CN115689259B - Method, equipment and medium for determining priority of cooperative scene of road section and vehicle - Google Patents
Method, equipment and medium for determining priority of cooperative scene of road section and vehicle Download PDFInfo
- Publication number
- CN115689259B CN115689259B CN202310004953.4A CN202310004953A CN115689259B CN 115689259 B CN115689259 B CN 115689259B CN 202310004953 A CN202310004953 A CN 202310004953A CN 115689259 B CN115689259 B CN 115689259B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- road
- layer
- weight
- scene
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of intelligent traffic, in particular to a method, a device, equipment and a medium for determining the priority of a cooperative scene of a road and a vehicle, wherein the method comprises the following steps: acquiring road section characteristics of a target road section of a target layer; classifying the scenes of the vehicle-road cooperation according to different criteria of a criterion layer, wherein the criteria comprise: safe running of the vehicle, efficient running of the vehicle and vehicle information service; according to the vehicle-road cooperative scene of the scheme layer corresponding to each criterion, constructing a vehicle-road cooperative scene hierarchical structure model required to be deployed for the target road section based on a hierarchical analysis method; determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relation between all the layers of the model; and solving the weights of the elements of the hierarchy according to the first judgment matrix and the second judgment matrix, and determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section. Through this scheme, more targeted improvement road intelligence reforms transform the upgrading demand.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a method, a device, equipment and a medium for determining the priority of a cooperative scene of a road and a vehicle.
Background
In a traffic system, vehicles and roads are complementary communities, and only when the vehicle capacity and the road environment can be matched and cooperatively operated, the balance of the whole road traffic system can be ensured, so that the safety, the high efficiency and the economy of traffic and transportation are ensured. However, along with continuous promotion and popularization of the intelligent capability of the vehicle, the current level development of road intelligence cannot keep pace with the development steps of the vehicle, while the standard and the standard of Guan Zhi intelligent road construction have been issued at home and abroad, the guidance on the macroscopic level is focused, the problems that the intelligent level of the road section can be improved only by applying what scene to what road section, and the like are solved, and clear guidance is provided. Therefore, how to define the requirements of the cooperative scene of the vehicle and the road in the intelligent process of each road section with targeting, and reasonably and scientifically arrange the cooperative scene of the vehicle and the road for the road section, thereby improving the intelligent environment of the road in a targeted way, ensuring the safety, the high efficiency and the comfort of the vehicle in the running process of the current road section, and being the key problem to be solved in the current urgent need.
Whether the priority method of the road section and vehicle road collaborative scene deployment is correct or not is directly related to the cost and efficiency of intelligent road construction or upgrading and reconstruction engineering.
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides the method and the device for determining the priority of the cooperative scene of the road and the vehicle, thereby improving the intelligent requirement of the road more pertinently, guiding the construction of the current intelligent road effectively, reducing the repeatability of the construction of the scene such as road infrastructure and the like, reasonably distributing material resources and manpower resources and saving the cost.
According to a first aspect of an embodiment of the present invention, there is provided a method for determining a priority of a cooperative scene of a road and a vehicle, the method including:
acquiring road section characteristics of a target road section of a target layer;
classifying the scenes of the vehicle-road cooperation according to different criteria of a criterion layer, wherein the criteria of the criterion layer comprise: safe running of the vehicle, efficient running of the vehicle and vehicle information service;
according to the vehicle-road cooperative scene of the scheme layer corresponding to each criterion, constructing a vehicle-road cooperative scene hierarchical structure model required to be deployed for the target road section based on a hierarchical analysis method;
determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relation among all the layers of the vehicle-road collaborative scene hierarchical structure model;
and solving the weights of all the elements of the hierarchy according to the first judgment matrix and the second judgment matrix, and determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section.
In one embodiment, preferably, the road cooperative scene in which the vehicle safely runs includes: emergency braking early warning, blind area early warning, slow-speed vehicle early warning, abnormal vehicle early warning, weak traffic participant collision early warning, road danger prompt, curve early warning, speed limit early warning, forward collision early warning and side collision early warning;
the vehicle-road cooperative scene for efficiently driving the vehicle comprises: emergency vehicle yielding, front congestion early warning, traffic limiting management, vehicle speed guidance, route guidance and guidance;
the vehicle cooperative scene of the vehicle information service includes: near field payment, remote payment, and service information alerting.
In one embodiment, preferably, determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to a scale relation between each level of the vehicle-road cooperative scene hierarchical structure model includes:
and determining the scale relation value among all the levels of the road section characteristics by adopting a consistent matrix method and an questionnaire method according to the hierarchical structure model of the vehicle-road cooperative scene and a scale method of 1-9, and calculating an average value to obtain the first judgment matrix and the second judgment matrix.
In one embodiment, preferably, determining a vehicle-road cooperative scene deployment priority allocation scheme of the target road section according to the first judgment matrix and the second judgment matrix includes:
calculating the maximum characteristic root of each first judgment matrix and the corresponding characteristic vector thereof, and carrying out normalization processing on the characteristic vectors to obtain first single-order weight vectors, and carrying out ordering and consistency verification, wherein the first single-order weight vectors are used for representing weight values of relative importance of a target layer relative to a criterion layer;
calculating the maximum feature root of each second judgment matrix and the corresponding feature vector thereof, and carrying out normalization processing on the feature vectors to obtain second single-order weight vectors, and carrying out order and consistency verification, wherein the second single-order weight vectors are used for representing weight values of relative importance of the criterion layer relative to the scheme layer;
determining a first-level weight total ordering of weight values of relative importance of all criteria of a criterion layer to a target layer and a second-level weight total ordering of weight values of relative importance of all schemes of a scheme layer to the target layer according to the first single-order weight vector and the second single-order weight vector, and performing consistency verification;
and determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the ordering results of the first-level weight total ordering and the second-level weight total ordering.
In one embodiment, preferably, the step of checking the consistency of the first judgment matrix and the second judgment matrix includes:
according to the maximum eigenvalue of the judgment matrix and the order of the judgment matrix, calculating a consistency index by adopting the following first calculation formula;
wherein, the liquid crystal display device comprises a liquid crystal display device,
the index of the consistency is indicated as such,
representing the largest feature root of the decision matrix,
representing the order of the decision matrix,
indicating complete consistency;
close to 0, indicating satisfactory consistency;
determining a random consistency index corresponding to the order of the judgment matrix according to the corresponding relation between the preset order and the random consistency index;
according to the consistency index and the random consistency index, calculating a consistency deviation check coefficient by adopting the following third calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the consistency deviation checking coefficient,
representing the random consistency index;
when the consistency deviation checking coefficient is smaller than 0.1, determining that the feature vector (after normalization) is a weight vector after consistency checking; otherwise, the consistency check is not passed, and the judgment matrix value needs to be readjusted.
In one embodiment, preferably, determining a first hierarchical total ordering of weights of all criteria of the criterion layer for the weight values of the relative importance of the target layer and a second hierarchical total ordering of weights of all schemes of the scheme layer for the weight values of the relative importance of the target layer according to the first single ordering weight vector and the second single ordering weight vector, and performing the consistency check includes:
synthesizing and arranging the first single-ranking weight vectors in the order from top to bottom to obtain the first-level weight total ranking a 1 ，a 2 ，…，a m Wherein m represents the criterion number of the criterion layer,
a weight value representing an mth criterion;
and calculating the second-level weight total sequencing according to the first-level weight total sequencing and the second single-sequencing weight vector by adopting the following calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
a weight value representing a j-th criterion in the first hierarchical weight total ranking,
representing the first in the second single rank weight vector
For the first aspect
Weight values for the individual criteria;
the consistency deviation check coefficient of the total sequence of the hierarchical weights is calculated by adopting the following calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represent the first
The individual criteria correspond to the scheme layer
The value of the sum of the values,
represent the first
The individual criteria correspond to the scheme layer
A value;
and when the consistency deviation check coefficient is smaller than 0.1, determining that the consistency check is passed, otherwise, failing to pass the consistency check, and re-considering the model or re-constructing a judgment matrix with larger consistency ratio.
In one embodiment, preferably, a vehicle-road collaborative scene deployment priority allocation scheme of the target road section is determined according to the sequence of the weights from large to small according to the sorting result of the first-level weight total sorting and the second-level weight total sorting.
According to a second aspect of the embodiment of the present invention, there is provided a road section and vehicle cooperation scene priority determining apparatus, the apparatus including:
the acquisition module is used for acquiring the road section characteristics of the target road section of the target layer;
the classification module is used for classifying the scenes of the vehicle-road cooperation according to different criteria of the criterion layer, wherein the criteria of the criterion layer comprise: safe running of the vehicle, efficient running of the vehicle and vehicle information service;
the construction module is used for constructing a vehicle-road collaborative scene hierarchical structure model required to be deployed on the target road section based on a hierarchical analysis method according to the vehicle-road collaborative scene of the scheme layer corresponding to each criterion;
the first determining module is used for determining a first judging matrix between the target layer and the criterion layer and a second judging matrix between the criterion layer and the scheme layer according to the scale relation among all the layers of the vehicle-road collaborative scene hierarchical structure model;
and the second determining module is used for determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the first judging matrix and the second judging matrix.
According to a third aspect of embodiments of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the road segment and vehicle co-scenario priority determination method according to any one of the embodiments of the first aspect when the program is executed.
According to a fourth aspect of embodiments of the present invention there is provided a non-transitory computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of the first aspects.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
in the embodiment of the invention, the characteristics of the road section are extracted and analyzed, the intelligent vehicle-road cooperative scenes are classified from three aspects of safety, high efficiency and information service, a hierarchical analysis method is adopted, a hierarchical structure model is constructed for the intelligent vehicle-road cooperative scenes of the road section, a questionnaire method is adopted, a road intelligent judgment matrix is constructed, the vehicle-road cooperative scene weight coefficients corresponding to the three dimensions of the road section safety, high efficiency and information service are obtained, the consistency is sequenced and checked, and the deployment priority of each intelligent vehicle-road cooperative scene of the road section is determined. On one hand, the intelligent road requirement is improved more pertinently, and the current intelligent road construction is effectively guided; on the other hand, the repeatability of scene construction such as road infrastructure and the like can be reduced, material resources and human resources are reasonably distributed, and the cost is saved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart illustrating a road segment and vehicle co-scene priority determination method according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating step S105 in a road segment and vehicle cooperative scene priority determination method according to an exemplary embodiment.
Fig. 3 is a specific flowchart illustrating a method for determining a priority of a cooperative scene of a road segment and a vehicle according to an exemplary embodiment.
Fig. 4 is a block diagram illustrating a road section and vehicle cooperation scene priority determining apparatus according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
Fig. 1 is a flowchart illustrating a method for determining a priority of a cooperative scene of a road and a vehicle, according to an exemplary embodiment, and the method includes:
step S101, obtaining a link characteristic of a target link of the target layer. The method is characterized by analyzing the characteristics of road sections under different terrains such as mountain areas, plain, hills and the like by adopting standard consulting, field investigation methods, questionnaires and the like, and comprises the characteristics of road line-shaped characteristics, whether traffic sign marks are perfect, whether tunnels are arranged, whether effective bridges are arranged, whether side slopes are arranged, whether sharp turns are arranged, whether long downgrades are arranged, whether shielding is arranged, the surrounding environment is arranged and the like.
Step S102, classifying the scenes of the vehicle-road cooperation according to different criteria of a criterion layer, wherein the criteria of the criterion layer comprise: safe running of the vehicle, efficient running of the vehicle and vehicle information service;
the method can divide the dimensions of the cooperative scene of the vehicle and the road into 3 dimensions of safety, high efficiency and information service by combining published road related standards or guidelines.
In one embodiment, preferably, the road cooperative scene in which the vehicle safely runs includes: emergency braking early warning, blind area early warning, slow-speed vehicle early warning, abnormal vehicle early warning, weak traffic participant collision early warning, road danger prompt, curve early warning, speed limit early warning, forward collision early warning and side collision early warning;
the vehicle-road cooperative scene for efficiently driving the vehicle comprises: emergency vehicle yielding, front congestion early warning, traffic limiting management, vehicle speed guidance, route guidance and guidance;
the vehicle cooperative scene of the vehicle information service includes: near field payment, remote payment, and service information alerting.
Step S103, constructing a vehicle-road collaborative scene hierarchical structure model required to be deployed for the target road section based on a hierarchical analysis method according to the vehicle-road collaborative scenes of the scheme layers corresponding to the various criteria; the vehicle-road cooperative scene is an influence factor of the road section capable of reflecting the functions of safe running, efficient running and information service of the vehicle.
The hierarchical analysis principle is utilized to construct an intelligent hierarchical structure model of a certain road section, wherein the road section is an ultimate target, the safety, the high efficiency and the information service are embodied by the intelligent function of the road section, and the typical vehicle-road cooperative scene is a road section intelligent scheme means, so that a target layer in the hierarchical structure model is the road section, a criterion layer is 3 dimensions of the safety, the high efficiency and the information service, and a scheme layer is the vehicle-road cooperative scene.
Step S104, determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relation among all the layers of the vehicle-road collaborative scene hierarchical structure model;
in one embodiment, preferably, determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to a scale relation between each level of the vehicle-road cooperative scene hierarchical structure model includes:
and determining the scale relation value among all the levels of the road section characteristics by adopting a consistent matrix method and an questionnaire method according to the hierarchical structure model of the vehicle-road cooperative scene and a scale method of 1-9, and calculating an average value to obtain the first judgment matrix and the second judgment matrix.
The consistent matrix method, namely the element-to-element comparison, adopts the relative scale, namely the scale method of 1-9, to determine the important relation between the elements, so as to reduce the difficulty of comparing different factors with each other and improve the accuracy. The judgment matrix has the following properties:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing elements
And elements
The significance comparisons are shown in Table 1 with reference to the scale meanings of 1-9.
TABLE 1
Step S105, determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the first judgment matrix and the second judgment matrix.
In the embodiment, the characteristics of the road section are extracted and analyzed, the intelligent vehicle-road cooperative scenes are classified from three aspects of safety, high efficiency and information service, a hierarchical analysis method is adopted, a hierarchical structure model is constructed for the intelligent vehicle-road cooperative scenes of the road section, a questionnaire method is adopted, a road intelligent judgment matrix is constructed, vehicle-road cooperative scene weight coefficients corresponding to three dimensions of the road section safety, high efficiency and information service are obtained, consistency is ordered and checked, and the deployment priority of each intelligent vehicle-road cooperative scene of the road section is determined, so that the cost of upgrading, reconstruction and reconstruction of the existing road can be reduced, and the running requirements of vehicle safety, high efficiency and information service are met.
Fig. 2 is a flowchart illustrating step S105 in a road segment and vehicle cooperative scene priority determination method according to an exemplary embodiment.
As shown in fig. 2, in one embodiment, the step S105 preferably includes:
step S201, calculating the maximum feature root of each first judgment matrix and the corresponding feature vector thereof, normalizing the feature vectors to obtain first single-order weight vectors, and carrying out ordering and consistency verification, wherein the first single-order weight vectors are used for representing weight values of relative importance of a target layer relative to a criterion layer; the method comprises the steps of calculating single-order weight vectors, ordering and checking consistency, wherein the single-order weight vectors comprise hierarchical single-order and consistency check thereof and hierarchical total-order and consistency check thereof. The hierarchical single ranking refers to the step of obtaining the feature vector W of the maximum feature root lambda of the corresponding judgment matrix, and the normalized omega is the ranking weight of the relative importance of the corresponding factor of the same hierarchy to a certain factor of the upper layer. The consistency check is to determine the allowable range of the inconsistent judgment matrix.
The hierarchical total sequencing and consistency check thereof means that the hierarchical total sequencing of the weight of all factors of a certain layer on the relative importance of a target layer is calculated, and particularly the sequencing weight of each scheme in the bottommost layer on the target layer is calculated, so that scheme selection is carried out. The total sorting weight is important to synthesize the weights under the single quasi side from top to bottom.
Step S202, calculating the maximum feature root of each second judgment matrix and the corresponding feature vector thereof, normalizing the feature vectors to obtain second single-order weight vectors, and carrying out ordering and consistency verification, wherein the second single-order weight vectors are used for representing weight values of relative importance of the criterion layer relative to the scheme layer;
step S203, according to the first single-order weight vector and the second single-order weight vector, determining a first-level weight total ordering of weight values of the relative importance of all criteria of the criterion layer to the target layer and a second-level weight total ordering of weight values of the relative importance of all schemes of the scheme layer to the target layer, and performing consistency verification;
step S204, determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the ordering results of the first-level weight total ordering and the second-level weight total ordering.
In one embodiment, preferably, the step of checking the consistency of the first judgment matrix and the second judgment matrix includes:
according to the maximum eigenvalue of the judgment matrix and the order of the judgment matrix, calculating a consistency index by adopting the following first calculation formula;
wherein, the liquid crystal display device comprises a liquid crystal display device,
the index of the consistency is indicated as such,
representing the largest feature root of the decision matrix,
representing the order of the decision matrix,
indicating complete consistency;
close to 0, indicating satisfactory consistency;
the larger the disparity, the more serious the disparity.
Introducing random consistency index
Measurement of
Wherein, the random uniformity index
Regarding the order of the judgment matrix, in general, the larger the matrix order is, the greater the probability of random deviation of consistency is, and the corresponding relation is shown in table 2;
TABLE 2
According to the consistency index and the random consistency index, calculating a consistency deviation check coefficient by adopting the following second calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the consistency deviation checking coefficient,
representing the random consistency index;
when the consistency deviation checking coefficient is smaller than 0.1, determining that the feature vector (after normalization) is a weight vector after consistency checking; otherwise, the consistency check is not passed, and the judgment matrix value needs to be readjusted.
In one embodiment, preferably, determining a first hierarchical total ordering of weights of all criteria of the criterion layer for the weight values of the relative importance of the target layer and a second hierarchical total ordering of weights of all schemes of the scheme layer for the weight values of the relative importance of the target layer according to the first single ordering weight vector and the second single ordering weight vector, and performing the consistency check includes:
synthesizing and arranging the first single-ranking weight vectors in the order from top to bottom to obtain the first-level weight total ranking
Wherein, the method comprises the steps of, wherein,
the number of criteria representing the layer of criteria,
represent the first
Weight values for the individual criteria;
layer A is arranged
Individual factors
The total ranking weight of the layers of the total target Z is that
。
The n factors of the B layer are sequenced to the hierarchical list with the Aj factor in the upper layer A
Layer B total ordering
：
B 1 :a 1 b 11 +a 2 b 12 +...+a m b 1m ；
B 2 :a 1 b 21 +a 2 b 22 +...+a m b 2m ；
...
B n :a 1 b n1 +a 2 b n2 +...+a m b nm ；
And calculating the second-level weight total sequencing according to the first-level weight total sequencing and the second single-sequencing weight vector by adopting the following calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the first level of total ordering of weights
The weight value of the individual criteria is set,
representation ofThe second single rank weight vector
For the first aspect
Weight values for the individual criteria;
the consistency deviation check coefficient of the total sequence of the hierarchical weights is calculated by adopting the following calculation formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represent the first
The individual criteria correspond to the scheme layer
The value of the sum of the values,
represent the first
The individual criteria correspond to the scheme layer
A value;
and when the consistency deviation check coefficient is smaller than 0.1, determining that the consistency check is passed, otherwise, failing to pass the consistency check, and re-considering the model or re-constructing a judgment matrix with larger consistency ratio.
In one embodiment, preferably, a vehicle-road collaborative scene deployment priority allocation scheme of the target road section is determined according to the sequence of the weights from large to small according to the sorting result of the first-level weight total sorting and the second-level weight total sorting.
The above technical solution of the present invention will be described in detail below by taking a target link as an example of a curved link.
As shown in fig. 3, the vehicle-road cooperative scene hierarchical structure model corresponding to the curve section comprises a target layer, a criterion layer and a scheme layer, wherein the target layer is the curve section (O), the criterion layer comprises safety (A1), high efficiency (A2) and information service (A3), the vehicle-road cooperative scene of the vehicle safety running comprises emergency braking early warning (B1), blind area early warning (B2), slow vehicle early warning (B3), abnormal vehicle early warning (B4), weak traffic participant collision early warning (B5), road danger prompt (B6), curve early warning (B7), speed limit early warning (B8), forward collision early warning (B9) and side collision early warning (B10),
the vehicle-road cooperative scene for efficiently driving the vehicle comprises: emergency vehicle let-off (B1), front congestion pre-warning (B2), limit management (B3), vehicle speed guidance (B4) and route guidance and guidance (B5);
the vehicle cooperative scene of the vehicle information service includes: near field payment (B1), remote payment (B2) and service information reminder (B3).
And determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relation among all the layers of the vehicle-road collaborative scene hierarchical structure model.
The first judgment matrix O-A is shown in Table 3:
TABLE 3 Table 3
| O | A1 | A2 | A3 |
| A1 | 1 | 5 | 9 |
| A2 | 1/5 | 1 | 5 |
| A3 | 1/9 | 1/5 | 1 |
Computing the maximum feature root of the O-A matrix
Feature vectors corresponding to the feature vectors
And normalizing the feature vector to obtain a first single-order weight vector. By means of the calculation of the number of the parameters,
feature vector
=（2.295，0.386，0.391） T Normalized to
=（0.765，0.0128，0.011） T Consistency index ci=0.019, random consistency index ri=0.52 (table look-up 2), consistency ratio cr=ci/ri=0.037
Pass the consistency check.
The second judgment matrices A1-B are shown in Table 4:
TABLE 4 Table 4
| A1 | B1 | B2 | B3 | B4 | B5 | B6 | B7 | B8 | B9 | B10 |
| B1 | 1 | 1/3 | 1/3 | 1/5 | 1/5 | 1/5 | 1/3 | 1/3 | 1/3 | 1/3 |
| B2 | 3 | 1 | 1/2 | 1/5 | 1/4 | 1/2 | 1/3 | 2 | 1/3 | 1/3 |
| B3 | 3 | 2 | 1 | 1/3 | 1/5 | 1/3 | 1/3 | 3 | 1/3 | 1/3 |
| B4 | 5 | 5 | 3 | 1 | 1/3 | 4 | 3 | 5 | 1/2 | 1/2 |
| B5 | 5 | 4 | 5 | 3 | 1 | 3 | 2 | 5 | 3 | 3 |
| B6 | 5 | 2 | 3 | 1/4 | 1/3 | 1 | 0.5 | 4 | 2 | 2 |
| B7 | 3 | 3 | 3 | 1/3 | 1/2 | 2 | 1 | 2 | 2 | 2 |
| B8 | 3 | 1/2 | 1/3 | 1/5 | 1/5 | 1/4 | 1/2 | 1 | 1/2 | 1/2 |
| B9 | 3 | 3 | 3 | 2 | 1/3 | 1/2 | 1/2 | 2 | 1 | 1 |
| B10 | 3 | 3 | 3 | 2 | 1/3 | 1/2 | 1/2 | 2 | 1 | 1 |
And (3) calculating to obtain:
feature vector w= (0.26,0.456,0.545,1.628,2.329,1.136,1.234,0.404,1.004,1.004) T Normalized to
=（0.026，0.0456，0.0545，0.1628，0.2329，0.1136，0.1234，0.0404，0.1004，0.1004） T Consistency index ci=0.132, random consistency index ri=1.49 (table look-up 2), consistency ratio cr=ci/ri=0.089
Pass the consistency check.
The second judgment matrix A2-B is shown in Table 5:
TABLE 5
| A2 | B1 | B2 | B3 | B4 | B5 |
| B1 | 1 | 1/5 | 2 | 1/3 | 1/3 |
| B2 | 5 | 1 | 3 | 2 | 2 |
| B3 | 1/2 | 1/3 | 1 | 1/3 | 1/3 |
| B4 | 3 | 1/2 | 3 | 1 | 2 |
| B5 | 3 | 1/2 | 3 | 1/2 | 1 |
And (3) calculating to obtain:
feature vector w= (0.536,2.268,0.45,1.552,1.176) T Normalized to
=（0.08967，037908，0.07527，0.25939，0.19658） T Consistency index ci=0.05, random consistency index ri=1.12 (table look-up 2), consistency ratio cr=ci/ri=0.044
Pass the consistency check.
The second judgment matrix A2-B is shown in Table 6:
TABLE 6
| A3 | B1 | B2 | B3 |
| B1 | 1 | 1/4 | 1/6 |
| B2 | 4 | 1 | 1/3 |
| B3 | 6 | 3 | 1 |
And (3) calculating to obtain:
feature vector w= (0.347,1.101,2.621) T Normalized to
=（0.08522，0.27056，0.64422） T Consistency index ci=0.027, random consistency index ri=0.52 (table look-up 2), consistency ratio cr=ci/ri=0.052
Pass the consistency check.
The hierarchical single ranking weight coefficients are shown in table 7:
TABLE 7
The total ordering weight vector of the scheme layer is calculated, consistency is checked, and the weight of the scheme layer to the target layer is obtained as shown in table 8.
TABLE 8
As can be seen from table 8, CR =
According to the above results, the safety weight value is highest for the curve section, 76.506%, and safety running is most important in the curve section. The road section is improved to obtain the whole intelligent level, and the scene of the weak traffic participants, abnormal vehicle early warning, curve early warning, road danger condition prompt, front collision early warning and side collision early warning can be deployed preferentially.
Fig. 4 is a block diagram illustrating a road section and vehicle cooperation scene priority determining apparatus according to an exemplary embodiment.
As shown in fig. 4, according to a second aspect of the embodiment of the present invention, there is provided a road section vehicle-road cooperation scene priority determining apparatus, including:
an acquisition module 41, configured to acquire a road segment characteristic of a target road segment of a target layer;
the classification module 42 is configured to classify the scene of the vehicle-road collaboration according to different criteria of a criterion layer, where the criteria of the criterion layer include: safe running of the vehicle, efficient running of the vehicle and vehicle information service;
the construction module 43 is configured to construct a hierarchical structure model of the vehicle-road collaboration scene required to be deployed on the target road section based on a hierarchical analysis method according to the vehicle-road collaboration scenes of the scheme layers corresponding to the respective criteria;
a first determining module 44, configured to determine a first judgment matrix between the target layer and the criterion layer, and a second judgment matrix between the criterion layer and the scheme layer according to a scale relationship between each level of the vehicle-road collaborative scene hierarchy model;
and the second determining module 45 is configured to determine a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the first judging matrix and the second judging matrix.
According to a third aspect of embodiments of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the road segment and vehicle co-scenario priority determination method according to any one of the embodiments of the first aspect when the program is executed.
According to a fourth aspect of embodiments of the present invention there is provided a non-transitory computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of the first aspects.
It is further understood that the term "plurality" in this disclosure means two or more, and other adjectives are similar thereto. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It is further understood that the terms "first," "second," and the like are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the expressions "first", "second", etc. may be used entirely interchangeably. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the invention.
It will further be appreciated that although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (4)
1. The method for determining the priority of the cooperative scene of the road and the vehicle is characterized by comprising the following steps:
step S101, obtaining the road section characteristics of a target road section of a target layer;
step S102, classifying the scenes of the vehicle-road cooperation according to different criteria of a criterion layer, wherein the criteria of the criterion layer comprise: safe running of the vehicle, efficient running of the vehicle and vehicle information service;
step S103, constructing a vehicle-road collaborative scene hierarchical structure model required to be deployed for the target road section based on a hierarchical analysis method according to the vehicle-road collaborative scenes of the scheme layers corresponding to the various criteria;
step S104, determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relation among all the layers of the vehicle-road collaborative scene hierarchical structure model;
step S105, the weights of all elements of the hierarchy are obtained according to the first judgment matrix and the second judgment matrix, and a vehicle-road collaborative scene deployment priority allocation scheme of the target road section is determined;
in the step S101, a standard consulting, on-site investigation and questionnaire method is adopted to perform feature analysis on road sections under different terrains, including road line shape features, whether traffic sign marks are intact, whether tunnels are present, whether effective bridges are present, whether side slopes are present, whether sharp turns are present, whether long downgrades are present, whether shielding is present, and surrounding environments are present;
in the step S102, the vehicle-road cooperative scenario for safe driving of the vehicle includes: emergency braking early warning, blind area early warning, slow-speed vehicle early warning, abnormal vehicle early warning, weak traffic participant collision early warning, road danger prompt, curve early warning, speed limit early warning, forward collision early warning and side collision early warning;
the vehicle-road cooperative scene for efficiently driving the vehicle comprises: emergency vehicle yielding, front congestion early warning, traffic limiting management, vehicle speed guidance, route guidance and guidance;
the vehicle cooperative scene of the vehicle information service includes: near field payment, remote payment and service information reminding;
in the step S103, the target layer in the hierarchical structure model is a road section, the criterion layer is 3 dimensions of safety, high efficiency and information service, and the scheme layer is a vehicle-road cooperative scene;
in the step S104, determining a first judgment matrix between the target layer and the criterion layer and a second judgment matrix between the criterion layer and the scheme layer according to the scale relationship between the layers of the hierarchical structure model of the cooperative scene of the vehicle, including:
according to the hierarchical structure model of the vehicle-road cooperative scene, a consistent matrix method and an questionnaire method are adopted, scale relation values among all levels of the road section characteristics are determined according to a scale method of 1-9, and an average value is obtained to obtain the first judgment matrix and the second judgment matrix;
in the step S105, determining a deployment priority allocation scheme of the cooperative vehicle-road scene of the target road section according to the first judgment matrix and the second judgment matrix, including:
step S201, calculating the maximum feature root of each first judgment matrix and the corresponding feature vector thereof, normalizing the feature vectors to obtain first single-order weight vectors, and carrying out ordering and consistency verification, wherein the first single-order weight vectors are used for representing weight values of relative importance of a target layer relative to a criterion layer;
step S202, calculating the maximum feature root of each second judgment matrix and the corresponding feature vector thereof, normalizing the feature vectors to obtain second single-order weight vectors, and carrying out ordering and consistency verification, wherein the second single-order weight vectors are used for representing weight values of relative importance of the criterion layer relative to the scheme layer;
step S203, according to the first single-order weight vector and the second single-order weight vector, determining a first-level weight total ordering of weight values of the relative importance of all criteria of the criterion layer to the target layer and a second-level weight total ordering of weight values of the relative importance of all schemes of the scheme layer to the target layer, and performing consistency verification;
step S204, determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the ordering results of the first-level weight total ordering and the second-level weight total ordering;
in the step S203, a first-level total weight ranking of the weight values of the relative importance of all the criteria of the criterion layer to the target layer and a second-level total weight ranking of the weight values of the relative importance of all the schemes of the scheme layer to the target layer are determined according to the first single-order weight vector and the second single-order weight vector, and consistency verification is performed, including:
synthesizing and arranging the first single-ranking weight vectors in the order from top to bottom to obtain the first-level weight total ranking a 1 ，a 2 ，…，a m Wherein m represents the criterion number of the criterion layer, a m A weight value representing an mth criterion;
and calculating the second-level weight total sequencing according to the first-level weight total sequencing and the second single-sequencing weight vector by adopting the following calculation formula:
wherein a is j Weight value, b, representing the jth criterion in the first hierarchical weight total ranking ij A weight value for a j criterion representing an i-th scheme in the second single rank weight vector;
the consistency deviation check coefficient of the total sequence of the hierarchical weights is calculated by adopting the following calculation formula:
wherein CI is m CI value, RI, representing the mth criterion and its corresponding scheme layer m An RI value representing a scheme layer corresponding to the mth criterion;
and when the consistency deviation check coefficient is smaller than 0.1, determining that the consistency check is passed, otherwise, not passing the consistency check, and readjusting the judgment matrix.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
and determining a vehicle-road collaborative scene deployment priority allocation scheme of the target road section according to the sequence of the weights from big to small according to the ordering results of the first-level weight total ordering and the second-level weight total ordering.
3. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the road segment and vehicle co-scene priority determination method according to any one of claims 1 to 2 when the program is executed.
4. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the road segment vehicle co-scene priority determination method according to any of claims 1 to 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310004953.4A CN115689259B (en) | 2023-01-04 | 2023-01-04 | Method, equipment and medium for determining priority of cooperative scene of road section and vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310004953.4A CN115689259B (en) | 2023-01-04 | 2023-01-04 | Method, equipment and medium for determining priority of cooperative scene of road section and vehicle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115689259A CN115689259A (en) | 2023-02-03 |
| CN115689259B true CN115689259B (en) | 2023-06-30 |
Family
ID=85056904
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310004953.4A Active CN115689259B (en) | 2023-01-04 | 2023-01-04 | Method, equipment and medium for determining priority of cooperative scene of road section and vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115689259B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116714579B (en) * | 2023-08-04 | 2023-10-24 | 交通运输部公路科学研究所 | Predictive safety control method and system for automatic driving vehicle based on vehicle-road cooperation |
| CN117669364A (en) * | 2023-11-22 | 2024-03-08 | 中汽研汽车检验中心(广州)有限公司 | Method, server and medium for extracting test scene of lane keeping auxiliary system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016127918A1 (en) * | 2015-02-13 | 2016-08-18 | 北京嘀嘀无限科技发展有限公司 | Transport capacity scheduling method and system |
| CN110880236B (en) * | 2018-09-05 | 2022-06-14 | 阿里巴巴集团控股有限公司 | Road condition information processing method, device and system |
| CN111862600A (en) * | 2020-06-22 | 2020-10-30 | 中汽研智能网联技术(天津)有限公司 | Traffic efficiency assessment method based on vehicle-road cooperation |
| CN112525551B (en) * | 2020-12-10 | 2023-08-29 | 北京百度网讯科技有限公司 | Drive test method, device, equipment and storage medium for automatic driving vehicle |
| CN112907955A (en) * | 2021-01-28 | 2021-06-04 | 天津职业技术师范大学(中国职业培训指导教师进修中心) | Evaluation method of vehicle-road cooperative system based on information fusion |
-
2023
- 2023-01-04 CN CN202310004953.4A patent/CN115689259B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN115689259A (en) | 2023-02-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115689259B (en) | Method, equipment and medium for determining priority of cooperative scene of road section and vehicle | |
| Wan et al. | The extended VIKOR method for multi-attribute group decision making with triangular intuitionistic fuzzy numbers | |
| Duleba et al. | A dynamic analysis on public bus transport's supply quality by using AHP | |
| CN111611524B (en) | Gas risk assessment and safety supervision resource matching method and device | |
| CA2530909C (en) | System and method of optimizing a fixed route transit network | |
| CN107466469B (en) | Map drawing method, cloud platform and server thereof | |
| WO2020147391A1 (en) | Driving proficiency evaluation method and apparatus, computer device and storage medium | |
| Fogue et al. | A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms | |
| CN110288825B (en) | Traffic control subregion clustering division method based on multi-source data fusion and SNMF | |
| CN110942198A (en) | Passenger path identification method and system for rail transit operation | |
| CN110288826B (en) | Traffic control subregion clustering division method based on multi-source data fusion and MILP | |
| CN111785023A (en) | Vehicle collision risk early warning method and system | |
| Duran et al. | Transit network design with pollution minimization | |
| CN113988705A (en) | Traffic safety risk assessment method and device | |
| Kim et al. | Iterative optimization algorithm with parameter estimation for the ambulance location problem | |
| Shah et al. | Relationship between road traffic features and accidents: An application of two-stage decision-making approach for transportation engineers | |
| Huo et al. | A correlated random parameters model with heterogeneity in means to account for unobserved heterogeneity in crash frequency analysis | |
| Prakisya et al. | Utilization of K-nearest neighbor algorithm for classification of white blood cells in AML M4, M5, and M7 | |
| CN116386316A (en) | Traffic risk prediction method and device, electronic equipment and storage medium | |
| Tan et al. | “robust-squared” imputation models using bart | |
| CN115345347A (en) | Traffic risk prediction method, device, computer equipment and storage medium | |
| Turoń | Selection of Car Models with a Classic and Alternative Drive to the Car-Sharing Services from the System’s Rare Users Perspective | |
| CN105976099A (en) | Fuzzy information-based aerospace model scientific research and production management level evaluation method | |
| Park et al. | Data-driven dynamic stacking strategy for export containers in container terminals | |
| Shi et al. | Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |