CN114475599A - Queue control method for curved road scene - Google Patents

Queue control method for curved road scene Download PDF

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CN114475599A
CN114475599A CN202210205186.9A CN202210205186A CN114475599A CN 114475599 A CN114475599 A CN 114475599A CN 202210205186 A CN202210205186 A CN 202210205186A CN 114475599 A CN114475599 A CN 114475599A
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vehicle
control
vehicles
following
distance
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胡广地
周红梅
胡坚耀
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Sichuan Jiaya Automobile Technology Co ltd
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Sichuan Jiaya Automobile Technology Co ltd
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    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • 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
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • 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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • G—PHYSICS
    • G08—SIGNALLING
    • G08C—TRANSMISSION SYSTEMS FOR MEASURED VALUES, CONTROL OR SIMILAR SIGNALS
    • G08C17/00—Arrangements for transmitting signals characterised by the use of a wireless electrical link
    • G08C17/02—Arrangements for transmitting signals characterised by the use of a wireless electrical link using a radio link
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/65—Data transmitted between vehicles
    • 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
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
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  • Human Computer Interaction (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention discloses a queue control method aiming at a curved road scene, which comprises the following steps of S1 vehicles longitudinally queuing and numbering, S2 all vehicles send own vehicle information to other vehicles, following vehicles adjust self states according to the received information, S3 establishes vehicle dynamics models for the vehicles in a queue, S4 each following vehicle takes a reference road track as a control target and the minimum safe distance with the front vehicle as a constraint condition, designs a cost function and solves the cost function to obtain an optimal control sequence in a prediction time domain, S5 acts a first element of the control sequence on a vehicle system to control, and S6 repeats S4 and S5 until the queue driving is finished. The method can effectively supplement the vehicle queue control method which is lacked at present and relates to the curved road scene, and realize better vehicle queue tracking effect.

Description

Queue control method for curved road scene
Technical Field
The invention relates to the field of intelligent traffic systems and intelligent networked automobiles, in particular to a queue control method for a curved road scene.
Background
With the increasing number of automobiles and the increasing traffic jam problem, the traditional traffic management model and the vehicle safety technology are more difficult to meet the challenges. And the intelligent transportation system provides an effective way for solving the problems. The intelligent traffic system is an intelligent traffic network management system which integrates multiple subject advanced technologies such as information, sensing, communication, control and the like, and has the advantages of high efficiency, real time, accuracy and the like. The intelligent networked automobile is an important component of an intelligent traffic system, and has important research significance for realizing intelligent traffic.
Vehicle queue control is an important research direction in the field of intelligent networked automobiles. According to the method, all vehicles located in the same lane automatically adjust the transverse and longitudinal movement states of the vehicle according to the state information of the vehicles adjacent to the vehicles, such as the inter-vehicle distance, the vehicle speed and the like, so that the speeds of all vehicles in a vehicle queue are consistent and the expected inter-vehicle distance is realized. Because the running states of all vehicles are similar, the vehicles running in the vehicle queue have great potential in the aspects of relieving traffic jam, improving traffic capacity, enhancing driving safety and the like, and the method is a hot spot in the research field of intelligent networked automobiles.
At present, there are many patents for researching vehicle queue control methods, such as a patent owned by the university of Hunan, "a vehicle queue control method and system considering economy," a patent owned by the university of northwest industry, "a vehicle queue optimal cooperative control method," a patent owned by the university of Qinhuang island of northeast university, "a finite time fleet control method based on a preset performance function," and the like, but these patents generally research the vehicle queue control method from the perspective of optimal economy or optimal control method, and less research the road scene realized by vehicle queue control. The actual road has a straight road and a curved road, and when vehicles run in a queue, different consideration must be given to different road scenes.
Disclosure of Invention
Aiming at the defects of the existing vehicle queue control technology, the invention aims to provide a queue control method under a curved road scene, and when vehicle queue control is carried out, the vehicle speed is controlled in the longitudinal direction to keep a relatively safe distance with other vehicles, and the front wheel turning angle is also controlled in the transverse direction to accurately track the road center. The method can ensure that the vehicle queue can realize good tracking effect in a curved road scene.
In order to achieve the purpose, the invention provides the following technical scheme:
a queue control method for a curved road scene comprises the following steps:
firstly, vehicles are longitudinally queued to run, information exchange is carried out between each vehicle and an adjacent vehicle through a wireless communication technology, a first vehicle is marked as a first vehicle, also called a pilot vehicle and numbered as 0, and the rest vehicles are called following vehicles and numbered from 1 to N-1, wherein N is the total number of the vehicles in a queue;
secondly, all vehicles in the queue are intelligent vehicles and have automatic driving related functions such as environment sensing, positioning planning and motion control capacity, the first vehicle runs on the road planned by automatic driving, state information of the first vehicle, such as position, speed, acceleration and the like, is sent to other vehicles, and the following vehicles adjust the states of the following vehicles according to the received information;
step three, establishing a vehicle dynamic model considering dynamic characteristics for each vehicle in the queue, and laying a foundation for realizing transverse tracking tracks and longitudinally adjusting the vehicle speed of the vehicle;
step four, designing and solving a cost function by each following vehicle by taking the reference road track as a control target and the distance between each following vehicle and the preceding vehicle as a constraint condition based on a model prediction control algorithm to obtain an optimal control sequence in a prediction time domain;
step five, each following vehicle acts the first element of the control sequence on the own vehicle to control, actual state information of the own vehicle at the moment is obtained, and the information is sent to other vehicles for use by the other vehicles;
step six, repeating the step four and the step five until finishing the queue driving;
the technical scheme of the invention has the following characteristics:
(1) in the third step, the established model is a three-degree-of-freedom transverse and longitudinal coupling dynamic model and is established by the following parameters of the vehicleObtaining the following components by a die: vehicle mass, gravitational acceleration, rotational inertia, front wheel yaw stiffness, rear wheel yaw stiffness, distance from center of mass to front wheel, distance from center of mass to rear wheel, rolling resistance coefficient, air longitudinal resistance coefficient, air vertical lift force coefficient czLongitudinal speed v of the vehiclexVehicle lateral velocity vyYaw rate of vehicle
Figure BDA0003528983530000031
Vehicle longitudinal position X, vehicle lateral position Y, vehicle yaw angle psi, tire longitudinal force FxAnd the front wheel turns δ. The three-degree-of-freedom transverse and longitudinal coupling dynamic model obtained by modeling is as follows:
Figure BDA0003528983530000032
Figure BDA0003528983530000033
Figure BDA0003528983530000034
Figure BDA0003528983530000035
Figure BDA0003528983530000036
writing the model into a state space equation form:
Figure BDA0003528983530000041
y＝g(x)
the selection state quantity is:
Figure BDA0003528983530000042
the selection control quantity is as follows: u ═ Fx,δ]T；
The selection output is:
Figure BDA0003528983530000043
vxis the longitudinal speed, v, of the vehicleyIs the lateral speed of the vehicle,
Figure BDA0003528983530000044
is the vehicle yaw rate, X is the vehicle longitudinal position, Y is the vehicle lateral position, psi is the vehicle yaw angle, FxIs the tire longitudinal force, δ is the front wheel corner;
(2) the control targets in the fourth step are selected as follows:
Figure BDA0003528983530000045
Figure BDA0003528983530000046
Figure BDA0003528983530000047
Figure BDA0003528983530000048
Figure BDA0003528983530000049
the longitudinal position difference, the transverse position difference, the yaw angular speed difference and the reference transverse and longitudinal speed difference of the vehicle and the reference road are close to 0 through control;
Xrreference longitudinal coordinate, Y, for reference road centerrFor reference purposesThe reference lateral coordinate of the road center,
Figure BDA00035289835300000410
for reference yaw rate, vxrTo the desired vehicle speed, vyrIs a reference lateral vehicle speed;
(3) the cost function in step four is set as follows:
Figure BDA0003528983530000051
wherein eta (k + i | k) is the output quantity of the prediction equation at the moment of k + i, etaref(k + i) is the desired reference at time k + i, Δ u (k + i | k) is the control increment at time k + i, ρ is the weight coefficient, ε is the relaxation factor, QQ,RRAs a weight matrix, NpTo predict the time domain, NcIs a control time domain;
first item
Figure BDA0003528983530000052
Can be disassembled into the following five items:
Figure BDA0003528983530000053
respectively expressed in a prediction time domain NpThe speed error between the internal following vehicle and the reference vehicle speed, the transverse speed error, the longitudinal position error, the transverse position error and the yaw angle error of the reference road center reflect the tracking capability of the system to the reference quantity;
second item
Figure BDA0003528983530000061
Is represented in the control time domain NcThe size of the internal control increment reflects the requirement of the system for stable change of the control increment. Setting corresponding weight for each control target, and adjusting control weight QQ,RRThe value of (A) can adjust the control requirements of various performances;
(4) and the constraint condition of the first vehicle in the fourth step considers the constraint of the road geometry and the constraint of the executing mechanism of the vehicle. Road geometry constraints are reflected in the influence of road curvature and road friction coefficient on vehicle speed, i.e.:
Figure BDA0003528983530000062
wherein: g is the gravitational acceleration, μ is the road friction coefficient, and ρ is the road curvature.
The vehicle-body actuator constraints are embodied in the maximum acceleration and deceleration limits when the vehicle accelerates and decelerates, and the turning angle limits when the vehicle turns, that is, constraints on the control amount and the control increment.
The following vehicle considers the minimum safe distance constraint with the front vehicle, so that the difference between the actual distance of the two vehicles and the expected distance approaches to zero:
τ≥dfl-ddes≥0
dflto follow the distance between the car and the front car, ddesTau is a very small constant for the expected distance between the following vehicle and the front vehicle;
in particular, since the control is performed for a curved road scene, it is not reasonable to consider only the distance calculation in the longitudinal direction, taking the absolute distance value of two vehicles as the actual distance of the two vehicles:
Figure BDA0003528983530000071
(X, Y) is a position point of the following vehicle, (X)l，Yl) Is the position point of the front vehicle (suppose (X)l，Yl) The value is known);
the expected distance between the two vehicles is calculated by a safety distance strategy based on emergency braking:
Figure BDA0003528983530000072
the performance difference of different vehicles is considered in the distance strategy, and the braking capability of the vehicle is considered, so that the safety of the vehicle is ensured, and a more reasonable distance between the two vehicles is obtained;
vxto follow the current speed of the vehicle, axTo follow the current acceleration of the vehicle, α is the vehicle speed control coefficient, β is the acceleration control coefficient, amaxFor following the maximum braking deceleration of the vehicle, vlTo guide the current speed of the vehicle, amax，lMaximum braking deceleration for the lead vehicle;
(5) the constraint conditions of the control quantity and the control increment in the model predictive control algorithm used in the fourth step are as follows:
umin≤u(k+i|k)≤umax，i＝0，1，…，Nc-1
Δumin≤Δu(k+i|k)≤Δumax，i＝0，1，…，Nc-1
(6) the vehicle dynamics model and the constraints used in the fourth step need to be linearized and discretized so as to be convenient for control by using a model predictive control method.
Using Taylor's formula, will
Figure BDA0003528983530000075
At the operating point [ x ]t，ut-1]And (4) performing linearization processing, and neglecting high-order terms, then:
Figure BDA0003528983530000073
note the book
Figure BDA0003528983530000074
Then linearizing the vehicle dynamics model to obtain a state space equation for model predictive control, where the equation is as follows:
Figure BDA0003528983530000081
Figure BDA0003528983530000082
wherein,
Figure BDA0003528983530000083
Figure BDA0003528983530000084
discretizing by adopting forward Euler, namely approximating the derivative of x to time by the following formula;
Figure BDA0003528983530000085
wherein: t is sampling time, then:
x(k+1)＝(I+TA)x(k)+TBu(k)
recording: a. thek＝I+TA,Bk＝TB,CkC, wherein: i is the identity matrix, and the above state space equation can be:
Figure BDA0003528983530000086
Figure BDA0003528983530000087
according to the model prediction control principle, a prediction equation is obtained by derivation:
Y＝Ψξ(k)+ΘΔU
wherein:
Figure BDA0003528983530000091
Figure BDA0003528983530000092
Figure BDA0003528983530000093
the linearization and discretization of the constraint by the same method comprises the following steps:
Figure BDA0003528983530000094
thus dfl-ddesAfter linearization can be expressed as:
Figure BDA0003528983530000101
in the formula: dflIs about (X, Y, v)x,ax) Of (a) in which (X)r,Yr,vr,ar) Information (X) as a reference pointl,Yl,vl,al) The information of the preceding vehicles is known quantity.
To facilitate the uniformity of the state quantities, the state quantities are selected as
Figure BDA0003528983530000102
Form, then can remember dflr-ddesr＝a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e。
Wherein:
Figure BDA0003528983530000103
Figure BDA0003528983530000104
Figure BDA0003528983530000105
the following constraints are to be satisfied due to the difference in distance between the front vehicle and the rear vehicle:
0≤dfl-ddes≤τ
therefore, the method comprises the following steps:
0≤a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e≤τ
transforming the above inequalities into a matrix multiplication form, and writing together the constraints of the road geometry on the vehicle speed, then there are:
Figure BDA0003528983530000111
here:
Figure BDA0003528983530000112
Figure BDA0003528983530000113
a method for output quantity constraint processing of a reference model predictive control algorithm, wherein the constraint can be:
Figure BDA0003528983530000114
wherein:
Figure BDA0003528983530000115
Figure BDA0003528983530000116
Figure BDA0003528983530000117
the constraints on the control quantities and control increments may be converted to:
Umin≤DkΔU+Ut≤Umax
ΔUmin≤ΔU≤ΔUmax
namely:
Figure BDA0003528983530000121
wherein:
Figure BDA0003528983530000122
I2Ncis a column vector with the number of rows 2Nc, U (k-1) is the actual control quantity at the last moment, Umax,UminRespectively, a maximum value and a minimum value set of control quantity in a control time domain, delta Umax,ΔUminThe control time domain control increment is a minimum value set and a maximum value set of the control increment in the control time domain.
Figure BDA0003528983530000123
The above-mentioned model predictive control problem with constraints can be finally converted into the following quadratic programming problem solution:
Figure BDA0003528983530000124
s.t.MΔU≤γ
wherein, H is 2 (theta)TQQΘ+RR),gT＝2ETQQΘ,Ω＝ETQQE+ρε2And omega is a constant, the number of the first and second coils is,
Figure BDA0003528983530000125
(7) step five, completing the operation in the pair (6) in each control periodSolving the optimization problem to obtain a control time domain NcThe optimal control increment sequence in (1) is as follows:
ΔU*＝[Δu*(k)，Δu*(k+1)，...，Δu*(k+Nc-1)]T
applying the first of the optimal control increment sequence as the actual control increment to the system, namely:
u(k)＝u(k-1)+Δu*(k)
and repeating the process in the next control period, and thus, the vehicle queue control can be realized.
The invention has the beneficial effects that:
the queue control method under the curved road scene can effectively supplement the vehicle queue control method under the curved road scene which is lacked at present, and realize better vehicle queue tracking effect.
Drawings
Fig. 1 is a flowchart of a queue control method for a curved road scene according to the present invention.
Detailed Description
The following describes the technical solution of the present invention in detail with reference to the accompanying drawings.
Example 1, reference to fig. 1:
1. firstly, vehicles are longitudinally queued to run, information exchange is carried out between each vehicle and an adjacent vehicle through a wireless communication technology, a first vehicle is marked as a first vehicle, also called a pilot vehicle and numbered as 0, and the rest vehicles are called following vehicles and numbered from 1 to N-1, wherein N is the total number of the vehicles in a queue;
2. step two, all vehicles in the queue are intelligent vehicles and have related functions of automatic driving, such as environment sensing, positioning planning, motion control and the like, the first vehicle runs according to the road planned by the automatic driving, state information of the first vehicle, such as position, speed, acceleration and the like, is sent to other vehicles, the following vehicles adjust the state of the following vehicles according to the received information, and the information of the following vehicles is also sent to other vehicles;
3. step three, pairEstablishing a three-degree-of-freedom transverse and longitudinal coupling dynamic model for each vehicle in the queue, wherein the model comprises the following parameters: mass m of vehicle, acceleration of gravity g, moment of inertia IzFront wheel cornering stiffness CfRear wheel cornering stiffness CrDistance l from center of mass to front wheelfDistance l from center of mass to rear wheelrCoefficient of rolling resistance frCoefficient of air longitudinal resistance cxCoefficient of vertical lift of air czLongitudinal speed v of the vehiclexVehicle lateral velocity vyYaw rate of vehicle
Figure BDA0003528983530000141
Vehicle longitudinal position X, vehicle lateral position Y, vehicle yaw angle psi, tire longitudinal force FxAnd the front wheel turns δ. The three-degree-of-freedom transverse and longitudinal coupling dynamic model obtained by modeling is as follows:
Figure BDA0003528983530000142
Figure BDA0003528983530000143
Figure BDA0003528983530000144
Figure BDA0003528983530000145
Figure BDA0003528983530000146
the state space equation form of the model is:
Figure BDA0003528983530000147
y＝g(x)
the selection state quantity is:
Figure BDA0003528983530000151
the selection control quantity is as follows: u ═ Fx，δ]T；
The selection output is:
Figure BDA0003528983530000152
vxis the longitudinal speed, v, of the vehicleyIs the lateral speed of the vehicle,
Figure BDA0003528983530000153
is the vehicle yaw rate, X is the vehicle longitudinal position, Y is the vehicle lateral position, psi is the vehicle yaw angle, FxIs the tire longitudinal force, δ is the front wheel corner;
calculating to obtain a control quantity by utilizing a model predictive control algorithm, and controlling the acceleration, the deceleration and the turning of the vehicle by controlling the longitudinal force of the tire and the front wheel steering angle;
4. step four, each following vehicle takes the longitudinal position difference, the transverse position difference, the yaw angular velocity difference and the reference transverse and longitudinal velocity difference with the center of the reference road as targets, namely, the following vehicles are obtained:
Figure BDA0003528983530000154
Figure BDA0003528983530000155
Figure BDA0003528983530000156
Figure BDA0003528983530000157
Figure BDA0003528983530000158
wherein, XrReference longitudinal coordinate, Y, for reference road centerrTo reference the reference lateral coordinates of the road center,
Figure BDA0003528983530000159
for reference yaw rate, vxrTo the desired vehicle speed, vyrIs a reference lateral vehicle speed;
5. with the above control objectives clear, the cost function in the model predictive control algorithm can be set as follows:
Figure BDA0003528983530000161
wherein eta (k + i | k) is the output quantity of the prediction equation at the moment of k + i, etaref(k + i) is the desired reference at time k + i, Δ u (k + i | k) is the control increment at time k + i, ρ is the weight coefficient, ε is the relaxation factor, QQ，RRAs a weight matrix, NpTo predict the time domain, NcIs a control time domain;
first item
Figure BDA0003528983530000162
Can be disassembled into the following five items:
Figure BDA0003528983530000163
respectively expressed in a prediction time domain NpThe speed error between the internal following vehicle and the reference vehicle speed, the transverse speed error, the longitudinal position error, the transverse position error and the yaw angle error of the reference road center reflect the tracking capability of the system to the reference quantity;
second item
Figure BDA0003528983530000171
Is represented in the control time domain NcThe size of the internal control increment reflects the requirement of the system for stable change of the control increment. Setting corresponding weight for each control target, and adjusting control weight QQ，RRThe value of (A) can adjust the control requirements of various performances;
6. the algorithm constraints are in addition to considering the control quantity and control increment constraints:
umin≤u(k+i|k)≤umax，i＝0，1，…，Nc-1
Δumin≤Δu(k+i|k)≤Δumax，i＝0，1，…，Nc-1
the first vehicle constraints also take into account road geometry constraints and vehicle actuator constraints. Road geometry constraints are reflected in the influence of road curvature and road friction coefficient on vehicle speed, i.e.:
Figure BDA0003528983530000172
wherein: g is the gravitational acceleration, μ is the road friction coefficient, and ρ is the road curvature.
The vehicle-body actuator constraints are embodied in the maximum acceleration and deceleration limits when the vehicle accelerates and decelerates, and the turning angle limits when the vehicle turns, that is, constraints on the control amount and the control increment.
The constraint condition of the following vehicle considers the minimum safe distance constraint with the front vehicle on the basis of the constraint condition of the front vehicle, so that the difference between the actual distance of the two vehicles and the expected distance approaches to zero:
τ≥dfl-ddes≥0
dflto follow the distance between the car and the front car, ddesTau is a very small constant for the expected distance between the following vehicle and the front vehicle;
in particular, since the control is performed for a curved road scene, it is not reasonable to consider only the distance calculation in the longitudinal direction, taking the absolute distance value of two vehicles as the actual distance of the two vehicles:
Figure BDA0003528983530000181
(X, Y) is a position point of the following vehicle, (X)l，Yl) Is the position point of the front vehicle (suppose (X)l，Yl) The value is known);
the expected distance between the two vehicles is calculated by a safety distance strategy based on emergency braking:
Figure BDA0003528983530000182
the performance difference of different vehicles is considered in the distance strategy, and the braking capability of the vehicle is considered, so that the safety of the vehicle is ensured, and a more reasonable distance between the two vehicles is obtained;
wherein v isxTo follow the current speed of the vehicle, axTo follow the current acceleration of the vehicle, α is the vehicle speed control coefficient, β is the acceleration control coefficient, amaxTo follow the maximum braking deceleration of the vehicle, vlTo guide the current speed of the vehicle, amax，lMaximum braking deceleration for the lead vehicle;
7. the established vehicle dynamics model and the constraints need to be subjected to linearization and discretization processing so as to be convenient for control by using a model predictive control method.
Using Taylor's formula, will
Figure BDA0003528983530000183
At the operating point [ x ]t，ut-1]And (4) performing linearization processing, and neglecting high-order terms, then:
Figure BDA0003528983530000184
note the book
Figure BDA0003528983530000185
Then the vehicle will be turnedAfter the vehicle dynamics model is linearized, a state space equation for model predictive control can be obtained, and the equation is as follows:
Figure BDA0003528983530000191
Figure BDA0003528983530000192
wherein,
Figure BDA0003528983530000193
Figure BDA0003528983530000194
forward euler is used for discretization, i.e. the derivative of x with respect to time is approximated by the following equation.
Figure BDA0003528983530000195
Wherein: t is sampling time, then:
x(k+1)＝(I+TA)x(k)+TBu(k)
recording: a. thek＝I+TA,Bk＝TB,CkC, wherein: i is the identity matrix, and the above state space equation can be:
Figure BDA0003528983530000196
Figure BDA0003528983530000197
according to the model prediction control principle, a prediction equation is obtained by derivation:
Y＝Ψξ(k)+ΘΔU
wherein:
Figure BDA0003528983530000201
Figure BDA0003528983530000202
Figure BDA0003528983530000203
the linearization and discretization of the constraint by the same method comprises the following steps:
Figure BDA0003528983530000204
thus dfl-ddesAfter linearization can be expressed as:
Figure BDA0003528983530000211
in the formula: dflIs about (X, Y, v)x，ax) Of (a) in which (X)r，Yr，vr，ar) Is information of a reference point, (X)l，Yl，vl，al) The information of the preceding vehicles is known quantity.
To facilitate the uniformity of the state quantities, the state quantities are selected as
Figure BDA0003528983530000212
Form, then can remember
dflr-ddesr＝a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e。
Wherein:
Figure BDA0003528983530000213
Figure BDA0003528983530000214
Figure BDA0003528983530000215
the following constraints are to be satisfied due to the difference in distance between the front vehicle and the rear vehicle:
0≤dfl-ddes≤τ
therefore, the method comprises the following steps:
0≤a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e≤τ
transforming the above inequalities into a matrix multiplication form, and writing together the constraints of the road geometry on the vehicle speed, then there are:
Figure BDA0003528983530000221
here:
Figure BDA0003528983530000222
Figure BDA0003528983530000223
a method for output quantity constraint processing of a reference model predictive control algorithm, wherein the constraint can be:
Figure BDA0003528983530000224
wherein:
Figure BDA0003528983530000225
Figure BDA0003528983530000226
Figure BDA0003528983530000227
the constraints on the control quantities and control increments may be converted to:
Umin≤DkΔU+Ut≤Umax
ΔUmin≤ΔU≤ΔUmax
namely:
Figure BDA0003528983530000231
wherein:
Figure BDA0003528983530000232
I2Ncis a column vector with the number of rows 2Nc, U (k-1) is the actual control quantity at the last moment, Umax，UminRespectively, a maximum value and a minimum value set of control quantity in a control time domain, delta Umax，ΔUminThe control time domain control increment is a minimum value set and a maximum value set of the control increment in the control time domain.
Figure BDA0003528983530000233
The above model predictive control problem with constraints can be finally converted into the following quadratic programming problem solution:
Figure BDA0003528983530000234
s.t.MΔU≤γ
wherein, H is 2 (theta)TQQΘ+RR)，gT＝2ETQQΘ，Ω＝ETQQE+ρε2And omega is a constant, the number of the first and second coils is,
Figure BDA0003528983530000235
8. in the fifth step, the optimization problem in the step 7 is solved in each control period to obtain a control time domain NcThe optimal control increment sequence in (1) is as follows:
ΔU*＝[Δu*(k)，Δu*(k+1)，...，Δu*(k+Nc-1)]T
applying the first of the optimal control increment sequence as the actual control increment to the vehicle system, namely:
u(k)＝u(k-1)+Δu*(k)
then the actual state of the self vehicle is sent to other vehicles, and the next cycle is started;
9. and repeating the steps four and five by each following vehicle until the queue control is finished.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (8)

1. A queue control method for a curved road scene is characterized by comprising the following steps:
s1: the method comprises the following steps that vehicles are longitudinally queued to run, information exchange is carried out between each vehicle and adjacent vehicles through a wireless communication technology, the first vehicle is marked as a first vehicle, namely a pilot vehicle and is numbered as 0, the rest vehicles are marked as following vehicles and are numbered from 1 to N-1, wherein N is the total number of vehicles in a queue;
s2: the first vehicle drives according to the road planned by automatic driving, the state information of the first vehicle is sent to other vehicles, and the following vehicle adjusts the state of the following vehicle according to the received state information, wherein the state information comprises position, speed and acceleration;
s3: for each vehicle in the queue, a vehicle dynamic model considering dynamic characteristics is established, and a foundation is laid for realizing transverse tracking tracks and longitudinally adjusting the vehicle speed of the vehicle;
s4: each following vehicle takes the reference road track as a control target, takes the distance between the following vehicle and the preceding vehicle as a constraint condition, designs a cost function and solves the cost function based on a model predictive control algorithm, and obtains an optimal control sequence in a prediction time domain;
s5: each following vehicle acts the first element of the control sequence on the own vehicle to control, actual state information of the own vehicle at the moment is obtained, and the information is sent to other vehicles for use by the other vehicles;
s6: steps S4 and S5 are repeated until the queued travel is ended.
2. The method as claimed in claim 1, wherein the step of establishing a vehicle dynamics model considering dynamics characteristics for each vehicle in the fleet specifically comprises:
establishing a three-degree-of-freedom transverse and longitudinal coupling dynamic model, and modeling by the following parameters of the vehicle: mass m of vehicle, acceleration of gravity g, moment of inertia IzFront wheel cornering stiffness CfRear wheel cornering stiffness CrDistance l from center of mass to front wheelfDistance l from center of mass to rear wheelrCoefficient of rolling resistance frCoefficient of air longitudinal resistance cxCoefficient of vertical lift of air czLongitudinal speed v of the vehiclexVehicle lateral velocity vyYaw rate of vehicle
Figure FDA0003528983520000021
Vehicle longitudinal position X, vehicle lateral position Y, vehicle yaw angle psi, tire longitudinal force FxFront wheel steering angle δ; three-degree-of-freedom transverse and longitudinal coupling dynamic modelThe type is as follows:
Figure FDA0003528983520000022
Figure FDA0003528983520000023
Figure FDA0003528983520000024
Figure FDA0003528983520000025
Figure FDA0003528983520000026
writing the three-degree-of-freedom transverse and longitudinal coupling dynamic model into a state space equation form:
Figure FDA0003528983520000027
y＝g(x)
the selection state quantity is:
Figure FDA0003528983520000028
the selection control quantity is as follows: u ═ Fx，δ]T；
The selection output is:
Figure FDA0003528983520000029
vxis the longitudinal speed, v, of the vehicleyIs the lateral speed of the vehicle,
Figure FDA00035289835200000210
is the vehicle yaw rate, X is the vehicle longitudinal position, Y is the vehicle lateral position, psi is the vehicle yaw angle, FxIs the tire longitudinal force, δ is the front wheel angle.
3. The queue control method for the curved road scene as claimed in claim 1, wherein the control targets in S4 are selected as:
Figure FDA0003528983520000031
Figure FDA0003528983520000032
Figure FDA0003528983520000033
Figure FDA0003528983520000034
Figure FDA0003528983520000035
the longitudinal position difference, the transverse position difference, the yaw angular speed difference and the reference transverse and longitudinal speed difference of the vehicle and the reference road are close to 0 through control;
Xrreference longitudinal coordinate, Y, for reference road centerrTo reference the reference lateral coordinates of the road center,
Figure FDA0003528983520000036
for reference yaw rate, vxrIn order to achieve the desired speed of the vehicle,vyris a reference lateral vehicle speed.
4. The queue control method for the curved road scene as claimed in claim 1, wherein: the cost function is set as follows:
Figure FDA0003528983520000037
wherein eta (k + i | k) is the output quantity of the prediction equation at the moment of k + i, etaref(k + i) is the desired reference at time k + i, Δ u (k + i | k) is the control increment at time k + i, ρ is the weight coefficient, ε is the relaxation factor, QQ，RRAs a weight matrix, NpTo predict the time domain, NcIs a control time domain;
first item
Figure FDA0003528983520000041
Can be disassembled into the following five items:
Figure FDA0003528983520000042
respectively expressed in a prediction time domain NpThe speed error between the internal following vehicle and the reference vehicle speed, the transverse speed error, the longitudinal position error, the transverse position error and the yaw angle error of the reference road center reflect the tracking capability of the system to the reference quantity;
second item
Figure FDA0003528983520000043
Is represented in the control time domain NcThe size of the internal control increment reflects the requirement of the system on the stable change of the control increment; setting corresponding weight for each control target, and adjusting control weight QQ，RRThe values of (c) can adjust the control requirements for each performance.
5. The queue control method for the curved road scene as claimed in claim 2, wherein the constraint condition of the first vehicle takes into account the road geometry constraint and the vehicle actuator constraint; road geometry constraints are reflected in the influence of road curvature and road friction coefficient on vehicle speed, i.e.:
Figure FDA0003528983520000051
wherein: g is the gravity acceleration, mu is the road friction coefficient, and rho is the road curvature;
the constraint of the executing mechanism of the vehicle is embodied in the maximum acceleration and deceleration limit when the vehicle accelerates and decelerates and the corner limit when the vehicle turns, namely the constraint of the control quantity and the control increment;
the constraint condition of the following vehicle is based on the constraint condition of the front vehicle, and the minimum safe distance constraint with the front vehicle is considered, so that the difference between the actual distance of the two vehicles and the expected distance approaches to zero:
τ≥dfl-ddes≥0
dflto follow the distance between the car and the front car, ddesTau is a very small constant for the expected distance between the following vehicle and the front vehicle;
in particular, since the control is performed for a curved road scene, it is not reasonable to consider only the distance calculation in the longitudinal direction, taking the absolute distance value of two vehicles as the actual distance of the two vehicles:
Figure FDA0003528983520000052
(X, Y) is a position point of the following vehicle, (X)l，Yl) Is the position point of the front vehicle (suppose (X)l，Yl) The value is known);
the expected distance between the two vehicles is calculated by a safety distance strategy based on emergency braking:
Figure FDA0003528983520000053
the performance difference of different vehicles is considered in the distance strategy, and the braking capability of the vehicle is considered, so that the safety of the vehicle is ensured, and a more reasonable distance between the two vehicles is obtained;
wherein: v. ofxTo follow the current speed of the vehicle, axTo follow the current acceleration of the vehicle, α is the vehicle speed control coefficient, β is the acceleration control coefficient, amaxFor following the maximum braking deceleration of the vehicle, vlTo guide the current speed of the vehicle, amax，lIs the maximum braking deceleration of the lead vehicle.
6. The queue control method for the curved road scene as claimed in claim 5, wherein the constraints on the control quantity and the control increment are as follows:
umin≤u(k+i|k)≤umax，i＝0，1，…，Nc-1
Δumin≤Δu(k+i|k)≤Δumax，i＝0，1，…，Nc-1。
7. the method as claimed in claim 5, wherein the three-degree-of-freedom transverse-longitudinal coupling dynamic model and the constraints on the control quantity and the control increment are linearized and discretized so as to facilitate control by a model predictive control method;
using Taylor's formula, will
Figure FDA0003528983520000061
At the operating point [ x ]t，ut-1]And (4) performing linearization processing, and neglecting high-order terms, then:
Figure FDA0003528983520000062
note the book
Figure FDA0003528983520000063
Then linearizing the vehicle dynamics model to obtain a state space equation for model predictive control, where the equation is as follows:
Figure FDA0003528983520000064
Figure FDA0003528983520000065
wherein,
Figure FDA0003528983520000071
Figure FDA0003528983520000072
discretizing by adopting forward Euler, namely approximating the derivative of x to time by the following formula;
Figure FDA0003528983520000073
wherein: t is sampling time, then there are:
x(k+1)＝(I+TA)x(k)+TBu(k)
recording: a. thek＝I+TA，Bk＝TB，CkC, wherein: i is the identity matrix, and the above state space equation can be:
Figure FDA0003528983520000074
Figure FDA0003528983520000075
according to the model prediction control principle, a prediction equation is obtained by derivation:
Y＝Ψξ(k)+ΘΔU
wherein:
Figure FDA0003528983520000081
Figure FDA0003528983520000082
Figure FDA0003528983520000083
the method for carrying out linearization and discretization on the control quantity and control increment constraints by adopting the same method comprises the following steps:
Figure FDA0003528983520000084
thus dfl-ddesAfter linearization can be expressed as:
Figure FDA0003528983520000091
in the formula: dflIs about (X, Y, v)x，ax) A linear function of (a), whereinr，Yr，vr，ar) Is information of a reference point, (X)l，Yl，vl，al) The information of the front vehicle is known quantity;
to facilitate the uniformity of the state quantities, the state quantities are selected as
Figure FDA0003528983520000092
Form, then can remember dflr-ddesr＝a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e；
Wherein:
Figure FDA0003528983520000093
Figure FDA0003528983520000094
Figure FDA0003528983520000095
the following constraints are to be satisfied due to the difference in distance between the front vehicle and the rear vehicle:
0≤dfl-ddes≤τ
therefore, the method comprises the following steps:
0≤a(X-Xr)+b(Y-Yr)+c(vx-vr)+d(ax-ar)+e≤τ
transforming the above inequalities into a matrix multiplication form, and writing together the constraints of the road geometry on the vehicle speed, then there are:
Figure FDA0003528983520000101
here:
Figure FDA0003528983520000102
Figure FDA0003528983520000103
a method for output quantity constraint processing of a reference model predictive control algorithm, wherein the constraint can be:
Figure FDA0003528983520000104
wherein:
Figure FDA0003528983520000105
Figure FDA0003528983520000106
Figure FDA0003528983520000107
the constraints on the control quantities and control increments may be converted to:
Umin≤DkΔU+Ut≤Umax
ΔUmin≤ΔU≤ΔUmax
namely:
Figure FDA0003528983520000111
wherein:
Figure FDA0003528983520000112
I2Ncis a column vector with the number of rows 2Nc, U (k-1) is the actual control quantity at the last moment, Umax，UminRespectively, a maximum value and a minimum value set of control quantity in a control time domain, delta Umax，ΔUminRespectively a minimum value and a maximum value set of control increment in the control time domain;
Figure FDA0003528983520000113
the above-mentioned model predictive control problem with constraints can be finally converted into the following quadratic programming problem solution:
Figure FDA0003528983520000114
s.t.MΔU≤γ
wherein, H is 2 (theta)TQQΘ+RR)，gT＝2ETQQΘ，Ω＝ETQQE+ρε2And omega is a constant, the number of the first and second coils is,
Figure FDA0003528983520000115
8. the method as claimed in claim 7, wherein the solving of the optimization problem is completed in each control period to obtain a control time domain NcThe optimal control increment sequence in (1) is as follows:
ΔU*＝[Δu*(k)，Δu*(k+1)，...，Δu*(k+Nc-1)]T
applying the first of the optimal control increment sequence as the actual control increment to the system, namely:
u(k)＝u(k-1)+Δu*(k)
and repeating the process in the next control period, and thus, the vehicle queue control can be realized.
CN202210205186.9A 2022-03-02 2022-03-02 Queue control method for curved road scene Pending CN114475599A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116564118A (en) * 2023-07-11 2023-08-08 蘑菇车联信息科技有限公司 Intersection passing control method, device and system of vehicles and electronic equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116564118A (en) * 2023-07-11 2023-08-08 蘑菇车联信息科技有限公司 Intersection passing control method, device and system of vehicles and electronic equipment
CN116564118B (en) * 2023-07-11 2023-10-03 蘑菇车联信息科技有限公司 Intersection passing control method, device and system of vehicles and electronic equipment

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