CN107856737A - A kind of man-machine coordination rotating direction control method based on degree of danger variable weight - Google Patents
A kind of man-machine coordination rotating direction control method based on degree of danger variable weight Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
Abstract
The invention discloses a kind of man-machine coordination rotating direction control method based on degree of danger variable weight, comprise the following steps：Establish simplified model；Determine vehicle safe driving road boundary；Determine driving environment hazard index and driver's operational hazards exponential expression, it is then determined that driving environment hazard index and the membership function of driver's operational hazards index and automatic Pilot weight coefficient, different classes of dangerous degree are divided according to fuzzy rule, automatic Pilot weight coefficient is obtained on driving environment hazard index and the three-dimensional map of driver's operational hazards index, driving environment hazard index and driver's operational hazards index are determined in real time, and automatic Pilot weight coefficient is obtained using three-dimensional map；The man-machine coordination steering controller based on degree of danger variable weight is carried out to design and complete to control.The present invention changes driving weight by differentiating driver and vehicle risk degree, using Constrained Model Predictive Control, enables the vehicle to meet driver's driving intention as far as possible.
Description
Technical field
The present invention relates to a kind of man-machine coordination rotating direction control method based on degree of danger variable weight, be consider driver and
The man-machine coordination rotating direction control method of vehicle risk degree, belong to advanced auxiliary driving field.
Background technology
In the last few years, unmanned technology had been to be concerned by more and more people, and U.S. SAE has been divided into five by unmanned
Level, wherein, man-machine coordination control is in centre position, to realize complete unmanned, and man-machine coordination is the only stage which must be passed by, man-machine
Collaboration is people and controller co- controlling vehicle, when people controls automobile, and when controller is controlled vapour
Car, when the two mutual Collaborative Control vehicle, be man-machine coordination control research root problem.When people is in driving procedure
Effect it is more and more weaker, until controller substitutes people to carry out the control of vehicle completely, unpiloted purpose just reaches.It is man-machine
It is to improve drive safety to cooperate with most important task.Secondly it is also contemplated that the comfortableness of driver, and the manipulation of vehicle are steady
The index such as qualitative.Give more preferable driving experience in the case where ensureing driving safety.
The content of the invention
In order to solve above mentioned problem existing for prior art, the present invention provides a kind of based on the man-machine of degree of danger variable weight
Rotating direction control method is cooperateed with, it changes driving weight by differentiating driver and vehicle risk degree, pre- using restricted model
Observing and controlling system, on the premise of avoidance safety is met, enable the vehicle to meet driver's driving intention as far as possible.
It is of the invention that purpose is achieved through the following technical solutions：
1. a kind of man-machine coordination rotating direction control method based on degree of danger variable weight, comprises the following steps：
Step 1: establish comprehensive vehicle dynamics and kinematic simplified model：
In formula,
X=[yo ψ β r]T, u=δf.
In formula, x is the state vector of system；U is system control amount；A is sytem matrix；B is input matrix；yoFor vehicle
Barycenter o lateral position, unit：m；ψ is vehicle course angle, unit：rad；V be vehicle centroid at longitudinal velocity, unit：m/
s；β be vehicle side slip angle, unit：rad；R be vehicle yaw velocity, unit：rad/s；CfFor vehicle front-wheel wheel
The cornering stiffness of tire, unit：N/rad；CrFor the cornering stiffness of vehicle rear wheel tire, unit：N/rad；M is the quality of vehicle,
Unit：kg；IzRotary inertia for vehicle around z-axis, unit：kg·m2；A is vehicle centroid o to the distance of automobile front-axle, unit：
m；B is vehicle centroid o to the distance of vehicle rear axle, unit：m；δfFor the front wheel angle of vehicle, unit：rad；
Step 2: determine vehicle safe driving road boundary：
In formula, fl(x) it is that the left margin for post-processing obtained front connecting way region is scanned by sensory perceptual system；fr(x)
For the right margin in the front connecting way region obtained by sensory perceptual system scanning post processing；W is vehicle width, unit, m；lf
Distance for vehicle centroid o to vehicle front point F, unit, m；lrDistance for vehicle centroid o to rear vehicle end point R, unit,
m；ψ is vehicle course angle, unit, rad；
Step 3: determine automatic Pilot weight coefficient：
Driving environment hazard index and driver's operational hazards exponential expression are determined first, it is then determined that driving environment is endangered
Dangerous index and the membership function of driver's operational hazards index and automatic Pilot weight coefficient, according to fuzzy rule division not
Same classes of dangerous degree, automatic Pilot weight coefficient is obtained on driving environment hazard index and driver's operational hazards index
Three-dimensional map, it is last to determine driving environment hazard index and driver's operational hazards index in real time, obtained using the three-dimensional map
Automatic Pilot weight coefficient；
Step 4: the automatic Pilot weight coefficient obtained using step 3, using MPC methods based on degree of danger become
The man-machine coordination steering controller design of weight：
Meet：X (k+i+1)=Acx(k+i)+Bcu(k+i)
In formula：
Cψ=[0 10 0]
Wherein, J is the object function of majorized function；δhFor the desired front wheel angle of driver, unit：rad；lfFor vehicle
Barycenter o is to vehicle front point F distance, unit：m；lrDistance for vehicle centroid o to rear vehicle end point R, unit：m；u(k+
I) for the k+i moment system control amount, as vehicle front wheel steering angle, unit：rad；X (k+i) is the system shape at k+i moment
State vector；Y (k+i) is the system output quantity at k+i moment；P is prediction time domain, and N is control time domain；ΓdAnd ΓhRespectively control
Measure the weight coefficient and driver's target weight coefficient of increment；ΓyAnd ΓβRespectively road axis follows weight coefficient and matter
Heart side drift angle weight coefficient；Γ is automatic Pilot weight coefficient；fl(k+i) it is front connecting way region left side boundary line fl(x) exist
Moment k+i sampled value, unit：m；fr(k+i) it is then boundary line f on the right of the connecting way region of frontr(x) in moment k+i sampling
Value, unit：m；TsFor sampling time, unit：s；X is the state vector of system；A is sytem matrix；B is input matrix；
Step 5: choose controlled quentity controlled variable and complete to control：
Choosing control rate u is：
U=U*(1) (4)
Wherein, U*To optimize obtained optimal control sequence；First amount of optimal control sequence is chosen as control
Amount is applied on controlled vehicle；To subsequent time, the shared steering controller based on Model Predictive Control will be according to Current vehicle
State recalculates an optimum control amount；It is reciprocal with this, realize rolling optimization control.
By the implementation of above scheme, beneficial effects of the present invention are：
1st, the present invention makes vehicle to be travelled with avoiding obstacles in safety zone.
2nd, the present invention considers the shape of vehicle when avoidance security constraint is chosen.
3rd, the present invention considers driver intention and driving environment degree of danger.
Brief description of the drawings
Fig. 1 is the man-machine steering cooperative control method flow chart of the present invention based on degree of danger variable weight
Fig. 2 is auto model schematic diagram
Fig. 3 is vehicle of the present invention and road relation model schematic
Fig. 4 is road hazard parameter membership function schematic diagram
Fig. 5 is driver's risk parameters membership function schematic diagram
Fig. 6 is automatic Pilot weight coefficient membership function schematic diagram
Fig. 7 is automatic Pilot weight coefficient membership function on environmental hazard index and driver's operational hazards index
Three-dimensional map schematic diagrames
Embodiment
Below in conjunction with accompanying drawing, the present invention is described in detail：
The present invention is a kind of man-machine coordination rotating direction control method based on degree of danger variable weight, as shown in figure 1, specific real
It is as follows to apply step：
Step 1: establish comprehensive dynamics of vehicle and kinematic simplified model
(1) vehicle dynamic model is established
Vehicle dynamic model only considers the side of vehicle as shown in Fig. 2 wherein vehicle centroid o is the origin of coordinates herein
To kinematics and weaving kinematics, ignore the longitudinal dynamics of vehicle.Then we can obtain one it is simplified
Two degrees of freedom auto model.Vehicle body direction of advance is x-axis positive direction, perpendicular to x-axis it is upward for y-axis positive direction.According to power
Gaining knowledge, it is as shown in Equation 5 to obtain two degrees of freedom kinetics equation.
Wherein, β is side slip angle, unit, rad；V be vehicle centroid at longitudinal velocity, unit, m/s；R is vehicle
Yaw velocity, unit, rad/s；CfFor the cornering stiffness of vehicle front tyre, unit, N/rad；CrFor vehicle rear wheel tire
Cornering stiffness, unit, N/rad；M be vehicle quality, unit, kg；IzRotary inertia for vehicle around z-axis, unit, kg
m2；A is vehicle centroid o to the distance of automobile front-axle, unit, m；B is vehicle centroid o to the distance of vehicle rear axle, unit, m；δf
For vehicle front wheel steering angle, unit, rad；
(2) vehicle kinematics model
Dynamics of vehicle equation schematic diagram is as illustrated in fig. 2, it is assumed that vehicle is a rigid body, due to vehicle in the process of moving
The road curvature perceived is smaller, and course angle ψ and side slip angle β also change in smaller range, so we can
It is shown with the vehicle kinematics equation such as formula (6) after being simplified：
In formula, β is side slip angle, unit, rad；xoFor vehicle centroid o lengthwise position, unit, m；yoFor vehicle matter
Heart o lateral position, unit, m；R be vehicle yaw velocity, unit, rad/s；ψ is vehicle course angle, unit, rad；
(3) auto model is established
We assume that the longitudinal velocity v of vehicle keeps constant, convolution (5) and formula (6) can obtain dynamics of vehicle with
Shown in the kinematic differential equation such as formula (7)：
We choose [yoψ β r] system state variables is used as, choose front wheel angle δfAs system control input.Then I
Can obtain shown in system state equation such as formula (8)：
Wherein：
X=[yo ψ β r]T, u=δf.
Wherein, x is the state vector of system；U is system control amount；A is sytem matrix；B is input matrix；
Step 2: determine vehicle safe driving road boundary：
Two-dimentional bus or train route relational model is illustrated in figure 3, ignores body width in this figure, vehicle is considered into a rigidity
Bar, wherein F are vehicle front point, and R is rear vehicle end point, and o is vehicle centroid.Meanwhile in order to ensure to simplify reasonability, Wo Mentong
Upper and lower road boundary is respectively shortened half vehicle commander by sample.Then road boundary such as formula (9) institute after we can be simplified
Show：
Wherein fl' (x) and fr' (x) be simplify after road boundary.flAnd f (x)r(x) it is original road boundary, w is
Body width.
As long as we ensure that front and rear end points F and R cans in road boundary of vehicle realize anticollision.Fig. 4 gives just
Property rod end point and barycenter between relation, it is same we assume that vehicle course angle ψ and side slip angle β is also in the process of moving
Change in smaller range.We can be obtained shown in final road boundary expression formula such as formula (10)：
Step 3: determine automatic Pilot weight coefficient
Automatic Pilot weight coefficient Γ is drawn by considering road and driver's comprehensive condition.
(1) shown in driving environment hazard index expression formula such as formula (11)：
Wherein, YvehicleRepresent vehicle centroid changing coordinates；YcenterRepresent road-center line coordinates；EAIt is one and is more than 0
Determination coefficient.The position of vehicle is so converted into embodying for safe coefficient.The bigger representative of driving environment hazard index
Vehicle distances road boundary is nearer, and degree of danger is higher.
(2) shown in driver's operational hazards exponential expression such as formula (12)：
Wherein, δhumanRepresent the desired front wheel angle of driver, δpredictionThe front wheel angle of system prediction is represented, it is
Prediction obtains in last moment PREDICTIVE CONTROL, EBIt is adjustment factor.The index characterizes driver's practical operation and meets system
It is expected that the degree of operation.
(3) automatic Pilot weight coefficient Γ is on driving environment hazard index and the three-dimensional of driver's operational hazards index
map：Utilize driving environment hazard index EroadWith driver's operational hazards index Edriver, using fuzzy method, obtain automatic
Weight coefficient Γ is driven on EroadAnd EdriverThree-dimensional map.
First by driving environment hazard index EroadWith driver's operational hazards index EdriverObscure respectively and turn to 5 collection
Close：S (safety), MS (safer), M (in), MD (relatively hazardous), D (danger).As shown in Figure 4, Figure 5, adjustment parameter E is utilizedAWill
EroadExcursion is set as [0,1], shown in the expression formula such as formula (13) of its membership function；Utilize adjustment parameter EBBy Edriver
Excursion be set to [0,1], shown in the expression formula such as formula (14) of its membership function：
Equally automatic Pilot weight coefficient Γ is also obscured and turns to 5 set：S (small), MS (smaller), M (in), MB (compared with
Greatly), B (big).As shown in fig. 6, shown in its corresponding membership function expression formula such as formula (15)：
Wherein Aij、Bij、Cij(i=1,2,3；J=1,2,3,4,5) it is constant.
Then the foundation of fuzzy rule is carried out.Specific fuzzy rule is as shown in table 1：
The fuzzy rule of table 1
Finally, determine automatic Pilot weight coefficient Γ on E by fuzzy rule and above-mentioned membership functionroadWith
EdriverThree-dimensional map, as shown in Figure 7；
(4) automatic Pilot weight coefficient Γ real-time determination
It is last to determine driving environment hazard index and driver's operational hazards index in real time, obtained using above-mentioned three-dimensional map
To automatic Pilot weight coefficient Γ；
Step 4: the automatic Pilot weight coefficient obtained using step 3, using MPC (Model Predictive
Control, Model Predictive Control) method carry out based on degree of danger variable weight man-machine coordination steering controller design
(1) control targe of the invention is as follows：
1) system control is made to follow driver intention as far as possible, while controlled quentity controlled variable keeps smooth-going.
2) ensure vehicle in lane boundary line during travelling.
(2) the man-machine coordination steering controller design based on degree of danger variable weight
The present invention makes hypothesis below：Assuming that autonomous land vehicle is predicted in time domain at one keeps constant speed drive.Formula (8)
For the continuous model of Vehicular system, for the design for the domain type path following control algorithm based on Model Predictive Control, need
By formula (8) discretization, the Vehicular system model of discrete time is obtained, as shown in formula (13)：
X (k+1)=Acx(k)+Bcu(k) (16)
In formula,Wherein TsFor the sampling time.
It is assumed that prediction time domain is P, it is N to control time domain, and meets N≤P.Assume to control the controlled quentity controlled variable outside time domain to protect simultaneously
Hold it is constant, i.e. u (k+N)=u (k+N+1)=...=u (k+P-1), can derive P step status predication equation, such as formula (17)：
Definition：
Then we can be obtained shown in driver's object function such as formula (18)：
Wherein, δhFor the desired front wheel angle of driver, unit, rad.U (k) is control input, Δ u (k+i)=u (k+
I)-u (k+i-1) is increment of the front wheel angle in sampling interval.ΓdAnd ΓhThe respectively weight coefficient of controlled quentity controlled variable increment and driving
Member's target weight coefficient.
Then we can be obtained shown in controller object function such as formula (19)：
Wherein yc(k+i) it is the lateral position of road axis, the expectation y (k+i) as vehicle lateral position is vehicle
Actual lateral position, β (k+i)=CβX (k+i), Cβ=[0 01 0].ΓyAnd ΓβRespectively road axis follows weight system
Number and side slip angle weight coefficient.
Mutually tied with automatic Pilot weight coefficient determined by driver's risk parameters further according to road hazard parameter in Fig. 7
Close, we are obtained shown in final Controlling object function such as formula (20)：
J=JH+ΓJA (20)
Wherein, JHFor driver's object function, JAFor automatic controller object function, Γ is automatic Pilot weight coefficient.
The lateral position of vehicle centroid meets the constraint in formula (10), and the output constraint can be written to as shown in formula (21)
Form：
In formula, ψ (k+i)=Cψx(k+i),Cψ=[0 10 0], fl(k+i) it is front connecting way region left side boundary line
fl(x) in moment k+i sampled value, unit, m；fr(k+i) it is then boundary line f on the right of the connecting way region of frontr(x) in moment k+
I sampled value, unit, m.
Shared steering controller design is carried out using restricted model Forecasting Methodology, arranges and is；
Meet：X (k+i+1)=Acx(k+i)+Bcu(k+i)
In formula：
Cψ=[0 10 0]
Wherein, J is the object function of majorized function；δhFor the desired front wheel angle of driver, unit, rad；lfFor vehicle
Barycenter o is to vehicle front point F distance, unit, m；lrDistance for vehicle centroid o to rear vehicle end point R, unit, m；u(k+
I) for the k+i moment system control amount, as vehicle front wheel steering angle, unit, rad；X (k+i) is the system shape at k+i moment
State vector；Y (k+i) is the system output quantity at k+i moment；P is prediction time domain, and N is control time domain；ΓdAnd ΓhRespectively control
Measure the weight coefficient and driver's target weight coefficient of increment；ΓyAnd ΓβRespectively road axis follows weight coefficient and matter
Heart side drift angle weight coefficient；Γ is automatic Pilot weight coefficient；fl(k+i) it is front connecting way region left side boundary line fl(x) exist
Moment k+i sampled value, unit, m；fr(k+i) it is then boundary line f on the right of the connecting way region of frontr(x) in moment k+i sampling
Value, unit, m；TsFor sampling time, unit s；X is the state vector of system；A is sytem matrix；B is input matrix.
Step 5: choosing controlled quentity controlled variable and completing to control, choosing control rate u is：
U=U*(1) (23)
Wherein, U*To optimize obtained optimal control sequence；
First amount for choosing optimal control sequence is applied on controlled vehicle as controlled quentity controlled variable.To subsequent time, base
An optimum control amount will be recalculated according to current vehicle condition in the shared steering controller of Model Predictive Control, it is past with this
It is multiple, that is, realize rolling optimization control.
Claims (2)
1. a kind of man-machine coordination rotating direction control method based on degree of danger variable weight, it is characterised in that comprise the following steps：
Step 1: establish comprehensive vehicle dynamics and kinematic simplified model：
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X=[yo ψ β r]T, u=δf.
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In formula, x is the state vector of system；U is system control amount；A is sytem matrix；B is input matrix；yoFor vehicle centroid o
Lateral position, unit：m；ψ is vehicle course angle, unit：rad；V be vehicle centroid at longitudinal velocity, unit：m/s；β is
The side slip angle of vehicle, unit：rad；R be vehicle yaw velocity, unit：rad/s；CfFor the side of vehicle front tyre
Inclined rigidity, unit：N/rad；CrFor the cornering stiffness of vehicle rear wheel tire, unit：N/rad；M be vehicle quality, unit：
kg；IzRotary inertia for vehicle around z-axis, unit：kg·m2；A is vehicle centroid o to the distance of automobile front-axle, unit：m；B is
Vehicle centroid o is to the distance of vehicle rear axle, unit：m；δfFor the front wheel angle of vehicle, unit：rad；
Step 2: determine vehicle safe driving road boundary：
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In formula, fl(x) it is that the left margin for post-processing obtained front connecting way region is scanned by sensory perceptual system；fr(x) it is logical
Cross the right margin in the front connecting way region that sensory perceptual system scanning post processing obtains；W is vehicle width, unit, m；lfFor car
Barycenter o is to vehicle front point F distance, unit, m；lrDistance for vehicle centroid o to rear vehicle end point R, unit, m；ψ is
Vehicle course angle, unit, rad；
Step 3: determine automatic Pilot weight coefficient：
Driving environment hazard index and driver's operational hazards exponential expression are determined first, it is then determined that driving environment danger refers to
The membership function of number and driver's operational hazards index and automatic Pilot weight coefficient, different danger are divided according to fuzzy rule
Dangerous intensity grade, automatic Pilot weight coefficient is obtained on driving environment hazard index and the three-dimensional of driver's operational hazards index
Map, it is last to determine driving environment hazard index and driver's operational hazards index in real time, obtained automatically using the three-dimensional map
Drive weight coefficient；
Step 4: the automatic Pilot weight coefficient obtained using step 3, carries out being based on degree of danger variable weight using MPC methods
Man-machine coordination steering controller design：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<munder>
<mi>min</mi>
<mrow>
<mi>U</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</munder>
<mi>J</mi>
<mo>=</mo>
<msub>
<mi>&Gamma;</mi>
<mi>h</mi>
</msub>
<mrow>
<mo>(</mo>
<mo>|</mo>
<msub>
<mi>&delta;</mi>
<mi>h</mi>
</msub>
<mo>-</mo>
<mi>u</mi>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
<mo>|</mo>
<mo>+</mo>
<msub>
<mi>&Gamma;</mi>
<mi>d</mi>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>P</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<mi>&Delta;</mi>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mi>&Gamma;</mi>
<mo>(</mo>
<msub>
<mi>&Gamma;</mi>
<mi>y</mi>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>y</mi>
<mi>c</mi>
</msub>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mo>+</mo>
<msub>
<mi>&Gamma;</mi>
<mi>&beta;</mi>
</msub>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>P</mi>
</munderover>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<mi>&beta;</mi>
<mrow>
<mo>(</mo>
<mrow>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
</mrow>
<mo>)</mo>
</mrow>
</mrow>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>)</mo>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Meet：X (k+i+1)=Acx(k+i)+Bcu(k+i)
<mrow>
<msub>
<mi>f</mi>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
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<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mi>w</mi>
<mn>2</mn>
</mfrac>
<mo>-</mo>
<msub>
<mi>l</mi>
<mi>f</mi>
</msub>
<msub>
<mi>C</mi>
<mi>&psi;</mi>
</msub>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>f</mi>
<mi>l</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mfrac>
<mi>w</mi>
<mn>2</mn>
</mfrac>
<mo>-</mo>
<msub>
<mi>l</mi>
<mi>f</mi>
</msub>
<msub>
<mi>C</mi>
<mi>&psi;</mi>
</msub>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>f</mi>
<mi>r</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mfrac>
<mi>w</mi>
<mn>2</mn>
</mfrac>
<mo>+</mo>
<msub>
<mi>l</mi>
<mi>r</mi>
</msub>
<msub>
<mi>C</mi>
<mi>&psi;</mi>
</msub>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<mi>y</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>&le;</mo>
<msub>
<mi>f</mi>
<mi>l</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mfrac>
<mi>w</mi>
<mn>2</mn>
</mfrac>
<mo>+</mo>
<msub>
<mi>l</mi>
<mi>r</mi>
</msub>
<msub>
<mi>C</mi>
<mi>&psi;</mi>
</msub>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
In formula：
<mrow>
<mi>U</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "[" close = "]">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mo>.</mo>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>u</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>+</mo>
<mi>N</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
<mrow>
<msub>
<mi>A</mi>
<mi>c</mi>
</msub>
<mo>=</mo>
<msup>
<mi>e</mi>
<mrow>
<msub>
<mi>AT</mi>
<mi>s</mi>
</msub>
</mrow>
</msup>
<mo>,</mo>
<msub>
<mi>B</mi>
<mi>c</mi>
</msub>
<mo>=</mo>
<msubsup>
<mo>&Integral;</mo>
<mn>0</mn>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</msubsup>
<msup>
<mi>e</mi>
<mrow>
<mi>A</mi>
<mi>&tau;</mi>
</mrow>
</msup>
<mi>d</mi>
<mi>&tau;</mi>
<mo>&CenterDot;</mo>
<mi>B</mi>
<mo>;</mo>
</mrow>
Cψ=[0 10 0]
Wherein, J is the object function of majorized function；δhFor the desired front wheel angle of driver, unit：rad；lfFor vehicle centroid o
To vehicle front point F distance, unit：m；lrDistance for vehicle centroid o to rear vehicle end point R, unit：m；U (k+i) is k+
The front wheel steering angle of the system control amount at i moment, as vehicle, unit：rad；X (k+i) be the k+i moment system mode to
Amount；Y (k+i) is the system output quantity at k+i moment；P is prediction time domain, and N is control time domain；ΓdAnd ΓhRespectively controlled quentity controlled variable increases
The weight coefficient and driver's target weight coefficient of amount；ΓyAnd ΓβRespectively road axis follows weight coefficient and barycenter side
Drift angle weight coefficient；Γ is automatic Pilot weight coefficient；fl(k+i) it is front connecting way region left side boundary line fl(x) at the moment
K+i sampled value, unit：m；fr(k+i) it is then boundary line f on the right of the connecting way region of frontr(x) in moment k+i sampled value,
Unit：m；TsFor sampling time, unit：s；X is the state vector of system；A is sytem matrix；B is input matrix；
Step 5: choose controlled quentity controlled variable and complete to control：
Choosing control rate u is：
U=U*(1)
Wherein, U*To optimize obtained optimal control sequence；First amount for choosing optimal control sequence acts on as controlled quentity controlled variable
Onto controlled vehicle；To subsequent time, the shared steering controller based on Model Predictive Control will be according to current vehicle condition weight
Newly calculate an optimum control amount；It is reciprocal with this, realize rolling optimization control.
2. a kind of man-machine coordination rotating direction control method based on degree of danger variable weight as claimed in claim 1, its feature exist
In the step 3 determines that automatic Pilot weight coefficient specifically includes procedure below：
3.1) driving environment hazard index and driver's operational hazards exponential expression are determined：
Driving environment hazard index expression formula is：
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>=</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>Y</mi>
<mrow>
<mi>v</mi>
<mi>e</mi>
<mi>h</mi>
<mi>i</mi>
<mi>c</mi>
<mi>l</mi>
<mi>e</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>Y</mi>
<mrow>
<mi>c</mi>
<mi>e</mi>
<mi>n</mi>
<mi>t</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<msub>
<mi>E</mi>
<mi>A</mi>
</msub>
</msup>
</mrow>
In formula, YvehicleRepresent vehicle centroid changing coordinates；YcenterRepresent road-center line coordinates；EAIt is one and is more than 0 really
Determine coefficient；
Driver's operational hazards exponential expression is：
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mo>|</mo>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>h</mi>
<mi>u</mi>
<mi>m</mi>
<mi>a</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>&delta;</mi>
<mrow>
<mi>p</mi>
<mi>r</mi>
<mi>e</mi>
<mi>d</mi>
<mi>i</mi>
<mi>c</mi>
<mi>t</mi>
<mi>i</mi>
<mi>o</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>|</mo>
</mrow>
<msub>
<mi>E</mi>
<mi>B</mi>
</msub>
</mfrac>
</mrow>
In formula, δhumanRepresent the desired front wheel angle of driver, δpredictionThe front wheel angle of system prediction is represented, it is upper one
Prediction obtains in moment PREDICTIVE CONTROL, EBIt is adjustment factor；
3.2) driving environment hazard index E is utilizedroadWith driver's operational hazards index Edriver, using fuzzy method, obtain
Automatic Pilot weight coefficient Γ is on EroadAnd EdriverThree-dimensional map：
First by driving environment hazard index EroadWith driver's operational hazards index EdriverObscure respectively and turn to 5 set：Peace
Full S, safer MS, middle M, relatively hazardous MD, dangerous D；Utilize adjustment parameter EABy EroadExcursion is set as [0,1], and it is subordinate to
The expression formula for spending function is as follows：
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<mtable>
<mtr>
<mtd>
<mrow>
<mi>M</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>13</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>13</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>13</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>13</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>13</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>13</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>12</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>12</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
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<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
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<mi>B</mi>
<mn>12</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>12</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>12</mn>
</msub>
</mrow>
</mfrac>
<mrow>
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<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>12</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>D</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>14</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>14</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
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</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>14</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>14</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>14</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>14</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mn>2</mn>
<mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mfrac>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>B</mi>
<mn>11</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<mfrac>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mrow>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
</mrow>
</mfrac>
</mrow>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mn>11</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>D</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>A</mi>
<mn>15</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>15</mn>
</msub>
</mrow>
<mrow>
<msub>
<mi>B</mi>
<mn>15</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>15</mn>
</msub>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>A</mi>
<mn>15</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>B</mi>
<mn>15</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>15</mn>
</msub>
</mrow>
<mrow>
<msub>
<mi>C</mi>
<mn>15</mn>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>15</mn>
</msub>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mn>15</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>C</mi>
<mn>15</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>r</mi>
<mi>o</mi>
<mi>a</mi>
<mi>d</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>C</mi>
<mn>15</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Utilize adjustment parameter EBBy EdriverExcursion be set to [0,1], the expression formula of its membership function is as follows：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>M</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>23</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>23</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>23</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>23</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>23</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>23</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>22</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>22</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>22</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>22</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>22</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>22</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>D</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>24</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>24</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>24</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>24</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>24</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>24</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mn>2</mn>
<mfrac>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>21</mn>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>C</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>11</mn>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mfrac>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>A</mi>
<mn>21</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>B</mi>
<mn>21</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>2</mn>
<msup>
<mrow>
<mo>(</mo>
<mrow>
<msub>
<mi>C</mi>
<mn>21</mn>
</msub>
<mo>-</mo>
<mfrac>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mrow>
<msub>
<mi>C</mi>
<mn>21</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>21</mn>
</msub>
</mrow>
</mfrac>
</mrow>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mn>21</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>C</mi>
<mn>21</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>C</mi>
<mn>21</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>D</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>A</mi>
<mn>25</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>25</mn>
</msub>
</mrow>
<mrow>
<msub>
<mi>B</mi>
<mn>25</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>25</mn>
</msub>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>A</mi>
<mn>25</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>B</mi>
<mn>25</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<msup>
<mrow>
<mo>(</mo>
<mfrac>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>25</mn>
</msub>
</mrow>
<mrow>
<msub>
<mi>C</mi>
<mn>25</mn>
</msub>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>25</mn>
</msub>
</mrow>
</mfrac>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mtd>
<mtd>
<mrow>
<msub>
<mi>B</mi>
<mn>25</mn>
</msub>
<mo>&le;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msub>
<mi>C</mi>
<mn>25</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>1</mn>
</mtd>
<mtd>
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>d</mi>
<mi>r</mi>
<mi>i</mi>
<mi>v</mi>
<mi>e</mi>
<mi>r</mi>
</mrow>
</msub>
<mo>&GreaterEqual;</mo>
<msub>
<mi>C</mi>
<mn>25</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Equally automatic Pilot weight coefficient Γ is also obscured and turns to 5 set：Small S, smaller MS, middle M, larger MB, big B, its is right
The expression formula for the membership function answered is as follows：
<mfenced open = "" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>M</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>33</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>33</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<mi>&Gamma;</mi>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>33</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>33</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>33</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<mi>&Gamma;</mi>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>33</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>S</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>32</mn>
</msub>
<mo>-</mo>
<msub>
<mi>A</mi>
<mn>32</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<mi>&Gamma;</mi>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>32</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
<mn>32</mn>
</msub>
<mo>-</mo>
<msub>
<mi>C</mi>
<mn>32</mn>
</msub>
</mrow>
</mfrac>
<mrow>
<mo>(</mo>
<mi>&Gamma;</mi>
<mo>-</mo>
<msub>
<mi>B</mi>
<mn>32</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>1</mn>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>M</mi>
<mi>D</mi>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mfrac>
<mn>1</mn>
<mrow>
<msub>
<mi>B</mi>
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In formula, Aij、Bij、Cij(i=1,2,3；J=1,2,3,4,5) it is constant；
Then the foundation of fuzzy rule, specific fuzzy rule such as following table are carried out：
Finally, determine automatic Pilot weight coefficient Γ on E by fuzzy rule and above-mentioned membership functionroadAnd EdriverThree
Tie up map；
3.3) driving environment hazard index and driver's operational hazards index are determined in real time, are obtained certainly using above-mentioned three-dimensional map
It is dynamic to drive weight coefficient Γ.
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