CN114863678B - Optimized layout method and system for road safety risk detectors in intelligent networking environment - Google Patents
Optimized layout method and system for road safety risk detectors in intelligent networking environment Download PDFInfo
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Abstract
The invention discloses an optimized layout method and system of a road safety risk detector under 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; calculating the mixing rate of the automatic driving vehicles in each road section, and 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 output matrix C based on a mixed traffic flow model i (ii) a Calculating Rank (A) by using the Rank criterion of system controllability m ,C i ) Whether the value of (d) is n; if yes, the output matrix C is reserved i (ii) a Screening output matrixes meeting all mixing rates, and selecting the output matrix with the best layout position and the least quantity as a control matrix; and (4) arranging the risk detectors based on the control matrix. The invention adopts the system observability theory to optimize the layout of the road safety risk detectors and ensure the safe and efficient operation of the road network system.
Description
Technical Field
The invention relates to the technical field of mixed traffic flow safety risk detection, in particular to an optimized layout method and system of road safety risk detectors in an intelligent network connection environment.
Background
With the continuous development and maturity of the automatic driving technology, a scene that traditional manual driving vehicles and automatic driving vehicles of different grades are mixed can appear for a long time in the future, some driving rules of automatic driving vehicles are different from driving habits of human drivers, and the difference often becomes a cause of collision between the automatic driving vehicles and the manual driving vehicles, especially intelligent internet vehicles with the technology maturity yet to be improved in road tests and the manual driving vehicles are in mixed intelligent interactive operation, so that the potential safety risk hazard under the scene of mixed traffic flow is increased easily.
The existing effective solution is to arrange a safety risk detector at the roadside, detect risks in time and adopt early warning and control strategies in time for high risks; however, for such a large-scale road network, if detectors are arranged on all road sections, the investment and operation and maintenance costs are greatly increased, and the road network does not accord with the traffic economy characteristics; therefore, how to set the minimum number of safety risk detectors on a proper road section is a difficult problem to realize accurate detection of safety risks in the whole road network.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides the optimal layout method and the system of the road safety risk detectors in the intelligent network connection environment, and the safe and efficient operation of the road network system in a complex mixed traffic flow scene is ensured by arranging the risk detectors at the proper positions of the road network.
The invention discloses an optimized layout method of a road safety risk detector under 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:
wherein x = [ x ] 1 ,…,x n ] T Traffic flow density vector representing road network, u = [ u = 1 ,u 2 …,u q ] T Representing the control input of a road network, and q is the number of controllers; y = [ y 1 ,y 2 …,y p ]Represents the measurement of the traffic detector, p is the number of detectors deployed; a. The σ(t) Is a system matrix, B σ(t) Is the input matrix of the system, C is the designed output matrix; f σ(t) Is 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＝{A 1 ,A 2 ,…,A m }
in the formula, A m The 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 output matrix C based on the mixed traffic flow model:
calculating Rank (A) by using the Rank criterion of system controllability m ,C i ) Whether the value of (d) is n; if yes, the output matrix C is reserved i If not, the output matrix C is deleted i ；
Screening output matrixes meeting the condition of the mixing rate of all the automatic driving vehicles, and selecting the output matrix with the best layout position and the least quantity from the screened output matrixes as a control matrix;
and arranging the detectors 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 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 the automatically driven vehicles include:
obtaining the number N of vehicles entering each road section based on a video detector Video drive-in And number of vehicles exiting N Video launch And calculating the number N of vehicles on the road section Video (ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detector Microwave drive-in And number of vehicles exiting N Microwave coming out And calculating the number N of vehicles on the road section Microwave oven ；
Will N Video In N Microwave oven One or more results obtained after fusion
The total number of vehicles N as the road section;
automatic driving vehicle detector based on network connectionTaking the number M of the driving automatic driving vehicles on each road section Upstream drive-in And number of driven-out autonomous vehicles M Downstream exit And calculating the number M of the automatically driven vehicles in the road section 1 (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 section 2 (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 segment 3 ；
Will M 1 、M 2 And M 3 One or more of the results obtained after fusion
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,
N video ＝N Road section +N Video drive-in -N Video launch
N Microwave oven ＝N Road section +N Microwave drive-in -N Microwave coming out
M 1 ＝M Road section +M Upstream drive-in -M Downstream exit
In the formula, N Road section For all vehicles of the road section in the last sampling period, alpha 1 、α 2 As a weight, M Road section For the last sampling periodNumber of all autonomous vehicles, lambda, of the road section in the period 1 、λ 2 、λ 3 Is a weight value.
As a further improvement of the invention, the weight value alpha 1 、α 2 The determination method comprises the following steps:
performing cyclic calculation in each sampling period, and solving a weight combination pair { alpha ] corresponding to the minimum value of the standard deviation 1 ，α 2 As the final weight:
in the formula,
as a further improvement of the invention, the weight value lambda 1 、λ 2 、λ 3 The determination method 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 deviation 1 ，λ 2 ，λ 3 As the final weight:
in the formula,
as a further improvement of the invention, the mixing ratio phi of the automatic driving vehicle in the current road section is as follows:
as a further improvement of the invention, the total number N of output matrices designed based on the mixed traffic flow model Output matrix Comprises the following steps:
as a further improvement of the present invention, the method for determining the control matrix includes:
screening output matrixes meeting the conditions of the mixing rates of all automatic driving vehicles and positions corresponding to the risk detectors;
judging whether the same number of risk detectors exist or not;
if the control matrix exists, selecting the output matrix with the best layout position as the control matrix;
if not, selecting the output matrix with the least quantity as the control matrix.
The invention also discloses an optimized layout system of the road safety risk detectors in the intelligent networking environment, which is used for realizing the optimized layout method of the road safety risk detectors in the intelligent networking environment.
Compared with the prior art, the invention has the beneficial effects that:
the road network is regarded as a complex traffic network system, a mixed traffic flow model is modeled, and the optimal layout of the road safety risk detector is carried out by adopting the observability theory of the system; the risk detector with optimized layout can detect safety risks appearing in different road sections in time so as to adopt a rapid and accurate early warning prevention and control strategy and ensure safe and efficient operation of a road network system.
Drawings
FIG. 1 is a schematic diagram of a layout of risk detectors for different rates of automatic vehicle cut-in according to one embodiment of the present disclosure;
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 security risk detector in an intelligent networking 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 attached drawing figures:
aiming at the safety risks in the scene of mixed traffic of networked automatic driving vehicles and manually driven vehicles, a risk detector is required to be arranged at a proper position of a road network, and the safety risks in different road sections are detected in time, so that a rapid and accurate early warning prevention and control strategy is adopted, and the safe and efficient operation of a road network system is ensured; the road network is regarded as a complex traffic network system and is modeled into a mixed traffic flow model, the optimized layout of the road safety risk detectors is carried out by adopting the system observability theory, and the detectors with the minimum number and relatively uniform positions are guaranteed to be laid on the premise of meeting the observability of the system, so that the real-time detection of the safety risk of the mixed traffic flow is realized.
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 a control center (based on V2I) can be realized, and the information such as the position, the speed, the acceleration 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 detector is arranged, the mixed traffic flow modeling is 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:
wherein x = [ x ] 1 ,…,x n ] T Representing traffic flow density vectors of a road network, wherein n represents the number of cells; t is a sampling period, and the optimal sampling period is 5min in practical use; u = [ u = 1 ,u 2 …,u q ] T Representing the control input of a road network, wherein q is the number of controllers which are known parameters, namely risk early warning controllers are arranged in certain cells; y = [ y 1 ,y 2 …,y p ]Representing the measurements of the traffic detectors, p being the number of deployed risk detectors; a. The σ(t) The system matrixes corresponding to the mixing rates of different automatic driving vehicles are different from the system matrixes corresponding to the different cell combination modes with different mixing rates; b is σ(t) Is the input matrix of the system, which is a known parameter; c represents a matrix related to the road network layout safety risk detector, namely an output matrix to be designed in the invention, aiming at different mixing rates of the automatic driving vehicles (namely different system matrixes), a corresponding output matrix needs to be designed to meet the condition that the system can observe; f σ(t) Is an affine vector.
As shown in fig. 3, after the mixed traffic flow modeling is completed, the method for optimally arranging the road safety risk detectors 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 the vehicles and the number of the automatically driven vehicles;
the specific calculation method of the mixing ratio includes:
step 211, constructing an automatic driving vehicle mixing rate calculation system shown in fig. 2; wherein,
the method comprises the steps that a portal frame 1 or other mounting frames are arranged on the upstream boundary and the downstream boundary of each road section, and a video detector 2 and/or an internet automatic driving vehicle detector 3 are mounted on the portal frame 1 on the upstream boundary and the downstream boundary of each road section, namely, the number of all vehicles driving into the road section is collected based on the upstream video detector, the number of all vehicles driving out of the road section is collected based on the downstream video detector, the number of all automatic driving vehicles driving into the road section is collected based on the upstream internet automatic driving vehicle detector, and the number of all automatic driving vehicles driving out of the road section is collected based on the downstream internet automatic driving vehicle detector; meanwhile, the microwave detectors 4 can be installed on the upstream boundary and the downstream boundary of each road section, namely, all vehicles running into the road section are collected on the basis of the upstream microwave detectors, and all vehicles running out of the road section are collected on the basis of the downstream microwave detectors; 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 of the vehicle; meanwhile, real-time communication can be carried out between the networked automatic driving vehicles based on V2V, information such as 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 calculating 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 detector Video drive-in And number of vehicles coming out N Video launch And calculating the number N of vehicles on the road section Video (ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detector Microwave drive-in And driving out of the vehicleNumber of vehicles N Microwave drive-out And calculating the number N of vehicles on the road section Microwave oven (ii) a Wherein,
N video ＝N Road section +N Video drive-in -N Video outbound
N Microwave oven ＝N Road section +N Microwave drive-in -N Microwave coming out
In the formula, N Road section The number of all vehicles in the road section in the last sampling period is;
step 213, adding N Video In N Microwave oven One or more of the results obtained after fusion
The total number of vehicles N as the road segment; wherein,
in the formula, alpha 1 、α 2 Is the weight;
furthermore, the design principle and weight α of the above fusion processing of the present invention 1 、α 2 The 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, the number of vehicles is calculated by adopting a fusion algorithm;
suppose that the weight values of the video detection value and the microwave detection value are respectively alpha 1 And alpha 2 Thus, a weight combination pair { α } can be calculated 1 ，α 2 And the weighted summation result of the number of vehicles in the road section is as follows:
respectively calculate the viewsFrequency detection result N Video And microwave detection result N Microwave oven And with
The difference of (a) to (b), namely:
performing cyclic calculation in each sampling period to obtain a weight combination pair { alpha ] corresponding to the minimum value of the standard deviation 1 ，α 2 As the final weight:
according to the obtained weight value alpha 1 And alpha 2 Calculating the number of all vehicles in the road section
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 detector Upstream drive-in And number of driven-out autonomous vehicles M Downstream run-out And calculating the number M of the automatically driven vehicles in the road section 1 (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 section 2 (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 segment 3 (ii) a Wherein,
M 1 ＝M road section +M Upstream drive-in -M Downstream run-out
In the formula, M Road section The number of all automatic driving vehicles in the road section in the last sampling period is counted;
step 215, adding M 1 、M 2 And M 3 One or more results obtained after fusion
The number of autonomous vehicles M as the road section; wherein,
in the formula, λ 1 、λ 2 、λ 3 Is the weight;
further, λ 1 、λ 2 、λ 3 The method for determining (1) comprises the following steps:
the weights of the number of the online automatic driving vehicles in the current road section obtained by the three methods are assumed to be lambda respectively 1 、λ 2 And λ 3 Thus, a weight combination pair λ can be calculated 1 ，λ 2 ，λ 3 And the weighted summation result of the number of the networked automatic driving vehicles in the road section is as follows:
respectively calculating three recognition results M 1 、M 2 And M 3 And with
The difference of (a) to (b), namely:
performing cyclic calculation in each sampling period, and calculating a weight combination pair { lambda ] corresponding to the minimum value of the standard deviation 1 ，λ 2 ，λ 3 As the final weight:
according to the obtained weight value lambda 1 、λ 2 And λ 3 Calculating the number of the networked automatic driving vehicles in the current road section
Further, the invention can also use lambda 1 、λ 2 And λ 3 One of the weights is 0 to realize M 1 、M 2 And M 3 And fusing any 2 results, and obtaining the fused result as the final automatic driving vehicle number M.
Step 216, based on the total number of vehicles and the number of automatically driven vehicles on the road section, calculating the mixing ratio phi of the automatically driven vehicles on the current road section as follows:
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＝{A 1 ,A 2 ,…,A m }
in the formula, A m The 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% in sequence.
Step 23, designing an output matrix C based on the mixed traffic flow model, wherein the output matrix is kXn, and k is more than or equal to 1 and less than or equal to n:
that is, as long as the detector is arranged in the ith cell, the element of the corresponding position of the output matrix is 1, otherwise, the element is 0;
finally obtaining the total number N of the output matrixes Output matrix Comprises the following steps:
step 24, calculating Rank (A) by using the Rank criterion of system controllability m ,C i ) Whether the value of (d) is n; if yes, the output matrix C is reserved i If not, deleting the output matrix C i (ii) a Wherein,
the observability discrimination algorithm is:
if Rank (A) m ,C i ) If n, the satisfied system can be observed, and finally only all the satisfied system output matrixes C are reserved i ；
If Rank (A) m ,C i ) N, deleting all output matrixes C which do not satisfy the observability of the system i 。
Step 25, screening output matrixes meeting the conditions of the mixing rate of all the automatic driving vehicles, and selecting the output matrix with the best layout position and the least quantity from the screened output matrixes as a control matrix;
the method specifically comprises the following steps:
step 251, screening output matrixes meeting the conditions of the mixing rates of all automatic driving vehicles and positions corresponding to risk detectors;
step 252, judging whether the same number of risk detectors exist;
step 253, if the control matrix exists, selecting the output matrix with the best layout position as the control matrix; the optimal layout position refers to an output matrix with relatively uniform position distribution;
step 254, if the output matrix does not exist, the output matrix with the least quantity is selected as the control matrix;
and finally, selecting the output matrix with the least quantity and the best layout position as the control matrix.
26, laying detectors based on the control matrix; the safety risk detector is mainly used for timely detecting and identifying existing safety risks, a control center can conveniently and directly control the networked automatic driving vehicle by adopting a corresponding control strategy, the manual driving vehicle is subjected to induction control, and the safety risk of a road is reduced to the maximum extent.
The invention provides an optimized layout system of a road safety risk detector in an intelligent networking environment, which is used for realizing the optimized layout method of the road safety risk detector in the intelligent networking environment and can realize the optimized layout method of the road safety risk detector in the intelligent networking environment based on the system and the control center shown in figure 2.
Example (b):
a section of highway with 4 lanes, the highest speed limit of 120km/h and the length of 2km is taken as an example for specific explanation:
the selected road section is averagely divided into 10 cells, the length of each cell is 200 meters, and the corresponding system observability under the conditions of different automatic driving vehicle mixing rates is analyzed.
(1) The mixing rate of the automatic driving vehicles is 0, namely all vehicles are artificial driving vehicles, and a corresponding system matrix is calculated according to the established mixed traffic flow model as follows:
an output matrix set C can be obtained by the design method based on the output matrix, and further according to the observability criterion of the system, all output matrices C meeting the controllability of the system can be calculated as follows:
from the calculation results it can be seen that at least the security risk detector is arranged on the cell 10, i.e. it can be satisfied that the system is observable, and whether the arrangement of other cells does not affect the observability of the system.
(2) The mixing rates of the automatic driving vehicles are respectively 10%, and according to the established mixed traffic flow model, the corresponding system matrix is calculated as follows:
as can be seen from the calculation results, this case is the same as the system matrix with the autopilot mix ratio of 0, and therefore the safety risk detector layout is the same as it is.
(3) The autonomous vehicle mix rates were 20%, 30%, 40%, 50%, 60%, and 70%, respectively, and the corresponding system matrices were calculated as follows:
based on a design method of an output matrix and a system observability criterion, the same output matrix C is obtained by calculation under the condition that the mixing rate of the automatic driving vehicle meets the system observability, and the method is specifically as follows:
thus, in these cases it is sufficient to arrange at least the security risk detectors on cell 1 and cell 10 that the system is observable without the observability of the system being affected by the presence or absence of other cells.
(4) The mix-in rates of the autonomous vehicles are 80% and 90%, respectively, and the corresponding system matrix is calculated as follows:
based on a design method of an output matrix and a system observability criterion, the same output matrix C is obtained by calculation under the condition that the mixing rate of the automatic driving vehicle meets the system observability, and the method is specifically as follows:
thus, in this case, at least the security risk detectors are arranged on cell 1, cell 6 and cell 10, such that the system is observable without affecting the observability of the system by arranging other cells.
(5) The mixing rate of the automatic driving vehicle is 100%, and a corresponding system matrix is calculated as follows:
as can be seen from the calculation results, this case is the same as the system matrix with the autopilot mix ratio of 0, and therefore the safety risk detector layout is the same as it is.
In summary, in order to make the system observable at all the mixing rates, that is, to control the whole system at different mixing rates, the safety risk detectors are arranged at least at the cell 1, the cell 6 and the cell 10 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 (9)
1. An optimized layout method for a road security risk detector in an intelligent network connection 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 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:
wherein x = [ x ] 1 ,…,x n ] T Traffic flow density vector representing road network, u = [ u = 1 ,u 2 …,u q ] T Representing the control input of a road network, and q is the number of controllers; y = [ y) 1 ,y 2 …,y p ]Represents the measurement of the traffic detector, p is the number of detectors deployed; a. The σ(t) Is a system matrix, B σ(t) Is the input matrix of the system, C is the designed output matrix; f σ(t) Is 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; the method specifically comprises the following steps: obtaining the number N of vehicles entering each road section based on a video detector Video drive-in And number of vehicles exiting N Video launch And calculating the number N of vehicles on the road section Video (ii) a And/or acquiring the number N of vehicles entering each road section based on the microwave detector Microwave drive-in And number of vehicles exiting N Microwave drive-out And calculating the number N of vehicles on the road section Microwave oven (ii) a Will N Video In N Microwave oven One or more results obtained after fusion
The total number of vehicles N as the road section; obtaining number M of driving-in automatic driving vehicles of each road section based on network connection automatic driving vehicle detector Upstream drive-in And number of outgoing autonomous vehicles M Downstream exit And calculating the number M of the automatically driven vehicles in the road section 1 (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 section 2 (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 segment 3 (ii) a Will M 1 、M 2 And M 3 One or more of the results obtained after fusion
As the road sectionThe number of autonomous vehicles M; 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;
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＝{A 1 ,A 2 ,…,A m }
in the formula, A m The 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 output matrix C of the ith cell based on a mixed traffic flow model i ：
Calculating Rank (A) by using the Rank criterion of system controllability m ,C i ) Whether the value of (d) is n; if yes, the output matrix C is reserved i If not, the output matrix C is deleted i ；
Screening output matrixes meeting the condition of the mixing rate of all the automatic driving vehicles, and selecting the output matrix with the best layout position and the least quantity from the screened output matrixes as a control matrix;
and arranging the detectors based on the control matrix.
2. The method for optimizing the layout of road security risk detectors in an 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 method as claimed in claim 1, wherein the road security risk detector is arranged in an intelligent networking environment,
N video ＝N Road section +N Video drive-in -N Video outbound
N Microwave oven ＝N Road section +N Microwave drive-in -N Microwave coming out
M 1 ＝M Road section +M Upstream drive-in -M Downstream exit
M 1 、M 2 And M 3 Obtained after fusion
The calculation formula of (2) is as follows:
in the formula, N Road section For all vehicles of the road section in the last sampling period, alpha 1 、α 2 As a weight, M Road section For all the number of autonomous vehicles, lambda, of the road section in the last sampling period 1 、λ 2 、λ 3 Is a weight value.
4. The method as claimed in claim 3, wherein the weight α is a weight value 1 、α 2 The 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 deviation 1 ，α 2 As the final weight:
in the formula,
5. the method as claimed in claim 3, wherein the weight λ is a value obtained by optimizing the distribution of the road security risk detectors in the environment of an intelligent network 1 、λ 2 、λ 3 The determination method comprises the following steps:
performing cyclic calculation in each sampling period to obtain weight combination pair { lambda ] corresponding to the minimum value of standard deviation 1 ，λ 2 ，λ 3 As the final weight:
in the formula,
6. the method as claimed in claim 1, wherein the blending ratio Φ of the autonomous driving vehicles in the current road section is:
7. the optimal layout method of road safety risk detectors in intelligent network-linked environment as claimed in claim 1, wherein the total number N of output matrices designed based on the mixed traffic flow model Output matrix Comprises the following steps:
8. the method for optimizing the layout of road security risk detectors in an intelligent networking environment as claimed in claim 1, wherein the method for determining the control matrix comprises:
screening output matrixes meeting the conditions of the mixing rates of all automatic driving vehicles and positions corresponding to the risk detectors;
judging whether the same number of risk detectors exist or not;
if the control matrix exists, selecting the output matrix with the best layout position as the control matrix;
if not, selecting the output matrix with the least quantity as the control matrix.
9. An optimized layout system of the road security risk detectors in the intelligent network connection environment, which is used for realizing the optimized layout method of the road security risk detectors in the intelligent network connection environment according to any one of claims 1 to 8.
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