and the track curvature sample data included by the target data group into the polynomial model to obtain an estimated steering wheel corner corresponding to the target data group;
and carrying out error analysis on the pre-estimated steering wheel angle and steering wheel angle sample data included in the target data group to obtain a correction factor corresponding to a low-speed large-turning scene.
In a second aspect of the embodiments of the present invention, a system for adjusting a steering angle of a steering wheel of a vehicle is disclosed, the system including:
the acquiring unit is used for acquiring the real-time speed of the vehicle and the real-time track curvature of the vehicle when the vehicle runs on a curve;
the first calculation unit is used for substituting the real-time vehicle speed, the real-time track curvature and the real-time factor coefficient into a preset polynomial model to obtain a target steering wheel corner of the vehicle; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle;
and the adjusting unit is used for adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel angle.
A third aspect of the embodiment of the invention discloses a vehicle including the steering wheel angle adjustment system of the vehicle disclosed in the second aspect of the embodiment of the invention.
A fourth aspect of the present invention discloses a system for adjusting a steering angle of a steering wheel of a vehicle, including:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute the method for adjusting the steering angle of the steering wheel of the vehicle disclosed by the first aspect of the embodiment of the invention.
A fifth aspect of the embodiments of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute a method for adjusting a steering angle of a steering wheel of a vehicle disclosed in the first aspect of the embodiments of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, a polynomial model is constructed based on the operational relationship among the steering wheel corner, the vehicle speed and the track curvature of the vehicle, sample data of the three are respectively collected to carry out polynomial regression on the polynomial model, and a real-time factor coefficient related to the calculation of the steering wheel corner is obtained, so that the corresponding steering wheel corner is directly obtained according to the vehicle speed and the track curvature when the vehicle passes a curve by combining the real-time factor coefficient, the complex steps of manually debugging and calibrating individual parameters of the vehicle are omitted, and the factor coefficient in the polynomial model can be updated in time by continuously collecting and updating the sample data to adapt to the change of the vehicle parameters, thereby improving the flexibility of adjusting the steering wheel corner; in addition, the calculation process of the steering wheel turning angle is irrelevant to individual parameters of the vehicle, so the scheme can be suitable for various vehicle speeds and track curvature scenes, the universality of the scheme is greatly improved, and the scheme can also be independently applied to different vehicles and is convenient to transplant on different vehicle types and platforms.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a method for adjusting a steering angle of a steering wheel of a vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a simplified model of a vehicle according to an embodiment of the present disclosure;
FIG. 3 is a flow chart illustrating another method for adjusting the steering angle of a steering wheel of a vehicle according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a steering wheel angle adjustment system for a vehicle according to an embodiment of the present invention;
FIG. 5 is a schematic structural diagram of another vehicle steering wheel angle adjustment system according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of another vehicle steering wheel angle adjustment system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first", "second", "third", "fourth", and the like in the description and the claims of the present invention are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a method and a system for adjusting a steering wheel angle of a vehicle and the vehicle, which can adaptively adjust the steering wheel angle and reduce complicated manual debugging steps. The following detailed description is made with reference to the accompanying drawings.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a method for adjusting a steering angle of a steering wheel of a vehicle according to an embodiment of the present invention. As shown in fig. 1, the method for adjusting the steering angle of the steering wheel of the vehicle is applied to the vehicle, and may specifically include the following steps.
101. When the vehicle runs on a curve, the real-time speed of the vehicle and the real-time track curvature of the vehicle are obtained.
In the embodiment of the invention, the real-time vehicle speed is obtained in real time through a wheel speed sensor when a vehicle turns, and the real-time track curvature is calculated and obtained on the basis of the real-time yaw angular velocity and the real-time vehicle speed; the real-time yaw rate is a vehicle yaw rate acquired in real time by the IMU during turning of the vehicle, and the manner of acquiring any one of the physical quantities is not particularly limited.
Optionally, the vehicle may obtain a real-time trajectory curvature of the vehicle by combining a real-time vehicle speed of the vehicle and the following formula (1); the formula (1) is:
(1)
wherein rho is the real-time track curvature, w is the real-time yaw angular velocity, and v is the real-time vehicle speed.
102. And substituting the real-time speed, the real-time track curvature and the real-time factor coefficient of the vehicle into a preset polynomial model by the vehicle to obtain the target steering wheel corner of the vehicle.
In the embodiment of the invention, the real-time factor coefficient of the vehicle is obtained by performing polynomial regression processing on the polynomial model by using steering wheel angle sample data, vehicle speed sample data and track curvature sample data of the vehicle, and the steering wheel angle sample data, the vehicle speed sample data and the track curvature sample data are respectively a steering wheel steering angle, a vehicle speed and a track curvature corresponding to a certain data acquisition moment in the driving process of the vehicle. The vehicle may acquire a steering angle of a steering wheel of the vehicle through a steering wheel angle sensor disposed on the steering wheel, acquire a vehicle speed of the vehicle through a wheel speed sensor, and measure a yaw rate of the vehicle through an Inertial Measurement Unit (IMU), without limitation.
In addition, a vehicle simplified model is established for the current vehicle, and a polynomial model for calculating the steering wheel angle can be derived. Referring to fig. 2, fig. 2 is a schematic diagram of a simplified model of a vehicle according to an embodiment of the present invention. In fig. 2, the vehicle front wheels 201 serve as steering wheels, and the vehicle rear wheels 202 serve as driving wheels. f is the front axle center point of the vehicle 20, r is the rear axle center point of the vehicle 20, i is the inter-axle center point of the vehicle 20, and the inter-axle center point i is generally related to the centroid position of the vehicle 20. The vehicle coordinate system on the horizontal plane is established by taking the vehicle forward direction as an x axis and taking the direction vertical to the x axis as a y axis, and the vehicle speed v of the vehicle can be decomposed into a longitudinal vehicle speed v in the x axis directionxAnd a lateral vehicle speed v in the y-axis directiony. By performing the force analysis on the vehicle 20, the following equations (2) and (3) can be obtained:
(2)
(3)
wherein w is the vehicle yaw angleSpeed, L is the distance between the front and rear axle centers between the front axle center point f and the rear axle center point r,fis the wheel angle of the front wheels 201 of the vehicle, K is the understeer coefficient, m is the mass of the vehicle 20, LfIs the distance between the center point f of the front axle and the center point i between the axles, LrIs the distance between the center point r of the rear axle and the center point i between the axles, alphafIs the front wheel side slip angle, alpha, between the corresponding instantaneous trajectory tangential direction of the vehicle front wheel 201 and the vehicle front wheel 201rIs the rear wheel side slip angle between the corresponding instantaneous trajectory tangent to the vehicle rear wheel 202 and the vehicle rear wheel 202.
Further, in consideration of the influence of zero-offset of the steering wheel on the steering angle of the steering wheel, equation (4) can be derived by combining equation (1), equation (2) and equation (3), that is:
(4)θ＝θ0+Af＝θ0+ALρ+ALKvx 2ρ；
where θ is the steering wheel angle of the vehicle 20 and θ0For zero yaw of the steering wheel of the vehicle 20, A is the gear ratio of the vehicle 20.
Therefore, a polynomial model for representing the operational relationship between the steering wheel angle, the trajectory curvature and the vehicle speed can be determined according to equation (4), namely:
θ＝θ0+θ1ρ+θ2(v2ρ)；
wherein, in a physical sense, θ0Representing zero deflection of the steering wheel, theta1Representing the product of the transmission ratio A and the distance L between the front and rear axle centers, i.e. theta1＝AL；θ2Represents the product of the transmission ratio A, the front-rear axle center distance L and the understeer coefficient K, namely theta2＝ALK。
It can be understood that the polynomial regression learning is performed on the polynomial model by using the steering wheel corner sample data, the vehicle speed sample data and the track curvature sample data, an optimal combined polynomial can be fitted, and the real-time factor coefficient in the combined polynomial includes theta0、θ1And theta2The respective corresponding optimal solutions.
103. The vehicle adjusts the steering angle of the steering wheel of the vehicle according to the target steering wheel angle.
Therefore, by implementing the method described in fig. 1, the corresponding steering wheel corner can be obtained directly according to the vehicle speed and the track curvature when the vehicle passes a curve, so that the complex steps of manually debugging and calibrating individual parameters of the vehicle are omitted, and the factor coefficients in the polynomial model can be updated in time by continuously acquiring and updating sample data so as to adapt to the change of the vehicle parameters and improve the flexibility of adjusting the steering wheel corner; in addition, the calculation process of the steering wheel turning angle is irrelevant to individual parameters of the vehicle, so the scheme can be suitable for various vehicle speeds and track curvature scenes, the universality of the scheme is greatly improved, and the scheme can also be independently applied to different vehicles and is convenient to transplant on different vehicle types and platforms.
Example two
Referring to fig. 3, fig. 3 is a schematic flow chart illustrating another method for adjusting a steering angle of a steering wheel of a vehicle according to an embodiment of the present invention. As shown in fig. 3, the method for adjusting the steering angle of the vehicle steering wheel may include the following steps.
301. The method comprises the steps of collecting steering wheel corner sample data, vehicle speed sample data and track curvature sample data of a vehicle in the driving process of the vehicle.
In the embodiment of the present invention, the steering wheel angle sample data, the vehicle speed sample data, and the track curvature sample data of the vehicle are respectively a steering angle, a vehicle speed, and a track curvature of the steering wheel, which are acquired at a certain data acquisition time in the driving process of the vehicle, and the acquisition manner of each physical quantity may refer to the first embodiment, which is not described herein again.
302. The vehicle takes the steering wheel corner sample data, the vehicle speed sample data and the track curvature sample data as a group of sample data groups to be stored in a preset database.
303. The vehicle judges whether a sufficient number of sample data sets are stored in a preset database, if so, the step 304-step 308 are executed; if not, continue to step 301.
304. The vehicle reads a specified number of sample data sets from a preset database.
It can be seen that, by implementing the above steps 301 to 304, the steering wheel angle sample data, the vehicle speed sample data, and the track curvature sample data of the vehicle are continuously collected as the newly added sample data set, and the preset database is continuously updated by using the newly added sample data set until the number of the sample data sets reaches the specified number, the polynomial regression learning is performed on the polynomial model by using the sample data sets of the specified number in the preset database, so that the calculation method of the steering wheel angle is periodically updated, and the parameter change caused by the increase of the use time of the vehicle can be adapted.
Optionally, after step 304, the vehicle may further mark all sample data sets in the preset database as historical data sets, so that the number of the sample data sets in the preset database is cleared, and step 301 is continuously performed, so as to continuously update the preset database, and ensure that all the sample data sets used for performing polynomial regression learning on the polynomial model are the newly acquired sample data sets of the specified number.
305. And the vehicle performs polynomial regression processing on the preset polynomial model by using the sample data sets with the specified number to obtain the real-time factor coefficient of the vehicle.
In the embodiment of the invention, the real-time factor coefficient comprises a first factor coefficient, a second factor coefficient and a third factor coefficient; step 305 specifically includes:
the vehicle carries out polynomial regression processing on the preset polynomial model by utilizing the specified number of sample data sets to solve the parameter theta in the polynomial model0Corresponding first factor coefficient, parameter theta1Corresponding second factor coefficient and parameter theta2A corresponding third factor coefficient, i.e.;
θ＝θ0+θ1ρ+θ2(v2ρ)；
the parameter theta is used for substituting steering wheel corner sample data included in each group of sample data groups, the parameter rho is used for substituting track curvature sample data included in each group of sample data groups, and the parameter v is used for substituting vehicle speed sample data included in each group of sample data groups.
306. When the vehicle runs on a curve, the real-time speed of the vehicle and the real-time track curvature of the vehicle are obtained.
307. And substituting the real-time speed, the real-time track curvature and the real-time factor coefficient of the vehicle into a preset polynomial model by the vehicle to obtain the target steering wheel corner of the vehicle.
It can be understood that if a sufficient number of sample data sets are not stored in the preset database and the vehicle leaves the factory for the first time, the vehicle can directly obtain an initial factor coefficient corresponding to the vehicle type of the vehicle, and the real-time vehicle speed, the real-time track curvature and the initial factor coefficient are substituted into the polynomial model to obtain a target steering wheel corner of the vehicle;
or, if a sufficient number of sample data sets are not stored in the preset database and the vehicle already records a historical factor coefficient obtained by one calculation before the current time, the vehicle can continue to use the historical factor coefficient and substitute the real-time vehicle speed, the real-time track curvature and the historical factor coefficient into the polynomial model to obtain the target steering wheel angle of the vehicle.
As an optional implementation manner, step 307 may specifically include:
the vehicle determines a fitting polynomial according to the first factor coefficient, the second factor coefficient, the third factor coefficient and the polynomial model:
substituting the real-time speed and the real-time track curvature into the fitting polynomial by the vehicle to obtain a target steering wheel corner of the vehicle; wherein the fitting polynomial (5) is:
(5)θ'＝θ0'+θ1'ρ'+θ2'(v'2ρ')；
where θ' is the target steering wheel angle of the vehicle, θ0' is a first factor coefficient, θ1' is a second factor coefficient, θ2' is a third factor coefficient, rho ' is the real-time trajectory curvature, and v ' is the real-time vehicle speed.
Therefore, after the polynomial model is fitted by adopting the sample data set, the specific physical meanings of the first factor coefficient, the second factor coefficient and the third factor coefficient do not need to be considered, so that the steps of quantitatively evaluating individual parameters of the vehicle are reduced, the target steering wheel corner of the vehicle can be directly obtained according to the real-time vehicle speed and the real-time track curvature, and the adaptability and the flexibility of adjusting the steering wheel corner of the vehicle are improved.
308. The vehicle judges whether the vehicle meets the low-speed large-turning driving condition or not based on the real-time vehicle speed and the real-time track curvature, and if not, the step 309 is executed; if yes, go to step 310 to step 311.
In the embodiment of the invention, the low-speed and large-turning running condition can be as follows: the real-time speed of the vehicle is less than or equal to the preset speed, and the real-time track curvature is less than or equal to the preset track curvature.
309. The vehicle adjusts the steering angle of the steering wheel of the vehicle according to the target steering wheel angle.
310. And the vehicle corrects the target steering wheel angle by using the correction factor corresponding to the low-speed large-turning scene to obtain the corrected steering wheel angle.
It can be understood that through learning and analyzing the sample data set in the low-speed and large-turning scene, the fact that the steering wheel angle calculated by the vehicle through the fitting polynomial (5) in the low-speed and large-turning scene has a nonlinear relation with the actual steering wheel angle of the vehicle can be found, and therefore a correction factor is required to be obtained to correct the target steering wheel angle. In the embodiment of the present invention, step 310 may specifically include:
the vehicle corrects the target steering wheel angle by using a correction factor corresponding to a low-speed large-turning scene and combining the following formula (6) to obtain a corrected steering wheel angle, namely:
(6)θfinal＝θ*(k*θ)；
wherein, thetafinalTo correct the steering wheel angle, θ is the target steering wheel angle; k is a correction factor and is obtained by analyzing a sample data set corresponding to a low-speed large-turning scene.
It can be seen that the accuracy of adaptively adjusting the steering wheel angle can be improved by performing the above steps 308 to 310 and further correcting the target steering wheel angle obtained in the low-speed large-turning scene by using the correction factor.
As an optional implementation manner, before step 310, the present solution further includes:
selecting a target data group meeting the low-speed and large-turning driving condition from the sample data groups with the specified number by the vehicle;
substituting the real-time factor coefficient, the vehicle speed sample data included by the target data group and the track curvature sample data included by the target data group into the polynomial model by the vehicle to obtain the estimated steering wheel angle corresponding to the target data group;
and the vehicle performs error analysis on the estimated steering wheel angle and steering wheel angle sample data included in the target data set to obtain a correction factor corresponding to a low-speed large-turning scene.
Suppose that the predicted steering wheel angle is thetagThe target data group includes steering wheel angle sample data of thetasThe correction factor is obtained according to equation (6):
it can be seen that, by implementing the above alternative embodiment, the correction factor is updated with the update of the sample data set in the preset database, which can further improve the accuracy of adaptively adjusting the steering wheel angle.
Optionally, if the vehicle cannot select the target data set meeting the low-speed and large-turning driving condition from the specified number of sample data sets, continuing to correct the target steering wheel angle by using the correction factor obtained by the previous calculation at the current time, so as to obtain the corrected steering wheel angle. It can also be understood that, at the time of initial shipment of the vehicle, the correction factor corresponding to the low-speed large-turning scene is set to an initial value matching the model of the vehicle.
311. The vehicle adjusts the steering angle of the steering wheel of the vehicle in accordance with the corrected steering wheel angle.
It can be seen that, by implementing the method described in fig. 3, the corresponding steering wheel corner can be directly obtained according to the vehicle speed and the track curvature when the vehicle passes a curve, without considering the specific physical meaning of each factor coefficient, so that not only is the complicated step of manually debugging and calibrating individual parameters of the vehicle omitted, but also the factor coefficients in the polynomial model can be periodically updated by continuously acquiring and updating sample data, so as to adapt to the parameter change of the vehicle caused by the increase of the use time, and improve the flexibility of adjusting the steering wheel corner; in addition, the calculation process of the steering wheel turning angle is irrelevant to individual parameters of the vehicle, so the scheme can be suitable for various vehicle speeds and track curvature scenes, the universality of the scheme is greatly improved, and the scheme can also be independently applied to different vehicles and is convenient to transplant on different vehicle types and platforms; furthermore, the target steering wheel angle obtained in a low-speed large-turning scene is further corrected by using the correction factor, so that the accuracy of self-adaptive steering wheel angle adjustment can be improved; furthermore, the correction factor is updated along with the update of the sample data set in the preset database, so that the accuracy of adaptively adjusting the steering wheel angle can be further improved.
EXAMPLE III
Referring to fig. 4, fig. 4 is a schematic structural diagram of a system for adjusting a steering angle of a vehicle steering wheel according to an embodiment of the present invention, where the system for adjusting a steering angle of a vehicle steering wheel is applied to a vehicle, so that the vehicle can perform any one of the methods for adjusting a steering angle of a vehicle steering wheel in fig. 1 or fig. 3. As shown in fig. 4, the adjustment system for the steering angle of the vehicle steering wheel may include an acquisition unit 401, a first calculation unit 402, and an adjustment unit 403, in which:
an obtaining unit 401, configured to obtain a real-time vehicle speed of the vehicle and a real-time trajectory curvature of the vehicle when the vehicle is traveling in a curve.
A first calculating unit 402, configured to substitute a real-time vehicle speed, a real-time trajectory curvature, and a real-time factor coefficient of the vehicle into a preset polynomial model, to obtain a target steering wheel angle of the vehicle; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel angle sample data, vehicle speed sample data and track curvature sample data of the vehicle.
An adjusting unit 403, configured to adjust the steering angle of the steering wheel of the vehicle according to the target steering angle.
In the embodiment of the invention, the real-time vehicle speed is obtained in real time through a wheel speed sensor when a vehicle turns, and the real-time track curvature is calculated and obtained on the basis of the real-time yaw angular velocity and the real-time vehicle speed; the real-time yaw rate is a vehicle yaw rate acquired in real time by the IMU during turning of the vehicle, and the manner of acquiring any one of the physical quantities is not particularly limited.
Optionally, the manner of acquiring the real-time trajectory curvature of the vehicle by the acquiring unit 401 specifically is: an obtaining unit 401, configured to obtain a real-time yaw rate of the vehicle, and obtain a real-time trajectory curvature of the vehicle by combining a real-time vehicle speed of the vehicle and the following formula (1); the formula (1) is:
(1)
wherein rho is the real-time track curvature, w is the real-time yaw angular velocity, and v is the real-time vehicle speed.
It can be seen that, by implementing the system described in fig. 4, the corresponding steering wheel corner can be obtained directly according to the vehicle speed and the track curvature when the vehicle passes a curve, so that not only are the complicated steps of manually debugging and calibrating individual parameters of the vehicle omitted, but also the factor coefficients in the polynomial model can be updated in time by continuously acquiring and updating sample data so as to adapt to the change of vehicle parameters, and the flexibility of adjusting the steering wheel corner is improved; in addition, the calculation process of the steering wheel turning angle is irrelevant to individual parameters of the vehicle, so the scheme can be suitable for various vehicle speeds and track curvature scenes, the universality of the scheme is greatly improved, and the scheme can also be independently applied to different vehicles and is convenient to transplant on different vehicle types and platforms.
Example four
Referring to fig. 5, fig. 5 is a schematic structural diagram of another vehicle steering wheel angle adjusting system according to an embodiment of the present invention. The adjustment system for the steering angle of the vehicle shown in fig. 5 is optimized from the adjustment system for the steering angle of the vehicle shown in fig. 4. Compared with the adjusting system of the steering angle of the steering wheel of the vehicle shown in fig. 4, the system shown in fig. 5 further includes a collecting unit 404, a storage unit 405, a first judging unit 406, a reading unit 407, a second calculating unit 408, a second judging unit 409, and a correcting unit 410, wherein:
the acquisition unit 404 is configured to acquire steering wheel angle sample data, vehicle speed sample data, and track curvature sample data of the vehicle before the first calculation unit 402 substitutes the real-time vehicle speed, the real-time track curvature, and the real-time factor coefficient of the vehicle into the preset polynomial model and during the driving process of the vehicle;
the storage unit 405 is configured to store steering wheel corner sample data, vehicle speed sample data, and track curvature sample data of a vehicle as a set of sample data sets to a preset database;
a first determining unit 406, configured to determine whether a sufficient number of sample data sets have been stored in the preset database;
a reading unit 407, configured to read a specified number of sample data sets from the preset database when the first determining unit 406 determines that a sufficient number of sample data sets have been stored in the preset database;
the second calculating unit 408 is configured to perform polynomial regression processing on a preset polynomial model by using a specified number of sample data sets to obtain a real-time factor coefficient of the vehicle.
Optionally, the collecting unit 404 is further configured to collect steering wheel angle sample data, vehicle speed sample data, and track curvature sample data of the vehicle during the driving process of the vehicle when the first determining unit 406 determines that the preset database does not store a sufficient number of sample data sets.
Further optionally, the system further includes a marking unit, configured to mark all sample data sets in the preset database as historical data sets after the reading unit 407 reads a specified number of sample data sets from the preset database, so that the number of the sample data sets in the preset database is cleared, and the acquisition unit 404 is triggered to acquire steering wheel corner sample data, vehicle speed sample data, and track curvature sample data of the vehicle. Therefore, the preset database can be continuously updated, and the sample data groups used for performing polynomial regression learning on the polynomial model each time are all newly acquired sample data groups with the specified number.
In the embodiment of the invention, the real-time factor coefficient comprises a first factor coefficient, a second factor coefficient and a third factor coefficient; optionally, the second calculating unit 408 is specifically configured to perform polynomial regression processing on the following preset polynomial model by using the sample data set with the specified number, and solve a parameter θ in the polynomial model0Corresponding first factor coefficient, parameter theta1Corresponding second factor coefficient and parameter theta2A corresponding third factor coefficient, i.e.;
θ＝θ0+θ1ρ+θ2(v2ρ)；
the parameter theta is used for substituting steering wheel corner sample data included in each group of sample data groups, the parameter rho is used for substituting track curvature sample data included in each group of sample data groups, and the parameter v is used for substituting vehicle speed sample data included in each group of sample data groups.
Still further optionally, the first calculating unit 402 includes:
a determining subunit 4021, configured to determine a fitting polynomial according to the first factor coefficient, the second factor coefficient, the third factor coefficient, and the polynomial model:
a calculating subunit 4022, configured to substitute the real-time vehicle speed and the real-time trajectory curvature into the fitting polynomial to obtain a target steering wheel angle of the vehicle; wherein the fitting polynomial is:
θ'＝θ0'+θ1'ρ'+θ2'(v'2ρ')；
where θ' is the target steering wheel angle of the vehicle, θ0' is a first factor coefficient, θ1' is a second factor coefficient, θ2' is a third factor coefficient, rho ' is the real-time trajectory curvature, and v ' is the real-time vehicle speed.
A second determination unit 409, configured to determine whether the vehicle satisfies a low-speed large-turn driving condition based on the real-time vehicle speed and the real-time trajectory curvature after the first calculation unit 402 obtains the target steering wheel angle of the vehicle and before the adjustment unit 403 adjusts the steering wheel steering angle of the vehicle according to the target steering wheel angle;
the adjusting unit 403 is specifically configured to adjust the steering angle of the steering wheel of the vehicle according to the target steering wheel angle:
an adjusting unit 403 for adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel angle when the second judging unit 409 judges that the vehicle does not satisfy the low-speed large-turn running condition;
the correcting unit 410 is configured to correct the target steering wheel angle by using a correction factor corresponding to the low-speed large-turning scene when the second determining unit 409 determines that the vehicle meets the low-speed large-turning driving condition, so as to obtain a corrected steering wheel angle;
and an adjusting unit 403, configured to adjust a steering angle of the steering wheel of the vehicle according to the corrected steering wheel angle.
Still further, as an optional implementation, the system may further include:
a selecting unit, configured to select a target data set that satisfies a low-speed large-turning driving condition from a specified number of sample data sets before the correcting unit 410 corrects the target steering wheel angle by using the correction factor corresponding to the low-speed large-turning scene;
the third calculating unit is used for substituting the real-time factor coefficient, the vehicle speed sample data included by the target data group and the track curvature sample data included by the target data group into the polynomial model to obtain the estimated steering wheel corner corresponding to the target data group;
and the analysis unit is used for carrying out error analysis on the pre-estimated steering wheel angle and steering wheel angle sample data included in the target data set to obtain a correction factor corresponding to a low-speed large-turning scene.
Optionally, the correcting unit 410 is further configured to, when the selecting unit cannot select the target data group satisfying the low-speed and large-turning driving condition from the sample data groups of the specified number, continue to correct the target steering wheel angle by using the correction factor calculated once before the current time, so as to obtain a corrected steering wheel angle. It can also be understood that, at the time of initial shipment of the vehicle, the correction factor corresponding to the low-speed large-turning scene is set to an initial value matching the model of the vehicle.
It can be seen that, by implementing the system described in fig. 5, the corresponding steering wheel corner can be directly obtained according to the vehicle speed and the track curvature when the vehicle passes a curve, without considering the specific physical meaning of each factor coefficient, so that not only is the complicated step of manually debugging and calibrating individual parameters of the vehicle omitted, but also the factor coefficients in the polynomial model can be periodically updated by continuously acquiring and updating sample data, so as to adapt to the parameter change of the vehicle caused by the increase of the use time, and improve the flexibility of adjusting the steering wheel corner; in addition, the calculation process of the steering wheel turning angle is irrelevant to individual parameters of the vehicle, so the scheme can be suitable for various vehicle speeds and track curvature scenes, the universality of the scheme is greatly improved, and the scheme can also be independently applied to different vehicles and is convenient to transplant on different vehicle types and platforms; furthermore, the target steering wheel angle obtained in a low-speed large-turning scene is further corrected by using the correction factor, so that the accuracy of self-adaptive steering wheel angle adjustment can be improved; furthermore, the correction factor is updated along with the update of the sample data set in the preset database, so that the accuracy of adaptively adjusting the steering wheel angle can be further improved.
EXAMPLE five
Referring to fig. 6, fig. 6 is a schematic structural diagram of another vehicle steering wheel angle adjusting system according to an embodiment of the present invention. As shown in fig. 6, the adjustment system for the steering angle of the vehicle steering wheel may include:
a memory 601 in which executable program code is stored;
a processor 602 coupled to a memory 601;
the processor 602 calls the executable program code stored in the memory 601 to execute a method for adjusting the steering angle of the steering wheel of the vehicle shown in fig. 1 or 3.
The embodiment of the invention discloses a vehicle which comprises a vehicle steering wheel angle adjusting system shown in any one of figures 4 or 5.
The embodiment of the invention also discloses a computer readable storage medium which stores a computer program, wherein the computer program enables a computer to execute the adjusting method of the steering wheel angle of the vehicle shown in the figure 1 or the figure 3.
It will be understood by those skilled in the art that all or part of the steps in the methods of the embodiments described above may be implemented by instructions associated with hardware, which may be stored in a computer-readable storage medium, such as Read-Only Memory (ROM), Random Access Memory (RAM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Compact Disc Read-Only Memory (CD-ROM), or other Memory, magnetic disk, magnetic tape, or magnetic tape, Or any other medium which can be used to carry or store data and which can be read by a computer.
The method and the system for adjusting the steering angle of the steering wheel of the vehicle and the vehicle disclosed by the embodiment of the invention are described in detail, a specific example is applied in the description to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (9)
1. A method of adjusting a steering angle of a vehicle steering wheel, the method comprising:
when the vehicle runs on a curve, acquiring the real-time speed of the vehicle and the real-time track curvature of the vehicle;
substituting the real-time speed, the real-time track curvature and the real-time factor coefficient of the vehicle into a preset polynomial model to obtain a target steering wheel corner of the vehicle; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle;
judging whether the vehicle meets a low-speed large-turning driving condition or not based on the real-time vehicle speed and the real-time track curvature;
if not, adjusting the steering angle of the steering wheel of the vehicle according to the target steering wheel angle;
if so, correcting the target steering wheel corner by using a correction factor corresponding to a low-speed large-turning scene to obtain a corrected steering wheel corner; and adjusting the steering angle of the steering wheel of the vehicle according to the corrected steering wheel angle.
2. The method of claim 1, wherein prior to substituting the real-time vehicle speed, the real-time trajectory curvature, and the real-time factor coefficients of the vehicle into a preset polynomial model, the method further comprises:
collecting steering wheel corner sample data, vehicle speed sample data and track curvature sample data of a vehicle in the driving process of the vehicle, and storing the steering wheel corner sample data, the vehicle speed sample data and the track curvature sample data of the vehicle as a group of sample data groups to a preset database;
judging whether a sufficient number of sample data sets are stored in the preset database;
if not, continuing to execute the step of collecting steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle in the running process of the vehicle;
if so, reading a specified number of sample data groups from the preset database, and performing polynomial regression processing on a preset polynomial model by using the specified number of sample data groups to obtain the real-time factor coefficient of the vehicle.
3. The method of claim 2, wherein the real-time factor coefficients of the vehicle include a first factor coefficient, a second factor coefficient, and a third factor coefficient;
the performing polynomial regression processing on a preset polynomial model by using the sample data group with the specified number to obtain the real-time factor coefficient of the vehicle comprises:
performing polynomial regression processing on the preset polynomial model by using the sample data group with the specified number to solve a parameter theta in the polynomial model0Corresponding first factor coefficient, parameter theta1Corresponding second factor coefficient and parameter theta2A corresponding third factor coefficient, i.e.;
θ＝θ0+θ1ρ+θ2(v2ρ)；
the parameter theta is used for substituting steering wheel corner sample data included in each group of sample data groups, the parameter rho is used for substituting trajectory curvature sample data included in each group of sample data groups, and the parameter v is used for substituting vehicle speed sample data included in each group of sample data groups.
4. The method of claim 3, wherein said substituting said real-time vehicle speed, said real-time trajectory curvature and said real-time factor coefficient into a preset polynomial model to obtain a target steering wheel angle of said vehicle comprises:
determining a fitting polynomial according to the first factor coefficient, the second factor coefficient, the third factor coefficient and a preset polynomial model:
substituting the real-time vehicle speed and the real-time track curvature into the fitting polynomial to obtain a target steering wheel corner of the vehicle; wherein the fitting polynomial is:
θ'＝θ0'+θ1'ρ'+θ2'(v'2ρ')；
wherein θ' is a target steering wheel angle of the vehicle, and θ0' is the first factor coefficient, the theta1' is the second factor coefficient, the theta2'is the third factor coefficient, and the rho' is the real-time trajectory curveAnd v' is the real-time vehicle speed.
5. The method of claim 2, wherein before the correcting the target steering wheel angle with the correction factor corresponding to the slow-speed and large-turn scenario, the method further comprises:
selecting a target data group meeting the low-speed large-turning driving condition from the specified number of sample data groups;
substituting the real-time factor coefficient, the vehicle speed sample data included by the target data group and the track curvature sample data included by the target data group into the polynomial model to obtain an estimated steering wheel corner corresponding to the target data group;
and carrying out error analysis on the pre-estimated steering wheel angle and steering wheel angle sample data included in the target data group to obtain a correction factor corresponding to a low-speed large-turning scene.
6. A system for adjusting a steering angle of a steering wheel of a vehicle, the system comprising:
the acquiring unit is used for acquiring the real-time speed of the vehicle and the real-time track curvature of the vehicle when the vehicle runs on a curve;
the first calculation unit is used for substituting the real-time vehicle speed, the real-time track curvature and the real-time factor coefficient into a preset polynomial model to obtain a target steering wheel corner of the vehicle; the real-time factor coefficient is obtained by performing polynomial regression processing on the polynomial model by using steering wheel corner sample data, vehicle speed sample data and track curvature sample data of the vehicle;
the second judgment unit is used for judging whether the vehicle meets the low-speed large-turning running condition or not based on the real-time vehicle speed and the real-time track curvature;
an adjustment unit configured to adjust a steering angle of the vehicle in accordance with the target steering wheel angle when the second determination unit determines that the vehicle does not satisfy the low-speed large-turn running condition;
a correction unit, configured to correct the target steering wheel angle by using a correction factor corresponding to a low-speed large-turning scene when the second determination unit determines that the vehicle satisfies the low-speed large-turning driving condition, to obtain a corrected steering wheel angle;
the adjusting unit is further configured to adjust a steering angle of the steering wheel of the vehicle according to the corrected steering wheel angle.
7. A vehicle characterized by comprising the vehicle steering wheel angle adjusting system according to claim 6.
8. A system for adjusting a steering angle of a steering wheel of a vehicle, the system comprising:
a memory storing executable program code;
a processor coupled with the memory;
the processor calls the executable program code stored in the memory to execute a method for adjusting the steering angle of a vehicle steering wheel according to any one of claims 1 to 5.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program causes a computer to execute a method of adjusting a steering angle of a vehicle according to any one of claims 1 to 5.
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| CN111158379B (en) * | 2020-01-16 | 2022-11-29 | 合肥中科智驰科技有限公司 | Steering wheel zero-bias self-learning unmanned vehicle track tracking method |
| CN113119947A (en) * | 2021-05-21 | 2021-07-16 | 前海七剑科技(深圳)有限公司 | Vehicle control method and device |
| CN113325695B (en) * | 2021-05-31 | 2024-07-05 | 广州极飞科技股份有限公司 | Vehicle direction control model generation method, vehicle direction control method and device |
| CN115489602A (en) * | 2021-06-18 | 2022-12-20 | 博泰车联网(南京)有限公司 | Intelligent driving method based on steering wheel turning angle, storage medium and electronic equipment |
| CN113624520B (en) * | 2021-07-29 | 2023-05-16 | 东风汽车集团股份有限公司 | System, method and medium for calculating vehicle understeer gradient coefficient in real time based on machine vision technology |
| CN113962023B (en) * | 2021-10-20 | 2024-05-03 | 北京轻舟智航科技有限公司 | Steering wheel zero offset online identification method |
| CN114137971B (en) * | 2021-11-25 | 2023-06-09 | 北京轻舟智航科技有限公司 | Off-line identification method for steering system delay |
| CN114954654B (en) * | 2022-06-22 | 2023-11-28 | 阿波罗智能技术(北京)有限公司 | Calculation method, control method and device for zero offset compensation angle of steering wheel of vehicle |
| CN115180017B (en) * | 2022-08-18 | 2023-10-17 | 苏州轻棹科技有限公司 | Processing method for compensating steering wheel rotation angle |
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| US6408687B1 (en) * | 2000-04-26 | 2002-06-25 | Visteon Global Technologies, Inc. | Misalignment detection system for a steering system of an automotive vehicle |
| JP4280678B2 (en) * | 2004-05-26 | 2009-06-17 | トヨタ自動車株式会社 | Vehicle steering device |
| CN105279309B (en) * | 2015-09-16 | 2018-11-09 | 南京航空航天大学 | Active steering ideal orientation disk torque design method based on aligning torque estimation |
| CN106066644A (en) * | 2016-06-17 | 2016-11-02 | 百度在线网络技术（北京）有限公司 | Set up the method for intelligent vehicle control model, intelligent vehicle control method and device |
| US11254351B2 (en) * | 2017-03-13 | 2022-02-22 | Steering Solutions Ip Holding Corporation | Steering mode selection using machine learning |
| CN107380169B (en) * | 2017-06-02 | 2020-03-27 | 广州小鹏汽车科技有限公司 | Online prediction method and system for automobile steering control characteristics |
| CN109017780B (en) * | 2018-04-12 | 2020-05-05 | 深圳市布谷鸟科技有限公司 | Intelligent driving control method for vehicle |
| CN108820039B (en) * | 2018-05-11 | 2020-08-28 | 江苏大学 | System and method for transversely controlling curve of unmanned vehicle |
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