CN114387787A - 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 PDF

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CN114387787A
CN114387787A CN202210293317.3A CN202210293317A CN114387787A CN 114387787 A CN114387787 A CN 114387787A CN 202210293317 A CN202210293317 A CN 202210293317A CN 114387787 A CN114387787 A CN 114387787A
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vehicle
fleet
state
equation
intelligent networked
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CN114387787B (en
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何书贤
邱志军
安德玺
任学锋
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Ismartways Wuhan Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G08G1/202—Dispatching vehicles on the basis of a location, e.g. taxi dispatching
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/80—Technologies aiming to reduce greenhouse gasses emissions common to all road transportation technologies
    • Y02T10/84—Data processing systems or methods, management, administration

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  • General Physics & Mathematics (AREA)
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  • Hybrid Electric Vehicles (AREA)
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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

Vehicle track control method and device, electronic equipment and storage medium
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 technical problem, the invention provides a vehicle trajectory control method for controlling the trajectory of each vehicle in a hybrid fleet, wherein the hybrid fleet comprises a plurality of intelligent networked vehicles and a plurality of fully manually driven vehicles, and the vehicle trajectory control method comprises the following steps:
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 a vehicle position and a 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;
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:
Figure 494706DEST_PATH_IMAGE001
Figure 688927DEST_PATH_IMAGE002
Figure 978701DEST_PATH_IMAGE003
Figure 526357DEST_PATH_IMAGE004
Figure 821072DEST_PATH_IMAGE005
Figure 186194DEST_PATH_IMAGE006
Figure 605674DEST_PATH_IMAGE007
in the formula (I), the compound is shown in the specification,
Figure 81655DEST_PATH_IMAGE008
is composed of
Figure 902981DEST_PATH_IMAGE009
The position of the intelligent networked vehicle of the mth sub-fleet at the moment;
Figure 206048DEST_PATH_IMAGE010
is composed of
Figure 112824DEST_PATH_IMAGE009
The speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
Figure 126917DEST_PATH_IMAGE011
the position of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 396224DEST_PATH_IMAGE012
the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 978515DEST_PATH_IMAGE013
the acceleration of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 497221DEST_PATH_IMAGE014
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
Figure 190371DEST_PATH_IMAGE015
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;
Figure 609457DEST_PATH_IMAGE016
the real-time distance difference between the current vehicle and the preceding vehicle,
Figure 97070DEST_PATH_IMAGE017
an optimal speed model is obtained;
Figure 368652DEST_PATH_IMAGE018
is the first derivative;
Figure 865492DEST_PATH_IMAGE019
for mth sub-fleet
Figure 843812DEST_PATH_IMAGE020
A state vector of a time;
Figure 626961DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 995625DEST_PATH_IMAGE022
the mth sub-fleet is
Figure 656676DEST_PATH_IMAGE023
A state vector of a time;
Figure 427186DEST_PATH_IMAGE024
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain;
Figure 381235DEST_PATH_IMAGE025
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:
Figure 502775DEST_PATH_IMAGE026
Figure 200473DEST_PATH_IMAGE027
Figure 825489DEST_PATH_IMAGE028
the output equation of the augmentation state is as follows:
Figure 419281DEST_PATH_IMAGE029
Figure 120128DEST_PATH_IMAGE030
Figure 762462DEST_PATH_IMAGE031
Figure 632198DEST_PATH_IMAGE032
Figure 68995DEST_PATH_IMAGE033
Figure 758602DEST_PATH_IMAGE034
Figure 266944DEST_PATH_IMAGE035
Figure 600974DEST_PATH_IMAGE036
Figure 100350DEST_PATH_IMAGE037
Figure 152620DEST_PATH_IMAGE038
Figure 995811DEST_PATH_IMAGE039
Figure 449926DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 556422DEST_PATH_IMAGE041
to be composed of
Figure 486201DEST_PATH_IMAGE042
Augmented state vector prediction of time of day
Figure 8449DEST_PATH_IMAGE043
An augmented state vector of time;
Figure 143502DEST_PATH_IMAGE044
is composed of
Figure 217637DEST_PATH_IMAGE042
An augmented state vector of time;
Figure 244499DEST_PATH_IMAGE045
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
Figure 695072DEST_PATH_IMAGE046
a state transition matrix in an augmented form within a prediction horizon;
Figure 186096DEST_PATH_IMAGE047
is the mth childAn input matrix of an augmented form of a fleet of vehicles;
Figure 306499DEST_PATH_IMAGE048
the change value of the control vector at the k moment;
Figure 446756DEST_PATH_IMAGE049
to be composed of
Figure 576386DEST_PATH_IMAGE042
Output vector prediction of time of day
Figure 718654DEST_PATH_IMAGE043
An output vector of time;
Figure 275537DEST_PATH_IMAGE050
a state transition matrix in a discrete time domain;
Figure 136046DEST_PATH_IMAGE051
an input matrix in a discrete time domain;
Figure 131684DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 269404DEST_PATH_IMAGE024
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:
Figure 847057DEST_PATH_IMAGE052
Figure 194862DEST_PATH_IMAGE053
Figure 666295DEST_PATH_IMAGE054
Figure 783155DEST_PATH_IMAGE055
in the formula (I), the compound is shown in the specification,
Figure 9737DEST_PATH_IMAGE056
is the optimization objective function;
Figure 720204DEST_PATH_IMAGE057
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
Figure 559110DEST_PATH_IMAGE058
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
Figure 796056DEST_PATH_IMAGE059
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
Figure 865643DEST_PATH_IMAGE060
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;
Figure 188040DEST_PATH_IMAGE061
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
Figure 329171DEST_PATH_IMAGE062
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 trajectories of vehicles in a hybrid fleet, where the hybrid fleet includes 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 a vehicle position and a vehicle speed 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 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 vehicle fleet, determining a 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 augmentation form state equation, 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 provided by the invention, firstly, the intelligent networked vehicle is taken as a 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 a plurality of intelligent networked vehicles and a plurality of fully-manually-driven vehicles is realized.
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 full-manual driving vehicle in the hybrid fleet based on the optimization objective function, so that the energy consumption of each vehicle in the hybrid fleet can be optimized while the traffic efficiency of the hybrid fleet is improved.
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 schematic flow chart diagram 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 schematic flow chart of one embodiment of S103 of FIG. 1;
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 disclosure illustrate operations implemented according to 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 performed in reverse order or 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:
Figure 295990DEST_PATH_IMAGE063
wherein the content of the first and second substances,
Figure 97331DEST_PATH_IMAGE064
a state matrix representing all vehicles in the mth sub-fleet. When the vehicle position and the vehicle speed are taken as the state variables, the state variables can be expressed as:
Figure 907024DEST_PATH_IMAGE065
wherein the content of the first and second substances,
Figure 258371DEST_PATH_IMAGE066
respectively representing the position and the speed of the intelligent networked vehicle in the mth sub-fleet;
Figure 204330DEST_PATH_IMAGE067
respectively representing the position and the speed of the w-th full-man driving vehicle in the mth sub-fleet;
Figure 881299DEST_PATH_IMAGE068
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-car 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:
Figure 115971DEST_PATH_IMAGE069
in the formula (I), the compound is shown in the specification,
Figure 897108DEST_PATH_IMAGE008
and
Figure 838519DEST_PATH_IMAGE070
are respectively as
Figure 483127DEST_PATH_IMAGE009
The position and the speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
Figure 1833DEST_PATH_IMAGE011
and
Figure 694983DEST_PATH_IMAGE012
the position and the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t are respectively;
Figure 615534DEST_PATH_IMAGE013
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 human-driven vehicle is the following vehicle in the sub-fleet, it cannot be trajectory controlled. However, in the actual operation process, a following vehicle driver can drive along with a front vehicle according to the actual situation, so that the track is indirectly optimized, 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-reflection 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, if the following vehicle following process is described based on the velocity difference stress-response model, the acceleration of any following vehicle satisfies:
Figure 634306DEST_PATH_IMAGE071
wherein the content of the first and second substances,
Figure 138843DEST_PATH_IMAGE014
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
Figure 698000DEST_PATH_IMAGE015
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;
Figure 348424DEST_PATH_IMAGE016
the real-time distance difference between the current vehicle and the preceding vehicle,
Figure 662731DEST_PATH_IMAGE017
is an Optimal Velocity (OVM) model.
Wherein, the OVM model satisfies:
Figure 31395DEST_PATH_IMAGE072
wherein the content of the first and second substances,
Figure 190981DEST_PATH_IMAGE073
v1, V2, C1, C2 are all constants, specifically:
Figure 525273DEST_PATH_IMAGE073
=0.85，V1=6.75，V2=7.91，C1=0.13，C2=1.57。
the following vehicle state equation is:
Figure 620268DEST_PATH_IMAGE074
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:
Figure 866442DEST_PATH_IMAGE075
order to
Figure 767401DEST_PATH_IMAGE007
And establishing a following vehicle linear time-varying state equation:
Figure 392418DEST_PATH_IMAGE076
wherein the content of the first and second substances,
Figure 48527DEST_PATH_IMAGE018
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:
Figure 218215DEST_PATH_IMAGE001
Figure 188445DEST_PATH_IMAGE002
Figure 667968DEST_PATH_IMAGE003
Figure 494979DEST_PATH_IMAGE004
Figure 387848DEST_PATH_IMAGE005
Figure 302715DEST_PATH_IMAGE006
in the formula (I), the compound is shown in the specification,
Figure 528422DEST_PATH_IMAGE019
for mth sub-fleet
Figure 401700DEST_PATH_IMAGE020
A state vector of a time;
Figure 578603DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 297161DEST_PATH_IMAGE077
the mth sub-fleet is
Figure 875910DEST_PATH_IMAGE023
A state vector of a time;
Figure 982406DEST_PATH_IMAGE024
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain;
Figure 521972DEST_PATH_IMAGE025
is the input vector of the mth sub-fleet.
In order to reduce or eliminate static errors when the integration is introduced, thereby further improving the accuracy of the predicted 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:
Figure 932968DEST_PATH_IMAGE078
wherein k represents the step length after unit time discretization, and the continuous time domain state transition equation is discretized to obtain:
Figure 241589DEST_PATH_IMAGE079
Figure 518987DEST_PATH_IMAGE080
wherein the content of the first and second substances,
Figure 404903DEST_PATH_IMAGE081
state vectors at the time k +1 in the discrete time domain;
Figure 324318DEST_PATH_IMAGE082
the control vector is the control vector of k time in a discrete time domain;
Figure 284184DEST_PATH_IMAGE050
a state transition matrix in a discrete time domain;
Figure 233947DEST_PATH_IMAGE051
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:
Figure 13685DEST_PATH_IMAGE083
in the formula (I), the compound is shown in the specification,
Figure 2369DEST_PATH_IMAGE084
the state vector difference between the kth +1 th moment and the kth moment of the mth sub-fleet is obtained;
Figure 20004DEST_PATH_IMAGE085
the output vector difference of the mth sub-fleet at the k +1 th moment and the kth moment is obtained;
Figure 701521DEST_PATH_IMAGE086
state vector at time k;
Figure 765292DEST_PATH_IMAGE087
the vector is an output vector at the moment k + 1;
Figure 167454DEST_PATH_IMAGE088
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:
Figure 397185DEST_PATH_IMAGE089
in the formula (I), the compound is shown in the specification,
Figure 921707DEST_PATH_IMAGE090
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:
Figure 207195DEST_PATH_IMAGE091
Figure 803262DEST_PATH_IMAGE092
Figure 61068DEST_PATH_IMAGE093
transforming the above equation into a standard augmented state transition and state output form:
Figure 818808DEST_PATH_IMAGE094
in the formula (I), the compound is shown in the specification,
Figure 358636DEST_PATH_IMAGE095
state vectors in an augmented form for the mth sub-fleet k + 1;
Figure 368180DEST_PATH_IMAGE096
a state transition matrix in an augmented form for the mth sub-fleet;
Figure 605127DEST_PATH_IMAGE097
state vectors in an augmented form for the mth sub-fleet k moment;
Figure 674714DEST_PATH_IMAGE098
an input matrix in an augmented form for the mth sub-fleet;
Figure 934794DEST_PATH_IMAGE099
the variation of the input vector at the k moment of the mth sub-fleet is obtained;
Figure 138242DEST_PATH_IMAGE100
an output vector of an amplification form at the moment k +1 of the mth sub-fleet;
Figure 665913DEST_PATH_IMAGE101
an output vector of an amplification form at the k moment of the mth sub-fleet;
Figure 968718DEST_PATH_IMAGE102
a predetermined matrix for the mth sub-fleet in an augmented 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:
Figure 778411DEST_PATH_IMAGE103
Figure 129758DEST_PATH_IMAGE027
Figure 341297DEST_PATH_IMAGE028
the output equation of the augmentation state is:
Figure 487107DEST_PATH_IMAGE029
Figure 223244DEST_PATH_IMAGE030
Figure 768495DEST_PATH_IMAGE031
Figure 444327DEST_PATH_IMAGE037
Figure 151252DEST_PATH_IMAGE038
Figure 873221DEST_PATH_IMAGE039
Figure 566370DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 985457DEST_PATH_IMAGE041
to be composed of
Figure 4228DEST_PATH_IMAGE042
Augmented state vector prediction of time of day
Figure 744651DEST_PATH_IMAGE043
An augmented state vector of time;
Figure 241492DEST_PATH_IMAGE044
is composed of
Figure 219812DEST_PATH_IMAGE042
An augmented state vector of time;
Figure 737381DEST_PATH_IMAGE045
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
Figure 466565DEST_PATH_IMAGE046
a state transition matrix in an augmented form within a prediction horizon;
Figure 829413DEST_PATH_IMAGE047
an input matrix in an augmented form for the mth sub-fleet;
Figure 458977DEST_PATH_IMAGE048
the change value of the control vector at the k moment;
Figure 819552DEST_PATH_IMAGE049
to be composed of
Figure 534567DEST_PATH_IMAGE042
Output vector prediction of time of day
Figure 223078DEST_PATH_IMAGE043
An output vector of time;
Figure 848095DEST_PATH_IMAGE050
a state transition matrix in a discrete time domain;
Figure 238625DEST_PATH_IMAGE051
an input matrix in a discrete time domain;
Figure 316302DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 817691DEST_PATH_IMAGE024
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, obtaining 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, obtaining 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.
According to 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 value of the acceleration may be 0m/s 2.
Specifically, the optimization objective function is:
Figure 562793DEST_PATH_IMAGE052
Figure 124224DEST_PATH_IMAGE053
Figure 423619DEST_PATH_IMAGE054
Figure 230163DEST_PATH_IMAGE055
in the formula (I), the compound is shown in the specification,
Figure 829771DEST_PATH_IMAGE056
to optimize the objective function;
Figure 234208DEST_PATH_IMAGE057
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
Figure 348795DEST_PATH_IMAGE058
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
Figure 129669DEST_PATH_IMAGE059
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
Figure 708418DEST_PATH_IMAGE060
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;
Figure 221439DEST_PATH_IMAGE061
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
Figure 620059DEST_PATH_IMAGE062
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:
Figure 142307DEST_PATH_IMAGE104
the reference control vector in the ith control step length of the intelligent networked vehicle of the mth sub-fleet meets the following requirements:
Figure 74098DEST_PATH_IMAGE105
the instantaneous fuel consumption of the vehicle conforms to the following formula:
Figure 23599DEST_PATH_IMAGE106
wherein the content of the first and second substances,
Figure 175095DEST_PATH_IMAGE107
、
Figure 32192DEST_PATH_IMAGE108
、
Figure 460900DEST_PATH_IMAGE109
are all constant terms; m represents the mass of the automobile,
Figure 705936DEST_PATH_IMAGE110
and
Figure 688936DEST_PATH_IMAGE111
respectively representing the instantaneous acceleration and speed of the vehicle; g (t) represents the total power of work done by the automobile engine, and meets the following requirements:
Figure 677620DEST_PATH_IMAGE112
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:
Figure 757572DEST_PATH_IMAGE113
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
Figure 48876DEST_PATH_IMAGE114
Then:
Figure 410849DEST_PATH_IMAGE115
Figure 78591DEST_PATH_IMAGE116
the time required for the first vehicle to pass of the (m-1) th sub-vehicle fleet is obtained;
Figure 544207DEST_PATH_IMAGE117
the number of following vehicles in the m-1 th sub-fleet.
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:
Figure 131046DEST_PATH_IMAGE118
if the lowest speed limit does not exist for the road,
Figure 354217DEST_PATH_IMAGE119
the maximum speed limit is 60km/h,
Figure 950284DEST_PATH_IMAGE120
。
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:
Figure 942511DEST_PATH_IMAGE121
according to the empirical value of the acceleration range, take
Figure 372355DEST_PATH_IMAGE122
，
Figure 705991DEST_PATH_IMAGE123
。
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 configured to select an acceleration of the intelligent networked vehicle as a control variable, and establish 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 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;
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: the processor 701, when executing the vehicle trajectory control program in the memory 702, may also implement other functions in addition to 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), etc. 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, the vehicle track control device, the electronic device and the storage medium provided by the invention are described in detail, specific examples are applied in the description to explain the principle and the implementation of the invention, and the description of the embodiments is only used to help understanding the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

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 a vehicle position and a 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;
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.
2. The vehicle trajectory control method of claim 1, wherein establishing a fleet continuous time domain state transition equation based on the control variables, a vehicle kinematics model, and a speed difference stress-response model comprises:
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.
3. The vehicle trajectory control method of claim 2, wherein the fleet continuous time domain state transition equation is:
Figure 201223DEST_PATH_IMAGE001
Figure 834330DEST_PATH_IMAGE002
Figure 669431DEST_PATH_IMAGE003
Figure 413396DEST_PATH_IMAGE004
Figure 384763DEST_PATH_IMAGE005
Figure 454350DEST_PATH_IMAGE006
Figure 511168DEST_PATH_IMAGE007
in the formula (I), the compound is shown in the specification,
Figure 121141DEST_PATH_IMAGE008
is composed of
Figure 822380DEST_PATH_IMAGE009
Intelligent internet connection vehicle for mth sub-vehicle fleet at momentA location of the vehicle;
Figure 686038DEST_PATH_IMAGE010
is composed of
Figure 839939DEST_PATH_IMAGE009
The speed of the intelligent networked vehicle of the mth sub-fleet at the moment;
Figure 315919DEST_PATH_IMAGE011
the position of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 137245DEST_PATH_IMAGE012
the speed of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 345372DEST_PATH_IMAGE013
the acceleration of the intelligent networked vehicle of the mth sub-fleet at the moment t;
Figure 376782DEST_PATH_IMAGE014
instantaneous acceleration of the nth fully manually driven vehicle in any mth sub-fleet;
Figure 266241DEST_PATH_IMAGE015
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;
Figure 66707DEST_PATH_IMAGE016
the real-time distance difference between the current vehicle and the preceding vehicle,
Figure 648998DEST_PATH_IMAGE017
an optimal speed model is obtained;
Figure 839808DEST_PATH_IMAGE018
is the first derivative;
Figure 159056DEST_PATH_IMAGE019
for mth sub-fleet
Figure 954973DEST_PATH_IMAGE020
A state vector of a time;
Figure 301641DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 183009DEST_PATH_IMAGE022
the mth sub-fleet is
Figure 804484DEST_PATH_IMAGE023
A state vector of a time;
Figure 251646DEST_PATH_IMAGE024
the input matrix is the input matrix of the mth sub-fleet in the continuous time domain;
Figure 175739DEST_PATH_IMAGE025
is the input vector of the mth sub-fleet.
4. 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.
5. The vehicle locus control method according to claim 4, wherein the augmented form state equation includes an augmented state transition equation and an augmented state output equation;
the augmented state transition equation is:
Figure 403458DEST_PATH_IMAGE026
Figure 438410DEST_PATH_IMAGE027
Figure 589947DEST_PATH_IMAGE028
the output equation of the augmentation state is as follows:
Figure 684942DEST_PATH_IMAGE029
Figure 399957DEST_PATH_IMAGE030
Figure 35338DEST_PATH_IMAGE031
Figure 660355DEST_PATH_IMAGE032
Figure 785305DEST_PATH_IMAGE033
Figure 862983DEST_PATH_IMAGE034
Figure 364371DEST_PATH_IMAGE035
Figure 843894DEST_PATH_IMAGE036
Figure 77429DEST_PATH_IMAGE037
Figure 268502DEST_PATH_IMAGE038
Figure 448947DEST_PATH_IMAGE039
Figure 642031DEST_PATH_IMAGE040
in the formula (I), the compound is shown in the specification,
Figure 249730DEST_PATH_IMAGE041
to be composed of
Figure 98737DEST_PATH_IMAGE042
Augmented state vector prediction of time of day
Figure 207508DEST_PATH_IMAGE043
An augmented state vector of time;
Figure 396043DEST_PATH_IMAGE044
is composed of
Figure 33698DEST_PATH_IMAGE042
An augmented state vector of time;
Figure 573264DEST_PATH_IMAGE045
a state transition matrix in an augmented form for the mth sub-fleet within the prediction horizon;
Figure 718681DEST_PATH_IMAGE046
a state transition matrix in an augmented form within a prediction horizon;
Figure 824040DEST_PATH_IMAGE047
an input matrix in an augmented form for the mth sub-fleet;
Figure 773542DEST_PATH_IMAGE048
the change value of the control vector at the k moment;
Figure 925037DEST_PATH_IMAGE049
to be composed of
Figure 719818DEST_PATH_IMAGE042
Output vector prediction of time of day
Figure 7580DEST_PATH_IMAGE043
An output vector of time;
Figure 190300DEST_PATH_IMAGE050
a state transition matrix in a discrete time domain;
Figure 438878DEST_PATH_IMAGE051
an input matrix in a discrete time domain;
Figure 427563DEST_PATH_IMAGE021
the state transition matrix is the state transition matrix of the mth sub-fleet in the continuous time domain;
Figure 445198DEST_PATH_IMAGE024
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.
6. 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.
7. The vehicle trajectory control method according to claim 6, characterized in that the optimization objective function is:
Figure 362600DEST_PATH_IMAGE052
Figure 98475DEST_PATH_IMAGE053
Figure 890851DEST_PATH_IMAGE054
Figure 825309DEST_PATH_IMAGE055
in the formula (I), the compound is shown in the specification,
Figure 287514DEST_PATH_IMAGE056
is the optimization objective function;
Figure 635319DEST_PATH_IMAGE057
actual output vectors in the ith prediction step length of the intelligent networked vehicles of the mth sub-fleet are obtained;
Figure 575593DEST_PATH_IMAGE058
a reference output vector in the ith prediction step length of the intelligent networked vehicle of the mth sub-fleet is obtained;
Figure 692453DEST_PATH_IMAGE059
the actual control vector is in the ith control step length of the intelligent networked vehicle of the mth sub-fleet;
Figure 591139DEST_PATH_IMAGE060
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;
Figure 98344DEST_PATH_IMAGE061
instantaneous fuel consumption of intelligent networked vehicles in each sub-fleet;
Figure 465478DEST_PATH_IMAGE062
instantaneous fuel consumption for all manually driven vehicles in each sub-vehicle; and | | is a norm.
8. 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 a vehicle position and a vehicle speed 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 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 vehicle fleet, determining a 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 augmentation form state equation, the optimization objective function and the constraint condition.
9. 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 7.
10. A computer-readable storage medium storing a computer-readable program or instructions, which when executed by a processor, is capable of implementing the steps in the vehicle trajectory control method of any one of claims 1 to 7.
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CN116804852A (en) * 2023-06-25 2023-09-26 北京交通大学 Mixed motorcade ecological driving track optimization method

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