CN109733395A - It is a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation - Google Patents
It is a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation Download PDFInfo
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Abstract
The invention discloses a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation, designs upper controller: obtaining the lateral position deviation e of vehicle and road axisp, vehicle and road axis course deviationYaw rateAnd road axis curvature ρ, it is separately input to PID/feedback controller and PID forward-feedback controller, corresponding output vehicle front wheel angle δf1And δf2It brings output valve into evaluation index prediction model, acquires two groups of corresponding vehicles-road predicted state amount, vehicle-road predicted state amount is evaluated using optimal degree evaluation method, select goodness high as a result, being input in lower layer's controller using its corresponding front wheel angle as output valve；Lower layer's controller passes through the lateral position deviation e of current vehicle and road axispAnd road axis curvature ρ calculates correlation function K (S), using correlation function K (S) by δfCoordinated, the front wheel angle after output coordinating obtains actual front wheel corner to vehicle-road condition space equation.
Description
Technical field
The invention belongs to autonomous driving vehicle control system technical field, in particular to a kind of based on can open up optimal evaluation
Autonomous driving vehicle horizontal coordination control method.
Background technique
Intelligent vehicle is as a main research contents in intellectual traffic control field, by various modern electronic information
Integration ofTechnology is in one.As current social is higher and higher for the intelligence of modern vehicle, the demand of safe, intelligent vehicle
The hot issue and frontline technology mutually studied unexpectedly as every country in the world in field of traffic.Grinding in terms of vehicle lateral control
Study carefully a part of (lane holding, vehicle lane-changing) as intelligent vehicle research field, has been subjected to people and has more and more paid close attention to.
Vehicle lateral control refers to that vehicle in along expected path driving process, is executed by algorithm decision and bottom
The effect of device, so that lateral position deviation and course deviation are small as far as possible, while vehicle should have certain stability
And driving safety.Lateral Control Algorithm used at present mainly has feedback control, Feedforward-feedback control, fuzzy control, sliding formwork
Control strategy, Single-point preview strategy, Model Predictive Control, optimum control etc..But there are many limitations for above-mentioned control method
Property, or control effect is preferable under specific operating condition, and in the case where mixing complex working condition, overall control is ineffective.
And vehicle control is a multi-input multi-output system, and also needs to consider locating for vehicle in vehicle lateral control
Road environment problem, so that there are many limitations for above-mentioned control method.The application is mainly for single control strategy control
The limited problem in region processed opens up theory based on what Cai Wen was proposed, and the optimal degree evaluation method of generation is that one is evaluated in extension science
Object, the basic skills of the superiority and inferiority including things, strategy, method etc..According to the needs of practical problem, formulation meets technical requirements
Evaluation criterion, determine measurement index, reflect pros and cons process and they agree situations of change, thus evaluation one object
Superiority and inferiority.It is targetedly used according to optimal degree evaluation method and is preferably controlled using different control strategies according to varying environment
System strategy, designs a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation, can entirely to control
Process processed obtains good control effect.
Summary of the invention
It is a kind of based on the autonomous driving vehicle horizontal coordination control method of optimal evaluation can be opened up, including design two parts: on
Layer controller and lower layer's controller.The controller output valve that upper controller selects goodness high by optimal degree evaluation method, lower layer
Controller is based on the degree of association and carries out coordination output.Specifically includes the following steps:
In upper controller: obtaining vehicle-road condition amount, the lateral position deviation e of vehicle and road axisp、
The course deviation of vehicle and road axisYaw rateAnd road axis curvature ρ is as input value point
It is not input to PID/feedback controller and PID forward-feedback controller, corresponding output vehicle front wheel angle δf1And δf2, and will be defeated
It is worth out and brings evaluation index prediction model into respectively, two groups of corresponding vehicles-road predicted state amount is acquired, using optimal evaluation side
Method evaluates two groups of vehicles-road predicted state amount, and preferably goodness is high as a result, output quantity front wheel angle that goodness is high
Output valve as upper controller is input in lower layer's controller；In lower layer's controller: by current vehicle and road
The lateral position deviation e of heart linepAnd road axis curvature ρ calculates correlation function K (S), is designed based on correlation function K (S)
Coordinate o controller, by upper controller output valve front wheel angle δfCoordination o controller is input to, after output coordinating
Front wheel angle, i.e., practical control result take aim at buggy model according to vehicle two degrees of freedom kinetic model, path trace in advance and establish vehicle
- road condition space equation, as control object, the validity of authentication control method.
Further, the vehicle-road condition space equation is pre- according to vehicle two degrees of freedom kinetic model, path trace
Take aim at buggy model foundation:
Wherein:
Further, thus state space equation is transformed the evaluation index prediction model:
Further, measurement index the optimal degree evaluation method: is determined according to vehicle-road condition firstAnd according to the importance degree of each measurement index, the weight coefficient α=(α of (0,1) is assigned respectively1, α2,
α3, α4), wherein α1+α2+α3+α4=1；Secondly, requiring according to each measurement index, correlation function K is established1(x1), K2(x2), K3
(x3), K4(x4) is object δf1, δf2About each measurement index MIiCorrelation function value be abbreviated as Ki(δfj), then each object δf1,
δf2About MIiThe degree of association be Ki=(Ki(δf1), Ki(δf2)), i=1,2,3,4. standardize the above-mentioned degree of association:I=1,2,3,4, j=1,2, then each object δf1, δf2About MIiThe specification degree of association be ki=(ki1,
ki2), i=1,2,3,4；Then goodness calculating is carried out.
Further, the goodness calculates: setting computing object ZjAbout each measurement index MI1, MI2, MI3, MI4Specification association
Degree isThen computing object ZjGoodness are as follows:
To δfjGoodness be compared: ifThen object δf0It is more excellent.
Further, when calculating correlation function K (S), deviation e is laterally taken aim in characteristic quantity selection in advancepWith road axis curvature ρ group
At significant condition S (ep,ρ)。
Further, when calculating correlation function K (S), the section for the Classical field that two-dimentional Region place value divides are as follows: lateral position is inclined
Poor ep[-ep1,ep1], road axis curvature ρ [- ρ1,ρ1]；Extension range section are as follows: lateral position deviation ep[-ep2,-ep1)∪
(ep1,ep2], road axis curvature ρ [- ρ2,ρ1)∪(ρ1,ρ2]；Non- domain section are as follows: lateral position deviation ep(-∞,-ep2)∪
(ep2,+∞), road axis curvature ρ (- ∞ ,-ρ2)∪(ρ2,+∞)。
Further, the calculating of the correlation function K (S) needs first that opening up in two-dimentional Region place value is one-dimensional away from being converted into
Can open up away from.
Further, the correlation function are as follows:Wherein D (P3,<P5,P2>,<
P4,P1>)=ρ (P3,<P5,P2>)-ρ(P3,<P4,P1>), ρ (P3,<P4,P1>) and ρ (P3,<P5,P2>) it is respectively point P3To classics
Domain and extension range are opened up away from P3Collected vehicle-road model characteristic quantity (e when for vehicle drivingp, ρ) and real-time status value,
P3Hand over Classical field boundary in point P with origin line1、P4, hand over extension range boundary in point P2、P5。
Further, the coordination o controller uses respectively using 1-K (S), K (S) as previous second output valve δf(t-1)With
Current output value δf(t)Weighting coefficient, final output value are as follows: δf=[1-K (S)] δf(t-1)+K(S)δf(t)
The invention has the benefit that
(1) satisfactory degree of the control output quantity about measurement index is determined by optimal degree evaluation method, calculated each
The synthesis goodness of control output quantity to be evaluated enables to automatic driving car to differentiate the superiority and inferiority of control output quantity to be evaluated
Crosswise joint object: never zone of control is converted into stably and controllable region to vehicle-roadnet quantity of state, gets rid of vehicle
The restriction for the factors such as dynamics-road model complexity or control method use condition are limited, improves automatic driving vehicle
The ability of crosswise joint and Trajectory Tracking Control.
(2) present invention is by extension control, so that automatic driving vehicle is by not only there is Chinese yeast rate road, but also has big
Under curvature big corner bend COMPLEX MIXED operating condition, optimum control can be realized by optimal degree evaluation method in two kinds of controllers, made
It obtains entire control process and obtains smaller control error.
(3) present invention can significantly improve control by the coordination o controller of the weighting coefficient based on correlation function K (S)
The jitter conditions of device switching, so that entire control process is more stable, so that comfort is higher.
Detailed description of the invention
Fig. 1 is based on the autonomous driving vehicle horizontal coordination control method flow chart that can open up optimal evaluation
Fig. 2 is that PID/feedback controls control flow chart；
Fig. 3 is PID Feedforward-feedback control control flow chart；
Fig. 4 is two degrees of freedom vehicle dynamic model figure；
Fig. 5 is laterally to take aim at buggy model figure in advance；
Fig. 6 is course deviation illustraton of model；
Fig. 7 is optimal degree evaluation method flow chart
Fig. 8 is two-dimentional Region place value figure；
Fig. 9 is one-dimensional Region place value figure；
Specific embodiment
Below in conjunction with Detailed description of the invention and specific embodiment, specifically the present invention is further illustrated, but of the invention
Protection scope is not limited to that.
Fig. 1 is based on the autonomous driving vehicle horizontal coordination control method flow chart that can open up optimal evaluation, the method for the present invention
It is specific as follows including design upper controller and lower layer's controller:
Upper controller: it obtains and acquires vehicle-road condition amount, the cross of vehicle and road axis from vehicle sensors
To position deviation ep, vehicle and road axis course deviationYaw rateAnd road axis curvature
ρ.Vehicle-road condition amount is inputted into PID/feedback controller and PID forward-feedback controller as input value respectively, correspondence is defeated
Vehicle front wheel angle δ outf1And δf2, and output valve is brought into evaluation index prediction model respectively, acquire two groups of corresponding vehicles-
Road predicted state amount evaluates two groups of vehicles-road predicted state amount using optimal degree evaluation method, and preferably goodness is high
Scheme is input in lower layer's controller using its controller output quantity front wheel angle as the output valve of upper controller.
Lower layer's controller: pass through the lateral position deviation e of current vehicle and road axispAnd road axis curvature
ρ calculating correlation K (S) is based on degree of association K (S) Design coordination o controller, by upper controller output valve front wheel angle δf
It is input to coordination o controller, the front wheel angle after output coordinating, i.e., practical control result, according to vehicle two degrees of freedom power
Model, path trace take aim at buggy model in advance and establish vehicle-road condition space equation, as control object, access control side
The validity of method.
It is illustrated in figure 2 and control flow chart is controlled based on the PID/feedback for taking aim at deviation in advance, it is smaller suitable for road curvature, it is horizontal
It is realized to control and only needs the desired trajectory in tracking, it is usually required mainly for solution is to try to lateral position in small angle tower
Deviation and lateral deviation are reduced to minimum.It is illustrated in figure 3 the PID forward-feedback controller control stream based on road curvature
Cheng Tu, smaller suitable for vehicle turn radius, road curvature is larger, needs vehicle quick response, guarantees that vehicle can be in time by vehicle
Wheel is gone on required corner, so that vehicle front wheel angle quick response, improves control system and small curvature path is interfered
Response speed, while also reducing hysteresis quality and fluctuation of the vehicle to control.
The present embodiment use two degrees of freedom vehicle dynamic model, the model only consider along vehicle y-axis lateral movement and
Around the weaving of z-axis, and do following hypothesis:
(1) ignore the air drag that automobile is subject to；
(2) road surface of hypothesis running car is horizontal, and ground is 0 in face of the road resistance of vehicle；
(3) it is 0 that hypothesis automobile two front wheels and the steering angle size of two rear-wheels, which distinguish equal and wheel camber angle,；
(4) ignore effect of steering system during Vehicular turn, directly using the wheel steering angle of front-wheel as the control of system
System input；
(5) assume that the suspension of automobile is rigid, vehicle body is not present perpendicular to the up and down motion on ground, pitch and vehicle
The inclination of body, the Motion Parallel of vehicle body is in ground；
(6) ignore the left and right offset of car mass during turning, it is believed that hanging down on ground suffered by the wheel of the left and right sides
Histogram to holding power be equal.
It is illustrated in figure 4 vehicle two degrees of freedom kinetic model schematic diagram.It is available according to Newton's second law theorem
Along y-axis resultant force ∑ Fy,iEquilibrium equation and resultant moment ∑ M around z-axis directionzEquilibrium equation:
Wherein, m is vehicle mass (kg)；vx、vyRespectively vehicular longitudinal velocity, lateral velocity (ms-1)；For vehicle
Course angle (rad)；δfFor vehicle front wheel angle (rad)；IzIt is vehicle around the rotary inertia (kgm of z-axis2)；A, b is respectively vehicle
Front axle distance and rear axle distance (m)；k1、k2Respectively cornering stiffness (the Nrad of front and rear wheel tire-1)；ωrFor vehicle cross
Pivot angle speed (rads-1)。
For vehicle during track following, motion process includes the translational motion and rotary motion of vehicle, and path trace is pre-
Taking aim at buggy model includes laterally to take aim at buggy model and course deviation model in advance, as shown in Figure 3 and Figure 4 respectively.
In Fig. 5, A, B two o'clock are respectively vehicle rear and front end point, and C point is vehicle centroid, and D point is to take aim at a little in advance, and a, b are respectively
Automobile front-axle distance and rear axle distance (m), L are preview distance (m), epTo take aim at lateral deviation (m) a little in advance, e is vehicle centroid
Locate lateral position deviation (m),For the course deviation (rad) of vehicle and road axis, c1、c2Respectively front of the car and phase
Hope the lateral position deviation (m) between path.
It can be obtained according to geometrical relationship:
Then
By scheming also to can be obtained:
Then
I.e.
Due toSo
To epDerivation can obtain:
In Fig. 6,For the course deviation of vehicle and road axis；For vehicle course angle；For the angle of road axis tangent line and the earth abscissa.It is available according to geometrical relationship:
Wherein, vehicular longitudinal velocity vx(m·s-1) it is constant, road axis curvature ρ (m-1) it is road axis circle half
The inverse of diameter is known quantity.
Therefore have
The change rate of lateral position deviation at Vehicle tracing process mass center between road axis are as follows:
Due toSo
Formula (13) substitution formula (8) can be obtained
Vehicle state quantity side velocity v can be obtained in abbreviationy, side accelerationYaw rateVehicular yaw
Angular accelerationTake aim at the relationship of parameter in buggy model in advance with path trace, i.e. buggy model is taken aim in path trace in advance:
Path trace is taken aim to buggy model in conjunction with vehicle two degrees of freedom kinetic model in advance, constitutes vehicle-road and take aim in advance
Buggy model chooses ep、For the quantity of state of state space equation, the state of available vehicle-road model
Space equation
Wherein:
It is as follows that evaluation index prediction model can be obtained by formula (4) (5) (10) (15)
Optimal degree evaluation method is mainly used for choosing the higher controller output valve of goodness, such as Fig. 7 optimal degree evaluation method process
Figure, detailed process is as follows:
1) measurement index is determined
Evaluate the superiority and inferiority of an object it may first have to provide measurement index.Superiority and inferiority is for certain standard.
One object, is advantageous about certain measurement indexs, may be to have disadvantage to other measurement indexs.Therefore evaluation one is right
The superiority and inferiority of elephant must reflect the degree and their possible situations of change of pros and cons.This requires the need according to practical problem
It wants, makes satisfactory evaluation criterion, determine measurement index
2) weight coefficient is determined
Evaluate a controller output valve δfj(j=1,2) superiority and inferiorityEach measurement index MIiHave light
Point of weight, the importance degree of each measurement index is indicated with weight coefficient, assigns the value of (0,1) respectively.Weight coefficient is denoted as
α=(α1, α2, α3, α4) (18)
Wherein, α1+α2+α3+α4=1.
3) correlation function, calculating correlation are established
Measurement indexWeight coefficient is assigned as α=(α1, α2, α3, α4), it is wanted according to each measurement index
It asks, object δf1, δf2About each measurement index MIiCorrelation function value be abbreviated as Ki(δfj), then each object δf1, δf2About MIi
The degree of association be
Ki=(Ki(δf1), Ki(δf2)), i=1,2,3,4. (19)
The above-mentioned degree of association is standardized:
Then each object δf1, δf2About MIiThe specification degree of association be ki=(ki1, ki2), i=1,2,3,4 (21)
4) goodness is calculated
Object ZjAbout each measurement index MI1, MI2, MI3, MI4The specification degree of association be
Object ZjGoodness be
5) the higher scheme of goodness is chosen
To δfjGoodness be compared: ifThen object δf0To be more excellent, δf0It is upper layer
Controller is input to the value of lower layer's controller.
It is as shown in the table for optimal evaluation table:
Since optimal degree evaluation method upper one second the controller output valve chosen and the controller currently chosen are non-same, because
This is exported twice perhaps can be widely different, and front-wheel steer is uncoordinated, it may occur that jitter conditions make to export controller coordinate
Entire vehicle-roadnet is more stable, uses using 1-K (S), K (S) as previous second output valve δf(t-1)And current output value
δf(t)Weighting coefficient, final output value are as follows:
δf=[1-K (S)] δf(t-1)+K(S)δf(t) (24)
It is as follows for correlation function K (S) calculating process:
1) Characteristic Extraction
It is inclined to evaluate position of the most important index of automatic driving vehicle crosswise joint between vehicle and road axis
Difference, and the design and road conditions of entire control system strategy are closely related, and the index of reaction this situation of Road Base is road
Curvature, in addition, automatic driving vehicle crosswise joint is using the Foundation of Feedforward-feedback control in this parameter of road curvature
On, therefore, deviation e is laterally taken aim in characteristic quantity selection in advancepWith road axis curvature ρ composition characteristic state S (ep,ρ)。
2) Region place value divides
As shown in fig. 7, establishing two-dimentional Region place value, determine that extension range takes aim at lateral deviation e in advancepMaximum allowable range [-
ep2,ep2], maximum allowable the range [- ρ of extension range road axis curvature ρ2,ρ2].For Classical field, due to the control plan of use
Slightly traditional PID/feedback control, control ability is limited, in vehicle tracking big corner road-center line tracking, is not achieved full
The control effect of meaning, therefore, Classical field lateral deviation epMaximum allowable range [- ep1,ep1], the maximum allowable range of road curvature ρ
For [- ρ1,ρ1].So in two-dimentional Region place value, the section of Classical field are as follows: lateral position deviation ep[-ep1,ep1], road-center
Line curvature ρ [- ρ1,ρ1]；Extension range section are as follows: lateral position deviation ep[-ep2,-ep1)∪(ep1,ep2], road axis curvature ρ
[-ρ2,ρ1)∪(ρ1,ρ2]；Non- domain section are as follows: lateral position deviation ep(-∞,-ep2)∪(ep2,+∞), road axis curvature ρ
(-∞,-ρ2)∪(ρ2,+∞)。
3) calculating of correlation function
The characteristic quantity that traditional extension controller is chosen is deviation and deviation differential, and the present embodiment is chosen takes aim at lateral deviation e in advancep
With the road curvature ρ amount of being characterized, calculating can be opened up away from correlation function value is determined, be illustrated in figure 8 two-dimentional Region place value.
In two-dimentional Region place value, origin (0,0) is characterized the optimum point of state.Assuming that there are a point P in extension range3, P3
For current vehicle-roadnet state in which, origin and P are connected3Point obtains P3Approach the shortest distance of optimum point (0,0) |
OP3|.Straight line hands over Classical field boundary in P where the line segment1And P4Point hands over extension range boundary in P2And P5Point.Guaranteeing P3It levels off to
Under the shortest precondition of initial point distance, P can determine according to these intersection points3With the minimum distance of extension range, Classical field.
In one-dimensional Region place value, it can open up away from the minimum range for arriving interval border is substantially put, can be incited somebody to action according to this principle
Opening up away from being converted into one-dimensional open up away from as shown in Figure 9 in two-dimentional Region place value.P3Point to Classical field and extension range open up away from
Respectively ρ (P3,<P4,P1>) and ρ (P3,<P5,P2>), it solves as follows:
Then it can determine correlation function are as follows:
Wherein: D (P3,<P5,P2>,<P4,P1>)=ρ (P3,<P5,P2>)-ρ(P3,<P4,P1>)。
The series of detailed descriptions listed above only for feasible embodiment of the invention specifically
Protection scope bright, that they are not intended to limit the invention, it is all without departing from equivalent implementations made by technical spirit of the present invention
Or change should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation, which is characterized in that design upper layer
Controller and lower layer's controller；The vehicle front wheel angle output valve that upper controller selects goodness high by optimal degree evaluation method,
Lower layer's controller is coordinated based on the front wheel angle that the degree of association exports upper controller, and the front wheel angle after output coordinating is given
Control object.
2. it is according to claim 1 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the specific implementation of the control method:
Upper controller: vehicle-road condition amount, the lateral position deviation e of vehicle and road axis are obtainedp, vehicle and road
The course deviation of center lineYaw rateAnd road axis curvature ρ is separately input to PID as input value
Feedback controller and PID forward-feedback controller, corresponding output vehicle front wheel angle δf1And δf2, and output valve is brought into respectively
Evaluation index prediction model acquires two groups of corresponding vehicles-road predicted state amount, using optimal degree evaluation method to two groups of vehicles
- road predicted state amount evaluated, select preference value high as a result, using its corresponding front wheel angle as upper controller
Output valve be input in lower layer's controller.
Lower layer's controller: pass through the lateral position deviation e of current vehicle and road axispAnd road axis curvature ρ is calculated
Correlation function K (S), using correlation function K (S) by upper controller output valve front wheel angle δfCoordinated, after output coordinating
Front wheel angle to vehicle-road condition space equation.
3. it is according to claim 2 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the vehicle-road condition space equation is taken aim at partially in advance according to vehicle two degrees of freedom kinetic model, path trace
Poor model foundation obtains:
Wherein:
4. it is according to claim 3 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the vehicle two degrees of freedom kinetic model are as follows:
Wherein, m is vehicle mass (kg)；vx、vyRespectively vehicular longitudinal velocity, lateral velocity (ms-1)；For vehicle course
Angle (rad)；δfFor vehicle front wheel angle (rad)；IzIt is vehicle around the rotary inertia (kgm of z-axis2)；A, before b is respectively vehicle
Wheelbase from (m) with a distance from rear axle；k1、k2Respectively cornering stiffness (the Nrad of front and rear wheel tire-1)；ωrFor Vehicular yaw angle
Speed (rads-1)；
Buggy model is taken aim in the path trace in advance:
Wherein, vyFor vehicle state quantity side velocity, vxVehicular longitudinal velocity,For the course deviation of vehicle and road axis,
L is preview distance, and ρ is road axis curvature.
5. it is according to claim 3 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the evaluation index prediction model is converted to obtain by vehicle-road condition space equation:
6. it is according to claim 3 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the optimal degree evaluation method:
Measurement index is determined according to vehicle-road condition firstAnd according to the importance of each measurement index
Degree assigns the weight coefficient α=(α of (0,1) respectively1, α2, α3, α4), wherein α1+α2+α3+α4=1；
Secondly, requiring according to each measurement index, correlation function K is established1(x1), K2(x2), K3(x3), K4(x4) is object δf1, δf2
About each measurement index MIiCorrelation function value be abbreviated as Ki(δfj), then each object δf1, δf2About MIiThe degree of association be
Ki=(Ki(δf1), Ki(δf2)), i=1,2,3,4. standardize the above-mentioned degree of association:
Then each object δf1, δf2About MIiThe specification degree of association be ki=
(ki1, ki2), i=1,2,3,4；Then goodness calculating is carried out.
7. it is according to claim 6 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the method that the goodness calculates:
If computing object ZjAbout each measurement index MI1, MI2, MI3, MI4The specification degree of association beThen goodness computing object ZjGoodness are as follows:
To δfjGoodness be compared: ifThen object δf0It is more excellent.
8. it is according to claim 7 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, the front wheel angle after the output coordinating are as follows:
δf=[1-K (S)] δf(t-1)+K(S)δf(t)。
9. it is according to claim 8 a kind of based on the autonomous driving vehicle horizontal coordination control method that can open up optimal evaluation,
It is characterized in that, correlation function K (S) calculation method is as follows:
1) Characteristic Extraction
Characteristic quantity is selected as laterally to take aim at deviation e in advancepWith road axis curvature ρ composition characteristic state S (ep,ρ)；
2) Region place value divides
Two-dimentional Region place value is established, determines that extension range takes aim at lateral deviation e in advancepMaximum allowable range [- ep2,ep2], extension range road
Maximum allowable the range [- ρ of Lu Zhizheng line curvature ρ2,ρ2], for Classical field, Classical field lateral deviation epMaximum allowable range [- ep1,
ep1], the maximum allowable range of road curvature ρ is [- ρ1,ρ1]；Therefore, in two-dimentional Region place value, the section of Classical field is set as: laterally
Position deviation ep[-ep1,ep1], road axis curvature ρ [- ρ1,ρ1]；Extension range section are as follows: lateral position deviation ep[-ep2,-
ep1)∪(ep1,ep2], road axis curvature ρ [- ρ2,ρ1)∪(ρ1,ρ2]；Non- domain section are as follows: lateral position deviation ep(-∞,-
ep2)∪(ep2,+∞), road axis curvature ρ (- ∞ ,-ρ2)∪(ρ2,+∞)；
3) calculating of correlation function
It chooses and takes aim at lateral deviation e in advancepWith the road curvature ρ amount of being characterized, calculating can be opened up away from determining correlation function value；It can be opened up in two dimension
In set, origin (0,0) is characterized the optimum point of state, it is assumed that there are a point P in extension range3, P3For current vehicle-road system
Unite state in which, connection origin and P3Point obtains P3Approach the shortest distance of optimum point (0,0) | OP3|, it is straight where the line segment
Line hands over Classical field boundary in P1And P4Point hands over extension range boundary in P2And P5Point；Guaranteeing P3Before the initial point distance that levels off to is shortest
Under the conditions of mentioning, P is determined using these intersection points3With the minimum distance of extension range, Classical field；
In one-dimensional Region place value, by opening up away from being converted into one-dimensional open up away from P in two-dimentional Region place value3Point arrives Classical field and can
Open up the opening up away from respectively ρ (P of domain3,<P4,P1>) and ρ (P3,<P5,P2>), it solves as follows:
Then correlation function is determined are as follows:
Wherein: D (P3,<P5,P2>,<P4,P1>)=ρ (P3,<P5,P2>)-ρ(P3,<P4,P1>)。
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| CN113104037A (en) * | 2019-12-24 | 2021-07-13 | 浙江吉利汽车研究院有限公司 | Method and system for determining steering angle of vehicle steering wheel |
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| CN113032880A (en) * | 2021-03-26 | 2021-06-25 | 武汉理工大学 | Method for evaluating design rationality of circular curve road section |
| CN113032880B (en) * | 2021-03-26 | 2022-06-14 | 武汉理工大学 | Method for evaluating design rationality of circular curve road section |
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