CN107792062A - Automatic parking control system - Google Patents
Automatic parking control system Download PDFInfo
- Publication number
- CN107792062A CN107792062A CN201710975324.0A CN201710975324A CN107792062A CN 107792062 A CN107792062 A CN 107792062A CN 201710975324 A CN201710975324 A CN 201710975324A CN 107792062 A CN107792062 A CN 107792062A
- Authority
- CN
- China
- Prior art keywords
- msub
- mrow
- mtd
- mtr
- msup
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 claims abstract description 15
- 238000004088 simulation Methods 0.000 claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims abstract description 8
- 230000005484 gravity Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 230000006870 function Effects 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 8
- 238000012549 training Methods 0.000 abstract description 3
- 239000000284 extract Substances 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/06—Automatic manoeuvring for parking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Geometry (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
Abstract
The invention provides an automatic parking control system. The system divides the whole automatic parking process into an input layer, a strategy layer, a planning layer and a control layer. In the automatic parking control process, the input layer generates control instructions and parking track data sets of different types of vehicles through simulation; the strategy layer learns the simulation data by using a deep neural network algorithm and extracts the general relation between the control instruction and the parking track; the planning layer finds a proper parking strategy through training in a few steps, gives a control instruction under the parking scene and generates a planning track; and the control layer performs control feedback according to the deviation of the actual parking track and the planned track, so that the parking track is closest to the ideal track planned by the system.
Description
Technical Field
The invention relates to the field of automatic parking, in particular to an automatic parking control system for a general scene.
Background
In-line parking is a painful experience for many drivers, and large cities have limited parking space, and it has become a necessary skill to drive cars into narrow spaces. There are few situations where the vehicle is parked without taking a turn, which can result in traffic jams, nerve fatigue, and bumper jostling. With the development of the automatic parking technology, the above problems are greatly improved. The automatic parking technology can help a driver to automatically park and also help to solve some parking and traffic problems in densely populated urban areas. Sometimes, whether to park in a narrow space is limited by the skill of the driver. The automatic parking technique can park the automobile in a small space, which is much smaller than the space in which most drivers can park themselves. This makes it easier for the owner to find the parking space, while the same number of cars takes up less space.
In the prior art, as disclosed in publication No. CN107102642A, an automatic parking system for an unmanned vehicle is provided, which focuses mainly on an automatic parking monitoring sensing system, and estimates a vehicle trajectory using detection data of a geomagnetic sensor. As disclosed in publication No. CN106427996A, there is provided a multifunctional parking control method and system by acquiring obstacle information around a vehicle; and selecting an automatic parking mode or a remote control parking mode according to the information of obstacles around the vehicle. For example, publication No. CN 106043282A provides a full-automatic parking system for a vehicle and a control method thereof, which plans a parking path according to surrounding environment information of the vehicle and running state information of the vehicle, and controls an electric power steering system, an electronic stability system, and a transmission control unit to complete full-automatic parking according to the parking path.
In the prior art, the related content of 'automatic parking' mainly focuses on the hardware structure of an automatic parking system, how each part of modules works and the communication mode among the modules, most of technical content of the automatic parking system does not relate to the consideration of parking scenes, and the automatic parking system cannot solve the parking problems of various parking scenes and different parking types.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides an automatic parking control system.
The following technical scheme is adopted specifically:
the system comprises an input layer, a strategy layer, a planning layer and a control layer; the input layer is used for receiving and sensing the speed and the position of the vehicle and the position and the size of the parking space in the current state; the strategy layer gives a control operation instruction of automatic parking according to the related information of the current vehicle and the parking space; the planning layer combines a dynamic model of a specific vehicle to give a planned parking track; and the control layer carries out real-time feedback control according to the deviation of the actual parking track and the planned parking track.
Preferably, the input layer defines the vehicle type according to the size of the vehicle body, the position of the center of gravity, the front-rear wheel base and the front-rear wheel rotation inertia; and respectively generating the track of different vehicle types: dividing the whole parking process into N stages, wherein the control instruction sets of the N stages are deltaf N＝{δf(1),…,δf(N) }; the specific vehicle control command in each stage is from a set S of K control anglesδ＝{δ1,…,δkGet K available for each type of vehicleNCombining the results; the time length of each stage is TNThe total time length is T ═ T1+…+TN(ii) a The simulation step size is T, where TN＝K*t；
The strategy layer obtains a control instruction combination of the vehicle through deep neural network learning according to the final position, the deflection angle and the speed of the vehicle;
the programming layer is based on a set of control instructions { δ }1,δ2,…,δtObtaining a vehicle parking track by combining the current specific vehicle dynamics model parameters;
and the control layer performs feedback control according to the deviation of the actual parking track and the planned track to generate a final parking track.
Preferably, the specific way of obtaining the parking trajectory of the vehicle by the planning layer is as follows:
1) in the forward mode, the equation is transferred according to the vehicle state
And
δf＝δmax，δr＝0，
to obtain
Wherein,for the last time vehicle system state variables in the forward mode,the state variable of the vehicle system at the next moment in the forward mode; deltafFor the steering angle, delta, of the front wheelsrIs the rear wheel steering angle, δmaxIs the maximum steering angle;
2) in reverse mode, according to the vehicle state transfer equation
And
δr＝0，δf＝δmax
to obtain
Wherein,the last moment vehicle system state variable in the reverse mode,is the vehicle system state variable at the next moment in the reverse mode.
Preferably, the control layer feedback control specifically includes the steps of:
and performing feedback control according to an included angle β between the vehicle speed and the longitudinal axis of the vehicle and the rotation angular speed r under an inertial coordinate system:
the state space vector of the original system is expressed as
The actual system of change of the system parameters is
The state space vector of the feedback system is represented as
Wherein x is [ β, r ═ r]TIs a state variable of an ideal system, x ' ═ β ', r ']TIs a state variable of the actual system; a and B are state constants; u is a vehicle corner; k is a feedback matrix;
from an objective functionAt a minimum, a state feedback controller is obtained as
u＝-Kx。
Preferably, the control layer comprises the following specific steps of:
and performing feedback control according to the vehicle position and the turning angle deviation:
from an objective function
At a minimum, a state feedback controller is obtained as
u＝-Kx
Wherein:
(x ', y') represents the coordinates of the actual trace points;
(x, y) represents the coordinates of the ideal trace point;
psi' represents the corner of the actual trace point;
psi denotes the corner of the ideal trace point;
k ═ K1, K2 denotes a feedback matrix.
The invention has the following beneficial effects:
(1) the automatic parking system provided by the invention is oriented to a common scene, and a parking strategy suitable for the current scene is found through algorithm learning and field training, so that the problem that the type of a parking space suitable for the current parking system is single is solved;
(2) two vehicle trajectory control modes based on control theory algorithms are provided, which are more accurate than the mode of simply giving a corrected value of speed or deflection angle in the prior art.
Drawings
Fig. 1 is a schematic diagram of an automatic parking system.
Fig. 2 is a schematic diagram of simulation trajectories of two different vehicle types.
Fig. 3 is a schematic diagram of a neural network structure.
Fig. 4 is a schematic diagram of a planned trajectory.
FIG. 5 is a schematic view of a vehicle dynamics model.
Fig. 6 is a schematic diagram of the feedback control principle of δ _ f according to β and r.
Fig. 7 is a schematic diagram illustrating the principle of feedback control of the position and rotation angle deviation.
Detailed Description
The automatic parking control system oriented to the general scene divides the whole automatic parking process into four layers: an input layer, a strategy layer, a planning layer and a control layer. As shown in fig. 1:
the input layer is used for receiving and sensing the speed and the position of the vehicle and the direction and the size of the parking space in the current state; the strategy layer gives a control operation instruction of automatic parking according to the related information of the current vehicle and the parking space; the planning layer combines a dynamic model of a specific vehicle to give a planned parking track; and the control layer carries out real-time feedback control according to the deviation of the actual parking track and the planned parking track.
Firstly, generating control instructions and parking track data sets of different types of vehicles through MATLAB simulation software and a parallel learning algorithm; secondly, learning the simulation data by using a deep neural network algorithm, and extracting a general relation between a control command and a parking track, so that when any parking scene is given, a proper parking strategy can be found through training in a few steps, and the control command under the parking scene is given; then, aiming at a specific vehicle dynamics model of the current vehicle, a parking track under a theoretical condition is given; and finally, performing control feedback according to the deviation generated in the actual parking process to enable the parking track to be closest to the ideal track planned by the system.
(1) Different vehicle types are defined: different vehicle types have different characteristic parameters, such as vehicle body size, gravity center position, front and rear wheel distance, front and rear wheel moment of inertia and the like, and two vehicle types are selected, wherein A represents a car and B represents an SUV.
TABLE 1 different vehicle type parameter examples
(2) Generating a control instruction and a parking track in a simulation mode: trajectory generation is performed separately for two different vehicle types a and B. Dividing the whole parking process into N stages, wherein the control instruction sets of the N stages are deltaf N＝{δf(1),…,δf(N) }; the specific vehicle control command in each stage is from a set S of K control anglesδ＝{δ1,…,δkThus, each type of vehicle can obtain KNCombining the results; the time length of each stage is TNThe total time length is T ═ T1+…+TN(ii) a The simulation step length is T, TNK × t. FIG. 1 is a simulated trajectory, δ, for two types of vehiclesf N＝{-0.6,-0.4,-0.2,0,0.2,0.4,0.6}，K＝7，N＝4，t＝0.01s，TN2401 traces are formed together for 3 s.
(3) Learning a neural network: in order to determine a combination of control commands with knowledge of the final position, the yaw angle and the speed of the vehicleTo achieve accurate parking of the vehicle at the final position, we use a deep neural network algorithm to solve this problem. Fig. 2 is a schematic of the structure of a neural network, divided into an input layer, a hidden layer, and an output layer. Inputting a position as a target state of a vehicleAnd a velocity vector v, the output being a set of required control commands { δ }1,δ2,…,δt}。
(4) Planning a track: at a given set of control instructions { δ1,δ2,…,δtAnd on the basis of the calculation, the current specific vehicle dynamics model parameters are combined to give the vehicle parking track. Fig. 3 is an example of a planned trajectory.
FIG. 4 is a simplified schematic diagram of a vehicle dynamics model, in which the following parameters are used:
● v-vehicle speed
● v _ x is the component of velocity in the horizontal direction
● v _ y being the component of velocity in the vertical direction
● β -angle between vehicle speed and longitudinal axis of vehicle
●Angular velocity of rotation in inertial frame
● x is the abscissa of the center of gravity of the vehicle
● y is the vehicle center of gravity ordinate
● F is the lateral force at the center of gravity
●δf(δr) Front (rear) steering angle
● theta is the included angle between the front wheel of the vehicle and the long axis direction of the berth, and the critical observation angle for the vehicle direction conversion
● psi being the angle of rotation from the x axis to the long axis of the vehicle
1) Forward mode
The vehicle state transfer equation is
WhereinIn order to be the last time the system state variable,is the system state variable at the next moment.
Wherein:
Cf＝μcf,Cr＝μcr
coefficient of tire stiffness mgv pairs of vehicle steering angle control quantity Cf、CrThe influence coefficient of (a);
coefficient of tire stiffness mgv pairs of vehicle angular rate CflfThe influence coefficient of (a);
moment of inertia of tire IgzFor vehicle turning angle rate CflfThe influence coefficient of (a);
inertia of tire versus vehicle angular velocity CflfThe influence coefficient of (a);
influence coefficients of the front wheel stiffness coefficients on the front wheel steering angle control quantity;
influence coefficients of the rear wheel stiffness coefficients on rear wheel steering angle control quantities;
influence coefficients of the rotational inertia of the front wheel tires on the front wheel steering angle control quantity;
influence coefficients of the rotational inertia of the rear wheel tires on the rear wheel steering angle control quantity;
Cf＝μcf: the rigidity coefficient of the front wheel tire when the road surface adhesion coefficient is mu;
Cr＝μcr: the rigidity coefficient of the rear wheel tire when the road surface adhesion coefficient is mu;
(dry road surface μ ═ 1, wet road surface μ ═ 0.5).
While advancing, deltaf＝δmax，δrWhen the value is 0, then:
namely:
2) reverse mode
If the model is not influenced by the front driving and the rear driving of the vehicle, the model for backing the vehicle can be regarded as taking the tail of the vehicle as the head of the vehicle, and the model for advancing is still used, so that the front and rear parameters of the vehicle are exchanged:
wherein:
thus:
when backing a car deltaf *＝δr＝0，δr *＝δf＝δmaxThus, therefore, it is
Then:
β and r are defined identically since they are in increments, whether advancing or backing, but ψ is an accumulated amount that, when advancing,
ψ*＝ψ+r*Δt；
when backing, the tail of the vehicle is regarded as the head of the vehicle, so
ψ*＝ψ+r*Δt+π；
Component of velocity
v_x*＝|v|*cos(β+ψ*)；
v_y*＝|v|*sin(β+ψ*)；
Position coordinates
x*＝x+vx*Δt；
y*＝y+vy*Δt；
Deflection angle
ψ*ψ + r Δ t; (Advance)
ψ*＝ψ+r*Δt+π；(reversing).
(5) Feedback control: due to vehicle wear, environmental changes, etc., model parameters in the actual vehicle operating system may differ from those of the ideal planning system, resulting in a deviation between the actual parking trajectory and the planned trajectory. In order to eliminate or reduce the deviation as much as possible, a linear control algorithm is adopted to perform feedback control on the system.
In actual parking systems, due to vehicle-inherent parameters such as mg、lf(lr)、Cf(Cr) The vehicle running track may be different from the track under the ideal system. To eliminate this difference, negative feedback control is added to the system to adjust the vehicle trajectory.
The feedback control of the vehicle track regulation is divided into two types, namely, the deviation of the internal variation β and r of the system is used for deltafPerforming feedback control; second, according to the system cumulant, namely the deviation of position and angle, to deltafAnd performing feedback control.
1) According to β and r to deltafPerforming feedback control
From the vehicle dynamics model, it can be seen that changes in the system parameters cause changes in the system variables β and r, which in turn affect the vehicle trajectory.
The state space vector of the original system can be expressed as
The actual system is due to the change of system parameters
According to the LQR control method, a state feedback controller u-Kx is designedObtaining an objective functionAnd minimum. The state space vector of the feedback system can then be expressed as
Wherein x is [ β, r ═ r]T: state variables of the ideal system;
x′＝[β′,r′]T: state variables of the actual system;
a and B are state constants;
u＝δf: turning the vehicle;
k: the feedback matrix K ═ K1, K2], and the specific calculation method refers to the LQR control algorithm.
In the MATLAB discrete system, the simulation step number is 12000 steps, the step length is 0.001s, and the simulation step number is divided into four stages:
stage1：step1-step3000，δf＝-0.4(v＝-1)/δf＝0.4(v＝1)；
stage2：step3001-step6000，δf＝0；
stage3：step6001-step9000，δf＝0.4(v＝-1)/δf＝-0.4(v＝1)；
stage4：step9001-step12000，δf0. In the MATLAB discrete system, the simulation step number is 12000 steps, the step length is 0.001s, and the simulation step number is divided into four stages:
stage1：step1-step3000，δf＝-0.4(v＝-1)/δf＝0.4(v＝1)；
stage2：step3001-step6000，δf＝0；
stage3：step6001-step9000，δf＝0.4(v＝-1)/δf＝-0.4(v＝1)；
stage4：step9001-step12000，δf＝0。
2) feedback control is performed based on the vehicle position and the steering angle deviation, and the feedback principle is shown in fig. 6.
On the premise of knowing the feedback of the ideal track point and the actual track point at the previous moment, a steering angle can be found, so that the next actual track of the vehicle is closest to the track of the planned path, and an objective function is enabled to be obtained
And minimum. Wherein:
(x ', y') represents the coordinates of the actual trace points;
(x, y) represents the coordinates of the ideal trace point;
psi' represents the corner of the actual trace point;
psi denotes the corner of the ideal trace point;
k ═ K1, K2 denote feedback matrices, where K1, K2 ═ 1;
taking delta according to the limitation of the maximum steering angle of the vehiclef∈[-0.70,0.70]. The feedback process is as follows:
● step 1: original system deltaf0.4, feedback system δf＝0.4
● step2-step 300: original system deltaf＝0.4，[-0.70,0.70]
● step301-step 600: original system deltaf0, feedback system δf＝[-0.70,0.70]
● step601-step 900: original system deltaf-0.4, feedback system δf＝[-0.70,0.70]
● step900-step 1200: original system deltaf0, feedback system δf＝[-0.70,0.70]。
Claims (5)
1. An automatic parking control system is characterized in that,
the system comprises an input layer, a strategy layer, a planning layer and a control layer;
the input layer is used for receiving and sensing the speed and the position of the vehicle and the position and the size of the parking space in the current state;
the strategy layer gives a control operation instruction of automatic parking according to the related information of the current vehicle and the parking space;
the planning layer combines a dynamic model of a specific vehicle to give a planned parking track;
and the control layer carries out real-time feedback control according to the deviation of the actual parking track and the planned parking track.
2. The automatic parking control system according to claim 2,
the input layer defines the vehicle type according to the size of the vehicle body, the position of the gravity center, the wheelbase and the front and rear wheel rotating inertia; and respectively generating the track of different vehicle types: dividing the whole parking process into N stages, wherein the control instruction sets of the N stages are deltaf N＝{δf(1),…,δf(N) }; the specific vehicle control command in each stage is from a set S of K control anglesδ＝{δ1,…,δkGet K available for each type of vehicleNCombining the results; the time length of each stage is TNThe total time length is T ═ T1+…+TN(ii) a The simulation step size is T, where TN＝K*t；
The strategy layer obtains a control instruction combination of the vehicle through deep neural network learning according to the final position, the deflection angle and the speed of the vehicle;
the programming layer is based on a set of control instructions { δ }1,δ2,…,δtObtaining a vehicle parking track by combining the current specific vehicle dynamics model parameters;
and the control layer performs feedback control according to the deviation of the actual parking track and the planned track to generate a final parking track.
3. The automatic parking control method for the general parking scene as claimed in claim 2, wherein the specific way of obtaining the parking trajectory of the vehicle by the planning layer is as follows:
1) in the forward mode, the equation is transferred according to the vehicle state
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mover> <mi>&beta;</mi> <mo>&CenterDot;</mo> </mover> </mtd> </mtr> <mtr> <mtd> <mover> <mi>r</mi> <mo>&CenterDot;</mo> </mover> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>a</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>12</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>a</mi> <mn>21</mn> </msub> </mtd> <mtd> <msub> <mi>a</mi> <mn>22</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <mi>&beta;</mi> </mtd> </mtr> <mtr> <mtd> <mi>r</mi> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>b</mi> <mn>11</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>12</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>b</mi> <mn>21</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>22</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>&delta;</mi> <mi>f</mi> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>&delta;</mi> <mi>r</mi> </msub> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>11</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>12</mn> </msub> <mo>=</mo> <mo>-</mo> <mn>1</mn> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>-</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <msup> <mi>v</mi> <mn>2</mn> </msup> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>21</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>-</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>a</mi> <mn>22</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msup> <msub> <mi>l</mi> <mi>f</mi> </msub> <mn>2</mn> </msup> <mo>+</mo> <msub> <mi>c</mi> <mi>r</mi> </msub> <msup> <msub> <mi>l</mi> <mi>r</mi> </msub> <mn>2</mn> </msup> </mrow> <mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>b</mi> <mn>11</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>c</mi> <mi>f</mi> </msub> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>b</mi> <mn>21</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>c</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> </mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced>
And
δf＝δmax，δr＝0，
to obtain
<mrow> <mover> <mi>&beta;</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>a</mi> <mn>11</mn> </msub> <mi>&beta;</mi> <mo>+</mo> <msub> <mi>a</mi> <mn>12</mn> </msub> <mi>r</mi> <mo>+</mo> <msub> <mi>b</mi> <mn>11</mn> </msub> <msub> <mi>&delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
<mrow> <mover> <mi>r</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <msub> <mi>a</mi> <mn>21</mn> </msub> <mi>&beta;</mi> <mo>+</mo> <msub> <mi>a</mi> <mn>22</mn> </msub> <mi>r</mi> <mo>+</mo> <msub> <mi>b</mi> <mn>21</mn> </msub> <msub> <mi>&delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
Wherein,for the last time vehicle system state variables in the forward mode,the state variable of the vehicle system at the next moment in the forward mode; deltafFor the steering angle, delta, of the front wheelsrIs the rear wheel steering angle, δmaxIs the maximum steering angle;
2) in reverse mode, according to the vehicle state transfer equation
<mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <mover> <mi>&beta;</mi> <mo>&CenterDot;</mo> </mover> <mo>*</mo> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mover> <mi>r</mi> <mo>&CenterDot;</mo> </mover> <mo>*</mo> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>=</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>a</mi> <mn>11</mn> </msub> </mtd> <mtd> <mrow> <mo>-</mo> <mn>2</mn> <mo>-</mo> <msub> <mi>a</mi> <mn>12</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mo>-</mo> <msub> <mi>a</mi> <mn>21</mn> </msub> </mrow> </mtd> <mtd> <msub> <mi>a</mi> <mn>22</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <mi>&beta;</mi> <mo>*</mo> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <mi>r</mi> <mo>*</mo> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>+</mo> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msub> <mi>b</mi> <mn>12</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>11</mn> </msub> </mtd> </mtr> <mtr> <mtd> <msub> <mi>b</mi> <mn>22</mn> </msub> </mtd> <mtd> <msub> <mi>b</mi> <mn>21</mn> </msub> </mtd> </mtr> </mtable> </mfenced> <mfenced open = "[" close = "]"> <mtable> <mtr> <mtd> <msup> <msub> <mi>&delta;</mi> <mi>f</mi> </msub> <mo>*</mo> </msup> </mtd> </mtr> <mtr> <mtd> <msup> <msub> <mi>&delta;</mi> <mi>r</mi> </msub> <mo>*</mo> </msup> </mtd> </mtr> </mtable> </mfenced> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>b</mi> <mn>12</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>C</mi> <mi>r</mi> </msub> <mrow> <msub> <mi>m</mi> <mi>g</mi> </msub> <mi>v</mi> </mrow> </mfrac> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>b</mi> <mn>22</mn> </msub> <mo>=</mo> <mo>-</mo> <mfrac> <mrow> <msub> <mi>C</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <msub> <mi>I</mi> <mrow> <mi>g</mi> <mi>z</mi> </mrow> </msub> </mfrac> </mrow> </mtd> </mtr> </mtable> </mfenced>
And
δr＝0，δf＝δmax
to obtain
<mrow> <msup> <mover> <mi>&beta;</mi> <mo>&CenterDot;</mo> </mover> <mo>*</mo> </msup> <mo>=</mo> <msub> <mi>a</mi> <mn>11</mn> </msub> <msup> <mi>&beta;</mi> <mo>*</mo> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mo>-</mo> <mn>2</mn> <mo>-</mo> <msub> <mi>a</mi> <mn>12</mn> </msub> <mo>)</mo> </mrow> <msup> <mi>r</mi> <mo>*</mo> </msup> <mo>+</mo> <msub> <mi>b</mi> <mn>11</mn> </msub> <msub> <mi>&delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
<mrow> <msup> <mover> <mi>r</mi> <mo>&CenterDot;</mo> </mover> <mo>*</mo> </msup> <mo>=</mo> <mo>-</mo> <msub> <mi>a</mi> <mn>21</mn> </msub> <msup> <mi>&beta;</mi> <mo>*</mo> </msup> <mo>+</mo> <msub> <mi>a</mi> <mn>22</mn> </msub> <msup> <mi>r</mi> <mo>*</mo> </msup> <mo>+</mo> <msub> <mi>b</mi> <mn>21</mn> </msub> <msub> <mi>&delta;</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> </msub> </mrow>
Wherein,the last moment vehicle system state variable in the reverse mode,for the next moment of the vehicle system state variable in the reverse mode, a11Is the coefficient of stiffness m of the tiregv influence coefficient on the vehicle turning angle control amount; a is12Is the coefficient of stiffness m of the tiregv pairs of vehicle angular rate CflfThe influence coefficient of (a); a is21Is the moment of inertia of the tyre IgzA coefficient of influence on a vehicle turning rate; a is22The influence coefficient of the rotational inertia of the tire on the vehicle rotation angular rate is shown; b11The influence coefficient of the rigidity coefficient of the front wheel on the steering angle control quantity of the front wheel is obtained; b12The influence coefficient of the rigidity coefficient of the rear wheel on the steering angle control quantity of the rear wheel is obtained; b21The influence coefficient of the rotational inertia of the front wheel tire on the front wheel steering angle control quantity is obtained; b22The influence coefficient of the rotational inertia of the rear wheel tire on the rear wheel steering angle control quantity is obtained; cfThe rigidity coefficient of the front wheel tire when the road adhesion coefficient is mu; crThe rigidity coefficient of the rear wheel tire is the road surface adhesion coefficient mu.
4. An automatic parking control method oriented to a general parking scene as claimed in claim 2, characterized in that said control layer feedback control comprises the specific steps of:
and performing feedback control according to an included angle β between the vehicle speed and the longitudinal axis of the vehicle and the rotation angular speed r under an inertial coordinate system:
the state space vector of the original system is expressed as
<mrow> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>=</mo> <mi>A</mi> <mi>x</mi> <mo>+</mo> <mi>B</mi> <mi>u</mi> </mrow>
The actual system of change of the system parameters is
<mrow> <msup> <mover> <mi>x</mi> <mo>&CenterDot;</mo> </mover> <mo>&prime;</mo> </msup> <mo>=</mo> <msup> <mi>A</mi> <mo>&prime;</mo> </msup> <msup> <mi>x</mi> <mo>&prime;</mo> </msup> <mo>+</mo> <msup> <mi>B</mi> <mo>&prime;</mo> </msup> <mi>u</mi> </mrow>
The state space vector of the feedback system is represented as
<mrow> <msup> <mi>x</mi> <mo>&prime;</mo> </msup> <mo>=</mo> <msup> <mi>A</mi> <mo>&prime;</mo> </msup> <msup> <mi>x</mi> <mo>&prime;</mo> </msup> <mo>+</mo> <msup> <mi>B</mi> <mo>&prime;</mo> </msup> <mover> <mo>&lsqb;</mo> <mo>&CenterDot;</mo> </mover> <mi>u</mi> <mo>-</mo> <mi>K</mi> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>x</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow>
Wherein x is [ β, r ═ r]TIs a state variable of an ideal system, x ' ═ β ', r ']TIs a state variable of the actual system; a and B are state constants; u is a vehicle corner; k is a feedback matrix;
from an objective functionAt a minimum, a state feedback controller is obtained as
u＝-Kx。
5. An automatic parking control method oriented to a general parking scene as claimed in claim 2, characterized in that said control layer control feedback comprises the following specific steps:
and performing feedback control according to the vehicle position and the turning angle deviation:
from an objective function
<mrow> <mi>d</mi> <mi>i</mi> <mi>s</mi> <mo>=</mo> <mi>k</mi> <mn>1</mn> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <msup> <mi>x</mi> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>x</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <msup> <mi>y</mi> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>y</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> </mrow> </msqrt> <mo>+</mo> <mi>k</mi> <mn>2</mn> <mo>|</mo> <msup> <mi>&psi;</mi> <mo>&prime;</mo> </msup> <mo>-</mo> <mi>&psi;</mi> <mo>|</mo> </mrow>At a minimum, a state feedback controller is obtained as
-Kx wherein:
(x ', y') represents the coordinates of the actual trace points;
(x, y) represents the coordinates of the ideal trace point;
psi' represents the corner of the actual trace point;
psi denotes the corner of the ideal trace point;
k ═ K1, K2 denotes a feedback matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710975324.0A CN107792062B (en) | 2017-10-16 | 2017-10-16 | Automatic parking control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710975324.0A CN107792062B (en) | 2017-10-16 | 2017-10-16 | Automatic parking control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107792062A true CN107792062A (en) | 2018-03-13 |
| CN107792062B CN107792062B (en) | 2019-11-05 |
Family
ID=61534130
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710975324.0A Expired - Fee Related CN107792062B (en) | 2017-10-16 | 2017-10-16 | Automatic parking control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107792062B (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108407805A (en) * | 2018-03-30 | 2018-08-17 | 中南大学 | A kind of vehicle automatic parking method based on DQN |
| CN108860139A (en) * | 2018-04-11 | 2018-11-23 | 浙江零跑科技有限公司 | A kind of automatic parking method for planning track based on depth enhancing study |
| CN109823348A (en) * | 2019-02-19 | 2019-05-31 | 百度在线网络技术（北京）有限公司 | Unmanned vehicle reversing model control method, device, equipment and computer-readable medium |
| CN110525428A (en) * | 2019-08-29 | 2019-12-03 | 合肥工业大学 | A kind of automatic parking method based on the study of fuzzy deeply |
| WO2020001423A1 (en) * | 2018-06-29 | 2020-01-02 | 华为技术有限公司 | Method and device for determining automatic parking strategy |
| CN110654374A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Automatic parking simulation system and test method thereof |
| CN110654373A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Automatic parking method and device and vehicle |
| WO2020056875A1 (en) * | 2018-09-20 | 2020-03-26 | 初速度（苏州）科技有限公司 | Parking strategy based on deep reinforcement learning |
| CN111047948A (en) * | 2019-11-27 | 2020-04-21 | 康忠文 | Auxiliary method and system for learning to learn to train |
| CN111098852A (en) * | 2019-12-02 | 2020-05-05 | 北京交通大学 | Parking path planning method based on reinforcement learning |
| CN111098850A (en) * | 2018-10-25 | 2020-05-05 | 北京初速度科技有限公司 | Automatic parking auxiliary system and automatic parking method |
| CN111434550A (en) * | 2019-01-15 | 2020-07-21 | 初速度（苏州）科技有限公司 | Simulation-based parking strategy generation method and system |
| CN113110500A (en) * | 2021-05-10 | 2021-07-13 | 的卢技术有限公司 | Transverse control method for low-speed automatic parking of automatic-driving automobile |
| CN113252366A (en) * | 2021-06-23 | 2021-08-13 | 奇瑞新能源汽车股份有限公司 | Automatic parking evaluation method and device for vehicle, vehicle and storage medium |
| CN113978547A (en) * | 2021-10-21 | 2022-01-28 | 江铃汽车股份有限公司 | Automatic driving steering control method and system |
| CN115303256A (en) * | 2022-08-10 | 2022-11-08 | 浙江零跑科技股份有限公司 | Automatic parking path tracking control method and device and computer equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104057950A (en) * | 2014-05-21 | 2014-09-24 | 重庆长安汽车股份有限公司 | Full-automatic parking system |
| CN104260722A (en) * | 2014-09-23 | 2015-01-07 | 北京理工大学 | Automatic parking system |
| CN105857306A (en) * | 2016-04-14 | 2016-08-17 | 中国科学院合肥物质科学研究院 | Vehicle autonomous parking path programming method used for multiple parking scenes |
| US20170294121A1 (en) * | 2016-04-12 | 2017-10-12 | Ford Global Technologies, Llc | Detecting available parking spaces |
-
2017
- 2017-10-16 CN CN201710975324.0A patent/CN107792062B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104057950A (en) * | 2014-05-21 | 2014-09-24 | 重庆长安汽车股份有限公司 | Full-automatic parking system |
| CN104260722A (en) * | 2014-09-23 | 2015-01-07 | 北京理工大学 | Automatic parking system |
| US20170294121A1 (en) * | 2016-04-12 | 2017-10-12 | Ford Global Technologies, Llc | Detecting available parking spaces |
| CN105857306A (en) * | 2016-04-14 | 2016-08-17 | 中国科学院合肥物质科学研究院 | Vehicle autonomous parking path programming method used for multiple parking scenes |
Non-Patent Citations (2)
| Title |
|---|
| 吴冰等: "基于RBF神经网络的自动泊车路径规划", 《合肥工业大学学报(自然科学版)》 * |
| 张放等: "基于模糊逻辑的自动平行泊车控制方法", 《汽车工程》 * |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108407805A (en) * | 2018-03-30 | 2018-08-17 | 中南大学 | A kind of vehicle automatic parking method based on DQN |
| CN108860139A (en) * | 2018-04-11 | 2018-11-23 | 浙江零跑科技有限公司 | A kind of automatic parking method for planning track based on depth enhancing study |
| CN108860139B (en) * | 2018-04-11 | 2019-11-29 | 浙江零跑科技有限公司 | A kind of automatic parking method for planning track based on depth enhancing study |
| CN110654374B (en) * | 2018-06-29 | 2022-03-18 | 比亚迪股份有限公司 | Automatic parking simulation system and test method thereof |
| US11897454B2 (en) | 2018-06-29 | 2024-02-13 | Huawei Technologies Co., Ltd. | Method and apparatus for determining automatic parking strategy |
| WO2020001423A1 (en) * | 2018-06-29 | 2020-01-02 | 华为技术有限公司 | Method and device for determining automatic parking strategy |
| CN110654374A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Automatic parking simulation system and test method thereof |
| CN110654373A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Automatic parking method and device and vehicle |
| WO2020056875A1 (en) * | 2018-09-20 | 2020-03-26 | 初速度（苏州）科技有限公司 | Parking strategy based on deep reinforcement learning |
| CN111098850A (en) * | 2018-10-25 | 2020-05-05 | 北京初速度科技有限公司 | Automatic parking auxiliary system and automatic parking method |
| CN111434550A (en) * | 2019-01-15 | 2020-07-21 | 初速度（苏州）科技有限公司 | Simulation-based parking strategy generation method and system |
| CN111434550B (en) * | 2019-01-15 | 2021-12-31 | 魔门塔(苏州)科技有限公司 | Simulation-based parking strategy generation method and system |
| CN109823348A (en) * | 2019-02-19 | 2019-05-31 | 百度在线网络技术（北京）有限公司 | Unmanned vehicle reversing model control method, device, equipment and computer-readable medium |
| CN110525428B (en) * | 2019-08-29 | 2020-09-04 | 合肥工业大学 | Automatic parking method based on fuzzy depth reinforcement learning |
| CN110525428A (en) * | 2019-08-29 | 2019-12-03 | 合肥工业大学 | A kind of automatic parking method based on the study of fuzzy deeply |
| CN111047948A (en) * | 2019-11-27 | 2020-04-21 | 康忠文 | Auxiliary method and system for learning to learn to train |
| CN111098852A (en) * | 2019-12-02 | 2020-05-05 | 北京交通大学 | Parking path planning method based on reinforcement learning |
| CN113110500A (en) * | 2021-05-10 | 2021-07-13 | 的卢技术有限公司 | Transverse control method for low-speed automatic parking of automatic-driving automobile |
| CN113252366A (en) * | 2021-06-23 | 2021-08-13 | 奇瑞新能源汽车股份有限公司 | Automatic parking evaluation method and device for vehicle, vehicle and storage medium |
| CN113978547A (en) * | 2021-10-21 | 2022-01-28 | 江铃汽车股份有限公司 | Automatic driving steering control method and system |
| CN113978547B (en) * | 2021-10-21 | 2022-08-02 | 江铃汽车股份有限公司 | Automatic driving steering control method and system |
| CN115303256A (en) * | 2022-08-10 | 2022-11-08 | 浙江零跑科技股份有限公司 | Automatic parking path tracking control method and device and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107792062B (en) | 2019-11-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107792062B (en) | Automatic parking control system | |
| CN107697065B (en) | Automatic parking control method for general parking scene | |
| Pérez et al. | Cascade architecture for lateral control in autonomous vehicles | |
| Li et al. | A potential field approach-based trajectory control for autonomous electric vehicles with in-wheel motors | |
| CN106515722B (en) | A kind of method for planning track of vertically parking | |
| CN107380161B (en) | A kind of active steering control device for aiding in driver to realize desired ride track | |
| CN104742959B (en) | The track of vehicle keeps control device | |
| CN108216231B (en) | One kind can open up united deviation auxiliary control method based on steering and braking | |
| CN106681327B (en) | A kind of the intelligent driving transverse and longitudinal decoupling control method and system of big inertial electrodynamic car | |
| Chen et al. | Dynamic drifting control for general path tracking of autonomous vehicles | |
| Zhang et al. | A novel steering system for a space-saving 4WS4WD electric vehicle: design, modeling, and road tests | |
| CN103057436B (en) | Yawing moment control method of individual driven electromobile based on multi-agent | |
| CN106915385A (en) | A kind of line traffic control differential steering system and method for distributed-driving electric automobile | |
| CN107618504A (en) | It is a kind of applied to the crawl speed control method and device automatically parked | |
| CN106515716A (en) | Coordination control device and method for chassis integrated control system of wheel driving electric vehicle | |
| WO2019170346A1 (en) | Method for calculating a trajectory limitation, and method for regulating a drive dynamic | |
| Lin et al. | Coordinated control architecture for motion management in ADAS systems | |
| Mashadi et al. | Integrated AFS/DYC sliding mode controller for a hybrid electric vehicle | |
| CN105966263A (en) | Differential turning road sense control method of motor-wheel vehicle driven by hub motors | |
| Guo et al. | Regional path moving horizon tracking controller design for autonomous ground vehicles | |
| US20240075981A1 (en) | Model predictive brake-to-steer control for automated vehicles | |
| Chang et al. | Combined longitudinal and lateral control of vehicle platoons | |
| CN115268464A (en) | Automatic driving automobile track tracking control method and device based on hierarchical constraint | |
| Chen et al. | Ribbon model based path tracking method for autonomous ground vehicles | |
| Kim et al. | Experimental verification of the power slide driving technique for control strategy of autonomous race cars |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20191105 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |