CN115320596A - Intelligent internet motorcade plug-in cooperative lane change control method - Google Patents
Intelligent internet motorcade plug-in cooperative lane change control method Download PDFInfo
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Abstract
The invention relates to an intelligent networked fleet plug-in cooperative lane change control method, which comprises the following steps: the central controller collects information of controlled vehicles and origin of reference coordinates in the intelligent networked vehicle fleet, and makes behavior, speed and lane change decisions aiming at the controlled vehicle fleet; establishing a backward following information topological structure, wherein the controlled vehicle takes a following vehicle as a target; the central controller receives the decision control command and adopts a corresponding speed decision and a corresponding motion control command according to the behavior command; in the lane changing process, a backward following information topological structure is adopted, the acceleration, braking and steering processes of each controlled vehicle are optimally controlled in a time domain, and an optimized control instruction is generated to a power system, a braking system and a steering system of each controlled vehicle to complete the cooperative lane changing. Compared with the prior art, the method can realize plug-in type cooperative lane change and plug, thereby greatly improving the lane change capability of the intelligent networked motorcade in a high-density traffic environment and the maneuverability of the intelligent networked motorcade in a real traffic flow.
Description
Technical Field
The invention relates to the technical field of intelligent networked motorcade control, in particular to an intelligent networked motorcade plug-in cooperative lane change control method.
Background
The intelligent internet automobile is provided with advanced sensors, decision-making devices, controllers, actuators and other devices, integrates modern communication technology and network technology, and has the functions of automobile-to-automobile, automobile-to-road communication, vehicle-mounted sensing and the like. The vehicle-mounted communication equipment and the vehicle-mounted sensing equipment enable the intelligent networked automobile to have the capability of sensing the environment, the environment sensing information is subjected to decision-making through the decision-making system so as to generate a decision-making command, the control system receives the decision-making command so as to control the acceleration, braking and steering processes of the automobile, and finally the control operation of automatic driving is completed by the automobile actuator.
The intelligent networked automobile cooperative formation driving is a key technology in the intelligent networked automobile application. The formation driving can effectively improve the mobility, the safety and the sustainable development of the traffic system. The intelligent networked automobile consists of a plurality of intelligent networked automobiles to form a motorcade, so that the following distance between the automobiles in the motorcade can be reduced, the road traffic capacity can be improved by nearly one time, and the fuel consumption and the carbon emission of 10 to 20 percent can be reduced. In order to realize formation driving of the intelligent networked automobiles, perfect intelligent networked automobile formation control, namely multi-automobile cooperative transverse and longitudinal coupling control in multiple scenes is often required. In order to achieve the control target, at present, a central controller is mainly used for collecting vehicle information in a motorcade through a multi-vehicle communication technology, and the vehicle information is processed and a vehicle control instruction is sent in combination with data information, so that acceleration, braking and steering of intelligent networked vehicles in the motorcade are correspondingly controlled.
However, most of current intelligent networked automobile collaborative formation driving technologies only consider a longitudinal level, which makes the controlled vehicle easily depart from the fleet when taking lane change measures, and because only the transverse control is performed on a single vehicle level, the continuity and stability of formation driving cannot be ensured. Although researches in the prior art consider the intelligent networked automobile formation lane changing behaviors and some intelligent networked automobile fleet lane changing methods are provided, the existing intelligent networked automobile fleet lane changing methods still have the following obvious defects:
1. the most prominent problem in the conventional control method is that conditions such as a lane change gap of a vehicle team and speeds and accelerations of vehicles in front of the vehicle team and in front of the lane change gap are required to be high. Under the high-density traffic environment, the lane change clearance is small, and the success rate of the lane change of the motorcade by adopting the existing control method is very low, so that the maneuverability of the intelligent networked motorcade and the adaptability to the actual traffic scene are greatly reduced.
2. In the existing control method, the stability of the fleet is not verified, and the following error of the controlled vehicle may increase from beginning to end along the fleet, so that the probability of collision is increased, and traffic oscillation is caused.
3. The existing control method has the disadvantages that the calculation time is too long and uncertain, the hardware is stressed greatly, and the algorithm instantaneity cannot be guaranteed.
4. In the existing control method, the transverse control of the vehicle only aims at optimizing the current offset or a certain position offset, and the integral consideration of errors in a period of time in the future cannot be realized, so that the control precision is low, and the phenomenon of overshoot swing is easy to occur in vehicle formation.
The defects can cause that the vehicles in formation driving can not accurately follow the track of the reference target vehicle, and the intelligent internet connection vehicle team can not realize the cooperative lane change by utilizing smaller lane change gaps in the high-density traffic environment.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an intelligent networked fleet plug-in type collaborative lane change control method, so that a fleet can complete lane change of the fleet in a high-density traffic environment by using small lane change gaps, and the lane change capability and the lane change efficiency of the fleet in the high-density traffic environment are effectively improved.
The purpose of the invention can be realized by the following technical scheme: an intelligent networked fleet plug-in cooperative lane change control method comprises the following steps:
s1, selecting an initial reference coordinate system for formation and lane change according to different lane change scenes;
s2, the central controller collects information of the controlled vehicles and information of reference coordinate origin in the intelligent networked fleet, communication connection is initiated between the controlled vehicles and the central controller, if the communication connection fails, the step S3 is executed, and if not, the step S4 is executed;
s3, judging that data packet loss occurs, reading the last piece of information from the database by the central controller to serve as the current controlled vehicle information, and then executing the step S4;
s4, storing the information of all controlled vehicles in the fleet to a database;
s5, the central controller carries out behavior decision, speed decision and lane change decision aiming at the controlled motorcade according to the information of each controlled vehicle and the vehicles around the motorcade to generate a corresponding decision command, and the information of the decision command is stored in a database;
s6, establishing a backward following information topological structure in the lane changing process, wherein the controlled vehicle takes a following vehicle as a target, the state of an adjacent following vehicle in a fleet where the controlled vehicle is located is taken as a reference state, and the backward following information topological structure is used for realizing insertion and expanding smaller intervals so as to make a space for formation lane changing;
s7, the central controller receives the decision control command generated in the step S5, and takes a corresponding speed decision and motion control command according to the action command;
in the lane changing process, the backward following information topological structure established in the step S6 is adopted, the acceleration, braking and steering processes of each controlled vehicle are optimally controlled in a time domain according to the information of each controlled vehicle, the optimized control instruction is transmitted to a power system, a braking system and a steering system of each controlled vehicle, and the optimized state information is stored in a database;
and S8, correspondingly executing the optimized control command generated in the step S7 by a power system, a braking system and a steering system of each controlled vehicle to complete the cooperative lane change.
Further, in the step S1, the initial reference coordinate system of the formation lane change includes a transverse relative reference coordinate system and a longitudinal relative reference coordinate system, and the initial reference coordinate system does not need to be fixed in the whole course of the formation operation, and only needs to be made explicit in the lane change process;
for a scene that a vehicle team changes lanes cooperatively at a fixed position on a road, taking the fixed position on the road as a longitudinal coordinate origin and taking the direction along the road as a longitudinal positive direction;
for a scene of changing lanes at a fixed position in a traffic stream, if the lane is changed by utilizing a gap which can be penetrated in front of a certain vehicle in the traffic stream, the position of the vehicle is taken as a longitudinal coordinate origin, and the direction along a road is taken as a positive direction;
the coordinate origin of the lateral control is uniformly set as the left edge of the road, and the direction pointing to the right side from the vertical road direction is taken as the positive direction of the lateral coordinate system.
Further, in step S2, the controlled vehicle information collected by the central controller includes: longitudinal position, transverse position, yaw angle, vehicle speed, steering wheel angle, acceleration, wheelbase, front wheel angle of a road coordinate system; the longitudinal direction controls the vehicle speed of the reference object and the longitudinal position in the road coordinate system.
Further, the step S5 specifically includes the following steps:
s51, in the action decision part, the central controller makes decisions on the states of the controlled fleet of vehicles, including states of cruising, following and lane changing;
s52, in the speed decision part, the central controller makes a decision on the reference speed of the fleet;
and S53, in the lane change decision-making part, the central controller makes a lane change decision on the controlled vehicles in the fleet, including finding a lane change gap and performing lane change.
Further, the step S51 specifically includes the following steps:
s511, tracking the global path, and if the motorcade faces a forced lane change requirement, entering a lane change state; if the forced lane change requirement is not satisfied, executing step S512;
s512, if the vehicle exists in front of the vehicle team and the vehicle team faces the random lane changing requirement, the vehicle team enters the lane changing state; otherwise, go to step S513;
s513, if the vehicle exists in front of the motorcade, the motorcade enters a following state; if no vehicle exists in front of the motorcade, the motorcade enters a cruising state.
Further, the step S52 specifically includes the following steps:
s521, when the motorcade is in a cruising state, the motorcade reference speed is as follows:
wherein,
a desired speed for the fleet;
s522, when the motorcade is in the following state, the motorcade reference speed is as follows:
wherein,
the speed of the front vehicle of the motorcade;
and S523, when the motorcade is in a state of searching for the lane change gap, if the motorcade faces the random lane change requirement, the motorcade reference speed is as follows:
if the motorcade faces the requirement of forced lane change, the reference speed of the motorcade is as follows:
wherein,
the expected speed difference in the gap search process;
s524, when the motorcade is in the lane changing state, the motorcade reference speed is as follows:
wherein,
in order to change the speed of the vehicle ahead of the gap,
to account for the expected speed difference during the lane change gap enlargement.
Further, the step S53 specifically includes the following steps:
s531, calculating the safe lane change distance between the vehicle and the controlled vehicle after the lane change gap:
wherein,
response time for human driving, t b In order to delay the braking operation,
for the speed of the vehicle after changing the gap, v is the speed of the controlled vehicle, a min Is the vehicle maximum deceleration;
and then calculating the safe lane change distance between the front vehicle and the controlled vehicle in the lane change gap:
wherein,
in order to realize the reaction time of the intelligent networked vehicle,
the speed of the vehicle before the lane changing gap;
s532, calculating the actual distance between the controlled vehicle and the rear vehicle and the front vehicle in the lane changing gapThe corresponding safe lane changing distances are compared, and if the actual distance between the controlled vehicle and the vehicle after the lane changing gap is met simultaneously
And the actual distance between the controlled vehicle and the vehicle ahead of the lane change gap
The controlled vehicle takes lane-change measures under the current lane-change clearance condition.
Further, the step S6 specifically includes the following steps:
s61, selecting a lane changing gap of a target lane;
s62, operating the last controlled vehicle of the fleet to adopt a lane change measure to enter a lane change gap;
s63, reducing the speed of the last controlled vehicle of the fleet to expand the lane changing gap;
s64, operating the fleet to successively take lane change measures from the last controlled vehicle to the first controlled vehicle;
s65, if the cut-in of other vehicles occurs, regarding the cut-in vehicles as the front vehicles of the lane change gap;
and S66, continuously operating the controlled vehicles to take lane changing measures until all the controlled vehicles in the fleet complete lane changing.
Further, in step S7, the state information of the controlled vehicles in the fleet includes speed, position, heading, and time, and the control information includes acceleration and front wheel slip angle;
the specific process for optimizing the acceleration, braking and steering process of each controlled vehicle is as follows:
s71, calculating an initial state vector of a fleet consisting of n controlled vehicles:
wherein phi is x,n Is a longitudinal state vector of the fleet y,n Is a transverse state vector of the vehicle fleet, v j Speed of jth controlled vehicle, v j -v j+1 Is the relative speed, x, of the jth controlled vehicle and the jth +1 controlled vehicle j Is the longitudinal position, x, of the jth controlled vehicle j -x j+1 Relative longitudinal position of jth controlled vehicle and jth +1 controlled vehicle, g d It is the desired following spacing that is desired,
is the heading angle error of the jth controlled vehicle relative to the road direction, y j Is the lateral position of the jth controlled vehicle,
is the desired lateral position of the jth controlled vehicle;
the initial control vector for the fleet is:
wherein u is x,n For longitudinal control vectors of the fleet, u y,n Is a transverse control vector of the fleet of vehicles, a j Is the acceleration of the jth controlled vehicle,
is the front wheel slip angle of the vehicle;
s72, calculating a state updating equation coefficient matrix:
wherein A is x,n ，B x,n ，C x,n Is a matrix of longitudinal state update equation coefficients, A y,n ，B y,n ，C y,n Is a matrix of transverse state update equation coefficients, d t Is the time domain step size of the vehicle control, I is the identity matrix, v is the vehicle speed, L fr Is the wheelbase of the vehicle, k is the road curvature;
s73, calculating a cost function matrix:
wherein Q is x，n ，R x，n Is a longitudinal control cost function matrix, Q y，n ，R y，n Is a matrix of transverse control cost functions, q v ，q g ，r a ，q θ ，q y ，r σ The data are positive numbers, and specifically, debugging is selected according to control preference in the vehicle control process;
s74, defining a adjoint matrix of the final state
S75, calculating an adjoint matrix reversely;
and S76, forward calculation of a control vector and a state vector.
Further, the step S75 specifically includes the following steps:
s751, respectively calculating:
s752, calculating a cost function fitting matrix
Wherein,
is a desired state quantity;
s753, respectively calculating:
wherein,
is a companion matrix to the cost function matrix,
a companion matrix to the cost function is fitted.
Further, the step S76 specifically includes the following steps:
s761, calculating a control vector and a state vector according to the maximum value principle of Pontryagin:
s762, if u i ＞u max Then u is i ＝u max ；
If u is i ＜u min Then u is i ＝u min ；
Wherein u is max And u min Maximum and minimum values of the control quantity, u i Iteratively calculating a control quantity for the ith step;
s763, if phi i ＞φ max Then phi is i ＝φ max ；
If phi is i ＜φ min Then phi is i ＝φ min ；
Wherein phi max And phi min Maximum and minimum values of the state quantities, respectively i And iterating the calculated state quantity for the ith step.
Compared with the prior art, the method has the advantages that the running state of the vehicle is obtained by collecting the information of the controlled vehicle in the fleet and the vehicles around the fleet in the coordinate system, and then the decision is made respectively according to the vehicle behavior, the fleet speed and the lane change of the vehicle, so that a corresponding decision command is generated; and then based on the decision command, adopting a backward following fleet information topological structure, comprehensively considering longitudinal and transverse errors of the controlled vehicles in a period of time in the future, optimizing longitudinal and transverse driving behaviors in a time domain, further performing optimized control on the acceleration, braking and steering processes of each controlled vehicle, and finally outputting a control result to a power system, a braking system and a steering system of each controlled vehicle to realize a cooperative lane change control process. According to the invention, the motorcade can realize plug-in cooperative lane changing, so that the motorcade can complete lane changing of the motorcade by using small lane changing gaps in a high-density traffic environment, the lane changing capability and the lane changing efficiency of the motorcade in the high-density traffic environment are greatly improved, and the maneuverability of the motorcade in a traffic flow is improved.
According to the method, the error of the vehicle in a future period of time is considered in the time domain, so that the optimization control precision of the vehicle in the current state can be effectively improved, and the vehicle in formation driving can accurately follow the track of the reference target vehicle; the performance of the control algorithm in the invention has excellent reliability and robustness, can be still suitable for the situation of traffic jam, can ensure the stability of the intelligent networked fleet, has the calculation efficiency of engineering application level, and can reduce the operation load.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a plug-in cooperative lane change control method for an intelligent internet fleet includes the following steps:
s1, selecting an initial reference coordinate system for formation lane changing according to different lane changing scenes;
s2, the central controller collects information of the controlled vehicles and information of reference coordinate origin in the intelligent networked fleet, communication connection is initiated between the controlled vehicles and the central controller, if the communication connection fails, the step S3 is executed, and if not, the step S4 is executed;
s3, judging that data packet loss occurs, reading the last piece of information from the database by the central controller to serve as the current controlled vehicle information, and then executing the step S4;
s4, storing the information of all controlled vehicles in the fleet to a database;
s5, the central controller carries out behavior decision, speed decision and lane change decision aiming at the controlled motorcade according to the information of each controlled vehicle and the vehicles around the motorcade, generates a corresponding decision command, and stores the information of the decision command in a database;
s6, establishing a backward following information topological structure in the lane changing process, wherein the controlled vehicle takes the following vehicle as a target, the state of an adjacent following vehicle in a fleet where the controlled vehicle is located is taken as a reference state, and the backward following information topological structure is used for realizing insertion and expanding smaller intervals so as to make a space for formation lane changing;
s7, the central controller receives the decision control command generated in the step S5, and takes a corresponding speed decision and motion control command according to the action command;
in the lane changing process, the backward following information topological structure established in the step S6 is adopted, the acceleration, braking and steering processes of each controlled vehicle are optimized and controlled in a time domain according to the information of each controlled vehicle, the optimized control instruction is transmitted to a power system, a braking system and a steering system of each controlled vehicle, and the optimized state information is stored in a database;
and S8, correspondingly executing the optimized control command generated in the step S7 by a power system, a braking system and a steering system of each controlled vehicle to complete the cooperative lane change.
The embodiment, applying the above technical solution, mainly includes the following contents:
step 1, selecting an initial reference coordinate system for formation and lane change according to different lane change scenes.
In step 1, the reference coordinate system does not need to be fixed in the whole course of the queuing operation, and only needs to be determined in the track changing process. The transverse and longitudinal directions may each have different relative reference frames, and the origin of the relative reference frames may be any point that is stationary or moving. For a scene that a vehicle team changes lanes cooperatively at a fixed position on a road, taking the position as a longitudinal coordinate origin and taking the direction along the road as a longitudinal positive direction; for a scene of changing lanes at a fixed position in the traffic stream, if a gap which can be crossed by a certain vehicle in the traffic stream is utilized to change lanes, the position of the vehicle is taken as a longitudinal coordinate origin, and the direction along the road is taken as a positive direction. The coordinate origin of the lateral control is uniformly set as the left edge of the road, and the direction pointing to the right side from the vertical road direction is taken as the positive direction of the lateral coordinate system.
And 2, the central controller collects information of the controlled vehicles and the reference coordinate origin in the intelligent networked fleet, and the controlled vehicles are communicated with the central controller. And if the communication fails, entering step 3, otherwise, entering step 4.
In step 2, each piece of controlled vehicle information collected by the central controller includes: longitudinal position, transverse position, yaw angle, vehicle speed, steering wheel angle, acceleration, wheelbase, front wheel angle of a road coordinate system; additionally included is the vehicle speed of the longitudinal control reference and the longitudinal position in the road coordinate system.
In order to collect such information, the vehicle to which the present invention is applied should have: the system comprises a vehicle information acquisition device, a communication device, a vehicle database and a central controller arranged on any vehicle in a fleet.
In the present invention, the control device of the controlled vehicle is connected to the power system, the brake system, and the steering system of the controlled vehicle.
And 3, judging that data packet loss occurs, reading the last piece of information from the database by the central controller, regarding the last piece of information as the current controlled vehicle information, and then entering the step 4.
And 4, storing the information of all the controlled vehicles in the fleet to a database.
And step 5, the central controller carries out behavior decision, speed decision and lane change decision aiming at the controlled motorcade according to the information of each controlled vehicle and the vehicles around the motorcade, generates a corresponding decision command, and stores the information of the decision command in a database.
In step 5, making a decision from the three parts of vehicle behavior, fleet speed and vehicle lane change to generate a corresponding decision command, comprising the following steps:
step 5.1, in the behavior decision part, the central controller makes decisions on the states of the controlled fleet of vehicles, including states of cruising, following and lane changing;
specifically, the decision-making and judgment process of the central controller on the states (cruising, following and lane changing) of the controlled motorcade is as follows:
step 5.1.1, tracking the global path, and if the motorcade faces the forced lane change requirement (such as a ramp, a turn and the like), entering a lane change state; if the forced lane change requirement is not met, entering the step 5.1.2 for decision judgment;
step 5.1.2, if the vehicle exists in front of the motorcade and the motorcade is in random lane changing requirements (such as being blocked by a slow running vehicle), the motorcade enters a lane changing state; if the condition is not met, entering step 5.1.3 for decision judgment;
step 5.1.3, if the vehicle exists in front of the motorcade, the motorcade enters a following state; if no vehicle is present in front of the platoon, the platoon enters a cruise state.
Step 5.2, in the speed decision part, the central controller makes a decision on the reference speed of the fleet;
specifically, the decision calculation process of the reference speed of the fleet is as follows:
step 5.2.1, when the motorcade is in a cruising state, the motorcade reference speedDegree of
Wherein
Is the fleet expected speed;
step 5.2.2, when the motorcade is in the following state, the motorcade reference speed is
Wherein
Is the speed of the vehicle ahead of the fleet;
step 5.2.3, when the motorcade is in the state of searching for the lane change gap, if the motorcade faces the random lane change requirement (such as being blocked by a slow running vehicle), the motorcade reference speed is
If the fleet is faced with a forced lane change requirement (such as a requirement that a ramp, turn, etc. follow a global path), the fleet reference speed is
Wherein
Is the expected speed difference during the gap search;
step 5.2.4, when the motorcade is in the lane changing state, the motorcade reference speed is
Wherein
In order to change the speed of the vehicle ahead of the gap,
to account for the desired speed differential during the lane change gap enlargement.
Step 5.3, in the lane change decision-making part, the central controller makes a lane change decision for the controlled vehicles in the fleet, including finding a lane change gap and carrying out lane change;
specifically, the decision process of the lane change action adopted by the controlled vehicles in the fleet is as follows:
step 5.3.1, calculating the safe lane change distance between the vehicle and the controlled vehicle after the lane change gap:
wherein,
response time for human driving, t b In order to delay the braking operation,
for the speed of the vehicle after changing the track clearance, v is the speed of the controlled vehicle, a min Is the vehicle maximum deceleration;
calculating the safe lane change distance between the front vehicle and the controlled vehicle in the lane change gap:
wherein,
in order to realize the reaction time of the intelligent networked vehicle,
the speed of the vehicle before the lane changing gap is adopted;
step 5.3.2, mixingThe actual distance between the controlled vehicle and the rear vehicle and the actual distance between the controlled vehicle and the front vehicle in the lane changing gap are compared with the corresponding safe lane changing distances, and if the actual distances between the controlled vehicle and the rear vehicle in the lane changing gap are met simultaneously
And the actual distance between the controlled vehicle and the vehicle before the lane-changing gap
The controlled vehicle takes lane-change measures under the current lane-change clearance condition.
And 6, establishing a backward following information topological structure in the lane changing process, wherein the controlled vehicle takes a following vehicle as a target and takes the state of the following vehicle in the fleet as a reference state. The information topological structure can achieve the purpose of inserting and expanding smaller space so as to manufacture space for formation and track changing.
In step 6, based on the information topology structure of backward following, the process that the motorcade adopts plug-in type collaborative lane changing and plug adding specifically comprises the following steps:
6.1, selecting a lane changing gap of the target lane;
6.2, operating the last controlled vehicle of the fleet to adopt a lane change measure to enter a lane change gap;
6.3, reducing the speed of the last controlled vehicle of the fleet to expand the lane changing gap;
6.4, operating the fleet to successively take lane changing measures from the last controlled vehicle to the first controlled vehicle;
step 6.5, if the situation of cutting in of other vehicles occurs, regarding the cut-in vehicle as a front vehicle of the lane change gap;
and 6.6, continuing to operate the controlled vehicles to adopt lane changing measures until all the controlled vehicles in the fleet complete lane changing.
And 7, the central controller receives the decision control command generated in the step 5, and takes a corresponding speed decision and motion control command according to the action command. And in the lane changing process, optimally controlling the acceleration, braking and steering processes of each controlled vehicle in a time domain by adopting the backward following information topological structure established in the step 6 according to the information of each controlled vehicle, transmitting an optimized control instruction to a power system, a braking system and a steering system of each controlled vehicle, and storing the optimized state information into a database.
In step 7, the state information of the controlled vehicles in the fleet comprises speed, position, course and time, and the control information comprises acceleration and front wheel declination; the contents for optimizing the acceleration, braking and steering processes of each controlled vehicle include:
step 7.1, calculating the initial state vector of a fleet consisting of n controlled vehicles as follows:
wherein phi x，n Is a longitudinal state vector of the fleet y，n Is a transverse state vector of the fleet, v j Is the speed of the jth controlled vehicle in m/s; v. of j -v j+1 Is the relative speed of the jth controlled vehicle and the jth +1 controlled vehicle in m/s; x is the number of j Is the longitudinal position of the jth controlled vehicle, in units of m; x is a radical of a fluorine atom j -x j+1 Is the relative longitudinal position of the jth controlled vehicle and the jth +1 controlled vehicle, in units of m; g d Is the desired following distance, in units of m;
is the heading angle error of the jth controlled vehicle relative to the road direction, in units rad; y is j Is the lateral position of the jth controlled vehicle, in units m;
is the desired lateral position of the jth controlled vehicle, in m. The initial control vector of the fleet is
Wherein u x，n Longitudinal control vectors, u, for a fleet of vehicles y，n Is a transverse control vector of the fleet of vehicles, a j Is the acceleration of the jth controlled vehicle in m/s 2 ；
Is the front wheel deflection angle of the vehicle, unit rad;
step 7.2, calculating a state updating equation coefficient matrix:
wherein, A x，n ，B x，n ，C x，n Is a longitudinal state update equation coefficient matrix; a. The y，n ，B y，n ，C y，n Is a transverse state update equation coefficient matrix; d t Is the time domain step size of vehicle control, in units of s; i is an identity matrix; v is vehicle speed, in m/s; l is fr Is the wheelbase, unit of the vehiclem; k is the road curvature;
step 7.3, calculating a cost function matrix:
wherein Q x，n ，R x，n Is a longitudinal control cost function matrix, Q y，n ，R y，n Is a matrix of transverse control cost functions, q v ，q g ，r a ，
q y ，r δ If the number is positive, the debugging can be selected according to the control preference in the vehicle control process;
step 7.4, define the adjoint matrix of the final state
7.5, reversely calculating the adjoint matrix;
in step 7.5, the backward computation of the adjoint comprises the steps of:
step 7.5.1, four matrixes are calculated:
step 7.5.2, calculating a cost function fitting matrix
Wherein,
is a desired state quantity;
step 7.5.3, calculating two corresponding adjoint matrixes:
and 7.6, forward computing a control vector and a state vector.
In step 7.6, forward computing the control vector and the state vector comprises the steps of:
step 7.6.1, calculating a control vector and a state vector according to the Pontryagin maximum principle:
step 7.6.2, if u i ＞u max Then u is i ＝u max (ii) a If u is i ＜u min Then u is i ＝u min (ii) a Wherein u is max And u min Maximum and minimum values of the control quantity, u i Iteratively calculating a control quantity for the ith step;
step 7.6.3, if phi i ＞φ max Then phi is i ＝φ max (ii) a If phi is i ＜φ min Then phi is i ＝φ min (ii) a Wherein phi is max And phi min Maximum and minimum values of the state quantities, respectively i And (4) iteratively calculating the state quantity for the ith step.
And 8, executing the optimized control command in the step 7 by the power system, the braking system and the steering system of each controlled vehicle, and then returning to the step 2 or stopping the coordinated lane change control process.
It should be noted that, in the present technical solution, if the control step is set to 0.1 second, it is necessary to determine a control amount every 0.1 second, and then d t ＝0.1s；
In calculation of fleet reference speeds
Parameters can be actively set according to actual requirements and improved according to experience
Can increase the successful probability of the channel change gap search in the forced channel change process and improve
The speed of expanding the lane change gap can be increased. This embodiment will be described
Taking the ratio as 3m/s;
q in a cost function matrix v ，q g ，r a ，
q y ，r δ Parameters are actively set according to actual needs, and q is increased according to experience v ，q g ，
q y Size, which is beneficial for the vehicle to quickly reach the control target and improve r a And r δ Vehicle control oscillations are advantageously reduced.
In conclusion, the technical scheme provides the intelligent networking vehicle fleet collaborative lane changing method for manufacturing space aiming at the phenomenon that the lane changing space of the vehicle fleet is insufficient in the high-density traffic environment. The method comprises the steps of acquiring information of controlled vehicles in a fleet and vehicles around the fleet, adopting a backward following fleet information topological structure, making decisions from three parts of vehicle behaviors, fleet speeds and vehicle lane changing, optimizing longitudinal and transverse driving behaviors in a time domain, namely based on corresponding decision commands, adopting the backward following fleet information topological structure, taking adjacent rear vehicles in the fleet as reference targets, coordinating and controlling the controlled vehicles in the fleet to follow the reference target tracks, combining the controlled vehicle information and the reference target information, comprehensively considering errors in a period of time ahead, optimizing acceleration, braking and steering processes of the controlled vehicles in the fleet by using an optimization control principle under the constraints of safe following, comfortable acceleration and deceleration, speed threshold values and the like, and transmitting an optimization result to a power device, a braking device and a steering device of the controlled vehicles. Therefore, the motorcade can complete the lane change of the motorcade by using small lane change gaps in a high-density traffic environment, and the maneuverability of the motorcade in an actual traffic flow is greatly improved. According to the technical scheme, the error of the vehicle in a future period of time is considered in the time domain, so that the optimization control precision of the vehicle in the current state is improved. The control algorithm performance of the technical scheme has reliability and robustness, can ensure the stability of the intelligent networked fleet, has the calculation efficiency of the engineering application level, and can reduce the operation load.
Claims (10)
1. An intelligent networked fleet plug-in type cooperative lane change control method is characterized by comprising the following steps:
s1, selecting an initial reference coordinate system for formation and lane change according to different lane change scenes;
s2, the central controller collects information of the controlled vehicles and information of reference coordinate origin in the intelligent networked fleet, communication connection is initiated between the controlled vehicles and the central controller, if the communication connection fails, the step S3 is executed, and if not, the step S4 is executed;
s3, judging that data packet loss occurs, reading the last piece of information from the database by the central controller to serve as the current controlled vehicle information, and then executing the step S4;
s4, storing the information of all controlled vehicles in the fleet to a database;
s5, the central controller carries out behavior decision, speed decision and lane change decision aiming at the controlled motorcade according to the information of each controlled vehicle and the vehicles around the motorcade to generate a corresponding decision command, and the information of the decision command is stored in a database;
s6, establishing a backward following information topological structure in the lane changing process, wherein the controlled vehicle takes the following vehicle as a target, the state of an adjacent following vehicle in a fleet where the controlled vehicle is located is taken as a reference state, and the backward following information topological structure is used for realizing insertion and expanding smaller intervals so as to make a space for formation lane changing;
s7, the central controller receives the decision control command generated in the step S5, and takes a corresponding speed decision and a corresponding motion control command according to the behavior command;
in the lane changing process, the backward following information topological structure established in the step S6 is adopted, the acceleration, braking and steering processes of each controlled vehicle are optimized and controlled in a time domain according to the information of each controlled vehicle, the optimized control instruction is transmitted to a power system, a braking system and a steering system of each controlled vehicle, and the optimized state information is stored in a database;
and S8, correspondingly executing the optimized control command generated in the step S7 by a power system, a braking system and a steering system of each controlled vehicle to complete the cooperative lane change.
2. The intelligent networked fleet plug-in cooperative lane change control method according to claim 1, wherein in step S1, the initial reference coordinate system for the formation lane change comprises a transverse relative reference coordinate system and a longitudinal relative reference coordinate system, and the initial reference coordinate system does not need to be fixed in the whole course of the formation operation, but only needs to be defined in the lane change process;
for a scene that a vehicle team changes lanes cooperatively at a fixed position on a road, taking the fixed position on the road as a longitudinal coordinate origin and taking the direction along the road as a longitudinal positive direction;
for a scene of changing lanes at a fixed position in the traffic stream, if a gap which can be penetrated by the front of a certain vehicle in the traffic stream is utilized to change lanes, the position of the vehicle is taken as a longitudinal coordinate origin, and the direction along the road is taken as a positive direction;
the coordinate origin of the transverse control is uniformly set as the left edge of the road, and the direction pointing to the right side along the direction vertical to the road is taken as the positive direction of the transverse coordinate system;
in step S2, the controlled vehicle information collected by the central controller includes: longitudinal position, transverse position, yaw angle, vehicle speed, steering wheel angle, acceleration, wheelbase, front wheel angle of a road coordinate system; the longitudinal direction controls the vehicle speed of the reference object and the longitudinal position in the road coordinate system.
3. The method as claimed in claim 1, wherein the step S5 specifically comprises the steps of:
s51, in the action decision part, the central controller makes decisions on the states of the controlled fleet of vehicles, including states of cruising, following and lane changing;
s52, in the speed decision part, the central controller makes a decision on the reference speed of the fleet;
and S53, in the lane changing decision-making part, the central controller makes a lane changing decision for the controlled vehicles in the fleet, including finding a lane changing gap and changing lanes.
4. The method according to claim 3, wherein the step S51 specifically comprises the steps of:
s511, tracking the global path, and if the motorcade is in a forced lane change requirement, enabling the motorcade to enter a lane change state; if the forced lane change requirement is not satisfied, executing step S512;
s512, if the vehicle exists in front of the vehicle team and the vehicle team faces the random lane changing requirement, the vehicle team enters the lane changing state; otherwise, go to step S513;
s513, if the vehicle exists in front of the motorcade, the motorcade enters a following state; if no vehicle exists in front of the motorcade, the motorcade enters a cruising state.
5. The method as claimed in claim 3, wherein the step S52 specifically comprises the steps of:
s521, when the motorcade is in a cruising state, the motorcade reference speed is as follows:
wherein,
a desired speed for the fleet;
s522, when the motorcade is in the following state, the motorcade reference speed is as follows:
wherein,
the speed of the front vehicle of the motorcade;
s523, when the motorcade is in a state of searching for lane change gaps, if the motorcade is in a random lane change requirement, the reference speed of the motorcade is as follows:
if the motorcade faces the requirement of forced lane change, the reference speed of the motorcade is as follows:
wherein,
the expected speed difference in the gap search process;
s524, when the motorcade is in the lane changing state, the motorcade reference speed is as follows:
wherein,
in order to change the speed of the vehicle ahead of the gap,
to account for the expected speed difference during the lane change gap enlargement.
6. The method as claimed in claim 5, wherein the step S53 specifically comprises the steps of:
s531, calculating the safe lane change distance between the vehicle and the controlled vehicle after the lane change gap:
wherein,
response time for human driving, t b In order to delay the braking of the vehicle,
for the speed of the vehicle after changing the track clearance, v is the speed of the controlled vehicle, a min Is the vehicle maximum deceleration;
and then calculating the safe lane changing distance between the vehicle before the lane changing gap and the controlled vehicle:
wherein,
in order to realize the reaction time of the intelligent networked vehicle,
the speed of the vehicle before the lane changing gap;
s532, comparing the actual distance between the controlled vehicle and the rear vehicle and the front vehicle in the lane changing gap with the corresponding safe lane changing distance, and if the actual distance between the controlled vehicle and the rear vehicle in the lane changing gap is met simultaneously
And the actual distance between the controlled vehicle and the vehicle ahead of the lane change gap
The controlled vehicle takes lane-change measures under the current lane-change clearance condition.
7. The intelligent networked fleet plug-in cooperative lane change control method according to claim 1, wherein the step S6 specifically comprises the steps of:
s61, selecting a lane changing gap of a target lane;
s62, operating the last controlled vehicle of the fleet to adopt a lane change measure to enter a lane change gap;
s63, reducing the speed of the last controlled vehicle of the fleet to expand the lane changing gap;
s64, operating the fleet to successively take lane change measures from the last controlled vehicle to the first controlled vehicle;
s65, if the cut-in of other vehicles occurs, regarding the cut-in vehicles as the front vehicles of the lane change gap;
and S66, continuously operating the controlled vehicles to take lane changing measures until all the controlled vehicles in the fleet complete lane changing.
8. The intelligent networked fleet plug-in cooperative lane-changing control method according to claim 1, wherein in step S7, the status information of the controlled vehicles in the fleet comprises speed, position, heading, time, and the control information comprises acceleration and front wheel slip angle;
the specific process for optimizing the acceleration, braking and steering process of each controlled vehicle is as follows:
s71, calculating an initial state vector of a fleet consisting of n controlled vehicles:
wherein phi is x，n Is a longitudinal state vector of the vehicle fleet, phi y，n Is a transverse state vector of the fleet, v j Speed of jth controlled vehicle, v j -v j+1 Is the relative speed, x, of the jth controlled vehicle and the jth +1 controlled vehicle j Is the jth vehicleLongitudinal position of the controlled vehicle, x j -x j+1 Is the relative longitudinal position of the jth controlled vehicle and the (j + 1) th controlled vehicle, g d It is the desired following spacing that,
is the heading angle error of the jth controlled vehicle relative to the road direction, y j Is the lateral position of the jth controlled vehicle,
is the desired lateral position of the jth controlled vehicle;
the initial control vector for the fleet is:
wherein u is x，n For longitudinal control vectors of the fleet, u y，n Is a transverse control vector of the fleet of vehicles, a j Is the acceleration of the jth controlled vehicle,
is the front wheel slip angle of the vehicle;
s72, calculating a state updating equation coefficient matrix:
wherein A is x，n ，B x，n ，C x，n Is a matrix of longitudinal state update equation coefficients, A y，n ，B y，n ，C y，n Is a matrix of transverse state update equation coefficients, d t Is the time domain step size of the vehicle control, I is the identity matrix, v is the vehicle speed, L fr Is the wheelbase of the vehicle, k is the road curvature;
s73, calculating a cost function matrix:
wherein，Q x，n ，R x，n Is a matrix of longitudinal control cost functions, Q y，n ，R y，n Is a matrix of transverse control cost functions, q v ，q g ，r a ，q θ ，q y ，r σ The values are positive numbers, and specifically, debugging is selected according to control preference in the vehicle control process;
s74, defining a adjoint matrix of the final state
S75, calculating an adjoint matrix reversely;
and S76, forward calculation of a control vector and a state vector.
9. The method as claimed in claim 8, wherein the step S75 specifically includes the steps of:
s751, respectively calculating:
s752, calculating a cost function fitting matrix
Wherein,
is a desired state quantity;
s753, respectively calculating:
wherein,
is a companion matrix to the cost function matrix,
a adjoint of the matrices is fitted to the cost function.
10. The method as claimed in claim 9, wherein the step S76 includes the following steps:
s761, calculating a control vector and a state vector according to the Pontryagin maximum principle:
S762、if u is i ＞u max Then u is i ＝u max ；
If u is i ＜u min Then u is i ＝u min ；
Wherein u is max And u min Maximum and minimum values of the control quantity, u i Iteratively calculating a control quantity for the ith step;
s763, if phi i ＞φ max Then phi is i ＝φ max ；
If phi is i ＜φ min Then phi is i ＝φ min ；
Wherein phi is max And phi min Maximum and minimum values of the state quantities, respectively i And (4) iteratively calculating the state quantity for the ith step.
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