CN110598311B - Automatic driving vehicle track tracking method - Google Patents
Automatic driving vehicle track tracking method Download PDFInfo
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- CN110598311B CN110598311B CN201910846823.9A CN201910846823A CN110598311B CN 110598311 B CN110598311 B CN 110598311B CN 201910846823 A CN201910846823 A CN 201910846823A CN 110598311 B CN110598311 B CN 110598311B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18172—Preventing, or responsive to skidding of wheels
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
Abstract
The invention relates to a track tracking method, in particular to a track tracking method for an automatic driving vehicle; in particular, it is disclosed to add a virtual representation of the front wheel inclination δ actually acting on an autonomous vehicle to the cost function of a predictive model true The model is used as a cost-effective amount for model prediction control, so that the wheel slip of the vehicle is prevented and corrected, and the track tracking effect is improved; since the wheel slip phenomenon mainly occurs when the model of the vehicle deviates from the ideal model, that is, the wheel inclination deviates from the driving track of the vehicle, the virtual front wheel inclination δ is determined true As a cost-effective amount, the problem of wheel slip is effectively solved.
Description
Technical Field
The invention relates to a track tracking method, in particular to a track tracking method for an automatic driving vehicle.
Background
The key technologies of automatic driving can be sequentially divided into environment perception, behavior decision, path planning and motion control, wherein the motion control mainly comprises two basic design methods, one is a method based on driver simulation, and the other is a control method based on dynamics modeling. The vehicle control method widely studied and applied in the control method based on the dynamics modeling is model predictive control, and can optimize the punctuality, comfort and energy-saving indexes so as to obtain optimized control; in addition, compared with the traditional serial ring PID control, the preview tracking control, the linear model prediction control and the linear quadratic LQR control, the method has the advantages of high calculation precision, strong robustness, small overshoot and good tracking effect.
However, most of the current model-based predictive control only depends on the robustness, and in the control process, if the autonomous vehicle deviates from the reference model (i.e. has a sideslip situation), the behavior decision of the autonomous vehicle is needed to reduce the problem of deviation of the set trajectory, but the deviation of the model cannot be well compensated, so that the phenomenon that the autonomous vehicle deviates from the reference model, such as the autonomous vehicle deviates from the reference model, cannot be avoided.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for tracking the track of an automatic driving vehicle, which can optimize the original model prediction control by increasing indexes reflecting deviation under the condition that the automatic driving vehicle deviates from a reference model, such as driving out or turning, and the like, and can effectively control the automatic driving vehicle to approach to the accurate model as much as possible by the model prediction control, thereby preventing skidding.
The purpose of the invention is realized by the following technical scheme:
a method for tracking the trajectory of an autonomous vehicle, comprising the steps of:
s1: establishing a ground coordinate system and a body coordinate system, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis coordinates of the center of mass of the autonomous vehicle in the ground coordinate system, psi is the heading angle of the autonomous vehicle in the ground coordinate system, v is the forward speed of the autonomous vehicle, a is the forward acceleration of the autonomous vehicle, t represents the amount of the current time, t +1 represents the amount of the next time, L f Is the wheelbase between the front and rear wheels; delta true As a virtual quantity, representing the current true front wheel inclination, isThe vehicle assumes a corresponding front wheel inclination angle under the condition that the front wheel does not slip in the current running direction;
s2: obtaining self state vector X of body t And last output vector U t-1 (ii) a Wherein the content of the first and second substances,
X t ＝[x，y，ψ，v] T (2)
U t-1 ＝[a t-1 ，δ t-1 ] (3)
delta is the front wheel inclination of the autonomous vehicle; when the self state vector X of the machine body at the next moment needs to be calculated t+1 Then, the X at that moment is calculated t Substituting into formula (1) to calculate X at the next time t+1 Is related to parameter x t+1 、y t+1 、ψ t+1 And v t+1 ；
S3: obtaining a virtual quantity which is the current real front wheel inclination angle delta true ；
S4: obtaining discrete waypoint paths P through sampling, carrying out interpolation calculation on the waypoint paths P, and obtaining x by length l respectively ref ，y ref ，ψ ref ，v ref Obtaining a continuous track f (P); wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P 'by setting a reference speed v, wherein P' comprises the state of N points;
P′＝[X 1 ，X 2 ，…，XN] T (5)
X n ＝[f 1 (l n ) f 2 (l n ) f 3 (l n ) f 4 (l n )] (6)
l n ＝(n+1)×v×dt (7)
wherein n is the number of the point to be taken;
s5: according to self state X t And a waypoint path P' for obtaining the acceleration of the current time by predicting the minimum value of the cost function J through a calculation modelDegree a t And the front wheel inclination angle delta at the present moment t So as to obtain the optimal output vector as the current output vector U t ；
The calculation formula of the model prediction cost function J is as follows:
n is a model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual condition;
s6: output vector U t Acting on the autonomous vehicle, S2 to S6 are repeated.
In a preferred embodiment of the present invention, in S3, the current true front wheel inclination angle δ is acquired true The method comprises the following steps:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle delta true Radius of turning in time, L f D ψ represents a heading angle difference of two time intervals, dx represents an x-coordinate difference of two time intervals, dy represents a y-coordinate difference of two time intervals, dt represents a time interval, and ω is an angular velocity of the vehicle.
Preferably, a large amount of δ is obtained by equations (9) and (10) true 、X t And U t Obtaining approximate function g through multivariate least square polynomial fitting; wherein the functions g and X t X, y, ψ in (b) has no relation with the output vector U at that time t Velocity v t Acceleration a t And front wheel inclination angle delta t And the output vector U at the previous moment t-1 Velocity v t-1 Acceleration a t-1 And true front wheel inclination
(ii) related; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting.
In a preferred embodiment of the present invention, in S2, the current body state vector X is obtained by sensor filtering t 。
In a preferred embodiment of the present invention, in S4, the waypoint path P is acquired by the path planner.
Compared with the prior art, the invention has the following beneficial effects:
1. when the model of the vehicle has a certain deviation from the ideal model, mainly the wheel slip phenomenon occurs, namely the wheel inclination angle has a deviation from the driving track of the vehicle; in this case, the virtual representation is used to indicate the actual front wheel inclination δ acting on the autonomous vehicle true The method is used as a cost-effective amount for model prediction control, so that the contact ratio of an actually-operated vehicle model and a preset ideal model is improved, the prevention and correction of wheel slip of the vehicle are facilitated, and the track tracking effect is improved.
2. The invention adds one more cost-substituting quantity delta only in the original model predictive control true The method has small influence on the calculated amount of the model and good effect.
Drawings
Fig. 1 is a schematic view of vehicle trajectory processing of the automatic driving vehicle trajectory tracking method of the present invention.
FIG. 2 is a schematic view of a vehicle model for the trajectory tracking method of an autonomous vehicle according to the present invention.
FIG. 3 is a schematic diagram of a vehicle trajectory tracking system of the method for tracking a trajectory of an autonomous vehicle according to the present invention.
FIG. 4 shows δ in the trajectory tracking method of an autonomous vehicle according to the present invention t And delta true Schematic representation of (a).
Detailed Description
The present invention will be further described with reference to the following examples and drawings, but the embodiments of the present invention are not limited thereto.
Referring to fig. 1 to 4, the method for tracking a trajectory of an autonomous vehicle according to the present embodiment includes the following steps:
s1: establishing a ground coordinate system (xoy)1 and a body coordinate system (x ' o ' y ') 2, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis coordinates of the center of mass of the autonomous vehicle under the ground coordinate system 1, psi is the heading angle of the autonomous vehicle under the ground coordinate system, v is the advancing speed of the autonomous vehicle, a is the advancing acceleration of the autonomous vehicle, t represents the current time quantity, t +1 represents the next time quantity, L f Is the wheelbase between the front and rear wheels; delta true The virtual quantity represents the current real front wheel inclination angle, and is the corresponding front wheel inclination angle of the vehicle under the condition that the front wheels do not slip in the current driving direction.
S2: obtaining self state vector X of body by sensor filtering (or filter) t Obtaining last output vector U through recording information of the system t-1 (ii) a Wherein the content of the first and second substances,
X t ＝[x，y，ψ，v] T (2)
U t-1 ＝[a t-1 ，δ t-1 ] (3)
delta is the angle of inclination of the front wheels of the autonomous vehicle(ii) a When the next moment of the body self-state vector X needs to be calculated t+1 Then, the X at that moment is calculated t Substituting into formula (1) to calculate X at the next time t+1 Is related to parameter x t+1 、y t+1 、ψ t+1 And v t+1 。
S3: obtaining a virtual quantity through an observer, wherein the virtual quantity is the current real front wheel inclination angle delta true (ii) a The observer is specifically modeled as follows:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle delta true Radius of turning in time, L f D ψ represents a heading angle difference of two time intervals, dx represents an x-coordinate difference of two time intervals, dy represents a y-coordinate difference of two time intervals, dt represents a time interval, and ω is an angular velocity of the vehicle.
In addition, a large amount of δ is obtained by the formulas (9) and (10) true 、X t And U t Obtaining approximate function g through multivariate least square polynomial fitting; wherein the functions g and X t X, y, ψ in (b) has no relation with the output vector U at that time t Velocity v t Acceleration a t And front wheel inclination angle delta t And the output vector U at the previous moment t-1 Velocity v t-1 Acceleration a t-1 And true front wheel inclination
(ii) related; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting. In this way, the current true front wheel inclination δ at each instant can be obtained directly from the function g true The calculation amount is simplified, and the calculation efficiency is improved.
S4: referring to fig. 2, discrete waypoint paths P3 are obtained by sampling by the path planner and interpolated for the waypoint paths P3 to obtain x with length l ref ，y ref ，ψ ref ，v ref Obtaining a continuous track f (P) 4; wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P '5 by setting a reference speed v, wherein P' 5 comprises the state of N points;
P′＝[X 1 ，X 2 ，…，X N ] T (5)
X n ＝[f 1 (l n ) f 2 (l n ) f 3 (l n ) f 4 (l n )] (6)
l n ＝(n+1)×v×dt (7)
where n is the number of points taken.
S5: according to self state X t And a waypoint path P' 5, and obtaining the acceleration a at the current moment by calculating the minimum value of the model prediction cost function J t And the front wheel inclination angle delta at the present moment t So as to obtain the optimal output vector as the current output vector U t ；
The calculation formula of the model prediction cost function J is as follows:
wherein, N is the model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual situation.
S6: output vector U t Act on the autonomous vehicle, repeat S2 to S6.
Referring to fig. 4, when a slipping state occurs at the front wheel of the vehicle, the direction of the front wheel of the vehicle is not consistent with the direction in which the wheel should be, that is, the direction of the front wheel of the vehicle is not consistent with the traveling direction v of the vehicle Vehicle with wheels Inconsistency; however, the direction in which the front wheels of the vehicle should be located can be determined by calculation or in some other way, so that this time by introducing a virtual quantity δ true (current true front wheel inclination angle) as the direction in which the front wheel of the vehicle should be located, and the deviation value between the two
The calculation is carried out in the cost function, so that the problem about wheel slip is solved in the whole model prediction control, and the vehicle track tracking is more accurate. The boxes in fig. 4 represent the front wheels of the vehicle.
The present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents and are included in the scope of the present invention.
Claims (5)
1. A method for tracking the trajectory of an autonomous vehicle, comprising the steps of:
s1: establishing a ground coordinate system and a body coordinate system, and performing dynamic modeling on the vehicle; the vehicle dynamics model is as follows:
wherein x and y are respectively the x-axis and y-axis coordinates of the center of mass of the autopilot vehicle in the ground coordinate system, psi is the heading angle of the autopilot vehicle in the ground coordinate system, v is the forward speed of the autopilot vehicle, a is the forward acceleration of the autopilot vehicle, t represents the current time quantity, t +1 represents the next time quantity, L f Is the wheelbase between the front and rear wheels; delta true The real front wheel inclination angle is a virtual quantity, represents the current real front wheel inclination angle, and is the corresponding front wheel inclination angle of the vehicle under the condition that the front wheel is not in a slipping state in the current driving direction;
s2: obtaining self state vector X of body t And last output vector U t-1 (ii) a Wherein the content of the first and second substances,
X t ＝[x，y，ψ，v] T (2)
U t-1 ＝[a t-1 ，δ t-1 ] (3)
delta is the front wheel inclination of the autonomous vehicle; when the self state vector X of the machine body at the next moment needs to be calculated t+1 Then, the X at that moment is calculated t Substituting into formula (1) to calculate X at the next time t+1 Is related to parameter x t+1 、y t+1 、ψ t+1 And v t+1 ；
S3: obtaining a virtual quantity which is the current real front wheel inclination angle delta true ；
S4: obtaining discrete waypoint paths P through sampling, carrying out interpolation calculation on the waypoint paths P, and obtaining x by length l respectively ref ，y ref ，ψ ref ，v ref Obtaining a continuous track f (P); wherein the content of the first and second substances,
substituting the following formula to obtain a discrete waypoint path P 'by setting a reference speed v, wherein P' comprises the state of N points;
P′＝[X 1 ，X 2 ，…，X N ] T (5)
X n ＝[f 1 (l n ) f 2 (l n ) f 3 (l n ) f 4 (l n )] (6)
l n ＝(n+1)×v×dt (7)
wherein n is the number of the point to be taken;
s5: according to self state X t And a waypoint path P', and obtaining the acceleration a at the current moment by calculating the minimum value of the model prediction cost function J t And the front wheel inclination angle delta at the present moment t So as to obtain the optimal output vector as the current output vector U t ；
The calculation formula of the model prediction cost function J is as follows:
n is a model predictive control calculation step length, w represents the weight of each index, and the weight of each index can be assigned according to the actual condition;
s6: output vector U t Act on the autonomous vehicle, repeat S2 to S6.
2. The autonomous-vehicle trajectory tracking method of claim 1, wherein in S3, a current true front-wheel inclination δ is obtained true The method comprises the following steps:
first, by geometric relationships one can obtain:
therefore, the method comprises the following steps:
wherein R is the front wheel inclination angle delta true Radius of turning in time, L f D ψ represents a heading angle difference of two time intervals, dx represents an x-coordinate difference of two time intervals, dy represents a y-coordinate difference of two time intervals, dt represents a time interval, and ω is an angular velocity of the vehicle.
3. The autonomous vehicle trajectory tracking method of claim 2, wherein the plurality of δ is obtained by equations (9) and (10) true 、X t And U t Obtaining approximate function g through multivariate least square polynomial fitting; wherein the functions g and X t X, y, ψ in (b) has no relation with the output vector U at that time t Velocity v t Acceleration a t And front wheel inclination angle delta t And the output vector U at the previous moment t-1 Velocity v t-1 Acceleration a t-1 And true front wheel inclination
Related to; the following can be obtained:
meanwhile, the calculation can be simplified to obtain:
and finally, obtaining a function g through multivariate least square polynomial fitting.
4. The automatic driven vehicle trajectory tracking method according to claim 1, characterized in that in S2, the current body own state vector X is acquired through sensor filtering t 。
5. The autonomous vehicle trajectory tracking method of claim 1, wherein in S4, a waypoint path P is obtained by the path planner.
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