CN115424456A - Expressway intersection area cooperative self-adaptive cruise optimization control method - Google Patents
Expressway intersection area cooperative self-adaptive cruise optimization control method Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
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Abstract
The invention discloses a cooperative self-adaptive cruise optimization control method for an intersection area of a highway, and belongs to the technical field of calculation, reckoning or counting. The method defines a locomotive headway control strategy of the collaborative self-adaptive cruise marshalling vehicle; a feedback-feedforward combined comprehensive control system is provided; a control method of a feedforward control module in a comprehensive control system is designed, a variable acceleration change limiting control strategy is proposed and applied to the feedforward control module. The invention also adopts a model predictive control algorithm based on a rolling time domain to identify the traffic behaviors of the vehicles in the adjacent lanes, and limits the acceleration of the controlled vehicle according to the identified traffic behaviors of the vehicles in the adjacent lanes. The optimization control method improves the running stability of the whole motorcade in complex traffic environments such as an expressway intersection area and the like, and improves the driving comfort and safety of the system for automatically driving the vehicles.
Description
Technical Field
The invention relates to the field of vehicle networking and intelligent driving, mainly relates to key technologies in the field of vehicle networking and vehicle automatic control, discloses a cooperative self-adaptive cruise optimization control method for an expressway intersection area, and belongs to the technical field of calculation, calculation or counting.
Background
The expressway intersection area is a key road section for limiting the traffic capacity of the expressway and is also a road section with high collision accident occurrence frequency in the expressway. The attention of researchers is increasing on how to improve the vehicle traffic capacity in an intersection area of a highway and improve the driving stability and safety of vehicles in the intersection area. In recent years, with continuous progress of wireless communication technology, the field of car networking is also continuously perfected, wherein development of car-to-car communication and vehicle-to-road Cooperative communication technology brings new development opportunities to Cooperative Adaptive Cruise Control (CACC) technology, and the car networking technology is a basis for realizing Cooperative driving between vehicles and is one of effective ways for realizing information transmission between vehicles.
Coordinated Adaptive Cruise autopilot technology is used as an extension of Adaptive Cruise Control (ACC). The ACC vehicle mainly depends on a vehicle-mounted radar and a view screen processing device to sense the traffic environment of the ACC vehicle; the CACC vehicles not only sense the environment by using radar and video equipment, but also establish a self-organizing local communication network by using vehicle-mounted network communication equipment, and the formed local V2V and V2I networks transmit the vehicle running information of the traffic upstream in real time, so that the vehicles can realize the cooperative running on the basis of running state information sharing. The cooperative driving can not only improve the traffic capacity of roads, but also improve the driving safety of vehicles and reduce the risk of rear-end collision of the vehicles.
The highway intersection area is usually a bottleneck area in a highway, and not only influences the overall traffic efficiency of the highway, but also is a key area for traffic safety management. At present, the research on the collaborative formation control of the automatic driving vehicles is basically in a test stage, the considered traffic scene is simpler, and the requirement of realizing stable collaborative automatic driving of the automatic driving vehicle fleet in an expressway intersection area cannot be fully met. Current research is relatively lacking in the concern of CACC fleet wide-spread cut-in-cut-out vehicle interference in highway intersections. The existing adaptive cruise control algorithm mainly considers team cooperative driving of a conventional road section of a high-speed road, and along with the development of an internet of vehicles technology and a cooperative control marshalling driving technology, a control system which meets the inevitable requirement of scientific and technological development that a CACC team can realize cooperative driving in each section of the high-speed road is required. The invention aims to provide a highway interleaved area collaborative self-adaptive cruise optimization control method, and integrates a feedforward control module (embedded acceleration control algorithm) aiming at non-internet vehicle cut-in and cut-out interference in an interleaved area, compared with a traditional self-adaptive cruise control system, the internet collaborative cruise control algorithm provided by the invention has the main advantages that the negative influence caused by uncertain cut-in and cut-out interference is reduced through the feedforward module and a built-in algorithm thereof, and the driving stability and driving comfort of an internet collaborative fleet in the highway interleaved area are improved.
Disclosure of Invention
The invention aims to provide a coordinated self-adaptive cruise optimization control method for an expressway interlacing area aiming at the defects of the background art, integrates feed-forward control aiming at cut-in and cut-out interference of non-networked vehicles in the interlacing area, and realizes the aim of self-adaptive cruise optimization of the expressway interlacing area.
The invention adopts the following technical scheme for realizing the aim of the invention:
and the controlled vehicles in the cooperative driving fleet acquire the positions of the cooperative driving vehicles in the fleet under the current road environment through the vehicle-mounted device. Assuming that the current fleet consists of n +1 networked autonomous vehicles, the first vehicle of the fleet is designated as the head vehicle Car 0 (ii) a Any vehicle in the queue except the head vehicle is marked as Car i Where the subscript i indicates the following position of the vehicle in the queue.
Step S1: the cooperative running vehicle utilizes the vehicle-mounted radar and the positioning device to acquire the real-time position, speed and acceleration information of each vehicle in the CACC fleet.
Step S2: inside the CACC grouping fleet, all controlled vehicles realize the sharing of vehicle running information through V2V internet of vehicles communication. The vehicle-mounted computing system can calculate the relative position d of the vehicle and the front vehicle according to the shared running information of each vehicle i ＝x i -x i-1 Wherein x is i ,x i-1 Respectively showing the absolute position coordinates of the vehicle and the preceding vehicle.
And step S3: and adjusting control output by the controller according to the current speed and the current running state of the vehicle according to the distance error and the speed error obtained in the previous two steps, and compensating the distance error between the vehicles in the current fleet.
Wherein C is 1 The specific gravity value of the front vehicle and the queue head vehicle of the controlled vehicle is represented and generally takes between (0, 1); ξ represents the system's assistant factor ratio, the critical damping can be set to 1; omega n Representing the bandwidth of the controller. With the CACC fleet organized according to the above formula, vehicles can track the lead vehicle at a constant distance.
And S4, updating the position, speed and acceleration information of the vehicle by the vehicle according to the expected acceleration output by the current vehicle, transmitting the information to other vehicles in the CACC grouping fleet again, and calculating and updating the distance error between the current vehicles.
Step S5: a feedforward control module of automatic driving control monitors whether each vehicle in a CACC fleet has a specific traffic behavior request such as: leave the CACC queue or wait for a request to join the CACC queue. When a request for joining or leaving the cooperative fleet of vehicles in the adjacent lanes is monitored, the highway interleaved area cooperative adaptive cruise control module with feedforward acceleration change limitation performs feedforward control on cut-in and cut-out CACC vehicle platoon interference, and a specific algorithm expression of an acceleration change limitation algorithm in the module is as follows:
wherein equation (1) represents the built-in control algorithm in the feedforward control module that is activated when the CACC control system receives a signal that a fleet of vehicles is about to switch out of the queue and leave the CACC bank,
represents the maximum acceleration of the ith vehicle;
representing an acceleration value at a current time; f. of i (Δt/t al ) A limit function representing an acceleration limit. Equation (2) represents the built-in control algorithm in the feed-forward control module that is activated when the CACC control system receives a signal from a vehicle with an adjacent lane to join the CACC bank,
the maximum deceleration value of the i-th vehicle is indicated.
Step S6: the control system selects whether to trigger an acceleration change limiting module in the feed forward control system based on actual traffic conditions. If a CACC vehicle leaves the CACC fleet and the control system of the adjacent rear CACC vehicle triggers the feedforward acceleration change limiting module, dangerous driving behaviors caused by too large sudden acceleration changes are avoided.
Further, in step S5, a rolling prediction algorithm based on MPC is adopted to predict the traffic behavior of the leading vehicle and the adjacent lane that may occur cut-in and cut-out, specifically: predicting the transverse position of the monitored vehicle in a period of time in the future, judging that the monitored vehicle is cut into the CACC fleet when the transverse position function value calculated according to each predicted value in the prediction sequence does not exceed a threshold value, and judging that the monitored vehicle is cut out of the CACC fleet when the transverse position function value calculated according to each predicted value in the prediction sequence exceeds the threshold value.
By adopting the technical scheme, the invention has the following beneficial effects:
(1) The invention provides a cooperative adaptive cruise control method, which comprises the design of information transmission of vehicles, wherein in the process of cooperative grouping driving of controlled vehicles, the position, the speed and the acceleration of the controlled vehicles are transmitted to other vehicles in a queue in real time, meanwhile, an information receiving device also uninterruptedly receives driving information transmitted by other vehicles, the controlled vehicles mainly pay attention to the driving information of a front vehicle and a head vehicle of the CACC queue, and the aim of stably following the front vehicle by each vehicle in the CACC fleet can be fulfilled.
(2) The method for controlling the adaptive cruise of the vehicles in the intersection area of the expressway provided by the invention is added with the feedforward acceleration change limiting operation, so that the behavior that the controlled vehicle suddenly and violently accelerates or decelerates due to the cut-in and cut-out of the vehicles in the adjacent lanes can be effectively avoided, and the dangerous driving behavior caused by the cut-in and cut-out interference can be avoided.
Drawings
Other technical features, objects, and performance advantages of the present application will become apparent from the following detailed description of a motorway intersection fleet coordination control system with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of data acquisition and processing processes designed for a control system for motorway intersection region motorway cooperative driving, which is provided based on an improved motorway cooperative driving control algorithm.
Fig. 2 is a schematic diagram of an application example of the improved fleet coordinated driving control algorithm in an intersection of a highway.
Fig. 3 is a schematic diagram of the model-based predictive control algorithm for identifying the traffic behavior of the leading vehicle.
Fig. 4 is a schematic diagram of a control framework of a highway interleaved zone cooperative adaptive cruise control algorithm provided by the invention.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached drawings.
As shown in fig. 2, the present invention provides an improved VAL-CACC (Variable access Limit-CACC) adaptive cruise control method, which variably controls the Acceleration of an autonomous vehicle in a fleet where CACCs cooperatively run in an intersection of highways, thereby preventing severe buffeting from occurring due to external interference. The vehicle-mounted communication equipment is used for sensing the lane change request of the vehicle in the interleaving area in advance, and a feedforward control module in the VAL-CACC control system performs feedforward control on lane change interference. The invention is mainly used for the cooperative driving of the vehicle queue in the expressway intersection area, and inhibits the interference on the CACC queue caused by the lane changing behavior of the vehicle. Under the control of the control system structure and the control algorithm, the change of the acceleration is limited, the integral traffic capacity of the highway can be improved, the travel time of the automatic driving fleet in the interleaving area is shortened, and the stability and the safety of the CACC automatic driving vehicle queue in the interleaving area of the highway are improved.
As shown in fig. 1, the method for the acceleration change limitation feedforward cooperative adaptive cruise control in the expressway intersection area based on V2X communication includes steps S1 to S6.
Step S1: and acquiring the relative position and the absolute position of each automobile in the coordinated running fleet under the current road environment through the vehicle-mounted positioning device. As shown in FIG. 2, assuming the current fleet consists of n +1 networked autonomous vehicles, the first vehicle in the fleet is designated as the head Car 0 (ii) a Any vehicle in the queue other than the first vehicle in the queue is marked as Car i Where the subscript i indicates the following position of the vehicle in the queue. When the vehicles loaded with the vehicle-mounted communication system form a self-organization local vehicle networking, the construction of the cooperative self-adaptive cruise fleet can be completed. As shown in fig. 2, the V2X internet of vehicles system includes not only a vehicle-to-vehicle communication system (V2V) but also a vehicle-to-road cooperative communication system (V2I). The vehicle-mounted communication system mainly adopts the IEEE 802.11p intelligent traffic system special communication mobile ad hoc networking technology. On one hand, the driving information of the front vehicle can be mastered in real time through the V2V communication system; on the other hand, through V2I communication, all vehicles in the CACC fleet can obtain the running information of the queue head vehicle, which is the communication basis of the invention adopting constant head distance. Controlled vehicles in real-time queue during coordinated consist drivingThe vehicle sends the position, speed and acceleration of the vehicle, and the information receiving device also continuously receives the driving information sent by other vehicles, the controlled vehicle mainly focuses on the driving information of the front vehicle and the head vehicle of the CACC queue, and the driving information of the vehicle which is focused on is extracted by a vehicle ID identification method.
Step S2: real-time position, speed and acceleration information of each vehicle in the CACC fleet is obtained by utilizing a vehicle-mounted radar and a positioning device. In the CACC grouping fleet, all controlled vehicles realize the sharing of vehicle running information through V2V internet of vehicles communication. The vehicle-mounted computing system can calculate the relative position d of the vehicle and the front vehicle according to the shared running information of each vehicle i ＝x i -x i-1 Wherein x is i ,x i-1 Respectively showing the absolute position coordinates of the vehicle and the preceding vehicle.
And step S3: obtaining the position error of two adjacent vehicles and the difference value of the speeds of the two adjacent vehicles according to the previous two steps, wherein the position error of the two adjacent vehicles is epsilon i ＝x i -x i-1 +L i ,，ε i Is the position error value of the current vehicle i and the previous vehicle, i.e. the head spacing error of the current vehicle and the previous vehicle, L i And adjusting control output for a constant head distance value between the current vehicle i and the previous vehicle by the controller according to the current vehicle speed and the current running state of the vehicle, and compensating the distance error between the vehicles in the current motorcade. The control algorithm for the controlled vehicle is as follows:
wherein, a i,des Represents a desired acceleration of the controlled vehicle i; a is a 0 Representing the acceleration of the head vehicle of the queue; a is i-1 Represents the acceleration of the preceding vehicle of the controlled vehicle i; c 1 The weight value representing the front vehicle and the head vehicle of the queue of the controlled vehicle i represents the reaction of the controlled vehicle to the control signal of the head vehicle of the queue, and the value is generally (0, 1); ξ represents the system's assistant factor ratio, critical damping can be set to 1; omega n Represents the bandwidth of the controller; epsilon i Respectively representThe spacing error of the controlled vehicle i from its immediately following vehicle,
is epsilon i A derivative of (a); v. of i -v 0 Representing the difference between the speed of the controlled vehicle i and the speed of the lead vehicle. The CACC fleet is organized according to the above formula so that each vehicle in the CACC fleet can track the leading vehicle at a constant distance.
And S4, updating the position, speed and acceleration information of the vehicle by the vehicle according to the expected acceleration output by the current vehicle, transmitting the information to other vehicles in the CACC grouping fleet again, and calculating and updating the distance error between the current vehicles.
Step S5: the feedforward control module of automatic driving control monitors whether each vehicle in the CACC fleet has a specific traffic behavior request, and here the feedforward control module mainly monitors the occupation of the adjacent lanes and whether the vehicles in the queue have a request to leave the queue, such as: leave the CACC queue or wait for a request to join the CACC queue.
In order to identify the cut-out behavior of the front vehicle and the cut-in behavior of the adjacent lane, the invention adopts a model prediction algorithm to predict the track of the front vehicle and the lateral displacement of the vehicle of the adjacent lane and judges whether the cut-in and cut-out behavior exists. As shown in fig. 3, the present invention utilizes MPC-based rolling prediction algorithms to predict the likely cut-in-cut-out traffic behavior of the leading vehicle and the adjacent lanes. MPC predictive control algorithms take a finite prediction time domain to solve for future optimal control inputs. To achieve rolling predictions of vehicle behavior, the MPC algorithm takes the first prediction component of a set of optimal solutions computed at each time instant as the controller's execution component.
The rolling model prediction algorithm is used for predicting the transverse movement of the front vehicle mainly through the rolling prediction algorithm, predicting the x position of the vehicle, and judging whether the front vehicle has substantial cut-in or cut-out lane changing behaviors or not according to the predicted value. The roll prediction algorithm (MPC) will predict the lateral position X = [ X ] of its listening vehicle at a future time k (k),x k (k+1),x k (k+2),...x k (k+n)]Wherein x is k (k + n) represents the lateral position at the time when k + n of the monitored vehicle is predicted. By using the obtained predicted position, the invention uses the formula (3) as the judgment basis for judging whether the vehicle has cut-in and cut-out interference:
where f (X) represents a vehicle lateral position function designed based on a rolling predicted vehicle lateral displacement vector, where the most common f (X) function may be represented as a linear transition function, i.e. f (X) = AX T Wherein A = (α) 0 ,α 1 ,α 2 ,....,α n )，α i The value of (A) can be changed according to the needs of engineering practice, and in general, alpha is 0 ,α 1 ,α 2 ,....,α n The value of (A) is gradually decreased, and may be, for example, 0.8,0.1,0.05 \8230. In addition, x in the above criterion k (z) represents the lane boundary position at time k. The MPC algorithm executes a local optimal solution in a limited time domain every time, rolling prediction is carried out on line in real time, external interference can be resisted, and the traffic behavior of a front vehicle can be accurately predicted in real time. Step S6: when a request for joining or leaving the cooperative fleet is monitored for vehicles in an adjacent lane, the highway interleaved area cooperative adaptive cruise control module with feedforward acceleration change limitation eliminates interference for the cut-in and cut-out CACC vehicles, performs feedforward control, updates the position and the speed of the current vehicle after the acceleration of the current vehicle is limited, broadcasts the updated current vehicle information to other vehicles through V2X, and returns to the step S1 when the request for joining or leaving the cooperative fleet is not monitored, wherein the specific algorithm expression of the acceleration change limitation algorithm in the feedforward control module is as follows:
wherein, the formula (1) represents a built-in control algorithm in a feedforward control module which is started when a CACC control system receives a signal that a vehicle team wants to switch out a queue and leave a CACC bank,
representing the maximum acceleration of the controlled vehicle i;
representing the acceleration value of the controlled vehicle i at the current moment; u. of i (t + Δ t) represents a real-time control input amount of the controlled vehicle; a is i (t + Δ t) represents an acceleration limit value of the controlled vehicle i within a moment t + Δ t when the controlled vehicle i cuts out of or into the fleet of vehicles ahead, f i (Δt/t al ) Function representing the limiting acceleration, t al The acceleration limit duration is indicated and is typically 2-3s. Furthermore, f i (Δt/t al ) The functions can be designed into different functions according to actual engineering requirements, and typical functions include linear functions, nonlinear sigmmod functions and the like. Equation (2) represents the built-in control algorithm in the feed-forward control module that is activated when the CACC control system receives a signal from a vehicle with an adjacent lane to join the CACC bank,
representing the maximum deceleration value of the controlled vehicle i.
As shown in fig. 4, when the feedforward control module monitors the switching-in or switching-out of the vehicle k, the feedforward control module outputs uk' and pk to the feedback control module, limits the acceleration of the controlled vehicle i, outputs the expected acceleration of the controlled vehicle, and updates the position, speed and acceleration information of the controlled vehicle according to the expected acceleration of the controlled vehicle, wherein,
wherein Q i Represent the controlled vehicle in the cooperative driving queueLaplace transform of vehicle i position, U i Laplace transform, τ, representing the desired execution acceleration of the controlled vehicle i i Representing the time lag constant, theta, of the driveline of the controlled vehicle i The time delay of communication representing information transfer between vehicles, the subscript i represents the order of the controlled vehicles in the fleet, and s represents the laplacian.
Here, the number of the first and second electrodes,
whether the feedback control module conforms to the control law H i (s)＝1+h i s, represents the spacing control strategy for vehicles in the fleet, where h i Representing the head time distance constant. The transfer function between the acceleration of the controlled vehicle and the acceleration of the preceding vehicle can be expressed as:
wherein, U i (s),U k (s) represents the laplace transform between the acceleration of the controlled vehicle and the acceleration of the vehicle associated therewith, respectively; d ff (s) a control module representing a hysteresis effect generated by a time delay during the communication of the V2X vehicle; g i (s) represents a dynamic transfer function of the vehicle;
represents a conventional closed-loop feedback control module;
is a feedforward acceleration control module in the control strategy.
The cooperative adaptive cruise control method for the expressway intersection area is characterized in that transfer functions of the feedback and feedforward control modules are as follows:
in fact, the acceleration from the preceding vehicle is not amplified or reduced, but is passed directly through the unity gain, so K ff The value of (A) is usually taken to be 1; k fb Representing a feedback control vector comprising two elements, each K fb (1) And K fb (2)；σ i Represents the time lag constant of the transmission system to the control system; h is a total of i And representing the preset time and distance of the vehicle head under different traffic conditions.
Step S6: the control system selects whether to trigger an acceleration change limiting module in the feed forward control system based on actual traffic conditions. If a CACC vehicle leaves a CACC fleet group and the control system of the next CACC vehicle immediately after the CACC vehicle triggers the feedforward acceleration change limiting module, dangerous driving behaviors caused by too large sudden acceleration changes are avoided.
Claims (7)
1. A cooperative self-adaptive cruise optimization control method for an expressway intersection area is characterized in that,
acquiring the position, speed and acceleration of each vehicle in a CACC fleet, and sharing the position, speed and acceleration of each vehicle in the CACC fleet through a V2X communication system;
solving the head spacing error of the current vehicle and the previous vehicle;
the expected acceleration of the current vehicle is adjusted according to the distance error and the speed error between the current vehicle and the previous vehicle, the speed and the position of the current vehicle are updated, and the position, the speed and the acceleration of the current vehicle are shared with all vehicles in the CACC fleet through a V2X communication system;
and monitoring whether each vehicle in the CACC fleet has a request for cutting into or out of the CACC fleet, when a vehicle cuts into or out of the CACC fleet, limiting the acceleration of the vehicle immediately behind the cut-in or cut-out vehicle, updating the position and speed of the vehicle immediately behind the cut-in or cut-out vehicle, and sharing the position, speed and acceleration of the vehicle immediately behind the cut-in or cut-out vehicle with each vehicle in the CACC fleet through a V2X communication system.
2. Root of herbaceous plantsThe cooperative adaptive cruise optimization control method for the intersection area of the expressway according to claim 1, wherein the expression for adjusting the expected acceleration of the current vehicle according to the headway error and the speed error of the current vehicle and the preceding vehicle is as follows:
wherein, a i,des Represents a desired acceleration of the current vehicle i; a is 0 Representing the acceleration of the head vehicle of the queue; a is i-1 Represents the acceleration of the vehicle immediately preceding the current vehicle i; c 1 Representing the reaction of the current vehicle to the control signal of the head vehicle of the queue instead of the weight value of the front vehicle and the head vehicle of the queue of the current vehicle i, wherein the value is generally (0, 1); ξ represents the system's assistant factor ratio, critical damping is set at 1; omega n Represents the bandwidth of the controller; epsilon i Respectively representing the distance error of the current vehicle i and the immediately following vehicle,
is epsilon i A derivative of (d); v. of i -v 0 Representing the difference between the speed of the current vehicle i and the speed of the head vehicle in the platoon.
3. The method for optimal control of coordinated adaptive cruise in an intersection area of a highway according to claim 1, wherein the specific method for judging whether vehicles cut in or cut out of a CACC fleet comprises the following steps: and predicting a current vehicle transverse position sequence by adopting a rolling prediction method based on the MPC, judging that the current vehicle cuts into the CACC fleet when a transverse position function value corresponding to the transverse position sequence does not exceed a threshold value, and judging that the current vehicle cuts out the CACC fleet when the transverse position function value corresponding to the transverse position sequence exceeds the threshold value.
4. The method of claim 1, wherein when a vehicle cuts into the CACC fleet, the acceleration of the vehicle immediately following the cut-in vehicle is limited according to the following expression,
wherein u is i (t + Δ t) represents a real-time control input amount of the current vehicle i; a is i max Represents the maximum acceleration of the current vehicle i; a is i (t + Δ t) represents the acceleration limit of the current vehicle i within the instant t + Δ t that the previous vehicle cuts into the fleet;
an acceleration value representing a current time of the current vehicle i; f. of i (Δt/t al ) Representing the limiting acceleration function, t, of the preceding vehicle i al Indicating the acceleration limit time period.
5. The method for optimal control of coordinated adaptive cruise in an intersection area of a highway according to claim 1, wherein when a CACC fleet is cut out of vehicles, the acceleration of the vehicle immediately after the cut-out vehicle is limited according to the following expression,
wherein u is i (t + Δ t) represents a real-time control input amount of the current vehicle i; d is a radical of i min Representing a maximum deceleration value of the current vehicle i; a is a i (t + Δ t) represents an acceleration limit value of the current vehicle i within an instant t + Δ t at which the current vehicle i cuts out the fleet of vehicles ahead;
an acceleration value representing a current time of the current vehicle i; f. of i (Δt/t al ) Representing the limiting acceleration function, t, of the preceding vehicle i al Indicating the acceleration limit time period.
6. The cooperative adaptive cruise optimization control method for the intersection area of the expressway according to claim 1, wherein the head space error between the current vehicle and the front vehicle is epsilon i ＝x i -x i-1 +L i ,，ε i For the current vehicle iHead spacing error from the leading vehicle, x i 、x i-1 Absolute position coordinates, L, of the current vehicle i and the preceding vehicle i-1, respectively i And the constant head spacing value of the current vehicle i and the front vehicle is obtained.
7. The method as claimed in claim 3, wherein the lateral position function is f (X) = AX T Wherein X is a current vehicle transverse position sequence predicted by adopting a rolling prediction method based on MPC, f (X) is a transverse position function value corresponding to the transverse position sequence, A is a linear coefficient matrix, and A = (alpha) 0 ,α 1 ,α 2 ,....,α n )，α 0 ,α 1 ,α 2 ,....,α n Gradually decreases in value.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108263383A (en) * | 2016-12-30 | 2018-07-10 | 现代自动车株式会社 | The device and method that speed is controlled in cooperative self-adapted cruise control system |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation |
| CN113204863A (en) * | 2021-04-09 | 2021-08-03 | 燕山大学 | Manual-CACC automatic driving vehicle mixed flow simulation method based on cellular automata |
| CN113299107A (en) * | 2021-05-08 | 2021-08-24 | 东南大学 | Multi-target fusion intersection dynamic vehicle internet speed guiding control method |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | Automatic driving vehicle management method based on stability and safety |
| CN114664078A (en) * | 2022-03-18 | 2022-06-24 | 河北工业大学 | Road confluence area cooperation convergence control method based on automatic driving vehicle queue |
-
2022
- 2022-08-19 CN CN202210998575.1A patent/CN115424456B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108263383A (en) * | 2016-12-30 | 2018-07-10 | 现代自动车株式会社 | The device and method that speed is controlled in cooperative self-adapted cruise control system |
| CN112116822A (en) * | 2020-09-21 | 2020-12-22 | 长沙理工大学 | Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation |
| CN113204863A (en) * | 2021-04-09 | 2021-08-03 | 燕山大学 | Manual-CACC automatic driving vehicle mixed flow simulation method based on cellular automata |
| CN113299107A (en) * | 2021-05-08 | 2021-08-24 | 东南大学 | Multi-target fusion intersection dynamic vehicle internet speed guiding control method |
| CN113345268A (en) * | 2021-07-16 | 2021-09-03 | 长沙理工大学 | CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene |
| CN113781788A (en) * | 2021-11-15 | 2021-12-10 | 长沙理工大学 | Automatic driving vehicle management method based on stability and safety |
| CN114664078A (en) * | 2022-03-18 | 2022-06-24 | 河北工业大学 | Road confluence area cooperation convergence control method based on automatic driving vehicle queue |
Non-Patent Citations (2)
| Title |
|---|
| 华雪东;阳建强;王炜;: "车辆协同巡航控制系统设计改进与试验评价", 交通运输系统工程与信息 * |
| 秦严严;王昊;王炜;NI DAI-HENG;: "自适应巡航控制车辆跟驰模型综述", 交通运输工程学报 * |
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