CN115206093A - Traffic flow control method based on intelligent network connection vehicle - Google Patents
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- G08G1/00—Traffic control systems for road vehicles
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- G—PHYSICS
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- 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
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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
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
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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
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- G—PHYSICS
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- 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
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Abstract
The invention relates to a traffic flow control method based on an intelligent internet vehicle, which comprises the following steps: s1, establishing a system dynamic equation of a novel mixed traffic flow of a road section by taking the expected speed of an intelligent internet vehicle as a control quantity; s2, obtaining a linearized expression of the system dynamic equation based on a feedback linearization theory; s3, designing a controller based on a linear expression of a system dynamic equation; and S4, calculating the expected speed of the intelligent networked automatic driving vehicle based on the designed controller, and controlling the intelligent networked vehicle to run based on the expected speed. Compared with the prior art, the method has the advantages of high control precision, strong applicability, high calculation efficiency, quick control response and the like.
Description
Technical Field
The invention relates to the technical field of internet automatic driving, in particular to a traffic flow control method based on an intelligent internet vehicle.
Background
With the development of network communication technology and automatic driving technology, internet-connected automatic driving vehicles have become mature. Compared with the traditional automobile, the automobile can greatly reduce fuel consumption, reduce traffic pollution, improve traffic operation efficiency and ensure traffic safety. Meanwhile, the vehicle-mounted automatic driving control system is provided with advanced sensors, actuators and other devices, and organically integrates modern communication technologies, so that the vehicle-mounted automatic driving control system has vehicle-to-vehicle communication, vehicle-to-road communication and environment sensing capabilities, and can make decisions on the obtained information through a control system to generate control instructions, and finally, the vehicle actuators complete automatic driving control operation.
The speed coordination control technology of the networked automatic driving vehicle is a novel intelligent networked technology. The technology can reduce the difference of different vehicle speeds in the traffic flow by changing the speed of the controlled vehicle, so that the traffic system is more stable and safer, and the problem of congestion of a road bottleneck point can be solved to a certain extent. In order to realize the technology, a novel mixed traffic flow running state evolution mechanism is firstly deconstructed, the influence of the speed change of the networked automatic driving vehicles on the traffic flow state change rule is quantitatively depicted, then a mixed traffic flow road section speed coordination control method is established, and a mixed traffic flow macro network coordination control method is established on the basis of the speed coordination control method.
However, the existing speed coordination control method for the road section mixed traffic flow has the following obvious defects:
1. most of the existing control methods are in the initial stage, few and few methods which can be used for practical engineering application are available, most of researches are based on pure network connection automatic driving vehicles, and the existing control methods have no much practical application value.
2. The existing control method can only be applied to a single-lane expressway, and has a large difference from practical application.
3. The existing control method can not realize good self-adaptive control for dynamic traffic flow, and has a large gap with practical application.
4. The most outstanding problem of the existing control method is that the practicability is too poor, namely, the complete control method cannot be formed in the face of mixed traffic flow, the set requirement cannot be met, and the engineering requirement cannot be met.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a traffic flow control method based on an intelligent network vehicle, which has the advantages of high control precision, strong applicability, high calculation efficiency, quick control response and the like.
The purpose of the invention can be realized by the following technical scheme:
a traffic flow control method based on an intelligent networked vehicle comprises the following steps:
s1, establishing a system dynamic equation of a novel mixed traffic flow of a road section by taking the expected speed of an intelligent internet vehicle as a control quantity;
s2, obtaining a linearized expression of a system dynamic equation based on a feedback linearization theory;
s3, designing a controller based on a linearized expression of a system dynamic equation;
and S4, calculating the expected speed of the intelligent networked automatic driving vehicle based on the designed controller, and controlling the intelligent networked vehicle to run based on the expected speed.
Preferably, step S1 comprises:
s11, expressing the evolution of the road section traffic state by using a differential equation of the road section density;
s12, establishing rho k And rho k+1 Where k is the control time interval, p k Is the average traffic flow density of unit road section in the k time interval, rho k+1 The average traffic flow density of the unit road section in the k +1 time interval;
s13, establishing a basic relation between the flow and the density
And chi k In which
The outflow rate of the unit road section in the k +1 time interval k The expected speed of the intelligent networked automobile in the kth time interval is obtained;
s14, assuming that the inflow flow does not change with time, will be established
And chi k Bringing in relationships ofAnd obtaining a system dynamic equation of the novel mixed traffic flow of the road section by using a differential equation of the road section density.
Preferably, the differential equation of the section density in step S11 is expressed as:
wherein rho is the road section density, t is the time, delta t is the control updating time interval,
the unit road section inflow flow rate in the k +1 time interval,
the flow rate of the unit road section in the k +1 time interval,
the unit section inflow flow rate in the k time interval,
the flow rate of the unit road section in the kth time interval is shown, and L is the length of the controlled road section.
Preferably, ρ in step S12 k And rho k+1 The relationship between them is expressed as:
wherein ,ok Is the ratio of the vehicles influenced by the speed change of the intelligent networked vehicles in the kth time interval, v k Is the average speed, χ, of all vehicles in the kth time interval k For the desired speed of the intelligent networked automobile in the kth time interval,
is the average vehicle length, t LC Average lane change duration, μ for vehicle k The average permeability of the smart grid car in the kth time interval is shown.
Preferably, in step S13
And chi k The relationship of (c) is expressed as:
where eta is the traffic wave velocity ρ jam Is the blocking density.
Preferably, the system dynamic equation of the new mixed traffic flow of the road segment in step S14 is expressed as:
preferably, step S2 comprises:
s21, listing a traffic flow state space expression, including a system dynamic equation and an output equation:
q＝η(ρ jam -ρ)
wherein q is the section outflow flow;
s22, calculating a first derivative of the section outflow flow q based on a feedback linearization theory:
s23, setting a new control vector u * Let us order
S24, according to
Can obtain
Further, the linear expression of the system dynamics is as follows:
preferably, step S3 is based on the new control quantity u * And designing a controller.
Preferably, the controller comprises a PID controller, and the designing process comprises:
s31, giving an expression of the error
e is the flow tracking error, q d Is the road segment target flow;
s32, calculating a new control vector based on the design thought of the PID controller
wherein ,kP 、k I 、k D The integral term, the proportional term and the differential term of the PID controller are respectively.
Preferably, in step S4, the calculated desired speed is corrected when the intelligent networked vehicle is controlled to operate based on the desired speed, so as to obtain a desired speed for effective execution, and the intelligent networked vehicle is controlled to operate based on the desired speed for effective execution, where the desired speed for effective execution may be represented as:
wherein ,
desired velocity for effective execution, χ k Desired speed, v, for a smart grid-connected autonomous vehicle calculated based on a design controller k Is the average speed of all vehicles in the kth time interval, Δ T is the control update time interval, T s Is the delay duration.
Compared with the prior art, the invention has the following advantages:
(1) The invention gets rid of the passivity of the traditional traffic control, takes the intelligent networked vehicles as a breakthrough point to carry out active and accurate traffic control, is fundamentally superior to the traditional traffic control method, and has the advantages of high control precision, strong applicability, high calculation efficiency, quick control response and the like.
(2) The invention comprehensively considers the traffic system optimization and the intelligent network vehicle-connected individual optimization, establishes the traffic flow control method for maximizing the entropy benefit of mixed traffic flow, and realizes the balance between the group benefit and the individual optimization.
(3) The invention establishes an integral control frame based on system dynamic, so that the relation among the internal elements of the system is clear, the system evolution rule is clear and clear, and a solid foundation is laid for scientific control.
Drawings
Fig. 1 is a flow chart of a traffic flow control method based on an intelligent internet vehicle according to the present invention;
FIG. 2 is a schematic view of a traffic flow control segment according to the present invention;
FIG. 3 is a schematic diagram of a controller according to the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, the present embodiment provides a traffic flow control method based on an intelligent internet vehicle, the method including:
s1, establishing a system dynamic equation of a novel mixed traffic flow of a road section by taking the expected speed of an intelligent internet vehicle as a control quantity;
s2, obtaining a linearized expression of a system dynamic equation based on a feedback linearization theory;
s3, designing a controller based on a linear expression of a system dynamic equation;
and S4, calculating the expected speed of the intelligent networking automatic driving vehicle based on the designed controller, and controlling the intelligent networking vehicle to operate based on the expected speed.
Wherein, step S1 includes:
s11, representing the evolution of the road section traffic state by using a differential equation of road section density rho, wherein rho is the average traffic flow density of the road section and has the unit of pcu/km;
s12, establishing rho k And rho k+1 Where k is the control time interval, p k The average traffic flow density of the unit road section in the kth time interval is pcu/km, rho k+1 The average traffic flow density of the unit road section in the k +1 time interval is pcu/km;
s13, establishing a basic relation between the flow and the density
And chi k In which
The unit is the outflow rate of the unit road section in the k +1 time interval and the unit is pcu/h, χ k The expected speed of the intelligent networked automobile in the kth time interval is obtained;
s14, assuming that the inflow flow does not change with time, will be established
And chi k The system dynamic equation of the novel mixed traffic flow of the road section is obtained by substituting the relation into the differential equation of the road section density.
The differential equation of the section density in step S11 is expressed as:
wherein rho is the road section density, t is the time, delta t is the control updating time interval,
the unit road section inflow flow rate in the k +1 time interval,
the outflow rate of the unit road section in the k +1 time interval,
the unit section inflow flow rate in the k time interval,
the flow rate of the unit road section in the kth time interval is shown, and L is the length of the controlled road section.
ρ in step S12 k And rho k+1 The relationship between them is expressed as:
wherein ,ok Is the ratio of the vehicles influenced by the speed change of the intelligent networked vehicles in the kth time interval, v k Is the average speed, χ, of all vehicles in the kth time interval k For the desired speed of the intelligent networked automobile in the kth time interval,
in order to average the length of the vehicle,
the average value is 4.75m,t LC The average lane change duration of the vehicle is generally 3 to 5 seconds mu k The average permeability of the smart grid car in the kth time interval is shown.
In step S13
And chi k The relationship of (c) is expressed as:
where eta is the traffic wave velocity ρ jam Is the blocking density.
The system dynamic equation of the novel mixed traffic flow of the road segment in the step S14 is expressed as:
the step S2 comprises the following steps:
s21, listing a traffic flow state space expression, including a system dynamic equation and an output equation:
q＝η(ρ jam -ρ)
wherein q is the section outflow flow;
s22, calculating a first derivative of the outflow flow q of the road section based on a feedback linearization theory:
s23, a function E (ρ) is defined, representing a feedback linearized decoupling matrix.
And order
wherein Lf h (ρ) is the first lie derivative of function h in the direction of function f;
s24, setting a new control vector u * Let us order
Based on the steps S22 and S23, the original control quantity u and the new control quantity u are obtained * A mapping relation of (i.e.)
wherein ,u* The new controlled variable is also called decoupling control variable and has the unit of km/h;
s25, according to
u is the inverse of the expected speed of an Intelligent networked Vehicle (ICV), and is available
Further, the linear expression of the system dynamics is as follows:
step S3 is based on the new control quantity u * Designing a controller, wherein the controller comprises a PID controller, and the designing process comprises the following steps:
s31, giving an expression of the error
e is the flow tracking error, q d Is the road segment target flow;
s32, baseCalculating new control vector in PID controller design idea
wherein ,kP 、k I 、k D The integral term, the proportional term and the differential term of the PID controller are respectively.
Furthermore, the specific process of calculating the expected speed of the intelligent networked vehicle is as follows: calculated new control vector u * Will u * As inputs, the desired speed of the intelligent networked vehicle may be expressed as:
in a preferred embodiment, the calculated desired speed is corrected when the intelligent networked vehicle is controlled to operate based on the desired speed in step S4, so as to obtain an effectively executed desired speed, and the intelligent networked vehicle is controlled to operate based on the effectively executed desired speed, where the effectively executed desired speed may be represented as:
wherein ,
desired velocity for effective execution, χ k Desired speed, v, for a smart grid-connected autonomous vehicle calculated based on a design controller k Is the average speed of all vehicles in the kth time interval, Δ T is the control update time interval, T s Is the delay duration.
The method is used for traffic flow control of the road section shown in figure 2, the controller based on the method is controlled as shown in figure 3, and for the controller designed by the method, the saturation of the road section and the permeability of the intelligent networked vehicle influence the control effect. In general, the greater the saturation, the higher the permeability, and the better the control. The reason is mainly that the larger the saturation is, the higher the permeability is, the greater the influence of the intelligent networked vehicle on the human-driven vehicle is, and the more the controller can play a role.
The controller designed by the invention can meet the control requirement within 10 seconds, can achieve the control precision of more than 85% in most scenes, has the calculation efficiency and the control effect of engineering application level, and can be used as an effective method for active traffic control.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (10)
1. A traffic flow control method based on an intelligent internet vehicle is characterized by comprising the following steps:
s1, establishing a system dynamic equation of a novel mixed traffic flow of a road section by taking the expected speed of an intelligent internet vehicle as a control quantity;
s2, obtaining a linearized expression of the system dynamic equation based on a feedback linearization theory;
s3, designing a controller based on a linear expression of a system dynamic equation;
and S4, calculating the expected speed of the intelligent networked automatic driving vehicle based on the designed controller, and controlling the intelligent networked vehicle to run based on the expected speed.
2. The traffic flow control method based on the intelligent networked vehicle according to claim 1, wherein the step S1 comprises:
s11, expressing the evolution of the road section traffic state by using a differential equation of the road section density;
s12, establishing rho k And rho k+1 Where k is the control time interval, p k Is the average traffic flow density of unit road section in the k time interval, rho k+1 The average traffic flow density of the unit road section in the k +1 time interval;
s13, based onThe basic relationship between the flow and the density is established
And chi k In which
The outflow rate of the unit road section in the k +1 time interval k The expected speed of the intelligent networked automobile in the kth time interval is obtained;
s14, assuming that the inflow flow does not change with time, will be established
And chi k The relation of the road section density is brought into a differential equation of the road section density, and a system dynamic equation of the novel mixed traffic flow of the road section is obtained.
3. The traffic flow control method based on the intelligent networked vehicle as claimed in claim 2, wherein the differential equation of the road section density in the step S11 is expressed as:
wherein rho is the road section density, t is the time, Δ t is the control update time interval,
the unit road section inflow flow rate in the k +1 time interval,
the outflow rate of the unit road section in the k +1 time interval,
the unit section inflow flow rate in the k time interval,
the flow rate of the unit road section in the kth time interval is shown, and L is the length of the controlled road section.
4. The traffic flow control method based on the intelligent networked vehicle as claimed in claim 3, wherein p in step S12 k And rho k+1 The relationship between them is expressed as:
wherein ,ok The ratio of the vehicles influenced by the speed change of the intelligent networked vehicles in the kth time interval, v k Is the average speed, χ, of all vehicles in the kth time interval k For the desired speed of the intelligent networked automobile in the kth time interval,
is the average vehicle length, t LC Average lane change duration, μ for vehicle k The average permeability of the intelligent networked automobile in the kth time interval is shown.
5. The traffic flow control method based on the intelligent networked vehicle as claimed in claim 4, wherein in step S13
And chi k The relationship of (c) is expressed as:
wherein eta is the speed of traffic wave, rho jam Is the blocking density.
6. The traffic flow control method based on the intelligent networked vehicle as claimed in claim 5, wherein the system dynamic equation of the novel road segment mixed traffic flow in the step S14 is expressed as:
7. the traffic flow control method based on the intelligent networked vehicle according to claim 6, wherein the step S2 comprises:
s21, listing a traffic flow state space expression, including a system dynamic equation and an output equation:
q＝η(ρ jam -ρ)
wherein q is the section outflow flow;
s22, calculating a first derivative of the section outflow flow q based on a feedback linearization theory:
s23, setting a new control vector u * Let us order
S24, according to
Can obtain the product
Further, the linear expression of the system dynamics is as follows:
8. the traffic flow control method based on the intelligent networked vehicle according to claim 7, wherein the controller is designed based on the new control quantity u in the step S3.
9. The method as claimed in claim 8, wherein the controller includes a PID controller, and the designing process includes:
s31, giving an expression of the error
e is the flow tracking error, q d Is the road segment target flow;
s32, calculating a new control vector based on the design thought of the PID controller
wherein ,kP 、k I 、k D The integral term, the proportional term and the differential term of the PID controller are respectively.
10. The traffic flow control method based on the intelligent networked vehicle as claimed in claim 1, wherein in step S4, the calculated desired speed is corrected when the intelligent networked vehicle is controlled to operate based on the desired speed, so as to obtain the desired speed for effective execution, and the intelligent networked vehicle is controlled to operate based on the desired speed for effective execution, wherein the desired speed for effective execution can be expressed as:
wherein ,
desired speed, χ, for efficient implementation k Desired speed, v, for an intelligent networked autonomous vehicle calculated based on a controller of the design k Is the average speed of all vehicles in the kth time interval, Δ T is the control update time interval, T s Is the delay duration.
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