CN116279475B - Cooperative control method for consistency of operation speeds of network-connected automatic driving vehicle queues - Google Patents

Cooperative control method for consistency of operation speeds of network-connected automatic driving vehicle queues Download PDF

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CN116279475B
CN116279475B CN202310124084.9A CN202310124084A CN116279475B CN 116279475 B CN116279475 B CN 116279475B CN 202310124084 A CN202310124084 A CN 202310124084A CN 116279475 B CN116279475 B CN 116279475B
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driving vehicle
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CN116279475A (en
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王立超
杨敏
张霁扬
覃柏霑
马可
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Southeast University
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention discloses a cooperative control method for consistency of queue running speeds of an online automatic driving vehicle, which utilizes an intelligent driver model of a comprehensive online vehicle driving strategy to establish an online running environment of the automatic driving vehicle; each network automatic driving vehicle in the network running environment obtains the motion state information of the vehicle and sends the motion state information to a control center; the control center calculates the optimal acceleration and deceleration control strategy of each networked automatic driving vehicle according to the received motion state information, and returns the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle; and each network automatic driving vehicle regulates and controls the speed according to the optimal acceleration and deceleration control strategy, so that the consistency cooperative control of the running speeds of the network automatic driving vehicles in a queue is realized. The invention takes CAV vehicles as main bodies, aims to reduce the speed difference of adjacent vehicles, and provides a method for promoting the consistency of CAV queue speeds, thereby realizing the high-efficiency and stable operation of high-density CAV in the same lane.

Description

Cooperative control method for consistency of operation speeds of network-connected automatic driving vehicle queues
Technical Field
The invention relates to the field of intelligent traffic vehicle cooperative automatic control, in particular to a cooperative control method for the consistency of the queue running speed of a network-connected automatic driving vehicle.
Background
Along with the continuous increase of the quantity of motor vehicles in China, the traffic jam situation is increasingly strong, and under the high-density traffic flow state, the traffic flow which is stopped and walked is easier to generate concussion propagation of a traffic system, so that the phenomena of traffic running blocking, unsmooth traffic and the like are further aggravated, and therefore, the high-efficiency smooth traffic of urban roads is ensured to be more important.
The intelligent decision and cooperative control are carried out by the networked automatic driving vehicles step by step through technical means such as vehicle-vehicle cooperation, vehicle-road cooperation, instant messaging, networked intercommunication and the like, the powerful technical support is provided for the operation modes such as higher-level cooperative driving, queue operation and the like of the networked automatic driving vehicles, the speed optimization among adjacent vehicles is realized, the queue operation of the adjacent vehicles is promoted, the speed consistency control tends to be carried out, and the intelligent decision and cooperative control method has practical significance in the aspects of reducing the speed difference of the adjacent vehicles, reducing the fluctuation range of the traffic flow speed, slowing down the speed oscillation of a traffic system and the like. The existing part of related technologies can eliminate or slow down traffic wave oscillation through the speed dynamic guiding of an automatic driving vehicle, and the existing vehicle cooperative method can not alleviate the problem through the most direct queue speed.
Disclosure of Invention
The invention aims to: aiming at the problems, the invention aims to provide a cooperative control method for consistency of the operating speeds of the network-connected automatic driving vehicle queues, which reduces traffic concussion and slows down the phenomenon of frequent stop-and-go in a real traffic system.
The technical scheme is as follows: the invention discloses a cooperative control method for the consistency of the queue running speeds of networked automatic driving vehicles, which comprises the following steps:
establishing an online operation environment of the automatic driving vehicle by utilizing an intelligent driver model of the comprehensive online vehicle driving strategy;
each network automatic driving vehicle in the network running environment obtains the motion state information of the vehicle and sends the motion state information to a control center;
the control center calculates the optimal acceleration and deceleration control strategy of each networked automatic driving vehicle according to the received motion state information, and returns the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle;
and each network automatic driving vehicle regulates and controls the speed according to the optimal acceleration and deceleration control strategy, so that the consistency cooperative control of the running speeds of the network automatic driving vehicles in a queue is realized.
Further, the motion state information comprises running speed, acceleration and deceleration, relative positions and distances between two adjacent networked automatic driving vehicles.
Further, the expression of the intelligent driver model of the comprehensive internet-connected vehicle driving strategy is as follows:
in the formula, v i CAV for indicating network automatic driving vehicle i Operating speed, L i Representing networked automatic driving vehicle i and preceding vehicle CAV i-1 Distance between Deltav i Representing CAV i CAV with front car i-1 The speed difference between, i represents the ith CAV,representing the running speed v i First derivative at time t, a max Representing the maximum acceleration of CAV, +.>Representing CAV i Delta represents the free acceleration index, L * Representing the desired distance between two adjacent CAVs, < >>Representing the minimum safe distance, t, between adjacent two networked autonomous vehicles CAV i Representing CAV i Decision time, -a min Represents the maximum deceleration of CAV, a i Representing CAV i Acceleration of, -a i Representing CAV i Is a deceleration of (a).
Further, when the distributed cooperative control strategy is adopted, every two adjacent CAVs are taken as units, and each control unit takes a front vehicle as a control center of the control unit; when the global cooperative control strategy is adopted, the head car of the whole queue is used as a control center.
Further, when a distributed cooperative control strategy is adopted, for a queue consisting of n CAVs, model predictive control is utilized to control the operation of vehicles in the MPC cooperative control queue, at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to the adjacent networked automatic driving vehicles for cooperative control.
Further, when the global cooperative control strategy is adopted, for a queue consisting of n CAVs, the model prediction is utilized to control the vehicle operation in the MPC cooperative control queue, and at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle for cooperative control.
Further, the adoption of the distributed cooperative control strategy comprises the following steps:
starting from the head vehicle of the network-connected driving vehicle queue, two CAVs of the automatic driving vehicle are sequentially connected in adjacent network n-1 And CAV (computer aided V) n For controlling the unit, the control center is CAV n-1 Calculating the average speed of the current control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein n-1 is the number of the n-1 th CAV, v i-in Representing CAV i Initial velocity, t k Represents the sampling time point, Δt represents the prediction period, a best To control input, CAV is represented n-1 And CAV (computer aided V) n M represents the number of prediction periods, CAV in M prediction periods 1 -CAV n-1 Speed consistency coordination control is realized, and m represents a predicted period sequence number;
at [ t ] k+ MΔt,t k +(M+1)Δt]During prediction, whenAt the time of CAV n Is optimized to->When (when)At the time of CAV n Is optimized to v d,n 。
Further, the global cooperative control strategy includes:
the average speed of the current control unit is calculated by a control center by taking a queue consisting of n CAVs as the control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein a is best Representing optimal acceleration and deceleration of all CAVs for control input;
at [ t ] k ,t k +Δt]During prediction, whenAt the time of CAV n Is optimized to->When->At the time of CAV n Is optimized to v d,n 。
The beneficial effects are that: compared with the prior art, the invention has the remarkable advantages that: the invention fully considers the phenomena that traffic waves such as stop and go and the like are likely to occur in the running process of the high-density CAV in the same lane under the networked automatic driving environment, and provides a method for promoting the consistency of the CAV queue speed by taking the CAV vehicle as a main body and aiming at reducing the speed difference of adjacent vehicles, thereby realizing the high-efficiency stable running of the high-density CAV in the same lane, and practically providing technical references for the development of intelligent traffic, networked automatic driving, vehicle-vehicle coordination, queue control and the like.
Drawings
FIG. 1 is a flow chart of a method for collaborative control of queue operating speed consistency for networked autonomous vehicles;
FIG. 2 is a flow chart of a distributed cooperative control strategy;
FIG. 3 is a schematic diagram of a CAV queue speed consistency cooperative control strategy in a distributed networked automatic driving environment;
FIG. 4 is a flow chart of a global cooperative control strategy;
FIG. 5 is a schematic diagram of a collaborative control method for CAV queue speed consistency in a global networked automatic driving environment;
FIG. 6 is a graph of the effect of CAV queue speed consistency cooperative control in a distributed networked autopilot environment;
fig. 7 is a graph of the effect of CAV queue speed consistency cooperative control in a global networked automatic driving environment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples.
Fig. 1 is a flowchart of a method for controlling consistency of operation speeds of a queue of networked automatic driving vehicles according to the embodiment, including the following steps:
(1) Establishing a network connection operation environment of the automatic driving vehicle by utilizing an intelligent driver model of the comprehensive network connection vehicle driving strategy;
the method comprises the steps of establishing an online running environment of an automatic driving vehicle based on a CVDS-IDM, realizing interconnection of vehicles in the running process of the automatic driving vehicle, wherein the vehicles in the online running environment are all online automatic driving vehicles, and realizing information sharing, collaborative decision and intelligent interconnection of the vehicles on the premise that each vehicle realizes autonomous running decision in the online running environment;
(2) Each network automatic driving vehicle in the network running environment obtains the motion state information of the vehicle and sends the motion state information to the control center;
the control center is a vehicle with a control function of a control unit;
(3) The control center calculates the optimal acceleration and deceleration control strategy of each networked automatic driving vehicle according to the received motion state information, and returns the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle;
the optimal acceleration and deceleration control strategy refers to an operation speed optimization strategy obtained by calculation according to the motion state information;
(4) And each network automatic driving vehicle regulates and controls the speed according to the optimal acceleration and deceleration control strategy, so that the consistency cooperative control of the running speeds of the network automatic driving vehicles in a queue is realized.
In one embodiment, the motion state information includes a running speed, acceleration and deceleration, a relative position, and a distance between two adjacent networked autopilot vehicles.
In one embodiment, the expression of the intelligent driver model of the comprehensive internet-connected vehicle driving strategy is:
in the formula, v i CAV for indicating network automatic driving vehicle i Operating speed, L i Representing networked automatic driving vehicle i and preceding vehicle CAV i-1 Distance between Deltav i Representing CAV i CAV with front car i-1 The speed difference between, i represents the ith CAV,representing the running speed v i First derivative at time t, a max Representing the maximum acceleration of CAV, +.>Representing CAV i Delta represents the free acceleration index, L * Representing the desired distance between two adjacent CAVs, < >>Representing the minimum safe distance, t, between adjacent two networked autonomous vehicles CAV i Representing CAV i Decision time, -a min Represents the maximum deceleration of CAV, a i Representing CAV i Acceleration of, -a i Representing CAV i Is a deceleration of (a).
In one embodiment, when a distributed cooperative control strategy is adopted, every two adjacent CAVs are taken as units, and each control unit takes a preceding vehicle as a control center of the control unit; when the global cooperative control strategy is adopted, the head car of the whole queue is used as a control center.
As shown in fig. 2 and 5, when a distributed cooperative control strategy is employed, the control centers are sequentially changed during the operation of the queues, such as when using CAV 1 And CAV (computer aided V) 2 When the control unit performs consistency control, the control center is CAV 1 When CAV 1 And CAV (computer aided V) 2 For the uniform speed and then CAV 2 And CAV (computer aided V) 3 When the speed consistency control is performed for the control unit, the control center is CAV 2 . When the global cooperative control strategy is adopted, the control center is always the head car of the queue.
In one embodiment, when a distributed cooperative control strategy is employed, for a train of n CAVs, the operation of the vehicles in the MPC coordinated control train is controlled using model prediction at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to the adjacent networked automatic driving vehicles for cooperative control.
In one embodiment, when a global cooperative control strategy is employed, for a train of n CAVs, the operation of the vehicles in the MPC cooperative control train is controlled using model predictions, at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle for cooperative control.
In one embodiment, as shown in fig. 3, the above-mentioned adoption of the distributed cooperative control strategy includes:
starting from the head vehicle of the network-connected driving vehicle queue, two CAVs of the automatic driving vehicle are sequentially connected in adjacent network n-1 And CAV (computer aided V) n For controlling the unit, the control center is CAV n-1 Calculating the average speed of the current control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein n-1 is the number of the n-1 th CAV, v i-in Representing CAV i Initial velocity, t k Represents the sampling time point, Δt represents the prediction period, a best To control input, CAV is represented n-1 And CAV (computer aided V) n M represents the number of prediction periods, CAV in M prediction periods 1 -CAV n-1 Speed consistency coordination control is realized, and m represents a predicted period sequence number;
at [ t ] k+ MΔt,t k +(M+1)Δt]During prediction, whenAt the time of CAV n Is optimized to->When (when)At the time of CAV n Is optimized to v d,n 。
Specifically, the implementation process of the distributed cooperative control strategy is illustrated in a queue consisting of 3 networked automatic driving vehicles, and the implementation process comprises the following steps:
(a) Vehicle CAV connected with head net by using queue 1 And adjacent net-connected vehicle CAV 2 Is a speed consistency control unit, and the control center is CAV 1 Calculating the average speed of the current control unit by the control centerAnd a common desired velocity v d,n The expressions are respectively:
in the method, in the process of the invention,refers to v 1 And v 2 Average value v of (v) d,2 Refers to CAV 1 And CAV (computer aided V) 2 V of the common desired speed of (v) i-in Refers to CAV i An initial velocity of a) best For control input, CAV 1 And CAV (computer aided V) 2 Is used for optimal acceleration and deceleration.
If it isRepresenting the time t of sampling k Previously, CAV 1 And CAV (computer aided V) 2 Cannot provide further space for optimizing speed uniformity for each vehicle, and the desired speed and the speed of each vehicle are cooperatively controlled in accordance with the average speed, at [ t ] k ,t k +Δt]During CAV 2 Will be along +.>Optimizing;
if it isRepresenting at the sampling instant t k Thereafter, CAV 1 And CAV (computer aided V) 2 Can provide further speed consistency optimization space for each vehicle, and cooperatively control the running speed and the addition (subtraction) speed of each vehicle according to the desired speed, at [ t ] k ,t k +Δt]During CAV 2 Will be along v d,2 And (5) optimizing.
When CAV 1 And CAV (computer aided V) 2 Achieving speed uniformity after m prediction periods at time t k +mDeltat, will start for CAV 2 And CAV (computer aided V) 3 Is sampled and speed consistency optimization is performed on the first 3 CAVs.
(b)、CAV 2 And CAV (computer aided V) 3 Speed consistency control, wherein the control center is CAV 2 Calculating a current control unitAverage velocity of (2)And a common desired velocity v d,n The expressions are respectively:
in the method, in the process of the invention,refers to v 2 And v 3 Average value v of (v) d,3 Refers to CAV 2 And CAV (computer aided V) 3 V of the common desired speed of (v) i-in Refers to CAV i An initial velocity of a) best For control input, CAV 2 And CAV (computer aided V) 3 Optimal acceleration and deceleration of (a)
If it isAt [ t ] k+ mΔt,t k +(m+1)Δt]During CAV 3 Will be along->Optimizing;
if it isAt [ t ] k+ mΔt,t k +(m+1)Δt]During CAV 3 Will be along v d,3 And (5) optimizing.
Through the steps, the queue formed by 4 internet-connected vehicles CAV can achieve speed consistency control under the action of the distributed cooperative control strategy.
In one embodiment, as shown in fig. 4, the flow chart includes:
the average speed of the current control unit is calculated by a control center by taking a queue consisting of n CAVs as the control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein a is best Representing optimal acceleration and deceleration of all CAVs for control input;
at [ t ] k ,t k +Δt]During prediction, whenAt the time of CAV n Is optimized to->When->At the time of CAV n Is optimized to v d,n . After repeated prediction periods, the queue consisting of n CAVs will reach speed consistency under the action of the global cooperative control strategy.
In order to further clearly explain the generating effect of the cooperative control method for the consistency of the operation speeds of the online automatic driving vehicle queue in the embodiment, the following specific examples are used for simulation and display. Let the maximum expected speedIs 100km/h, maximum plus (minus) speed + -a i Is 3m/s 2 Desired distance L between two adjacent CAVs * 30m, CAV i Is of the decision time of (a)t i Is set to be constant for 0.6s, the free acceleration index delta is set to be 0.8, and the minimum safety distance between two adjacent CAVs is +.>Set to 20m. The scene in the simulation process is set as a highway section around a city with obvious stop-and-go phenomenon, only a single lane is selected, the saturation is 0.8, the lane width is 3.75m, and the lane length is not limited under a certain time condition (400 s). The simulation process takes CVDS-IDM as an operation and control basis, and the distributed and global speed consistency cooperative control is adopted to simulate the CAV queue operation control process in the networked automatic driving environment.
Fig. 6 shows a CAV queue speed consistency cooperative control effect in a distributed network automatic driving environment, and fig. 7 shows a CAV queue speed consistency cooperative control effect in a global network automatic driving environment. As shown in fig. 6-7, both cooperative control strategies achieve speed consistency cooperative control of the networked autonomous vehicle queues within a certain time. In the optimization process, the speed change trend is approximately the same in the simulation process adopting two cooperative control strategies, the oscillation is larger in the initial process, the distributed oscillation time range is 0-100s, the global oscillation time range is 0-13s, the intermediate process oscillation is smaller, the distributed oscillation is 100-350s, the global oscillation is 13-79s, and finally, the speed consistency control is realized for all CAVs in the online automatic driving environment. In addition, the optimization process of the two control strategies has obvious time difference, and the distributed optimization time is longer than the global time under the same experimental condition. In the distributed simulation process, the CAV queue speed is consistent after about 370s, and the consistency control of the CAV queue speed is realized by adopting the global method only about 80 s. The result shows that the two methods can realize CAV queue speed consistency control under the network connection automatic driving condition, and under the same operation condition, the global speed consistency cooperative control method can realize CAV queue speed consistency cooperative control faster than the distributed speed consistency cooperative control method.

Claims (7)

1. The cooperative control method for the consistency of the operating speeds of the network-connected automatic driving vehicle queues is characterized by comprising the following steps of:
establishing an online operation environment of the automatic driving vehicle by utilizing an intelligent driver model of the comprehensive online vehicle driving strategy;
each network automatic driving vehicle in the network running environment obtains the motion state information of the vehicle and sends the motion state information to a control center;
the control center calculates the optimal acceleration and deceleration control strategy of each networked automatic driving vehicle according to the received motion state information, and returns the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle;
each network automatic driving vehicle regulates and controls the speed according to the optimal acceleration and deceleration control strategy, so as to realize the consistent cooperative control of the running speeds of the network automatic driving vehicles;
the expression of the intelligent driver model of the comprehensive network-connected vehicle driving strategy is as follows:
in the formula, v i CAV for indicating network automatic driving vehicle i Operating speed, L i Representing CAV i CAV with front car i-1 Distance between Deltav i Representing CAV i CAV with front car i-1 The speed difference between, i represents the ith CAV,representing the running speed v i First derivative at time t, a max Representing the maximum acceleration of CAV, +.>Representing CAV i Delta represents the free acceleration index, L * Representing the desired distance between two adjacent CAVs, < >>Representing the minimum safe distance, t, between adjacent two networked autonomous vehicles CAV i Representing CAV i Decision time, -a min Represents the maximum deceleration of CAV, a i Representing CAV i Acceleration of, -a i Representing CAV i Is a deceleration of (a).
2. The method for collaborative control of queue operating speed consistency for networked autonomous vehicles according to claim 1, wherein the motion state information includes operating speed, acceleration and deceleration, relative position, and distance between two adjacent networked autonomous vehicles.
3. The cooperative control method for the consistency of the operation speeds of the network-connected automatic driving vehicle queues according to claim 1, wherein when a distributed cooperative control strategy is adopted, every two adjacent CAVs are taken as units, and a preceding vehicle is taken as a control center of the control unit in each control unit; when the global cooperative control strategy is adopted, the head car of the whole queue is used as a control center.
4. The cooperative control method for the consistency of the operation speeds of a train of networked autonomous vehicles according to claim 3, wherein when a distributed cooperative control strategy is adopted, model predictive control is used for a train consisting of n CAVsVehicle operation in MPC coordinated control queues at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to the adjacent networked automatic driving vehicles for cooperative control.
5. A networked autonomous vehicle queue operation speed consistency cooperative control method as recited in claim 3, wherein when a global cooperative control strategy is employed, vehicle operation in the MPC cooperative control queue is controlled by model prediction for a queue consisting of n CAVs at each sampling time point t k And the control center sends the optimal acceleration and deceleration control strategy to each networked automatic driving vehicle for cooperative control.
6. The method for collaborative control of queue operating speed consistency for networked autonomous vehicles according to claim 4, wherein the employing a distributed collaborative control strategy includes:
starting from the head vehicle of the network-connected driving vehicle queue, two CAVs of the automatic driving vehicle are sequentially connected in adjacent network n-1 And CAV (computer aided V) n For controlling the unit, the control center is CAV n-1 Calculating the average speed of the current control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein n-1 represents the number of the n-1 th CAV, v i-in Representing CAV i Initial velocity, t k Represents the sampling time point, Δt represents the prediction period, a best To control input, CAV is represented n-1 And CAV (computer aided V) n M represents the number of prediction periods, CAV in M prediction periods 1 -CAV n-1 Speed consistency coordination control is realized, and m represents a predicted period sequence number;
at [ t ] k+ MΔt,t k +(M+1)Δt]During prediction, whenAt the time of CAV n Is optimized to->When (when)At the time of CAV n Is optimized to v d,n 。
7. The networked autonomous vehicle queue operation speed consistency cooperative control method of claim 5, wherein the global cooperative control strategy comprises:
the average speed of the current control unit is calculated by a control center by taking a queue consisting of n CAVs as the control unitAnd a common desired velocity v d,n The expressions are respectively:
wherein a is best Representing optimal acceleration and deceleration of all CAVs for control input;
at [ t ] k ,t k +Δt]During prediction, whenAt the time of CAV n Is optimized to->When->At the time of CAV n Is optimized to v d,n 。
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CN114489067A (en) * 2022-01-21 2022-05-13 东南大学 Intelligent networked vehicle queue cooperative driving model prediction control method
CN114516328A (en) * 2022-03-08 2022-05-20 武汉科技大学 Rule-based motorcade following model method in intelligent network environment

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