CN114120688B - Method for establishing following model considering front vehicle information under V2V environment - Google Patents
Method for establishing following model considering front vehicle information under V2V environment Download PDFInfo
- Publication number
- CN114120688B CN114120688B CN202111407065.4A CN202111407065A CN114120688B CN 114120688 B CN114120688 B CN 114120688B CN 202111407065 A CN202111407065 A CN 202111407065A CN 114120688 B CN114120688 B CN 114120688B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- model
- speed
- nth
- adjacent
- 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.)
- Active
Links
Images
Classifications
-
- 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
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Traffic Control Systems (AREA)
Abstract
A method for establishing a following model considering front vehicle information under a V2V environment belongs to the technical field of vehicle-road cooperation and intelligent traffic. The invention aims at the problems that the influence of the following model in the existing V2V environment on target vehicles is assumed to be the same, so that the description effect of the model on the stability of the traffic flow is poor, and the model is difficult to be applied to the artificial driving background in the V2V environment. The method comprises the following steps: based on the full differential model, the full differential model is:
introducing an average speed of a preceding neighboring vehicle and a preceding next neighboring vehicle to an all-speed-differential model
Obtaining a follow-up model after primary optimization:
the invention can effectively improve the stability of the traffic flow.
Description
Technical Field
The invention relates to a method for establishing a following model considering front vehicle information under a V2V environment, and belongs to the technical field of vehicle-road cooperation and intelligent traffic.
Background
In recent years, with the further development of motorization, the number of vehicles on roads is increased sharply, and traffic congestion frequently occurs, so that the traffic congestion becomes one of the most common and major problems in urban traffic, and the development of cities and the prosperity of economy are severely restricted. To solve this problem, research on traffic flow characteristics and mechanisms of traffic congestion formation are being conducted, and among them, a car-following model describing individual behaviors of drivers is receiving wide attention.
Under the V2V communication environment, a driver can accurately and widely acquire information of surrounding vehicles, so a large number of follow-up model researches aiming at information such as multi-vehicle average speed, average expected speed, average head distance and the like are developed on the basis of information interaction among vehicles, and a follow-up model considering multi-vehicle speed fluctuation, a follow-up model considering a generalized front vehicle, a follow-up model considering a front vehicle group average expected speed, an average speed effect and the like are provided.
Regarding the follow-up model research under the environment of V2V, most of the assumptions assume that the influence of the surrounding vehicles on the target vehicle is the same, and the introduced information is the average value of the expected speed and the distance between the two heads of the vehicle group, and the assumption can be better applied to the automatic driving environment. However, since vehicles on roads are currently driven primarily by human beings, the vehicles present in the field of view tend to be the front adjacent, second adjacent vehicles, in which case providing average information does not necessarily play a good role in traffic flow stability, city road motor vehicle operation benefits. On the other hand, although some follow-up model studies in the V2V environment suggest that the nearby vehicle has a different influence on the target vehicle, they are not sufficiently concerned about the expected speed, the magnitude of the action strength of the speed information, and the influence of the action strength relationship.
Disclosure of Invention
Aiming at the problems that the influence of peripheral vehicles on target vehicles is assumed to be the same in the conventional car following model under the V2V environment, so that the description effect of the model on the stability of a traffic flow is poor, and the model is difficult to be applied to the V2V environment manual driving background, the invention provides a car following model establishing method considering front vehicle information under the V2V environment.
The invention relates to a method for establishing a following model considering information of a front vehicle under a V2V environment, wherein the front vehicle comprises a front adjacent vehicle and a front secondary adjacent vehicle; comprises the steps of (a) preparing a substrate,
based on the full differential model, the full differential model is:
wherein alpha represents the sensitivity coefficient of the nth vehicle driver; v (Δ x)n(t)) represents the expected speed of the nth vehicle, vn(t) represents the speed of the nth vehicle at time t, Δ vn(t) represents a speed difference, Δ x, between the vehicle n and the preceding adjacent vehicle n +1 at time tn(t) represents the vehicle-to-vehicle head spacing difference between the vehicle n and the adjacent vehicle n +1 in front at the time t, xn(t) represents the position of the nth vehicle at the time t, and lambda represents the sensitivity coefficient of the nth vehicle driver to the relative speed stimulation;
wherein V (Δ x)n(t))＝V1+V2tanh[C1(Δxn(t)-lc)-C2]，
In the formula V1、V2、C1、C2And lcAre all the parameters which are constant and are constant,
V1＝6.75m/s,V2＝7.91m/s,C1＝0.13m-1,C2＝1.57，lc＝5m；
Δxn(t)＝xn+1(t)-xn(t)，
in the formula xn+1(t) represents the position x of the (n + 1) th vehicle at time tn(t)；
Introducing an average speed of a preceding neighboring vehicle and a preceding next neighboring vehicle to an all-speed-differential model 
Obtaining a follow-up model after primary optimization:
in the formulapIndicating the intensity of the action of the speed of the preceding adjacent vehicle n +1 on the nth vehicle at the time t,qrepresents the action intensity of the average speed of the preceding adjacent vehicle and the preceding next adjacent vehicle on the nth vehicle, and p + q is 1;
in the formula vn+1(t) is the speed of the preceding neighboring vehicle, vn+2(t) is the speed of the next preceding neighboring vehicle.
According to the method for establishing the following model considering the information of the front vehicle under the V2V environment, the expected speed V (delta x) of the front adjacent vehicle is introduced into the following model after primary optimizationn+1(t)) and the expected speed V (Deltax) of the next preceding neighboring vehiclen+2(t)), obtaining a follow-up model after the second optimization:
in the formula beta1Intensity of action, β, on nth vehicle for expected speed of preceding adjacent vehicle2The strength of the action on the nth vehicle for the expected speed of the preceding next-adjacent vehicle satisfies the following expression:
β＝β1+β2,β≤0.5，β2＝γβ1,γ≤1，γis a control coefficient;
where β represents the sum of the expected speed of the preceding neighboring vehicle and the expected speed of the preceding next neighboring vehicle with respect to the nth vehicle action intensity, further satisfying the following expression:
βVm(Δxn(t))＝β1(V(Δxn+1(t))-V(Δxn(t)))+β2(V(Δxn+2(t))-V(Δxn(t)))，
Vmthe parameter is a self-defined parameter and represents the current expected speed of the nth vehicle obtained by the driver of the nth vehicle according to the expected speed of the front adjacent vehicle and the expected speed of the front next adjacent vehicle.
According to the following model establishing method considering the information of the preceding vehicle in the V2V environment of the present invention,
assuming that the distance between the heads of the adjacent vehicles is h, the stability condition of the following model after primary optimization is as follows:
α>2V'(h)-(2p+3q)λ，
in the formula V: (h) Current vehicle obtained for corresponding vehicle head interval hThe expected speed of the vehicle.
According to the following model establishing method considering the information of the preceding vehicle in the V2V environment of the present invention,
assuming that the distance between the heads of the adjacent vehicles is h, the stability condition of the follow-up model after the secondary optimization is as follows:
where v (h) is the current vehicle expected speed obtained for the corresponding headway h.
The invention has the beneficial effects that: the invention provides a following model considering information of front adjacent vehicles and secondary adjacent vehicles in a V2V environment, and theoretical and numerical simulation results show that the model can effectively improve the stability of traffic flow, the stability of the traffic flow is improved along with the increase of the sum of action strengths of expected speed information, and the influence generated by the change of the action strength relationship is small; the average speed information has small contribution to the stability of the traffic flow, but has good benefit when being superposed with the action of the expected speed information; the model can provide smoother vehicle starting and braking processes at the signalized intersection, and can reduce oil consumption and emission.
Drawings
FIG. 1 is a flow chart of a method for establishing a follow-up model considering information of a vehicle ahead in a V2V environment according to the present invention;
in fig. 2, when λ is 0.3, γ is 0.5, and q is 0.2, the equation β is obtained2＝0.5β1Neutral stability curves and coexistence curves at different β;
fig. 3 shows the neutral stability curve and coexistence curve for different γ values when λ is 0.3, β is 0.2, and q is 0.2;
fig. 4 shows the neutral stability curve and coexistence curve under different p, q relationships when λ is 0.3, γ is 0.5, and β is 0.2;
fig. 5 shows the relationship between p and q and the relationship between β and the neutral stability curve and coexistence curve when λ is 0.3 and γ is 0.5;
FIG. 6 is the time-space evolution of the density wave of the FVD model after t is 2700 s;
fig. 7 is the spatio-temporal evolution of the density wave for the case where β is 0.1, γ is 0.5, and q is 0.2 after t is 2700 s;
fig. 8 is the spatio-temporal evolution of the density wave for the case where β is 0.2, γ is 0.5, and q is 0.2 after t is 2700 s;
fig. 9 is the spatio-temporal evolution of the density wave for the case where β is 0.3, γ is 0.5, and q is 0.2 after t is 2700 s;
fig. 10 shows the change in the FVD model vehicle speed after t is 2700 s;
fig. 11 shows the change of the vehicle speed of the following model after the second optimization under the conditions that t is 2700s, β is 0.1, γ is 0.5, and q is 0.2;
Fig. 12 shows the variation of the vehicle speed of the following model after the secondary optimization under the conditions that t is 2700s, β is 0.2, γ is 0.5, and q is 0.2;
fig. 13 shows the variation of the vehicle speed of the following model after the secondary optimization under the conditions that t is 2700s, β is 0.3, γ is 0.5, and q is 0.2;
fig. 14 is the locomotive spacing distribution of the FVD model and the secondarily optimized following model under different β when t is 2700 s;
fig. 15 is a velocity-headway trajectory of the FVD model and the following model after the second optimization at different β when γ is 0.5 and q is 0.2;
fig. 16 is the vehicle headway distribution of the following car model after the secondary optimization under different gamma when beta is 0.2;
fig. 17 is the vehicle headway distribution of the following car model after the secondary optimization under different gamma when beta is 0.3;
fig. 18 is a velocity-headway trajectory of a follow-up model after quadratic optimization at different γ when β is 0.2;
fig. 19 is a velocity-headway trajectory of a follow-up model after quadratic optimization at different γ when β is 0.3;
FIG. 20 is a time velocity plot of the FVD model under basic parameter set conditions;
FIG. 21 is a time velocity plot of a quadratic optimized heel model under basic parameter set conditions;
FIG. 22 is a time acceleration curve of the FVD model under basic parameter set conditions;
FIG. 23 is a time acceleration curve of a secondarily optimized following model under basic parameter setting conditions;
FIG. 24 is a time velocity profile of the FVD model during braking;
FIG. 25 is a time velocity profile of a follow-up model after a second optimization during braking;
FIG. 26 is a time acceleration curve of the FVD model during braking;
fig. 27 is a time acceleration curve of the follow-up model after quadratic optimization during braking.
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.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
In a first embodiment, referring to fig. 1, the present invention provides a method for establishing a following model considering information of a preceding vehicle in a V2V environment, where the preceding vehicle includes a preceding neighboring vehicle and a preceding next neighboring vehicle; comprises the steps of (a) preparing a mixture of a plurality of raw materials,
Based on the full speed difference model, the full speed difference model is as follows:
wherein alpha represents the sensitivity coefficient of the nth vehicle driver; v (Δ x)n(t)) represents the number of n-th vehiclesPhase velocity, vn(t) represents the speed of the nth vehicle at time t, Δ vn(t) represents a speed difference, Δ x, between the vehicle n and the preceding neighboring vehicle n +1 at time tn(t) represents the vehicle-to-vehicle head spacing difference between the vehicle n and the adjacent vehicle n +1 in front at the time t, xn(t) represents the position of the nth vehicle at the time t, and lambda represents the sensitivity coefficient of the nth vehicle driver to the relative speed stimulation;
wherein V (Δ x)n(t))＝V1+V2tanh[C1(Δxn(t)-lc)-C2]，
In the formula V1、V2、C1、C2And lcAre all the parameters which are constant and are constant,
V1＝6.75m/s,V2＝7.91m/s,C1＝0.13m-1,C2＝1.57，lc＝5m；
Δxn(t)＝xn+1(t)-xn(t)，
in the formula xn+1(t) represents the position x of the (n + 1) th vehicle at time tn(t)；
Introducing an average speed of a preceding neighboring vehicle and a preceding next neighboring vehicle to an all-speed-differential model
Obtaining a follow-up model after primary optimization:
in the formulapIndicating the intensity of the action of the speed of the preceding adjacent vehicle n +1 on the nth vehicle at the time t,qrepresents the intensity of the action of the average speed of the preceding adjacent vehicle and the preceding next adjacent vehicle on the nth vehicle, and p + q is 1;
in the formula vn+1(t) is the speed of the preceding neighboring vehicle, vn+2(t) is the speed of the next preceding neighboring vehicle.
The once optimized following model can improve the stability of the traffic flow, and the driver can more clearly perceive the speed information of the next adjacent vehicle.
The model of the invention aims to explore the influence of providing expected speed information and average speed information on traffic flow characteristics and vehicle running characteristics in the application background of manual driving: the contribution and importance of the expected speed information and the average speed information to the traffic flow stability in the theoretical analysis model, and the influence of the action strength, the action strength relation and different information types of the information on the traffic flow stability are analyzed; by carrying out numerical simulation on three scenes, namely vehicle starting, vehicle braking and disturbance propagation, theoretical analysis results are deepened, the traffic flow characteristics of the model and the oil-saving and emission-reducing benefits are determined, and the defects in the existing research results can be supplemented.
The embodiment aims to research the traffic flow stability, the traffic flow characteristics and the oil-saving and emission-reducing benefits of models under the same conditions, so certain assumptions are made to facilitate the smooth development of research: (1) the driving environment is a traffic flow consisting of V2V vehicles completely and is manual driving; (2) only the operation of the small-sized vehicle is considered, and factors of other vehicle types, non-motor vehicles and pedestrians are not considered; (3) only fuel vehicles are considered, and electric vehicles or hybrid vehicles and the like are not considered; (4) there is no delay in the interaction and sharing between the vehicle information.
The full speed differential model (FVD) model in the embodiment can reflect actual traffic conditions more truly, and shows good performance in the aspect of researching traffic flow stability. However, the FVD model reflects only the interaction of the vehicle with its preceding vehicle, but in actual traffic conditions, the driver is concerned not only with the preceding nearby vehicle but also with the next-to-other vehicle, and the preceding nearby vehicle is more concerned than the other vehicles. In the V2V environment, the driver can obtain accurate motion information of the preceding vehicle, and can react to the information more effectively.
The advantage of this embodiment modeling the chosen V2V environment and the manual driving context is that vehicles on the road will still be largely manually driven for a future period of time. The V2V technology is rapidly developed and is more popular in the future, so that the model established based on the V2V can have certain application value.
The method is used for researching the influence of the action intensity sum, action intensity relation and different information types of the expected speed information on the traffic flow stability. It is assumed that the information provided in the context of a V2V environment human drive is the expected speed of the forward neighboring vehicle, the next neighboring vehicle, and the average speed of both.
Further, the expected speed V (deltax) of the front adjacent vehicle is introduced into the following model after the first optimizationn+1(t)) and the expected speed V (Deltax) of the next preceding neighboring vehiclen+2(t)), obtaining a follow-up model after the second optimization:
in the formula beta1For the intensity of the effect of the speed expected of the preceding neighbouring vehicle on the nth vehicle, beta2The intensity of the action on the nth vehicle for the expected speed of the next preceding adjacent vehicle satisfies the following expression:
β＝β1+β2,β≤0.5，β2＝γβ1gamma is less than or equal to 1 and is a control coefficient;
where β represents the sum of the expected speed of the preceding neighboring vehicle and the expected speed of the preceding next neighboring vehicle with respect to the nth vehicle action intensity, further satisfying the following expression:
βVm(Δxn(t))＝β1(V(Δxn+1(t))-V(Δxn(t)))+β2(V(Δxn+2(t))-V(Δxn(t)))，
Vmthe parameter is a self-defined parameter and represents the current expected speed of the nth vehicle obtained by the driver of the nth vehicle according to the expected speed of the front adjacent vehicle and the expected speed of the front next adjacent vehicle.
The following model after the secondary optimization can further improve the stability of the traffic flow. By using the model, the driver can clearly perceive the speed information of the front adjacent vehicle and the next adjacent vehicle and can acquire the driving behavior to be taken by the front vehicle in advance.
Further, assuming that the distance between the vehicle heads of the adjacent vehicles is h, the stability condition of the following model after the primary optimization is as follows:
α>2V'(h)-(2p+3q)λ，
Where v (h) is the current vehicle expected speed obtained for the corresponding headway h.
The stability conditions of the traditional FVD model are as follows:
α>2V'(h)-2λ，
compared with the traditional FVD model, the model stability area after one-time optimization is increased, and the stability of the traffic flow can be improved.
Further, assuming that the distance between the heads of the adjacent vehicles is h, the stability condition of the follow-up model after the secondary optimization is as follows:
where v (h) is the current vehicle expected speed obtained for the corresponding headway h.
Similarly, compared with the traditional FVD model, the model stable region after the secondary optimization is increased, and the stability of the traffic flow can be further improved.
In order to explore the influence of the expected speed and the average speed of the front adjacent vehicle and the secondary adjacent vehicle on the stability of the traffic flow, the following model provided by the invention can be subjected to linear stability analysis. When the vehicle travels at a constant headway h and an optimum speed v (h), the traffic flow reaches a steady state.
When λ is 0.3, γ is 0.5, and q is 0.2, the product is obtained2＝0.5β1Then, neutral stability curves (solid lines) under different β and the vehicle head space-sensitivity space (Δ x) obtained from the mKdV equation are obtainednα), as shown in fig. 2, the vertex of the curve represents the critical point (h) under the corresponding condition c,αc). The neutral stability curve and corresponding coexistence curve divide the traffic flow into three regions: a stable region, a metastable region, and an unstable region. InThe area above the stability line is a stable area, and when small disturbance is added, traffic jam cannot occur; in a metastable zone and an unstable zone below the unstable zone, the traffic flow is unstable, and the traffic flow can be developed into congestion along with time when the traffic flow is disturbed. As is apparent from fig. 2, compared with the FVD model, the stability region of the following model after the secondary optimization is larger, and the stability of the traffic flow can be effectively improved. Alpha corresponding to the critical point as beta increasescAnd the neutral stability curve is gradually reduced, the unstable area of the model is gradually contracted, and the stable area is gradually enlarged, so that the stability of the traffic flow can be enhanced and the traffic jam can be inhibited to a certain extent if the driver is more concerned with the expected speed information of the front adjacent vehicle and the next adjacent vehicle. In addition, as can be seen from fig. 2, the change width of the stable region is large when β changes from 0 to 0.1 and from 0.1 to 0.2; when the beta is further increased, the improvement level of the beta on the traffic flow stability is not obvious, which indicates that a driver does not need to be excessively sensitive to the expected speed information and can also improve the traffic flow stability to a certain extent.
Fig. 3 shows the difference when λ is 0.3, β is 0.2, and q is 0.2γLower neutral stability curve and headway-sensitivity space (Δ x)nAnd α) in the above. As can be seen from FIG. 3, when β is constant, β is increased with γ2Increase of (c), corresponding to the critical pointcAnd reducing, gradually reducing the neutral stability curve, gradually expanding the stable area of the model, and gradually contracting the unstable area. This indicates that, in the case of a certain β, if the driver focuses more on the expected speed information of the next-adjacent vehicle, the stability of the traffic flow is improved, which has a positive effect on suppressing traffic congestion. It should be noted that the influence is small compared to the influence of the increase of β, which has a larger influence on the stability of the traffic flow than γ. In addition, from beta2＝0,β2＝β1And beta2＝0.25β1The corresponding three neutral stability curves can be seen: even if the influence of the secondary neighboring vehicles is fully considered, the improvement level of the stability of the traffic flow is not obvious; without fully considering the effect of sub-adjacent vehicles, whose contribution to traffic flow stability approaches β2Case 0. Further, it can be concluded that the influence of the expected speed information of the following vehicles on the traffic flow stability is smaller, especially in the case that the attention of the driver is insufficient, and even in the case that much information is distracted by the driver, the rationality of the following model after the secondary optimization can be verified.
Fig. 4 shows the neutral stability curve and the headway-sensitivity space (Δ x) under different p, q relationships when λ is 0.3, γ is 0.5, and β is 0.2nA) coexistence curve; fig. 5 shows the neutral stability curve and the headway-sensitivity space (Δ x) for different p, q relationships and different β when λ is 0.3 and γ is 0.5nAnd α) in the above. As can be seen from fig. 4 and 5, when only the average speed information of the preceding adjacent and next-adjacent vehicles is provided, the change in the stable region is small, and when the intensity of the action of the average speed information changes, the influence on the stability of the traffic flow is small. When only the expected speed information is provided, the stable region of the car following model after the secondary optimization is expanded greatly, and when the action intensity and the change of the expected speed information are influenced greatly on the traffic flow stability.
From the above analysis, it can be found that the average speed information of the front-adjacent and the second-adjacent vehicles contributes much less to the stability of the traffic flow than the expected speed information of both. Kuang et al consider that the expected speed information, the influence of speed information on the target vehicle, is the same for each vehicle ahead, and analyze the influence of the average value of the information on the traffic flow stability and the contribution of different average information types to the stability in a highly automated driving environment. In contrast, the present embodiment is carried out in the context of manual driving in a V2V environment, and it is considered that the expected speed information and speed information of the preceding neighboring vehicle have a greater influence on the target vehicle than the next neighboring vehicle. In addition, although the average speed information does not greatly contribute to the stability of the traffic flow, the stability of the traffic flow can be further improved to a certain extent by providing the corresponding average speed information on the basis of providing the expected speed information.
To further explore the effect of anticipated speed of front adjacent and next adjacent vehicles and average speed on traffic flow, a critical point (h) may be selectedc,αc) NearbyThe slow-changing behaviors of two slow variables of time and space are subjected to nonlinear analysis.
Nonlinear analysis revealed that the propagation velocity c and the critical sensitivity α were obtained in three cases, i.e., λ 0.3, γ 0.5, β 0.2, γ 0.2, λ 0.3, γ 0.5, β 0.2, p, and qcAs shown in tables 1, 2 and 3.
As can be seen from the three tables, when other parameters are controlled to be unchanged, no matter the beta is increased, the gamma is increased, or the q is increased, c and alpha are increasedcThe values of (a) and (b) are all gradually decreased, indicating that the performance of stabilizing the flow is improved. However, c and α increase compared to βcA significant decrease in γ and an increase in q would only result in c and αcThe small reduction can indicate that the improvement of the attention degree of the driver to the expected speed information of the next adjacent vehicle and the improvement of the attention degree of the average speed information of the adjacent and next adjacent vehicles have little effect on improving the traffic flow stability.
TABLE 1
λ 0.3, γ 0.5, p 0.8, q 0.2, the propagation velocity c and the critical sensitivity α for different β c
TABLE 2
λ is 0.3, β is 0.2, p is 0.8, and q is 0.2, respectivelyγLower propagation velocity c and critical sensitivity alphac
TABLE 3
Propagation velocity c and critical sensitivity α in the relationship of p and q when λ is 0.3, γ is 0.5, and β is 0.2c
The model traffic flow characteristics and the fuel-saving and emission-reducing benefits are explained by deepening the theoretical analysis result of the model in a numerical analysis mode and combining the numerical simulation results of the vehicle starting and braking scenes at the signalized intersection.
The following cycle boundary conditions are set: the road length L is 1500m, the total number of vehicles N is 100, α is 0.6, and λ is 0.3.
Fig. 6 to 9 show the time-space evolution of the density wave after t 2700s, the density wave propagates backwards with time, and the legend on the right represents the size of the headway. The parameters in fig. 6 to 9 fail to satisfy the stability condition, and after the initial small disturbance occurs, the traffic flow transits from the initial steady flow state to the unsteady flow state, and the phenomena of stop and go and traffic jam occur. Along with the increase of beta, the amplitude of the density wave in the density wave space-time evolution curved surface graph is reduced, and the stability of the traffic flow is enhanced. In particular, when β is 0.4, γ is 0.5, and q is 0.2, the stability condition is satisfied. When the initial small disturbance disappears, the density wave disappears along with the small disturbance, the distance between the car heads is approximately equal, the curved surface graph is almost a plane, and the traffic flow still keeps a stable state.
Fig. 10 to 13 show the change of the vehicle speed under different conditions after t equals 2700s, and the right side legend represents the magnitude of the speed. The parameters in fig. 10 to 13 are such that, in the event that the stability condition is not satisfied, the initial small disturbance changes the traffic flow to an unsteady flow state and the vehicle speed repeatedly changes as time passes. As can be seen from the figure, the vehicle speed of the FVD model is relatively extreme, and the follow-up model after secondary optimization can better solve the problems of the FVD model. And the speed change is smaller and smaller, the vehicle can advance at a stable speed, and the traffic flow stability is obviously improved. When β is 0.4, γ is 0.5, and q is 0.2, the stability condition is satisfied. When the initial small disturbance disappears, the speed change graph is of a single color, and the traffic flow keeps a stable state.
As can be seen from fig. 14, the amplitude of the FVD model headway distribution is larger than the amplitude fluctuation of the model under different β, which can fully show that the stability of the traffic flow can be improved by introducing the expected speed and average speed information of the front adjacent vehicle and the next adjacent vehicle. Furthermore, the fluctuation of the headway distance significantly decreases as β increases, which means that the traffic flow becomes more stable if the driver can sufficiently consider the expected speed information of the preceding adjacent, next-adjacent vehicle. In particular, when β is 0.4, the traffic flow changes from an unstable state to a stable state due to the stability condition being satisfied, and the phenomena of vehicle stop and acceleration and deceleration disappear.
As can be seen from fig. 15, the traffic state is almost stable over a sufficiently long time, and the hysteresis loop of the vehicle movement can be clearly observed. The size of the hysteresis loop can reflect the stability of the traffic flow, and a larger hysteresis loop represents a poorer stability of the traffic flow. As can be seen from fig. 14, the FVD model has the largest hysteresis loop and the worst traffic flow stability, which may cause severe traffic congestion, and has a negative speed that does not meet the actual traffic conditions. Compared with an FVD model, the model has smaller hysteresis loop no matter the size of beta, can overcome the defect of negative speed, and plays a positive role in stabilizing traffic flow. The increase of beta can further reduce the hysteresis loop, so that the traffic flow is more stable. Specifically, when β is 0.4, the stability condition is satisfied, the hysteresis loop contracts to the point H on the optimum speed curve, and the traffic flow reaches a steady state.
Fig. 16 and 17 show the headstock spacing distribution of different γ lower models after a sufficient period of time, when β is 0.2/0.3. As can be seen from the two figures, the fluctuation condition of the locomotive head distance is reduced to a certain extent along with the increase of gamma, but the reduction amplitude is not obvious. Fig. 18 and 19 show the speed-headstock spacing traces of different γ models when β is 0.2/0.3. Overall, the hysteresis loop tends to decrease with increasing γ, but the decrease is not significant. In addition, as can be seen from a transverse comparison of fig. 16 to 19, when β is 0.3, the fluctuation of the headway distance and the hysteresis loop are smaller than β is 0.2, which further explains that the change in β size has a larger influence on the stability of the traffic flow than the change in γ. Therefore, the results of the numerical simulation are matched with the theoretical analysis results.
The starting condition of the model vehicle under the condition that the signal lamp is turned from red to green is researched, and the traffic flow characteristic of the model is analyzed. The basic parameters of the simulation are set as follows:
α＝0.41,λ＝0.2,Vmax＝15m/s,Vx＝6.75m/s,Vy＝7.91m/s,C1＝0.13m-1,C2＝1.57m-1,lc＝5m
Vfree14.66m/s, β is 0.2, γ is 0.5, p is 0.8, q is 0.2, and the number of vehicles N is 15.
Setting initial parameters of the vehicle: vehicle initial position xn(0) (n-1) d, d 6.5m, vehicle initial speed 0, i.e., vn(0) 0. And selecting an FVD model for comparison, wherein the parameter setting is the same as the setting.
By analyzing fig. 20 and fig. 21, the fifteenth vehicle in the FVD model starts in 22.7s, and the fifteenth vehicle in the follow-up model after the second optimization starts in 18.5s, and the vehicle in the model has a faster starting speed, so that the starting delay can be reduced, and the starting efficiency can be improved. This corresponds to some extent to the driving psychology and driving behaviour of the actual driver: it is known that when a preceding vehicle is intended to be started, the driver has a stronger desire to start the vehicle forward.
By analyzing the graphs from FIG. 20 to FIG. 23, the driver can start the vehicle (when the speed is higher than 0, the acceleration is lower than 0.5 m/s)2The vehicle reaches a stable speed to complete the starting process), the acceleration of the secondarily optimized following model is obviously lower than that of the FVD model, the vehicle in the secondarily optimized following model can complete the process at a smoother acceleration, and the maximum value of the acceleration is lower than 3m/s 2. The time for completing the starting process of the vehicle is analyzed, the time for completing the starting process of the fifteenth vehicle in the FVD model is 31.8s, the time for completing the starting process of the fifteenth vehicle in the follow-up model after secondary optimization is 29.7s, the time consumed by the starting process of the FVD model is 9.1s, the time consumed by the starting process of the follow-up model after secondary optimization is 11.2s, the starting time consumed by the follow-up model after secondary optimization is longer, but the starting process can be completed earlier, and the start-up process is smootherAnd (4) processing.
In order to analyze the characteristics of the model after the secondary optimization in the aspects of oil consumption and emission, a VT-Micro model can be selected for calculation, and the VT-Micro model is obtained according to the speed and acceleration characteristic fitting, so that the oil consumption and emission conditions can be accurately reflected.
The average fuel consumption and emission generated by the driver during the starting process (from the third vehicle to the fifteenth vehicle) in the FVD model and the follow-up model after the second optimization are shown in table 4, and as can be seen from table 4, the fuel consumption and emission generated by the driver can be reduced to some extent by the model during the starting process of the driver, which is about 5%. The reason for this is that the model can reduce start-up delay, improve start-up efficiency, and have a smoother start-up process.
TABLE 4
Average oil consumption and emission table generated in starting process of FVD (frequency-vapor deposition) model and follow-up model after secondary optimization
The method is characterized in that research is carried out on the braking condition of the model vehicle under the condition that a signal lamp is changed from green to red, relevant characteristics of a follow-up model after secondary optimization are checked, the setting of simulation basic parameters is the same as the starting scene of the vehicle, and a red lamp is assumed to be arranged at a position 600m of a road. The vehicle initial parameters are set as follows: vehicle initial position xn(0) (n-1) d, d 40m, vehicle initial speed vn(0) 14.66 m/s. And selecting an FVD model for comparison, wherein the parameter setting is the same as the setting.
As can be seen from fig. 24 and 25, the fifteenth vehicle in the FVD model starts to decelerate at 86.9s and ends to brake at 96.4s, which takes 9.5 s; the fifteenth vehicle in the secondarily optimized follow-up model starts to decelerate at 82.8s and stops braking at 94.9s, which takes 12.1s, and the secondarily optimized follow-up model starts to brake earlier but takes more time to complete the braking process. This corresponds to some extent to the driving psychology and driving behaviour of the actual driver: it is known that when a preceding vehicle intends to brake for parking, it is easier for the driver to take a deceleration operation, but it is still necessary to gradually come to a signal intersection for parking. However, the braking end time difference of the two models is small and depends on the braking start time of the first vehicle.
The average oil consumption and emission of the third vehicle to the fifteenth vehicle in the fleet from the braking of the first vehicle to the braking of the last vehicle are calculated, and the fact that the braking process of each vehicle in the follow-up model after the second optimization is longer, the oil consumption and the emission are not improved, but are reduced by 2% -3% can be found. This result can be fully explained in conjunction with fig. 26 and 27: the follow-up model after the second optimization adopts smoother deceleration in the braking process, and the minimum value of the deceleration is basically less than-3 m/s2The FVD model then assumes a smaller deceleration, the minimum of which is close to-4 m/s2The model has a smoother braking process.
In the embodiment, the stability condition and the mKdV equation of the secondarily optimized car following model are obtained through linear stability analysis and nonlinear analysis, and the traffic flow stability of the secondarily optimized car following model is theoretically analyzed. Deepening the theoretical analysis result through numerical simulation, and analyzing the traffic flow characteristics and the oil-saving and emission-reducing benefits of two scenes of vehicle starting and braking at the signalized intersection to obtain the following conclusions:
(1) the car following model after secondary optimization can effectively improve the stability of the traffic flow, the action strength of the expected speed information has great influence on the stability of the traffic flow, and the influence of the action relation of the expected speed information is small;
(2) The average speed information in the car following model after the secondary optimization contributes little to the stability of the traffic flow, but the stability of the traffic flow can be further improved on the basis of providing expected speed information;
(3) the following model vehicle after secondary optimization has small starting delay, can finish the starting process earlier, improves the starting efficiency, can start braking earlier under the braking scene, smoothes the braking process, and can reduce oil consumption and emission to a certain extent.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that various dependent claims and the features described herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (4)
1. A following model building method in consideration of information of a preceding vehicle in a V2V environment, the preceding vehicle including a preceding neighboring vehicle and a preceding secondary neighboring vehicle; which is characterized by comprising the following steps of,
Based on the full speed difference model, the full speed difference model is as follows:
wherein alpha represents the sensitivity coefficient of the nth vehicle driver; v (Δ x)n(t)) represents the expected speed of the nth vehicle, vn(t) represents the speed of the nth vehicle at time t, Δ vn(t) represents a speed difference, Δ x, between the nth vehicle and the preceding adjacent vehicle n +1 at time tn(t) represents the vehicle-to-vehicle head distance difference between the nth vehicle and the adjacent vehicle n +1 in front at the time t, xn(t) denotes the nth vehicle istThe position of the moment, lambda represents the sensitivity coefficient of the nth vehicle driver to the relative speed stimulation;
wherein V (Δ x)n(t))＝V1+V2tanh[C1(Δxn(t)-lc)-C2]，
In the formula V1、V2、C1、C2And lcAre all the parameters which are constant and are constant,
V1＝6.75m/s,V2＝7.91m/s,C1＝0.13m-1,C2＝1.57，lc＝5m；
Δxn(t)＝xn+1(t)-xn(t)，
in the formula xn+1(t) indicates that the preceding neighboring vehicle n +1 istThe location of the time of day;
introducing an average speed of a preceding neighboring vehicle and a preceding next neighboring vehicle to an all-speed-differential model
Obtaining a follow-up model after primary optimization:
in the formulapIndicating the intensity of the action of the speed of the preceding adjacent vehicle n +1 on the nth vehicle at the time t,qrepresents the intensity of the action of the average speed of the preceding adjacent vehicle and the preceding next adjacent vehicle on the nth vehicle, and p + q is 1;
in the formula vn+1(t) is the speed of the preceding neighboring vehicle, vn+2(t) is the speed of the next preceding neighboring vehicle.
2. The method for building a follow-up model considering information of a preceding vehicle in a V2V environment according to claim 1, wherein an expected speed V (Δ x) of a preceding neighboring vehicle is introduced into the once optimized follow-up model n+1(t)) and the expected speed V (Deltax) of the next preceding neighboring vehiclen+2(t)), obtaining a follow-up model after the second optimization:
in the formula beta1For the intensity of the effect of the speed expected of the preceding neighbouring vehicle on the nth vehicle, beta2Is next to the frontThe action intensity of the expected speed of the vehicle on the nth vehicle meets the following expression:
β＝β1+β2,β≤0.5，β2＝γβ1,γ≤1，γis a control coefficient;
where β represents the sum of the expected speed of the preceding neighboring vehicle and the expected speed of the preceding next neighboring vehicle with respect to the nth vehicle action intensity, further satisfying the following expression:
βVm(Δxn(t))＝β1(V(Δxn+1(t))-V(Δxn(t)))+β2(V(Δxn+2(t))-V(Δxn(t)))，
Vmthe parameter is a self-defined parameter and represents the current expected speed of the nth vehicle obtained by the driver of the nth vehicle according to the expected speed of the front adjacent vehicle and the expected speed of the front next adjacent vehicle.
3. The following model establishing method taking into account preceding vehicle information in a V2V environment according to claim 1,
assuming that the distance between the vehicle heads of the adjacent vehicles is h, the stability condition of the following model after the primary optimization is as follows:
α＞2V'(h)-(2p+3q)λ，
where v (h) is the current vehicle expected speed obtained for the corresponding headway h.
4. The following model establishing method taking into account preceding vehicle information in a V2V environment according to claim 2,
assuming that the distance between the heads of the adjacent vehicles is h, the stability condition of the follow-up model after the secondary optimization is as follows:
Where v (h) is the current vehicle expected speed obtained for the corresponding headway h.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407065.4A CN114120688B (en) | 2021-11-24 | 2021-11-24 | Method for establishing following model considering front vehicle information under V2V environment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111407065.4A CN114120688B (en) | 2021-11-24 | 2021-11-24 | Method for establishing following model considering front vehicle information under V2V environment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114120688A CN114120688A (en) | 2022-03-01 |
| CN114120688B true CN114120688B (en) | 2022-06-28 |
Family
ID=80372708
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111407065.4A Active CN114120688B (en) | 2021-11-24 | 2021-11-24 | Method for establishing following model considering front vehicle information under V2V environment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114120688B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115171414B (en) * | 2022-06-10 | 2023-07-14 | 哈尔滨工业大学重庆研究院 | CACC follows car flow control system based on Frenet coordinate system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015099508A (en) * | 2013-11-19 | 2015-05-28 | 東芝テック株式会社 | Vehicular passage control system and vehicular passage control program |
| CN105448080A (en) * | 2015-11-16 | 2016-03-30 | 北京理工大学 | Modeling method considering influence of sub-adjacent vehicles to traffic flow time lag car-following model stability |
| JP2020100179A (en) * | 2018-12-20 | 2020-07-02 | トヨタ自動車株式会社 | Manufacturing method for vehicle |
| KR20200081526A (en) * | 2018-12-18 | 2020-07-08 | 현대자동차주식회사 | Autonomous vehicle and driving control method using the same |
-
2021
- 2021-11-24 CN CN202111407065.4A patent/CN114120688B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015099508A (en) * | 2013-11-19 | 2015-05-28 | 東芝テック株式会社 | Vehicular passage control system and vehicular passage control program |
| CN105448080A (en) * | 2015-11-16 | 2016-03-30 | 北京理工大学 | Modeling method considering influence of sub-adjacent vehicles to traffic flow time lag car-following model stability |
| KR20200081526A (en) * | 2018-12-18 | 2020-07-08 | 현대자동차주식회사 | Autonomous vehicle and driving control method using the same |
| JP2020100179A (en) * | 2018-12-20 | 2020-07-02 | トヨタ自動車株式会社 | Manufacturing method for vehicle |
Non-Patent Citations (3)
| Title |
|---|
| 交通流双车动力学模型与数值仿真;彭光含 等;《***仿真学报》;20080130;第20卷(第2期);272-275,292 * |
| 双车跟驰模型稳定性及非线性分析;彭光含;《四川大学学报(自然科学版)》;20090728;第46卷(第4期);1057-1064 * |
| 考虑车辆加速度和次邻近车辆速度差的跟驰模型及仿真研究;陈刚 等;《科学技术与工程》;20120528;第12卷(第15期);3672-3674,3684 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114120688A (en) | 2022-03-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113781806B (en) | Mixed traffic flow passing method used in intelligent network connection environment | |
| Manzie et al. | Fuel economy improvements for urban driving: Hybrid vs. intelligent vehicles | |
| CN114120688B (en) | Method for establishing following model considering front vehicle information under V2V environment | |
| CN112735126A (en) | Mixed traffic flow cooperative optimization control method based on model predictive control | |
| CN113628443B (en) | Signal intersection speed guiding method based on driver finiteness | |
| CN115641717B (en) | Highway main line-ramp vehicle cooperative confluence control method, device and storage medium based on mixed traffic flow | |
| CN115273500A (en) | Signalized intersection internet vehicle track optimization guiding method and system | |
| CN114333364B (en) | Ecological driving method for automatically-driven vehicle to pass through signalized intersection | |
| CN115817460B (en) | Intelligent network linkage vehicle active coordination collision avoidance method under mixed flow condition | |
| CN116895163A (en) | Multi-vehicle cooperative control method and system based on signal lamp active communication | |
| CN113312752A (en) | Traffic simulation method and device for main road priority control intersection | |
| CN115497315B (en) | Dynamic bus lane energy-saving optimization control method under vehicle-road cooperative environment | |
| CN107215333A (en) | A kind of cooperative self-adapted cruise strategy of the economy of use sliding mode | |
| CN114261399B (en) | Decision planning method for intelligent driving automobile under ice and snow road surface | |
| CN115116249B (en) | Method for estimating different permeability and road traffic capacity of automatic driving vehicle | |
| CN115713863A (en) | Trajectory planning and signal optimization method for special phase for automatic driving | |
| CN113823076B (en) | Instant-stop and instant-walking road section blockage relieving method based on networked vehicle coordination control | |
| CN111476491A (en) | Vehicle motion simulation analysis method in urban traffic scene | |
| CN115083164A (en) | Signal intersection double-layer optimization method for mixed traffic flow | |
| Du et al. | A fuel efficient control strategy for connected vehicles in multiple-lane urban roads | |
| Wang et al. | Towards higher efficiency and less consumption: Control Strategy and Simulation for CAV platooning | |
| CN117195464A (en) | Method for evaluating road truck special lane setting condition under automatic driving vehicle mixing condition | |
| CN114861391A (en) | Microcosmic simulation method for cooperative self-adaptive cruise control vehicle | |
| Wang et al. | Driving behavior of connected autonomous vehicles in mixed traffic flow within ramp segments on freeway based on an advanced cellular automata | |
| CN113256988A (en) | Stability analysis-based confluence area mixed traffic speed cooperative control method |
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 |