CN108045435A - A kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive - Google Patents
A kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive Download PDFInfo
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- CN108045435A CN108045435A CN201711223046.XA CN201711223046A CN108045435A CN 108045435 A CN108045435 A CN 108045435A CN 201711223046 A CN201711223046 A CN 201711223046A CN 108045435 A CN108045435 A CN 108045435A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D15/00—Steering not otherwise provided for
- B62D15/02—Steering position indicators ; Steering position determination; Steering aids
- B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
Abstract
The invention discloses a kind of intelligent vehicle empir-ical formulation control methods of pavement self-adaptive, belong to automobile intelligent and drive field.The defects of present invention can not meet deep camber path trace for the common position deviation control of intelligent vehicle problem, introduce the crosswise joint strategy of another tracking vehicle expectation state amount, and target road curvature and its curvature variation to monitor in real time is switching conditions, design fuzzy controller causes intelligent vehicle to switch in real time between two kinds of control strategies, to realize the high-precision path trace for being adapted to different road surfaces.The present invention can effectively reduce the steady-state error of intelligent vehicle tracking, and make its course changing control more accurate, steady.
Description
Technical field
The present invention relates to a kind of intelligent vehicle empir-ical formulation control methods of pavement self-adaptive, belong to automobile intelligent and drive neck
Domain.
Background technology
Widely used position deviation control methods (such as pure tracing control method) realize vehicle in intelligent automobile crosswise joint at present
Path trace performance, i.e., when destination path be Lane marking or small curvature track when, take aim in advance a little opposite with target track
Lateral deviation and azimuth deviation difference between the closest approach of vehicle is smaller, position and side between controlling vehicle at this time and taking aim at a little in advance
Position deviation most I realizes accurate Lane tracking performance substantially.But the target road operating mode of actual vehicle is relative complex, can deposit
In various operating mode that rectilinear stretch, small curvature bend, Duct With Strong Curvature etc. mutually merge, position deviation control methods common at this time
Obviously the track trace performance of Duct With Strong Curvature can not be met.Then there are experts and scholars for the existing control of position deviation control methods
The problem of precision processed is not high, it is proposed that it is a kind of to track the vehicle lateral control method for it is expected yaw velocity, and it is real to pass through emulation
Test the accurate tracking for showing that the control method can realize each curvature particularly deep camber path.But this control method robustness compared with
Difference, while there is a situation where that yaw velocity fluctuation is larger during the also non-complete stability tracking expected path of vehicle, this
The riding comfort of vehicle will be directly affected.Therefore vehicle how to be controlled steadily to track destination path under various curvature
It is a problem to be solved.
The content of the invention
To overcome more than technological deficiency, the present invention proposes a kind of intelligent vehicle empir-ical formulation controlling party of pavement self-adaptive
Method, including step：
The vehicle position information and its attitude information that step 1) is measured by the sensor device on intelligent vehicle are input to vehicle
In road identifying system, the input of its comprehensive target lane information and preview distance, determine current vehicle it is pre- take aim at target point and
Target track and detects vehicle and to take aim at the lateral separation error Y of target point in advance compared with the closest approach of vehicleeIt is missed with azimuth
Poor φeAnd target track is compared with the curvature ρ and its curvature variation of the closest approach of vehicleAnd by it with referring to curvature threshold
Value ρ0It makes the difference, by this error of curvature signal e and error of curvature change rate signalInput fuzzy controller；
Step 2) fuzzy controller is exported according to the error signal and its change rate of input as intelligent vehicle crosswise joint mould
Formula, i.e. γ=1 (position deviation control methods), γ=0 (yaw velocity control methods it is expected in tracking)；
If during step 3) γ=1；
Intelligent vehicle empir-ical formulation controller is switched to position deviation control method at this time, and PID controller is known according to track
The vehicle of other system output takes aim at a little relatively transverse range error Y in advanceeWith azimuth angle error φe, calculate and export suitable front wheel angle
Signal δ turns to executing agency and according to the angular signal intelligent vehicle is controlled to turn to, completes path tracking procedure.
If during step 4) γ=0；
Intelligent vehicle empir-ical formulation controller is switched to the control method that yaw velocity it is expected in tracking at this time, and upper strata it is expected
The vehicle that yaw velocity maker is exported according to lane recognition system takes aim at a little relatively transverse range error Y in advanceeIt is missed with azimuth
Poor φe, output vehicle body sensor signal, which generates, it is expected yaw velocity ωd, and it is expected yaw velocity tracing control as lower floor
The reference input of device.Lower floor it is expected that yaw velocity tracking control unit is calculated by analyzing, and exports suitable front wheel angle signal
δ turns to executing agency and according to the angular signal intelligent vehicle is controlled to turn to, completes path tracking procedure.
Further, the position of the vehicle and attitude information include automobile in the process of moving compared with earth coordinates XY
The information such as coordinate, displacement, speed, acceleration, yaw velocity, front wheel angle, the GPS by being loaded on intelligent vehicle respectively
The equipment such as alignment system, hall speed sensor, gyroscope, rotary angle transmitter gather in real time.
Further, the road curvature is that the traffic lane line of target road is identified based on lane recognition system, and
It carries out curve fitting to the graticule of identification, the road curvature and its curvature variation being calculated.
Further, the design of Fuzzy Controller of the step 2 is as follows：
Choose road curvature error signal e and error of curvature change rate signalIt is defeated as the input signal of fuzzy controller
Go out signal for crosswise joint pattern.By the error signal e of input and error of curvature change rate signalIt is divided into 5 fuzzy sets：NB
(negative big), NS (negative small), ZO (zero), PS (just small), PB (honest).Output γ is divided to for two fuzzy sets：0 (intelligent vehicle horizontal stroke
Tracking, which is switched to, to control it is expected yaw velocity control methods), 1 (intelligent vehicle crosswise joint is switched to position deviation control
Method).This controller road curvature error signal e domain is set to [- 0.04,0.04], error of curvature change rate signalDomain is set to
[- 0.04,0.04], the domain of γ are set to { 0,1 }.For input quantity error of curvature signal e and error of curvature change rate signalAll
Using the membership function of Gaussian, triangular membership is used for output quantity γ.Its fuzzy if-then rules table such as one institute of table
Show.
Further, the realization method of the PID controller can be expressed by following formula：
Wherein, kp、kdAnd kiFor lateral separation error YeRatio, differential and integral coefficient, k 'p、k′dWith k 'iFor orientation
Angle error φeRatio, differential and integral coefficient.By adjusting lateral separation error YeWith azimuth angle error φeRatio, product
Point and differential coefficient, export the front wheel angle signal δ at current time.
Further, the expectation yaw velocity maker is according to preview distance, lateral separation error YeIt is missed with azimuth
Poor φeInput signal, vehicle and it is pre- take aim at a little between construct virtual driving path, and when calculating current based on this virtual route
Carve the expectation yaw velocity ω that vehicle approaches target road along virtual routed, yaw velocity tracking then it is expected by lower floor
Controller controls vehicle corner δ in real time, reaches accurate tracking and it is expected yaw velocity ωdPurpose.
Beneficial effects of the present invention are：The present invention makes vehicle by the real-time monitoring to target road curvature and its change rate
It can it is expected freely to cut between yaw velocity control methods in position deviation control methods and tracking when tracing surface is to different curvature path
It changes, and then acquisition precision is higher, the preferable vehicle-following behavior of robustness.
Description of the drawings
Fig. 1 is a kind of embodiment controlling party of the intelligent vehicle empir-ical formulation control method of pavement self-adaptive of the present invention
Case figure.
Specific embodiment
Below in conjunction with drawings and the specific embodiments, the present invention will be further described.Intelligent vehicle in the process of moving, by
The current vehicle position and its attitude information that multiple sensors equipment thereon measures are input to lane recognition system and laterally mix
In hop controller, subsequent lane recognition system is according to target lane information, preview distance information and vehicle location attitude information
Calculate, determine that the pre- of current vehicle takes aim at the closest approach of target point and target track compared with vehicle, and detect vehicle with it is pre-
Take aim at the lateral separation error Y of target pointeWith azimuth angle error φeAnd target track is compared with the curvature ρ of the closest approach of vehicle
And its curvature variationAnd by it with referring to curvature threshold ρ0It makes the difference, by this error of curvature signal e and error of curvature change rate
SignalFuzzy controller is inputted, while by lateral separation error YeWith azimuth angle error φeIt is input to empir-ical formulation controller
In；
Table 1 is the fuzzy if-then rules table of fuzzy controller of the present invention
Fuzzy controller is exported as intelligent vehicle crosswise joint pattern, i.e., according to the error signal and its change rate of input
γ=1 (position deviation control methods), γ=0 (yaw velocity control methods it is expected in tracking)；
If during γ=1, show that the roadway segment curvature of vehicle tracking at this time is smaller, empir-ical formulation controller takes position deviation
Control methods track expected path.A little relatively transverse range error Y is taken aim at according to the vehicle that lane recognition system exports in advanceeAnd orientation
Angle error φe, PID controller is according to the following formula：
Wherein, kp、kdAnd kiFor lateral separation error YeRatio, differential and integral coefficient, k 'p、k′dWith k 'iFor orientation
Angle error φeRatio, differential and integral coefficient.
It is adjusted to deserved lateral separation error YeWith azimuth angle error φeRatio, integration and differential coefficient, calculate it is defeated
Go out suitable front wheel angle signal δ, turn to executing agency and according to the angular signal intelligent vehicle is controlled to turn to, complete path trace
Process.
If during γ=0, show that the roadway segment curvature of vehicle tracking at this time is larger, empir-ical formulation controller is switched to the tracking phase
Hope the control method of yaw velocity, upper strata it is expected that the vehicle that yaw velocity maker is exported according to lane recognition system is taken aim in advance
The relatively transverse range error Y of pointeWith azimuth angle error φe, vehicle and it is pre- take aim at a little between construct virtual driving path, and be based on
This virtual route calculates the expectation yaw velocity ω that current time vehicle approaches target road along virtual routed, and conduct
The reference input of yaw velocity tracking control unit it is expected by lower floor.Lower floor it is expected that yaw velocity tracking control unit passes through analysis meter
It calculates, exports suitable front wheel angle signal δ, turn to executing agency and according to the angular signal intelligent vehicle is controlled to turn to, complete road
Footpath tracks process.
To sum up, the intelligent vehicle empir-ical formulation control method of a kind of pavement self-adaptive of the invention, belongs to automobile intelligent and drives
Sail field.The defects of present invention can not meet deep camber path trace for the common position deviation control of intelligent vehicle problem, draws
Enter the crosswise joint strategy of another tracking vehicle expectation state amount, and become with the target road curvature and its curvature monitored in real time
Rate is switching condition, and design fuzzy controller causes intelligent vehicle to switch in real time between two kinds of control strategies, is adapted to realizing
High-precision path trace in different road surfaces.The present invention can effectively reduce the steady-state error of intelligent vehicle tracking, and make its steering
Control is more accurate, steady.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " illustrative examples ",
The description of " example ", " specific example " or " some examples " etc. means to combine specific features, the knot that the embodiment or example describe
Structure, material or feature are contained at least one embodiment of the present invention or example.In the present specification, to above-mentioned term
Schematic representation may not refer to the same embodiment or example.Moreover, specific features, structure, material or the spy of description
Point can in an appropriate manner combine in any one or more embodiments or example.
Although an embodiment of the present invention has been shown and described, it will be understood by those skilled in the art that：Not
In the case of departing from the principle of the present invention and objective a variety of change, modification, replacement and modification can be carried out to these embodiments, this
The scope of invention is limited by claim and its equivalent.
Claims (8)
1. the intelligent vehicle empir-ical formulation control method of a kind of pavement self-adaptive, which is characterized in that comprise the following steps：
The vehicle position information and its attitude information that step 1) is measured by the sensor device on intelligent vehicle are input to track knowledge
In other system, the input of its comprehensive target lane information and preview distance determines that the pre- of current vehicle takes aim at target point and target
Track and detects vehicle and to take aim at the lateral separation error Y of target point in advance compared with the closest approach of vehicleeAnd azimuth angle error
φeAnd target track is compared with the road curvature ρ and its curvature variation of the closest approach of vehicleAnd by its with reference to curvature
Threshold value ρ0It is poor to make, by this error of curvature signal e and error of curvature change rate signalInput fuzzy controller；
Step 2) fuzzy controller is exported as intelligent vehicle crosswise joint pattern according to the error signal and its change rate of input,
Output γ is divided to for two fuzzy sets：γ=0 represents that intelligent vehicle crosswise joint is switched to tracking and it is expected yaw velocity control
Method, γ=1 represent that intelligent vehicle crosswise joint is switched to position deviation control methods；
If during step 3) γ=1；
Intelligent vehicle empir-ical formulation controller is switched to position deviation control method at this time, and PID controller is according to lane identification system
The vehicle of system output takes aim at a little relatively transverse range error Y in advanceeWith azimuth angle error φe, calculate and export suitable front wheel angle signal
δ turns to executing agency and according to the angular signal intelligent vehicle is controlled to turn to, completes path tracking procedure；
If during step 4) γ=0；
Intelligent vehicle empir-ical formulation controller is switched to the control method that yaw velocity it is expected in tracking at this time, and yaw it is expected on upper strata
The vehicle that angular speed maker is exported according to lane recognition system takes aim at a little relatively transverse range error Y in advanceeWith azimuth angle error φe,
It exports vehicle body sensor signal and generates and it is expected yaw velocity ωd, and it is expected yaw velocity tracking control unit as lower floor
Reference input, lower floor it is expected that yaw velocity tracking control unit is calculated by analyzing, exports suitable front wheel angle signal δ, turns
According to the angular signal intelligent vehicle is controlled to turn to executing agency, complete path tracking procedure.
2. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
Vehicle position information and its attitude information include automobile in the process of moving compared with the coordinate of earth coordinates XY, displacement, speed
Degree, acceleration, yaw velocity, front wheel angle information, respectively by be loaded on intelligent vehicle GPS positioning system, Hall speed
Degree sensor, gyroscope, rotary angle transmitter equipment gather in real time.
3. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
Road curvature is that the traffic lane line of target road is identified based on lane recognition system, and carries out curve to the graticule of identification
Fitting, the road curvature and its curvature variation being calculated.
4. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
The design of Fuzzy Controller of the step 2) is as follows：
Choose road curvature error signal e and error of curvature change rate signalAs the input signal of fuzzy controller, output letter
Number for crosswise joint pattern, by the error signal e of input and error of curvature change rate signalIt is divided into 5 fuzzy sets：NB is born greatly,
NS bears small, and ZO zero, PS is just small, and PB is honest；For input quantity error of curvature signal e and error of curvature change rate signalAll use
The membership function of Gaussian uses triangular membership for output quantity γ.
5. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
The realization method of PID controller can be expressed by following formula：
<mrow>
<mi>&delta;</mi>
<mo>=</mo>
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</msub>
<msub>
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<mi>k</mi>
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<mfrac>
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<mi>dY</mi>
<mi>e</mi>
</msub>
</mrow>
<mrow>
<mi>d</mi>
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</mrow>
</mfrac>
<mo>+</mo>
<msub>
<mi>k</mi>
<mi>i</mi>
</msub>
<munderover>
<mo>&Integral;</mo>
<mn>0</mn>
<mi>t</mi>
</munderover>
<msub>
<mi>Y</mi>
<mi>e</mi>
</msub>
<mi>d</mi>
<mi>t</mi>
<mo>+</mo>
<msubsup>
<mi>k</mi>
<mi>p</mi>
<mo>&prime;</mo>
</msubsup>
<msub>
<mi>&phi;</mi>
<mi>e</mi>
</msub>
<mo>+</mo>
<msubsup>
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</msubsup>
<mfrac>
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<mi>d&phi;</mi>
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Wherein, kp、kdAnd kiFor lateral separation error YeRatio, differential and integral coefficient, k 'p、k′dWith k 'iFor azimuth angle error
φeRatio, differential and integral coefficient, by adjusting lateral separation error YeWith azimuth angle error φeRatio, integration and micro-
Divide coefficient, export the front wheel angle signal δ at current time.
6. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
It is expected yaw velocity maker according to preview distance, lateral separation error Y in upper strataeWith azimuth angle error φeInput signal,
Vehicle and it is pre- take aim at a little between construct virtual driving path, and current time vehicle is calculated along virtual route based on this virtual route
Approach the expectation yaw velocity ω of target roadd, then it is expected that yaw velocity tracking control unit controls vehicle in real time by lower floor
Corner δ reaches accurate tracking and it is expected yaw velocity ωdPurpose.
7. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 1, which is characterized in that
With reference to curvature threshold ρ0It is set as 0.04.
8. a kind of intelligent vehicle empir-ical formulation control method of pavement self-adaptive according to claim 4, which is characterized in that
In fuzzy controller, road curvature error signal e domain is set to [- 0.04,0.04], by error of curvature change rate signalBy
Domain is set to [- 0.04,0.04], and the domain of γ is set to { 0,1 }.
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| CN201711223046.XA CN108045435B (en) | 2017-11-29 | 2017-11-29 | Pavement self-adaptive intelligent vehicle transverse hybrid control method |
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| CN201711223046.XA CN108045435B (en) | 2017-11-29 | 2017-11-29 | Pavement self-adaptive intelligent vehicle transverse hybrid control method |
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ID=62120935
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| CN110160806A (en) * | 2019-06-17 | 2019-08-23 | 北京艾尔动力科技有限公司 | Automated driving system and test method for automobile durable test |
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