CN109144076A - A kind of more vehicle transverse and longitudinals coupling cooperative control system and control method - Google Patents
A kind of more vehicle transverse and longitudinals coupling cooperative control system and control method Download PDFInfo
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Abstract
The present invention discloses a kind of more vehicle transverse and longitudinal coupling cooperative control systems, comprising: context detection module, for detecting current environment, road and signal information；Queue global path planning module carries out path Global motion planning according to queue current location and target position, and routing information is passed to navigator's vehicle local paths planning module；Navigator's vehicle local paths planning module, its detection information for being used to receive the context detection module, and local paths planning and path trace are carried out, Following Car path modification module, it receives the local paths planning, and is corrected again to the local paths planning；Vehicle control module receives revised local paths planning, and carries out path trace.The present invention also provides a kind of more vehicle transverse and longitudinals to couple cooperative control method, can preferably carry out more vehicle transverse and longitudinal coupling Collaborative Controls.
Description
Technical field
The present invention relates to more vehicle Collaborative Control technical fields, and more particularly, the present invention relates to a kind of more vehicle transverse and longitudinals
To coupling cooperative control system and control method.
Background technique
Queue Collaborative Control is integrated with the communication technology, computer technology, artificial intelligence technology and intelligent vehicle control side
Method realizes the Collaborative Control between more vehicles by V2X exchange technology.By environment sensing, information fusion and optimization
Decision improves road by efficiency and energy utilization efficiency, increases road safety, therefore becomes the following intelligent network connection
With the important combination field of intelligent vehicle.
Existing queue cooperative control method focuses primarily upon vertical collaboration control direction, with the work such as speed and acceleration
To control variable, by certain longitudinal following distance control strategy, more vehicles is made to carry out road driving in the form of longitudinal formation.It is existing
Depositing control system the most mature is layered distribution type cooperative control system.Entire more vehicle control systems include assignment decisions
Layer, vehicle motion control layer and execution control layer.Wherein, queue is considered as a system by assignment decisions layer, according to queue matter
Heart position and destination carry out global path planning；Vehicle motion control layer includes navigator's vehicle and follows vehicle, is navigated
Vehicle carries out the sector planning and tracking in path, follows vehicle according to the driving path of navigator's vehicle, in conjunction with spacing control strategy into
Row is longitudinal to be followed；Control layer is executed according to the vehicle traveling information determined in vehicle motion control layer, by the system for controlling vehicle
The dynamic motion requirement for meeting vehicle with acceleration.More vehicle transverse and longitudinals involved in existing patent couple and can not have to cooperative control method
Effect controls the operating conditions such as steering, lane-change；Or lateral, longitudinal control is individually carried out, ignore coupling between the two
Relationship.It is specific there are the problem of it is as follows:
(1) kinetic model problem: a. in order to describe control information and vehicle response corresponding relationship, a variety of queue vehicles
Nodes dynamics model is suggested, and the control system based on these modellings contains only the longitudinal movement of vehicle and can not contain
The transverse movement of lid vehicle；B. existing vehicle dynamic model can not embody the heterogeneity of different vehicle in queue, i.e., can not
The difference of kinetic parameter between vehicle is described；C. existing vehicle dynamic model can only embody conventional truck front-wheel steer control
System can not be suitable for following four-wheel steering vehicle etc..
(2) in controller, without reference to more vehicle platoon horizontal spacing Optimal Control Strategies, make not carrying out between vehicle
Laterally optimal collaboration.
(3) dynamics of vehicle performance difference is affected to more vehicle Collaborative Controls in queue, and navigator's vehicle and Following Car
The environment faced ignores these elements and control easily initiation safety problem there is also difference.
Chinese invention patent 201410033746.2 discloses a kind of vehicle multi-objective coordinated lane-change auxiliary adaptive cruise
Control method is that control variable is tracked using the position of vehicle, speed and acceleration as state variable with front truck with acceleration
Property, more vehicle sports safeties and longitudinal drive comfort be optimization aim, carry out Model Predictive Control, guarantee following distance and vehicle
Between relative velocity error it is minimum.However vehicle multi-objective coordinated lane-change auxiliary self-adapting cruise control method cannot achieve in queue
The crosswise joint of vehicle.And due to the kinetic characteristics parameter in the kinetic model of use without reference to different automobile types, nothing
Method is accurately controlled for respective power performance；The ambient enviroment difference etc. for following vehicle and navigator's vehicle to face is not accounted for
Problem.
Chinese invention patent CN201711206133 discloses a kind of intelligent fleet lane-change method, with the vehicle of relative maturity
Lane-change technology realizes in fleet vehicle successively safe lane-change.However the lane-change method only considered the lane-change of vehicle in fleet
Opportunity, and not can guarantee the implementation of holding and the coupling control of vehicle transverse and longitudinal of fleet's formation when lane-change.
Chinese invention patent 201610957049.5 disclose it is a kind of control automobile is formed into columns in the form of cluster travel method,
Speed control model is given on horizontal and vertical formation model.However the invention only gives the motion control for interior vehicle of forming into columns
Function does not control vehicle from dynamic (dynamical) angle, and can not embody the Kinetic differences between vehicle.This will lead to
It is low that Controlling model controls precision when running at high speed, and the identical real road that do not meet of interior vehicle dynamics characteristics of forming into columns travels feelings
Border.
Chinese invention patent 201510896784.5 discloses a kind of device for controlling the speed of CACC system and side
Method provides a kind of device and method for controlling cooperating type adaptive cruise (CACC) system, to the vehicle of Collaborative Control
Between information streaming delivery method designed.However the device and method do not account for the specific movement between vehicle and vehicle
Control method.
Summary of the invention
It is an object of the invention to design and develop a kind of more vehicle transverse and longitudinal coupling cooperative control systems, Following Car energy
Enough according to the planning path of navigator's vehicle and environment, road and the signal information of Following Car, Following Car is carried out laterally and vertical
To coupling Collaborative Control.
Another method of the invention is to have designed and developed a kind of more vehicle transverse and longitudinal coupling cooperative control methods, Following Car
The path of Following Car can be carried out according to the planning path of navigator's vehicle and environment, road and the signal information of Following Car
It corrects and optimizes to obtain control variable, preferably carry out more vehicle transverse and longitudinal coupling Collaborative Controls.
The present invention can also be control variable with vehicle front vehicle wheel corner, rear wheel corner and longitudinal speed to auto model into
Row linearization process, and then the state variable of Following Car subsequent time is obtained by Linearized state equations.
The present invention can also be modified based on path of the BP neural network to Following Car and optimize to obtain control variable.
Technical solution provided by the invention are as follows:
A kind of more vehicle transverse and longitudinal coupling cooperative control systems, comprising:
Context detection module, for detecting current environment, road and signal information；
Queue global path planning module carries out path Global motion planning according to queue current location and target position, and
Routing information is passed into navigator's vehicle local paths planning module；
Navigator's vehicle local paths planning module is used to receive the detection information of the context detection module, and carry out office
Portion's path planning and path trace,
Following Car path modification module receives the local paths planning, and carries out again to the local paths planning
Amendment；
Vehicle control module receives revised local paths planning, and carries out path trace.
A kind of more vehicle transverse and longitudinal coupling cooperative control methods, include the following steps:
Step 1: it is control variable with vehicle front vehicle wheel corner, rear wheel corner and longitudinal speed, be displaced with lateral direction of car,
Length travel, lateral speed, acceleration and yaw angle are state variable, establish vehicle single track Three Degree Of Freedom model, line of going forward side by side
Propertyization processing, obtains Linearized state equations；
Step 2: according to queue current location and target position, carrying out path Global motion planning and obtain the road of navigator's vehicle planning
Diameter, and local paths planning is carried out according to current environment, road and signal information and obtains the path of Following Car planning；
Step 3: the path of Following Car planning being optimized, the control matrix of variables after being optimized；
J (k)=min | f (η, ηref)|+min|ΔU|
In formula, J (k) is optimization object function, and η is the revised path of Following Car, ηrefFor navigator's vehicle planning path,
Δ U is the variation value matrix in the path of Following Car planning and the control variable in the revised path of Following Car, and f () is error letter
Number；
Wherein, in optimization process, Following Car meets following constraint condition:
-12°≤β≤12°；
ay,min≤ay≤ay,max；
-2.5°≤αf,t≤2.5°；
-2.5°≤αr,t≤2.5°；
VC≤Vlight,i；
In formula, β is the side slip angle of Following Car, ayFor the side acceleration of Following Car, ay,min,ay,maxRespectively follow
The minimum value and maximum value of the side acceleration of vehicle, αf,t,αr,tThe side drift angle of tire and right side tire respectively on the left of Following Car,
XC,YCThe respectively lateral position of Following Car and lengthwise position, XO,YOThe respectively lateral position of barrier and lengthwise position, d
For the safe distance of Following Car and barrier, VCFor longitudinal speed of Following Car, Vlight,iFor the restriction vehicle under the i-th class signal lamp
Speed；
Step 4: by the control matrix of variables input linear state equation after optimization, obtaining after following optimization of vehicle
State variable matrix, and then the state variable after being optimized carries out Collaborative Control to vehicle.
Preferably, the Linearized state equations are as follows:
X (k+1)=[I+TA (t)] X (k)+TB (t) U (k)；
In formula, X (k+1) is the vehicle-state matrix of variables at+1 moment of kth, and I is unit matrix, and T is sampling time, A
(t), B (t) is parameter matrix, and X (k) is the vehicle-state matrix of variables at kth moment, and U (k) is that the vehicle control at kth moment becomes
Moment matrix, Ccf,CcrRespectively vehicle front-wheel, rear-wheel cornering stiffness, m are the quality of vehicle,Respectively longitudinal direction of car and cross
To speed, lf,lrRespectively automobile front-axle and rear axle wheelbase, IzFor vehicle rotary inertia,For Vehicular yaw angle,For vehicle
Yaw velocity.
Preferably, the vehicle single track Three Degree Of Freedom model are as follows:
In formula,Respectively longitudinal direction of car and transverse acceleration,For Vehicular yaw angular acceleration, δf,δrRespectively vehicle
Front-wheel, rear-wheel corner, Clf,ClrRespectively vehicle front-wheel, rear-wheel longitudinal force and slip rate proportionality coefficient, sf,srDivide than being vehicle
Front-wheel, rear wheel slip rate,The respectively longitudinally, laterally speed of vehicle in the queue.
Preferably, the vehicle front-wheel, rear wheel slip rate meet: sf=sr=0.2.
Preferably, in the step 3, control matrix of variables after optimization is specifically included:
When more vehicle platoons when driving, based on BP neural network to Following Car front vehicle wheel corner, rear wheel corner and longitudinal direction
Speed carries out optimising and adjustment, includes the following steps:
Step 1: according to the sampling period, acquiring the lateral displacement L of navigator's vehiclePV,t, length travel LPV,p, lateral vehicle velocity VPV,t、
Acceleration aPVAnd yaw angleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration
aFVAnd yaw angle
Step 2: the successively lateral displacement L of high-ranking military officer's airlinePV,t, length travel LPV,p, lateral vehicle velocity VPV,t, acceleration aPVWith
Yaw angleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration aFVAnd yaw angleIt standardizes, determines the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5,x6,x7,x8,x9,x10}；
Wherein, x1For the lateral displacement coefficient of navigator's vehicle, x2For the length travel coefficient of navigator's vehicle, x3For the lateral speed system of navigator's vehicle
Number, x4For the acceleration factor of navigator's vehicle, x5For the sideway ascent of navigator's vehicle, x6For the lateral displacement coefficient of Following Car, x7For
The length travel coefficient of Following Car, x8For the lateral speed coefficient of Following Car, x9For the acceleration factor of Following Car, x10To follow
The sideway ascent of vehicle；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；During m is
Interbed node number；
Step 4: obtaining output layer vector z={ z1,z2,z3}；Wherein, z1For Following Car front vehicle wheel corner adjustment factor, z2
For Following Car rear wheel corner adjustment factor, z3For Following Car longitudinal direction speed adjustment factor, make
Wherein, z1 i、z2 i、z3 iRespectively ith sample period output layer vector parameter, The maximum longitudinal vehicle of Following Car front vehicle wheel hard-over, Following Car rear wheel hard-over, the Following Car respectively set
Speed,Respectively the i+1 sampling period when Following Car front vehicle wheel corner, after Following Car
Wheel steering angle, Following Car longitudinal direction speed.
Preferably, in the step 1, under initial operating state, Following Car front vehicle wheel corner, Following Car rear car rotation
Angle, Following Car longitudinal direction speed meet empirical value:
δFV, f, 0=0,
Wherein,Respectively initially turn with the initial corner of Chinese herbaceous peony wheel, Following Car rear wheel
The initial longitudinal speed of angle, Following Car.
Preferably, in the step 2, the lateral displacement L of navigator's vehiclePV,t, length travel LPV,p, lateral vehicle velocity VPV,t、
Acceleration aPVAnd yaw angleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration
aFVAnd yaw angleCarry out normalization formulae are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter LPV,t、LPV,p、VPV,t、aPV、
LFV,t、LFV,p、VFV,t、aFV、J=1,2,3,4 ..., 10；XjmaxAnd XjminMaximum value in respectively corresponding measurement parameter
And minimum value.
Preferably, the middle layer node number m meets:Wherein n is input layer
Number, p are output layer node number.
Preferably, in the step 3, control matrix of variables after optimization is specifically included: using quadratic programming
The Optimal matrix for solving control variable, converts standard type for objective function:
min∫(XTQX+UTRU)dt
In formula, X is the state variable matrix of Following Car, and U is the control matrix of variables of Following Car, and Q and R are weight coefficient.
It is of the present invention the utility model has the advantages that
(1) more vehicle transverse and longitudinals provided by the invention couple cooperative control system, and Following Car can be according to the rule of navigator's vehicle
Environment, road and the signal information for drawing path and Following Car carry out transverse and longitude coupling Collaborative Control to Following Car.
(2) more vehicle transverse and longitudinals provided by the invention couple cooperative control method, and Following Car can be according to the rule of navigator's vehicle
Environment, road and the signal information for drawing path and Following Car, are modified and optimize to the path of Following Car and controlled
Variable, and then the state variable of Following Car subsequent time is obtained by Linearized state equations, preferably carry out more vehicle transverse and longitudinals
To coupling Collaborative Control.The present invention can also be modified based on path of the BP neural network to Following Car and optimize to obtain control change
Amount.
Detailed description of the invention
Fig. 1 is that more vehicle transverse and longitudinals of the present invention couple Collaborative Control schematic illustration.
Fig. 2 is Following Car path modification schematic illustration of the present invention.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
The present invention provides a kind of more vehicle transverse and longitudinal coupling cooperative control systems, comprising: context detection module, for detecting
Current environment, road and signal information；Queue global path planning module, according to queue current location and target position
It sets, carries out path Global motion planning, and routing information is passed into navigator's vehicle local paths planning module；Navigator's vehicle local path rule
Module is drawn, is used to receive the detection information of the context detection module, and carry out local paths planning and path trace, follows
Bus or train route diameter correction module receives the local paths planning, and is corrected again to the local paths planning；Vehicle control
Module receives revised local paths planning, and carries out path trace.
More vehicle transverse and longitudinals provided by the invention couple cooperative control system, and Following Car can be according to the planning road of navigator's vehicle
The environment of diameter and Following Car, road and signal information carry out transverse and longitude coupling Collaborative Control to Following Car.
As shown in Figure 1, 2, the present invention also provides a kind of more vehicle transverse and longitudinals to couple cooperative control method, including walks as follows
It is rapid:
Step 1: it is control variable with vehicle front vehicle wheel corner, rear wheel corner and longitudinal speed, be displaced with lateral direction of car,
Length travel, lateral speed, acceleration and yaw angle are state variable, establish vehicle single track Three Degree Of Freedom model:
In formula,Respectively longitudinal direction of car and transverse acceleration,For Vehicular yaw angular acceleration, δf,δrRespectively vehicle
Front-wheel, rear-wheel corner, Clf,ClrRespectively vehicle front-wheel, rear-wheel longitudinal force and slip rate proportionality coefficient, sf,srDivide than being vehicle
Front-wheel, rear wheel slip rate,The respectively longitudinally, laterally speed of vehicle in the queue, Ccf,CcrRespectively vehicle front-wheel,
Rear-wheel cornering stiffness, m are the quality of vehicle,Respectively longitudinal direction of car and lateral velocity, lf,lrRespectively automobile front-axle and
Rear axle wheelbase, IzFor vehicle rotary inertia,For Vehicular yaw angle,For yaw rate.
And linearization process is carried out to single track Three Degree Of Freedom model, obtain Linearized state equations:
X (k+1)=[I+TA (t)] X (k)+TB (t) U (k)；
In formula, X (k+1) is the vehicle-state matrix of variables at+1 moment of kth, and I is unit matrix, and T is sampling time, A
(t), B (t) is parameter matrix, and X (k) is the vehicle-state matrix of variables at kth moment, and U (k) is that the vehicle control at kth moment becomes
Moment matrix, it is to be understood that t is continuous time, k is step-length when continuous system being turned to non-continuous system.
Step 2: according to queue current location and target position, carrying out path Global motion planning and obtain the road of navigator's vehicle planning
Diameter, and local paths planning is carried out according to current environment, road and signal information and obtains the path of Following Car planning, (just
The navigator's vehicle planning path generated that begins and local planning path are consistent in ideal circumstances)；
Step 3: the path of Following Car planning being optimized, the control matrix of variables after being optimized；
J (k)=min | f (η, ηref)|+min|ΔU|
In formula, J (k) is optimization object function, and η is the revised path of Following Car, ηrefFor navigator's vehicle planning path,
Δ U is the variation value matrix in the path of Following Car planning and the control variable in the revised path of Following Car, and f () is error letter
Number；
Wherein, in optimization process, Following Car meets following constraint condition:
-12°≤β≤12°；
ay,min≤ay≤ay,max；
-2.5°≤αf,t≤2.5°；
-2.5°≤αr,t≤2.5°；
VC≤Vlight,i；
In formula, β is the side slip angle of Following Car, ayFor the side acceleration of Following Car, ay,min,ay,maxRespectively follow
The minimum value and maximum value of the side acceleration of vehicle, αf,t,αr,tThe side drift angle of tire and right side tire respectively on the left of Following Car,
XC,YCThe respectively lateral position of Following Car and lengthwise position, XO,YOThe respectively lateral position of barrier and lengthwise position, d
For the safe distance of Following Car and barrier, VCFor longitudinal speed of Following Car, Vlight,iFor the restriction vehicle under the i-th class signal lamp
Speed；
I.e. when being optimized to the path that Following Car is planned, while so that Following Car meets above-mentioned constraint condition, with
It is also wanted with the revised path of vehicle and the path of navigator's vehicle planning is similar as far as possible, so that whole queue more neat appearance.
Step 4: by the control matrix of variables input linear state equation after optimization, obtaining after following optimization of vehicle
State variable matrix:
Xop(k+1)=[I+TA (t)] X (k)+TB (t) Uop(k)
Wherein, XopIt (k+1) is to follow the state variable matrix after optimization of vehicle, UopIt (k) is the control variable square after optimization
Battle array.
Vehicle three-degrees-of-freedom dynamics model purpose is under theoretical description control variable effect that vehicle is longitudinal, horizontal
To and yaw direction motor imagination, to the formula carry out Rational Simplification, such as: under the premise of small angle tower, side drift angle is regarded
It is 0；Think that slip rate is maintained at 0.2 (coefficient of road adhesion peak value) left and right within protection scope of the present invention.Current mould
Type can be used for describing the Collaborative Control of multiple four-wheel steering-by-wire vehicles, when rear-wheel corner is set as 0, then to rotate before tradition
To vehicle.
Control matrix of variables after optimization is specifically included: when more vehicle platoons when driving, be based on BP neural network
Optimising and adjustment is carried out to Following Car front vehicle wheel corner, rear wheel corner and longitudinal speed, is included the following steps:
Step 1: establishing BP neural network model；
For the BP network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate that n detection signal of vehicle running state, these signal parameters are provided by data preprocessing module.The second layer is hidden layer,
Total m node is determined in an adaptive way by the training process of network.Third layer is output layer, total p node, by system
Actual needs output in response to determining that.
The mathematical model of the network are as follows:
Input layer vector: x=(x1,x2,…,xn)T
Middle layer vector: y=(y1,y2,…,ym)T
Output layer vector: z=(z1,z2,…,zp)T
In the present invention, input layer number is n=10, and output layer number of nodes is p=3.Hidden layer number of nodes m is estimated by following formula
It obtains:
According to the sampling period, 10 parameters of input are x1For the lateral displacement coefficient of navigator's vehicle, x2For the vertical of navigator's vehicle
To displacement coefficient, x3For the lateral speed coefficient of navigator's vehicle, x4For the acceleration factor of navigator's vehicle, x5For the yaw angle of navigator's vehicle
Coefficient, x6For the lateral displacement coefficient of Following Car, x7For the length travel coefficient of Following Car, x8For the lateral speed system of Following Car
Number, x9For the acceleration factor of Following Car, x10For the sideway ascent of Following Car；
Since the data that sensor obtains belong to different physical quantitys, dimension is different.Therefore, mind is inputted in data
Before network, need to turn to data requirement into the number between 0-1.
Specifically, for the lateral displacement L of navigator's vehiclePV,t, after being standardized, obtain the lateral displacement system of navigator's vehicle
Number x1:
Wherein,WithThe respectively minimum lateral displacement and maximum transversal displacement of navigator's vehicle.
Likewise, for the length travel L of navigator's vehiclePV,p, after being standardized, obtain the length travel coefficient x of navigator's vehicle2:
Wherein,WithThe respectively minimum length travel of navigator's vehicle and maximum length travel.
To the lateral vehicle velocity V of navigator's vehiclePV,t, after being standardized, obtain the lateral speed coefficient x of navigator's vehicle3:
Wherein,WithThe respectively minimum lateral speed and maximum transversal speed of navigator's vehicle.
To the acceleration a of navigator's vehiclePV, after being standardized, obtain the acceleration factor x of navigator's vehicle4:
Wherein,WithThe respectively minimum acceleration and peak acceleration of navigator's vehicle.
To the yaw angle of navigator's vehicleAfter being standardized, the sideway ascent x of navigator's vehicle is obtained5:
Wherein,WithThe respectively minimum yaw angle of navigator's vehicle and maximum yaw angle.
For the lateral displacement L of Following CarFV,t, after being standardized, obtain the lateral displacement coefficient x of G Following Car6:
Wherein,WithThe respectively minimum lateral displacement and maximum transversal displacement of Following Car.
Likewise, for the length travel L of Following CarFV,p, after being standardized, obtain the length travel coefficient x of navigator's vehicle7:
Wherein,WithThe respectively minimum length travel of Following Car and maximum length travel.
To the lateral vehicle velocity V of Following CarFV,t, after being standardized, obtain the lateral speed coefficient x of navigator's vehicle8:
Wherein,WithThe respectively minimum lateral speed and maximum transversal speed of Following Car.
To the acceleration a of Following CarFV, after being standardized, obtain the acceleration factor x of navigator's vehicle9:
Wherein,WithThe respectively minimum acceleration and peak acceleration of Following Car.
To the yaw angle of Following CarAfter being standardized, the sideway ascent x of Following Car is obtained10:
Wherein,WithThe respectively minimum yaw angle of Following Car and maximum yaw angle.
3 parameters of output signal respectively indicate are as follows: z1For Following Car front vehicle wheel corner adjustment factor, z2After Following Car
Wheel steering angle adjustment factor, z3For Following Car longitudinal direction speed adjustment factor；
Following Car front vehicle wheel corner adjustment factor z1Be expressed as Following Car front vehicle wheel corner in next sampling period with
The ratio between Following Car front vehicle wheel hard-over set in current sample period, i.e., it is collected to follow in the ith sample period
Chinese herbaceous peony wheel steering angle isThe Following Car front vehicle wheel corner adjustment factor in ith sample period is exported by BP neural network
z1 iAfterwards, controlling Following Car front vehicle wheel corner in the i+1 sampling period isMake its satisfaction
Following Car rear wheel corner adjustment factor z2Be expressed as Following Car rear wheel corner in next sampling period with
The ratio between Following Car rear wheel hard-over set in current sample period, i.e., it is collected to follow in the ith sample period
Vehicle rear wheel corner isThe Following Car rear wheel corner adjustment factor in ith sample period is exported by BP neural network
z2 iAfterwards, controlling Following Car rear wheel corner in the i+1 sampling period isMake its satisfaction
Following Car longitudinal direction speed adjustment factor z3It is expressed as Following Car longitudinal direction speed in next sampling period and current
The ratio between maximum longitudinal speed of the Following Car set in sampling period, i.e., in the ith sample period, collected Following Car is longitudinal
Speed isThe Following Car longitudinal direction speed adjustment factor z in ith sample period is exported by BP neural network3 iAfterwards, it controls
Following Car longitudinal direction speed in the i+1 sampling period isMake its satisfaction
Step 2: carrying out the training of BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.According to the experience number of product
According to the sample for obtaining training, and give the connection weight w between input node i and hidden layer node jij, hidden node j and output
Connection weight w between node layer kjk, the threshold θ of hidden node jj, export the threshold value w of node layer kij、wjk、θj、θkIt is -1
Random number between to 1.
In the training process, w is constantly correctedijAnd wjkValue, until systematic error be less than or equal to anticipation error when, complete
The training process of neural network.
As shown in table 1, given the value of each node in one group of training sample and training process.
Each nodal value of 1 training process of table
Step 3: acquisition data run parameter input neural network is regulated coefficient；
When more vehicle platoons when driving, i.e., under initial operating state, Following Car front vehicle wheel corner, Following Car rear car rotation
Angle, Following Car longitudinal direction speed meet empirical value:
δFV, f, 0=0,
Wherein,Respectively initially turn with the initial corner of Chinese herbaceous peony wheel, Following Car rear wheel
The initial longitudinal speed of angle, Following Car.
Meanwhile measuring the initial lateral displacement L of navigator's vehiclePV,t0, initial length travel LPV,p0, initial lateral vehicle velocity VPV,t0、
Initial acceleration aPV0With initial yaw angleAnd the initial lateral displacement L of Following CarFV,t0, initial length travel LFV,p0、
Initial transverse direction vehicle velocity VFV,t0, initial acceleration aFV0With initial yaw angleBy the way that above-mentioned parameter is standardized, BP mind is obtained
Initial input vector through networkIt is obtained by the operation of BP neural network
Initial output vector
Step 4: obtaining initial output vectorAfterwards, i.e., the front vehicle wheel corner of adjustable Following Car, rear car
Corner and longitudinal speed are taken turns, the front vehicle wheel corner, rear wheel corner and longitudinal speed difference of next sampling period Following Car are made
Are as follows:
The lateral displacement L of navigator's vehicle in the ith sample period is obtained by sensorPV,t, length travel LPV,p, laterally
Vehicle velocity VPV,t, acceleration aPVAnd yaw angleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral speed
VFV,t, acceleration aFVAnd yaw angleBy being standardized to obtain the input vector x in ith sample periodi=(x1 i,
x2 i,x3 i,x4 i,x5 i,x6 i,x7 i,x8 i,x9 i,x10 i), by the operation of BP neural network obtain the output in ith sample period to
Measure zi=(z1 i,z2 i,z3 i), the front vehicle wheel corner, rear wheel corner and longitudinal speed of Following Car are then controlled to adjust, i+1 is made
The front vehicle wheel corner of Following Car, rear wheel corner and longitudinal speed are respectively as follows: when a sampling period
Control matrix of variables after finally obtaining Following Car optimization.
It is, of course, also possible to obtain the control matrix of variables after Following Car optimization using the method for quadratic programming, that is, use two
The Optimal matrix of secondary programming evaluation control variable, converts standard type for objective function:
min∫(XTQX+UTRU)dt
In formula, X is the state variable matrix of Following Car, and U is the control matrix of variables of Following Car, and Q and R are weight coefficient.
The matrix obtained when the objective function minimum of above-mentioned standard type is Optimal matrix.
More vehicle transverse and longitudinals provided by the invention couple cooperative control method, and Following Car can be according to the planning road of navigator's vehicle
The environment of diameter and Following Car, road and signal information are modified the path of Following Car and optimize to obtain control variable,
And then the state variable of Following Car subsequent time is obtained by Linearized state equations, preferably carry out more vehicle transverse and longitudinal couplings
Collaborative Control.The present invention can also be modified based on path of the BP neural network to Following Car and optimize to obtain control variable.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (10)
1. a kind of more vehicle transverse and longitudinals couple cooperative control system characterized by comprising
Context detection module, for detecting current environment, road and signal information；
Queue global path planning module carries out path Global motion planning according to queue current location and target position, and by road
Diameter information passes to navigator's vehicle local paths planning module；
Navigator's vehicle local paths planning module, is used to receive the detection information of the context detection module, and carry out local road
Diameter planning and path trace,
Following Car path modification module receives the local paths planning, and is corrected again to the local paths planning；
Vehicle control module receives revised local paths planning, and carries out path trace.
2. a kind of more vehicle transverse and longitudinals couple cooperative control method, which comprises the steps of:
Step 1: being control variable with vehicle front vehicle wheel corner, rear wheel corner and longitudinal speed, with lateral direction of car displacement, longitudinal direction
Displacement, lateral speed, acceleration and yaw angle are state variable, establish vehicle single track Three Degree Of Freedom model, and linearized
Processing obtains Linearized state equations；
Step 2: according to queue current location and target position, carries out path Global motion planning and obtain the path of navigator's vehicle planning, and
Local paths planning, which is carried out, according to current environment, road and signal information obtains the path of Following Car planning；
Step 3: the path of Following Car planning being optimized, the control matrix of variables after being optimized；
J (k)=min | f (η, ηref)|+min|ΔU|
In formula, J (k) is optimization object function, and η is the revised path of Following Car, ηrefFor the path of navigator's vehicle planning, Δ U is
The variation value matrix of the control variable in the path and revised path of Following Car of Following Car planning, f () is error function；
Wherein, in optimization process, Following Car meets following constraint condition:
-12°≤β≤12°；
ay,min≤ay≤ay,max；
-2.5°≤αf,t≤2.5°；
-2.5°≤αr,t≤2.5°；
VC≤Vlight,i；
In formula, β is the side slip angle of Following Car, ayFor the side acceleration of Following Car, ay,min,ay,maxRespectively Following Car
The minimum value and maximum value of side acceleration, αf,t,αr,tThe side drift angle of tire and right side tire, X respectively on the left of Following CarC,YC
The respectively lateral position of Following Car and lengthwise position, XO,YOThe respectively lateral position of barrier and lengthwise position, d are to follow
The safe distance of vehicle and barrier, VCFor longitudinal speed of Following Car, Vlight,iFor the restriction speed under the i-th class signal lamp；
Step 4: by the control matrix of variables input linear state equation after optimization, obtaining following the state after optimization of vehicle
Matrix of variables, and then the state variable after being optimized carries out Collaborative Control to vehicle.
3. more vehicle transverse and longitudinals as claimed in claim 2 couple cooperative control method, which is characterized in that the linearisation state
Equation are as follows:
X (k+1)=[I+TA (t)] X (k)+TB (t) U (k)；
In formula, X (k+1) is the vehicle-state matrix of variables at+1 moment of kth, and I is unit matrix, and T is sampling time, A (t), B
It (t) is parameter matrix, X (k) is the vehicle-state matrix of variables at kth moment, and U (k) is the vehicle control variable square at kth moment
Battle array, Ccf,CcrRespectively vehicle front-wheel, rear-wheel cornering stiffness, m are the quality of vehicle,Respectively longitudinal direction of car and laterally speed
Degree, lf,lrRespectively automobile front-axle and rear axle wheelbase, IzFor vehicle rotary inertia,For Vehicular yaw angle,For Vehicular yaw angle
Speed.
4. more vehicle transverse and longitudinals as claimed in claim 3 couple cooperative control method, which is characterized in that the vehicle single track three
Degrees of Freedom Model are as follows:
In formula,Respectively longitudinal direction of car and transverse acceleration,For Vehicular yaw angular acceleration, δf,δrRespectively before vehicle
Wheel, rear-wheel corner, Clf,ClrRespectively vehicle front-wheel, rear-wheel longitudinal force and slip rate proportionality coefficient, sf,srDivide than for before vehicle
Wheel, rear wheel slip rate,The respectively longitudinally, laterally speed of vehicle in the queue.
5. more vehicle transverse and longitudinals as claimed in claim 4 couple cooperative control method, which is characterized in that the vehicle front-wheel,
Rear wheel slip rate meets: sf=sr=0.2.
6. more vehicle transverse and longitudinals as described in claim 2,3,4 or 5 couple cooperative control method, which is characterized in that the step
In rapid 3, control matrix of variables after optimization is specifically included:
When more vehicle platoons when driving, based on BP neural network to Following Car front vehicle wheel corner, rear wheel corner and longitudinal speed
Optimising and adjustment is carried out, is included the following steps:
Step 1: according to the sampling period, acquiring the lateral displacement L of navigator's vehiclePV,t, length travel LPV,p, lateral vehicle velocity VPV,t, accelerate
Spend aPVAnd yaw angleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration aFVWith
Yaw angle
Step 2: the successively lateral displacement L of high-ranking military officer's airlinePV,t, length travel LPV,p, lateral vehicle velocity VPV,t, acceleration aPVAnd sideway
AngleAnd the lateral displacement L of Following CarFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration aFVAnd yaw angleInto
Professional etiquette is formatted, and determines the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5,x6,x7,x8,x9,x10}；Wherein,
x1For the lateral displacement coefficient of navigator's vehicle, x2For the length travel coefficient of navigator's vehicle, x3For the lateral speed coefficient of navigator's vehicle, x4
For the acceleration factor of navigator's vehicle, x5For the sideway ascent of navigator's vehicle, x6For the lateral displacement coefficient of Following Car, x7To follow
The length travel coefficient of vehicle, x8For the lateral speed coefficient of Following Car, x9For the acceleration factor of Following Car, x10For Following Car
Sideway ascent；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the middle layer vector y={ y1,y2,…,ym}；M is middle layer
Node number；
Step 4: obtaining output layer vector z={ z1,z2,z3}；Wherein, z1For Following Car front vehicle wheel corner adjustment factor, z2For with
With vehicle rear wheel corner adjustment factor, z3For Following Car longitudinal direction speed adjustment factor, make
Wherein, z1 i、z2 i、z3 iRespectively ith sample period output layer vector parameter, Point
The maximum longitudinal speed of Following Car front vehicle wheel hard-over, Following Car rear wheel hard-over, the Following Car that Wei do not set,Respectively the i+1 sampling period when Following Car front vehicle wheel corner, Following Car rear car
Take turns corner, Following Car longitudinal direction speed.
7. more vehicle transverse and longitudinals as claimed in claim 6 couple cooperative control method, which is characterized in that in the step 1,
Under initial operating state, Following Car front vehicle wheel corner, Following Car rear wheel corner, Following Car longitudinal direction speed meet empirical value:
δFV,f,0=0,
Wherein, δFV,f,0、Respectively with the initial corner of Chinese herbaceous peony wheel, the initial corner of Following Car rear wheel, with
With the initial longitudinal speed of vehicle.
8. more vehicle transverse and longitudinals as claimed in claim 7 couple cooperative control method, which is characterized in that in the step 2,
The lateral displacement L of navigator's vehiclePV,t, length travel LPV,p, lateral vehicle velocity VPV,t, acceleration aPVAnd yaw angleAnd Following Car
Lateral displacement LFV,t, length travel LFV,p, lateral vehicle velocity VFV,t, acceleration aFVAnd yaw angleCarry out normalization formulae are as follows:
Wherein, xjFor the parameter in input layer vector, XjRespectively measurement parameter LPV,t、LPV,p、VPV,t、aPV、LFV,t、
LFV,p、VFV,t、aFV、J=1,2,3,4 ..., 10；XjmaxAnd XjminMaximum value and minimum in respectively corresponding measurement parameter
Value.
9. more vehicle transverse and longitudinals as claimed in claim 8 couple cooperative control method, which is characterized in that the middle layer node
Number m meets:Wherein n is input layer number, and p is output layer node number.
10. more vehicle transverse and longitudinals as described in claim 2,3,4 or 5 couple cooperative control method, which is characterized in that the step
In rapid 3, control matrix of variables after optimization is specifically included: using the Optimal matrix of Quadratic Programming Solution control variable,
Standard type is converted by objective function:
min∫(XTQX+UTRU)dt
In formula, X is the state variable matrix of Following Car, and U is the control matrix of variables of Following Car, and Q and R are weight coefficient.
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