CN113823076A - Instant-stop and instant-walking road section blockage relieving method based on networked vehicle coordination control - Google Patents
Instant-stop and instant-walking road section blockage relieving method based on networked vehicle coordination control Download PDFInfo
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Abstract
The invention discloses an instant stop and go road section blocking-relieving method based on networked vehicle coordination control, which comprises the following steps: analyzing traffic flow running characteristics and vehicle driving behaviors of the 'stop-and-go' road section; a coordination control strategy in the Internet of vehicles environment is proposed; respectively establishing a motorcade following coordination control model and a motorcade lane change coordination control model based on the state prediction of a head vehicle terminal; the method comprises the steps of improving a traditional particle swarm algorithm to solve a motorcade following coordination control model, and solving the motorcade lane change coordination control model by using simulation software to obtain the running speed and position of each time point of a motorcade head vehicle, and a lane change position point and a lane change time point when the motorcade head vehicle changes lanes. The method can effectively play the role of coordinated control of the networked vehicles in the traffic flow, and has the advantages of effectively relieving the congestion problem of the road section of 'stop-and-go-on-stop', improving the traffic speed of the vehicles in the road section, improving the driving comfort and the driving safety of the driver and the like.
Description
Technical Field
The invention relates to the technical field of vehicle networking technology and road section blockage relieving strategies, in particular to an 'stopping and walking-as-you-go' road section blockage relieving method based on networked vehicle coordination control.
Background
In recent years, the internet of vehicles technology has become a hot spot of research, and based on the increasing maturity of the internet of vehicles technology, the internet of vehicles technology has become a core component of the future intelligent transportation system. Based on the characteristics that the networked vehicles can acquire front and rear and surrounding vehicles and facilities in time, a plurality of students find that the networked vehicles can effectively reduce traffic jam and improve road traffic capacity under certain conditions. However, the current research on the block-slowing strategy of the road section of the road which is stopped and taken as soon as possible still mainly takes hardware improvement and reinforcement management, and the block-slowing research cannot be carried out by combining with the currently emerging technical means, so that a block-slowing method of the road section which is stopped and taken as soon as possible in a networked vehicle environment needs to be researched.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an 'stopping and walking' road section slow-blocking method based on the coordination control of networked vehicles.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a method for stopping and walking on road sections slowly based on networked vehicle coordination control comprises the following steps:
s1, dividing the road section of 'stop and go' into four areas, which are respectively as follows according to the advancing direction of the vehicle: the system comprises a following area, a lane changing area, a parking area and a termination area, wherein the following area is used for driving the vehicle only in a following way, the other areas are used for driving the vehicle in a following way and changing the lane, and the parking area is provided with parking spaces for the vehicle to temporarily park; the road section allowing the vehicle to temporarily stop but needing to leave within a specified time range is the road section of the vehicle which stops immediately;
s2, based on the driving behaviors of vehicles in each region, combining the networking characteristics of the vehicles, and aiming at the following and lane changing behaviors of the vehicles on the road section, providing a coordination control strategy under the vehicle networking environment, wherein the coordination control strategy comprises a following coordination control strategy and a lane changing coordination control strategy;
s3, respectively considering the driving safety and the driving comfort, establishing a motorcade following coordination control model based on the state prediction of the head vehicle terminal, and considering the driving safety and the driving delay, establishing a motorcade lane change coordination control model;
s4, the conventional particle swarm algorithm is improved to solve the motorcade following coordination control model based on the state prediction of the head car terminal, and simulation software is used to solve the motorcade lane changing coordination control model to obtain the running speed and position of each time point of the head car of the motorcade, and a lane changing position point and a lane changing time point when the head car of the motorcade changes lanes.
Further, in step S1, a one-way two-lane and in-road parking "stop-and-go" road segment is selected for study and division.
Further, in step S2, the following assumption is made with respect to the proposal of the cooperative control strategy:
a1, the motorcade already reaches a stable following state before a following area, and the traffic flow can be rapidly evacuated in a termination area without generating negative influence on the traffic flow in a parking area;
a2, in the process of coordination control, traffic flow takes the networked vehicles as the head, non-networked vehicles drive along with the states of a plurality of fleets driven by the networked vehicles, and the non-networked vehicles do not interfere with the formation of the fleets;
a3, in the process of Vehicle coordination control, the networked Vehicle exchanges information with the outside world through Vehicle-to-Vehicle or V2V and Vehicle-to-Infrastructure or V2I in real time communication, and the sent and acquired information comprises: the running speed, acceleration, position and running plan in a future time period of the own vehicle and the surrounding vehicles; the networked vehicles can accurately make and execute the coordination control strategy, the time for the system to calculate the coordination control strategy is not considered, and the influence of special conditions on the coordination control strategy is not considered;
a4, after entering the coordination control area, when the vehicle drives following, the non-networked vehicle drives following the front vehicle by following the Full Velocity Difference, namely, the FVD following model; the method comprises the following steps that the self vehicle always keeps running on the same lane under the condition of non-forced lane changing, lane changing is carried out by the vehicle according to the lane changing condition of a lane changing model selected in the previous section when the lane is changed, and lane changing is carried out by the vehicle in the unit of a vehicle fleet when the lane is changed;
a5, the whole process of traffic flow running only involves motor vehicles, the default is that pedestrians and non-motor vehicles strictly follow the traffic rules to walk on special roads, and the interference of the pedestrians and the non-motor vehicles to the main line traffic flow is not considered;
a6, vehicles normally change lanes and pass between a parking area and a lane changing area, and the situation of parking waiting caused by insufficient parking space can not occur;
b. and (3) following coordination control strategy: the following coordination control strategy is based on the following behavior and coordinates and controls the running states of the following vehicles of the motorcade by controlling the speed of the head vehicle-to-vehicle networking vehicle of the motorcade in real time;
firstly, the motorcade head networked vehicle communicates with a roadside detection unit through V2I in real time to send own vehicle information and acquire surrounding motorcade vehicle information, and communicates with the networked vehicle in real time through V2V to acquire information including acceleration, speed, position and coordination control strategy of the networked vehicle; after acquiring the position of a surrounding vehicle fleet, basic information of surrounding networked vehicles and a coordination control strategy, predicting the running condition of the front vehicle fleet, and then combining the current running condition of the networked vehicles with the current running condition of the self vehicle, the scale of non-networked vehicles in the current vehicle fleet, the running speed and the running characteristic, namely, following model information, with the overall optimal running stability and running safety of the vehicle fleet as a target, formulating the coordination control strategy considering the surrounding multi-vehicle information, outputting the real-time coordination speed of the networked vehicles, and communicating with the surrounding vehicles in real time to update the self vehicle information and the coordination control strategy;
c. and (3) a lane change coordination control strategy: the lane change coordination control strategy is based on the lane change behavior of the vehicles and controls the lane change behavior of the whole fleet of vehicles through controlling the lane change positions of the vehicles on the head vehicle networking of the fleet of vehicles, and the lane change coordination control strategy relates to the determination of a lane change position area and a specific lane change position;
c1, lane change position area determination: before changing lanes by taking a motorcade as a unit and outputting an optimal lane changing position, determining a lane changing position area of the motorcade by combining the running state and position of the motorcade and the running state and position of a motorcade on a left side, namely clearly determining that the motorcade changes lanes to the front or the back of an adjacent motorcade on the left side;
c2, determination of the specific lane change position: after the motorcade lane change position area is determined, the optimal specific lane change position of the motorcade is further determined, namely the lane change position point and the time point of the motorcade are determined.
Further, in step S3, a fleet following coordination control model based on head vehicle terminal state prediction is proposed for a following coordination control strategy, specifically as follows:
a. the terminal state prediction of the vehicle comprises terminal time prediction, terminal position prediction and terminal speed prediction; the time prediction of the fleet terminal in the lane change area and the following area is expressed as follows:
wherein:
is the terminal time of the current fleet of vehicles,
the terminal state of a front motorcade is shown, i represents the current motorcade, i-1 represents the front motorcade, and the front motorcade refers to the motorcade with the terminal time point in front;
the fleet terminal time forecast at the parking area is expressed as:
wherein: t is tlimitWhen the vehicle representing the parking area is parkedA time limit; t is tstop(n) represents the parked time of the vehicle parked in the nth parking space of the parking area, tstop(n)≤tlimit(ii) a k is the additional limit time for the vehicle to stop; k is the maximum additional limit time for the vehicle to stop;
the starting time of the vehicle at the nth parking space in the parking area under the restriction of the front motorcade position limit is obtained; t is tcChanging lane time for a single vehicle, namely starting to change lane from a parking space to finishing changing lane;
when the position of a head vehicle terminal is predicted, the terminal positions of the fleet tails at the terminal time points of the fleet tails around are considered, the fleet tails before the terminal time points are non-networked vehicles, the positions of the fleet tails are determined by combining a following model and the head positions of the fleet, and then the positions of the self-vehicle terminals are determined by combining a safe following distance; the prediction of the terminal position of the vehicle running in normal following with the following area and the lane changing area is expressed as follows:
wherein:
the terminal position of the head car of the motorcade of the terminal time point is obtained;
the terminal position of the head car of the fleet ahead of the terminal time point is obtained;
to be compared with the terminal speed of the fleet
The related vehicle following distance is determined by the Full Velocity Difference, namely, the related parameters of the FVD following model; l is the vehicle length; n is a radical ofi-1The scale of the motorcade is motorcade i-1;
the vehicle terminal position prediction of the parking area is expressed as follows:
wherein:
speed of vehicle travel for completion of lane change
The associated following distance;
when the speed of the first vehicle terminal is predicted, the terminal speed of the surrounding vehicle fleet at the terminal time point is obtained first, and the terminal speed of the current vehicle fleet at the first vehicle fleet is set as the running speed of the vehicle fleet before the terminal time point, namely:
wherein:
the running speed of the head car of the self motorcade at the terminal time point,
the driving speed of the head car of the motorcade before the terminal time point is obtained;
b. after the terminal state of the vehicle is predicted, the following vehicle fleet following coordination control model is established by considering the safety cost and the driving comfort in the driving process of the vehicle fleet:
in the formula: m is the total number of the motorcade vehicles; t is a certain simulation moment; t is the total number of simulation step lengths; delta t is the simulation time step length; TTCi(t) is the time of collision of the ith vehicle in the fleet at time t, and represents when the speed of the rear vehicle is greater than that of the front vehicle, the time of collision is keptFront velocity to the time of collision; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; a isiAnd (t) is the acceleration value of the ith vehicle at the time t.
Further, in step S3, a fleet lane change coordination control model is proposed for the lane change coordination control strategy, which includes a lane change position area model and a specific lane change position model, specifically as follows:
when the optimal lane change position area is determined, the influence of different lane change position areas on the motorcade passing time is considered, and the following lane change position area model is established:
wherein: n is the number of the vehicles in the self-motorcade, the left-side motorcade and the subsequent motorcade in the road section, and T (i) is the passing time of the ith vehicle, namely the lane changing strategy with the minimum total passing time is the optimal lane changing strategy;
on the basis of determining the optimal lane changing position area, the following specific lane changing position model is established by taking the fleet lane changing safety and the travel time of the vehicle on the current road section as optimization targets:
wherein:
calculated value of lane change safety for a fleet of vehicles changing lanes, NcFor the number of vehicles that actually need to change lanes,
in order to specify the time for the lane change termination,
tcfor changing track time for individual vehicles, i.e. from parking spaceStarting to switch lanes to end, wherein t is a certain simulation moment, and delta t is a simulation time step length; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; TTCj(t) is the collision time of the jth vehicle in the lane-changing fleet at the time t;
performing initial time for the specific lane changing behavior, namely, determining a lane changing time point corresponding to the optimal position obtained by the current lane changing position decision;
selecting delay influence on traffic flow for the passing time of all vehicles in the current road section, namely the lane change position; n is a radical oftotalT (e) is the passing time of the e-th vehicle.
Further, in step S4, the fleet following coordination control model predicted based on the head-end state is simulated and solved by using an improved particle swarm algorithm: on the basis that the traditional particle swarm algorithm can only solve one optimization target, when the two optimization targets are considered, the speed updating is divided into two stages, the first stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the safety in the running process of the fleet is optimal, the second stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the driving comfort is optimal, the steps are repeated in such a circulating way, the values of the two optimization targets when the two optimization targets are optimal are repeatedly considered, and finally, the bird groups can be gathered at the positions which are equal to the two food sources to obtain the speed and the position of each time point of the networked vehicles;
using MATLAB software, solving the lane change position area model by using an exhaustion method, enumerating all possible conditions during solving, solving different conditions, and obtaining the optimal lane change position area by taking the travel time caused by different lane change positions as a judgment basis;
after the optimal lane changing position area is determined, using MATLAB software to solve a specific lane changing position model under the condition of setting a motorcade running state, under the condition that all conditions are known, carrying out simulation operation on different lane changing positions to obtain the lane changing safety and the passing efficiency at the moment, obtaining the lane changing time point and the position point of a motorcade head vehicle lane changing according to the result of the motorcade lane changing coordination control model, and obtaining the speed and the position of each time point when the motorcade head vehicle, namely the networked vehicle, runs through a road section which is stopped and runs as soon as possible, and the lane changing time point and the position point when the lane changing is carried out according to the calculation results of the motorcade following coordination control model and the motorcade lane changing coordination control model.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the invention starts from the angle of networked vehicles, considers non-networked vehicles at the same time, can control the traffic flow running condition in real time according to the congestion degree aiming at the common congestion scene of the city, guides the vehicles to orderly pass through the road sections which are stopped and taken as soon as possible, can effectively relieve the congestion of the road sections which are stopped and taken as soon as possible, and has higher practical popularization value.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of the regions of the research road section and their functions.
FIG. 3 is a double optimization objective solution convergence curve.
FIG. 4 is a diagram illustrating the optimization results of the seek position area.
FIG. 5 is a schematic diagram of an optimized target variation curve corresponding to different lane-changing positions.
FIG. 6 is a flow chart of a MATLAB simulation experiment.
FIG. 7 is a proportional driving delay diagram for different networked vehicles.
FIG. 8 is a graph of proportional driving speeds for different networked vehicles.
FIG. 9 is a crash time profile for different proportions of networked vehicles.
FIG. 10 is a comfort profile for different proportions of networked vehicles.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
As shown in fig. 1, the present embodiment discloses an "stop-and-go" road section block-alleviating method based on networked vehicle coordination control, which has the following specific conditions:
1) firstly, a road section in front of a north 4-door of a third hospital affiliated to Zhongshan university in Guangzhou city is selected as an actual research scene, the road section is positioned on a river road and a side road in a Tianhe area in Guangzhou city, two lanes are arranged in one way, and the vehicle parking mode is in-road parking. And (4) surveying the research road section, and analyzing the running characteristics of the traffic flow of the road section of 'stop and go' and the driving behavior of the vehicle by combining survey data. The road section is divided into four parts according to the advancing direction of the vehicle: the system comprises a following area, a lane changing area, a parking area and a termination area, wherein vehicles only run with the following area, vehicles can run with the following area and change lanes in other areas, parking spaces are reserved in the parking area for the vehicles to temporarily park, and each divided area and function of a road section are shown in figure 2.
The road section field investigation data table 1 (a statistical table of 30-minute vehicle parking time at peak), the table 2 (15-minute parking space supply at peak) and the table 3 (60-minute collision times and position statistics at peak in lane change area) of the hospital scene are combined.
TABLE 1 statistics of 30 minute peak vehicle parking duration
TABLE 2 Peak 15 minute carport supply
Note: the effective supply amount is vehicles which stop in the parking area and get on or off passengers; the ineffective supply amount is a vehicle which is parked in a parking area but leaves for a certain period of time without getting on or off the bus.
TABLE 3 Lane change zone Peak 60 minute Conflict times and position statistics
Analyzing the traffic flow running characteristics and the driving behaviors of the vehicles of the researched road sections, and summarizing the characteristics as follows:
a. the lane change behavior in the termination area has a large impact on traffic. The lane change behavior of the road section is to change from a lane with high density to a lane with low density, so that the lane change condition is easily achieved, and the vehicles do not need to decelerate or even can accelerate when changing lanes, so that the lane change behavior of the vehicles in the area can generate a certain positive dispersion effect on the upstream traffic flow;
b. the lane change behavior of the parking area and the lane change area has great influence on the traffic flow. The frequent lane changing behavior of the road section is carried out between two lanes with high traffic flow density and equivalent traffic flow density, the lane changing condition is difficult to achieve, frequent acceleration and deceleration behaviors can be generated while lane changing is carried out, and the lane changing behavior in the area can cause negative influence on the driving safety and stability of the upstream traffic flow along with traffic waves; in addition, the turnover rate of the parking space is not high, the vehicle detention time is too long, the vehicle delay time of the whole area is too long to a certain extent, and even the current area and the lane changing area are seriously jammed. Therefore, the parking area and the lane change area are the key areas of the coordination control.
c. The following area is located at the upstream of the road section, the traffic flow of the road section is mainly based on the following behavior, and the driving speed is higher. The road traffic flow is influenced by the downstream operation condition and has certain influence on the downstream operation condition.
By combining the above conditions, the coordinated control of the vehicles on the road section can respectively perform the coordinated control of the following and lane changing behaviors in each functional area, thereby realizing the coordinated control of the whole road section. The following area mainly takes the following behavior coordination control as a main part, the lane changing area and the parking area need to consider both the following coordination control and the lane changing coordination control, and the key point is the lane changing coordination control as a main part.
2) Based on the research on the traffic flow operation characteristics of the road section and the driving behaviors of the vehicles analyzed in the step 1), and by combining the networking characteristics of the vehicles, aiming at the following and lane changing behaviors of the vehicles on the road section, a coordination control strategy under the vehicle networking environment is provided, wherein the coordination control strategy comprises a following coordination control strategy and a lane changing coordination control strategy. The following assumptions are made for the proposal of the coordination control strategy:
a1, the motorcade already reaches a stable following state before a following area, and the traffic flow can be rapidly evacuated in a termination area without generating negative influence on the traffic flow in a parking area;
a2, in the process of coordination control, traffic flow takes the networked vehicles as the head, non-networked vehicles drive along with the states of a plurality of fleets driven by the networked vehicles, and the non-networked vehicles do not interfere with the formation of the fleets;
a3, in the process of Vehicle coordination control, the networked Vehicle exchanges information with the outside world through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) in real time communication, and the information sent and obtained comprises: the running speed, acceleration, position and running plan in a future time period of the own vehicle and the surrounding vehicles; the networked vehicles can accurately make and execute the coordination control strategy, the time for the system to calculate the coordination control strategy is not considered, and the influence of special conditions on the coordination control strategy is not considered;
a4, after entering the coordination control area, when the vehicle drives along with the vehicle, the non-networked vehicle drives along with the front vehicle according to a Full Velocity Difference (FVD) following model; the method comprises the following steps that the self vehicle always keeps running on the same lane under the condition of non-forced lane changing, lane changing is carried out by the vehicle according to the lane changing condition of a lane changing model selected in the previous section when the lane is changed, and lane changing is carried out by the vehicle in the unit of a vehicle fleet when the lane is changed;
a5, the whole process of traffic flow running only involves motor vehicles, and the default is that pedestrians, non-motor vehicles and the like strictly follow the traffic rules to walk on special roads without considering the interference of the pedestrians and the non-motor vehicles to the main line traffic flow;
a6, vehicles normally change lanes and pass between a parking area and a lane changing area, and the situation of parking waiting caused by insufficient parking space can not occur;
b. and (3) following coordination control strategy: the following coordination control strategy is based on the following behavior and coordinates and controls the running states of the following vehicles of the motorcade by controlling the speed of the head vehicle-to-vehicle networking vehicle of the motorcade in real time;
firstly, the motorcade head networked vehicles communicate with a roadside detection unit through V2I in real time to send own vehicle information and acquire surrounding motorcade vehicle information, and communicate with other networked vehicles through V2V in real time to acquire information, such as acceleration, speed, position and coordination control strategies of the networked vehicles; after acquiring the position of a surrounding vehicle fleet, basic information of surrounding networked vehicles and a coordination control strategy, predicting the running condition of the front vehicle fleet, then combining the information of the networked vehicles, such as the current running condition of the own vehicle, the scale of non-networked vehicles of the current vehicle fleet, the running speed, the running characteristic (namely a following model) and the like, with the overall optimization of the running stability and the running safety of the vehicle fleet as a target, formulating the coordination control strategy considering the surrounding multi-vehicle information, outputting the real-time coordination speed of the networked vehicles, and communicating with the surrounding vehicles in real time to update the own vehicle information and the coordination control strategy;
c. and (3) a lane change coordination control strategy: the lane change coordination control strategy is based on the lane change behavior of the vehicles and controls the lane change behavior of the whole fleet of vehicles through controlling the lane change positions of the vehicles on the head vehicle networking of the fleet of vehicles, and the lane change coordination control strategy relates to the determination of a lane change position area and a specific lane change position;
c1, lane change position area determination: before changing lanes by taking a motorcade as a unit and outputting an optimal lane changing position, determining a lane changing position area of the motorcade by combining the running state and position of the motorcade and the running state and position of a motorcade on a left side, namely clearly determining that the motorcade changes lanes to the front or the back of an adjacent motorcade on the left side;
c2, determination of the specific lane change position: after the motorcade lane change position area is determined, the optimal specific lane change position of the motorcade is further determined, namely the lane change position point and the time point of the motorcade are determined.
3) And (3) based on the coordination control strategy formulated in the step 2), respectively considering the driving safety and the driving comfort to establish a motorcade following coordination control model based on the state prediction of the head-car terminal, and considering the driving safety and the driving delay to establish a motorcade lane change coordination control model.
The terminal state prediction of the vehicle comprises terminal time prediction, terminal position prediction and terminal speed prediction; the fleet terminal time prediction in the lane change area and the following area can be expressed as:
wherein:
is the terminal time of the current fleet of vehicles,
the terminal state of a front motorcade is shown, i represents the current motorcade, i-1 represents the front motorcade, and the front motorcade refers to the motorcade with the terminal time point in front;
the fleet terminal time prediction at a parking area may be expressed as:
wherein: t is tlimitA vehicle general parking time limit indicating a parking area; t is tstop(n) represents the parked time of the vehicle parked in the nth parking space of the parking area, tstop(n)≤tlimit(ii) a k is the additional limit time for the vehicle to stop; k is the maximum additional limit time for the vehicle to stop;
the starting time of the vehicle at the nth parking space in the parking area under the restriction of the front motorcade position limit is obtained; t is tcChanging lane time for a single vehicle, namely starting to change lane from a parking space to finishing changing lane;
when the position of a head vehicle terminal is predicted, the terminal positions of the fleet tails at the terminal time points of the fleet tails around are considered, the fleet tails before the terminal time points are generally non-networked vehicles, the positions of the fleet tails are determined by combining a following model and the head positions of the fleet, and then the positions of the self-vehicle terminals are determined by combining a safe following distance; the prediction of the position of the vehicle terminal in normal following driving in the following area and the lane changing area can be expressed as follows:
wherein:
the terminal position of the head car of the motorcade of the terminal time point is obtained;
the terminal position of the head car of the fleet ahead of the terminal time point is obtained;
to be compared with the terminal speed of the fleet
The related vehicle following distance is determined by the Full Velocity Difference, namely, the related parameters of the FVD following model; l is the vehicle length; n is a radical ofi-1The scale of the motorcade is motorcade i-1;
the vehicle terminal position prediction of the parking area is expressed as follows:
wherein:
speed of vehicle travel for completion of lane change
The relevant following distance is determined by a vehicle track change model based on the minimum safe distance (the model is published in the known literature);
when the speed of the first vehicle terminal is predicted, the terminal speed of the surrounding vehicle fleet at the terminal time point is obtained first, and the terminal speed of the current vehicle fleet at the first vehicle fleet is set as the running speed of the vehicle fleet before the terminal time point, namely:
wherein:
the running speed of the head car of the self motorcade at the terminal time point,
the driving speed of the head car of the motorcade before the terminal time point is obtained;
after the terminal state of the vehicle is predicted, the following vehicle fleet following coordination control model is established by considering the safety cost and the driving comfort in the driving process of the vehicle fleet:
in the formula: m is the total number of the motorcade vehicles; t is a certain simulation moment; t is the total number of simulation step lengths; delta t is the simulation time step length; TTCi(t) is the collision time of the ith vehicle in the fleet at the time t, which represents the time from keeping the current speed to the collision of the two vehicles when the speed of the rear vehicle is higher than that of the front vehicle; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; a isiAnd (t) is the acceleration value of the ith vehicle at the time t.
The motorcade lane change coordination control model is provided for a lane change coordination control strategy, and comprises a lane change position area model and a specific lane change position model, wherein the specific conditions are as follows:
when the optimal lane change position area is determined, the influence of different lane change position areas on the motorcade passing time is considered, and the following lane change position area model is established:
wherein: n is the number of the vehicles in the self-motorcade, the left-side motorcade and the subsequent motorcade in the road section, and T (i) is the passing time of the ith vehicle, namely the lane changing strategy with the minimum total passing time is the optimal lane changing strategy;
on the basis of determining the optimal lane changing position area, the following specific lane changing position model is established by taking the fleet lane changing safety and the travel time of the vehicle on the current road section as optimization targets:
wherein:
calculated value of lane change safety for a fleet of vehicles changing lanes, NcFor the number of vehicles that actually need to change lanes,
in order to specify the time for the lane change termination,
tcchanging the lane for a single vehicle, namely, starting to change the lane from a parking space to finishing changing the lane, wherein t is a certain simulation moment, and delta t is a simulation time step length; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; TTCj(t) is the collision time of the jth vehicle in the lane-changing fleet at the time t;
performing initial time for the specific lane changing behavior, namely, determining a lane changing time point corresponding to the optimal position obtained by the current lane changing position decision;
is as followsThe passing time of all vehicles in the front road section, namely the influence of the lane change position selection on the delay of the traffic flow; n is a radical oftotalT (e) is the passing time of the e-th vehicle.
4) The method comprises the steps of improving a traditional particle swarm algorithm to solve a motorcade following coordination control model, and solving the motorcade lane change coordination control model by using simulation software to obtain the running speed and position of each time point of a motorcade head vehicle, and a lane change position point and a lane change time point when the motorcade head vehicle changes lanes.
And (3) carrying out simulation solution on the fleet following coordination control model based on the head vehicle terminal state prediction by using an improved particle swarm optimization (the solution result is shown in figure 3): on the basis that the traditional particle swarm algorithm can only solve one optimization target, when the two optimization targets are considered, the speed updating is divided into two stages, the first stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the safety in the running process of the fleet is optimal, the second stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the driving comfort is optimal, the steps are repeated in such a circulating way, the values of the two optimization targets when the two optimization targets are optimal are repeatedly considered, and finally, the bird groups can be gathered at the positions which are equal to the two food sources, so that the speed and the position of each time point of the networked vehicles are obtained. The image form of the vehicle is slightly different from that of a single factor because the consideration factor is different from that of the single factor, but after 100 iterations, the curve has no large fluctuation and basically reaches the convergence state, only the fluctuation is generated when the speed of different optimization targets is considered to be updated, but through comparison, the driving speed curves of the vehicles are completely consistent, and the convergence state is reached.
The lane change position region model was solved using MATLAB software using an exhaustive method, and the results are shown in fig. 4. The right graph result of fig. 4 shows that the area with the grid lines indicates that the Z value is greater than zero, that is, the travel time from the lane change to the front of the left fleet is longer, and the right fleet is selected to change to the left fleet as the optimal strategy. As can be seen from fig. 4, the top view Z value and the positive and negative regions are basically connected together, which shows that the effect of the lane change strategy is gradually changed along with the change of the independent variable, which is consistent with the actual situation, that is, the result of the lane change strategy at both ends of a certain lane change critical point is opposite, and the boundary line where the Z value is equal to 0 in the diagram is the lane change critical point.
The specific lane-change position model was solved using MATLAB software, and the results are shown in fig. 5. It can be seen that the difference between the slopes of the speed change curves corresponding to the adjacent position points is not large, which means that the average driving speed of the vehicles gradually increases as the lane change position approaches the end point of the parking area; however, as the lane change position point approaches the lane change area terminal, the TTC value of lane change performed in the front 285m on a 300m long road is 3, which means that the lane change of the vehicle is absolutely safe at this time, as the lane change position moves backward, the lane change safety gradually decreases after 285m, there is an inflection point with a large slope at 290m, and the simulation result shows that the lane change performed at this point can take account of both the safety and the travel time, and the lane change safety is suddenly reduced due to the fact that the lane change is performed at a position closer to the lane change area terminal. The optimum lane change position point in this case is therefore 290m, i.e. 10m from the end of the lane change zone.
And after modeling and solving the coordination control strategy, setting an experiment to evaluate and analyze the slow blockage effect. Firstly, combining field investigation data and reasonable basic setting, and performing analog simulation on the current situation by using VISSIM software; numerical simulations were then performed using MATLAB software according to the simulation experiment flow of FIG. 6, based on the simulation logic of the coordinated control strategy.
Simulation software is used for simulating the research road section, and the simulation software comprises the following steps:
a. simulating the current road section by using classical simulation software VISSIM, and setting the basic scene in the simulation as follows by combining the characteristics of the VISSIM software:
a1, establishing a one-way two-lane road with the length of 460m, wherein the last 80m is a parking area and is divided into 10 parking spaces.
a2, setting the basic parking time of the vehicle to be 60s during simulation.
a3, thus only vehicles are generated, by default pedestrians and non-vehicles are driving on dedicated roads.
a4, generating 1080veh every 3600 simulation seconds by setting the traffic flow, and the speed range is 18km/h to 36 km/h.
b. Carrying out numerical simulation on the networked vehicle coordination control strategy by using MATLAB, wherein the basic scene and data are set as follows:
b1, road segment length setting. Setting the lengths of all path sections as follows: a car-following area 300m, a lane-changing area 80m and a parking area 80 m.
b2, vehicle ratio setting. The networked vehicles with a proportion of 10% to 100% were generated during the simulation and were tested separately. And when the proportion of the networked vehicles is lower than 25%, setting all the networked vehicles as the networked vehicles, and setting the rest of the networked vehicles as private vehicles.
b3, vehicle generation. The generated vehicles are divided into two types of hired vehicles and private vehicles, the ratio of the two types of hired vehicles to the private vehicles is 1:3, wherein the taxis are respectively pickup taxis and passing taxis, and the ratio of the two types of hired vehicles to the passing taxis is 2: 5; the private cars are divided into pickup and passing private cars, and the ratio of the pickup to passing private cars is set to be 1: 10. The positions of the various types of vehicles in the fleet are randomly generated.
b4, generating a rule by the vehicle. The initial speed of the upstream vehicle is randomly generated in an interval of 5m/s-10m/s, and 1080 vehicles are uniformly generated at time intervals in 3600 simulation seconds.
b5, parking area setting. The first half section of parking area supplies the networking vehicle to park, and the second half section supplies non-networking vehicle to park, and first half section length setting is according to the ratio setting of networking vehicle at the total vehicle that parks of berthing.
b6, vehicle driving model. The following model adopts an FVD following model, and the lane changing model adopts a lane changing model based on a safe distance.
b7, and data acquisition. And collecting the generated travel time T, vehicle driving safety index TIT and vehicle driving comfort index CI of all vehicles in the whole driving process from the road section starting point to the road section terminal point.
The simulation experiment flow based on the simulation logic of the coordination control strategy is shown in fig. 6.
And comparing and analyzing each index by combining the current situation obtained by simulation and the data after the blockage relieving strategy. The delay and the passing speed of the vehicle are shown in fig. 7 and 8, and the following conclusions can be obtained by comparing and analyzing the simulation results:
1. after the coordination control strategy is used, the driving delay can be reduced, and the driving speed is increased.
As can be seen from fig. 6 to 8 and 6 to 9, the driving delay is greatest when the ratio of the networked vehicles is 0, which is the current situation. And the driving delay is gradually reduced along with the increase of the proportion of the networked vehicles. The average vehicle passing speed is also the minimum when the proportion of the networked vehicles is 0, and the running speed is gradually increased along with the increase of the proportion of the networked vehicles. The coordination control strategy has obvious blockage relieving effect, can obviously improve the traffic speed of traffic flow and reduce the driving delay.
2. The coordinated control strategy begins to play a significant role when the proportion of networked vehicles reaches 30%.
When the proportion of networked vehicles in the traffic flow is less than 30%, the driving delay is reduced by a small amount compared with that before the coordinated control, and meanwhile, the average passing speed of the vehicles hardly changes, so that the coordinated control strategy plays a very little role; when the proportion of the networked vehicles is greater than 30%, the traffic delay is greatly reduced, and the average traffic speed of the vehicles is greatly increased, so that the coordinated control strategy fully exerts the effect of the coordinated control strategy, namely the coordinated control strategy has an obvious blockage relieving effect when the proportion of the networked vehicles reaches 30%.
3. The coordinated control strategy is more affected by the proportion of networked vehicles when the proportion of networked vehicles lies between 30% and 60%.
When the proportion of the networked vehicles is between 30% and 60%, the delay time of the vehicle is greatly reduced, and the traffic speed of the vehicle is greatly increased, which shows that the coordination control strategy plays an important role at the moment. Because the blocking-slowing effect of the coordinated control strategy can be well reflected by the fleet scale when the proportion of the networked vehicles is greater than 30%, the proportion of the networked vehicles is only changed in the proportion interval of the networked vehicles, which shows that the effect of the coordinated control strategy is greatly influenced by the proportion of the networked vehicles.
4. The best coordination control effect is achieved when the traffic flow is a fully networked vehicle.
When the proportion of the networked vehicles is 100%, the driving delay and the passing speed both reach the optimal values. At the moment, the driving delay time is 21s, which is reduced by 145s compared with the time before the coordination control, and the delay time is reduced by 87.3 percent on a year-by-year basis; the passing speed at the moment is 5.61m/s, is increased by 3.58m/s compared with the passing speed before the coordination control, and is improved by 176.4 percent in the same ratio. The data show that the coordination control effect is the best at this time, and the influence of the stop-and-go area on traffic flow passage is small.
The numerical simulation experiment results based on different proportions of networked vehicles are compared, and the comparison of the average collision time of the descending safety indicator vehicles in different proportions of networked vehicles is shown in fig. 9. Analysis of fig. 9 leads to the following conclusions:
1. traffic safety is gradually increasing as the proportion of networked vehicles increases.
When the proportion of the networked vehicles is minimum, the value of the traffic safety index TTC is minimum, which indicates that the traffic safety is the worst at the moment, and the safety index value is gradually increased and the traffic safety is gradually enhanced along with the increase of the proportion of the networked vehicles.
2. The traffic flow has reached a relatively safe state when the proportion of networked vehicles reaches 50%.
When the proportion of the networked vehicles is small, the safety level of the running of the partial vehicles is low, and when the proportion of the networked vehicles is 10%, the TTC value is only 1.92s, which means that the partial vehicles are in a dangerous state of collision within 1.9 s; when the proportion of networked vehicles reaches 50%, the TTC value is already above 2.5s, at which time the traffic flow has reached a relatively safe state, since normally 2.5s is already sufficient for the driver to react.
3. When the proportion of the networked vehicles reaches 70%, the coordination control strategy can completely control the traffic safety.
When the proportion of networked vehicles is less than 70%, the value of TTC will gradually increase as the proportion of networked vehicles increases, and when the proportion of networked vehicles reaches 70%, the value of time to collision TTC has stabilized at 3s (i.e. the time to collision threshold value defined herein), indicating that all vehicles in the traffic stream are in a safe state. At the moment, the coordination control strategy achieves the effect of completely coordinating and controlling the traffic flow safety.
For example, as shown in fig. 10, analysis of fig. 10 for ride comfort at different networked vehicle ratios leads to the following conclusions:
1. the driving comfort gradually becomes better as the proportion of networked vehicles becomes larger.
When the proportion of the networked vehicles is the minimum, the comfort index is in an extremely uncomfortable area, and the comfort gradually transits to the comfortable area along with the increase of the proportion of the networked vehicles, which shows that the riding experience is gradually comfortable along with the increase of the proportion of the networked vehicles.
2. The networked vehicle proportion of 50% is a demarcation point.
When the proportion of the networked vehicles in the traffic flow is lower, the comfort indexes of the vehicles are in uncomfortable areas; and when the proportion of networked vehicles is greater than 50%, the comfort index of the vehicle begins to approach from the less comfortable zone to the comfortable zone. Since less comfortable vehicles can already be defined in the comfortable category, a proportion of the networked vehicles of 50% is a cut-off point, with less comfort at proportions below the cut-off point and better comfort at proportions above the cut-off point.
3. When the proportion of the networked vehicles reaches 70%, the coordination control strategy can completely control the traffic comfort.
The experimental result shows that the riding comfort is gradually improved along with the improvement of the proportion of the networked vehicles, when the proportion of the networked vehicles reaches 70%, the value of the evaluation index CI is less than 0.315, and the riding comfort is in a comfort area, which shows that the comfort of traffic flow can be completely controlled through a coordinated control strategy at the moment.
Therefore, the overall experiment result shows that when the proportion of the networked vehicles is greater than 30%, the proposed coordination control strategy can effectively relieve the road congestion problem and can improve the traffic flow passing speed; in the comparison of different proportions of networked vehicles, the driving safety and comfort in traffic operation can be completely controlled by controlling the networked vehicles after the proportion of the networked vehicles reaches 70 percent.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (6)
1. A method for stopping and stopping road sections quickly based on coordinated control of networked vehicles is characterized by comprising the following steps:
s1, dividing the road section of 'stop and go' into four areas, which are respectively as follows according to the advancing direction of the vehicle: the system comprises a following area, a lane changing area, a parking area and a termination area, wherein the following area is used for driving the vehicle only in a following way, the other areas are used for driving the vehicle in a following way and changing the lane, and the parking area is provided with parking spaces for the vehicle to temporarily park; the road section allowing the vehicle to temporarily stop but needing to leave within a specified time range is the road section of the vehicle which stops immediately;
s2, based on the driving behaviors of vehicles in each region, combining the networking characteristics of the vehicles, and aiming at the following and lane changing behaviors of the vehicles on the road section, providing a coordination control strategy under the vehicle networking environment, wherein the coordination control strategy comprises a following coordination control strategy and a lane changing coordination control strategy;
s3, respectively considering the driving safety and the driving comfort, establishing a motorcade following coordination control model based on the state prediction of the head vehicle terminal, and considering the driving safety and the driving delay, establishing a motorcade lane change coordination control model;
s4, solving the motorcade following coordination control model by improving the traditional particle swarm algorithm, and solving the motorcade lane changing coordination control model by using simulation software to obtain the driving speed and position of each time point of the motorcade head car, and the lane changing position point and the lane changing time point of the motorcade head car during lane changing.
2. The method for alleviating congestion on an 'stop-and-go' road segment based on coordinated control of networked vehicles as claimed in claim 1, wherein in step S1, a one-way two-lane and in-road stopped 'stop-and-go' road segment is selected for research and division.
3. The method for road block mitigation based on networked vehicle coordination control, according to claim 1, wherein in step S2, the following assumptions are made for the proposal of the coordination control strategy:
a1, the motorcade already reaches a stable following state before a following area, and the traffic flow can be rapidly evacuated in a termination area without generating negative influence on the traffic flow in a parking area;
a2, in the process of coordination control, traffic flow takes the networked vehicles as the head, non-networked vehicles drive along with the states of a plurality of fleets driven by the networked vehicles, and the non-networked vehicles do not interfere with the formation of the fleets;
a3, in the process of Vehicle coordination control, the networked Vehicle exchanges information with the outside world through Vehicle-to-Vehicle or V2V and Vehicle-to-Infrastructure or V2I in real time communication, and the sent and acquired information comprises: the running speed, acceleration, position and running plan in a future time period of the own vehicle and the surrounding vehicles; the networked vehicles can accurately make and execute the coordination control strategy, the time for the system to calculate the coordination control strategy is not considered, and the influence of special conditions on the coordination control strategy is not considered;
a4, after entering the coordination control area, when the vehicle drives following, the non-networked vehicle drives following the front vehicle by following the Full Velocity Difference, namely, the FVD following model; the method comprises the following steps that the self vehicle always keeps running on the same lane under the condition of non-forced lane changing, lane changing is carried out by the vehicle according to the lane changing condition of a lane changing model selected in the previous section when the lane is changed, and lane changing is carried out by the vehicle in the unit of a vehicle fleet when the lane is changed;
a5, the whole process of traffic flow running only involves motor vehicles, the default is that pedestrians and non-motor vehicles strictly follow the traffic rules to walk on special roads, and the interference of the pedestrians and the non-motor vehicles to the main line traffic flow is not considered;
a6, vehicles normally change lanes and pass between a parking area and a lane changing area, and the situation of parking waiting caused by insufficient parking space can not occur;
b. and (3) following coordination control strategy: the following coordination control strategy is based on the following behavior and coordinates and controls the running states of the following vehicles of the motorcade by controlling the speed of the head vehicle-to-vehicle networking vehicle of the motorcade in real time;
firstly, the motorcade head networked vehicle communicates with a roadside detection unit through V2I in real time to send own vehicle information and acquire surrounding motorcade vehicle information, and communicates with the networked vehicle in real time through V2V to acquire information including acceleration, speed, position and coordination control strategy of the networked vehicle; after acquiring the position of a surrounding vehicle fleet, basic information of surrounding networked vehicles and a coordination control strategy, predicting the running condition of the front vehicle fleet, and then combining the current running condition of the networked vehicles with the current running condition of the self vehicle, the scale of non-networked vehicles in the current vehicle fleet, the running speed and the running characteristic, namely, following model information, with the overall optimal running stability and running safety of the vehicle fleet as a target, formulating the coordination control strategy considering the surrounding multi-vehicle information, outputting the real-time coordination speed of the networked vehicles, and communicating with the surrounding vehicles in real time to update the self vehicle information and the coordination control strategy;
c. and (3) a lane change coordination control strategy: the lane change coordination control strategy is based on the lane change behavior of the vehicles and controls the lane change behavior of the whole fleet of vehicles through controlling the lane change positions of the vehicles on the head vehicle networking of the fleet of vehicles, and the lane change coordination control strategy relates to the determination of a lane change position area and a specific lane change position;
c1, lane change position area determination: before changing lanes by taking a motorcade as a unit and outputting an optimal lane changing position, determining a lane changing position area of the motorcade by combining the running state and position of the motorcade and the running state and position of a motorcade on a left side, namely clearly determining that the motorcade changes lanes to the front or the back of an adjacent motorcade on the left side;
c2, determination of the specific lane change position: after the motorcade lane change position area is determined, the optimal specific lane change position of the motorcade is further determined, namely the lane change position point and the time point of the motorcade are determined.
4. The method for road section slow blocking based on networked vehicle coordination control and stop-and-go road section as claimed in claim 1, wherein in step S3, a fleet following coordination control model based on head end state prediction is proposed for the following coordination control strategy, as follows:
a. the terminal state prediction of the vehicle comprises terminal time prediction, terminal position prediction and terminal speed prediction; the time prediction of the fleet terminal in the lane change area and the following area is expressed as follows:
wherein:
is the terminal time of the current fleet of vehicles,
the terminal state of a front motorcade is shown, i represents the current motorcade, i-1 represents the front motorcade, and the front motorcade refers to the motorcade with the terminal time point in front;
the fleet terminal time forecast at the parking area is expressed as:
wherein: t is tlimitA vehicle parking time limit representing a parking area; t is tstop(n) represents the parked time of the vehicle parked in the nth parking space of the parking area, tstop(n)≤tlimit(ii) a k is the additional limit time for the vehicle to stop; k is the maximum additional limit time for the vehicle to stop;
the starting time of the vehicle at the nth parking space in the parking area under the restriction of the front motorcade position limit is obtained; t is tcChanging lane time for a single vehicle, namely starting to change lane from a parking space to finishing changing lane;
when the position of a head vehicle terminal is predicted, the terminal positions of the fleet tails at the terminal time points of the fleet tails around are considered, the fleet tails before the terminal time points are non-networked vehicles, the positions of the fleet tails are determined by combining a following model and the head positions of the fleet, and then the positions of the self-vehicle terminals are determined by combining a safe following distance; the prediction of the terminal position of the vehicle running in normal following with the following area and the lane changing area is expressed as follows:
wherein:
the terminal position of the head car of the motorcade of the terminal time point is obtained;
the terminal position of the head car of the fleet ahead of the terminal time point is obtained;
to be compared with the terminal speed of the fleet
The related vehicle following distance is determined by the Full Velocity Difference, namely, the related parameters of the FVD following model; l is the vehicle length; n is a radical ofi-1The scale of the motorcade is motorcade i-1;
the vehicle terminal position prediction of the parking area is expressed as follows:
wherein:
speed of vehicle travel for completion of lane change
The associated following distance;
when the speed of the first vehicle terminal is predicted, the terminal speed of the surrounding vehicle fleet at the terminal time point is obtained first, and the terminal speed of the current vehicle fleet at the first vehicle fleet is set as the running speed of the vehicle fleet before the terminal time point, namely:
wherein:
the running speed of the head car of the self motorcade at the terminal time point,
the driving speed of the head car of the motorcade before the terminal time point is obtained;
b. after the terminal state of the vehicle is predicted, the following vehicle fleet following coordination control model is established by considering the safety cost and the driving comfort in the driving process of the vehicle fleet:
in the formula: m is the total number of the motorcade vehicles; t is a certain simulation moment; t is the total number of simulation step lengths; delta t is the simulation time step length; TTCi(t) is the collision time of the ith vehicle in the fleet at the time t, which represents the time from keeping the current speed to the collision of the two vehicles when the speed of the rear vehicle is higher than that of the front vehicle; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; a isiAnd (t) is the acceleration value of the ith vehicle at the time t.
5. The method for blocking road sections slowly when stopping and walking based on the coordinated control of networked vehicles as claimed in claim 1, wherein in step S3, a fleet lane change coordinated control model is proposed for the lane change coordinated control strategy, which includes a lane change location area model and a specific lane change location model, as follows:
when the optimal lane change position area is determined, the influence of different lane change position areas on the motorcade passing time is considered, and the following lane change position area model is established:
wherein: n is the number of the vehicles in the self-motorcade, the left-side motorcade and the subsequent motorcade in the road section, and T (i) is the passing time of the ith vehicle, namely the lane changing strategy with the minimum total passing time is the optimal lane changing strategy;
on the basis of determining the optimal lane changing position area, the following specific lane changing position model is established by taking the fleet lane changing safety and the travel time of the vehicle on the current road section as optimization targets:
wherein:
calculated value of lane change safety for a fleet of vehicles changing lanes, NcFor the number of vehicles that actually need to change lanes,
in order to specify the time for the lane change termination,
tcchanging the lane for a single vehicle, namely, starting to change the lane from a parking space to finishing changing the lane, wherein t is a certain simulation moment, and delta t is a simulation time step length; TTC*The threshold value of TTC collision time is defined as unsafe state when the threshold value is lower than the threshold value, and the safe state when the threshold value is higher than the threshold value; TTCj(t) is the collision time of the jth vehicle in the lane-changing fleet at the time t;
performing initial time for the specific lane changing behavior, namely, determining a lane changing time point corresponding to the optimal position obtained by the current lane changing position decision;
selecting delay influence on traffic flow for the passing time of all vehicles in the current road section, namely the lane change position; n is a radical oftotalT (e) is the passing time of the e-th vehicle.
6. The method for road section blocking mitigation based on networked vehicle coordination control, according to claim 1, characterized in that in step S4, the fleet following coordination control model based on head-end state prediction is solved by simulation using improved particle swarm optimization: on the basis that the traditional particle swarm algorithm can only solve one optimization target, when the two optimization targets are considered, the speed updating is divided into two stages, the first stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the safety in the running process of the fleet is optimal, the second stage considers the influence of the self-history optimal position and the group-history optimal position on the self speed when the driving comfort is optimal, the steps are repeated in such a circulating way, the values of the two optimization targets when the two optimization targets are optimal are repeatedly considered, and finally, the bird groups can be gathered at the positions which are equal to the two food sources to obtain the speed and the position of each time point of the networked vehicles;
using MATLAB software, solving the lane change position area model by using an exhaustion method, enumerating all possible conditions during solving, solving different conditions, and obtaining the optimal lane change position area by taking the travel time caused by different lane change positions as a judgment basis;
after the optimal lane changing position area is determined, using MATLAB software to solve a specific lane changing position model under the condition of setting a motorcade running state, under the condition that all conditions are known, carrying out simulation operation on different lane changing positions to obtain the lane changing safety and the passing efficiency at the moment, obtaining the lane changing time point and the position point of a motorcade head vehicle lane changing according to the result of the motorcade lane changing coordination control model, and obtaining the speed and the position of each time point when the motorcade head vehicle, namely the networked vehicle, runs through a road section which is stopped and runs as soon as possible, and the lane changing time point and the position point when the lane changing is carried out according to the calculation results of the motorcade following coordination control model and the motorcade lane changing coordination control model.
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| CN112233413A (en) * | 2020-07-20 | 2021-01-15 | 北方工业大学 | Multilane space-time trajectory optimization method for intelligent networked vehicle |
| CN112437412A (en) * | 2020-10-30 | 2021-03-02 | 北方工业大学 | Mixed-driving vehicle formation control method based on vehicle-road cooperation |
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| CN115331441A (en) * | 2022-08-09 | 2022-11-11 | 贵阳信息技术研究院 | Narrow curve passage control system and method based on vehicle-road cooperation |
| CN116311867A (en) * | 2023-01-18 | 2023-06-23 | 东南大学 | Multi-vehicle collaborative lane changing method based on intelligent network vehicle linkage queue control |
| CN116311867B (en) * | 2023-01-18 | 2024-03-19 | 东南大学 | Multi-vehicle collaborative lane changing method based on intelligent network vehicle linkage queue control |
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