CN115424456B - Highway interweaving area cooperative self-adaptive cruise optimization control method - Google Patents
Highway interweaving area cooperative self-adaptive cruise optimization control method Download PDFInfo
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Abstract
The invention discloses a highway interweaving area cooperative self-adaptive cruise optimization control method, and belongs to the technical field of calculation, calculation or counting. The invention defines a headway control strategy of a cooperative self-adaptive cruising marshalling vehicle; a feedback-feedforward combined comprehensive control system is provided; a control method of a feedforward control module in the integrated control system is designed, a variable acceleration change limiting control strategy is provided, and the variable acceleration change limiting control strategy is applied to the feedforward control module. The invention also adopts a model predictive control algorithm based on rolling time domain to identify the traffic behavior of the adjacent lane vehicles, and limits the acceleration of the controlled vehicle according to the identified traffic behavior of the adjacent lane vehicles. The optimization control method improves running stability of the whole motorcade in complex traffic environments such as expressway interweaving areas and improves driving comfort and safety of automatic driving vehicles of the system.
Description
Technical Field
The invention relates to the field of Internet of vehicles and intelligent driving, mainly relates to a key technology in the field of Internet of vehicles and automatic vehicle control, discloses a highway interweaving area cooperative self-adaptive cruise optimization control method, and belongs to the technical field of calculation, calculation or counting.
Background
The highway interweaving area is a key road section for limiting the traffic capacity of the highway and is also a road section with higher occurrence frequency of collision accidents in the highway. How to improve the traffic capacity of vehicles in the highway interweaving area and the driving stability and safety of the vehicles in the interweaving area is more and more concerned by researchers. In recent years, along with the continuous progress of wireless communication technology, the field of internet of vehicles is continuously perfected, wherein the development of the technology of vehicle-to-vehicle communication and vehicle-road cooperative communication brings new development opportunities to the technology of cooperative adaptive cruise control (Cooperative Adaptive Cruise Control, CACC), and the technology of internet of vehicles is the basis for realizing cooperative driving among vehicles and is one of effective modes for realizing information transmission among vehicles.
Cooperative adaptive cruise autopilot technology is an extension of adaptive cruise technology (Adaptive Cruise Control, ACC). ACC vehicles mainly rely on vehicle-mounted radar and video processing equipment to sense the traffic environment of the ACC vehicles; the CACC vehicles not only use the radar and the video screen equipment to sense the environment, but also use the vehicle-mounted network communication equipment to construct a self-organizing local communication network, and the formed local V2V and V2I networks transmit the vehicle running information of the upstream traffic in real time, so that the vehicles can realize cooperative running on the basis of the running state information sharing. The cooperative driving can not only improve the traffic capacity of the road, but also improve the driving safety of the vehicle and reduce the risk of rear-end collision of the vehicle.
The highway interweaving area is usually a bottleneck area in the highway, which affects not only the overall traffic efficiency of the highway, but also is a critical area for traffic safety management. At present, the research on the cooperative formation control of the automatic driving vehicles is basically in a test stage, the considered traffic scene is simpler, and the requirement of the automatic driving vehicles on realizing stable cooperative automatic driving in the expressway interweaving area cannot be fully met. Current research is also relatively lacking in concerns about the wide-spread cut-in-cut vehicle disturbances of CACC fleets in highway interleaving areas. The existing self-adaptive cruise control algorithm mainly considers the cooperative driving of the motorcade of the conventional road section of the expressway, and along with the development of the internet of vehicles technology and the cooperative control marshalling driving technology, the control system which can realize the cooperative driving of the CACC motorcade in each section of the expressway becomes the necessary requirement of technological development. Compared with the traditional self-adaptive cruise control system, the online collaborative cruise control algorithm provided by the invention has the main advantages that negative influence caused by uncertain on-off interference is reduced through the feedforward module and the built-in algorithm thereof, and the running stability and driving comfort of the online collaborative motorcade in the highway interweaving area are improved.
Disclosure of Invention
The invention aims to solve the technical problems that the existing vehicle queues can not well eliminate the negative influence on traffic safety and vehicle stability caused by the interference of switching-in-out and the like of lane-changing vehicles in the highway interweaving zone when the cooperative self-adaptive cruise control is used.
The invention adopts the following technical scheme for realizing the purposes of the invention:
the controlled vehicles in the cooperative driving vehicle team acquire the positions of the various cooperative driving vehicles in the vehicle team under the current road environment through the vehicle-mounted device. Assuming that the current fleet consists of n+1 networked autopilot cars, the first Car of the fleet is denoted as the lead Car 0 The method comprises the steps of carrying out a first treatment on the surface of the Any vehicle in the queue that removes the first vehicle is designated Car i Where the index i indicates the vehicle's following position in the queue.
Step S1: the cooperative driving vehicle obtains real-time position, speed and acceleration information of each vehicle in the CACC motorcade by using the vehicle-mounted radar and the positioning device.
Step S2: and in the CACC marshalling motorcade, each controlled vehicle realizes the sharing of vehicle running information through the communication of the V2V vehicle networking. The relative position d of the vehicle and the front vehicle can be calculated according to the shared running information vehicle-mounted operation system of each vehicle i ＝x i -x i-1 Wherein x is i ,x i-1 And respectively representing the absolute position coordinates of the vehicle and the front vehicle.
Step S3: and according to the vehicle distance error and the speed error obtained in the previous two steps, the controller adjusts the control output according to the current vehicle speed and the current running state of the vehicle, and compensates the distance error among the vehicles in the current vehicle team.
Wherein C is 1 The specific gravity value of the front vehicle and the queue head vehicle representing the controlled vehicle is generally between (0, 1); ζ represents the assistant coefficient ratio of the system, the critical damping may be set to 1; omega n Representing the bandwidth of the controller. The CACC fleet, organized according to the above formula, can track the lead vehicles at a constant pitch.
And S4, according to the expected acceleration output by the current vehicle, the vehicle updates the position, speed and acceleration information of the vehicle, and transmits the information to other vehicles in the CACC marshalling vehicle team again, and meanwhile, the distance error between the current vehicles is calculated and updated.
Step S5: the feedforward control module of the automatic driving control monitors whether each vehicle in the CACC motorcade 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 a cooperative vehicle team is detected from vehicles in an adjacent lane, the cooperative self-adaptive cruise control module of the highway interweaving area with the limitation of feedforward acceleration changes performs feedforward control on cut-out CACC vehicle row interference, and a specific algorithm expression of an acceleration change limitation algorithm in the module is as follows:
wherein equation (1) represents a 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 to be dequeued off the CACC bank,
representing the maximum acceleration of the ith vehicle; />
An acceleration value representing the current time; f (f) i (Δt/t al ) A restriction function representing the acceleration restriction. Equation (2) shows the built-in control algorithm in the feedforward control module that is started when the CACC control system receives the signal to join the CACC row from the vehicle with the adjacent lane,>
indicating the maximum deceleration value of the i-th vehicle.
Step S6: and selecting whether to trigger an acceleration change limiting module in the feedforward control system according to the actual traffic condition control system. If the CACC vehicles leave the CACC fleet to group the immediately following CACC vehicles, the control system of the CACC vehicles triggers the feedforward acceleration change limiting module to avoid dangerous driving behaviors caused by too large sudden acceleration change.
Further, in step S5, a rolling prediction algorithm based on MPC is used to predict the traffic behavior of the preceding vehicle and the adjacent lane, which may be in-and-out, specifically: and predicting the transverse position of the monitored vehicle in a future period, judging that the monitored vehicle cuts into the CACC fleet when the transverse position function value calculated according to each predicted value in the prediction sequence does not exceed the threshold value, and judging that the monitored vehicle cuts 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.
The invention adopts the technical scheme and has the following beneficial effects:
(1) The invention provides a cooperative self-adaptive cruise control method, which comprises the design of information transmitted by vehicle information, wherein in the process of cooperative marshalling driving, a controlled vehicle sends the position, speed and acceleration of the vehicle to other vehicles in a queue in real time, and meanwhile, an information receiving device also continuously receives driving information sent by the other vehicles, and the controlled vehicle mainly pays attention to the driving information of a front vehicle and a CACC queue head vehicle, so that the aim of stabilizing the following front vehicle of each vehicle in a CACC vehicle queue can be realized.
(2) The method for controlling the vehicle self-adaptive cruising in the expressway interweaving area adds the feedforward acceleration change limiting operation, so that the behavior of suddenly and violently accelerating or decelerating the controlled vehicle caused by cutting in and cutting out the vehicles in adjacent lanes can be effectively avoided, and dangerous driving behavior caused by cutting in and cutting out interference is avoided.
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Other technical features, objects, and performance advantages of the present application will become more apparent upon reading the following detailed description of the highway interleaving area fleet cooperative control system with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a data acquisition and processing process designed for a control system for motorway interleaving area motorcade cooperative driving based on an improved motorcade cooperative driving control algorithm.
Fig. 2 is a schematic diagram of an application example of the improved motorcade cooperative driving control algorithm in the highway interleaving region.
Fig. 3 is a schematic diagram of the recognition of the traffic behavior of the preceding vehicle based on the model predictive control algorithm proposed by the present invention.
Fig. 4 is a schematic diagram of a control framework of the highway interleaving region cooperative adaptive cruise control algorithm according to the present invention.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings.
As shown in FIG. 2, the present invention provides an improved VAL-CACC (Variable Acceleration Limit-CACC) adaptive cruise control method for variably controlling acceleration of an autonomous vehicle in a fleet of vehicles traveling in tandem with CACC in a highway interleaving area, thereby avoiding severe jerkiness due to external disturbances. A vehicle-mounted communication device is utilized to sense a lane change request of a vehicle in an interleaving area in advance, and a feedforward control module in the VAL-CACC control system carries out feedforward control on lane change interference. The invention is mainly used for the cooperative running of the vehicle queues in the highway interweaving area and can inhibit the interference to the CACC queues caused by the lane changing behavior of the vehicles. Under the control of the control system structure and the control algorithm, the acceleration change is limited, the overall traffic capacity of the expressway can be improved, the travel time of the automatic driving vehicle queues in the interweaving area is shortened, and the stability and the safety of the CACC automatic driving vehicle queues in the expressway interweaving area are improved.
As shown in fig. 1, the highway interleaved region acceleration variation limiting feedforward cooperative adaptive cruise control method 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 cooperative driving motorcade under the current road environment through the vehicle-mounted positioning device. Assuming that the current fleet consists of n+1 networked autopilot cars, as shown in FIG. 2, the first Car of the fleet is denoted as the lead Car 0 The method comprises the steps of carrying out a first treatment on the surface of the Any vehicle in the queue other than the head vehicle in the queue is noted as Car i Where the index i indicates the vehicle's following position in the queue. When the vehicles loaded with the vehicle-mounted communication system form the self-organizing local internet of vehicles, the construction of the cooperative self-adapting cruising vehicle team 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 special communication mobile self-organizing networking technology of an IEEE 802.11p intelligent transportation system. On one hand, the driving information of the front vehicle can be controlled in real time through the V2V communication system; on the other hand, through V2I communication, all vehicles in the CACC vehicle team can acquire the driving information of the train head, which is the communication basis of adopting constant head space. In the process of collaborative marshalling driving, the controlled vehicle continuously receives driving information sent by other vehicles while sending the position, the speed and the acceleration of the vehicle to other vehicles in the queue in real time, and mainly focuses on the driving information of the front vehicle and the CACC queue head vehicle, and the important focusing on the driving information of the vehicle is extracted by a vehicle ID identification method.
Step S2: and acquiring real-time position, speed and acceleration information of each vehicle in the CACC motorcade by using the vehicle-mounted radar and the positioning device. And in the CACC marshalling motorcade, each controlled vehicle realizes the sharing of vehicle running information through the communication of the V2V internet of vehicles. Vehicle-mounted operation system according to shared running information of each vehicleTo calculate the relative position d of the vehicle and the front vehicle i ＝x i -x i-1 Wherein x is i ,x i-1 And respectively representing the absolute position coordinates of the vehicle and the front vehicle.
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 For the position error value of the current vehicle i and the front vehicle, namely, the head space error of the current vehicle and the front vehicle, L i For a constant head distance value between the current vehicle i and the front vehicle, the controller adjusts control output according to the current vehicle speed and the current running state of the vehicle, and compensates the distance error between vehicles in the current vehicle team. The control algorithm of the controlled vehicle is as follows:
wherein a is i,des Representing a desired acceleration of the controlled vehicle i; a, a 0 Indicating acceleration of the queuing head car; a, a i-1 Representing the acceleration of the preceding vehicle of the controlled vehicle i; c (C) 1 The weight value of the front vehicle and the queue head vehicle of the controlled vehicle i is represented by the representative, the reaction of the controlled vehicle to the control signal of the queue head vehicle is represented by the representative, and the value is generally (0, 1); ζ represents the assistant coefficient ratio of the system, the critical damping may be set to 1; omega n Representing the bandwidth of the controller; epsilon i Respectively representing the spacing error of the controlled vehicle i from its immediately following vehicle,
is epsilon i Is a derivative of (2); v i -v 0 Representing the difference in speed between the controlled vehicle i and the ride-on vehicle. Organizing the CACC fleet according to the above formula, each vehicle in the CACC fleet can track the lead vehicle at a constant pitch.
And S4, according to the expected acceleration output by the current vehicle, the vehicle updates the position, speed and acceleration information of the vehicle, and transmits the information to other vehicles in the CACC marshalling vehicle team again, and meanwhile, the distance error between the current vehicles is calculated and updated.
Step S5: the feedforward control module of the automatic driving control monitors whether each vehicle in the CACC fleet has a specific traffic behavior request, wherein the feedforward control module mainly monitors the occupation condition of the adjacent lanes and whether the vehicles in the queue have requests for exiting the queue, such as: leave the CACC queue or wait for a request to join the CACC queue.
In order to identify the cutting-out behavior of a front vehicle and the cutting-in behavior of an adjacent lane, a model prediction algorithm is adopted to predict the lateral displacement of the front vehicle track and the vehicles of the adjacent lanes, and whether the cutting-in cutting-out behavior exists or not is judged. As shown in fig. 3, the present invention utilizes an MPC-based rolling prediction algorithm to predict the likely entrance-exit traffic behavior of a lead vehicle and adjacent lanes. The MPC predictive control algorithm takes a finite prediction time domain to solve for future optimal control inputs. In order to realize rolling prediction of vehicle behavior, the MPC algorithm takes the first predicted component of a set of optimal solutions calculated at each moment as the execution component of the controller.
The rolling model prediction algorithm is used herein to predict lateral movement of the front vehicle, predict the x position of the vehicle, and determine whether the front vehicle has substantial track-in or track-out behavior based on the predicted value. The rolling prediction algorithm (MPC) will predict the lateral position x= [ X ] of its listening vehicle at a certain time in the future k (k),x k (k+1),x k (k+2),...x k (k+n)]Wherein x is k (k+n) represents the lateral position of the monitored vehicle at time k+n predicted. By using the obtained predicted position, the invention uses the formula (3) as a judging basis for judging whether the vehicle has cut-in-cut-out interference or not:
wherein f (X) represents a vehicle lateral position function designed based on a rolling predicted vehicle lateral displacement vector, the most common of which is the f (X) functionThe number can be expressed as a linear function of transition, 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 requirements of engineering practice, and alpha is generally 0 ,α 1 ,α 2 ,....,α n The value of (c) is gradually decreased, for example, 0.8,0.1,0.05 … may be taken. In addition, x in the above criterion k (z) represents the lane demarcation location at time k. The MPC algorithm performs local optimal solution in a limited domain each time, performs rolling prediction online in real time, and can not only resist external interference, but also accurately predict traffic behavior of a preceding vehicle in real time. Step S6: when a request for joining or leaving a cooperative vehicle team is monitored by vehicles in an adjacent lane, the highway interweaving area cooperative self-adaptive cruise control module with limited feedforward acceleration changes eliminates interference to cut-out CACC vehicles, feedforward control is carried out, after the acceleration of the current vehicle is limited, the position and speed of the current vehicle are updated, updated current vehicle information is broadcast to other vehicles through V2X, and if no request for joining or leaving the cooperative vehicle team is monitored by vehicles in the adjacent lane, a specific algorithm expression of an acceleration change limiting algorithm in the feedforward control module in the step S1 is returned to:
wherein, the formula (1) represents a built-in control algorithm in a feedforward control module started when the CACC control system receives a signal that a vehicle queue needs to cut out and leave the CACC row,
representing the maximum acceleration of the controlled vehicle i; />
The acceleration value of the controlled vehicle i at the current moment is represented; u (u) i (t+Δt) represents a real-time control input amount of the controlled vehicle; a, a i (t+Δt) represents the acceleration limit of the controlled vehicle i within the instant t+Δt of the cut-out or cut-in of the preceding vehicle into the fleet, f i (Δt/t al ) Representing a function of limiting acceleration, t al Representing the acceleration limit duration, typically 2-3s. In addition, f i (Δt/t al ) The function can be designed into different functions according to actual engineering requirements, and typical functions are linear functions, nonlinear sigmod functions and the like. Equation (2) shows the built-in control algorithm in the feedforward control module that is started when the CACC control system receives the signal to join the CACC row from the vehicle with the adjacent lane,>
representing the maximum deceleration value of the controlled vehicle i.
The block diagram model of the controller is shown in fig. 4, when the feedforward control module monitors that the vehicle k is cut in or cut out, the feedforward control module outputs uk', pk to the feedback control module, after limiting the acceleration of the controlled vehicle i, the feedforward control module 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 Q is i Representing Laplacian transformation of i position of controlled vehicle in cooperative driving queue, U i Laplacian transformation, τ, representing the desired execution acceleration of the controlled vehicle i i Time delay constant, θ, representing the driveline of a controlled vehicle i Representing the time delay of the communication of the information transfer between vehicles, the subscript i represents the order of the controlled vehicles in the fleet and s represents the laplace operator. />
Here, the->
Whether the feedback control module accords with the control rule H i (s)＝1+h i s represents a spacing control strategy for vehicles in a 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 is i (s),U k (s) represent the Laplace transform between the acceleration of the controlled vehicle and the acceleration of the vehicle associated therewith, respectively; d (D) ff (s) a control module representing a hysteresis effect generated by a time delay during V2X vehicle communication; g i (s) represents a kinetic transfer function of the vehicle;
representing a conventional closed loop feedback control module; />
Is a feedforward acceleration control module in the control strategy.
The highway interweaving region cooperative self-adaptive cruise control method is characterized in that the transfer function of the feedback and feedforward control module is as follows:
in practice, the acceleration from the preceding vehicle is not amplified or reduced, but directly through the unity gain, therefore K ff The value of (2) is typically 1; k (K) fb Representing a feedback control vector comprising two elements K fb (1) And K fb (2)；σ i Representing a time lag constant of the transmission system to the control system; h is a i And the preset head time distance under different traffic conditions is indicated.
Step S6: and selecting whether to trigger an acceleration change limiting module in the feedforward control system according to the actual traffic condition control system. If the CACC vehicles leave the CACC fleet to group the immediately following CACC vehicles, the control system of the CACC vehicles triggers the feedforward acceleration change limiting module to avoid dangerous driving behaviors caused by too large sudden acceleration change.
Claims (1)
1. The highway interweaving area cooperative self-adaptive cruise optimizing control method is characterized in that,
acquiring the position, the speed and the acceleration of each vehicle in the CACC vehicle team, and sharing the position, the speed and the acceleration of each vehicle in the CACC vehicle team through a V2X communication system;
solving a head space error between a current vehicle and a front vehicle;
according to the head space error and speed error of the current vehicle and the front vehicle, the expected acceleration of the current vehicle is adjusted, the speed and the position of the current vehicle are updated, the position, the speed and the acceleration of the current vehicle are shared with each vehicle in the CACC train through a V2X communication system, and the expression for adjusting the expected acceleration of the current vehicle according to the head space error and the speed error of the current vehicle and the front vehicle is as follows:
wherein a is i,des Representing a desired acceleration of the current vehicle i; a, a 0 Indicating acceleration of the queuing head car, a i-1 Representing the acceleration of the preceding vehicle of the current vehicle i, C 1 The weight value of the front vehicle and the queue head vehicle of the current vehicle i is represented by C 1 Representing the current vehicle's response to the control signal of the lead vehicle, C 1 The value of (1, 0) is generally taken, ζ represents the assistant coefficient ratio of the system, and critical damping is set to be 1, ω n Representing the bandwidth, epsilon, of the controller i Respectively representing the distance error between the current vehicle i and its following vehicle,/respectively>
Is epsilon i Derivative of v i -v 0 Representing the difference between the speeds of the current vehicle i and the aligned head vehicle, the head space error between the current vehicle and the front vehicleIs epsilon i ＝x i -x i-1 +L i ,ε i For the head space error of the current vehicle i and the front vehicle, x i 、x i-1 The absolute position coordinates of the current vehicle i and the preceding vehicle i-1 are respectively L i The constant head distance value of the current vehicle i and the front vehicle is obtained;
monitoring whether each vehicle in the CACC vehicle team has a request for cutting in or cutting out the vehicle team, limiting the acceleration of the vehicle immediately after cutting in or cutting out the vehicle when the vehicle cuts in or cuts out the CACC vehicle team, updating the position and the speed of the vehicle immediately after cutting in or cutting out the vehicle, sharing the position, the speed and the acceleration of the vehicle immediately after cutting in or cutting out the vehicle with each vehicle in the CACC vehicle team through a V2X communication system,
the specific method for judging whether the vehicle enters or exits the CACC motorcade comprises the following steps: predicting a current vehicle transverse position sequence by adopting a rolling prediction method based on MPC, judging that the current vehicle cuts into a CACC vehicle team when a transverse position function value corresponding to the transverse position sequence does not exceed a threshold value, judging that the current vehicle cuts out the CACC vehicle team when the transverse position function value corresponding to the transverse position sequence exceeds the threshold value, wherein the transverse 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 Is gradually decreased in value of (a) and (b),
when a vehicle cuts into the CACC fleet, the acceleration of the vehicle immediately following the cut into the vehicle is limited as follows,
wherein u is i (t+Δt) represents the real-time control input amount, a, of the current vehicle i i max Representing the maximum acceleration of the current vehicle i, a i (t+Δt) represents the acceleration limit of the current vehicle i within the instant t+Δt that the preceding vehicle cut into the fleet, +.>
Acceleration value f representing current moment of current vehicle i i (Δt/t al ) A limiting acceleration function, t, representing the preceding vehicle i al Indicating the acceleration limit duration of time,
when there is a vehicle cutting out of the CACC fleet, the acceleration of the vehicle immediately after the vehicle cutting out is limited according to the following expression,
wherein u is i (t+Δt) represents the real-time control input quantity of the current vehicle i, d i min Representing the maximum deceleration value, a, of the current vehicle i i (t+Δt) represents the acceleration limit of the current vehicle i in the instant t+Δt of the preceding vehicle cutting out the fleet, +.>
Acceleration value f representing current moment of current vehicle i i (Δt/t al ) A limiting acceleration function, t, representing the preceding vehicle i al Indicating the acceleration limit duration. />
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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 |
|---|
| 自适应巡航控制车辆跟驰模型综述;秦严严;王昊;王炜;NI Dai-heng;;交通运输工程学报(03);全文 * |
| 车辆协同巡航控制***设计改进与试验评价;华雪东;阳建强;王炜;;交通运输***工程与信息(06);全文 * |
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