CN114783186A - Optimal layout method and system for road risk early warning controller in intelligent networking environment - Google Patents

Optimal layout method and system for road risk early warning controller in intelligent networking environment Download PDF

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CN114783186A
CN114783186A CN202210450766.4A CN202210450766A CN114783186A CN 114783186 A CN114783186 A CN 114783186A CN 202210450766 A CN202210450766 A CN 202210450766A CN 114783186 A CN114783186 A CN 114783186A
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vehicles
road
automatic driving
road section
matrix
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CN114783186B (en
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郭宇奇
朱丽丽
李唯琛
高剑
李婉君
张金金
牛树云
李茜瑶
贺瑞华
车晓琳
黄烨然
卢立阳
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Research Institute of Highway Ministry of Transport
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
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    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G—PHYSICS
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Abstract

The invention discloses an optimized layout method and system of a road risk early warning controller in an intelligent network connection environment, which comprises the following steps: dividing a road network into n cells, and establishing a mixed traffic flow model containing the n cells; acquiring the total number of vehicles and the number of automatic driving vehicles in each road section, and calculating the mixing rate of the automatic driving vehicles; calculating a system matrix set A under different mixing rates of the automatic driving vehicles based on the mixed traffic flow model and the mixing rates of the automatic driving vehicles; designing an input matrix B based on a mixed traffic flow modeli(ii) a Calculating Rank (A) by using the Rank criterion of system controllabilitym,Bi) Whether the value of (d) is n; if yes, the input matrix B is reservedi(ii) a Screening input matrixes meeting all mixing rates, and selecting the input matrix with the best layout position and the least quantity as a control matrix; and (4) laying out the controllers based on the control matrix. The invention can be realized in a complexAnd under a mixed traffic flow scene, the safe and efficient operation of a road network system is guaranteed.

Description

Optimized layout method and system for road risk early warning controller in intelligent networking environment
Technical Field
The invention relates to the technical field of mixed traffic flow safety risk early warning and control, in particular to an optimized layout method and system of a road risk early warning controller in an intelligent network connection environment.
Background
In the continuous development of the automatic driving technology, a scene that a traditional vehicle and automatic driving vehicles of different grades are mixed can be generated inevitably, some driving rules of the automatic driving vehicles are different from the driving habits of human drivers, and the difference often becomes the reason of collision between the automatic driving vehicles and manual driving vehicles, especially, intelligent internet automobiles with the technology maturity yet to be improved in road testing are in mixed intelligent interactive operation with the manual driving vehicles, so that the oscillation of traffic flow is easily caused, and the potential safety risk hazard under the scene of mixed traffic flow is increased.
The existing effective solution is to arrange a safety risk early warning controller at the roadside and adopt a timely early warning and control strategy for high-risk driving behaviors; however, in the case of such a large-scale road network, how to set the minimum number of risk early warning controllers on a suitable road section to achieve the control effect and save the input cost is a problem to be solved at present.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an optimized layout method and system for a road risk early warning controller in an intelligent network connection environment, which ensure that a road network system can operate safely and efficiently in a complex mixed traffic flow scene.
The invention discloses an optimized layout method of a road risk early warning controller in an intelligent network connection environment, which comprises the following steps:
dividing a road network into n road sections, each road section is called a cell, and the divided cells are sequentially marked with serial numbers;
taking the traffic flow density of the cells as a state variable, establishing a mixed traffic flow model comprising n cells:
x(t+1)＝Aσ(t)x(t)+Bσ(t)u(t)+Fσ(t)
wherein x is [ x ]1,…,xn]TA traffic flow density vector representing a road network, u ═ u [ u ]1,…,uk]TRepresenting the traffic demand of a road network, wherein A represents a system matrix, B represents an input matrix, and F represents an affine vector;
acquiring the total number of vehicles and the number of automatic driving vehicles in each road section, and calculating the mixing rate of the automatic driving vehicles based on the total number of the vehicles and the number of the automatic driving vehicles;
based on the mixed traffic flow model and the mixing rate of the automatic driving vehicles, calculating a system matrix set A under different mixing rates of the automatic driving vehicles:
A＝{A1,A2,…,Am}
in the formula, AmThe system matrix is a system matrix of the mixing rate of the mth automatic driving vehicle, and the system matrix is n multiplied by n;
designing an input matrix B based on a mixed traffic flow modeliThe input matrix is nxk, k is more than or equal to 1 and less than or equal to n:
Figure BDA0003617131910000021
calculating Rank (A) by using the Rank criterion of system controllabilitym,Bi) Whether the value of (d) is n; if yes, the input matrix B is reservediIf not, deleting the input matrix Bi；
Screening input matrixes meeting the condition of the mixing rate of all automatic driving vehicles, and selecting the input matrix with the best layout position and the least quantity from the screened input matrixes as a control matrix;
and arranging the controllers based on the control matrix.
As a further improvement of the present invention, the road network segmentation method includes:
dividing the road network into n road sections according to the number and the positions of the entrance ramps and the exit ramps in the road network, the change positions of the number of the lanes and the change positions of the curvature radius of the road.
As a further improvement of the present invention, the acquiring the total number of vehicles and the number of automatically driven vehicles in each road section, and calculating the mixing rate of the automatically driven vehicles based on the total number of vehicles and the number of automatically driven vehicles includes:
obtaining the number N of vehicles entering each road section based on a video detectorVideo drive-inAnd number of vehicles exiting NVideo outboundAnd calculating the number N of vehicles on the road sectionVideo(ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detectorMicrowave drive-inAnd number of vehicles exiting NMicrowave coming outAnd calculating the number N of vehicles on the road sectionMicrowave oven；
Will NVideoIn NMicrowave ovenOne or more results obtained after fusion
Figure BDA0003617131910000022
The total number of vehicles N as the road segment;
obtaining the number M of the driving automatic driving vehicles of each road section based on the network connection automatic driving vehicle detectorUpstream drive-inAnd number of outgoing autonomous vehicles MDownstream run-outAnd calculating the number M of the automatically driven vehicles in the road section1(ii) a And/or based on the vehicle information of the automatic driving vehicles received by the edge calculation unit in real time, and calculating the number M of the automatic driving vehicles in the road section2(ii) a And/or, based on real-time vehicle information transfer between the autonomous vehicles, calculating the number of autonomous vehicles in the current road segment and in the current road segment near any autonomous vehicle, and calculating the number of autonomous vehicles M in the road segment3；
Will M1、M2And M3One or more results obtained after fusion
Figure BDA0003617131910000038
The number of autonomous vehicles M as the road section;
and calculating the mixing rate of the automatic driving vehicles on the current road section based on the total number of the vehicles on the road section and the number of the automatic driving vehicles.
As a further improvement of the present invention,
Nvideo＝NRoad section+NVideo drive-in-NVideo launch
NMicrowave oven＝NRoad section+NMicrowave drive-in-NMicrowave coming out
Figure BDA0003617131910000031
M1＝MRoad section+MUpstream drive-in-MDownstream exit
Figure BDA0003617131910000032
In the formula, NRoad sectionFor all vehicles on the road section in the last sampling period, alpha1、α2As a weight, MRoad sectionFor all the number of autonomous vehicles, lambda, of the road section in the last sampling period1、λ2、λ3Is a weight value.
As a further improvement of the inventionThen, the weight value α1、α2The determination method comprises the following steps:
performing cyclic calculation in each sampling period to obtain a weight combination pair { alpha ] corresponding to the minimum value of the standard deviation1，α2As the final weight:
Figure BDA0003617131910000033
in the formula (I), the compound is shown in the specification,
Figure BDA0003617131910000034
Figure BDA0003617131910000035
Figure BDA0003617131910000036
as a further improvement of the invention, the weight value lambda1、λ2、λ3The method for determining (1) comprises the following steps:
performing cyclic calculation in each sampling period to obtain weight combination pair { lambda ] corresponding to the minimum value of standard deviation1，λ2，λ3As the final weight:
Figure BDA0003617131910000037
in the formula (I), the compound is shown in the specification,
Figure BDA0003617131910000041
Figure BDA0003617131910000042
Figure BDA0003617131910000043
Figure BDA0003617131910000044
as a further improvement of the invention, the mixing rate phi of the automatic driving vehicle in the current road section is as follows:
Figure BDA0003617131910000045
as a further improvement of the invention, the total number N of input matrices designed based on the mixed traffic flow modelInput matrixComprises the following steps:
Figure BDA0003617131910000046
as a further improvement of the present invention, the method for determining the control matrix includes:
screening input matrixes and positions of corresponding controllers under the condition that all the automatic driving vehicle mixing rates are met;
judging whether the same number of controllers exist or not;
if the input matrix exists, selecting the input matrix with the best layout position as a control matrix;
and if the input matrix does not exist, selecting the input matrix with the least quantity as the control matrix.
The invention also discloses an optimized layout system of the road risk early warning controller in the intelligent networking environment, which is used for realizing the optimized layout method of the road risk early warning controller in the intelligent networking environment.
Compared with the prior art, the invention has the beneficial effects that:
the invention can realize the optimized layout of the road risk early warning controller, ensure the safe and efficient operation of a road network system under a complex mixed traffic flow scene, and timely send out a control instruction at high risk to directly control the network automatic driving vehicle and perform induced control on the manual driving vehicle, thereby reducing the safety risk of the road to the maximum extent.
Drawings
FIG. 1 is a schematic diagram of a traffic network cell partition according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a road vehicle information collection system according to an embodiment of the present disclosure;
fig. 3 is a flowchart of an optimized layout method of a road risk early warning controller in an intelligent network-connected environment according to an embodiment of the present invention.
In the figure:
1. a gantry; 2. a video detector; 3. a networked autonomous vehicle detector; 4. a microwave detector; 5. an edge calculation unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the following drawings:
aiming at the safety risks in the mixed traffic scene of automatic driving vehicles and manual driving vehicles in the intelligent network connection environment, a risk early warning controller needs to be arranged at a proper position of a road network, so that the safe and efficient operation of a road network system is ensured, and the operation risk of mixed traffic is reduced; therefore, the invention provides an optimal layout method and system of road risk early warning controllers in an intelligent network connection environment, which treats a road network as a complex traffic network system and models the road network into a mixed traffic flow model, adopts the system controllability theory to perform optimal layout of the controllers, ensures that the controllers with the best positions and the least quantity are laid on the premise of meeting the system controllability, and realizes early warning prevention and control of mixed traffic flow safety risks.
Because the automatic driving vehicles in the mixed traffic flow scene have the intelligent network connection function, the real-time communication between vehicles (based on V2V) and between the vehicles and the control center (based on V2I) can be realized, and the information of the positions, the speeds, the accelerations and the like of the vehicles can be transmitted; the manually driven vehicles do not have an intelligent networking function, and the information such as the positions and the numbers of the vehicles can be detected and obtained only through a sensor on the road side; the mixing of the automatic driving vehicles causes the change of the traffic flow transmission rule, and the basic parameters of the mixed traffic flow can be changed along with the difference of the mixing rate of the automatic driving vehicles.
Therefore, before the risk early warning controller is arranged, the mixed traffic flow modeling is required to be realized, and the method comprises the following steps:
step 11, cellular division is carried out on the road network:
dividing a road network into n road sections (n is a positive integer greater than 1) according to the number and the positions of entrance ramps and exit ramps in the road network, the change positions of the number of lanes and the change positions of the curvature radius of the road, wherein each road section is called a cell, and the serial numbers of the divided cells are sequentially calibrated, so that the system controllability research is facilitated, as shown in fig. 1;
step 12, mixed traffic flow modeling:
with the traffic flow density of the cells as a state variable, a mixed traffic flow model comprising n cells is established for the conditions of different mixing rates of the automatic driving vehicles:
x(t+1)＝Aσ(t)x(t)+Bσ(t)u(t)+Fσ(t)
wherein x is [ x ]1,…,xn]TA traffic flow density vector representing a road network, u ═ u [ u ]1,…,uk]TThe method comprises the steps of representing the traffic demand of a road network, wherein n is the number of cells, k corresponds to the number of cells provided with a controller, t is a sampling period, and the optimal sampling period can be 5min in actual use; a. theσ(t)System matrix representing different pairs of cellular combination modalities at different mixing rates, different autopilotsThe system matrixes corresponding to the mixing rates of the vehicles are different; b isσ(t)The method comprises the steps that an input matrix or a control matrix corresponding to different cellular combination modes with different mixing rates is represented, and corresponding control matrices need to be designed to meet system controllability according to different mixing rates of automatic driving vehicles (namely different system matrices); fσ(t)An affine vector is represented.
As shown in fig. 3, after the mixed traffic flow modeling is completed, the method for optimally arranging the road risk early warning controllers in the intelligent network connection environment includes:
step 21, acquiring the total number of vehicles and the number of automatically driven vehicles in each road section, and calculating the mixing rate of the automatically driven vehicles based on the total number of vehicles and the number of the automatically driven vehicles;
the specific method for calculating the mixing ratio includes:
step 211, constructing an automatic driving vehicle mixing rate calculation system shown in fig. 2; wherein the content of the first and second substances,
the method comprises the steps that portal frames 1 or other mounting frames are arranged on the upstream boundary and the downstream boundary of each road section, and video detectors 2 and/or internet automatic driving vehicle detectors 3 are mounted on the portal frames 1 on the upstream boundary and the downstream boundary of each road section, namely, all vehicles entering the road section are collected on the basis of the upstream video detectors, all vehicles leaving the road section are collected on the basis of the downstream video detectors, all automatic driving vehicles entering the road section are collected on the basis of the upstream internet automatic driving vehicle detectors, and all automatic driving vehicles leaving the road section are collected on the basis of the downstream internet automatic driving vehicle detectors; meanwhile, the microwave detectors 4 can be installed at the upstream boundary and the downstream boundary of each road section, namely, all the vehicles entering the road section are collected based on the upstream microwave detector, and all the vehicles leaving the road section are collected based on the downstream microwave detector; an edge calculating unit 5 is arranged on each road section, on one hand, the road section edge calculating unit 5 can be in real-time communication with the video detector 2 and the microwave detector 4 to obtain vehicle information collected by the video detector 2 and the microwave detector 4, and all the vehicle number of the current road section is calculated based on an embedded vehicle fusion algorithm; on the other hand, the vehicle information acquisition module can be communicated with the internet automatic driving vehicle detector 3 in real time to acquire the vehicle information acquired by the internet automatic driving vehicle detector 3; the vehicle can also be communicated with the internet automatic driving vehicle in real time based on V2I to obtain information such as speed, position, acceleration, driving direction and the like; meanwhile, real-time communication can be carried out between the networked automatic driving vehicles based on V2V, the information of the position, the speed, the acceleration, the driving direction and the like of the vehicles can be mutually transmitted, and the information is transmitted to the road section edge calculation unit; the road section edge calculation unit 5 calculates the number of the internet automatically driven vehicles of the current road section based on the embedded internet automatically driven vehicle fusion algorithm.
Meanwhile, the invention can also carry on two-stage division according to road and highway section, set up the distributed logic control architecture; the highway is provided with a road control center and a plurality of road section edge calculation units, the road section edge calculation units perform statistical analysis on all vehicles running in each cell of a jurisdiction area and perform real-time information transmission with networked automatic driving vehicles, and adjacent road section edge calculation units perform real-time information transmission and synchronously upload information to the road control center.
Step 212, acquiring the number N of the vehicles entering each road section based on the video detectorVideo drive-inAnd number of vehicles exiting NVideo outboundAnd calculating the number N of vehicles on the road sectionVideo(ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detectorMicrowave drive-inAnd number of vehicles exiting NMicrowave coming outAnd calculating the number N of vehicles on the road sectionMicrowave oven(ii) a Wherein the content of the first and second substances,
Nvideo＝NRoad section+NVideo drive-in-NVideo launch
NMicrowave oven＝NRoad section+NMicrowave drive-in-NMicrowave coming out
In the formula, NRoad sectionThe number of all vehicles in the road section in the last sampling period is;
step 213, adding NVideoIn NMicrowave ovenOne or more results obtained after fusion
Figure BDA0003617131910000074
The total number of vehicles N as the road section; wherein the content of the first and second substances,
Figure BDA0003617131910000075
in the formula, alpha1、α2Is the weight;
furthermore, the design principle and weight α of the fusion process of the present invention1、α2The determination method comprises the following steps:
under normal conditions, the video detector has higher inspection precision, but severe weather such as severe haze weather can affect the precision of the video detector, and the microwave detector is less affected by the visibility of weather; therefore, in order to reduce the influence of weather on the inspection result to the maximum extent, a fusion algorithm is adopted to calculate the number of vehicles;
the weight values of the video detection value and the microwave detection value are assumed to be alpha respectively1And alpha2Thus, a weight combination pair { α } can be calculated1，α2And f, the weighted summation result of the number of vehicles in the road section is as follows:
Figure BDA0003617131910000071
respectively calculating video detection results NVideoAnd microwave detection result NMicrowave ovenAnd
Figure BDA0003617131910000072
the difference of (a) to (b), namely:
Figure BDA0003617131910000073
Figure BDA0003617131910000081
performing cyclic calculation in each sampling period to obtainWeight combination pair { alpha ] corresponding to minimum standard deviation value1，α2As the final weight:
Figure BDA0003617131910000082
according to the obtained weight value alpha1And alpha2Calculating the number of all vehicles in the road section
Figure BDA0003617131910000089
Step 214, acquiring the number M of the driven-in automatic driving vehicles of each road section based on the network connection automatic driving vehicle detectorUpstream drive-inAnd number of outgoing autonomous vehicles MDownstream run-outAnd calculating the number M of the automatically driven vehicles in the road section1(ii) a And/or based on the vehicle information of the automatic driving vehicles received by the edge calculation unit in real time, and calculating the number M of the automatic driving vehicles in the road section2(ii) a And/or, based on real-time vehicle information transfer between the autonomous vehicles, calculating the number of autonomous vehicles in the current road section and near any autonomous vehicle in the current road section, and calculating the number M of autonomous vehicles in the road section3(ii) a Wherein the content of the first and second substances,
M1＝Mroad section+MUpstream drive-in-MDownstream run-out
In the formula, MRoad sectionThe number of all automatic driving vehicles on the road section in the last sampling period is counted;
step 215, adding M1、M2And M3One or more results obtained after fusion
Figure BDA00036171319100000810
The number of autonomous vehicles M as the road segment; wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003617131910000083
in the formula of lambda1、λ2、λ3Is the weight;
further, λ1、λ2、λ3The determination method comprises the following steps:
the weights of the number of the networked automatic driving vehicles in the current road section obtained by the three methods are respectively assumed to be lambda1、λ2And λ3Thus, a weight combination pair { λ } can be calculated1，λ2，λ3And the weighted summation result of the number of the networked automatic driving vehicles in the road section is as follows:
Figure BDA0003617131910000084
respectively calculating three recognition results M1、M2And M3And with
Figure BDA0003617131910000085
The difference of (a) is:
Figure BDA0003617131910000086
Figure BDA0003617131910000087
Figure BDA0003617131910000088
performing cyclic calculation in each sampling period to obtain weight combination pair { lambda ] corresponding to the minimum value of standard deviation1，λ2，λ3As the final weight:
Figure BDA0003617131910000091
according to the obtained weight value lambda1、λ2And λ3Calculating the current road sectionIn-line autonomous driving vehicle number
Figure BDA0003617131910000092
Furthermore, the invention can also convert lambda into1、λ2And λ3One of the weights is 0 to realize M1、M2And M3And fusing any 2 results, and obtaining the final automatic driving vehicle number M by using the fusion result.
Step 216, based on the total number of vehicles in the road section and the number of automatically driven vehicles, calculating a mixing rate phi of the automatically driven vehicles in the current road section as:
Figure BDA0003617131910000093
step 22, calculating a system matrix set A under different blending rates of the automatic driving vehicles based on the mixed traffic flow model and the blending rates of the automatic driving vehicles:
A＝{A1,A2,…,Am}
in the formula, AmThe system matrix is a system matrix of the mixing rate of the mth automatic driving vehicle, and the system matrix is n multiplied by n; m can be reasonably valued according to requirements, and one embodiment is that m is 10, namely the mixing rate is 10%, 20%, 100% and.
Step 23, designing an input matrix B based on the mixed traffic flow modeliThe input matrix is nxk, k is more than or equal to 1 and less than or equal to n:
Figure BDA0003617131910000094
namely, a controller is arranged in the ith cell, and the corresponding element of the corresponding control matrix is 1; otherwise, the corresponding element is 0;
the total number of input matrices N finally obtainedInput matrixComprises the following steps:
Figure BDA0003617131910000095
step 24, corresponding system matrix A for the m-th type of automatic driving vehicle mixing ratemCalculating Rank (A) by using the Rank criterion of system controllabilitym,Bi) Whether the value of (d) is n;
(1) if Rank (A)m,Bi) If n, the input matrix satisfies the system controllability, and finally all the input matrices B satisfying the system controllability are reservedi；
(2) If Rank (A)m,Bi) If n, the input matrix is not satisfactory to be system controllable, and all the input matrices B which are not satisfactory to be system controllable are deletedi。
Step 25, screening input matrixes meeting the conditions of the mixing rate of all the automatic driving vehicles, and selecting the input matrix with the best layout position and the least quantity from the screened input matrixes as a control matrix;
the method specifically comprises the following steps:
step 251, screening input matrixes and positions of corresponding controllers under the condition that the mixing rates of all automatic driving vehicles are met;
step 252, judging whether the same number of controllers exist;
step 253, if the control matrix exists, selecting the input matrix with the best layout position as the control matrix; the optimal layout position refers to an input matrix with relatively uniform position distribution;
and 254, if the input matrix does not exist, selecting the input matrix with the least quantity as the control matrix.
And 26, laying controllers based on the control matrix, wherein the controllers mainly realize timely sending of control instructions in high risk, directly control the internet automatic driving vehicle, and perform induction control on the manual driving vehicle, so that the safety risk of the road is reduced to the maximum extent.
The invention provides an optimized layout system of a road risk early warning controller in an intelligent networking environment, which can realize the optimized layout method of the road risk early warning controller in the intelligent networking environment based on the system and the control center shown in figure 2.
The embodiment is as follows:
a section of highway with 4 lanes, the highest speed limit of 120km/h and the length of 3km is taken as an example for specific explanation:
and averagely dividing the selected road section into 15 cells, wherein the length of each cell is 200 meters, and analyzing the corresponding system controllability under the conditions of different automatic driving vehicle mixing rates.
(1) The mixing rate of the automatic driving vehicles is 0, namely all vehicles are manually driven vehicles, and a corresponding system matrix is calculated according to the established mixed traffic flow model as follows:
Figure BDA0003617131910000111
an input matrix set B can be obtained based on the design method of the input matrix, and further according to the system controllability criterion, all the input matrices B meeting the system controllability can be calculated as follows:
Figure BDA0003617131910000112
according to the calculation result, at least the controllers are arranged on the cell 1 and the cell 15, that is, the system is controllable, and whether the controllers are arranged on other cells does not influence the controllability of the system.
(2) The mix rates of the automatically-driven vehicles are respectively 10%, 20%, 30%, 40%, 50%, 60%, 70% and 80%, and corresponding system matrixes are respectively calculated according to the established mixed traffic flow model as follows:
Figure BDA0003617131910000113
Figure BDA0003617131910000121
Figure BDA0003617131910000122
Figure BDA0003617131910000123
Figure BDA0003617131910000131
Figure BDA0003617131910000132
Figure BDA0003617131910000133
Figure BDA0003617131910000141
based on the design method of the input matrix and the system controllability criterion, the same input matrix B is obtained when the system controllability is satisfied under the condition that the blending rate of the automatically-driven vehicle is obtained by calculation, and the matrix B is as follows as in the case that the blending rate of the automatically-driven vehicle is 0:
Figure BDA0003617131910000142
therefore, in these cases, at least the controllers are arranged on the cells 1 and 15, that is, the system is controllable, and the controllability of the system is not affected by the arrangement of other cells.
(3) The mix-in rate of the autonomous vehicle is 90%, and a system matrix is calculated as follows:
Figure BDA0003617131910000143
based on the design method of the input matrix and the system controllability criterion, the input matrix B meeting the system controllability is obtained by calculation as follows:
Figure BDA0003617131910000151
therefore, in this case, at least the controllers are disposed on the unit cell 1, the unit cell 14, and the unit cell 15, so that the system is controllable, and the controllability of the system is not affected by the presence or absence of the other unit cells.
(4) The mixing rate of the automatic driving vehicles is 100%, namely all the vehicles are automatic driving vehicles, and a system matrix is calculated as follows:
Figure BDA0003617131910000152
based on the design method of the input matrix and the system controllability criterion, the input matrix B meeting the system controllability is obtained by calculation as follows:
Figure BDA0003617131910000153
therefore, in this case, at least the arrangement of the controllers on the cell 1, the cell 13, the cell 14 and the cell 15 can satisfy the requirement that the system is controllable, and the arrangement of other cells does not affect the controllability of the system.
In summary, in order to make the system controllable at all mixing ratios, that is, the controller is arranged to control the whole system at different mixing ratios, the controller is arranged at least at cell 1, cell 13, cell 14 and cell 15 according to the above calculation and analysis results.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An optimized layout method of a road risk early warning controller in an intelligent network environment is characterized by comprising the following steps:
dividing a road network into n road sections, each road section is called a cell, and the serial numbers of the divided cells are calibrated in sequence;
taking the traffic flow density of the cells as a state variable, establishing a mixed traffic flow model comprising n cells:
x(t+1)＝Aσ(t)x(t)+Bσ(t)u(t)+Fσ(t)
wherein x is [ x ]1，…，xn]TA traffic flow density vector representing a road network, u ═ u [ u ]1，…，uk]TThe method comprises the steps of representing traffic demands of a road network, wherein A represents a system matrix, B represents an input matrix, and F represents an affine vector;
acquiring the total number of vehicles and the number of automatic driving vehicles in each road section, and calculating the mixing rate of the automatic driving vehicles based on the total number of the vehicles and the number of the automatic driving vehicles;
based on the mixed traffic flow model and the mixing rate of the automatic driving vehicles, calculating a system matrix set A under different mixing rates of the automatic driving vehicles:
A＝{A1，A2，…，Am}
in the formula, AmThe system matrix is a system matrix of the mixing rate of the mth automatic driving vehicle, and the system matrix is n multiplied by n;
designing an input matrix B based on a mixed traffic flow modeliThe input matrix is nxk, k is more than or equal to 1 and less than or equal to n:
Figure FDA0003617131900000011
calculating Rank (A) by using the Rank criterion of system controllabilitym，Bi) Whether the value of (d) is n; if yes, input matrix B is reservediIf not, deleting the input matrix Bi；
Screening input matrixes meeting the condition of the mixing rate of all automatic driving vehicles, and selecting the input matrix with the best layout position and the least quantity from the screened input matrixes as a control matrix;
and arranging the controllers based on the control matrix.
2. The optimal layout method of the road risk early warning controllers in the intelligent network connection environment as claimed in claim 1, wherein the road network partitioning method comprises:
dividing the road network into n road sections according to the number and the positions of the entrance ramps and the exit ramps in the road network, the change positions of the number of the lanes and the change positions of the curvature radius of the road.
3. The optimal layout method of road risk early warning controllers in an intelligent network environment as claimed in claim 1, wherein the obtaining of the total number of vehicles and the number of automatically driven vehicles in each road section and calculating the mixing rate of the automatically driven vehicles based on the total number of vehicles and the number of automatically driven vehicles comprises:
obtaining the number N of vehicles entering each road section based on a video detectorVideo drive-inAnd number of vehicles exiting NVideo outboundAnd calculating the number N of vehicles on the road sectionVideo(ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detectorMicrowave drive-inAnd number of vehicles coming out NMicrowave coming outAnd calculating the number N of vehicles on the road sectionMicrowave oven；
N is to beVideoIn NMicrowave ovenOne or more results obtained after fusion
Figure FDA0003617131900000021
The total number of vehicles N as the road section;
obtaining the number M of the driving automatic driving vehicles of each road section based on the network connection automatic driving vehicle detectorUpstream drive-inAnd number of outgoing autonomous vehicles MDownstream run-outAnd calculating the self in the road sectionNumber of moving vehicles M1(ii) a And/or based on the vehicle information of the automatic driving vehicles received by the edge calculation unit in real time, and calculating the number M of the automatic driving vehicles in the road section2(ii) a And/or, based on real-time vehicle information transfer between the autonomous vehicles, calculating the number of autonomous vehicles in the current road segment and in the current road segment near any autonomous vehicle, and calculating the number of autonomous vehicles M in the road segment3；
Will M1、M2And M3One or more results obtained after fusion
Figure FDA0003617131900000022
The number of autonomous vehicles M as the road segment;
and calculating the mixing rate of the automatic driving vehicles on the current road section based on the total number of the vehicles on the road section and the number of the automatic driving vehicles.
4. The optimized layout method of road risk early warning controllers in intelligent network connection environment as claimed in claim 3,
Nvideo＝NRoad section+NVideo drive-in-NVideo outbound
NMicrowave oven＝NRoad section+NMicrowave drive-in-NMicrowave drive-out
Figure FDA0003617131900000023
M1＝MRoad section+MUpstream drive-in-MDownstream exit
Figure FDA0003617131900000024
In the formula, NRoad sectionFor all vehicles of the road section in the last sampling period, alpha1、α2As a weight, MRoad sectionIs the upper oneNumber of all autonomous vehicles, λ, of the road section in the sampling period1、λ2、λ3Is a weight value.
5. The optimized distribution method for road risk early warning controllers in intelligent network connection environment as claimed in claim 4, wherein the weight α is1、α2The method for determining (1) comprises the following steps:
performing cyclic calculation in each sampling period to obtain a weight combination pair { alpha ] corresponding to the minimum value of the standard deviation1，α2As the final weight:
Figure FDA0003617131900000031
in the formula (I), the compound is shown in the specification,
Figure FDA0003617131900000032
Figure FDA0003617131900000033
Figure FDA0003617131900000034
6. the optimized layout method of road risk early warning controllers in intelligent network connection environment as claimed in claim 4, wherein the weight λ is1、λ2、λ3The method for determining (1) comprises the following steps:
performing cyclic calculation in each sampling period, and calculating a weight combination pair { lambda ] corresponding to the minimum value of the standard deviation1，λ2，λ3As the final weight:
Figure FDA0003617131900000035
in the formula (I), the compound is shown in the specification,
Figure FDA0003617131900000036
Figure FDA0003617131900000037
Figure FDA0003617131900000038
Figure FDA0003617131900000039
7. the optimal layout method of the road risk early warning controllers in the intelligent network connection environment as claimed in claim 3, wherein the mixing rate φ of the automatic driving vehicles in the current road section is:
Figure FDA00036171319000000310
8. the optimal layout method of road risk early warning controllers in intelligent network connection environment as claimed in claim 1, wherein the total number of input matrices N designed based on the mixed traffic flow modelInput matrixComprises the following steps:
Figure FDA00036171319000000311
9. the method for optimizing the layout of the road risk early warning controllers in the intelligent network connection environment as claimed in claim 1, wherein the method for determining the control matrix comprises:
screening input matrixes and positions of corresponding controllers under the condition that the mixing rate of all automatic driving vehicles is met;
judging whether the same number of controllers exist or not;
if the input matrix exists, selecting the input matrix with the best layout position as a control matrix;
and if the input matrix does not exist, selecting the input matrix with the least quantity as the control matrix.
10. An optimized layout system of the road risk early warning controllers in the intelligent network connection environment is characterized by being used for realizing the optimized layout method of the road risk early warning controllers in the intelligent network connection environment as claimed in any one of claims 1 to 9.
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