CN113345268A - CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene - Google Patents

CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene Download PDF

Info

Publication number
CN113345268A
CN113345268A CN202110805341.6A CN202110805341A CN113345268A CN 113345268 A CN113345268 A CN 113345268A CN 202110805341 A CN202110805341 A CN 202110805341A CN 113345268 A CN113345268 A CN 113345268A
Authority
CN
China
Prior art keywords
lane
cav
lane change
automatic driving
vehicle
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202110805341.6A
Other languages
Chinese (zh)
Other versions
CN113345268B (en
Inventor
郝威
吴其育
张兆磊
易可夫
王正武
田大新
戎栋磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Changsha University of Science and Technology
Original Assignee
Changsha University of Science and Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Changsha University of Science and Technology filed Critical Changsha University of Science and Technology
Priority to CN202110805341.6A priority Critical patent/CN113345268B/en
Publication of CN113345268A publication Critical patent/CN113345268A/en
Application granted granted Critical
Publication of CN113345268B publication Critical patent/CN113345268B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Atmospheric Sciences (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

The invention discloses a CAV lane change decision-making method for a lower ramp diversion area of a highway under an automatic driving special lane deployment scene, which comprises the following steps: constructing a forced lane change scene; calculating the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state, calculating the traffic volume of an automatic driving special lane and a general lane based on a lane management strategy, and constructing the running condition of the mixed traffic flow; and the CAV selects to switch the track when the cost function value J of the CAV track switching model is minimum. The invention analyzes the influence of CAV driving away from CAV-L on the traffic state based on the CAV lane change decision method of the expressway down-ramp diversion area under the automatic driving special lane deployment scene, establishes a CAV lane change intention generation point position and lane change decision model, provides important technical guidance for future expressway mixed traffic flow management and provides a theoretical basis for expressway automatic driving special lane deployment landing.

Description

CAV lane change decision-making method for expressway down-ramp diversion area based on automatic driving special lane deployment scene
Technical Field
The invention belongs to the technical field of intelligent transportation, and relates to a CAV lane change decision-making method for a ramp shunting area under a highway based on an automatic driving special lane deployment scene.
Background
In recent years, with the increasing maturity of artificial intelligence technology, the research and development of automatic driving technology gradually turns to the combination of theoretical research and specific real vehicle test from pure theoretical research, which means that the traffic mode of vehicle driving will enter the stage of mixing the CAV and the manually driven vehicle (HMV). However, the popularity of the automatic driving technology is limited by many factors, such as audience acceptance, road infrastructure matching, relevant laws and regulations, and the traffic mode still needs to go through a stage of mixing CAV and HMV for a long time in the process of technology maturity. In addition, the characteristics of mixed traffic flow (referring to mixed traffic flow of CAV and HMV) and the safety thereof are not clear. Therefore, many transportation researchers propose to deploy CAV-L (Connected and Automated Vehicles differentiated Lane) on the highway, and achieve the purpose of improving traffic efficiency and traffic safety by reasonably laying CAV-L.
The existing research proves the necessity and feasibility of setting an automatic driving special lane, and an optimization model is constructed to obtain the optimal scheme of special lane deployment, and most researches only consider the main line traffic capacity in a balanced state, establish an analytical model or analyze the profit of CAV-L by applying a simulation technology, neglect the influence of CAV driving-in/out on the traffic flow, belong to a bottleneck area limiting the traffic capacity of the highway, and determine the operation quality of the whole highway by the operation efficiency. Therefore, the influence of CAV driving-in/out on the traffic state needs to be researched, a CAV lane change decision method is established, and the CAV lane change safety is guaranteed.
Disclosure of Invention
In order to achieve the aim, the invention provides a CAV lane change decision-making method based on a lower ramp shunting area of an expressway under an automatic driving special lane deployment scene, which analyzes the influence of CAV driving away from CAV-L on the traffic state and establishes a CAV lane change intention generation point position and lane change decision model; then, a lane change cost function under different influence factors is constructed on the basis, the optimal balance point of CAV driving efficiency and lane change safety under different influence factors is analyzed, driving benefit and lane change safety are comprehensively considered, a CAV lane change decision model is provided, the influence of CAV lane change on traffic safety under different traffic demands, different lane change preparation distances and different CAV market penetration rates p is analyzed through numerical values and simulation, and the problem that influence of CAV driving-in/driving-out on traffic flow is ignored in the prior art is solved.
The invention adopts the technical scheme that a CAV lane change decision-making method of a lower ramp shunting area of an expressway under an automatic driving special lane deployment scene comprises the following steps of:
constructing a forced lane changing scene of a CAV (vehicle access vehicle) running on an automatic driving special lane under a highway in a lower ramp shunting area;
under a forced lane change scene, calculating the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state, calculating the traffic volume of an automatic driving special lane and a general lane based on a lane management strategy, and constructing a mixed traffic flow operation condition;
and constructing a cost function of the CAV lane change model, and selecting the lane change by the CAV when the cost function value J of the CAV lane change model is minimum.
The invention has the beneficial effects that:
(1) the embodiment of the invention aims at a highway with a deployed CAV-L, analyzes the influence of CAV driving away from the CAV-L on the traffic state, and establishes a CAV lane change intention generation point position and lane change decision model; then, constructing lane change cost functions under different influence factors on the basis, analyzing the optimal balance point of CAV driving efficiency and lane change safety under different influence factors, finally comprehensively considering driving benefit and lane change safety, providing a CAV lane change decision model and a CAV lane change decision method, and analyzing the influence of CAV lane change on traffic safety under different traffic demands, different lane change preparation distances and different CAV market penetration rates p through numerical values and simulation;
(2) the embodiment of the invention considers the self-formation technology between CAVs, provides a dynamic road traffic capacity calculation method based on the mixed traffic flow characteristics of the CAVs and the HMVs, conforms to the reality and conforms to the large environment of mixed running of future automatic driving vehicles and traditional vehicles;
(3) the embodiment of the invention analyzes the influence of the traffic demand D, the lane change preparation distance S and the market permeability p of the CAV on the CAV lane change decision, researches the generation point position distribution rule of the CAV lane change intention and the probability of the vehicle CAV entering a lower ramp from the automatic driving lane, and provides a cost function to couple the lane change safety and efficiency on the basis so as to construct a CAV lane change decision model under the mixed traffic flow;
(4) the embodiment of the invention designs a 4-lane expressway scene, and respectively performs numerical analysis and case simulation, and the result shows that: the probability that the vehicle CAV successfully changes the lane from the automatic driving lane to enter the lower ramp is jointly influenced by the traffic demand, the lane change preparation distance S and the market permeability p of the CAV, the increase of the market permeability p of the CAV and the lane change preparation distance S can improve the probability that the vehicle CAV successfully changes the lane from the automatic driving lane to enter the lower ramp, and the increase of the traffic demand can reduce the chance of changing the lane of the CAV; the change trend of the traffic flow safety index TIT under the condition of the same numerical analysis is the same; in addition, when p is<50％、D>4000(veh.h-1) When the vehicle CAV is in the off-ramp state, the probability that the CAV of the CAV vehicle is successfully changed from the automatic driving lane to the off-ramp is lower than 30 percent and finally approaches to 0 along with the increase of traffic demand, and when p is>When 70 percent of the time, the probability of the vehicle CAV entering a next ramp from the automatic driving lane by successfully changing the lane can be improved by 30 to 50 percent and the TIT can be reduced by 30 to 50 percent by reasonably dividing the lane changing preparation distance of the special lane; the research result can provide important technical guidance for the mixed traffic flow management of the highway in the future and also can provide theoretical basis for the arrangement and landing of the automatic driving special lane of the highway.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a view of a scene of forced lane change of a ramp on a lower ramp of a vehicle on an automatic driving dedicated highway according to an embodiment of the invention.
Fig. 2 is a mixed traffic flow balance state headway distribution diagram according to an embodiment of the present invention.
FIG. 3 is a diagram of a numerical and simulation analysis scenario in accordance with an embodiment of the present invention.
FIG. 4a shows an embodiment of the present invention with traffic volume of 2400veh-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 4b shows a traffic volume of 3200veh.h in an embodiment of the present invention-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 4c shows an example of 4000veh traffic volume-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 4d is a graph of 4800veh-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 4e is a schematic diagram of a traffic volume of 5600veh.h in the embodiment of the present invention-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 4f shows a traffic volume of 6400veh-1And a probability numerical analysis result graph of the vehicle CAV entering the next ramp from the automatic driving lane after the vehicle is successfully changed.
FIG. 5a shows an embodiment of the present invention with traffic volume of 2400veh-1When α is 0.And 3, analyzing a result graph by using a cost function.
FIG. 5b shows a traffic volume of 3200veh.h in an embodiment of the present invention-1When α is 0.3, the cost function analysis results are shown.
FIG. 5c shows an example of 4000veh traffic volume-1When α is 0.3, the cost function analysis results are shown.
FIG. 5d is a graph of 4800veh-1When α is 0.3, the cost function analysis results are shown.
FIG. 5e is a schematic diagram of a traffic volume of 5600veh.h in the embodiment of the present invention-1When α is 0.3, the cost function analysis results are shown.
FIG. 5f shows a traffic volume of 6400veh-1When α is 0.3, the cost function analysis results are shown.
FIG. 6a shows a traffic volume of 2400veh.h in an embodiment of the present invention-1When α is 0.5, the cost function analysis result is shown.
FIG. 6b shows a traffic volume of 3200veh.h in an embodiment of the present invention-1When α is 0.5, the cost function analysis result is shown.
FIG. 6c shows an example of 4000veh traffic volume-1When α is 0.5, the cost function analysis result is shown.
FIG. 6d is a graph of 4800veh-1When α is 0.5, the cost function analysis result is shown.
FIG. 6e shows a traffic volume of 5600veh.h in accordance with the present invention-1When α is 0.5, the cost function analysis result is shown.
FIG. 6f shows a traffic volume of 6400veh-1When α is 0.5, the cost function analysis result is shown.
FIG. 7a shows an embodiment of the present invention with traffic volume of 2400veh-1When α is 0.7, the cost function analysis results are shown.
FIG. 7b shows a traffic volume of 3200veh.h in an embodiment of the present invention-1When α is 0.7, the cost function analysis results are shown.
FIG. 7c shows an example of 4000veh traffic volume-1When α is 0.7, the cost function analysis results are shown.
FIG. 7d is an implementation of the present inventionExample traffic volume is 4800veh-1When α is 0.7, the cost function analysis results are shown.
FIG. 7e shows a traffic volume of 5600veh.h in accordance with the present invention-1When α is 0.7, the cost function analysis results are shown.
FIG. 7f shows a traffic volume of 6400veh-1When α is 0.7, the cost function analysis results are shown.
FIG. 8a is a schematic diagram of an embodiment of the invention with traffic volume of 2400veh-1And (5) a time simulation analysis result graph.
FIG. 8b shows a traffic volume of 3200veh.h in an embodiment of the present invention-1And (5) a time simulation analysis result graph.
FIG. 8c shows an example of 4000veh traffic volume-1And (5) a time simulation analysis result graph.
FIG. 8d is a graph of 4800veh-1And (5) a time simulation analysis result graph.
FIG. 8e is a schematic diagram of a traffic volume of 5600veh-1And (5) a time simulation analysis result graph.
FIG. 8f shows a traffic volume of 6400veh-1And (5) a time simulation analysis result graph.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The CAV lane change decision method for the off-ramp shunting area of the expressway under the scene of automatic driving special lane deployment comprises the following steps of:
s10, constructing a forced lane changing scene of the CAV running on the automatic driving special lane under the expressway in a lower ramp diversion area: as shown in figure 1, the total number of lanes of the highway is H, H is more than or equal to 4, and the lane farthest from the lower ramp is lAAutomatic driving lane (CA)V-L)，lAH-1,. 1, 2; the 1 st lane for automatic driving is the H-th lane and the l-th laneAThe lane special for automatic driving is the H +1-lAStrip lanes, withAThe adjacent general lane at one side of the automatic driving special lane close to the lower ramp is the H-lAStrip lane, H-lAThe adjacent general lane at one side of the strip lane close to the lower ramp is the H-lA-1 lane up to lane 1, lane 1 being adjacent to the down ramp;
when the CAV running on the automatic driving special lane generates the intention of exiting the expressway, the CAV firstly changes the lane from the automatic driving special lane to a General Lane (GL), then changes the lane from the General lane to a lower Ramp (Ramp) and finally exits the expressway from the lower Ramp, wherein the generation point of the CAV lane changing intention is an A section, and the lateral distance of a lane changing area of an H lane is LCHThe latest lane change transverse distance is LH(ii) a H +1-lAThe transverse distance of the lane change area of the strip lane is
Figure BDA0003166285610000041
The latest lane change transverse distance is
Figure BDA0003166285610000042
H to lAThe transverse distance of the lane change area of the strip lane is
Figure BDA0003166285610000043
The latest lane change transverse distance is
Figure BDA0003166285610000044
H to lA-1 lane change zone lateral distance of lane
Figure BDA0003166285610000045
The latest lane change transverse distance is
Figure BDA0003166285610000046
The travel keeping area of the CAV in the first lane after the forced lane change is LK1The track-changing termination point is B, S indicates the track-changing preparation distance, i.e. the CAV track-changing intentionGenerating the transverse displacement of the point A and the lane change termination point B.
"lane change area lateral distance" means the projected distance of the area where the CAV makes a lane change decision in the traffic flow direction.
The "latest lane change lateral distance" indicates a projected distance of a distance traveled by the CAV in the traffic flow direction within the predicted lane change execution time when the lane change is started at the end of the "lane change area lateral distance".
The present application relates to "lateral" all referring to projected distance in the direction of traffic flow.
S20, under the scene of forced lane changing of S10, calculating the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state, calculating the traffic volume of an automatic driving special lane and a general lane based on a lane management strategy, constructing the running condition of the mixed traffic flow, and laying a foundation for subsequently constructing a CAV lane changing decision model:
the process of calculating the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state specifically comprises the following steps:
first, the CAV formation theory is provided: under the environment of intelligent networking, because of the V2V and V2X communication technologies, CAVs distributed in the same direction and on the same lane in space continuously can find opportunities to actively form an intelligent networking fleet and carry out formation driving, the intelligent networking fleet usually has certain scale limitation (the upper limit of the scale of the intelligent networking fleet is u) in consideration of the effective range and stability of workshop communication, and when the scale r of the intelligent networking fleet exceeds u, the subsequent CAVs are established as another intelligent networking fleet;
secondly, based on the CAV formation theory, there are 4 types of vehicles in the mixed traffic flow mixed with the intelligent internet fleet on the highway, as shown in fig. 2, which are respectively: traditional manual driving vehicle HMV, AVC (automatic voltage control) of intelligent network connection fleet of follow-up CAV (vehicle control ) AVH (automatic voltage control) of intelligent network connection fleet of follow-up HMV, AVH (automatic voltage control) of intelligent network connection fleet of follow-up HMV and CAV (vehicle control, vehicle speed) in intelligent network connection fleet, and vehicle following distance t of traditional manual driving vehicle HMVHMV2s, following vehicle following distance t of AVC (automatic Voltage control) vehicle of CAV (vehicle automatic Voltage control) intelligent network connection fleet cluster head vehicleAVC1.1s, following vehicle following distance t of AVH (automatic vehicle height) of HMV (hybrid automotive vehicle) intelligent networking fleetAVH＝1.5s，Car following distance t of CAV in intelligent internet fleetCAV＝0.6s；
Thirdly, calculating the existence probability of the four types of vehicles when the mixed traffic flow is in a balanced state: when the market penetration rate of CAV in a mixed traffic flow mixed with an intelligent internet fleet is p, the existence probability of the 4 types of vehicles is shown as the formula (1):
Figure BDA0003166285610000051
in the formula (1), pHMVRepresenting the probability of existence, p, of a conventional manually driven vehicle HMVAVHIndicating probability of existence, p, of intelligent networked fleet cluster head vehicle AVH following HMVAVCProbability of existence, p, of AVC (advanced video coding) of intelligent networked fleet cluster head vehicle representing following CAV (vehicle-associated vehicle)CAVThe method comprises the steps of representing the existence probability of CAV in an intelligent networked fleet, wherein r represents the scale of the intelligent networked fleet, the range of r is preferably 2-6, and the unit is vehicle (veh);
the method comprises the following steps of calculating the traffic volume of an automatic driving special lane and a general lane based on a lane management strategy, and specifically comprises the following steps:
first, the lane management strategy is: the CAV preferentially runs on the automatic driving special lane CAV-L, when the CAV requirement is larger than the maximum traffic capacity of the automatic driving special lane, the rest CAVs are distributed on a general lane GL to run, and both the CAV and the HMV have the right of the general lane GL;
according to the lane management strategy, firstly, the traffic q of the CAV-L is calculatedAAs shown in formula (2):
qA＝min(pD,lACA),lA＝1,2…H-1 (2)；
wherein p is the market penetration of CAV and D is the highway traffic demand in veh-1，lANumber of lanes dedicated to autonomous driving, CAThe unit is veh.h for the traffic capacity of the special lane for automatic driving-1Application CAPreferably 3200veh-1H is the total number of lanes of the highway;
secondly, the traffic q of the general lane is calculatedmixAs shown in formula (3):
qmix＝D-qA (3)；
the method comprises the following steps of constructing a mixed traffic flow running condition, specifically:
the initial mixed traffic flow keeps a balanced state, all vehicles obey Poisson distribution, the vehicles keep running at proper intervals, and the lane changing behavior of original vehicles on the general lane GL is not considered;
the distance between the generation point A of the intention of changing the lane and the entrance and exit of the next ramp is forced to be far enough, and the current decision of changing the lane of the vehicle is not influenced;
there is a limit to CAV driving away from the autopilot lane within a certain time.
S30, constructing a CAV lane change decision model:
firstly, constructing a cost function of a CAV lane change model: in a real expressway down-ramp scene, the selection of the position of the lane change intention generation point A by the CAV is actually carried out comprehensive balance and decision based on two contradictory factors of lane change safety and driving efficiency. Therefore, the embodiment of the invention establishes a cost function of the CAV lane change model to couple the safety and the efficiency, and specifically calculates the following formula (4):
Figure BDA0003166285610000061
in the formula (4), J represents a cost function of the CAV lane change model, alpha is a weight parameter of driving efficiency and lane change safety, the larger the value of the weight parameter is, the more CAV-emphasized driving efficiency is shown, the smaller the value is, the more CAV-emphasized lane change safety is shown, and TtotalIndicating CAV lane change preparation time, TmaxAnd representing the corresponding lane change preparation time when the probability rho of the vehicle CAV entering the lower ramp from the automatic driving lane is not less than 95%, wherein rho represents the probability of the vehicle CAV entering the lower ramp from the automatic driving lane.
CAV selects to start changing the track when the cost function value J of the CAV track changing model is minimum, namely the CAV track changing is carried out at the position of the J minimum valueGenerating a point A section by the road intention, and determining a key variable of a cost function value J of the CAV road changing model as the preparation time T of the road changingtotalAnd probability ρ of the vehicle CAV entering the off-ramp from the automatic driving lane, wherein the lane change preparation time TtotalIs calculated from the lane change preparation distance S and the average traveling speed v of the traffic flow in the ith lane (i is 2 to H)iDetermining as shown in formula (5) and formula (6):
Figure BDA0003166285610000062
Figure BDA0003166285610000063
in the formula, H represents the total number of lanes of the highway; LC (liquid Crystal)iThe lane change area transverse distance of the ith lane is represented, i represents a lane serial number mark, and i is 2-H; l isiThe latest lane change lateral distance of the ith lane is represented.
viThe average driving speed of the traffic flow of the ith lane is represented, and the calculation mode is shown as the formula (7):
Figure BDA0003166285610000071
in the formula (I), the compound is shown in the specification,
Figure BDA0003166285610000072
free flow velocity, k, representing the flow in the ith laneiIs the current traffic density of the traffic flow in the ith lane,
Figure BDA0003166285610000073
indicating the jam density of the traffic flow in the ith lane.
The calculation process of the probability rho of the vehicle CAV entering the next ramp after successfully changing the lane from the automatic driving lane specifically comprises the following steps:
based on Bernoulli profile, the method abstracts the track changing process of searching the passable gap by CAV into the success probability of a single track changing experimentA binomial distribution problem of ξ: assuming that the CAV carries out n times of lane changing experiments within 1 hour, the success probability of a single lane changing experiment is xi, the failure probability of the single lane changing experiment is 1-xi, and the current locomotive time distance t meets the acceptable safe lane changing locomotive time distance tcIf not, no response is made and the next lane change experiment is carried out. The success rate of changing the channel of n times of experiments is
Figure BDA0003166285610000077
Calculating as shown in equation (8):
Figure BDA0003166285610000074
according to equation (8), the probability ρ of the vehicle CAV entering the next ramp after successfully changing lanes from the autonomous driving lane is calculated as shown in equation (9):
Figure BDA0003166285610000075
in the formula (9), niThe number of lane change experiments generated during the process of changing the CAV from the i lane to the i-1 lane is shown.
Therefore, the calculation of rho is converted into the success probability xi of a single lane change experiment and the number n of lane change experiments generated in the process of changing CAV from the i lane to the i-1 laneiAnd (4) solving.
The calculation of the success probability xi of the single lane change experiment specifically comprises the following steps: acceptable safe lane changing locomotive time interval is tcAnd the distribution meets the probability distribution function f (t) of the headway density, and the success probability xi of the single lane change experiment is that the current headway t meets the requirement that the headway of the acceptable safe lane change is tcProbability, i.e. equal to or greater than tcThe probability of the headway distribution accounting for the total headway distribution is calculated as shown in equation (10):
Figure BDA0003166285610000076
wherein f (t) represents a headway density probability distribution function, t represents the headway, assuming that the vehicle arrives according to poisson distribution, and f (t) is calculated as shown in formula (11):
f(t)＝λe-λt (11)；
in the formula (11), λ is an average arrival rate of the vehicle in a unit time interval, and the unit is vehicle/s; t is the headway, and the unit is s; e is a natural index.
When the CAV requirement is greater than the traffic capacity of the automatic driving special lane, the traffic is converged into the general lane to form a mixed traffic flow on the general lane to form an intelligent networked vehicle team, when the mixed traffic flow is stable, the vehicles do not need to be newly formed, the traffic volume of the general lane, the probability density distribution function of the time interval of the vehicle head of the general lane and the success probability of the CAV single lane changing experiment are updated, and the lane changing experiment times n generated in the process of changing the CAV from the i lane to the i-1 lane are obtainediThe updating process specifically comprises the following steps:
the vehicles on the CAV-L are always in a stable state, all vehicles keep constant head time distance and stably run, therefore, a networked fleet is formed without considering the vehicles, when the CAV requirement is greater than the traffic capacity of an automatic driving special lane, the vehicles can converge into a general lane, a mixed traffic flow is formed on the general lane, an intelligent networked fleet is formed, and the intelligent networked fleet is divided into networked fleets F with cluster-head vehicles following the CAV1Networking fleet F following AVH with cluster head vehicles2Of an overflowing autonomous vehicle CAV in a common traffic lane GL of permeability
Figure BDA0003166285610000081
Internet fleet F forming cluster head vehicles following CAV1Has a probability of
Figure BDA0003166285610000082
Internet fleet F forming cluster head vehicle following AVH2Has a probability of
Figure BDA0003166285610000083
r represents the scale of the intelligent networked fleet,
Figure BDA0003166285610000084
as shown in equation (12),
Figure BDA0003166285610000085
is calculated as shown in equation (13):
Figure BDA0003166285610000086
Figure BDA0003166285610000087
in the formula, pAVCRepresenting the existence probability of AVC (automatic voltage control) of intelligent networking fleet cluster head vehicles following CAV; p is a radical ofCAVRepresenting the probability of the presence of CAV, p, within a fleet of Smart Net vehiclesAVHIndicating the existence probability of the intelligent internet fleet cluster head vehicle AVH following the HMV.
The total number Q of CAV vehicles forming the intelligent networked fleet is calculated by the formula (12) and the formula (13)FThe number N of the intelligent networked fleet is shown as formula (14)FAnd F denotes a fleet identification, i.e. fleet F1And fleet F2As shown in formula (15):
Figure BDA0003166285610000088
Figure BDA0003166285610000089
in the above formula, qmixRepresenting the traffic volume of the general purpose lane.
When the traffic flow is stable, the vehicles do not need to be newly formed, and because the following distance between the vehicles in the intelligent networked fleet is small (0.6s), the intelligent networked fleet can be taken as a large vehicle, so that the traffic volume of the general lane GL is updated to be QnewIt is calculated as shown in equation (16):
Figure BDA00031662856100000810
the probability density distribution function of the headway of the general lane GL is updated into
Figure BDA00031662856100000811
It is calculated as shown in equation (17):
Figure BDA00031662856100000812
in the equation (17), the average arrival rate λ of the vehicle in the unit time interval is updated to
Figure BDA00031662856100000813
And t is the headway.
From this, the success probability of CAV single lane change experiment is updated to
Figure BDA00031662856100000814
It is calculated as shown in equation (18):
Figure BDA00031662856100000815
therefore, the lane change experiment times n generated in the process of changing the CAV from the i lane to the i-1 laneiIt is calculated as shown in equation (19):
Figure BDA0003166285610000091
in the formula, T(i,i-1)The method is characterized in that the lane change remaining time of the vehicle CAV from the i lane to the i-1 lane is shown, n represents the theoretical times of lane change experiments which can be carried out by the CAV within 1 hour, namely the maximum theoretical CAV number from the current lane to the next lane, and H is the total number of lanes of the highway.
Then solving the lane change remaining time T for the vehicle to change from the i lane to the i-1 lane(i,i-1)The calculation is shown as formula (20), and the constraint condition is shown as formula(21) And formula (22):
Figure BDA0003166285610000092
Figure BDA0003166285610000093
Figure BDA0003166285610000094
in the formula (I), the compound is shown in the specification,
Figure BDA0003166285610000095
indicating the execution time of a lane change operation for a CAV to change from i lane to i-1 lane, vi-1Represents the average traveling speed of the traffic flow of the i-1 st lane,
Figure BDA0003166285610000096
indicating the lateral position, x, of the CAV changing from lane i to lane i-1iShowing the lateral position of the i lane before the CAV lane change.
The CAV can calculate the theoretical times n of the lane change experiment within 1 hour, and specifically comprises the following steps:
the following time interval of the vehicle is tg，tcIndicating acceptable safe lane change headway, when tc≤t≤tc+tgWhen, it means that a vehicle is allowed to pass through; when t isc+(j-1)tg≤t≤tc+jtgWhen j vehicles are allowed to pass through, the probability P of the gap allowing the j vehicles to pass through theoretically appearsjComprises the following steps:
Figure BDA0003166285610000097
therefore, the calculation of the theoretical number n of times that the CAV can carry out lane change experiments within 1 hour can be obtained, and the formula (24) shows:
Figure BDA0003166285610000098
Figure BDA0003166285610000099
Figure BDA0003166285610000101
in the formula (I), the compound is shown in the specification,
Figure BDA0003166285610000102
indicates the permeability
Figure BDA0003166285610000103
The average headway of the lower mixing flow,
Figure BDA0003166285610000104
permeability, t, of an overflowing autonomous vehicle CAV on a general purpose lane GLHMVThe time interval of the HMV vehicle head is 1.8 s; t is tAVHThe time interval of the AVH vehicle head is 1.5 s; t is tAVCThe time interval of the AVC vehicle head is 1.1 s; t is tCAVThe time interval of the CAV head of the vehicle is 0.6 s; and r represents the intelligent networked fleet scale.
And (3) synthesizing the above steps to obtain a CAV lane change decision model of the lower ramp diversion area of the expressway: the CAV vehicle selects to start lane changing at the position where the cost function value J of the CAV lane changing model is minimum, namely, the minimum value of the formula (4) is solved, and the CAV vehicle can obtain the following conditions based on the formulas (5) to (26):
Figure BDA0003166285610000105
lane change experiment times n generated in the process of changing CAV from i lane to i-1 laneiThe calculation method of (2) is as follows:
Figure BDA0003166285610000106
numerical and simulation analysis
Setting a scene:
the forced lane change scene is shown in fig. 3, the innermost lane 4 of the road section is an automatic driving special lane CAV-L, the outer lanes 1-3 are general lanes GL, and the attribute parameters of each lane are shown in table 1.
TABLE 1 Attribute parameters of each lane in forced lane-change scenarios
Lane numbering Lowest speed limit (m/s) Highest speed limit (m/s) Blocking Density (veh/km) tg(s) tc(s)
Lane 1 16.7 22.2 125 2.0 8
Lane 2 22.2 27.8 125 1.8 8
Lane 3 27.8 33.3 125 1.5 8
Lane 4 27.8 33.3 125 1.1 8
Numerical analysis:
(1) probability influence factor analysis for vehicle CAV entering off-ramp from automatic driving lane successful lane change
Based on the above scenes, the influence of the lane change preparation distance S, the traffic volume of the automatic driving special lane and the general lane and the market permeability p of the CAV on the CAV lane change behavior is analyzed by applying the CAV lane change decision method based on the off-ramp diversion area of the expressway under the automatic driving special lane deployment scene provided by the embodiment of the invention, and the result is shown in fig. 4a to 4 f. In view of the lane change preparation distance S, fig. 4a to 4f show that the probability ρ of the vehicle CAV entering the lower ramp from the automatic lane change is increased as the lane change preparation distance S increases. However, the rate of increase of the probability ρ of the vehicle CAV successfully changing the lane from the autonomous lane to the next ramp decreases with the increase of the lane change preparation distance S, that is, when the lane change preparation distance S is smaller, the influence of the probability ρ of the vehicle CAV successfully changing the lane from the autonomous lane to the next ramp is stronger than when the lane change preparation distance S is larger. Further, fig. 4a to 4f show that when the market penetration p of the CAV is low and the lane change preparation distance S is less than 500m, the CAV lane change vehicle does not have sufficient lateral distance to perform the lane change process, and thus the probability ρ of the vehicle CAV successfully changing the lane from the autonomous lane to the lower ramp approaches 0.
Considering from the traffic level of the automatic driving special lane and the general lane, when the traffic volume of the automatic driving special lane and the general lane becomes larger, the average head time distance is obtained
Figure BDA0003166285610000111
Small such that the probability P of a gap allowing j vehicles to pass through theoretically occursjThe small number of the theoretical times n that the lane change vehicle can carry out the lane change experiment within 1 hour is reduced, so the success rate of the lane change of n experiments is reduced
Figure BDA0003166285610000112
And then decreases. On the other hand, as the average headway becomes smaller, for a single bernoulli experiment, the success probability ξ of the single lane change experiment is reduced, so that the image shows regular fluctuation. FIGS. 4 a-4 b show that the traffic is below 3200 (veh.h)-1) In the meantime, as the traffic volume increases, the rate of decrease of the probability ρ of the vehicle CAV successfully changing lane from the autonomous driving lane to the off-ramp is relatively moderate, and fig. 4c to 4f show that when the traffic volume is higher than 3200 (veh.h)-1) The rate of decline of the probability ρ of a successful lane change of the vehicle CAV from the autonomous lane to the down ramp is steep, and when the traffic volume is higher than 4800(veh-1) And when the vehicle CAV is in the lane change from the automatic driving lane to the next ramp, the probability rho of the vehicle CAV entering the next ramp is close to 0.
Considering the market penetration p level of the CAV, it is known in the common general knowledge that the necessary condition for setting the driving-only lane CAV-L is that the market penetration p of the CAV is greater than 30%, so the analysis of the present embodiment based on the market penetration p of the CAV starts from 30%, and as the market penetration p of the CAV increases, the driving-only lane CAV-L can share more traffic, so that the traffic Q of the general lanenewPressure reduction, i.e. average headway of the general traffic lane GL 3600/QnewWhen the vehicle CAV is increased, the probability rho that the vehicle CAV successfully changes the lane from the automatic driving lane to the next ramp is increased. As can be seen from FIGS. 4 a-4 f, when the market penetration p of CAV is higher than that of CAVAt 50%, the probability ρ of the vehicle CAV successfully changing lane from the autonomous driving lane to the next ramp rises significantly and eventually approaches 100%.
In summary, the probability ρ of the vehicle CAV successfully changing the lane from the autonomous lane to the next ramp is determined by the lane change preparation distance S, the traffic volumes of the autonomous lane and the general lane, and the market penetration rate p of the CAV, and when the lane change preparation distance S is larger, the probability ρ of the vehicle CAV successfully changing the lane from the autonomous lane to the next ramp is higher, however, the increased lane change preparation distance S increases the lane change frequency, reduces the operating efficiency of the autonomous lane CAV-L, and simultaneously applies more disturbance to the traffic flow, thereby reducing the driving safety, and thus balancing the driving safety and the operating efficiency becomes a main problem facing the autonomous lane management.
(2) Cost function analysis under different influence factors
According to the cost function of the CAV lane change model provided by the formula (4), two dependent variables in the formula need to be summed up in an order of magnitude, so that T needs to be addedtotal/TmaxAnd carrying out normalization processing, calibrating a weight parameter alpha of the driving efficiency and the lane changing safety, calculating minimum value points of a cost function of the CAV lane changing model under different factors, and finally determining the optimal balance point of the driving efficiency and the lane changing safety of the vehicle under different influence factors. Fig. 5a to 7f show the results of analysis in this section, in which three cases, that is, safety of the side-tracked vehicle, safety of the vehicle equal to the driving efficiency, and side-weight driving efficiency are considered, and α is 0.3, 0.5, and α is 0.7. Fig. 5a to 7f all show that the cost function value J of the CAV lane change model has an obvious inflection point along with the change of the market permeability p of the CAV, that is, the inflection point does not have a gradual decreasing relationship with the lane change preparation distance S, which shows that the increase of the lane change preparation distance S can improve the driving safety on one hand and also can lose the driving efficiency of the vehicle on the other hand. Specifically, when the traffic volume is 2400, 3200, 4000, 4800, 5600, 6400(veh-1) Then, the inflection points of the cost function corresponding to the CAV lane change model respectively correspond to 45%, 60%, 70%, 80%, 90% and 9%Market penetration p of 5% CAV. Under different traffic volumes, when CAV is lower than the market permeability p of the corresponding inflection point CAV, the cost function can be obviously optimized by increasing the lane change preparation distance S, and when the CAV is higher than the market permeability p of the corresponding inflection point CAV, the utilization rate of the automatic driving special lane reaches the maximum, more intelligent internet fleets are formed by general lanes, more passable gaps can be generated, the requirement of lane change safety on the lane change preparation distance S is smaller, the cost function can show a phenomenon of rising along with the increase of the lane change preparation distance S, and when the S is larger, the rising amplitude of the cost function is larger. In addition, when the traffic volume is high, if the market penetration rate p of the CAV is low, the cost function of the CAV lane change model cannot be optimized even if the lane change preparation distance S is increased, and when the traffic volume is 5600 (veh.h)-1) In the meantime, the market penetration rate p of the CAV at least reaches 50%, the cost function of the CAV lane change model is optimized, and when the traffic volume is higher than 6400 (veh.h)-1) At least 70% of the market penetration p of CAV is achieved.
It can be seen from fig. 5a to 5f that the cost function optimization space and amplitude of the CAV lane change model are maximum when the market permeability p is lower than the corresponding inflection point CAV, and the rising amplitude is smaller when the market permeability p is higher than the corresponding inflection point CAV, which is shown in fig. 6a to 6f, and fig. 7a to 7f perform the worst. Fig. 7a to 7f show that, although the cost function performs better at the market permeability p corresponding to the inflection point CAV than fig. 5a to 5f and fig. 6a to 6f, once the market permeability p is higher than the inflection point CAV, the cost function of the CAV lane change model has a huge expansion range, and finally, a negative optimization state is presented. In summary, when the traffic volume is lower than 4000(veh/h/lane), the cost function of the CAV lane change model with α being 0.3 performs better, and when the traffic volume is higher than 4000(veh/h/lane), the cost function of the CAV lane change model with α being 0.5 performs better. The results show that the passing clearance is more and the lane change pressure/resistance is smaller when the traffic volume is low, and the lane change safety is emphasized, and the lane change safety and the driving efficiency are emphasized equally when the traffic volume is high.
Simulation analysis
(1) Design of simulation experiment
In order to check the effectiveness of the CAV lane change decision model in the ramp diversion area under the expressway, which is improved by the embodiment of the invention, simulation environment under the same scene as that of the figure 3 is set up by using SUMO simulation software. In a simulation experiment, a CACC (computer aided control) following model is adopted by the whole intelligent network fleet following the CAV, an ACC (adaptive cruise control) following model is adopted by the whole intelligent network fleet following the HMV and the CAV not forming the intelligent network fleet, a built-in LC2013 model is simulated by an SUMO (speedometer analysis) in a lane changing model, the arrival speed of a CAV-L (computer aided design-L) vehicle is set to be 33.3m/s, the arrival speed of a GL vehicle is randomly determined in the range of 16.7-33.3 m/s, and the ratio of vehicles required by ramps under the CAV-L is set to be 0.1 time qAThe simulation mainly focuses on simulating the running condition of the traffic flow of the lower ramp, the simulation time is 1h, and the simulation step length is 0.1 s.
Based on the microscopic simulation data of the vehicle, a traffic Safety evaluation agent index TIT (Time Integrated Time-to-precision) in a substitute Safety measure ssm (Safety measures) is applied to evaluate the traffic flow operation Safety, and the following formulas (29) to (30) are shown:
Figure BDA0003166285610000131
Figure BDA0003166285610000132
in the formula, TTCω(τ) time of collision, TTC, of the ω -th vehicle at time τ*The collision time threshold is represented by 3s, delta tau is a simulation time step length and is set to be 0.1s, omega is the total number of collided vehicles in the simulation, gamma is a simulation total step length and is set to be 3600s,
Figure BDA0003166285610000133
position of vehicle ahead of the conflict point at time τ, xω(tau) is the position of the vehicle behind the conflict point at the time of tau, l is the length of the vehicle, and 5m, v are takenω-1(τ) front speed at the break-out point at time τ, vωAnd (tau) is the speed of the vehicle behind the conflict point at the time of tau.
Considering the randomness of the spatial positions of 4 vehicle types in the simulation, the market permeability p of each CAV is independently simulated for 3 times and averaged to serve as the simulation result of the market permeability p of the CAV.
(2) Analysis of simulation results
The simulation results of fig. 8a to 8f were obtained by performing the simulation experiments as described above. From the aspect of traffic volume, the probability p that the vehicle CAV successfully changes from the automatic driving lane to the lower ramp is reduced along with the increase of the traffic volume in numerical analysis, and the TIT trend is increased along with the increase of the traffic volume in simulation, namely the traffic flow operation safety risk is improved, which is consistent with the practical situation. Meanwhile, fig. 8a to 8b show that when the traffic is less than 3200 (veh.h)-1) When the traffic flow is higher than 3200 (veh.h), the overall TIT level is lower, which is consistent with the probability ρ of the high-speed vehicle CAV entering the lower ramp from the automatic driving lane successfully changing the lane, which is reflected in the numerical analysis, i.e. under the condition of the traffic flow, the lane-changing vehicle can successfully enter the lower ramp and has small influence on the traffic flow, and fig. 8c to 8f show that when the traffic flow is higher than 3200 (veh.h)-1) When the traffic flow is in a lane change state, the overall TIT level is obviously improved, which is consistent with the reduction of the probability rho of the vehicle CAV entering a lower ramp from the automatic driving lane in numerical analysis, namely, the lane change vehicle can obviously influence the overall traffic flow at the moment, and the lane change preparation distance S needs to be reasonably defined so as to reduce the influence to the maximum extent;
from the market permeability p of the CAV, the overall TIT level is reduced along with the improvement of the market permeability p of the CAV, and the overall TIT level also shows in the numerical analysis result, along with the improvement of the market permeability p of the CAV, the probability rho that the vehicle CAV successfully changes the lane from the automatic driving lane to the lower ramp is improved, namely the improvement of the CAV can generate a gain effect on the traffic flow operation. Meanwhile, fig. 8a to 8b show that when the traffic is less than 3200 (veh.h)-1) When the market permeability p of the CAV is more than 50%, the traffic flow can be remarkably gained, and when the traffic flow is 3200-5600 (veh-1) When the market penetration rate p of the CAV is more than 70%, the overall traffic flow is gained, and when the traffic volume is more than 5600 (veh.h)-1) In time, the effect is achieved only when the market penetration rate p of the CAV reaches more than 85%; FIG. 8a to FIG. 8f are tables as viewed from the lane change preparation distance SObviously, as the lane change preparation distance S increases, the overall TIT level thereof decreases, which coincides with an increase in the probability ρ of the vehicle CAV successfully changing from the autonomous lane to the off-ramp in the numerical analysis. Meanwhile, fig. 8d to 8f show that when the traffic is higher than 4000(veh-1) Increasing the lane change preparation distance S will significantly decrease the TIT.
In conclusion, the safety level of the traffic flow in the lower ramp area is influenced by the traffic volumes of the automatic driving special lane and the general lane, the market permeability p of the CAV and the lane change preparation distance S, and the simulation analysis result and the numerical analysis result have the same change trend, which also shows that the reasonable determination of the lane change preparation distance S according to the traffic volumes of the automatic driving special lane and the general lane and the market permeability p of the CAV has significant gains in improving the probability ρ of the vehicle CAV successfully changing lanes from the automatic driving lane to the lower ramp, the driving efficiency of the CAV and the traffic flow safety.
It is noted that, in the present application, relational terms such as first, second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.

Claims (10)

1. The CAV lane change decision method for the off-ramp diversion area of the expressway under the scene of the deployment of the automatic driving special lane is characterized by comprising the following steps of:
constructing a forced lane changing scene of a CAV (vehicle access vehicle) running on an automatic driving special lane under a highway in a lower ramp shunting area;
under a forced lane change scene, calculating the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state, calculating the traffic volume of an automatic driving special lane and a general lane based on a lane management strategy, and constructing a mixed traffic flow operation condition;
and constructing a cost function of the CAV lane change model, and selecting the lane change by the CAV when the cost function value J of the CAV lane change model is minimum.
2. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the deployment scenario of the automatic driving dedicated lane according to claim 1, wherein the method for constructing the forced lane change scenario of the CAV running on the off-ramp diversion area of the automatic driving dedicated lane under the expressway is specifically as follows:
the total number of lanes of the highway is H lanes, and the lane farthest from the lower ramp is lAAutomatic driving laneAH-1,. 1, 2; the 1 st lane for automatic driving is the H-th lane and the l-th laneAThe lane special for automatic driving is the H +1-lAStrip lanes, withAThe adjacent general lane at one side of the automatic driving special lane close to the lower ramp is the H-lAStrip lane, H-lAThe adjacent general lane at one side of the strip lane close to the lower ramp is the H-lA-1 lane up to lane 1, lane 1 being adjacent to the down ramp;
when the CAV running on the special automatic driving lane produces the intention of exiting the expressway, the CAV firstly changes the lane from the special automatic driving lane to the general vehicleAnd changing the lane from the general lane to the lower ramp and finally exiting the expressway from the lower ramp, wherein the generation point of the CAV lane change intention is an A section, and the transverse distance of the lane change area of the H lane is LCHThe latest lane change transverse distance is LH(ii) a H +1-lAThe transverse distance of the lane change area of the strip lane is
Figure FDA0003166285600000011
The latest lane change transverse distance is
Figure FDA0003166285600000012
H to lAThe transverse distance of the lane change area of the strip lane is
Figure FDA0003166285600000013
The latest lane change transverse distance is
Figure FDA0003166285600000014
H to lA-1 lane change zone lateral distance of lane
Figure FDA0003166285600000015
The latest lane change transverse distance is
Figure FDA0003166285600000016
The travel keeping area of the CAV in the first lane after the forced lane change is LK1The lane change termination point is B, and S indicates a lane change preparation distance.
3. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the automatic driving special lane deployment scene according to claim 1, wherein the calculating of the existence probabilities of four types of vehicles when the mixed traffic flow is in a balanced state is specifically as follows:
providing CAV formation theory: in the intelligent networking environment, CAV searching opportunities which are distributed continuously in the same direction and on the same lane are actively formed, formation driving is carried out, and when the scale r of the intelligent networking fleet exceeds u, the follow-up CAV is established into another intelligent networking fleet;
based on CAV formation theory, there are 4 types of vehicles in the mixed traffic flow that mixes intelligent net joining motorcade on the highway, do respectively: the method comprises the following steps that (1) traditional manually driven vehicles HMV, AVC (automatic voltage control) of intelligent networked fleet cluster head vehicles of follow-up CAV, AVH (automatic voltage control) of intelligent networked fleet cluster head vehicles of follow-up HMV and CAV (vehicle automatic voltage control) in the intelligent networked fleet;
when the market penetration rate of CAV in a mixed traffic flow mixed with an intelligent networked fleet is p, the existence probability of 4 types of vehicles is shown as formula (1):
Figure FDA0003166285600000021
in the formula (1), pHMVRepresenting the existence probability of the HMV of the traditional manual driving vehicle; p is a radical ofAVHRepresenting the existence probability of AVH of the intelligent networking fleet head vehicle following the HMV; p is a radical ofAVCRepresenting the existence probability of AVC (automatic voltage control) of intelligent networking fleet cluster head vehicles following CAV; p is a radical ofCAVRepresenting the probability of the presence of CAVs within the intelligent networked fleet.
4. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the deployment scene of the automatic driving special lane according to claim 1, wherein the traffic volume of the automatic driving special lane and the general lane is calculated based on a lane management strategy, and a mixed traffic flow operation condition is constructed, specifically:
and (3) lane management strategy: the CAV preferentially runs on the automatic driving special lane, when the CAV requirement is larger than the maximum traffic capacity of the automatic driving special lane, the rest CAVs are distributed to run on a general lane, and both the CAV and the HMV have the right of way of the general lane;
according to the lane management strategy, firstly, the traffic q of the automatic driving special lane is calculatedAAs shown in formula (2):
qA＝min(pD,lACA),lA＝1,2…H-1 (2)；
in the formula (2), D represents highTraffic demand of express highway in veh-1；lAThe number of lanes dedicated for autopilot; cAThe unit is veh.h for the traffic capacity of the special lane for automatic driving-1(ii) a H is the total number of lanes of the highway, and p is the market permeability of CAV in the mixed traffic flow;
secondly, the traffic q of the general lane is calculatedmixAs shown in formula (3):
qmix＝D-qA (3)；
constructing a mixed traffic flow operation condition: the initial mixed traffic flow keeps a balanced state, all vehicles obey Poisson distribution, the vehicles keep running at proper intervals, and the lane changing behavior of original vehicles on the general lane GL is not considered; the distance between the generation point A of the intention of changing the lane and the entrance and exit of the next ramp is forced to be far enough, and the current decision of changing the lane of the vehicle is not influenced; there is a limit to CAV driving away from the autopilot lane within a certain time.
5. The CAV lane change decision-making method based on the off-ramp shunting area of the expressway under the automatic driving special lane deployment scene as claimed in claim 1, wherein a cost function of the CAV lane change model is as shown in formula (4):
Figure FDA0003166285600000022
in the formula (4), J represents a cost function value of the CAV lane change model; alpha is a weight parameter of driving efficiency and lane change safety; t istotalIndicating CAV lane change preparation time, TmaxRepresenting the corresponding lane change preparation time when the probability rho of the vehicle CAV entering the next ramp from the automatic driving lane is more than or equal to 95%; ρ represents the probability of the vehicle CAV successfully changing lanes from the autonomous lane to the off-ramp.
6. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the automatic driving special lane deployment scene as claimed in claim 1, whereinThe CAV selects to start lane changing when the cost function value J of the CAV lane changing model is minimum, and specifically comprises the following steps: the minimum value of J is a point A section generated by the track changing intention of the CAV, and the key variable for determining the cost function value J of the CAV track changing model is track changing preparation time TtotalAnd probability ρ of the vehicle CAV entering the off-ramp from the automatic driving lane, wherein the lane change preparation time TtotalIs calculated as shown in equations (5) and (6):
Figure FDA0003166285600000031
Figure FDA0003166285600000032
in the formula, LCiThe lane change area transverse distance of the ith lane is represented, i represents a lane serial number mark, and i is 2-H; l isiRepresenting the latest lane change transverse distance of the ith lane; v. ofiThe average driving speed of the traffic flow of the ith lane is represented, and the calculation mode is shown as the formula (7):
Figure FDA0003166285600000033
in the formula (7), the reaction mixture is,
Figure FDA0003166285600000034
free flow velocity, k, representing the flow in the ith laneiIs the current traffic density of the traffic flow in the ith lane,
Figure FDA0003166285600000035
representing the traffic jam density of the ith lane;
the calculation process of the probability rho of the vehicle CAV entering the next ramp after successfully changing the lane from the automatic driving lane specifically comprises the following steps: assuming that CAV carries out n times of lane changing experiments within 1 hour, the success probability of a single lane changing experiment is xi, and the failure probability of the single lane changing experiment is 1-xiThe current headway t meets the acceptable safe lane-changing headway tcIf not, no response is made and the next lane change experiment is carried out, the success rate of lane change for n times of experiments is
Figure FDA0003166285600000036
Calculating as shown in equation (8):
Figure FDA0003166285600000037
according to equation (8), the probability ρ of the vehicle CAV entering the next ramp after successfully changing lanes from the autonomous driving lane is calculated as shown in equation (9):
Figure FDA0003166285600000038
in the formula (9), niRepresenting the times of lane change experiments generated in the process of changing the CAV from the i lane to the i-1 lane;
the success probability xi of the single lane change experiment is more than tcThe probability of the headway distribution accounting for the total headway distribution is calculated as shown in equation (10):
Figure FDA0003166285600000039
in the equation (10), f (t) represents a headway density probability distribution function, assuming that the vehicle arrival obeys poisson distribution, and f (t) is calculated as shown in the equation (11):
f(t)＝λe-λt (11)；
in the formula (11), λ is an average arrival rate of the vehicle in a unit time interval, and the unit is vehicle/s; e is a natural index.
7. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the automatic driving special lane deployment scene as claimed in claim 6, wherein when the CAV lane change decision-making method is usedWhen the CAV requirement is greater than the traffic capacity of the automatic driving special lane, the traffic can be converged into the general lane, a mixed traffic flow is formed on the general lane, an intelligent internet motorcade is formed, when the mixed traffic flow is stable, the vehicles do not need to be newly formed, the traffic volume of the general lane, the time interval probability density distribution function of the head time of the general lane and the success probability of the CAV single lane changing experiment are updated, and the lane changing experiment times n generated in the process of changing the CAV from the i lane to the i-1 lane are obtainediThe updating process specifically comprises the following steps:
when the CAV requirement is larger than the traffic capacity of the automatic driving special lane, the traffic flow can be converged into the general lane, a mixed traffic flow is formed on the general lane, an intelligent internet connection motorcade is formed, and the intelligent internet connection motorcade is divided into an internet connection motorcade F with cluster-head vehicles following the CAV1Networking fleet F following AVH with cluster head vehicles2Two, the permeability of the overflowing autonomous vehicle CAV on the common lane GL is
Figure FDA00031662856000000414
Internet fleet F forming cluster head vehicles following CAV1Has a probability of
Figure FDA0003166285600000041
Internet fleet F forming cluster head vehicle following AVH2Has a probability of
Figure FDA0003166285600000042
Figure FDA0003166285600000043
As shown in equation (12),
Figure FDA0003166285600000044
is calculated as shown in equation (13):
Figure FDA0003166285600000045
Figure FDA0003166285600000046
calculating the total number Q of CAV vehicles in the formed intelligent networked fleetFThe number N of the intelligent networked fleet is shown as formula (14)FAs shown in formula (15):
Figure FDA0003166285600000047
Figure FDA0003166285600000048
when the traffic flow is stable, the vehicles do not need to form a new formation, and the traffic volume of the general lane is updated to QnewIt is calculated as shown in equation (16):
Figure FDA0003166285600000049
the probability density distribution function of the headway time interval of the general lane is updated to
Figure FDA00031662856000000410
It is calculated as shown in equation (17):
Figure FDA00031662856000000411
from this, the success probability of CAV single lane change experiment is updated to
Figure FDA00031662856000000412
It is calculated as shown in equation (18):
Figure FDA00031662856000000413
therefore, the lane change experiment times n generated in the process of changing the CAV from the i lane to the i-1 laneiIt is calculated as shown in equation (19):
Figure FDA0003166285600000051
in the formula, T(i,i-1)The lane change remaining time of the vehicle CAV from the i lane to the i-1 lane is shown, and n represents the theoretical times of lane change experiments which can be carried out by the CAV within 1 hour.
8. The CAV lane change decision-making method based on the off-ramp diversion area of the expressway under the automatic driving special lane deployment scene as claimed in claim 7, wherein the lane change remaining time T for the vehicle CAV to change from the i lane to the i-1 lane(i,i-1)The calculation is shown as formula (20), and the constraint conditions are shown as formulas (21) and (22):
Figure FDA0003166285600000052
Figure FDA0003166285600000053
Figure FDA0003166285600000054
in the formula (I), the compound is shown in the specification,
Figure FDA0003166285600000055
indicating the execution time of a lane change operation for a CAV to change from i lane to i-1 lane, vi-1Represents the average traveling speed of the traffic flow of the i-1 st lane,
Figure FDA0003166285600000056
indicating a change of CAV from i lane to i lane1 lateral position of lane, xiShowing the lateral position of the i lane before the CAV lane change.
9. The CAV lane change decision-making method based on the off-ramp shunting area of the expressway under the automatic driving special lane deployment scene according to claim 6, wherein the CAV can calculate the theoretical number n of lane change experiments within 1 hour, specifically:
the following time interval of the vehicle is tgWhen t isc≤t≤tc+tgWhen, it means that a vehicle is allowed to pass through; when t isc+(j-1)tg≤t≤tc+jtgWhen j vehicles are allowed to pass through, the probability P of the gap allowing the j vehicles to pass through theoretically appearsjComprises the following steps:
Figure FDA0003166285600000057
therefore, the calculation of the theoretical number n of times that the CAV can carry out lane change experiments within 1 hour can be obtained, and the formula (24) shows:
Figure FDA0003166285600000058
Figure FDA0003166285600000059
Figure FDA0003166285600000061
in the formula (I), the compound is shown in the specification,
Figure FDA0003166285600000062
indicates the permeability
Figure FDA0003166285600000063
Down-mixingThe average head time distance of the confluence is calculated,
Figure FDA0003166285600000064
penetration of the autonomous vehicle CAV representing the overflow on the general lane, tHMVRepresenting the following headway, t, of a conventional manually driven vehicle HMVAVHExpress following vehicle distance, t, of intelligent networking fleet cluster head vehicle AVH following vehicle of HMVAVCExpress following CAV's intelligent networking motorcade cluster head vehicle AVC's with driving distance, tCAVAnd the following time interval of CAV in the intelligent networked fleet is shown.
10. The CAV lane change decision-making method based on the off-ramp shunting area of the expressway under the automatic driving special lane deployment scene according to any one of claims 5 to 9, wherein a minimum function of a cost function value J of the CAV lane change model is calculated as shown in a formula (27):
Figure FDA0003166285600000065
lane change experiment times n generated in the process of changing CAV from i lane to i-1 laneiThe calculation method of (2) is shown in equation (28):
Figure FDA0003166285600000066
CN202110805341.6A 2021-07-16 2021-07-16 CAV lane change decision-making method for expressway down-ramp shunting area Active CN113345268B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110805341.6A CN113345268B (en) 2021-07-16 2021-07-16 CAV lane change decision-making method for expressway down-ramp shunting area

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110805341.6A CN113345268B (en) 2021-07-16 2021-07-16 CAV lane change decision-making method for expressway down-ramp shunting area

Publications (2)

Publication Number Publication Date
CN113345268A true CN113345268A (en) 2021-09-03
CN113345268B CN113345268B (en) 2022-03-18

Family

ID=77479895

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110805341.6A Active CN113345268B (en) 2021-07-16 2021-07-16 CAV lane change decision-making method for expressway down-ramp shunting area

Country Status (1)

Country Link
CN (1) CN113345268B (en)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113744539A (en) * 2021-09-17 2021-12-03 合肥工业大学 Dynamic regulation and control method for network connection vehicle special lane based on permeability
CN113781788A (en) * 2021-11-15 2021-12-10 长沙理工大学 Automatic driving vehicle management method based on stability and safety
CN114023066A (en) * 2021-11-08 2022-02-08 河海大学 Traffic flow control method for special lane for automatic driving
CN114999158A (en) * 2022-05-31 2022-09-02 重庆大学 Hybrid traffic crowd-subordinate throttling control method for inhibiting negative effect of express way bottleneck
CN115035731A (en) * 2022-06-15 2022-09-09 东南大学 Intelligent networking queue converging method based on vehicle group
CN115116249A (en) * 2022-06-06 2022-09-27 苏州科技大学 Method for estimating different permeability and road traffic capacity of automatic driving vehicle
CN115188189A (en) * 2022-07-07 2022-10-14 清华大学 Special lane dynamic setting method for supporting automatic driving truck vehicle queue operation
CN115424456A (en) * 2022-08-19 2022-12-02 东南大学 Expressway intersection area cooperative self-adaptive cruise optimization control method
CN115472032A (en) * 2022-08-24 2022-12-13 武汉理工大学 Automatic lane change decision system and method for vehicles in ramp confluence area of expressway
CN115880884A (en) * 2022-09-23 2023-03-31 长安大学 Expressway ramp mixed traffic flow control method based on controllable intelligent network connection
CN116129627A (en) * 2023-01-18 2023-05-16 东南大学 Collaborative lane changing strategy in front of intelligent network connected vehicle under ramp
CN116363905A (en) * 2023-05-19 2023-06-30 吉林大学 Heterogeneous traffic flow converging region lane change time judging and active safety control method
CN116543595A (en) * 2023-05-24 2023-08-04 合肥工业大学 Dynamic regulation and control method for CAV special lane change decision point of expressway diversion area
CN116665442A (en) * 2023-05-31 2023-08-29 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity
CN116740945A (en) * 2023-08-11 2023-09-12 苏州观瑞汽车技术有限公司 Method and system for multi-vehicle collaborative grouping intersection of expressway confluence region in mixed running environment
CN117409572A (en) * 2023-09-04 2024-01-16 河北渤思科技有限公司 Road traffic flow data management method and system based on signal processing

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108460980A (en) * 2018-05-11 2018-08-28 西南交通大学 The optimal intention of ring road generates point calculating method under automatic driving vehicle
CN109598934A (en) * 2018-12-13 2019-04-09 清华大学 A kind of rule-based method for sailing out of high speed with learning model pilotless automobile
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
CN112258849A (en) * 2020-10-26 2021-01-22 上海市城市建设设计研究总院（集团）有限公司 Dynamic automatic driving special lane based on expressway and use method thereof
CN113022586A (en) * 2021-04-14 2021-06-25 福瑞泰克智能***有限公司 Vehicle behavior prediction method and device and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108460980A (en) * 2018-05-11 2018-08-28 西南交通大学 The optimal intention of ring road generates point calculating method under automatic driving vehicle
CN109598934A (en) * 2018-12-13 2019-04-09 清华大学 A kind of rule-based method for sailing out of high speed with learning model pilotless automobile
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
CN112258849A (en) * 2020-10-26 2021-01-22 上海市城市建设设计研究总院（集团）有限公司 Dynamic automatic driving special lane based on expressway and use method thereof
CN113022586A (en) * 2021-04-14 2021-06-25 福瑞泰克智能***有限公司 Vehicle behavior prediction method and device and storage medium

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
WEI HAO 等: "Research on Mandatory Lane-Changing Behavior in Highway Weaving Sections", 《JOURNAL OF ADVANCED TRANSPORTATION》 *
ZHIZHOU WU 等: "An Optimal Longitudinal Control Strategy of Platoons Using Improved Particle Swarm Optimization", 《JOURNAL OF ADVANCED TRANSPORTATION》 *
董长印 等: "混入智能车的下匝道瓶颈路段交通流建模与仿真分析", 《物理学报》 *
高志波 等: "智能网联车环境下交叉口车流轨迹优化模型", 《交通运输***工程与信息》 *

Cited By (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113744539B (en) * 2021-09-17 2022-05-10 合肥工业大学 Dynamic regulation and control method for network vehicle special lane based on permeability
CN113744539A (en) * 2021-09-17 2021-12-03 合肥工业大学 Dynamic regulation and control method for network connection vehicle special lane based on permeability
CN114023066A (en) * 2021-11-08 2022-02-08 河海大学 Traffic flow control method for special lane for automatic driving
CN114023066B (en) * 2021-11-08 2022-09-20 河海大学 Traffic flow control method for special lane for automatic driving
CN113781788A (en) * 2021-11-15 2021-12-10 长沙理工大学 Automatic driving vehicle management method based on stability and safety
CN114999158A (en) * 2022-05-31 2022-09-02 重庆大学 Hybrid traffic crowd-subordinate throttling control method for inhibiting negative effect of express way bottleneck
CN114999158B (en) * 2022-05-31 2024-05-14 重庆大学 Mixed traffic mass-slave throttling control method for inhibiting expressway bottleneck negative effect
CN115116249A (en) * 2022-06-06 2022-09-27 苏州科技大学 Method for estimating different permeability and road traffic capacity of automatic driving vehicle
CN115035731B (en) * 2022-06-15 2023-05-16 东南大学 Intelligent network joint queue and confluence method based on vehicle group
CN115035731A (en) * 2022-06-15 2022-09-09 东南大学 Intelligent networking queue converging method based on vehicle group
CN115188189A (en) * 2022-07-07 2022-10-14 清华大学 Special lane dynamic setting method for supporting automatic driving truck vehicle queue operation
CN115424456B (en) * 2022-08-19 2023-05-19 东南大学 Highway interweaving area cooperative self-adaptive cruise optimization control method
CN115424456A (en) * 2022-08-19 2022-12-02 东南大学 Expressway intersection area cooperative self-adaptive cruise optimization control method
CN115472032A (en) * 2022-08-24 2022-12-13 武汉理工大学 Automatic lane change decision system and method for vehicles in ramp confluence area of expressway
CN115472032B (en) * 2022-08-24 2024-05-28 武汉理工大学 Automatic lane change decision system and method for vehicles in ramp confluence area of expressway
CN115880884A (en) * 2022-09-23 2023-03-31 长安大学 Expressway ramp mixed traffic flow control method based on controllable intelligent network connection
CN115880884B (en) * 2022-09-23 2023-10-27 长安大学 Expressway ramp mixed traffic flow control method based on controllable intelligent network vehicle connection
CN116129627B (en) * 2023-01-18 2023-12-01 东南大学 Collaborative lane changing strategy in front of intelligent network connected vehicle under ramp
CN116129627A (en) * 2023-01-18 2023-05-16 东南大学 Collaborative lane changing strategy in front of intelligent network connected vehicle under ramp
CN116363905A (en) * 2023-05-19 2023-06-30 吉林大学 Heterogeneous traffic flow converging region lane change time judging and active safety control method
CN116363905B (en) * 2023-05-19 2023-09-05 吉林大学 Heterogeneous traffic flow converging region lane change time judging and active safety control method
CN116543595A (en) * 2023-05-24 2023-08-04 合肥工业大学 Dynamic regulation and control method for CAV special lane change decision point of expressway diversion area
CN116543595B (en) * 2023-05-24 2024-04-26 合肥工业大学 Dynamic regulation and control method for CAV special lane change decision point of expressway diversion area
CN116665442B (en) * 2023-05-31 2024-05-10 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity
CN116665442A (en) * 2023-05-31 2023-08-29 东南大学 Intelligent networking special lane design method considering mixed flow theoretical traffic capacity
CN116740945B (en) * 2023-08-11 2023-12-01 苏州观瑞汽车技术有限公司 Method and system for multi-vehicle collaborative grouping intersection of expressway confluence region in mixed running environment
CN116740945A (en) * 2023-08-11 2023-09-12 苏州观瑞汽车技术有限公司 Method and system for multi-vehicle collaborative grouping intersection of expressway confluence region in mixed running environment
CN117409572A (en) * 2023-09-04 2024-01-16 河北渤思科技有限公司 Road traffic flow data management method and system based on signal processing
CN117409572B (en) * 2023-09-04 2024-05-28 河北渤思科技有限公司 Road traffic flow data management method and system based on signal processing

Also Published As

Publication number Publication date
CN113345268B (en) 2022-03-18

Similar Documents

Publication Publication Date Title
CN113345268B (en) CAV lane change decision-making method for expressway down-ramp shunting area
CN112116822B (en) Expressway traffic capacity cooperative regulation and control method based on CAVs mixed traffic flow lane dynamic allocation
CN109035862B (en) Multi-vehicle cooperative lane change control method based on vehicle-to-vehicle communication
CN113781806B (en) Mixed traffic flow passing method used in intelligent network connection environment
Wang et al. Distributed consensus-based cooperative highway on-ramp merging using V2X communications
US20190206247A1 (en) Smart In-Vehicle Decision Support Systems and Methods with V2I Communications for Driving through Signalized Intersections
Jin et al. Improving traffic operations using real-time optimal lane selection with connected vehicle technology
CN111081065A (en) Intelligent vehicle collaborative lane change decision model under road section mixed traveling condition
CN113327441B (en) Network-connection automatic vehicle speed control and track optimization method based on highway confluence area
CN111243296B (en) Ramp confluence cooperative control method and system based on confluence time optimization
CN115116249B (en) Method for estimating different permeability and road traffic capacity of automatic driving vehicle
Dao et al. A strategy for optimisation of cooperative platoon formation
CN116596380A (en) Optimization determination method, platform, equipment and medium for expressway construction organization scheme and management and control scheme
CN113823076B (en) Instant-stop and instant-walking road section blockage relieving method based on networked vehicle coordination control
Bakibillah et al. Predictive car-following scheme for improving traffic flows on urban road networks
CN116153137B (en) Multi-lane ramp collaborative import optimization scheduling method based on vehicle conflict relation graph
CN116863699A (en) Active transverse and longitudinal cooperative control method for vehicles in confluence region under mixed flow environment
Choudhury et al. Lane selection model for urban intersections
CN114995138A (en) Distributed cooperative control method for mixed vehicle group in near signal control area
CN115171386A (en) Distributed cooperative driving method based on Monte Carlo tree search
Shiomi et al. A lane-change maneuver of automated vehicles for improving traffic flow on highways with multiple lanes
CN116665442B (en) Intelligent networking special lane design method considering mixed flow theoretical traffic capacity
Li et al. Simulation and Operation Evaluation of Cooperative Lane Changing Strategy of Intelligent Connected Vehicles in On-Ramp Areas
Ke et al. Lane-changing decision model for connected and automated vehicle based on back-propagation neural network
CN115394073B (en) Highway congestion propagation method based on CA-SIR model in foggy environment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant