Behavior conflict intensity quantification method and system in mixed traffic environment

Abstract

The invention discloses a behavior conflict intensity quantification method and system in a mixed traffic environment, and relates to the technical field of automatic driving. The method comprises the following steps: receiving motion states of a target vehicle and surrounding traffic participants; traffic participants in eight directions are selected as interaction objects, so that interaction pairs are formed between the target vehicle and different interaction objects; constructing a constant speed model to predict a target vehicle track and an interaction object track in future time, and judging whether a potential interaction conflict exists between the target vehicle and the interaction object; and if the target vehicle and the interaction object have the interaction conflict, the conflict intensity is obtained by solving the optimization problem. According to the method, the interaction relationship between different types of traffic participants is accurately described from the spatio-temporal trajectory level, the traffic participants around the target vehicle are selected as interaction objects, the future trajectory is predicted based on the constant speed model to judge potential conflicts, and compared with a traditional method, the dynamic interaction process between the traffic participants can be carefully captured, so that the dynamic interaction effect is improved. And the interaction risk is accurately reflected.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, specifically to a method and system for quantifying the intensity of behavioral conflicts in mixed traffic environments. Background Technology

[0002] With the continuous development of autonomous driving technology, autonomous vehicles are gradually integrating into the daily traffic environment, sharing road resources with heterogeneous traffic participants such as human-driven vehicles, pedestrians, and non-motorized vehicles. However, vehicle-to-vehicle connectivity technology is not yet fully widespread, and data sharing channels between vehicles and traffic participants are limited, making it difficult to obtain real-time information on the behavioral intentions and states of each party. Furthermore, the behavior of human-driven vehicles, pedestrians, and non-motorized vehicles exhibits strong time-varying characteristics; their driving intentions depend not only on the target path but also on interactions with other traffic participants. Different traffic participants possess different social attributes, such as vehicle type, speed, and weight, which are highly likely to influence the decision-making and planning of autonomous vehicles.

[0003] In mixed traffic environments, autonomous vehicles need to reasonably quantify the intensity of conflicts during interactions in order to understand the traffic environment and provide an interpretable basis for subsequent traffic situation perception, risk assessment, and decision-making. However, traditional risk assessment methods are mainly based on distance or speed difference, which have certain limitations in complex traffic environments and cannot accurately reflect the interaction risks between autonomous vehicles and other traffic participants. Therefore, this invention proposes a method and system for quantifying the intensity of behavioral conflicts in mixed traffic environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for quantifying the intensity of behavioral conflicts in mixed traffic environments. By extracting the spatiotemporal trajectories of heterogeneous traffic participants in the same scenario, the qualitative description of the participants' strength / weakness attributes and behavioral conflicts is transformed into an optimization problem for quantitative solution. This describes the intensity of conflict between different intelligent agents based on social attributes, thereby improving the scene understanding ability of autonomous vehicles.

[0005] According to a first aspect of the present invention, in order to achieve the above objective, the present invention provides the following technical solution: a method for quantifying the intensity of behavioral conflicts in a mixed traffic environment, comprising the following steps: The system receives the motion status of the target vehicle and surrounding traffic participants. The target vehicle is an autonomous vehicle with environmental perception capabilities, and the surrounding traffic participants include traffic participants in eight directions: directly in front, right front, right, right rear, directly rear, left rear, directly left, and left front of the target vehicle. Based on the received motion state of the target vehicle and surrounding traffic participants, traffic participants in eight directions are selected as interaction objects, so that the target vehicle and different interaction objects form interaction pairs. For the interaction pair consisting of the target vehicle and the interaction object, a constant speed model is constructed to predict the trajectory of the target vehicle and the trajectory of the interaction object in the future period, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction object. If there is an interaction conflict between the target vehicle and the interactive object, an optimization problem is established based on the minimum control amplitude required by the target vehicle and the interactive object to avoid the conflict risk, and the longitudinal acceleration control amount required by the target vehicle and the interactive object is obtained by solving the problem. By substituting the longitudinal acceleration control quantity into the interaction intensity function and interaction cost function that consider the social attributes of traffic participants, the conflict intensity is calculated.

[0006] Furthermore, the motion status of the target vehicle and surrounding traffic participants is acquired through onboard sensors, including cameras and LiDAR, as detailed below: (1) In the formula Let represent the longitudinal and lateral position coordinates of vehicle n at time t, respectively; These represent the longitudinal and lateral velocities, respectively. When n is 0, it represents the target vehicle.

[0007] Furthermore, the target vehicle forms interaction pairs with the interaction objects in eight directions, as detailed below: (2) In the formula, Represents an interactive set, Indicates the target vehicle. This represents the j-th interaction object.

[0008] Furthermore, for the interaction pair consisting of the target vehicle and the interaction object, a constant speed model is constructed to predict the trajectory of the target vehicle and the interaction object over a future period of time, thereby determining whether there are potential interaction conflicts between the target vehicle and the interaction object, as detailed below: (41) Predict the trajectory of the target vehicle and the trajectory of the interacting object within a future period based on the constant speed model: (3) In the formula, Let represent the predicted trajectories of the target vehicle and the interaction object j, respectively. Represents the set of prediction time steps. Indicates the prediction time domain, Represents the coordinates of the target vehicle's trajectory. Represents the coordinates of the trajectory of the interactive object; (42) Calculate whether the minimum value of the target vehicle trajectory and the interaction object trajectory meets the judgment condition. If the judgment condition is met, it is considered that there is a potential conflict between the target vehicle and the interaction object. The judgment condition is specifically expressed as follows: (4) In the formula, This indicates that the two predicted trajectories are respectively in The relative position at any given moment. These represent the times corresponding to the minimum relative positions of the two trajectories; (43) Calculate the arrival times of the target vehicle and the interactive object at the interaction point. If the difference between their arrival times is less than the conflict time threshold, then a conflict is considered to exist between the target vehicle and the interactive object, i.e.: (5) In the formula, Indicates the coordinates of the conflict point. Indicates the conflict time threshold. Indicates correspondence The target vehicle's trajectory coordinates at any given time. Indicates correspondence The coordinates of the trajectory of the interactive object at any given moment.

[0009] Furthermore, an optimization problem is established based on the minimum control amplitude required by the target vehicle and the interacting object to avoid conflict risks. The longitudinal acceleration control amount required by the target vehicle and the interacting object is obtained by solving the problem, as follows: (6) In the formula, This represents the longitudinal acceleration required to resolve the conflict; The distances from their initial positions to the point of conflict, respectively; Indicates the time when the two reach the point of conflict; This represents the speed of the target vehicle and the interacting object at time t; This represents the speed of both vehicles when they reach the destination; the time constraint in the formula ensures that the time difference between the target vehicle and the interactive object reaching the interaction point is not less than a safety threshold. This allows for risk avoidance.

[0010] Furthermore, by substituting the longitudinal acceleration control quantity into the interaction strength function and interaction cost function that consider the social attributes of traffic participants, the conflict intensity is calculated. The conflict quantification process considers social attributes represented by virtual mass, specifically including: (61) The longitudinal acceleration obtained from the solution is used to calculate the interaction force between the target vehicle and the interactive object: (7) In the formula, This indicates the level of interaction between the target vehicle and the interacting object; It represents the virtual quality of the target vehicle and the interactive object, and also represents the social attributes of different traffic participants; (62) Calculate the interaction cost between the target vehicle and the interactive object based on the concepts of self-kinetic energy and relative kinetic energy: (8) In the formula, This represents the interaction cost between the target vehicle and the interacting object; Indicates the speed of the target vehicle and the interacting object at time t; This indicates the relative velocity between the two. (63) Calculate the conflict intensity between the target vehicle and the interaction object based on the interaction strength and interaction cost: (9) In the formula, Indicates the intensity of the conflict between the target vehicle and the interacting object. It is calculated from equations (7) and (8).

[0011] According to a second aspect of the present invention, the present invention provides a system for quantifying the intensity of behavioral conflicts in a mixed traffic environment, for implementing the method for quantifying the intensity of behavioral conflicts in a mixed traffic environment described in the first aspect, comprising: The data receiving module is used to receive the motion status of the target vehicle and surrounding traffic participants. The target vehicle is an autonomous vehicle with environmental perception capabilities, and the surrounding traffic participants include traffic participants in eight directions: directly in front, right front, right, right rear, directly rear, left rear, directly left, and left front of the target vehicle. The interaction module is used to select traffic participants in eight directions as interaction objects based on the received motion state of the target vehicle and surrounding traffic participants, so that the target vehicle can form interaction pairs with different interaction objects. The judgment module is used to construct a constant speed model to predict the trajectory of the target vehicle and the trajectory of the interaction object in the future time for the interaction pair consisting of the target vehicle and the interaction object, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction object. The first calculation module is used to establish an optimization problem based on the minimum control amplitude required by the target vehicle and the interactive object to avoid the risk of interaction if there is an interaction conflict between the target vehicle and the interactive object, and solve the longitudinal acceleration control amount required by the target vehicle and the interactive object. The second calculation module is used to substitute the longitudinal acceleration control quantity into the interaction force function and interaction cost function that take into account the social attributes of traffic participants, and calculate the conflict intensity.

[0012] This invention has at least the following beneficial effects: 1. This invention can accurately depict the interaction relationships between different types of traffic participants at the spatiotemporal trajectory level. By selecting the nearest traffic participants in eight directions around the target vehicle as interaction objects, and predicting future trajectories based on a constant speed model to determine potential conflicts, this invention can capture the dynamic interaction process between traffic participants in greater detail compared to traditional methods, providing richer environmental information for autonomous vehicles.

[0013] 2. This invention incorporates the social attributes of traffic participants (such as virtual quality) into the conflict intensity calculation model, successfully transforming qualitative cognition into quantitative expression. This makes the previously vague description of conflict intensity concrete and measurable, providing a reliable quantitative basis for risk assessment and decision-making planning of autonomous vehicles, and enhancing the scientific nature and accuracy of decision-making.

[0014] 3. Compared to traditional risk assessment methods based on distance or speed differences, this invention can more accurately reflect the interactive risks of autonomous vehicles in complex traffic environments. By quantifying the intensity of conflicts, autonomous vehicles can anticipate potential dangers and make more reasonable decisions, thereby improving the rationality and interpretability of traffic situational awareness, enhancing the safety of autonomous vehicles in mixed traffic scenarios, and reducing the probability of accidents.

[0015] 4. This invention does not rely on external communication information; it can construct interaction pairs and determine potential conflicts solely based on the target vehicle's perception data. This effectively adapts to the mixed traffic environment where vehicle-to-vehicle connectivity is not yet widespread. This significantly improves the method's practicality and versatility, enabling it to play a crucial role with existing traffic infrastructure and vehicle configurations without requiring additional communication equipment.

[0016] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the conflict quantification method described in this invention; Figure 2 This is a schematic diagram of interactive objects in a mixed traffic environment according to the present invention; Figure 3 This is a schematic diagram of a scenario involving behavioral conflicts among heterogeneous traffic participants according to the present invention. Figure 4 This is a schematic diagram of the conflict intensity distribution after processing the dataset using the quantization method described in this invention. Figure 5 This is a schematic diagram illustrating the processing results of the present invention in a scenario where there is a potential conflict between the target vehicle and a non-motorized vehicle in front. Figure 6This is a schematic diagram illustrating the processing results of the present invention in a scenario where there is a potential conflict between the target vehicle and non-motorized vehicles and pedestrians crossing the street. Detailed Implementation

[0018] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] Example 1: Please see Figures 1-6 This invention provides a technical solution: a method for quantifying the intensity of behavioral conflicts in mixed traffic environments, comprising the following steps: S1: Utilize onboard sensors (such as cameras and LiDAR) to acquire the motion state of the target vehicle and surrounding traffic participants, where the target vehicle is an autonomous vehicle with environmental perception capabilities, represented as: (1) In the formula Let represent the longitudinal and lateral position coordinates of vehicle n at time t, respectively; These represent the longitudinal and lateral velocities, respectively. When n is 0, it represents the target vehicle. S2: Identify the nearest traffic participants in the eight directions directly in front, to the right, to the right, to the right rear, to the rear, to the left rear, to the left, and to the left front of the target vehicle as potential interaction targets. Figure 2 As shown, the target vehicle and the interaction object can form an interaction pair in pairs, represented as follows: (2) In the formula, Represents an interactive set, Indicates the target vehicle. This represents the j-th interaction object; S3: Define whether conflicting behaviors exist by predicting the trajectory distribution of the target vehicle and the interacting object, such as... Figure 3 As shown, it includes: S31: Predicting the trajectory of the target vehicle and the trajectory of the interacting object within a future time period based on a constant speed model: (3) In the formula, Let represent the predicted trajectories of the target vehicle and the interaction object j, respectively. Represents the set of prediction time steps. Indicates the prediction time domain; S32: Calculate whether the minimum value of the target vehicle trajectory and the interaction object trajectory satisfies the judgment condition. If the judgment condition is satisfied, it is considered that there is a potential conflict between the target vehicle and the interaction object. The judgment condition can be expressed as: (4) In the formula, This indicates that the two predicted trajectories are respectively in The relative position at any given moment. These represent the times corresponding to the minimum relative positions of the two trajectories; S33: When calculating the arrival time difference between the target vehicle and the interaction object at the interaction point, if the time difference is less than the conflict time threshold, then a conflict is considered to exist between the target vehicle and the interaction object. (5) In the formula, Indicates the coordinates of the conflict point. Indicates the conflict time threshold; S4: Establish an optimization problem based on the minimum control amplitude required by the target vehicle and the interactive object to avoid conflict risks, and solve it to obtain the required longitudinal acceleration control amount for each. The longitudinal acceleration required to avoid conflict behavior is solved by constructing the following optimization problem: (6) In the formula, This represents the longitudinal acceleration required to resolve the conflict; The distances from their initial positions to the point of conflict, respectively; Indicates the time when the two reach the point of conflict; This represents the speed of the target vehicle and the interacting object at time t; This represents the speed of both vehicles when they reach the destination; the time constraint in the formula ensures that the time difference between the target vehicle and the interactive object reaching the interaction point is not less than a safety threshold. This allows for risk avoidance. S5: Conflict intensity is quantified based on the required longitudinal acceleration, and the social attributes represented by virtual mass are considered in the conflict quantification process, including: S51: Use the solved longitudinal acceleration to calculate the interaction force between the target vehicle and the interactive object. (7) In the formula, This indicates the level of interaction between the target vehicle and the interacting object; It represents the virtual quality of the target vehicle and the interactive object, and also represents the social attributes of different traffic participants; S52: Calculate the interaction cost between the target vehicle and the interacting object based on the concepts of self-kinetic energy and relative kinetic energy. (8) In the formula, This represents the interaction cost between the target vehicle and the interacting object; This represents the speed of the target vehicle and the interacting object at time t; This indicates the relative velocity between the two. S53: Calculate the conflict intensity between the target vehicle and the interaction object based on interaction strength and interaction cost: (9) In the formula, Indicates the intensity of the conflict between the target vehicle and the interacting object. It is calculated from equations (7) and (8).

[0020] In autonomous driving scenarios, the actual mass of interacting agents is difficult to obtain. Furthermore, the purpose of this embodiment is not to construct an accurate physical model, but rather to capture the relative collision risk posed by interacting objects. Therefore, virtual mass is used when calculating the interaction force and cost. Specifically, the virtual mass of passenger vehicles is set to 1, while the virtual masses of vulnerable road users (pedestrians and non-motorized vehicles) and heavy vehicles are set to 0.5 and 1.5, respectively.

[0021] The conflict intensity quantification method proposed in this embodiment was tested and verified on the InD dataset. The InD dataset is an interactive dataset collected by RWTH Aachen University in Germany using drones. It contains rich interaction data of heterogeneous traffic participants, including the trajectories of passenger cars and heavy vehicles, as well as the trajectory data of vulnerable traffic participants such as bicycles and pedestrians. Figure 4 This study illustrates the distribution of conflict intensity between the target vehicle and different traffic participants. In the interaction scenario with passenger vehicles, since both parties have the same virtual mass and almost the same relative speed, the difference in conflict intensity mainly stems from the interaction force; that is, the target vehicle's acceleration during conflict resolution is typically lower than that of the other vehicle, reflecting a decision-making pattern similar to self-interested human driving behavior. In contrast, in the interaction scenarios with heavy vehicles, non-motorized vehicles, and pedestrians, the difference in conflict intensity is primarily caused by differences in virtual mass. Specifically, in the pedestrian scenario, the lower the pedestrian's speed and mass, the lower the interaction cost, resulting in a lower conflict intensity between the pedestrian and the target vehicle.

[0022] Figure 5 and Figure 6 This diagram illustrates the interaction scenarios between the target vehicle (ego) and pedestrians (ped), non-motorized vehicles (bic), and passenger vehicles (car). The numbers in parentheses indicate the intensity of conflict between the target vehicle and the interacting objects. Objects without a conflict intensity indicator represent those with which there is no potential conflict. Figure 5There is a potential conflict between the target vehicle and the non-motorized vehicle in front, and the conflict intensity of the target vehicle is 7, while the conflict intensity of the non-motorized vehicle is 4. Figure 6 The target vehicle has potential conflicts with non-motorized vehicles and pedestrians crossing the street. The conflict intensities for the target vehicle are 5 and 9, respectively, while those for non-motorized vehicles and pedestrians are 4 and 5, respectively. Quantitative results show that, from the perspective of the target vehicle, the conflict intensity from pedestrians crossing the street is significantly greater than that from non-motorized vehicles. This quantitative result is basically consistent with the understanding and cognition of human drivers regarding traffic situations, indicating that the constructed conflict intensity index can effectively reflect the potential interactive risks between different traffic participants. Through this index, not only can the dynamic interactive relationships between traffic subjects be quantified, but it can also provide an interpretable basis for subsequent traffic situation perception, risk assessment, and decision-making planning.

[0023] In summary, the behavioral conflict intensity quantification method provided by this invention in mixed traffic environments can characterize the interaction relationships between different types of traffic participants at the spatiotemporal trajectory level. By incorporating the social attributes of traffic participants into the conflict intensity calculation model, a transformation from qualitative cognition to quantitative expression is achieved. This method does not rely on external communication information; it can construct interaction pairs and judge potential conflicts solely based on the perception data of the target vehicle, effectively adapting to the mixed traffic environment where vehicle-to-vehicle connectivity is not yet widespread. Compared to traditional risk assessment methods based on distance or speed differences, this embodiment can more accurately reflect the interaction risks of autonomous vehicles in complex traffic environments, thereby improving the rationality and interpretability of traffic situational awareness, providing a reliable quantitative basis for risk assessment and decision-making planning of autonomous driving systems, and enhancing the safety of autonomous vehicles in mixed traffic scenarios.

[0024] Example 2: This embodiment provides a behavioral conflict intensity quantification system for a mixed traffic environment, used to implement the behavioral conflict intensity quantification method for a mixed traffic environment described in Embodiment 1, including: The data receiving module is used to receive the motion status of the target vehicle and surrounding traffic participants. The target vehicle is an autonomous vehicle with environmental perception capabilities, and the surrounding traffic participants include traffic participants in eight directions: directly in front, right front, right, right rear, directly rear, left rear, directly left, and left front of the target vehicle. The interaction module is used to select traffic participants in eight directions as interaction objects based on the received motion state of the target vehicle and surrounding traffic participants, so that the target vehicle can form interaction pairs with different interaction objects. The judgment module is used to construct a constant speed model to predict the trajectory of the target vehicle and the trajectory of the interaction object in the future time for the interaction pair consisting of the target vehicle and the interaction object, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction object. The first calculation module is used to establish an optimization problem based on the minimum control amplitude required by the target vehicle and the interactive object to avoid the risk of interaction if there is an interaction conflict between the target vehicle and the interactive object, and solve the longitudinal acceleration control amount required by the target vehicle and the interactive object. The second calculation module is used to substitute the longitudinal acceleration control quantity into the interaction force function and interaction cost function that take into account the social attributes of traffic participants, and calculate the conflict intensity.

[0025] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0026] For those skilled in the art, the specific meaning of the above terms in this invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on," "mounted on," "fixed to," or "set on" another element, it may be directly on the other element or there may be an intermediate element present. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element present. The terms "vertical," "horizontal," "upper," "lower," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible embodiments.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

[0028] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this disclosure. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

Claims

1. A method for quantifying the intensity of behavior conflicts in a mixed traffic environment, characterized in that, The method comprises the following steps: Receiving the motion state of a target vehicle and surrounding traffic participants, wherein the target vehicle is an automatic driving vehicle with environment perception capability, and the surrounding traffic participants include traffic participants in eight directions, i.e., front, right front, right, rear, left rear, left and left front of the target vehicle; Based on the received motion state of the target vehicle and the surrounding traffic participants, selecting the traffic participants in the eight directions as interaction objects, so that the target vehicle and different interaction objects form interaction pairs; For the interaction pair formed by the target vehicle and the interaction object, a constant speed model is constructed to predict the target vehicle trajectory and the interaction object trajectory in a future period of time, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction object; If there is an interaction conflict between the target vehicle and the interaction object, an optimization problem is established according to the minimum control amplitude required by the target vehicle and the interaction object when avoiding conflict risk, and the longitudinal acceleration control amount required by the target vehicle and the interaction object is solved; The longitudinal acceleration control amount is substituted into the interaction intensity function and the interaction cost function considering the social attribute of the traffic participant, and the conflict intensity is calculated.

2. The method of quantifying the strength of a behavior conflict in a mixed traffic environment according to claim 1, wherein: The motion state of the target vehicle and the surrounding traffic participants is obtained through a vehicle-mounted sensor, wherein the vehicle-mounted sensor includes a camera and a laser radar, and specifically comprises: （1） wherein respectively denote the longitudinal and lateral position coordinates of vehicle n at time t; respectively denote the longitudinal and lateral speed, when n is 0, the target vehicle is denoted.

3. The method of claim 2, wherein: The target vehicle and the interaction objects in the eight directions form interaction pairs, and specifically comprise: （2） In the formula, denotes a set of interaction pairs, denotes a target vehicle, denotes the jth interaction object.

4. The method of claim 3, wherein: For the interaction pair formed by the target vehicle and the interaction object, a constant speed model is constructed to predict the target vehicle trajectory and the interaction object trajectory in a future period of time, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction object, and specifically comprises: (41) Based on the constant speed model, the target vehicle trajectory and the interaction object trajectory in a future period of time are predicted: （3） wherein, respectively represent the predicted trajectory of the target vehicle and the interaction object j, represent a set of prediction time steps, represent a prediction time horizon, represent target vehicle trajectory coordinates, represent interaction object trajectory coordinates; (42) Whether the minimum value of the target vehicle trajectory and the interaction object trajectory satisfies a determination condition is calculated, if the determination condition is satisfied, it is considered that there is a potential conflict between the target vehicle and the interaction object, and the determination condition is specifically expressed as: （4） In the formula, represent the relative positions of the two predicted trajectories at respectively. respectively represent the time instants corresponding to the minimum relative positions of the two trajectories. (43) The time when the target vehicle and the interaction object reach the interaction point position is calculated, if the difference between the arrival times of the two is less than a conflict time threshold, it is considered that there is a conflict behavior between the target vehicle and the interaction object, i.e., （5） In the formula, represents the conflict point coordinates, represents the conflict time threshold, represents the corresponding moment target vehicle track coordinates, represents the corresponding moment interactive object track coordinates.

5. The method of claim 4, wherein: An optimization problem is established according to the minimum control amplitude required by the target vehicle and the interaction object when avoiding conflict risk, and the longitudinal acceleration control amount required by the target vehicle and the interaction object is solved, and specifically comprises: （6） wherein, denotes the longitudinal acceleration required for conflict resolution; denotes the distance of both from the initial position to the conflict point; denotes the time of both to reach the conflict point; denotes the speed of the target vehicle and the interacting object at time t; denotes the speed of both at the end point, the time constraint in the equation ensures that the time difference of the target vehicle and the interacting object to reach the interaction point is not less than the safety threshold , thus achieving risk avoidance.

6. The method of claim 5, wherein: The longitudinal acceleration control amount is substituted into the interaction intensity function and the interaction cost function considering the social attribute of the traffic participant, and the conflict intensity is calculated, and the social attribute represented by the virtual mass is considered in the conflict quantification process, and specifically comprises: (61) The longitudinal acceleration obtained by solving is used to calculate the interaction intensity of the target vehicle and the interaction object: （7） In the formula, represents the interaction force between the target vehicle and the interaction object; represents the virtual mass of the target vehicle and the interaction object, and also represents the social attribute of different traffic participants; (62) The interaction cost of the target vehicle and the interaction object is calculated based on the concept of self kinetic energy and relative kinetic energy: （8） In the formula, represents the interaction cost of the target vehicle and the interaction object; represents the speed of the target vehicle and the interaction object at time t; represents the relative speed of the two. (63) The conflict intensity of the target vehicle and the interaction object is calculated based on the interaction intensity and the interaction cost: （9） In the formula, represents the collision intensity of the target vehicle with the interaction object, is calculated from the formulae (7) and (8).

7. A system for quantifying the strength of a behavior conflict in a mixed traffic environment, for implementing the method for quantifying the strength of a behavior conflict in a mixed traffic environment according to any one of claims 1 to 6, characterized in that, The method comprises the following steps: The data receiving module is configured to receive motion states of a target vehicle and surrounding traffic participants, wherein the target vehicle is an automatic driving vehicle with environment perception capability, and the surrounding traffic participants include traffic participants in eight directions, i.e., front, right front, right, rear, left, left front, left rear and right rear of the target vehicle. The interaction module is configured to select the traffic participants in the eight directions as interaction objects based on the received motion states of the target vehicle and the surrounding traffic participants, so that the target vehicle and different interaction objects form interaction pairs. The judgment module is configured to construct a constant speed model to predict target vehicle trajectories and interaction object trajectories in a future period of time for the interaction pairs formed by the target vehicle and the interaction objects, so as to determine whether there is a potential interaction conflict between the target vehicle and the interaction objects. The first calculation module is configured to, if there is an interaction conflict between the target vehicle and the interaction objects, establish an optimization problem according to minimum control amplitudes required by the target vehicle and the interaction objects when avoiding conflict risks, and solve the optimization problem to obtain longitudinal acceleration control amounts required by the target vehicle and the interaction objects. The second calculation module is configured to substitute the longitudinal acceleration control amounts into an interaction strength function and an interaction cost function considering social attributes of the traffic participants, and calculate a conflict intensity.

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