CN111137288B - Multi-vehicle cooperative lane changing method under internet connection condition - Google Patents

Multi-vehicle cooperative lane changing method under internet connection condition Download PDF

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CN111137288B
CN111137288B CN202010057765.4A CN202010057765A CN111137288B CN 111137288 B CN111137288 B CN 111137288B CN 202010057765 A CN202010057765 A CN 202010057765A CN 111137288 B CN111137288 B CN 111137288B
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CN111137288A (en
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倪捷
董非
韩静文
刘志强
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Jiangsu University
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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
    • B60W30/10—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
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • 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
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • 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
    • 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
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/12—Lateral speed
    • B60W2520/125—Lateral acceleration
    • 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

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Abstract

The invention provides a multi-vehicle cooperative lane change method under the condition of network connection, which comprises the following steps: establishing a driver expectation following model, and predicting the vehicle state after the vehicle changes lanes; establishing a lane change revenue function model, and judging whether the current state can cooperate with lane change operation according to constraint conditions; providing a two-stage collaborative lane changing frame, dividing a lane changing process into a longitudinal distance adjusting stage and a lane changing stage, establishing a collaborative lane changing multi-target optimization control function based on model prediction control, and solving through a rolling optimization time domain algorithm to obtain an optimal control quantity; and transmitting the optimal control quantity to the cooperative vehicles to control the multi-vehicle cooperative lane changing operation. The invention realizes the distributed control of the lane changing process by establishing the cooperative lane changing multi-objective optimization control function, and is used for improving the road traffic capacity, the vehicle lane changing safety performance and the lane changing efficiency.

Description

Multi-vehicle cooperative lane changing method under internet connection condition
Technical Field
The invention relates to the technical field of vehicle control methods/vehicle active safety, in particular to a multi-vehicle cooperative lane changing method under the condition of network connection.
Background
Lane change considerations are more vehicles, the decision making process is more complex, and difficult to describe than following behavior. According to the statistics of European Union data, the traffic accidents caused by lane change account for about 5%, and the traffic delay caused by lane change reaches 10%. Meanwhile, 75% of lane change accidents are caused by driver identification errors, namely, the self-vehicle state information and the surrounding environment are not sufficiently sensed. In recent years, with the application of sensor technology and vehicle-to-vehicle communication technology to automobiles, interconnection between vehicles and vehicles or road facilities is becoming a reality. Under the networking environment, the safety and the comfort of vehicle driving are improved by optimizing parameters such as the following distance between vehicles, the vehicle driving speed and the like and simultaneously giving a more optimized lane changing control strategy to the vehicles through acquiring the vehicle information around the driving road section.
However, most researches are mainly based on unmanned environment assumption, the structural design of the multi-vehicle cooperative driving system is carried out, and the construction of the driving control algorithm of the self vehicle and the research on the surrounding vehicle cooperative driving control algorithm are relatively less; in addition, due to the dimensions of collision avoidance constraints and non-linearities in vehicle kinematics, optimization control functions are often difficult to solve.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-vehicle cooperative lane changing method under the condition of network connection, which realizes the distributed control of the lane changing process by establishing a cooperative lane changing multi-target optimization control function and is used for improving the road traffic capacity, the vehicle lane changing safety performance and the lane changing efficiency.
The present invention achieves the above-described object by the following technical means.
A multi-vehicle cooperative lane change method under the condition of network connection comprises the following steps:
establishing a driver expectation following model, and predicting the vehicle state after the vehicle changes lanes; establishing a lane change revenue function model, and judging whether the current state can cooperate with lane change operation according to constraint conditions;
providing a two-stage collaborative lane changing frame, dividing a lane changing process into a longitudinal distance adjusting stage and a lane changing stage, establishing a collaborative lane changing multi-target optimization control function based on model prediction control, and solving through a rolling optimization time domain algorithm to obtain an optimal control quantity; and transmitting the optimal control quantity to the cooperative vehicles to control the multi-vehicle cooperative lane changing operation.
Further, establishing a driver expected car following model as follows:
Figure GDA0003018100770000011
Vm(ΔSn)＝e1+e2tanh[c1(ΔSn-dc)-c2]
wherein the content of the first and second substances,
Figure GDA0003018100770000012
representing the longitudinal acceleration of the vehicle on each lane after the vehicle changes lanes;
Figure GDA0003018100770000021
indicating vehicle after changing laneAcceleration of (2);
Figure GDA0003018100770000022
accelerating the vehicle on the original lane after the vehicle changes lane;
Figure GDA0003018100770000023
representing the vehicle acceleration on the target lane after the vehicle changes the lane;
Vm(ΔSn) To optimize the speed function;
Vnis the current speed of the vehicle;
ΔSnthe distance between the car heads of the two workshops;
dcis the minimum safe separation including the length of the vehicle body;
r is a reaction coefficient;
e1，e2is a constant parameter;
c1，c2are the corresponding coefficients.
Further, establishing a lane change revenue function model, specifically:
Figure GDA0003018100770000024
Ni＝{j∈Si：0≤||xSV-xj||≤l}，i＝O or T；
wherein: g (SV, O, T) represents the total benefit of SV switching from O to T under synergistic conditions; SV represents a self vehicle, O represents a current lane, and T represents a target lane;
Noa set representing subsequent vehicles on the current lane within the communication lane; n is a radical ofTA set representing subsequent vehicles in the target lane within the communication lane; l represents a left lane, and R represents a right lane;
the polite factor eta represents the influence of lane changing operation on subsequent vehicles in the target lane;
the polite factor mu represents the speed advantage of the subsequent vehicle on the current lane due to the lane change of the vehicle;
asvrepresents the acceleration of the vehicle in the current state,
Figure GDA0003018100770000025
representing the acceleration of the vehicle after the lane change; a istThe acceleration of the vehicle in the target lane is represented,
Figure GDA0003018100770000026
representing the vehicle acceleration on the target lane after the vehicle changes the lane; a is0The acceleration of the vehicle on the original lane is shown,
Figure GDA0003018100770000027
accelerating the vehicle on the original lane after the vehicle changes lane;
Figure GDA0003018100770000028
and
Figure GDA0003018100770000029
the method is obtained through a driver expected car following model;
xSVis a self-parking position; x is the number ofjIs the subsequent vehicle j position on lane i; l is the communication range; siRepresenting a set of vehicles on lane i; i represents a current lane or a target lane; | | | | is the euclidean norm.
Further, the constraint conditions of the lane change revenue function model are as follows:
Q＝arg maxT∈{L，R}G(SV，O，T)
Figure GDA00030181007700000210
wherein: q is a candidate target lane, namely selecting T with the maximum benefit G (SV, O, T) in the set as the target lane;
Δathis a switching threshold, i.e. the lane change behaviour is better than the lane keeping behaviour under current traffic conditions;
asafeindicating a safe acceleration.
Further, the longitudinal distance adjusting stage specifically includes: before lane changing starts, the longitudinal distance between a lane changing vehicle and a front vehicle and a rear vehicle is adjusted to ensure that the distance between the vehicles is sufficiently sparse, and an objective function in a longitudinal distance adjusting stage is established as follows:
Figure GDA0003018100770000031
the constraints are as follows:
Figure GDA0003018100770000032
wherein N represents a prediction time domain; θ ═ SV, LV, AFV, ALV }; omegaμIs a weight factor; (h + p +1| h) represents that the value at the time h + p +1 is predicted based on the information at the time h; a isminMinimum comfortable acceleration acceptable to the driver; a ismaxMaximum comfortable acceleration acceptable to the driver;
||a′SVl | is an acceleration change rate of the lane change vehicle, a'SV＝αSV(h+p+1|h)-αSV(h+p|h)；
||a′LVL is the acceleration change rate of the vehicle ahead of the current lane, a'LV＝aLV(h+p+1|h)-aLV(h+p|h)；
||a′ALVL | is the acceleration change rate of the vehicle ahead of the target lane, a'ALV＝aALV(h+p+1|h)-aALV(h+p|h)；
||a′AFVL | is the acceleration change rate of the vehicle behind the target lane, a'AFV＝aAFV(h+p+1|h)-aAFV(h+p|h)；
δ is the maximum comfortable longitudinal acceleration rate acceptable to the driver;
Dhrepresenting the actual distance between the two vehicles at the moment h;
Dsafethe distance between vehicles is safe.
Further, the lane change stage specifically includes:
in the SV lane changing process, the longitudinal acceleration is changed along with the expected control input quantity, and the transverse acceleration is expressed by adopting a sine function:
Figure GDA0003018100770000041
wherein tau is the transverse movement duration time of the whole lane changing process, and W is the width of the road;
the change of the transverse displacement can be obtained by integrating the lane-changing transverse acceleration twice:
Figure GDA0003018100770000042
assuming that when the lateral displacement of the lane change vehicle reaches a road width W, the objective function of the lane change phase is established as follows:
Figure GDA0003018100770000043
the constraints are as follows:
Figure GDA0003018100770000044
wherein N represents a prediction time domain; β ═ SV, AFV, ALV };
Figure GDA0003018100770000045
λβand
Figure GDA0003018100770000048
is a weight factor for each item; in the objective function, the target function is,
Figure GDA0003018100770000046
representing the acceleration optimization of the vehicle and surrounding vehicles and representing the comfort cost in the lane changing process;
Figure GDA0003018100770000047
representing the following vehicle distance error of the self vehicle and the surrounding vehicles, and representing the trackability cost of the self vehicle and the surrounding vehicles; Δ aβ(h + p +1| h) represents the change in acceleration from time h to h + p +1, Δ Sβ(h + p +1| h) represents a change in following distance from the time h to the time h + p + 1.
Further, the objective function of the longitudinal distance adjusting stage and the objective function of the lane changing stage respectively solve the optimization control problem step by step through a rolling optimization time domain algorithm, and the deviation of each moment is repeatedly optimized and calculated in a rolling limited time interval, so that the expected input of each vehicle in the longitudinal distance adjusting stage is obtained.
The invention has the beneficial effects that:
1. the multi-vehicle cooperative lane changing method under the internet condition constructs a driving control algorithm for the own vehicle and a cooperative driving control algorithm for surrounding vehicles.
2. At present, research is mainly focused on a confluence area and a crossroad, and complex traffic environment under actual conditions cannot be met.
3. Due to the dimensions of collision avoidance constraints and the non-linearity of vehicle kinematics, optimization control functions are often difficult to solve. The multi-vehicle cooperative lane changing method under the network connection condition adopts a rolling optimization time domain algorithm to gradually and dynamically solve an optimization control problem.
Drawings
Fig. 1 is a flow chart of a multi-vehicle cooperative lane change method under the internet connection condition.
Fig. 2 is a scene diagram of the multi-vehicle cooperative lane change according to the present invention.
FIG. 3 is a schematic view of the SV lane change process of the present invention.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, but the scope of the invention is not limited thereto.
As shown in fig. 1, the method for changing lanes by multiple vehicles in cooperation under the internet connection condition includes the following steps: establishing a driver expectation following model, and predicting the vehicle state after the vehicle changes lanes; establishing a lane change revenue function model, and judging whether the current state can cooperate with lane change operation according to constraint conditions; namely, whether the lane change behavior is better than the lane keeping behavior under the current situation is judged. Providing a two-stage collaborative lane changing frame, dividing a lane changing process into a longitudinal distance adjusting stage and a lane changing stage, establishing a collaborative lane changing multi-target optimization control function based on model prediction control, solving through a rolling optimization time domain algorithm, and obtaining expected control input quantity of each vehicle in real time to obtain optimal control quantity; and transmitting the optimal control quantity to the cooperative vehicles, controlling the multi-vehicle cooperative lane changing operation, and realizing the distributed control of the lane changing process.
Step one, establishing a driver expected car following model, and predicting the state of the car after changing lanes of the car, namely
Figure GDA0003018100770000051
Calculating an expected car following model of a driver;
establishing a driver expected car following model as follows:
Figure GDA0003018100770000052
Vm(ΔSn)＝e1+e2tanh[c1(ΔSn-dc)-c2]
wherein the content of the first and second substances,
Figure GDA0003018100770000053
representing the longitudinal acceleration of the vehicle on each lane after the vehicle changes lanes;
Figure GDA0003018100770000054
representing the acceleration of the vehicle after the lane change;
Figure GDA0003018100770000055
accelerating the vehicle on the original lane after the vehicle changes lane;
Figure GDA0003018100770000056
representing the vehicle acceleration on the target lane after the vehicle changes the lane;
Vm(ΔSn) To optimize the speed function;
Vnis the current speed of the vehicle;
ΔSnthe distance between the car heads of the two workshops;
dcis the minimum safe separation including the length of the vehicle body;
r is a reaction coefficient;
e1，e2is a constant parameter;
c1，c2are the corresponding coefficients.
As shown in fig. 2, the lane change scene is an expressway environment, and the lane change process is that SV vehicles (own vehicles) are changed from an original lane to a target lane, ALV vehicles and AFV vehicles respectively represent front and rear vehicles on the target lane, and LV vehicles represent front vehicles on the same lane. The SV vehicle changes the lane from the current lane to the position between the front vehicle and the rear vehicle of the target lane. Wherein, realize changing the information interaction of way in-process through the car networking environment. The acquired vehicle state parameters all use the own vehicle as a reference object.
Since the lane change behavior affects both the upstream and downstream vehicles of the original lane and the target lane, it is necessary to receive information from a plurality of nearby vehicles to make a lane change feasibility determination. In order to model the collaborative lane change centralized decision-making behavior, a lane change profit function is defined to judge the benefit degree of the lane change operation.
The lane change revenue function model specifically comprises the following steps:
Figure GDA0003018100770000061
Ni＝{j∈Si：0≤||xSV-xj||≤l}，i＝O or T；
wherein: g (SV, O, T) represents the total benefit of SV switching from O to T under synergistic conditions; SV represents a self vehicle, O represents a current lane, and T represents a target lane;
Noa set representing subsequent vehicles on the current lane within the communication lane; n is a radical ofTA set representing subsequent vehicles in the target lane within the communication lane; l represents a left lane, and R represents a right lane;
the polite factor eta represents the influence of lane changing operation on subsequent vehicles in the target lane;
the polite factor mu represents the speed advantage of the subsequent vehicle on the current lane due to the lane change of the vehicle;
asvrepresents the acceleration of the vehicle in the current state,
Figure GDA0003018100770000062
representing the acceleration of the vehicle after the lane change; a istThe acceleration of the vehicle in the target lane is represented,
Figure GDA0003018100770000063
representing the vehicle acceleration on the target lane after the vehicle changes the lane; a is0The acceleration of the vehicle on the original lane is shown,
Figure GDA0003018100770000064
accelerating the vehicle on the original lane after the vehicle changes lane;
Figure GDA0003018100770000065
and
Figure GDA0003018100770000066
the method is obtained through a driver expected car following model;
xSVis a self-parking position; x is the number ofjIs the subsequent vehicle j position on lane i; l is the communication range; siRepresenting a set of vehicles on lane i; i represents a current lane or a target lane; | | | | is the euclidean norm.
And if and only if the gain function meets the constraint condition, G (SV, O, T) is greater than a switching threshold value and the acceleration is less than the safe acceleration, the system decides that the cooperative lane change is feasible, otherwise, the cooperative lane change is not feasible. The constraint conditions of the lane change revenue function model are as follows:
Q＝arg maxT∈{L，R}G(SV，O，T)
Figure GDA0003018100770000067
wherein: q is a candidate target lane, namely selecting T with the maximum benefit G (SV, O, T) in the set as the target lane;
Δathis a switching threshold, i.e. the lane change behaviour is better than the lane keeping behaviour under current traffic conditions;
asafeindicating a safe acceleration.
And step two, providing a two-stage collaborative lane changing frame, dividing the lane changing process into a longitudinal distance adjusting stage and a lane changing stage, establishing a collaborative lane changing multi-target optimization control function, and gradually and dynamically solving an optimization control problem by adopting a rolling optimization time domain algorithm to realize distributed control of the lane changing process.
Pro 1: longitudinal distance adjusting stage
And in the longitudinal distance adjusting stage, before lane changing is started, the longitudinal distance between the lane changing vehicle and the front and rear vehicles is adjusted, so that the distance between the vehicles is sufficiently sparse. The acceleration errors of the vehicle, the front vehicle of the original lane and the front and rear vehicles of the target lane are optimized, and the longitudinal driving comfort of the vehicle is ensured.
Establishing an objective function in a longitudinal distance adjusting stage as follows:
Figure GDA0003018100770000071
the constraints are as follows:
Figure GDA0003018100770000072
wherein N represents a predictionA time domain; θ ═ SV, LV, AFV, ALV }; omegaμIs a weight factor; (h + p +1| h) represents that the value at the time h + p +1 is predicted based on the information at the time h; a isminMinimum comfortable acceleration acceptable to the driver; a ismaxMaximum comfortable acceleration acceptable to the driver;
||a′SVl | is an acceleration change rate of the lane change vehicle, a'SV＝αSV(h+p+1|h)-αSV(h+p|h)；
||a′LVL is the acceleration change rate of the vehicle ahead of the current lane, a'LV＝aLV(h+p+1|h)-aLV(h+p|h)；
||a′ALVL | is the acceleration change rate of the vehicle ahead of the target lane, a'ALV＝aALV(h+p+1|h)-aALV(h+p|h)；
||a′AFVL | is the acceleration change rate of the vehicle behind the target lane, a'AFV＝aAFV(h+p+1|h)-aAFV(h+p|h)；
δ is the maximum comfortable longitudinal acceleration rate acceptable to the driver;
Dhrepresenting the actual distance between the two vehicles at the moment h;
Dsafethe distance between vehicles is safe.
Because the objective function in the longitudinal distance adjusting stage is a multi-objective coordination optimization control problem, a rolling time domain optimization algorithm is adopted to solve the problem. That is, with the advance of the sampling time, the deviation of each time is repeatedly optimized and calculated in the limited rolling time interval, and the expected input of each vehicle in the control stage is obtained, so that the active cooperation is realized. In a system prediction time domain, a rolling optimization time domain algorithm is adopted to gradually and dynamically solve an optimization control problem, meanwhile, a constraint management method is adopted to soften hard constraints, and a Dantiig-wolfe active set method is selected to obtain an optimal control variable:
X＝arg min U1(h)
extracting the first element X (0) of the optimal control quantity, inputting the first element X (0) into a vehicle model, and obtaining the expected optimal accelerator opening degree c* thr(0) And optimal brake pedal pressure c* brk(0) And the optimal control of the driving and the braking of the vehicle is realized.
Pro 2: and a lane change stage.
As shown in fig. 3, the SV lane change process is performed, the longitudinal acceleration is changed according to the desired control input amount, and the lateral acceleration is expressed by a sine function:
Figure GDA0003018100770000081
wherein tau is the transverse movement duration time of the whole lane changing process, and W is the width of the road;
the change of the transverse displacement can be obtained by integrating the lane-changing transverse acceleration twice:
Figure GDA0003018100770000082
assuming that when the lateral displacement of the lane change vehicle reaches a road width W, in order to realize smooth transition of the lane change process and ensure safety, an objective function of the lane change stage is established as follows:
Figure GDA0003018100770000083
the constraints are as follows:
Figure GDA0003018100770000084
wherein N represents a prediction time domain; β ═ SV, AFV, ALV };
Figure GDA0003018100770000085
λβand
Figure GDA0003018100770000087
is a weight factor for each item; in the objective function, the target function is,
Figure GDA0003018100770000086
representing the acceleration optimization of the vehicle and surrounding vehicles and representing the comfort cost in the lane changing process;
Figure GDA0003018100770000091
representing the following vehicle distance error of the self vehicle and the surrounding vehicles, and representing the trackability cost of the self vehicle and the surrounding vehicles; Δ aβ(h + p +1| h) represents the change in acceleration from time h to h + p +1, Δ Sβ(h + p +1| h) represents a change in following distance from the time h to the time h + p + 1.
The solution method refers to the rolling time domain optimization algorithm in Pro 1.
And step 3: transmitting the result obtained by the model to the cooperative vehicle; and obtaining expected control parameters of the longitudinal direction and the transverse direction of the vehicle according to a vehicle control dynamic model, and controlling the opening of an engine throttle valve, the brake hydraulic pressure and the automatic transmission of each vehicle to enable the vehicle to realize a control strategy of cooperating lane changing.
The present invention is not limited to the above-described embodiments, and any obvious improvements, substitutions or modifications can be made by those skilled in the art without departing from the spirit of the present invention.

Claims (6)

1. A multi-vehicle cooperative lane change method under the condition of network connection is characterized by comprising the following steps:
establishing a driver expectation following model, and predicting the vehicle state after the vehicle changes lanes; establishing a driver expected car following model as follows:
Figure FDA0003018100760000011
Vm(ΔSn)＝e1+e2tanh[c1(ΔSn-dc)-c2]
wherein the content of the first and second substances,
Figure FDA0003018100760000012
representing the longitudinal acceleration of the vehicle on each lane after the vehicle changes lanes;
Figure FDA0003018100760000013
representing the acceleration of the vehicle after the lane change;
Figure FDA0003018100760000014
representing the acceleration of the vehicle on the original lane after the vehicle changes the lane;
Figure FDA0003018100760000015
representing the vehicle acceleration on the target lane after the vehicle changes the lane;
Vm(ΔSn) To optimize the speed function;
Vnis the current speed of the vehicle;
ΔSnthe distance between the car heads of the two workshops;
dcis the minimum safe separation including the length of the vehicle body;
r is a reaction coefficient;
e1，e2is a constant parameter;
c1，c2is the corresponding coefficient;
establishing a lane change revenue function model, and judging whether the current state can cooperate with lane change operation according to constraint conditions;
providing a two-stage collaborative lane changing frame, dividing a lane changing process into a longitudinal distance adjusting stage and a lane changing stage, establishing a collaborative lane changing multi-target optimization control function based on model prediction control, and solving through a rolling optimization time domain algorithm to obtain an optimal control quantity; and transmitting the optimal control quantity to the cooperative vehicles to control the multi-vehicle cooperative lane changing operation.
2. The multi-vehicle cooperative lane change method under the internet connection condition according to claim 1, wherein a lane change revenue function model is established, and specifically comprises the following steps:
Figure FDA0003018100760000016
Ni＝{j∈Si：0≤||xSV-xj||≤l}，i＝O or T；
wherein: g (SV, O, T) represents the total benefit of SV switching from O to T under synergistic conditions; SV represents a self vehicle, O represents a current lane, and T represents a target lane;
no represents the set of subsequent vehicles on the current lane within the communication lane; n is a radical ofTA set representing subsequent vehicles in the target lane within the communication lane; l represents a left lane, and R represents a right lane;
the polite factor eta represents the influence of lane changing operation on subsequent vehicles in the target lane;
the polite factor mu represents the speed advantage of the subsequent vehicle on the current lane due to the lane change of the vehicle;
asvrepresents the acceleration of the vehicle in the current state,
Figure FDA0003018100760000021
representing the acceleration of the vehicle after the lane change; a istThe acceleration of the vehicle in the target lane is represented,
Figure FDA0003018100760000022
representing the vehicle acceleration on the target lane after the vehicle changes the lane; a is0The acceleration of the vehicle on the original lane is shown,
Figure FDA0003018100760000023
accelerating the vehicle on the original lane after the vehicle changes lane;
Figure FDA0003018100760000024
and
Figure FDA0003018100760000025
the method is obtained through a driver expected car following model;
xSVis a self-parking position; x is the number ofjIs the subsequent vehicle j position on lane i; l is the communication range; siRepresenting a set of vehicles on lane i; i represents a current lane or a target lane; | | | | is the euclidean norm.
3. The multi-vehicle cooperative lane change method under the internet connection condition according to claim 2, wherein the constraint conditions of the lane change revenue function model are as follows:
Q＝argmaxT∈{L，R}G(SV，O，T)
Figure FDA0003018100760000026
wherein: q is a candidate target lane, namely selecting T with the maximum benefit G (SV, O, T) in the set as the target lane;
Δ ath is the switching threshold, i.e., lane change behavior is superior to lane keeping behavior under current traffic conditions;
asafeindicating a safe acceleration.
4. The multi-vehicle cooperative lane change method under the internet connection condition according to claim 1, wherein the longitudinal distance adjusting stage specifically comprises: before lane changing starts, the longitudinal distance between a lane changing vehicle and a front vehicle and a rear vehicle is adjusted to ensure that the distance between the vehicles is sufficiently sparse, and an objective function in a longitudinal distance adjusting stage is established as follows:
Figure FDA0003018100760000027
the constraints are as follows:
Figure FDA0003018100760000028
wherein N represents a prediction time domain; θ ═ SV, LV, AFV, ALV }; omegaθIs a weight factor; (h + p +1| h) represents that the value at the time h + p +1 is predicted based on the information at the time h; a isminMinimum comfortable acceleration acceptable to the driver; a ismaxMaximum comfortable acceleration acceptable to the driver; p is a time variable; Δ aθ(h + p +1| h) represents the change of the acceleration from the moment h to the moment h + p +1 in the longitudinal distance adjusting stage;
‖a′SVII is the acceleration change rate of the lane change vehicle, a'SV＝aSV(h+p+1|h)-aSV(h+p|h)；
‖a′LVII is the acceleration rate of change, a 'of the vehicle ahead of the current lane'LV＝aLV(h+p+1|h)-aLV(h+p|h)；
‖a′ALVII is the jerk, a 'of the front vehicle of the target lane'ALV＝aALV(h+p+1|h)-aALV(h+p|h)；
‖a′AFVII is the acceleration change rate of the rear vehicle of the target lane, a'AFV＝aAFV(h+p+1|h)-aAFV(h+p|h)；
δ is the maximum comfortable longitudinal acceleration rate acceptable to the driver;
Dhrepresenting the actual distance between the two vehicles at the moment h;
Dsafethe distance between vehicles is safe.
5. The method for changing lanes under the networking condition in cooperation with multiple vehicles according to claim 4, wherein the lane change stage specifically comprises:
in the SV lane changing process, the longitudinal acceleration is changed along with the expected control input quantity, and the transverse acceleration is expressed by adopting a sine function:
Figure FDA0003018100760000031
wherein tau is the transverse movement duration time of the whole lane changing process, and W is the width of the road;
the change of the transverse displacement can be obtained by integrating the lane-changing transverse acceleration twice:
Figure FDA0003018100760000032
assuming that when the lateral displacement of the lane change vehicle reaches a road width W, the objective function of the lane change phase is established as follows:
Figure FDA0003018100760000033
the constraints are as follows:
Figure FDA0003018100760000034
wherein N represents a prediction time domain; β ═ SV, AFV, ALV };
Figure FDA0003018100760000035
λβand
Figure FDA0003018100760000036
is a weight factor for each item; in the objective function, the target function is,
Figure FDA0003018100760000041
representing the acceleration optimization of the vehicle and surrounding vehicles and representing the comfort cost in the lane changing process;
Figure FDA0003018100760000042
representing the following vehicle distance error of the self vehicle and the surrounding vehicles, and representing the trackability cost of the self vehicle and the surrounding vehicles; Δ aβ(h + p +1| h) represents the change in acceleration from time h to h + p +1 during the lane change phase, Δ Sβ(h + p +1| h) represents a change in following distance from the time h to the time h + p + 1.
6. The method according to claim 5, wherein the objective function of the longitudinal distance adjustment stage and the objective function of the lane change stage are respectively used for gradually and dynamically solving the optimization control problem through a rolling optimization time domain algorithm, and the deviation at each moment is repeatedly optimized and calculated within a rolling limited time interval to obtain the expected input of each vehicle in the longitudinal distance adjustment stage.
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