CN114387787B - Vehicle track control method and device, electronic equipment and storage medium - Google Patents
Vehicle track control method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a vehicle track control method, a vehicle track control device, electronic equipment and a storage medium, wherein the method comprises the following steps: dividing the mixed fleet into a plurality of sub fleets by taking the intelligent networked vehicles as head vehicles, wherein each sub fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting a state variable, and establishing a state vector of the hybrid fleet according to the state variable; selecting a control variable, and establishing a fleet continuous time domain state transfer equation based on the control variable, a vehicle kinematics model and a speed difference stress-response model; discretizing the continuous time domain state transition equation to obtain an augmented form state equation; and establishing an optimization objective function, determining constraint conditions of the optimization objective function, and acquiring the vehicle track of the hybrid fleet based on the optimization objective function and the constraint conditions. The invention can carry out cooperative control on the running of each vehicle in the mixed motorcade consisting of a plurality of intelligent networked vehicles and a plurality of full-manual driving vehicles, thereby improving the traffic efficiency of the mixed motorcade.
Description
Technical Field
The invention relates to the technical field of motorcade track control, in particular to a vehicle track control method, a vehicle track control device, electronic equipment and a storage medium.
Background
In the urban road network environment, the vehicle passing efficiency is greatly influenced by the signal timing of the road junction on the passing path, the green light resource distribution is unreasonable under the practical condition, the vehicle can not adjust the motion state according to the real-time condition of the traffic light, and the purpose of reasonably utilizing the traffic light resource is achieved. Along with the construction of intelligent network connection infrastructures in urban road networks, high-grade automatic driving automobiles gradually develop commercial operation, the significance of developing a crossing vehicle track control method in an intelligent network connection environment is great, and the aims of saving oil consumption and improving the operation efficiency of vehicles and urban traffic can be fulfilled.
The existing vehicle track control method mainly comprises two types of vehicle speed guidance and full-intelligent networked vehicle fleet passing. The method mainly comprises the steps of calculating based on the time distribution of traffic lights and the real-time motion state of vehicles, achieving the purpose of passing through the intersection without stopping as much as possible by giving reasonable suggested speed or acceleration, helping the vehicles to improve the operation efficiency and optimizing the travel service, but mostly considering single vehicle passing, being incapable of carrying out integral or differential passing speed optimization aiming at a motorcade formed by a plurality of vehicles at the intersection, and being limited in helping to improve the traffic efficiency at the intersection. The vehicle fleet composed of a plurality of intelligent networked vehicles is integrally researched, the vehicle fleet passing efficiency can be greatly improved based on longitudinal control of the passing vehicle fleet, however, considering that the current intelligent networked vehicles have limited permeability and cannot meet the harsh conditions of the vehicle fleet composed of full intelligent networked vehicles under the non-intentional actual operation scene, most of the vehicle flees are composed of the intelligent networked vehicles and full-manual driving vehicles in a mixed mode.
Therefore, it is urgently needed to provide a vehicle trajectory control method, a vehicle trajectory control device, an electronic device and a storage medium to solve the technical problem that the prior art cannot perform trajectory control on a hybrid fleet composed of intelligent networked vehicles and fully-manually-driven vehicles in an actual application scene, so that the traffic efficiency of the hybrid fleet is low.
Disclosure of Invention
In view of the above, it is necessary to provide a vehicle trajectory control method, device, electronic device and storage medium to solve the technical problem in the prior art that the trajectory control cannot be performed on a hybrid fleet composed of an intelligent internet vehicle and a fully-manually-driven vehicle in an actual application scene, so that the traffic efficiency of the hybrid fleet is low.
In order to solve the above technical problem, the present invention provides a vehicle trajectory control method for controlling the trajectory of each vehicle in a hybrid fleet, where the hybrid fleet includes a plurality of intelligent networked vehicles and a plurality of fully manually driven vehicles, and the vehicle trajectory control method includes:
dividing the mixed fleet into a plurality of sub fleets by taking the intelligent networked vehicle as a head vehicle, wherein each sub fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting the position and the speed of the vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
selecting the acceleration of the intelligent networked vehicle as a control variable, and establishing a fleet continuous time domain state transfer equation based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
discretizing the continuous time domain state transition equation to obtain an augmented form state equation;
and establishing an optimization objective function with the aim of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at a stop line of the intersection and minimizing the accumulated oil consumption of the hybrid fleet, determining a constraint condition of the optimization objective function, and obtaining vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition.
In some possible implementations, the establishing a fleet continuous time domain state transition equation based on the control variables, the vehicle kinematics model, and the speed difference stress-response model includes:
establishing a head-vehicle state equation of the intelligent networked vehicle based on the control variables and the vehicle kinematics model;
establishing a following vehicle linear time-varying state equation of the fully manually driven vehicle based on the control variables and the speed difference stress-response model;
and establishing a fleet continuous time domain state transfer equation of the sub fleet based on the head vehicle state equation and the following vehicle state equation.
In some possible implementations, the fleet continuous time domain state transition equation is:
in the formula (I), the compound is shown in the specification,
is composed of
The position of the intelligent networked vehicle of the mth sub-fleet at the moment;
is composed of
The speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
the position of the intelligent networked vehicle of the mth sub-fleet at the moment t;
the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t;
the acceleration of the intelligent networked vehicle of the mth sub-fleet at the moment t;
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
the driver sensitivity coefficient is used for representing the stress response speed of the fully-manually driven vehicle along with the change of the speed difference and is a constant;
is a real-time distance difference between the current vehicle and the preceding vehicle,
an optimal speed model is obtained;
is the first derivative;
for mth sub-fleet
A state vector of a time;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the mth sub-fleet is
A state vector of a time;
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain;
is the input vector of the mth sub-fleet.
In some possible implementations, the discretizing the continuous time-domain state-transition equation to obtain an augmented form state equation includes:
discretizing the continuous time domain state transition equation based on discrete sampling step length, and establishing a state transition and state output equation in a discrete time domain;
obtaining an incremental form state equation based on the state transition equation and the state output equation in the discrete time domain;
and obtaining an augmented form state equation in the prediction time domain based on the incremental form state equation.
In some possible implementations, the augmented form state equations include an augmented state transition equation and an augmented state output equation;
the augmented state transition equation is:
the augmented state output equation is:
in the formula (I), the compound is shown in the specification,
to be composed of
Augmented state vector prediction of time of day
An augmented state vector of time;
is composed of
An augmented state vector of time;
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
a state transition matrix in an augmented form within a prediction horizon;
an input matrix in an augmented form for the mth sub-fleet;
the change value of the control vector at the k moment;
to be composed of
Output vector prediction of time of day
An output vector of time;
a state transition matrix in a discrete time domain;
an input matrix in a discrete time domain;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain; ca is a preset matrix in an augmented form; e is a natural constant, and tau is a discretized control matrix; i is an identity matrix; o is a zero matrix.
In some possible implementations, the establishing an optimization objective function with the objective of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at the intersection stop line and minimizing the cumulative fuel consumption of the hybrid fleet includes:
acquiring a motion state vector difference between a motion state of the intelligent networked vehicle at a stop line of an intersection and an ideal state in a prediction time domain;
acquiring a control vector difference of the intelligent networked vehicle in a control time domain;
acquiring the instantaneous oil consumption of the intelligent networked vehicles and the full-manual driving vehicles in each sub-fleet;
and establishing the optimization objective function based on the motion state vector difference, the control vector difference and the instantaneous fuel consumption.
In some possible implementations, the optimization objective function is:
in the formula (I), the compound is shown in the specification,
is the optimization objective function;
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
the reference control vector is a reference control vector in the ith control step length of the intelligent networked vehicle of the mth sub-fleet; q is a weight factor matrix of the output vector; r is a weight factor matrix of the control vector;
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
instantaneous fuel consumption for all manually driven vehicles in each sub-vehicle; and | | is a norm.
In another aspect, the present invention further provides a vehicle trajectory control device for controlling the trajectory of each vehicle in a hybrid fleet, where the hybrid fleet includes a plurality of intelligent networked vehicles and a plurality of fully manually driven vehicles, the vehicle trajectory control device comprising:
the hybrid fleet division unit is used for dividing the hybrid fleet into a plurality of sub-fleets by taking the intelligent networked vehicles as head vehicles, and each sub-fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting the position and the speed of the vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
the time domain state transfer equation establishing unit is used for selecting the acceleration of the intelligent networked vehicle as a control variable and establishing a continuous time domain state transfer equation of the fleet based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
the augmented form state equation establishing unit is used for discretizing the continuous time domain state transfer equation to obtain an augmented form state equation;
and the vehicle track control unit is used for establishing an optimization objective function with the purposes of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at a stop line of an intersection and minimizing the accumulated oil consumption of the mixed fleet, determining the constraint condition of the optimization objective function, and obtaining the vehicle tracks of the intelligent networked vehicle and the fully-manually driven vehicle in the mixed fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition.
In another aspect, the present invention also provides an electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled to the memory, is configured to execute the program stored in the memory to implement the steps of the vehicle trajectory control method in any one of the above implementations.
In another aspect, the present invention further provides a computer-readable storage medium for storing a computer-readable program or instructions, which when executed by a processor, can implement the steps of the vehicle trajectory control method in any one of the above-mentioned implementation manners.
The beneficial effects of adopting the above embodiment are: according to the vehicle track control method, firstly, the intelligent internet vehicle is used as a head vehicle, the mixed fleet is divided into a plurality of sub-fleets, each sub-fleet comprises one intelligent internet vehicle and at least one fully manually driven vehicle, and the technical effect of cooperatively controlling the running of each vehicle in the mixed fleet consisting of the plurality of intelligent internet vehicles and the plurality of fully manually driven vehicles is achieved.
Furthermore, the vehicle track control method provided by the invention establishes an optimization objective function aiming at the minimum difference between the motion state and the ideal state of the intelligent networked vehicle at the stop line of the intersection and the minimum accumulated oil consumption of the hybrid fleet, and obtains the vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid fleet based on the optimization objective function, so that the traffic efficiency of the hybrid fleet can be improved, and the energy consumption of each vehicle in the hybrid fleet can be optimized.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flow chart illustrating a vehicle trajectory control method according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a hybrid fleet provided by the present invention;
FIG. 3 is a flowchart illustrating an embodiment of S101 in FIG. 1 according to the present invention;
FIG. 4 is a flowchart illustrating an embodiment of S103 of FIG. 1 according to the present invention;
FIG. 5 is a flowchart illustrating an embodiment of S104 of FIG. 1 according to the present invention;
FIG. 6 is a schematic structural diagram of an embodiment of a vehicle trajectory control device provided by the present invention;
fig. 7 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.
It should be understood that the schematic drawings are not necessarily to scale. The flowcharts used in this invention illustrate operations performed in accordance with some embodiments of the present invention. It should be understood that the operations of the flow diagrams may be performed out of order, and that steps without logical context may be reversed in order or performed concurrently. One skilled in the art, under the direction of this summary, may add one or more other operations to, or remove one or more operations from, the flowchart.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor systems and/or microcontroller systems.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides a vehicle track control method, a vehicle track control device, an electronic device and a storage medium, which are respectively described below.
Fig. 1 is a schematic flowchart of an embodiment of a vehicle trajectory control method provided by the present invention, where the vehicle trajectory control method is used to control trajectories of vehicles in a hybrid fleet, where the hybrid fleet includes a plurality of intelligent networked vehicles and a plurality of fully manually driven vehicles, and as shown in fig. 1, the vehicle trajectory control method includes:
s101, dividing a mixed fleet into a plurality of sub fleets by taking an intelligent networked vehicle as a head vehicle, wherein each sub fleet comprises an intelligent networked vehicle and at least one full-manual driving vehicle; selecting the position and the speed of the vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
s102, selecting the acceleration of the intelligent networked vehicle as a control variable, and establishing a fleet continuous time domain state transfer equation based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
s103, discretizing the continuous time domain state transfer equation to obtain an augmented form state equation;
s104, establishing an optimization objective function aiming at the minimum difference between the motion state and the ideal state of the intelligent networked vehicle at the stop line of the intersection and the minimum accumulated oil consumption of the hybrid fleet, determining a constraint condition of the optimization objective function, and obtaining vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition.
Specifically, step S104 specifically includes: obtaining target acceleration, target speed and target position of intelligent networked vehicles in the hybrid fleet as well as following speed and following position of full-manual driving vehicles based on the state equation of the augmentation form, the target function and the constraint condition; and obtaining a vehicle trajectory for the hybrid fleet based on the target speed, the target location, the following speed, and the following location.
Compared with the prior art, the vehicle track control method provided by the embodiment of the invention has the advantages that the intelligent networked vehicle is taken as the head vehicle, the mixed fleet is divided into a plurality of sub-fleets, each sub-fleet comprises an intelligent networked vehicle and at least one fully-manually-driven vehicle, and the technical effect of cooperatively controlling the running of each vehicle in the mixed fleet consisting of the intelligent networked vehicles and the fully-manually-driven vehicles is realized.
Further, the vehicle track control method provided by the embodiment of the invention establishes the optimization objective function with the purposes of minimizing the difference between the motion state and the ideal state of the intelligent internet vehicle at the intersection stop line and minimizing the accumulated oil consumption of the hybrid fleet, and obtains the vehicle tracks of the intelligent internet vehicle and the fully-manually-driven vehicle in the hybrid fleet based on the optimization objective function, so that the traffic efficiency of the hybrid fleet can be improved, and the energy consumption of each vehicle in the hybrid fleet can be optimized.
In one embodiment of the present invention, as shown in fig. 2, the hybrid fleet includes m sub-fleets, each sub-fleet includes an intelligent networked vehicle (CAV) and at least one full Human Driven Vehicle (HDV), and is represented by a matrix family:
wherein the content of the first and second substances,
a state matrix representing all vehicles in the mth sub-fleet. When the vehicle position and the vehicle speed are selected as the state variables, the state variables can be expressed as:
wherein the content of the first and second substances,
respectively representing the position and the speed of the intelligent networked vehicle in the mth sub-vehicle fleet;
respectively representing the position and the speed of the w-th full-man driving vehicle in the mth sub-fleet;
representing the acceleration of the w-th fully human-driven vehicle in the m-th sub-fleet.
In some embodiments of the present invention, as shown in fig. 3, step S101 includes:
s301, establishing a head-vehicle state equation of the intelligent networked vehicle based on the control variables and the vehicle kinematics model;
s302, establishing a following vehicle linear time-varying state equation of the full-manual driving vehicle based on the control variable and the speed difference stress-response model;
s303, establishing a continuous time domain state transfer equation of the sub-fleet of vehicles based on the state equation of the head vehicle and the state equation of the following vehicle.
The intelligent internet vehicle is used as a head vehicle of a sub-fleet with controllable tracks, the fleet is in a cruising state during green light is considered, a state equation is established according to a vehicle kinematics model, and the state equation of the head vehicle is as follows:
in the formula (I), the compound is shown in the specification,
and
are respectively as
The position and the speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
and
the position and the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t are respectively;
and the acceleration of the intelligent networked vehicle of the mth sub-fleet at the moment t.
The vehicle kinematic model refers to a basic relation model of position and speed and a basic relation model of speed and acceleration of the vehicle in the motion process.
Since the fully manually driven vehicle is the following vehicle in the sub-fleet, it cannot be trajectory controlled. In the actual running process, a following vehicle driver can drive along with a front vehicle according to the actual situation so as to indirectly optimize the track, and based on the indirect optimization principle, a model for describing the following process of the following vehicle and the head vehicle can be constructed, wherein the model is called as a speed difference stress-reflecting model in the specific embodiment of the invention; it is to be understood that the velocity difference stress-reflecting model is an indirectly followed model theory and does not need to be characterized by a specific formula or functional model.
Specifically, describing the following vehicle following process based on the speed difference stress-response model, the acceleration of any following vehicle satisfies:
wherein the content of the first and second substances,
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
the driver sensitivity coefficient is used for representing the stress response speed of the fully-manually driven vehicle along with the change of the speed difference and is a constant;
the real-time distance difference between the current vehicle and the preceding vehicle,
is an Optimal Velocity (OVM) model.
Wherein, the OVM model satisfies:
wherein the content of the first and second substances,
v1, V2, C1, C2 are all constants, specifically:
=0.85，V1=6.75，V2=7.91，C1=0.13，C2=1.57。
the following vehicle state equation is:
expanding the following vehicle state equation at the time t according to the Taylor series to obtain an acceleration first-order linear differential equation as follows:
order to
And establishing a following vehicle linear time-varying state equation:
wherein the content of the first and second substances,
is the first derivative.
Combining the head vehicle state equation and the following vehicle linear time-varying state equation to obtain a fleet continuous time domain state transfer equation, wherein the fleet continuous time domain state transfer equation is as follows:
in the formula (I), the compound is shown in the specification,
for mth sub-fleet
A state vector of a time;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the mth sub-fleet is
A state vector of a time;
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain;
is the input vector of the mth sub-fleet.
In order to reduce or eliminate static errors when the integration is introduced, so as to further improve the accuracy of predicting the vehicle trajectory, in some embodiments of the present invention, as shown in fig. 4, step S103 includes:
s401, discretizing a continuous time domain state transition equation based on discrete sampling step length, and establishing a state transition and state output equation in a discrete time domain;
s402, obtaining an incremental form state equation based on a state transition equation and a state output equation in a discrete time domain;
and S403, obtaining an augmented form state equation in the prediction time domain based on the incremental form state equation.
In some embodiments of the invention, the sampling step size in step S401 is determined by the fixed frequency of communication between the vehicle and the communication device (V2I). In one embodiment, the V2I communication message transmission period is 100 ms.
Specifically, let:
wherein k represents the step length after unit time discretization, and the continuous time domain state transition equation is discretized to obtain:
wherein the content of the first and second substances,
state vectors at the time k +1 in the discrete time domain;
the control vector is the control vector of k time in a discrete time domain;
a state transition matrix in a discrete time domain;
an input matrix in a discrete time domain; e is a natural constant, and tau is a discretized control matrix; c is a preset matrix; i is an identity matrix; o is a zero matrix.
In order to reduce or eliminate static errors when introducing integration, the above discretization equation is rewritten as an incremental equation, let:
in the formula (I), the compound is shown in the specification,
the state vector difference between the kth +1 moment and the kth moment of the mth sub-fleet is obtained;
the output vector difference of the mth sub-fleet at the k +1 th moment and the kth moment is obtained;
state vector at time k;
the vector is an output vector at the moment k + 1;
is the output vector at time k.
The incremental form state transition equation and the output equation are obtained through derivation, and the incremental form state transition equation and the output equation are as follows:
in the formula (I), the compound is shown in the specification,
the control vector difference between the kth +1 th moment and the kth moment of the mth sub-fleet is obtained.
Writing the incremental state transition equation and the output equation into a matrix form to obtain a state transition matrix, a control matrix and a state output matrix:
transforming the above equation into a standard augmented state transition and state output form:
in the formula (I), the compound is shown in the specification,
state vectors in an augmented form for the mth sub-fleet k + 1;
a state transition matrix in an augmented form for the mth sub-fleet;
state vectors in an augmented form for the mth sub-fleet k moment;
an input matrix in an augmented form for the mth sub-fleet;
the variable quantity of the input vector at the k moment of the mth sub-fleet is obtained;
an output vector of an amplification form at the moment k +1 of the mth sub-fleet;
an output vector of an amplification form at the k moment of the mth sub-fleet;
for the purpose of broadeningA predetermined matrix of the mth sub-fleet of vehicles in the form.
Because the augmented state vector of any kth sampling step length can be obtained, the augmented state transition equation in the prediction step length p is respectively solved by combining the control variable increment expression in the range of the output prediction step length p as follows:
the output equation of the augmentation state is:
in the formula (I), the compound is shown in the specification,
to be composed of
Augmented state vector prediction of time of day
An augmented state vector of time;
is composed of
An augmented state vector of time;
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
a state transition matrix in an augmented form within a prediction horizon;
an input matrix in an augmented form for an mth sub-fleet;
the change value of the control vector at the k moment;
to be composed of
Output vector prediction of time of day
An output vector of time;
a state transition matrix in a discrete time domain;
an input matrix in a discrete time domain;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain; ca is a preset matrix in an augmented form; e is a natural constant, and tau is a discretized control matrix; i is an identity matrix; o is a zero matrix.
In some embodiments of the present invention, as shown in fig. 5, step S104 comprises:
s501, acquiring a motion state vector difference between a motion state of an intelligent networked vehicle at a stop line of an intersection and an ideal state in a prediction time domain;
s502, acquiring a control vector difference of the intelligent networked vehicle in a control time domain;
s503, acquiring the instantaneous oil consumption of the intelligent networked vehicles and the fully-manually driven vehicles in each sub-fleet;
and S504, establishing an optimization objective function based on the motion state vector difference, the control vector difference and the instantaneous oil consumption.
In the embodiment of the invention, the optimized objective function is established based on the motion state vector difference, the control vector difference and the instantaneous oil consumption, so that the oil consumption of a hybrid fleet is minimized while the traffic efficiency of the hybrid fleet is ensured, and the energy consumption is reduced.
It should be understood that: ideally, the intelligent networked vehicle is traveling at a constant speed at a saturated flow rate. In an embodiment of the present invention, the speed of the intelligent networked vehicle may be 15m/s and the instantaneous acceleration value may be 0m/s 2.
Specifically, the optimization objective function is:
in the formula (I), the compound is shown in the specification,
to optimize the objective function;
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
the reference control vector is a reference control vector in the ith control step length of the intelligent networked vehicle of the mth sub-fleet; q is a weight factor matrix of the output vector; r is a weight factor matrix of the control vector;
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
instantaneous fuel consumption for all manually driven vehicles in each sub-vehicle; and | | is a norm.
Wherein, the reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet meets the following requirements:
the reference control vector in the ith control step of the intelligent networked vehicle of the mth sub-vehicle team meets the following requirements:
the instantaneous fuel consumption of the vehicle conforms to the following formula:
wherein the content of the first and second substances,
、
、
are all constant terms; m represents the mass of the automobile,
and
respectively representing the instantaneous position of the vehicleTime acceleration and velocity; g (t) represents the total power of work done by the automobile engine, and meets the following requirements:
since intersection signal timing and traffic light state need to be considered in the vehicle trajectory control process, in some embodiments of the present invention, the transit time T of all vehicles during the green light period should satisfy:
wherein g is the green light time length, th is the time interval between any adjacent vehicles, nm is the number of following vehicles in the mth sub-fleet, tm is the time required by the mth sub-fleet for the head vehicle to pass through, and the minimum green light time length is set as
Then:
According to the embodiment of the invention, the track control of the motorcade in signal periods can be realized by setting the time required by the first vehicle passing of the mth sub-motorcade, so that the controlled sub-motorcade can be ensured to smoothly pass along with the vehicles.
In some embodiments of the present invention, the constraint conditions in step S104 include a velocity constraint and an acceleration constraint, wherein the velocity constraint is:
the speed variable in the state vector meets the lowest and highest speed limit conditions of the road, and is expressed by a formula as follows:
if the lowest speed limit does not exist for the road,
the maximum speed limit is 60km/h,
。
the acceleration constraints are:
the acceleration in the control vector meets the maximum and minimum acceleration constraint conditions, and is expressed by a formula as follows:
according to the empirical value of the acceleration range, take
，
。
In order to better implement the vehicle trajectory control method in the embodiment of the present invention, on the basis of the vehicle trajectory control method, correspondingly, as shown in fig. 6, an embodiment of the present invention further provides a vehicle trajectory control device 600, configured to control trajectories of vehicles in a hybrid fleet, where the hybrid fleet includes a plurality of intelligent networked vehicles and a plurality of fully manually driven vehicles, and the vehicle trajectory control device 600 includes:
the hybrid fleet dividing unit 601 is configured to divide the hybrid fleet into a plurality of sub-fleets by using the intelligent networked vehicle as a head vehicle, where each sub-fleet includes one intelligent networked vehicle and at least one full-man driven vehicle; selecting the position and the speed of the vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
the time domain state transfer equation establishing unit 602 is used for selecting the acceleration of the intelligent networked vehicle as a control variable and establishing a fleet continuous time domain state transfer equation based on the control variable, the vehicle kinematics model and the speed difference stress-response model;
the augmented form state equation establishing unit 603 is configured to discretize the continuous time domain state transition equation to obtain an augmented form state equation;
and the vehicle track control unit 604 is used for establishing an optimization objective function aiming at the minimum difference between the motion state and the ideal state of the intelligent internet vehicle at the stop line of the intersection and the minimum accumulated oil consumption of the hybrid fleet, determining a constraint condition of the optimization objective function, and obtaining the vehicle tracks of the intelligent internet vehicle and the fully-manually driven vehicle in the hybrid fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition.
The vehicle trajectory control device 600 provided in the foregoing embodiment may implement the technical solutions described in the foregoing vehicle trajectory control method embodiments, and the specific implementation principles of the modules or units may refer to the corresponding contents in the foregoing vehicle trajectory control method embodiments, and are not described herein again.
As shown in fig. 7, the present invention further provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, and a display 703. Fig. 7 shows only some of the components of the electronic device 700, but it is to be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The storage 702 may in some embodiments be an internal storage unit of the electronic device 700, such as a hard disk or a memory of the electronic device 700. The memory 702 may also be an external storage device of the electronic device 700 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc., provided on the electronic device 700.
Further, the memory 702 may also include both internal storage units and external storage devices of the electronic device 700. The memory 702 is used for storing application software and various types of data for installing the electronic apparatus 700.
The processor 701 may be, in some embodiments, a Central Processing Unit (CPU), microprocessor or other data Processing chip for executing program codes stored in the memory 702 or Processing data, such as the vehicle trajectory control method of the present invention.
The display 703 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 703 is used for displaying information at the electronic device 700 and for displaying a visual user interface. The components 701 and 703 of the electronic device 700 communicate with each other via a system bus.
In one embodiment, when the processor 701 executes the vehicle trajectory control program in the memory 702, the following steps may be implemented:
dividing the mixed fleet into a plurality of sub fleets by taking the intelligent networked vehicles as head vehicles, wherein each sub fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting the vehicle position and the vehicle speed as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
selecting the acceleration of the intelligent networked vehicle as a control variable, and establishing a fleet continuous time domain state transfer equation based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
discretizing the continuous time domain state transition equation to obtain an augmented form state equation;
the method comprises the steps of establishing an optimization objective function with the aim of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at a stop line of an intersection and minimizing the accumulated oil consumption of a hybrid vehicle fleet, determining a constraint condition of the optimization objective function, and obtaining vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid vehicle fleet based on an augmentation form state equation, the optimization objective function and the constraint condition.
It should be understood that: when the processor 701 executes the vehicle trajectory control program in the memory 702, other functions may be implemented besides the above functions, which may be specifically referred to the description of the corresponding method embodiments above.
Further, the type of the electronic device 700 is not particularly limited in the embodiments of the present invention, and the electronic device 700 may be a portable electronic device such as a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a wearable device, and a laptop computer (laptop). Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices that carry an IOS, android, microsoft, or other operating system. The portable electronic device may also be other portable electronic devices such as laptop computers (laptop) with touch sensitive surfaces (e.g., touch panels) and the like. It should also be understood that in other embodiments of the present invention, the electronic device 700 may not be a portable electronic device, but may be a desktop computer having a touch-sensitive surface (e.g., a touch panel).
Accordingly, the present application further provides a computer-readable storage medium, which is used for storing a computer-readable program or instruction, and when the program or instruction is executed by a processor, the steps or functions of the vehicle trajectory control method provided by the above method embodiments can be implemented.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, to instruct associated hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The vehicle track control method, apparatus, electronic device and storage medium provided by the present invention are described in detail above, and specific examples are applied herein to explain the principles and embodiments of the present invention, and the descriptions of the above embodiments are only used to help understanding the method and its core idea of the present invention; meanwhile, for those skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed, and in summary, the content of the present specification should not be construed as limiting the present invention.
Claims (9)
1. A vehicle trajectory control method for controlling trajectories of vehicles in a hybrid fleet of vehicles, the hybrid fleet comprising a plurality of intelligent networked vehicles and a plurality of fully human driven vehicles, the vehicle trajectory control method comprising:
dividing the mixed fleet into a plurality of sub fleets by taking the intelligent networked vehicle as a head vehicle, wherein each sub fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting the vehicle position and the vehicle speed of the intelligent networked vehicle and the vehicle position, the vehicle speed and the vehicle acceleration of the fully-manually-driven vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
selecting the acceleration of the intelligent networked vehicle as a control variable, and establishing a fleet continuous time domain state transfer equation based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
discretizing the continuous time domain state transition equation to obtain an augmented form state equation;
establishing an optimization objective function with the purpose of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at a stop line of an intersection and minimizing the accumulated oil consumption of the hybrid vehicle fleet, determining a constraint condition of the optimization objective function, and obtaining vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid vehicle fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition;
the establishing of the fleet continuous time domain state transfer equation based on the control variables, the vehicle kinematics model and the speed difference stress-response model comprises the following steps:
establishing a head-vehicle state equation of the intelligent networked vehicle based on the control variables and the vehicle kinematics model;
establishing a following vehicle linear time-varying state equation of the fully manually driven vehicle based on the control variables and the speed difference stress-response model;
and establishing a fleet continuous time domain state transfer equation of the sub fleet based on the head vehicle state equation and the following vehicle state equation.
2. The vehicle trajectory control method of claim 1, wherein the fleet continuous time domain state transition equation is:
in the formula (I), the compound is shown in the specification,
is composed of
The position of the intelligent networked vehicle of the mth sub-fleet at the moment;
is composed of
The speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
the position of the intelligent networked vehicle of the mth sub-fleet at the moment t;
the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t;
the acceleration of the intelligent networked vehicle of the mth sub-fleet at the moment t;
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
the driver sensitivity coefficient is used for representing the stress response speed of the fully-manually driven vehicle along with the change of the speed difference and is a constant;
the real-time distance difference between the current vehicle and the preceding vehicle,
an optimal speed model is obtained;
is the first derivative;
for mth sub-fleet
A state vector of a time;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the mth sub-fleet is
A state vector of a time;
an input matrix of the mth sub-fleet in a continuous time domain;
is the input vector of the mth sub-fleet.
3. The vehicle trajectory control method of claim 1, wherein discretizing the continuous time domain state transition equation to obtain an augmented form state equation comprises:
discretizing the continuous time domain state transition equation based on discrete sampling step length, and establishing a state transition and state output equation in a discrete time domain;
obtaining an incremental form state equation based on the state transition equation and the state output equation in the discrete time domain;
and obtaining an augmented form state equation in the prediction time domain based on the incremental form state equation.
4. The vehicle trajectory control method according to claim 3, characterized in that the augmented form state equation includes an augmented state transition equation and an augmented state output equation;
the augmented state transition equation is:
the output equation of the augmentation state is as follows:
in the formula (I), the compound is shown in the specification,
to be composed of
Augmented state vector prediction of time of day
An augmented state vector of time;
is composed of
An augmented state vector of time;
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
a state transition matrix in an augmented form within a prediction horizon;
an input matrix in an augmented form for the mth sub-fleet;
the change value of the control vector at the k moment;
to be composed of
Output vector prediction of time of day
An output vector of time;
a state transition matrix in a discrete time domain;
as a discrete time domainAn input matrix within;
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain; ca is a preset matrix in an augmented form; e is a natural constant, and tau is a discretized control matrix; i is an identity matrix; o is a zero matrix.
5. The vehicle trajectory control method of claim 1, wherein the establishing an optimization objective function with the objective of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at the intersection stop line and minimizing the cumulative fuel consumption of the hybrid fleet comprises:
acquiring a motion state vector difference between a motion state of the intelligent networked vehicle at a stop line of an intersection and an ideal state in a prediction time domain;
acquiring a control vector difference of the intelligent networked vehicle in a control time domain;
acquiring the instantaneous oil consumption of the intelligent networked vehicles and the full-manual driving vehicles in each sub-fleet;
and establishing the optimization objective function based on the motion state vector difference, the control vector difference and the instantaneous fuel consumption.
6. The vehicle trajectory control method according to claim 5, characterized in that the optimization objective function is:
in the formula (I), the compound is shown in the specification,
is the optimization objective function;
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
the reference control vector is a reference control vector in the ith control step length of the intelligent networked vehicle of the mth sub-fleet; q is a weight factor matrix of the output vector; r is a weight factor matrix of the control vector;
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
instantaneous fuel consumption for all manually driven vehicles in each sub-vehicle; and | | is a norm.
7. A vehicle trajectory control device for controlling trajectories of vehicles in a hybrid fleet of vehicles, the hybrid fleet comprising a plurality of intelligent networked vehicles and a plurality of fully human driven vehicles, the vehicle trajectory control device comprising:
the hybrid fleet division unit is used for dividing the hybrid fleet into a plurality of sub-fleets by taking the intelligent networked vehicles as head vehicles, and each sub-fleet comprises one intelligent networked vehicle and at least one full-manual driving vehicle; selecting the vehicle position and the vehicle speed of the intelligent networked vehicle and the vehicle position, the vehicle speed and the vehicle acceleration of the full-manual driving vehicle as state variables, and establishing a state vector of the hybrid fleet according to the state variables;
the time domain state transfer equation establishing unit is used for selecting the acceleration of the intelligent networked vehicle as a control variable and establishing a continuous time domain state transfer equation of the fleet based on the control variable, a vehicle kinematics model and a speed difference stress-response model;
the augmented form state equation establishing unit is used for discretizing the continuous time domain state transfer equation to obtain an augmented form state equation;
the vehicle track control unit is used for establishing an optimization objective function with the purposes of minimizing the difference between the motion state and the ideal state of the intelligent networked vehicle at a stop line of an intersection and minimizing the accumulated oil consumption of the hybrid vehicle fleet, determining the constraint condition of the optimization objective function, and obtaining the vehicle tracks of the intelligent networked vehicle and the fully-manually-driven vehicle in the hybrid vehicle fleet based on the state equation of the augmented form, the optimization objective function and the constraint condition;
the establishing of the fleet continuous time domain state transfer equation based on the control variables, the vehicle kinematics model and the speed difference stress-response model comprises the following steps:
establishing a head-vehicle state equation of the intelligent networked vehicle based on the control variables and the vehicle kinematics model;
establishing a following vehicle linear time-varying state equation of the fully manually driven vehicle based on the control variables and the speed difference stress-response model;
and establishing a fleet continuous time domain state transfer equation of the sub fleet based on the head vehicle state equation and the following vehicle state equation.
8. An electronic device comprising a memory and a processor, wherein,
the memory is used for storing programs;
the processor, coupled with the memory, is configured to execute the program stored in the memory to implement the steps of the vehicle trajectory control method of any one of the preceding claims 1 to 6.
9. A computer-readable storage medium storing a computer-readable program or instructions, which when executed by a processor, implement the steps of the vehicle trajectory control method of any one of claims 1 to 6.
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