A method for quantifying the interaction intensity among drivers in traffic flow

Abstract

The present invention is applicable to the technical field of autonomous vehicles, and provides a method for quantifying the interaction intensity among drivers in a traffic flow. This method provides a visualization method and a quantification index for understanding the interaction behavior among drivers. On the one hand, it realizes the expression and understanding of the interaction behavior among drivers in a visual way, and evaluates the performance of autonomous driving algorithms during the interaction process in a quantitative way, so as to evaluate and optimize autonomous driving algorithms in a more credible way, and then improve people's trust and acceptance of autonomous driving. On the other hand, the quantification of the interaction behavior among drivers can reveal the prior assumptions implicit in human reasoning about driving interaction behavior, and can then be applied to the development of cognitive-based intelligent algorithms and the development of cost functions for autonomous driving interaction behavior planning, etc., so as to promote autonomous vehicles to better understand humans and promote their entry into market applications.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving vehicles, and in particular to a method for quantifying the intensity of interaction between drivers in a traffic flow. Background Art

[0002] Self-driving cars are gradually being integrated into urban traffic flows. When a driver is driving in traffic, he or she will inevitably interact with other drivers. This is because the urban traffic system is a complex and huge social system, in which individuals need to achieve their respective goals through mutual interaction, such as cooperation or competition. When self-driving cars provide services, they will inevitably interact with other drivers on the road. The interactive behavior of robots requires the guidance of algorithms, and the design of algorithms requires quantitative scientific basis. Therefore, scientifically and effectively identifying and quantifying the large number of interactive behaviors that exist in human driving has important inspiration and guiding significance for the popularization and development of self-driving and even service robots.

[0003] Existing studies have used game theory, partially objective Markov decision processes, social value orientation theory and other methods to generate interactive behaviors of autonomous driving, and obtained social attribute parameters related to interaction from human driving data through inverse reinforcement learning and other technologies. However, there is still a lack of a unified mathematical definition of interactive behaviors in driving, and the mechanism of interactive behaviors in driving is still unclear. As a result, most of these studies can only learn and imitate from massive driving data through machine learning methods under the guidance of qualitative sociological theories to generate driving interactive behaviors that are as consistent as possible with humans. In the field of sociology, the study of human interactive behaviors has always been a hot topic. Social psychology research has found that socially meaningful information plays a key role in people's recognition of other people's interactions. The more social significance an interaction has, the easier it is for people to recognize the interaction. Social space has also been defined to explain interactions between people. But to date, these theories are still approximate conceptual descriptions and cannot quantify interactive behaviors. The application of virtual reality technology enables psychophysicists to create virtual avatars and construct social scenes close to reality, which makes it possible to adjust social attributes under experimental conditions and conduct quantitative experiments. Sociologists and brain scientists have measured the main parameters that affect people's recognition of other people's conversations in virtual space, and based on this, they have established a social interaction field model, which successfully quantifies the intensity of interaction when people talk. However, this model can only recognize interaction behaviors in static or slow-moving scenes, and is unable to recognize and quantify interaction behaviors in more complex dynamic driving scenes. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method for quantifying the intensity of interaction between drivers in traffic flow, aiming to solve the problems raised in the above background technology.

[0005] The embodiment of the present invention is implemented by comprising the following steps:

[0006] Step 1: Model the traffic flow as a high-order network and use it as a framework to explain driver interaction to provide a mathematical definition of driver interaction behavior and its interaction intensity;

[0007] Step 2: Within a certain spatial area, the driver can have an impact on individuals that are not in contact with him, thus increasing the impact that the driver has on the surroundings. Modeling as a driver impedance field model, and defining the direction, shape and strength of the impedance field;

[0008] Step 3: Quantify the intensity of interaction between drivers and divide them into intervals.

[0009] A further technical solution is that in step 1, the traffic flow network is a A collection of nodes and A collection of edges The mathematical object consists of a node, each node represents a driver, and each edge represents a connected road topology. The drivers on the bus will interact with each other, their driving intentions and behaviors will affect each other, and the interactive behavior between drivers is the representation of this interaction; The influence of a driver on the surrounding drivers at a certain moment is recorded as , the interaction function between drivers is recorded as ,but The interaction intensity of the drivers at this moment, that is, the interaction behavior intensity, is shown in formula (1):

[0010] (1);

[0011] Assuming that all drivers follow the principle of utility maximization to plan their driving behavior, their reward function is: ; The road The current state is recorded as ,but On The interactive behavior of each driver at the next moment The definition of is shown in formula (2):

[0012] (2);

[0013] Expanding to the entire traffic flow network, considering each edge of the network as a whole, the intensity of the interaction behavior in the entire traffic flow is defined as shown in formula (3):

[0014] (3);

[0015] In summary, drivers in traffic flow Via the network The driver transmits his own influence and interacts with each other; under this framework, a mathematical definition of the interaction behavior and interaction intensity between drivers is given. In the future, the influence of the driver on the surrounding environment will be further given on this basis. The calculation method of the interaction function between different drivers .

[0016] A further technical solution is that in step 2, the vehicle impedance field model This includes the direction, shape and strength of the vehicle impedance field, as follows:

[0017] Direction of the driver's impedance field: The driver's impedance field should represent the driver's driving intention, and its direction should be the driver's planned driving direction; the direction of the impedance field Classification discussion: When the vehicle is moving, the direction of the impedance field should point to the driver's preview position; when the vehicle is stationary, the direction of the impedance field should point to the end point of the trajectory; the direction of the driver's impedance field The definition is shown in formula (4):

[0018] (4);

[0019] The angle starts from the positive direction of the horizontal axis and is positive in the counterclockwise direction. ; and are the horizontal and vertical coordinates of the vehicle, For the current moment, , is the time when the trajectory ends; is the preview time. Under natural driving conditions, the driver's preview time is between 0.5s and 2s, and is taken as 1.5s here; is the four-quadrant inverse tangent function, defined as shown in formula (5):

[0020] (5);

[0021] The shape of the driver's impedance field is defined as a shape with the position of the vehicle as the center of the upper and lower sides and extending in the direction of the impedance field. A diverging trapezoid with a top base width of , waistline length is , the angle between the two waists is , then the coordinates of the upper and lower vertices of the impedance field trapezoid are shown in formula (6):

[0022] (6);

[0023] Defining Directivity As shown in formula (7), if the impedance field direction It forms an acute angle with the horizontal axis of the coordinate system, then is 1, otherwise -1;

[0024] (7);

[0025] The coordinates of the lower vertex of the impedance field trapezoid can be deduced as follows:

[0026] (8);

[0027] The strength of the driver impedance field: The value of each point in the impedance field, that is, the strength, represents the impact of the vehicle on that point. The strength of is defined as formula (9):

[0028] (9);

[0029] It should be pointed out that the driver impedance field is an inherent attribute of a certain vehicle or driver in the traffic flow and has nothing to do with other drivers in the traffic flow. It can be regarded as an inherent parameter of a node in a high-order network of traffic flow. As long as the driver drives the vehicle in the traffic flow, a driver impedance field will be formed around him.

[0030] Further technical solution, in step 3, assuming , At one point, the two cars If there is interaction, the impedance fields of the two vehicles are , There must be an intersection at this moment ; will be at the intersection The sum of the field strengths at each point of the impedance field of the two vehicles in is recorded as and , as shown in formula (10):

[0031] (10);

[0032] Then, the interaction between the two vehicles can be defined as the intersection of the impedance fields of the two vehicles: The sum of the impedance field strength in ; Abstract the special situations in driving scenarios into two categories and record them as special operators and special operators , corresponding to the following two low interaction intensity situations: If the impedance fields of the two vehicles are approximately parallel, it means that the two vehicles are expected to travel in the same direction, and they are in the following or meeting condition. The special operator Take 0, as shown in formula (11), where To determine whether it is approximately parallel, ; If the bottom edge of the impedance field of the vehicle is completely in the impedance field of the other vehicle, and the impedance field of the vehicle is in the same direction as that of the other vehicle, it means that the vehicle is completely blocking the direction of travel of the other vehicle. If this is not a merging scene, then the special operator Take 0. The flag bit of whether it is a merging scene is recorded as , if the scene is a merging scene, then ,otherwise, ,but As shown in formula (12); As shown in formula (12);

[0033] (11);

[0034] (12);

[0035] In summary, at the time , , The interaction between the two workshops, that is, the interaction intensity The definition is shown in formula (13):

[0036] (13);

[0037] At this point, the intensity of workshop interaction has been successfully quantified.

[0038] A further technical solution is that in step 3, in order to clarify the actual meanings corresponding to different numerical intervals, the subjective judgment data of human beings on the workshop interaction, that is, the interaction probability data judged by the experimental participants, is collected. , and through a large number of repeated experiments to reduce the influence of individual factors and random factors, which are used as indicators to verify the effectiveness of the interaction intensity quantification method;

[0039] On this basis, a classification standard for interaction intensity was established, which classified the workshop interaction intensity into It is divided into three levels: strong, medium and weak, and the numerical ranges corresponding to different intensity levels are given to establish the quantitative indicators and reference standards of workshop interaction intensity; the reference standard intervals corresponding to different interaction intensity levels are determined by the percentile method in statistics: Determine the interaction probability It can be regarded as a weak interaction. It can be regarded as a strong interaction, and the interaction between the two is a medium interaction; assuming The corresponding data is Percentile point, The corresponding data is percentile, the lower limit of the interval of strong interaction level is the interaction intensity data The value corresponding to the percentile point , similarly, the upper limit of the interval of weak interaction level is ; In summary, the interaction intensity value range corresponding to weak interaction is , the interval corresponding to the interaction is , the interval corresponding to strong interaction is ;

[0040] Combined with the collected data, the reference value ranges corresponding to different interaction intensity categories were calibrated. When , it belongs to weak interaction strength. Belongs to medium interaction strength, Belongs to strong interaction strength.

[0041] An embodiment of the present invention provides a method for quantifying the intensity of interaction between drivers in traffic flow, which provides a visualization method and quantitative indicators for understanding the interaction between drivers. On the one hand, this method realizes the expression and understanding of the interaction between drivers in a visual way, and evaluates the performance of the autonomous driving algorithm in the interaction process in a quantitative way, so as to evaluate and optimize the autonomous driving algorithm in a more credible way, thereby improving people's trust and acceptance of autonomous driving; on the other hand, the quantification of the interaction between drivers by this method can reveal the implicit prior assumptions of humans when reasoning about driving interaction behavior, and can be applied to the development of cognitive-based intelligent algorithms, and used in the development of cost functions for autonomous driving interaction behavior planning, so as to promote autonomous driving vehicles to better understand humans and promote their market application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is the high-order network of traffic flow;

[0043] Figure 2 is the driver impedance field;

[0044] Figure 3 A flowchart of a method for quantifying the intensity of interaction between drivers in traffic flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0047] like Figure 1 and3 As shown, a method for quantifying the intensity of interaction between drivers in a traffic flow provided by an embodiment of the present invention includes the following steps:

[0048] Step 1: Construct a high-order traffic flow network;

[0049] The traffic flow high-order network constructed is as follows Figure 1 As shown in the figure, each edge of the network is circled in the same color. The network contains 10 nodes and 6 edges. Human interaction is defined as a process of joint participation and interaction. In traffic flow, the interaction between drivers manifests as mutual influence on driving decisions. However, the mechanism of this mutual influence remains unclear. To solve this problem, traffic flow is modeled as a high-order network and used as a framework to explain driver interaction, so as to provide a mathematical definition of driver interaction behavior and its interaction intensity.

[0050] Drivers transmit their influence through the connected road network and are influenced by other drivers to achieve cooperative interaction between vehicles. This behavior is similar to nodes in the Internet. Vehicle drivers are regarded as nodes in the traffic flow network, and the connected road topology is regarded as the edge of the network. Since a road may contain multiple vehicle nodes, and the traditional network can only describe the connection between two nodes, a high-order network is used to mathematically describe the traffic flow.

[0051] Traffic flow network is a A collection of nodes and A collection of edges A mathematical object composed of. Each node represents a driver, and each edge represents a connected road topology. The drivers on the vehicle will interact with each other, their driving intentions and behaviors will affect each other, and the interaction between drivers is the representation of this interaction. The influence of a driver on the surrounding drivers at a certain moment is recorded as , the interaction function between drivers is recorded as ,but The interaction intensity of the drivers at this moment, that is, the interaction behavior intensity, is shown in formula (1):

[0052] (1);

[0053] Assuming that all drivers follow the principle of utility maximization to plan their driving behavior, their reward function is: . The current state is recorded as ,but On The interactive behavior of each driver at the next moment The definition of is shown in formula (2):

[0054] (2);

[0055] Expanding to the entire traffic flow network, considering each edge of the network as a whole, the intensity of the interaction behavior in the entire traffic flow is defined as shown in formula (3):

[0056] (3);

[0057] In summary, drivers in traffic flow Via the network The driver transmits his own influence and interacts with each other. In this framework, the mathematical definition of the interaction behavior and interaction intensity between drivers is given. The influence of the driver on the surrounding environment will be further given on this basis. The calculation method of the interaction function between different drivers .

[0058] Step 2: Construct the driver impedance field model;

[0059] The driver impedance field model constructed is as follows: Figure 2 , where the brighter the color, the stronger the impact of the vehicle on the point; the darker the color, the weaker the impact of the vehicle on the point. The red line is the vehicle's driving trajectory, the green dot is the driver's preview point, and the direction of the driver's impedance field points to the preview point. In a certain spatial area, the driver can have an impact on individuals that are not in contact with him. This property is very similar to the field in physics. Inspired by this, the impact of the driver on the surrounding The model is a driver impedance field model. The vehicle impedance field model will be introduced from three aspects: direction, shape and intensity. .

[0060] The direction of the driver's impedance field: The driver's impedance field should represent the driver's driving intention, and its direction should be the driver's planned driving direction. Vehicles not only express their intentions through driving actions, but also exchange information through lights and horns, which cannot be intuitively displayed through vehicle trajectory data. To solve this problem, the direction of the impedance field is discussed in a classified manner. :When the vehicle is moving, the direction of the impedance field should point to the driver's preview position; when the vehicle is stationary, the direction of the impedance field should point to the end point of the trajectory. This classification is because the driving intention of the vehicle when it is stationary is mainly expressed through lights and horns, and this type of information cannot be reflected in the trajectory data in the short time domain. Therefore, the end point can only be used as the anchor point of the driver's driving intention. The direction of the driver's impedance field The definition is shown in formula (4):

[0061] (4);

[0062] The angle starts from the positive direction of the horizontal axis and is positive in the counterclockwise direction. . and are the horizontal and vertical coordinates of the vehicle, For the current moment, , is the time when the trajectory ends. is the preview time. Under natural driving conditions, the driver's preview time is between 0.5s and 2s, and is taken as 1.5s here. is the four-quadrant inverse tangent function, defined as shown in formula (5):

[0063] (5);

[0064] The shape of the driver's impedance field: The shape of the impedance field is defined as a trapezoid, rather than the symmetrical circle commonly seen in similar studies. Studies on human interaction behavior have shown that people's influence on their surroundings is not symmetrical. They only exert social influence in the direction their faces are facing, and have little influence on the rear. The same is true when driving. Drivers will only actively interact and influence each other with vehicles within their field of vision. The human field of vision is fan-shaped, and the shape of the impedance field should be similar. Taking into account the computational cost and driving reality, this study defines the shape of the driver's impedance field as a circle with the location of the vehicle as the center of the upper and lower sides, and extending in the direction of the impedance field. Diverging trapezoid. The width of the upper base of the trapezoid is , waistline length is , the angle between the two waists is , then the coordinates of the upper and lower vertices of the impedance field trapezoid are shown in formula (6):

[0065] (6);

[0066] Defining Directivity As shown in formula (7), if the impedance field direction It forms an acute angle with the horizontal axis of the coordinate system, then is 1, otherwise it is -1.

[0067] (7);

[0068] The coordinates of the lower vertex of the impedance field trapezoid can be deduced as follows:

[0069] (8);

[0070] The strength of the driver's impedance field: The value of each point in the impedance field, that is, the strength, represents the impact of the vehicle on that point. Similar to the interaction of humans at rest, the impact of the driver on a certain point in the surrounding area is obviously related to the relative distance and relative angle between the two. In addition, since driving is a dynamic scene, speed is also an important indicator for understanding driver behavior. Different vehicle speeds will have different effects on driving behavior. For example, the following distance at high speeds is farther, which is also in line with people's driving common sense. Taking all the above factors into consideration, any point in the driver's impedance field is The strength of is defined as formula (9):

[0071] (9);

[0072] In summary, the impact of drivers on the surroundings in traffic flow is modeled as the driver impedance field, and the direction, shape and intensity of the impedance field are defined, such as Figure 2 It should be pointed out that the driver impedance field is an inherent attribute of a certain vehicle or driver in the traffic flow, and has nothing to do with other drivers in the traffic flow. It can be regarded as an inherent parameter of a node in a high-order network of traffic flow. As long as the driver drives the vehicle in the traffic flow, a driver impedance field will be formed around him.

[0073] Step 3: Interaction intensity between drivers and its interval division;

[0074] As mentioned before, the driver's influence on the surroundings is limited to its impedance field, so it is assumed that , At one point, the two cars If there is interaction, the impedance fields of the two vehicles are , There must be an intersection at this moment . will be at the intersection The sum of the field strengths at each point of the impedance field of the two vehicles in is recorded as and , as shown in formula (10):

[0075] (10);

[0076] Then, the interaction between the two vehicles can be defined as the intersection of the impedance fields of the two vehicles: The sum of the impedance field strength in However, due to the complexity and variety of driving scenarios, there are still some special cases, such as two cars following each other on the main road, or two cars turning left at an intersection. In these cases, the impedance fields of the two cars overlap a lot, but the probability of interaction between the two cars is extremely low. These specific special cases are abstracted into two categories and recorded as special operators respectively. and special operators They correspond to the following two low interaction intensity situations: If the impedance fields of the two vehicles are approximately parallel, it means that the two vehicles are expected to travel in the same direction, and they are in the following or meeting condition. Take 0, as shown in formula (11), where The threshold for determining whether it is approximately parallel ( ). If the bottom edge of the impedance field of the vehicle is completely in the impedance field of the other vehicle, and the impedance field of the vehicle is in the same direction as that of the other vehicle, it means that the vehicle is completely blocking the direction of travel of the other vehicle. If this is not a merging scene, then the special operator Take 0. The flag bit of whether it is a merging scene is recorded as , if the scene is a merging scene, then ,otherwise, ,but As shown in formula (12); As shown in formula (12):

[0077] (11);

[0078] (12);

[0079] In special operators In the calculation of , the self-vehicle and other vehicles are distinguished. This is because in the actual vehicle experiment, it is found that even in the same scene, the judgments of drivers of different vehicles will be significantly different. The occurrence of this phenomenon is analyzed in the results and discussion. , , The interaction between the two workshops, that is, the interaction intensity The definition is shown in formula (13):

[0080] (13)

[0081] So far, the interaction intensity of the workshop has been successfully quantified. However, the numerical value of the interaction intensity alone is not applicable. The actual meaning of different numerical ranges should be clarified, and the effectiveness of the quantitative method should be verified through convincing experiments. This is a difficulty in this type of research.

[0082] In order to obtain reliable data for classifying interaction intensity, this method collects subjective judgment data of human interaction in workshops, and reduces the influence of individual factors and random factors through a large number of repeated experiments, so as to obtain data that can represent the general basis of human judgment. It should be pointed out that the data collected is the interaction probability data judged by the experimental participants. , rather than instantaneous interaction intensity data . The difference between the two is that the interaction probability is an indicator to measure whether two vehicles interact and the degree of mutual influence between the two vehicles within a period of time. This is because the interaction between vehicles is a continuous process over a period of time, so the time domain for determining whether two vehicles interact should be a continuous time window, not an instant. The interaction intensity is an indicator to measure the degree of instantaneous vehicle-to-vehicle influence, which characterizes the instantaneous value of the degree of mutual influence between the behaviors, decisions, and trajectories of the two vehicles. Since it is difficult for experimental participants to make accurate judgments on the instantaneous interaction intensity, it is impossible to measure accurate and consistent interaction intensity data; the interaction probability over a period of time can be measured more accurately through experiments, so we select the interaction probability data as an indicator to verify the effectiveness of the interaction intensity quantification method.

[0083] On this basis, a classification standard for interaction intensity was established. It is divided into three levels: strong, medium and weak, and the numerical ranges corresponding to different intensity levels are given to establish the quantitative indicators and reference standards of workshop interaction intensity. The reference standard intervals corresponding to different interaction intensity levels are determined by the percentile method in statistics: This method considers that the interaction probability It can be regarded as a weak interaction. It can be regarded as a strong interaction, and the interaction between the two is a medium interaction. The corresponding data is Percentile point, The corresponding data is percentile, the lower limit of the interval of strong interaction level is the interaction intensity data The value corresponding to the percentile point , similarly, the upper limit of the interval of weak interaction level is In summary, the interaction strength value range corresponding to weak interaction is , the interval corresponding to the interaction is , the interval corresponding to strong interaction is Combined with the collected data, this method calibrates the reference value intervals corresponding to different interaction intensity categories. When , it belongs to weak interaction strength. Belongs to medium interaction strength, Belongs to strong interaction strength.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for quantifying the intensity of interaction between drivers in traffic flow, characterized in that: The following steps are involved: Step 1: Model the traffic flow as a high-order network and use it as a framework to explain driver interaction to provide a mathematical definition of driver interaction behavior and its interaction intensity; Step 2: The impact of the driver on the surrounding Modeling as a driver impedance field model, and defining the direction, shape and strength of the impedance field; Step 3: Quantify the intensity of interaction between drivers and divide them into intervals; In step 1, the traffic flow network is a A collection of nodes and A collection of edges The mathematical object consists of a node, each node represents a driver, and each edge represents a connected road topology. The drivers on the bus will interact with each other, their driving intentions and behaviors will affect each other, and the interactive behavior between drivers is the representation of this interaction; The influence of a driver on the surrounding drivers at a certain moment is recorded as , the interaction function between drivers is recorded as ,but The interaction intensity of the drivers at this moment, that is, the interaction behavior intensity, is shown in formula (1): （1）； Assuming that all drivers follow the principle of utility maximization to plan their driving behavior, their reward function is: ; The road The current state is recorded as ,but On The interactive behavior of each driver at the next moment The definition of is shown in formula (2): （2）； Expanding to the entire traffic flow network, considering each edge of the network as a whole, the intensity of the interaction behavior in the entire traffic flow is defined as shown in formula (3): （3）； In summary, drivers in traffic flow Via the network The drivers transmit their own influence and interact with each other, and in this framework, mathematical definitions of the interaction behavior and interaction intensity between drivers are given; In step 2, the vehicle impedance field model This includes the direction, shape and strength of the vehicle impedance field, as follows: Direction of the driver's impedance field: The driver's impedance field should represent the driver's driving intention, and its direction should be the driver's planned driving direction; the direction of the impedance field Classification discussion: When the vehicle is moving, the direction of the impedance field should point to the driver's preview position; when the vehicle is stationary, the direction of the impedance field should point to the end point of the trajectory; the direction of the driver's impedance field The definition is shown in formula (4): （4）； The angle starts from the positive direction of the horizontal axis and is positive in the counterclockwise direction. ; and are the horizontal and vertical coordinates of the vehicle, For the current moment, , is the time when the trajectory ends; is the preview time. Under natural driving conditions, the driver's preview time is between 0.5s and 2s, and is taken as 1.5s here; is the four-quadrant inverse tangent function, defined as shown in formula (5): （5）； The shape of the driver's impedance field is defined as a shape with the position of the vehicle as the center of the upper and lower sides and extending in the direction of the impedance field. A diverging trapezoid with a top base width of , waistline length is , the angle between the two waists is , then the coordinates of the upper and lower vertices of the impedance field trapezoid are shown in formula (6): （6）； Defining Directivity As shown in formula (7), if the impedance field direction It forms an acute angle with the horizontal axis of the coordinate system, then is 1, otherwise -1; （7）； The coordinates of the lower vertex of the impedance field trapezoid can be deduced as follows: （8）； The strength of the driver impedance field: The value of each point in the impedance field, that is, the strength, represents the impact of the vehicle on that point. The strength of is defined as formula (9): （9）； The driver impedance field is an inherent property of a vehicle or driver in the traffic flow, and has nothing to do with other drivers in the traffic flow. It can be regarded as an inherent parameter of a node in a high-order network of traffic flow. As long as a driver drives a vehicle in the traffic flow, a driver impedance field will be formed around him. In step 3, it is assumed that At one point, the two cars If there is interaction, the impedance fields of the two vehicles are There must be an intersection at this moment ; will be at the intersection The sum of the field strengths at each point of the impedance field of the two vehicles in is recorded as and , as shown in formula (10): （10）； Then, the interaction between the two vehicles can be defined as the intersection of the impedance fields of the two vehicles: The sum of the impedance field strength in ; Abstract the special situations in driving scenarios into two categories and record them as special operators and special operators , corresponding to the following two low interaction intensity situations: If the impedance fields of the two vehicles are approximately parallel, it means that the two vehicles are expected to travel in the same direction, and they are in the following or meeting condition. The special operator Take 0, as shown in formula (11), where To determine whether it is approximately parallel, ; If the bottom edge of the impedance field of the vehicle is completely in the impedance field of the other vehicle, and the impedance field of the vehicle is in the same direction as that of the other vehicle, it means that the vehicle is completely blocking the direction of travel of the other vehicle. If this is not a merging scene, then the special operator Take 0; record the flag bit of whether it is a merging scene as , if the scene is a merging scene, then ,otherwise, ,but As shown in formula (12): （11）； （12）； In summary, at the time , The interaction between the two workshops, that is, the interaction intensity The definition is shown in formula (13): （13）。 2. The method for quantifying the intensity of interaction between drivers in traffic flow according to claim 1, characterized in that: In step 3, in order to clarify the actual meanings of different numerical ranges, the subjective judgment data of humans on the workshop interaction, that is, the interaction probability data judged by the experimental participants, are collected. , and through a large number of repeated experiments to reduce the influence of individual factors and random factors, which are used as indicators to verify the effectiveness of the interaction intensity quantification method; On this basis, a classification standard for interaction intensity was established, which classified the workshop interaction intensity into It is divided into three levels: strong, medium and weak, and the numerical ranges corresponding to different intensity levels are given to establish the quantitative indicators and reference standards of workshop interaction intensity; the reference standard intervals corresponding to different interaction intensity levels are determined by the percentile method in statistics: Determine the interaction probability It can be regarded as a weak interaction. It can be regarded as a strong interaction, and the interaction between the two is a medium interaction; assuming The corresponding data is Percentile point, The corresponding data is percentile, the lower limit of the interval of strong interaction level is the interaction intensity data The value corresponding to the percentile point , similarly, the upper limit of the interval of weak interaction level is ; In summary, the interaction intensity value range corresponding to weak interaction is , the interval corresponding to the interaction is , the interval corresponding to strong interaction is ; Combined with the collected data, the reference value ranges corresponding to different interaction intensity categories were calibrated. When , it belongs to weak interaction strength. Belongs to medium interaction strength, Belongs to strong interaction strength.

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