CN113345268B - CAV lane change decision-making method for expressway down-ramp shunting area - Google Patents

CAV lane change decision-making method for expressway down-ramp shunting area Download PDF

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CN113345268B
CN113345268B CN202110805341.6A CN202110805341A CN113345268B CN 113345268 B CN113345268 B CN 113345268B CN 202110805341 A CN202110805341 A CN 202110805341A CN 113345268 B CN113345268 B CN 113345268B
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CN113345268A (en
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郝威
吴其育
张兆磊
易可夫
王正武
田大新
戎栋磊
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Changsha University of Science and Technology
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    • G—PHYSICS
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    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
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    • G08G1/00—Traffic control systems for road vehicles
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Abstract

The invention discloses a CAV lane change decision method for a ramp diversion area under a highway, 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 CAV lane change decision method for the expressway down-ramp diversion area analyzes the influence of CAV driving away from CAV-L on the traffic state, 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 arrangement landing.

Description

CAV lane change decision-making method for expressway down-ramp shunting area
Technical Field
The invention belongs to the technical field of intelligent traffic, and relates to a CAV lane change decision-making method for a lower ramp diversion area of a highway.
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 for a shunting area of a lower ramp of a highway, 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-making 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 method for a diversion area of a lower ramp of an expressway comprises the following steps:
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.
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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 0.3, the cost function analysis results are shown.
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 is a schematic diagram of a traffic volume in accordance with an embodiment of the present inventionIs 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 a graph of 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 is the bookThe traffic volume of the embodiment of the invention is 4000veh-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 diversion area of the expressway 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 (CAV-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 driving on the exclusive driving lane generates the intention of exiting the expressway, the CAV first changes the lane from the exclusive driving lane to the General Lane (GL), then changes the lane from the General lane to the Ramp, and finally exits the expressway from the RampThe road, wherein, the CAV lane change intention generation point is an A section, and the lateral 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 GDA0003458810410000041
The latest lane change transverse distance is
Figure GDA0003458810410000042
H to lAThe transverse distance of the lane change area of the strip lane is
Figure GDA0003458810410000043
The latest lane change transverse distance is
Figure GDA0003458810410000044
H to lA-1 lane change zone lateral distance of lane
Figure GDA0003458810410000045
The latest lane change transverse distance is
Figure GDA0003458810410000046
The travel keeping area of the CAV in the first lane after the forced lane change is LK1And the track changing termination point is B, and S indicates a track changing preparation distance, namely the transverse displacement of the CAV track changing intention generation point A and the track changing 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 fleetAVH1.5s, the following vehicle distance t of CAV in the intelligent network 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 GDA0003458810410000051
in the formula (1), pHMVRepresenting the probability of existence, p, of a conventional manually driven vehicle HMVAVHIntelligent networked fleet head vehicle AVH to indicate following HMVProbability of existence, pAVCProbability 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 GDA0003458810410000061
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.
The CAV selects to start lane changing when the cost function value J of the CAV lane changing model is minimum, namely the J minimum value generates a point A section for the lane changing intention of the CAV, and the key variable for determining the cost function value J of the CAV lane changing model is lane 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 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 GDA0003458810410000062
Figure GDA0003458810410000063
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 GDA0003458810410000071
in the formula (I), the compound is shown in the specification,
Figure GDA0003458810410000072
free flow velocity, k, representing the flow in the ith laneiIs the current traffic density of the traffic flow in the ith lane,
Figure GDA0003458810410000073
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 a binomial distribution problem with success probability xi of a single track changing experiment: 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 GDA0003458810410000077
Calculating as shown in equation (8):
Figure GDA0003458810410000074
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 GDA0003458810410000075
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 GDA0003458810410000076
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 can be converged into the general lane, a mixed traffic flow is formed on the general lane, an intelligent internet motorcade is formed, and when the mixed traffic flow is stable, the vehicles do not exist any moreCarrying out new formation, updating the traffic volume of the general lane, the probability density distribution function of the headway time interval of the general lane and the success probability of the CAV single lane change experiment, and obtaining the lane change experiment times n generated in the process of changing the CAV from the i lane to the i-1 laneiThe 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 GDA0003458810410000081
Internet fleet F forming cluster head vehicles following CAV1Has a probability of
Figure GDA0003458810410000082
Internet fleet F forming cluster head vehicle following AVH2Has a probability of
Figure GDA0003458810410000083
r represents the scale of the intelligent networked fleet,
Figure GDA0003458810410000084
as shown in equation (12),
Figure GDA0003458810410000085
is calculated as shown in equation (13):
Figure GDA0003458810410000086
Figure GDA0003458810410000087
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 GDA0003458810410000088
Figure GDA0003458810410000089
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 GDA00034588104100000810
the probability density distribution function of the headway of the general lane GL is updated into
Figure GDA00034588104100000811
It is calculated as shown in equation (17):
Figure GDA00034588104100000812
in the formula (17), wherein the unit timeThe average arrival rate λ of the vehicles in the interval is updated to
Figure GDA00034588104100000813
And t is the headway.
From this, the success probability of CAV single lane change experiment is updated to
Figure GDA00034588104100000814
It is calculated as shown in equation (18):
Figure GDA00034588104100000815
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 GDA0003458810410000091
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 conditions are shown as formulas (21) and (22):
Figure GDA0003458810410000092
Figure GDA0003458810410000093
Figure GDA0003458810410000094
in the formula (I), the compound is shown in the specification,
Figure GDA0003458810410000095
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 GDA0003458810410000096
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 GDA0003458810410000097
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 GDA0003458810410000098
Figure GDA0003458810410000099
Figure GDA0003458810410000101
in the formula (I), the compound is shown in the specification,
Figure GDA0003458810410000102
indicates the permeability
Figure GDA0003458810410000103
The average headway of the lower mixing flow,
Figure GDA0003458810410000104
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 GDA0003458810410000105
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 GDA0003458810410000106
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 GDA0003458810410000111
Small such that the probability P of a gap allowing j vehicles to pass through theoretically occursjThe small number of times n which leads to the lane change experiment of the lane change vehicle within 1 hour is reduced, so that the lane change of n experiments is successfulRate of change
Figure GDA0003458810410000112
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 fig. 4a to 4f, when the market penetration p of the CAV is higher than 50%, the probability ρ of the vehicle CAV successfully changing from the automatic driving lane to the lower 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) The cost function inflection points of the CAV lane change model respectively correspond to the market penetration rates p of the CAV of 45%, 60%, 70%, 80%, 90% and 95%. 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 traffic volume is high, market penetration if CAVp is lower, 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 qASimulation is mainly focused on simulationAnd under 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 GDA0003458810410000131
Figure GDA0003458810410000132
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 GDA0003458810410000133
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 the traffic is lower 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%; from the lane change preparation distance S, fig. 8a to 8f show that as the lane change preparation distance S increases, the overall TIT level decreases, which coincides with the 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 (6)

1. The CAV lane change decision method for the off-ramp diversion area of the expressway 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;
constructing a cost function of a 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 calculating of the existence probability of the four types of vehicles when the mixed traffic flow is in a balanced state specifically comprises the following steps:
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 FDA0003458810400000011
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 existence probability of CAV in the intelligent networked fleet;
the traffic volume of the automatic driving special lane and the general lane is calculated based on the lane management strategy, and the running condition of the mixed traffic flow is constructed, which specifically comprises the following steps:
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 the traffic demand of the highway, and the unit is 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; the CAV driving away from the automatic driving special lane within a certain time is limited;
the cost function of the CAV lane change model is as shown in formula (4):
Figure FDA0003458810400000021
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%; p represents the successful change of the vehicle CAV from the automatic driving laneProbability of a lane entering a lower ramp;
the minimum function of the cost function value J of the CAV lane change model is shown as a formula (27):
Figure FDA0003458810400000022
wherein S represents a lane change preparation distance, viRepresenting the average running speed of the traffic flow of the ith lane; i represents a lane serial number mark, and i is 2-H; t is tcRepresenting the acceptable safe lane changing headway; qnewThe traffic volume updating of the general traffic lane GL is represented and calculated according to the traffic volume of the general traffic lane and the existence probability of four types of vehicles when the mixed traffic flow is in a balanced state; t represents headway; n isiRepresenting the times of lane change experiments generated in the process of changing the CAV from the i lane to the i-1 lane;
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 FDA0003458810400000023
in the formula (I), the compound is shown in the specification,
Figure FDA0003458810400000024
indicates the permeability
Figure FDA0003458810400000025
Averaging the head time interval of the lower mixed flow; pjRepresenting the probability of a gap theoretically occurring that allows j vehicles to cross;
Figure FDA0003458810400000026
representing the execution time of lane changing operation of changing the CAV from the i lane to the i-1 lane; LC (liquid Crystal)iRepresenting the lateral distance of the lane change area of the ith lane; l isiRepresenting the latest lane change transverse distance of the ith lane; v. ofiMean line representing flow in ith laneDriving speed; v. ofi-1The average traveling speed of the traffic flow of the i-1 st lane is shown.
2. The CAV lane change decision-making method for the off-ramp diversion area of the expressway according to claim 1, wherein the construction of a forced lane change scene of the CAV driving on the special driveway for the automatic driving under the expressway in the off-ramp diversion area specifically comprises:
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 automatic driving special lane generates the intention of exiting the highway, the CAV firstly changes the lane from the automatic driving special lane to the general lane, then changes the lane from the general lane to the lower ramp and finally exits the highway from the lower ramp, wherein the generation point of the CAV lane change intention is an A section, and the lateral 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 FDA0003458810400000031
The latest lane change transverse distance is
Figure FDA0003458810400000032
H to lAThe transverse distance of the lane change area of the strip lane is
Figure FDA0003458810400000033
The latest lane change transverse distance is
Figure FDA0003458810400000034
H to lA-1 lane change zone lateral distance of lane
Figure FDA0003458810400000035
The latest lane change transverse distance is
Figure FDA0003458810400000036
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 for the off-ramp diversion area of the expressway according to claim 1, wherein the CAV selects to start lane change when a cost function value J of a CAV lane change 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 FDA0003458810400000037
Figure FDA0003458810400000038
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 FDA0003458810400000039
in the formula (7), the reaction mixture is,
Figure FDA00034588104000000310
free flow velocity, k, representing the flow in the ith laneiIs the current traffic density of the traffic flow in the ith lane,
Figure FDA00034588104000000311
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 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 lane change for n times of experiments is
Figure FDA0003458810400000041
Calculating as shown in equation (8):
Figure FDA0003458810400000042
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 FDA0003458810400000043
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 FDA0003458810400000044
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.
4. The CAV lane change decision-making method for the off-ramp diversion area of the expressway of claim 3, wherein when the CAV demand is greater than the traffic capacity of the automatic driving special lane, the CAV demand is converged into a general lane, a mixed traffic flow is formed on the general lane, an intelligent networked vehicle fleet is formed, when the mixed traffic flow is stable, vehicles do not need to be newly formed, the traffic volume of the general lane, the head-time distance probability density distribution function of the general lane and the success probability of the CAV single lane change experiment are updated, and the lane change 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 FDA0003458810400000045
Internet fleet F forming cluster head vehicles following CAV1Has a probability of
Figure FDA0003458810400000046
Internet fleet F forming cluster head vehicle following AVH2Has a probability of
Figure FDA0003458810400000047
As shown in equation (12),
Figure FDA0003458810400000048
is calculated as shown in equation (13):
Figure FDA0003458810400000049
Figure FDA00034588104000000410
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 FDA0003458810400000051
Figure FDA0003458810400000052
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 FDA0003458810400000053
the probability density distribution function of the headway time interval of the general lane is updated to
Figure FDA0003458810400000054
It is calculated as shown in equation (17):
Figure FDA0003458810400000055
from this, the success probability of CAV single lane change experiment is updated to
Figure FDA0003458810400000056
It is calculated as shown in equation (18):
Figure FDA0003458810400000057
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 FDA0003458810400000058
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.
5. The CAV lane change decision-making method for the off-ramp diversion area of the expressway of claim 4, wherein the lane change remaining time T for the CAV of 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 conditions are shown as formulas (21) and (22):
Figure FDA0003458810400000059
Figure FDA00034588104000000510
Figure FDA00034588104000000511
in the formula (I), the compound is shown in the specification,
Figure FDA00034588104000000512
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 FDA00034588104000000513
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.
6. The CAV lane change decision-making method for the diversion area of the off-ramp of the expressway according to claim 3, 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 FDA0003458810400000061
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 FDA0003458810400000062
Figure FDA0003458810400000063
Figure FDA0003458810400000064
in the formula (I), the compound is shown in the specification,
Figure FDA0003458810400000065
indicates the permeability
Figure FDA0003458810400000066
The average headway of the lower mixing flow,
Figure FDA0003458810400000067
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.
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