Method for Evaluating Vehicle Collision Risk Association Mode Based on Directed Weighted Graph

Abstract

This application belongs to the technical field of driving risk assessment, and discloses a method for evaluating the vehicle collision risk association pattern based on a directed weighted graph, including the following steps. Step 1: Through computer vision technology, identify the vehicles passing through the work area section from the traffic image video and extract information, where the extracted information includes the positions, speeds and spacings of each vehicle. Step 2: Use the extracted information to quantify the collision risk between the target vehicle and the interaction object ITA , and form a vehicle risk quantification matrix. The target vehicle is the rear vehicle, and the interaction objects are the front vehicle and the isolation facility. Step 3: Introduce a directed multi-weighted graph to describe the risk association characteristics of the vehicle group, so as to achieve the purpose of visually and quantitatively analyzing the collision risk relationship between all vehicles and isolation facilities in the work area.

Description

Technical Field

[0001] This application belongs to the technical field of driving risk assessment, and specifically relates to a method for evaluating the vehicle collision risk association pattern based on a directed weighted graph. Background Art

[0002] Highway work areas are specific areas specially set to effectively ensure the safety of vehicles during construction operations. Their settings mainly involve daily maintenance operations and reconstruction and expansion projects, and the demands for these two aspects of projects in China are both increasing continuously. On the one hand, many sections of China's highways have entered the middle and late stages of their service life, and the frequency of work area settings will continue to rise. On the other hand, the continuous growth of traffic volume will not only increase the frequency of daily maintenance operations but also require the improvement of the bearing capacity of highways through reconstruction and expansion projects. To ensure regional traffic access, the reconstruction and expansion of highways often adopt the mode of "constructing while maintaining traffic flow". However, the setting of work areas has changed the existing traffic environment, thus significantly increasing the risk of vehicle collisions within this section of the road.

[0003] Within the work area, the interactions between different types of traffic participants (such as vehicles, traffic cones, construction vehicles, etc.) are complex and variable, which poses challenges to the accurate quantification and situation representation of collision risks. Due to limitations in theoretical research and data collection methods, existing research often ignores these multi-object interactions, resulting in a series of problems such as insufficient comprehensive risk assessment, unclear optimization objects for static safety measures, and low timeliness of dynamic early warning. Therefore, it is necessary to study how to accurately quantify and evaluate the collision risks between vehicles and different traffic participants from the perspectives of potential field theory and computer vision technology in the complex scenario of multi-object interactions in the work area. This has extremely important practical significance for significantly improving the traffic safety level of the work area section.

[0004] Based on the above defects of the existing technology, there is an urgent need to propose a method for evaluating the vehicle collision risk association pattern in the work area based on graph theory and directed weighted graphs to solve the above problems. Summary of the Invention

[0005] The purpose of this application is to solve the problems of the existing technology and provide a method for evaluating the vehicle collision risk association pattern based on a directed weighted graph, providing a theoretical basis for evaluating the collision risks between vehicles and different traffic participants in such sections to ensure the driving safety of the work area section.

[0006] To solve the technical problems, the technical solution of this application is: a method for evaluating the vehicle collision risk association pattern based on a directed weighted graph, including the following steps:

[0007] Step 1: Identify the vehicles passing through the work area section from the traffic image video through computer vision technology and extract information, including the position, speed, and spacing of each vehicle;

[0008] Step 2: Use the extracted information to quantify the collision risk between the target vehicle and the interaction object ITA , form a vehicle risk quantification matrix, where the target vehicle is the rear vehicle, and the interaction object is the front vehicle and the isolation facility;

[0009] Step 3: Introduce a directed multi-weighted graph to characterize the risk association characteristics of the vehicle group;

[0010] Step 3-1: Introduce a directed multi-weighted graph, and use the target vehicle passing through the work area and the interaction object as the node set of the directed multi-weighted graph, denoted as ;

[0011] Step 3-2: Take the interaction relationship between the target vehicle and the interaction object as the directed edge of the directed multi-weighted graph, denoted as , , and the directed edge refers to the direction from the target vehicle to the interaction object ;

[0012] Step 3-3: Take the collision risk between the target vehicle and the interaction object ITA as the weight of each directed edge, and denote it as the weight of the directed edge ;

[0013] Step 3-4: Use the directed multi-weighted graph to represent the collision risk relationship between all target vehicles in the work area and the interaction object , and obtain a vehicle collision risk system correlation characterization graph.

[0014] This application uses the improved time to accident ( ITA ), and modifies the severity of the conflict by combining the relative speed difference index. This comprehensive method enables ITA to more objectively and accurately evaluate the collision risk.

[0015] Preferably, step 2 is specifically as follows: Calculate the target vehicle according to the position, speed and spacing of the vehicle and the interaction object for the collision risk ITA , and construct a vehicle risk quantification matrix;

[0016] The formula for the collision risk ITA is:

[0017] ;

[0018] In the formula:

[0019] and are the position and speed of the target vehicle at t time;

[0020] , and are the position, speed and spacing of the interaction object at t time;

[0021] is a correction factor.

[0022] Preferably, the isolation facility is a traffic cone, a guardrail or an abnormally parked vehicle.

[0023] Preferably, step 3 is specifically as follows:

[0024] Step 3-1: Introduce a directed multi-weighted graph, and use all the vehicles and isolation facilities passing through the work area as the node set of the directed multi-weighted graph, denoted as ;

[0025] Step 3-2: Take the interaction relationships between vehicles and between vehicles and isolation facilities as the directed edges of the directed multi-weighted graph, denoted as , and the directed edge refers to the target vehicle pointing to the interaction object ;

[0026] Step 3-3: Take the collision risks ITA between vehicles and between vehicles and isolation facilities as the weight of each directed edge, and denote it as the weight of the directed edge ;

[0027] Step 3-4: Use the directed multi-weighted graph to represent all the target vehicles in the work area and the interaction object The collision risk relationship is obtained to get the correlation representation diagram of the vehicle collision risk system.

[0028] Preferably, when there are two isolation facilities, the correlation representation diagram of the vehicle collision risk system has two centers, that is, each vehicle node has at least two directed edges, and all passing vehicles point to these two centers, which is called the multi-center type.

[0029] When there is one isolation facility, the correlation representation diagram of the vehicle collision risk system has one center, which is called the single-center type.

[0030] When the isolation facility is not considered or high-risk vehicles are screened out, the correlation representation diagram of the vehicle collision risk system is multiple groups, which is called the non-center type.

[0031] Preferably, after obtaining the correlation representation diagram of the vehicle collision risk system, high-risk vehicles are screened according to the risk threshold of the extreme field force F V . The nodes corresponding to high-risk vehicles and the directed edges between high-risk vehicles are retained to realize the correlation representation of high-risk vehicles. The correlation representation diagram of the vehicle collision risk system after screening is divided into multiple groups.

[0032] Preferably, the F V is used to quantify the collision risk of the target vehicle. The calculation formula of the extreme field force F V is:

[0033] ;

[0034] In the formula:

[0035] F V is the extreme field force;

[0036] is the collision potential field strength;

[0037] is the field force between the target vehicle and the interaction object ;

[0038] is the risk quantity of the interaction object .

[0039] Preferably, according to the extreme field force F V and taking the vehicle behind as the target vehicle , determine the following-following and overtaking phenomena between vehicles and whether there is a high-risk interaction relationship and whether a vehicle is a high-risk vehicle through different types of the two-node or three-node association model. The two-node association model includes two-node type A and two-node type B, and the three-node association model includes three-node type A, three-node type B, three-node type C, three-node type D, three-node type E, and three-node type F.

[0040] Compared with the prior art, the advantages of the present application are as follows:

[0041] (1) In response to the need for vehicle group collision risk assessment, the present application introduces a directed multi-weighted graph and proposes a vehicle collision risk association pattern assessment method. This method visually displays the risk correlation between all vehicles and isolation facilities in the work area section using the directed multi-weighted graph, and screens high-risk vehicles according to the risk threshold of the extreme field force F V to further quickly identify high-risk vehicle groups, clarify high-risk interactions, and further improve the accuracy of risk quantification and the comprehensiveness of risk assessment in the work area;

[0042] (2) The assessment method of the present application combines a directed multi-weighted graph, which not only shows the directional relationship between nodes but also expresses the strength or cost of these relationships through the weights of the directed edges. When using the directed multi-weighted graph to characterize the system correlation of vehicle collisions in the work area, due to different isolation facilities, the correlation graph of the collision risk system shows different patterns, which can reflect the influence of different isolation facilities on the system correlation of the collision risk;

[0043] (3) The present application visually displays the correlation between vehicles through a directed multi-weighted graph, can effectively quantify the interaction risk relationship between vehicles and isolation facilities in the work area section, clarify high-risk interactions, improve the accuracy of the high-risk vehicle group identification method, and thus provide real-time and predictive support for the safety management of the work area. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a flowchart of the vehicle collision risk association pattern assessment method based on a directed weighted graph of the present application;

[0045] Figure 2 is a correlation graph of the vehicle collision risk system in Embodiment 1 of the present application;

[0046] Figure 3 is a correlation graph of the vehicle collision risk system of the multi-center type in Embodiment 1 of the present application;

[0047] Figure 4 is a correlation graph of the vehicle collision risk system of the single-center type in Embodiment 1 of the present application;

[0048] Figure 5 It is the correlation representation diagram of the centerless type vehicle collision risk system in Embodiment 1 of the present application;

[0049] Figure 6 It is the correlation representation diagram of the vehicle collision risk system of several types of high-risk vehicles in Embodiment 1 of the present application;

[0050] Figure 7 and Figure 9 It is the correlation representation diagram of the vehicle collision risk system in the upstream transition section during operation in Embodiment 2 of the present application;

[0051] Figure 8 and Figure 10 It is the correlation representation diagram of the vehicle collision risk system during non-operation in Embodiment 2 of the present application;

[0052] Figure 11 It is the high-risk interaction relationship diagram between high-risk vehicles in Embodiment 2 of the present application;

[0053] Figure 12 It is the high-risk interaction relationship diagram between high-risk vehicles in Embodiment 2 of the present application. Detailed implementation manners

[0054] The present application will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but the present application is not limited to these embodiments. The present application covers any alternatives, modifications, equivalent methods, and solutions made within the essence and scope of the present application. In order to enable the public to have a thorough understanding of the present application, specific details are described in detail in the following embodiments of the present application, and those skilled in the art can fully understand the present application without these detailed descriptions.

[0055] The present application discloses a method for evaluating a vehicle collision risk association pattern based on a directed weighted graph, including the following steps:

[0056] Step 1: Through computer vision technology, identify the vehicles passing through the work area section from the traffic image video and extract information, where the extracted information includes the position, speed, and spacing of each vehicle;

[0057] Step 2: Utilize the extracted information to quantify the collision risk between the target vehicle ITA and the interaction object to form a vehicle risk quantification matrix, where the target vehicle is the rear vehicle, and the interaction object

[0058] is the front vehicle and the isolation facility;

[0059] Step 3-1: Introduce a directed multi-weighted graph with the target vehicle passing through the work area and the interaction object as the node set of the directed multi-weighted graph, denoted as ;

[0060] Step 3-2: Take the interaction relationship between the target vehicle and the interaction object as the directed edge of the directed multi-weighted graph, denoted as , , and the directed edge refers to the direction from the target vehicle to the interaction object ;

[0061] Step 3-3: Take the collision risk between the target vehicle and the interaction object ITA as the weight of each directed edge, and denote it as the weight of the directed edge ;

[0062] Step 3-4: Use the directed multi-weighted graph to represent the collision risk relationship between all target vehicles in the work area and the interaction object , and obtain a vehicle collision risk system relevance characterization graph.

[0063] Preferably, the specific content of step 2 is: According to the position, speed and spacing of the vehicle (the units of the position, speed and spacing of the vehicle use conventional units and keep corresponding), calculate the collision risk between the target vehicle and the interaction object ITA , and construct a vehicle risk quantification matrix;

[0064] The calculation formula for the collision risk ITA (s -1 ) is:

[0065] ;

[0066] In the formula:

[0067] and are the position (m) and speed (m / s) of the target vehicle at the moment t ;

[0068] , and are the position, speed and acceleration of the interaction object At t the position (m), speed (m / s), and spacing (m) at the moment;

[0069] is the correction coefficient;

[0070] The units of the above parameters use conventional units and just keep corresponding.

[0071] Preferably, the isolation facility is a traffic cone, a guardrail, or a vehicle parked abnormally.

[0072] Preferably, step 3 is specifically as follows:

[0073] Step 3-1: Introduce a directed multi-weighted graph, and use all vehicles passing through the work area and isolation facilities as the node set of the directed multi-weighted graph, denoted as ;

[0074] Step 3-2: Take the interaction relationships between vehicles and between vehicles and isolation facilities as the directed edges of the directed multi-weighted graph, denoted as , and the directed edge refers to the target vehicle pointing to the interaction object ;

[0075] Step 3-3: Take the collision risks ITA between vehicles and between vehicles and isolation facilities as the weights of each directed edge, and denote it as the weight of the directed edge ;

[0076] Step 3-4: Use the directed multi-weighted graph to represent the collision risk relationships between all target vehicles in the work area and the interaction objects , and obtain the vehicle collision risk system correlation characterization graph.

[0077] Preferably, when there are two isolation facilities, the vehicle collision risk system correlation characterization graph has two centers, that is, each vehicle node has at least two directed edges, and all passing vehicles point to these two centers, which is called the multi-center type;

[0078] When there is one isolation facility, the vehicle collision risk system correlation characterization graph has one center, which is called the single-center type;

[0079] When the isolation facility is not considered, or high-risk vehicles are screened out, the vehicle collision risk system correlation characterization graph is multiple groups, which is called the non-center type.

[0080] Preferably, after obtaining the vehicle collision risk system correlation characterization graph, according to the extreme field force FV The risk threshold is used to screen high-risk vehicles, retain the nodes corresponding to the high-risk vehicles and the directed edges between the high-risk vehicles, realize the correlation representation of the high-risk vehicles, and the correlation representation graph of the vehicle collision risk system after screening is divided into multiple groups.

[0081] Preferably, the extreme field force F V The calculation formula is:

[0082] ;

[0083] In the formula:

[0084] F V is the extreme field force;

[0085] is the collision potential field strength;

[0086] is the field force between the target vehicle and the interaction object ;

[0087] is the risk quantity of the interaction object ;

[0088] The units of the above parameters use conventional units and just keep corresponding.

[0089] Preferably, according to the extreme field force F V and taking the vehicle behind as the target vehicle , through different types of the two-node or three-node correlation models, determine the following-following and overtaking phenomena between vehicles and whether there is a high-risk interaction relationship between each vehicle and whether it is a high-risk vehicle. The two-node correlation model includes two-node type A and two-node type B, and the three-node correlation model includes three-node type A, three-node type B, three-node type C, three-node type D, three-node type E and three-node type F.

[0090] Embodiment 1

[0091] As Figure 1 shown, the present application provides a method for evaluating a vehicle collision risk association mode based on a directed weighted graph, which includes the following steps:

[0092] Step 1: Through computer vision technology, identify the vehicles passing through the work area section from the traffic image video and extract information, and the extracted information includes the positions, speeds and spacings of each vehicle;

[0093] Step 2: Using the extracted information, quantify the target vehicle and the interaction object Collision risk ITA , a vehicle risk quantification matrix is formed, and the target vehicle is the vehicle behind, and the interaction object is the vehicle in front and the isolation facility, and the isolation facility is a traffic cone;

[0094] Step 3: Introduce a directed multi-weighted graph to characterize the risk association characteristics of the vehicle group;

[0095] Introduce a directed multi-weighted graph. The vehicles and traffic cones are used as the node set of the directed multi-weighted graph, denoted as ; The interaction relationships between vehicles and between vehicles and traffic cones are used as the directed edges of the graph, denoted as , and the directed edge refers to the direction from the target vehicle to the interaction object ; The collision risk ITA between vehicles is used as the weight of each edge and is denoted as the weight of the directed edge .

[0096] Use the directed multi-weighted graph to represent the collision risk relationships between all vehicles in the work area; for the purpose of being able to visually and quantitatively analyze the collision risk relationships between all vehicles and traffic cones in the work area; as Figure 2 shown, it is a representation diagram of the system relevance of vehicle collision risks in the work area based on the directed multi-weighted graph.

[0097] As Figures 3 - 5 shown, it is a representation diagram of the system relevance of three typical vehicle collision risks;

[0098] As Figure 3 shown, it is a representation diagram of the system relevance of vehicle collision risks of the multi-center type. When considering multiple isolation facilities (such as traffic cones, guardrails, abnormally parked vehicles) simultaneously, the representation diagram of the system relevance of vehicle collision risks has two centers, that is, each vehicle node has at least two edges, and all vehicles passing through the work area point to these two centers, which is called the multi-center type;

[0099] As Figure 4 shown, it is a representation diagram of the system relevance of vehicle collision risks of the single-center type. When only considering an object fixed in the road domain space, the representation diagram of the system relevance of vehicle collision risks has only one center, which is called the single-center type;

[0100] As Figure 5As shown in the figure, it is a correlation representation diagram of the vehicle collision risk system of the non - centralized type. When the isolation facilities are not considered or high - risk vehicles are screened out, the correlation representation diagram of the vehicle collision risk system shows multiple groups, which is called the non - centralized type.

[0101] Further, on the basis of the directed multi - weighted graph representing the correlation of the vehicle collision risk system in the work area, further according to the risk threshold of the extreme field force F V screen high - risk vehicles, that is, only retain the nodes corresponding to high - risk vehicles and the directed edges between high - risk vehicles, and the correlation representation of high - risk vehicles can be realized. After screening, the directed multi - weighted Figure 1 is generally divided into multiple groups.

[0102] Such as Figure 6 As shown in the figure, it is a correlation representation diagram of the vehicle collision risk system of several types of high - risk vehicles. According to the extreme field force F V and taking the vehicle behind as the target vehicle , it is possible to determine the following - following, overtaking phenomena between vehicles and whether there is a high - risk interaction relationship between vehicles through different types of the double - node or triple - node correlation model. Figure 6 In the figure, (a) is the double - node type A, (b) is the double - node type B, (c) is the triple - node type A, (d) is the triple - node type B, (e) is the triple - node type C, (f) is the triple - node type D, (g) is the triple - node type E, and (h) is the triple - node type F.

[0103] Embodiment 2

[0104] To make the purpose, technical solutions and advantages of this application clearer, in Embodiment 2 of this application, the collision risk relationships of specific cases AW, AN, BW, and BN are combined with the attached drawings to illustrate the specific implementation cases of this application.

[0105] Such as Figures 7 - 10 As shown in the figure, it is a correlation representation diagram of the vehicle collision risk system in four cases. It should be noted that due to considering the limited available space of the figure, the weights in the figure are omitted, and in this embodiment, only traffic cones are considered as an isolation facility, and all traffic cones are regarded as a linear entity.

[0106] Figure 7 And Figure 9 As shown in the figure, it is a correlation representation diagram of the vehicle collision risk system in the upstream transition section during the operation period ( Figure 7 represents case AW, Figure 9 represents case BW), and its style shows a single - center type, with the node representing the traffic cone as the center.

[0107] Figure 8 And Figure 10Vehicle collision risk system correlation characterization diagram during non-operation period shown ( Figure 8 representing case AN, Figure 10 representing case BN), whose style is of the non-centered type.

[0108] It should be noted that since the isolation facilities are not considered, many vehicles passing alone are not shown in the figure. At the same time, it is found from the figure that there are vehicle groups with multiple complex collision risk correlation relationships in the four cases, and the number of vehicle groups during operation is significantly higher than that during non-operation. In addition, by comparing Figure 8 and Figure 10 it can be found that: the complexity of the collision risk relationship and the number of vehicle groups on section B during non-operation are significantly higher than those on section A, which means that generally the collision risk on section B may be higher than that on section A, and the setting of the operation area increases this trend.

[0109] As Figures 11 - 12 shown, it is a high-risk interaction relationship diagram between high-risk vehicles.

[0110] Based on the vehicle collision risk system correlation characterization of the above four cases, high-risk vehicles are screened out, that is, only the edges and nodes related to high-risk vehicles are retained to identify high-risk vehicle groups and evaluate the interaction relationships between high-risk vehicles. Figure 11 Samples A and B in Figure 12 represent two relatively complex high-risk vehicle association model samples extracted from the surveillance video,

[0111] It can be seen from this that the high-risk interaction relationship with only two nodes is not easily affected by other vehicles, while the collision risks among cluster vehicles with more than two nodes are interrelated and are more likely to be affected by the improper risk avoidance behavior of one of the vehicles, resulting in accidents.

[0112] According to statistics, there are 17 vehicle groups with high-risk interaction relationships in case AW, among which 15 groups are two-node association models and 2 groups are complex association models with three nodes or more, accounting for 11.76%; there are 31 vehicle groups with high-risk interaction relationships in case BW, among which 19 groups are two-node association models and 12 groups are complex association models with three nodes or more, accounting for 38.71%. This shows that the collision risk correlation relationship in case BW is significantly higher than that in case AW, which also means that for the same form of operation area setting on different sections of the same highway, there may be significant differences in the impact on road traffic safety.

[0113] In summary, the vehicle collision risk association pattern evaluation method provided by the embodiment of the present application, by introducing a directed multi-weighted graph, uses the vehicles passing through the work area and traffic cones as the node set of the directed multi-weighted graph, and the interaction relationships between vehicles and between vehicles and traffic cones as the directed edges of the directed multi-weighted graph; quantifies the collision risk between the target vehicle and the interacting vehicle through the extreme field force of the collision risk potential field in the work area, and then uses the directed multi-weighted graph to represent the collision risk relationships between all vehicles in the work area. Finally, it aims to visually and quantitatively analyze the collision risk relationships between all vehicles and traffic cones in the work area. It realizes the rapid identification of high-risk vehicle groups, clarifies high-risk interaction relationships, and further improves the accuracy of risk quantification and the comprehensiveness of risk assessment in the work area.

[0114] The above has made a detailed description of the preferred implementation mode of the present application. However, the present application is not limited to the above implementation mode. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the purpose of the present application.

[0115] Many other changes and modifications can be made without departing from the concept and scope of the present application. It should be understood that the present application is not limited to a specific implementation mode, and the scope of the present application is defined by the appended claims.

Claims

1. A vehicle collision risk association pattern assessment method based on a directed weighted graph, characterized in that: The following steps are involved: Step 1: Using computer vision technology, identify vehicles passing through the work zone from traffic imagery and extract information, including the location, speed, and spacing of each vehicle. Step 2: Quantify the target vehicle using the extracted information Interact with objects Collision risk ITA , forming a vehicle risk quantification matrix, the target vehicle For the rear vehicle, the interaction object For the vehicle ahead and the isolation facility; Step 3: Introduce a directed multi-weighted graph to characterize the risk association characteristics of the vehicle group; Step 3-1: Introduce a directed multi-weighted graph to target vehicles passing through the work area Interact with objects As a node set of a directed multi-weighted graph, denoted as ; Step 3-2: Move the target vehicle Interact with objects The interaction relationship between them is taken as the directed edge of the directed multi-weighted graph, denoted as , , directed edges The target vehicle Point to interactive object ; Step 3-3: Move the target vehicle Interact with objects Risk of collision between ITA As the weight of each directed edge, and recorded as the directed edge Weight ; Step 3-4: Using Directed Multi-Weighted Graph Indicates all target vehicles in the operation area Interact with objects The collision risk relationship between them is used to obtain the vehicle collision risk system correlation representation diagram; After obtaining the vehicle collision risk system correlation representation diagram, according to the extreme field force F V The high-risk vehicles are screened based on the risk threshold, and the nodes corresponding to the high-risk vehicles and the directed edges between the high-risk vehicles are retained to achieve the correlation representation of the high-risk vehicles. The correlation representation diagram of the vehicle collision risk system after screening is divided into multiple groups; The extreme field force F V The calculation formula is: ； Where: F V For extreme field forces; is the collision potential field strength; Target vehicle Interact with objects The field forces between For interactive objects the amount of risk; According to the extreme field force FV and the vehicle behind as the target vehicle , the different types of double-node or three-node association models are used to determine the following and overtaking phenomena between vehicles, whether there is a high-risk interaction relationship between vehicles, and whether they are high-risk vehicles. The double-node association model includes double-node type A and double-node type B, and the three-node association model includes three-node type A, three-node type B, three-node type C, three-node type D, three-node type E and three-node type F.

2. The vehicle collision risk association pattern assessment method based on directed weighted graph according to claim 1, characterized in that: The step 2 is specifically as follows: Calculate the target vehicle according to the position, speed and distance of the vehicle Interact with objects Risk of collision between ITA , construct vehicle risk quantification matrix; The collision risk ITA The calculation formula is: ； Where: and Target vehicle exist t Position and velocity at a given moment; 、 and For interactive objects exist t Position, speed and spacing at each moment; is the correction factor.

3. The vehicle collision risk association pattern assessment method based on directed weighted graph according to claim 1, characterized in that: The isolation facilities are traffic cones, guardrails or abnormally parked vehicles.

4. The vehicle collision risk association pattern assessment method based on directed weighted graph according to claim 3 is characterized in that: The step 3 is specifically as follows: Step 3-1: Introduce a directed multi-weighted graph, with all vehicles and isolation facilities passing through the work area as the node set of the directed multi-weighted graph, denoted as ; Step 3-2: The interaction between vehicles and vehicles and between vehicles and isolation facilities is taken as the directed edges of the directed multi-weighted graph, denoted as , directed edges The target vehicle Point to interactive object ; Step 3-3: Assess the collision risk between vehicles and between vehicles and isolation facilities. ITA As the weight of each directed edge, and recorded as the directed edge Weight ; Step 3-4: Using Directed Multi-Weighted Graph Indicates all target vehicles in the operation area Interact with objects The collision risk relationship between them is obtained to obtain the vehicle collision risk system correlation representation diagram.

5. The vehicle collision risk association pattern assessment method based on a directed weighted graph according to claim 4, characterized in that: When there are two isolation facilities, the vehicle collision risk system correlation representation graph has two centers, that is, each vehicle node has at least two directed edges, and all passing vehicles point to these two centers, which is called a multi-center type; When there is only one isolation facility, the vehicle collision risk system correlation representation diagram has one center, which is called a single-center type; When isolation facilities are not considered or high-risk vehicles are screened out, the vehicle collision risk system correlation representation diagram is multiple groups, which is called the centerless type.

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