A quantitative method and system for interactive decision-making between bicycles and vehicles based on heterogeneous bounded rationality

Abstract

The present invention discloses a method and system for quantifying heterogeneous bounded rational bicycle-vehicle interaction decision-making, relating to the field of road safety technology and addressing the technical problem that existing vehicle-bicycle interaction strategies cannot be dynamically adjusted. The present invention comprises obtaining the trajectories of bicycles and vehicles in an unsignaled intersection; constructing a payoff function for bicycles and vehicles, and establishing a two-person non-zero and non-cooperative dynamic game model by taking into account risk cost and delay cost; introducing heterogeneous bounded rational parameters for bicycles and vehicles through quantum response equilibrium, quantifying the bounded rationality of bicycle-vehicle interaction behavior and how its rationality changes during the dynamic game process, and estimating the bounded rationality parameters using maximum likelihood; and performing simulations based on the constructed heterogeneous bounded rational bicycle-vehicle interaction decision-making model and comparing them with actual observations. The present invention can be used to assess the safety of unsignaled intersections on urban roads and better address urban road traffic safety issues.

Description

Technical Field

[0001] The present invention relates to the technical field of road traffic safety, and in particular to a quantified method and system for heterogeneous bounded rational bicycle and vehicle interactive decision-making. Background Art

[0002] Currently, the goal of zero road accidents remains elusive. A key factor is the complexity of the traffic environment, characterized by the unpredictable nature of bicycle movement on urban roads. Compared to vehicles, cyclists can change direction and speed at any moment while crossing the road, exhibiting a higher degree of dynamic randomness. This makes it difficult for vehicles to accurately predict their movements, forcing them to stop and reducing traffic efficiency. Therefore, in order to ensure cyclist safety while improving vehicle efficiency in the complex environment of urban roads, more research is needed on the interaction between vehicles and dynamic bicycles.

[0003] While game-theoretic Nash equilibrium offers a reasonable and valuable analytical approach for studying bicycle crossing decisions, it relies on two strong assumptions: (a) each player is a perfect optimizer, always adopting a strategy that maximizes their expected payoff; and (b) each player fully understands their opponent's decision-making model and can therefore perfectly predict their opponent's action strategy. However, complete rationality rarely exists in bicycle-vehicle game decisions. Due to the bounded rationality of both cyclists and vehicle drivers, neither of these assumptions holds true in bicycle-vehicle games. Cyclists cannot fully understand their opponents and cannot accurately assess vehicle motion information such as stopping distance and speed. For example, when the speed of an oncoming vehicle exceeds a certain threshold, cyclists are more likely to underestimate the vehicle's stopping distance and speed. While the concept of bounded rationality has been explored in the field of human-vehicle interaction, its application to bicycle crossing decisions has not been specifically studied. Furthermore, existing bicycle-vehicle interaction models assume that most bicycle crossing decisions are one-off, failing to theoretically explain the impact of the position and speed of the bicycle and vehicle on each individual bicycle movement decision. Once either the vehicle or the bicycle makes a yield decision, it cannot dynamically adjust to changes in the relationship between the bicycle and the vehicle. Bicycles' decision-making and movement during street crossings occur almost simultaneously, yet these studies typically separate these two processes, lacking a degree of realism. Summary of the Invention

[0004] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a heterogeneous bounded rational bicycle-vehicle interaction decision quantification method and system, which solves the technical problem that the existing vehicle-bicycle interaction strategy cannot be dynamically adjusted as the movement relationship between the bicycle and the vehicle changes when either the vehicle or the bicycle makes a yield decision.

[0005] A quantitative method for heterogeneous bounded rational bicycle-vehicle interaction decision-making is proposed. The results of the heterogeneous bounded rational bicycle-vehicle interaction decision-making are processed and analyzed through the following steps:

[0006] Step A: Data collection and processing: A drone is used to select an area of the intersection without signal control for video recording, and the semi-automated software PeTrack is used to extract the trajectories of bicycles and vehicles.

[0007] Step B: Considering the risk cost (safety) and delay cost (efficiency), construct the benefit function of bicycles and vehicles, establish a two-person non-zero and non-cooperative dynamic game model, and solve it using the n-person game model.

[0008] Step C: Introduce heterogeneous bounded rationality parameters of bicycles and vehicles through quantum response equilibrium, quantify the bounded rationality of the interaction between bicycles and vehicles and the changes in their rationality during the dynamic game process, use maximum likelihood to estimate the bounded rationality parameters, and substitute them into the two-person non-zero and non-cooperative dynamic game model to obtain a heterogeneous bounded rational bicycle and vehicle interaction decision-making model.

[0009] Step D: Conduct simulation based on the constructed heterogeneous bounded rational bicycle-vehicle interaction decision model and compare it with the actual observations to verify the model effect.

[0010] Furthermore, in step B, the interaction between the bicycle and the vehicle is divided into a decision cycle [t a ,t e ], including n time steps, each time step is Δt. Within a specific time step, the benefits of bicycles and vehicles under different strategy combinations are obtained, and a two-person non-zero and non-cooperative dynamic game model is constructed.

[0011] Furthermore, the action strategies of bicycles and vehicles can be categorized as acceleration, constant speed, and deceleration. The benefits of bicycles under different strategy combinations can be expressed by formula (1). The first and second terms in formula (1) represent the post-encroachment time (PET) and the bicycle's desired movement speed, respectively. PET is a commonly used safety margin for bicycles and the most commonly used indicator for evaluating conflict safety. It refers to the time difference between the first road user leaving the conflict zone and the next road user arriving in the conflict zone. The benefits of bicycles pursuing efficiency under each strategy combination can be expressed in terms of movement speed. Inspired by the approaching vehicle, cyclists want to move at the most convenient speed with the least energy consumption, as close to their desired movement speed as possible.

[0012]

[0013] in, represents the revenue of the bicycle at time t, δ, are the action strategy sets of bicycles and vehicles respectively; α1 and α2 represent weight coefficients, which are determined by the entropy weight method; d1 and d2 represent the distances between bicycles and vehicles to the conflict point respectively; They represent the speeds of bicycles and vehicles at time t under different strategies, is the speed of the bicycle and the vehicle at time t-1, v bd represents the desired speed of the bicycle, and Δt is the time step.

[0014] Furthermore, the benefits of the vehicle under different strategy combinations can be expressed by formula (3), where the first and second terms in formula (3) represent the potential collision risk and the vehicle's desired driving speed, respectively. When the vehicle's driving speed changes, the risk of the vehicle colliding with a bicycle increases sharply. At the same distance, the vehicle's current speed significantly affects the associated collision risk. During the interaction between the vehicle and the bicycle, the driving speed and relative distance have a significant impact on the potential collision risk. Therefore, the first term in formula (3) shows that under different strategy combinations, the vehicle's collision risk benefit depends on the relative position of the two participants and the speed of the vehicle. The higher the collision risk, the lower the vehicle's benefits. Similar to bicycles, the greater the deviation between the vehicle's acceleration strategy and the desired speed, the lower the benefit of the second term in formula (3), and vice versa.

[0015]

[0016] in, represents the vehicle's revenue at time t; β1 and β2 represent weight coefficients, which are determined by the entropy weight method; Indicates the relative distance between the bicycle and the vehicle; represents the speed of the vehicle under the corresponding strategy at time t; v vd Indicates the desired vehicle speed.

[0017] Furthermore, in step C, the heterogeneous bounded rationality of the interactive decision-making behaviors of bicycles and vehicles is specifically obtained through the following steps.

[0018] Step C1: Introducing quantum response equilibrium weakens the strong assumption that cyclists and vehicle drivers are completely rational, so that each player is no longer a perfect optimizer. Bicycles and vehicles will not always adopt strategies that maximize their benefits. The strategies adopted by each player are random, and both players cannot fully infer the strategies that the opponent may adopt. The bicycle crossing decision-making behavior is relaxed from complete rationality to bounded rationality. The bicycle chooses an advantageous strategy or an inferior strategy with a certain probability. As the decision time changes, the bounded rationality parameter β is introduced into the quantum response equilibrium to reflect the bounded rationality level in the interactive decision-making behavior of bicycles and vehicles. The bounded rationality level changes with the change of parameter β. The changes in the rationality of bicycles and vehicles include initial bounded rationality, final bounded rationality and learning rate. In the quantum response equilibrium, the bicycle's action strategy δ i is a random variable in the entire strategy set δ, and is also close to the vehicle action strategy of the bicycle is the strategy space For bicycles, each allowed action strategy δ i ∈δ are all with a certain probability P b (δ i ) is selected. Similarly, for the approaching vehicle, each action is performed with a certain probability is selected, see formula (4).

[0019]

[0020] Among them, P b (δ i ), Respectively represent the probability of each action strategy of bicycle and vehicle being selected; δ i is the action strategy of the bicycle, which is a random variable in the bicycle strategy set δ; is the vehicle action strategy, which is the vehicle strategy space The random variables in ; β is a bounded rational parameter; and denote the expected utility of bicycle and car respectively.

[0021] Furthermore, step C also includes step C2: according to the change of the bounded rationality parameter β over time, the bicycle crossing decision can be divided into the best case, the worst case and the acceptable case. Specifically, when β→0, bicycles and vehicles will definitely choose the crossing strategy that maximizes the benefits. In this case, the quantum response equilibrium can be considered to be consistent with the Nash equilibrium; when β→∞, bicycles and vehicles lack the ability to take any rational crossing behavior, so the probability of all strategies being selected is equal. Here, the dynamic heterogeneous bounded rationality parameter β of cyclists and vehicle drivers is introduced. b (t) and β v(t):

[0022]

[0023] Among them, α b and β b denote the initial and final bounded rational parameters of the cyclist, α v , β v denote the initial and final bounded rational parameters of the vehicle driver, γ b is the cyclist’s dynamic heterogeneous bounded rational parameter learning rate, γ v is the dynamic heterogeneous bounded rational parameter learning rate of the vehicle driver;

[0024] When the decision time t ranges from 1 to ∞, the bicycle bounded rationality parameter β b (t) will be b Reduced to β b , while the vehicle's finite rationality parameter β v (t) will be v Reduced to β v , the process of bicycle heterogeneity bounded rationality parameter β b (t) and vehicle heterogeneous bounded rationality parameter β v The parameter α of (t) b , β b , γ b , α v , β v , γ v Perform maximum likelihood estimation.

[0025] Furthermore, in the step D, simulation is performed based on the constructed heterogeneous bounded rationality bicycle and vehicle interaction decision model, including embedding the established bounded rationality decision model into the bicycle and vehicle motion dynamics model for simulation, using the natural data of bicycle and vehicle interaction collected through field observation, calibrating the bounded rationality parameters in the dynamic game model through maximum likelihood estimation, and comparing the simulation results with the observation results to verify the model effect.

[0026] A heterogeneous bounded rational bicycle and vehicle interaction decision quantification system is used to implement a heterogeneous bounded rational bicycle and vehicle interaction decision quantification method, including: a trajectory collection module, a model building module, a rational parameter analysis module and a comparison and verification module;

[0027] The trajectory collection module is used to obtain the trajectories of bicycles and vehicles in the unsignalized intersection area;

[0028] The model building module is used to consider risk costs and delay costs, construct the profit functions of bicycles and vehicles under different action strategy combinations, establish a two-person non-zero and non-cooperative dynamic game model, and then solve the game model. The action strategies of bicycles and vehicles include acceleration, constant speed, and deceleration;

[0029] The rational parameter analysis module is used to introduce heterogeneous bounded rational parameters of bicycles and vehicles through quantum response equilibrium, quantify the bounded rationality of the interaction between bicycles and vehicles and the changes in their rationality during the dynamic game process, and estimate the bounded rational parameters using maximum likelihood;

[0030] The comparison and verification module is used to simulate the constructed heterogeneous bounded rational bicycle and vehicle interaction decision model and compare the model effect with the actual observation value.

[0031] The beneficial effects of the present invention include:

[0032] Considering safety and efficiency, we establish payoff functions for bicycles and vehicles, and develop a two-person, non-zero, and non-cooperative game model. Then, based on the quantum response equilibrium theory of game theory, we construct a bounded rationality model for bicycles and vehicles, quantifying their bounded rationality in their decision-making and how their rationality changes over time.

[0033] It provides a comprehensive, accurate, objective and novel road safety assessment method with clear applicable scenarios. In particular, this method can be used to assess the safety of unsignalized intersections on urban roads and better solve urban road traffic safety problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flowchart of a heterogeneous bounded rational bicycle-vehicle interaction decision quantification method involved in an embodiment of the present application.

[0035] Figure 2 This is a schematic diagram of the framework of a bicycle and vehicle interaction case involved in an embodiment of the present application.

[0036] Figure 3 This is a graph showing how the rationality of bicycles and vehicles involved in the embodiments of the present application changes over time.

[0037] Figure 4 This is a model verification effect diagram involved in the embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0039] like Figure 1 As shown in the figure, a quantitative method for heterogeneous bounded rational bicycle and vehicle interaction decision-making is proposed. According to the research objectives, the following steps are performed to process and analyze the results of heterogeneous bounded rational bicycle and vehicle interaction decision-making:

[0040] Step A: Data collection and processing: A drone is used to select an area of the intersection without signal control to record video, and the semi-automated software PeTrack is used to extract the trajectories of bicycles and vehicles.

[0041] Step B: Considering the risk cost (safety) and delay cost (efficiency), construct the benefit function of bicycles and vehicles, establish a two-person non-zero and non-cooperative dynamic game model, and solve it using the n-person game model.

[0042] Step C: Introduce heterogeneous bounded rationality parameters of bicycles and vehicles through quantum response equilibrium, quantify the bounded rationality of the interaction between bicycles and vehicles and the changes in their rationality during the dynamic game process, and use maximum likelihood to estimate the bounded rationality parameters; substitute them into the two-person non-zero and non-cooperative dynamic game model to obtain a heterogeneous bounded rational bicycle and vehicle interaction decision-making model.

[0043] Step D: Simulate the constructed heterogeneous bounded rational bicycle-vehicle interaction decision model and compare it with the actual observation value.

[0044] In another embodiment, in step B, the bicycle and vehicle interaction case framework is as follows: Figure 2 As shown, the interaction between bicycles and vehicles is divided into a decision cycle [t a ,t e ], including n time steps, each time step is Δt. Within a specific time step, the benefits of bicycles and vehicles under different strategy combinations are obtained, and a two-person non-zero and non-cooperative dynamic game model is constructed.

[0045] Step B1: The action strategies of bicycles and vehicles can be categorized as acceleration, constant speed, and deceleration. The benefits of bicycles under different strategy combinations can be expressed by formula (1). The first and second terms in formula (1) represent the post-encroachment time (PET) and the bicycle's desired speed, respectively. PET is a commonly used safety margin for bicycles and the most commonly used indicator for evaluating conflict safety. It refers to the time difference between the first road user leaving the conflict zone and the next road user arriving in the conflict zone. The benefits of bicycles pursuing efficiency under each strategy combination can be expressed in terms of speed. Inspired by the approaching vehicle, cyclists want to move at the most convenient speed with the least energy consumption, as close to their desired speed as possible.

[0046]

[0047] in, represents the revenue of the bicycle at time t, δ, are the action strategy sets of bicycles and vehicles respectively; α1 and α2 represent weight coefficients, which are determined by the entropy weight method; d1 and d2 represent the distances between bicycles and vehicles to the conflict point respectively; They represent the speeds of bicycles and vehicles at time t under different strategies, is the speed of the bicycle and the vehicle at time t-1, v bd represents the desired speed of the bicycle, and Δt is the time step.

[0048] Step B2: The vehicle's payoff under different strategy combinations can be expressed by formula (3). The first and second terms in formula (3) represent the potential collision risk and the vehicle's desired speed, respectively. When the vehicle's speed changes, the risk of the vehicle colliding with a bicycle increases dramatically. At the same distance, the vehicle's current speed significantly affects the associated collision risk. During the vehicle's interaction with a bicycle, the vehicle's driving speed and relative distance have a significant impact on the potential collision risk. Therefore, the first term in formula (3) illustrates that under different strategy combinations, the vehicle's collision risk payoff depends on the relative positions of the two participants and the vehicle's speed. The higher the collision risk, the lower the vehicle's payoff. Similar to bicycles, the greater the deviation between the vehicle's acceleration strategy and the desired speed, the lower the payoff in the second term in formula (3), and vice versa.

[0049]

[0050] in, represents the vehicle's revenue at time t; β1 and β2 represent weight coefficients, which are determined by the entropy weight method; Indicates the relative distance between the bicycle and the vehicle; represents the speed of the vehicle under the corresponding strategy at time t; v vd Indicates the desired vehicle speed.

[0051] In another embodiment, in step C, the rationality of bicycles and vehicles changes over time as follows: Figure 3 As shown in Figure 2, the bounded rationality of the interaction decision-making behavior of bicycles and vehicles is obtained through the following steps.

[0052] Step C1: Introducing quantum response equilibrium weakens the strong assumption that cyclists and vehicle drivers are completely rational, so that each player is no longer a perfect optimizer. Bicycles and vehicles will not always adopt strategies that maximize their benefits. The strategies adopted by each player are random, and both players cannot fully infer the strategies that the opponent may adopt. The bicycle crossing decision-making behavior is relaxed from complete rationality to bounded rationality. The bicycle chooses an advantageous strategy or an inferior strategy with a certain probability. As the decision time changes, the bounded rationality parameter β is introduced into the quantum response equilibrium to reflect the bounded rationality level in the interactive decision-making behavior of bicycles and vehicles. The bounded rationality level changes with the change of parameter β. The changes in the rationality of bicycles and vehicles include initial bounded rationality, final bounded rationality and learning rate. In the quantum response equilibrium, the bicycle's action strategy δ i is a random variable in the entire strategy set δ, and is also close to the vehicle action strategy of the bicycle is the strategy space For bicycles, each allowed action strategy δ i ∈δ are all with a certain probability P b (δ i ) is selected. Similarly, for the approaching vehicle, each action is performed with a certain probability is selected, see formula (4).

[0053]

[0054] Among them, P b (δ i ), Respectively represent the probability of each action strategy of bicycle and vehicle being selected; δ i is the action strategy of the bicycle, which is a random variable in the bicycle strategy set δ; is the vehicle action strategy, which is the vehicle strategy space The random variables in ; β is a bounded rational parameter; and denote the expected utility of bicycle and car respectively.

[0055] Step C2: Based on the time-varying bounded rationality parameter β, the bicycle crossing decision can be divided into the best case, the worst case, and the acceptable case. Specifically, when β→0, bicycles and vehicles deterministically choose the benefit-maximizing crossing strategy. In this case, the quantum response equilibrium can be considered consistent with the Nash equilibrium. When β→∞, bicycles and vehicles lack the ability to take any rational crossing behavior, so the probability of all strategies being selected is equal. Here, the dynamic heterogeneous bounded rationality parameter β of cyclists and vehicle drivers is introduced. b (t) and β v (t):

[0056]

[0057] Among them, α b and β b denote the initial and final bounded rational parameters of the cyclist, α v , β v denote the initial and final bounded rational parameters of the vehicle driver, γ b is the cyclist’s dynamic heterogeneous bounded rational parameter learning rate, γ v is the dynamic heterogeneous bounded rational parameter learning rate of the vehicle driver;

[0058] When the decision time t ranges from 1 to ∞, the bicycle bounded rationality parameter β b (t) will be b Reduced to β b , while the vehicle's finite rationality parameter β v (t) will be v Reduced to β v , the process of bicycle heterogeneity bounded rationality parameter β b (t) and vehicle heterogeneous bounded rationality parameter β v The parameter α of (t) b , β b , γ b , α v , β v , γ v Perform maximum likelihood estimation.

[0059] In another embodiment, in step D, simulation is performed based on the constructed heterogeneous bounded rational bicycle and vehicle interaction decision model, including embedding the established bounded rational decision model into the bicycle and vehicle motion dynamics model for simulation, using the natural data of bicycle and vehicle interaction collected by field observation, calibrating the heterogeneous bounded rational parameters in the dynamic game model through maximum likelihood estimation, and comparing the simulation results with the observation results to verify the model effect. Figure 4As shown in Figure 2, the accuracy of the bounded rationality model gradually increases as decision time increases, but the accuracy of vehicles is higher than that of bicycles, with bicycles achieving an average accuracy of 85% and vehicles achieving an average accuracy of 95%. This difference is due to the more random nature of cyclists' movements, making them more difficult to predict.

[0060] In another embodiment, a heterogeneous bounded rational bicycle and vehicle interaction decision quantification system is provided, which is used to implement a heterogeneous bounded rational bicycle and vehicle interaction decision quantification method, including: a trajectory collection module, a model building module, a rational parameter analysis module, and a comparison and verification module;

[0061] The trajectory collection module is used to obtain the trajectories of bicycles and vehicles in the unsignalized intersection area;

[0062] The model building module is used to consider risk costs and delay costs, construct the profit functions of bicycles and vehicles under different action strategy combinations, establish a two-person non-zero and non-cooperative dynamic game model, and then solve the game model. The action strategies of bicycles and vehicles include acceleration, constant speed, and deceleration;

[0063] The rational parameter analysis module is used to introduce heterogeneous bounded rational parameters of bicycles and vehicles through quantum response equilibrium, quantify the bounded rationality of the interaction between bicycles and vehicles and the changes in their rationality during the dynamic game process, and estimate the bounded rational parameters using maximum likelihood;

[0064] The comparison and verification module is used to simulate the constructed heterogeneous bounded rational bicycle and vehicle interaction decision model and compare the model effect with the actual observation value.

[0065] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

Claims

1. A quantified method for heterogeneous bounded rational bicycle-vehicle interaction decision making, characterized by: The following steps are involved: Step A: Obtain the trajectories of bicycles and vehicles in the unsignalized intersection area; Step B: Considering risk cost and delay cost, construct the profit function of bicycles and vehicles under different action strategy combinations, establish a two-person non-zero and non-cooperative dynamic game model, and then solve the game model. The action strategies of bicycles and vehicles include acceleration, constant speed, and deceleration; Step C: Dynamically heterogeneous bounded rationality parameters of bicycles and vehicles are introduced through quantum response equilibrium to quantify the bounded rationality of the interaction between bicycles and vehicles and how their rationality changes during the dynamic game. The bounded rationality parameters are substituted into the two-person non-zero and non-cooperative dynamic game model using maximum likelihood estimation to obtain a heterogeneous bounded rational bicycle-vehicle interaction decision-making model. Step D: Simulate the constructed heterogeneous bounded rational bicycle-vehicle interaction decision model and compare it with the actual observation value to verify the effect of the model.

2. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: In step B, the interaction between the bicycle and the vehicle is divided into a decision cycle [t a ,t e ], including n time steps, each time step is Δt. Within a specific time step, the benefits of bicycles and vehicles under different strategy combinations are obtained, and a two-person non-zero and non-cooperative dynamic game model is constructed.

3. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: The profit function of the bicycle under different action strategy combinations in step B is as follows: in, represents the revenue of the bicycle at time t, δ, are the action strategy sets of bicycles and vehicles respectively; α1 and α2 represent weight coefficients, which are determined by the entropy weight method; d1 and d2 represent the distances of bicycles and vehicles to the conflict point respectively; They represent the speed of bicycles and vehicles at time t under different strategies, is the speed of the bicycle and vehicle at time t-1, v bd represents the desired speed of the bicycle, and Δt is the time step.

4. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: The vehicle's profit function under different action strategy combinations in step B is as follows: in, represents the vehicle's revenue at time t; β1 and β2 represent weight coefficients respectively; Indicates the relative distance between the bicycle and the vehicle; represents the speed of the vehicle under the corresponding strategy at time t; v vd Indicates the desired vehicle speed.

5. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: After introducing heterogeneous bounded rational parameters of bicycles and vehicles through quantum response equilibrium in step C, the probability of each action strategy of bicycles and vehicles being selected is as follows: Among them, P b (δ i ), Respectively represent the probability of each action strategy of bicycle and vehicle being selected; δ i is the bicycle action strategy, which is a random variable in the bicycle action strategy set δ; is the vehicle action strategy, which is the vehicle action strategy set The random variables in ;β is a bounded rational parameter; and denote the expected utility of bicycle and vehicle respectively.

6. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: In step C, the dynamic heterogeneous bounded rationality parameter β of cyclists and vehicle drivers is introduced. b (t) and β v (t): Among them, α b and β b denote the initial and final bounded rational parameters of the cyclist, α v , β v denote the initial and final bounded rational parameters of the vehicle driver, γ b is the cyclist’s dynamic heterogeneous bounded rational parameter learning rate, γ v is the dynamic heterogeneous bounded rational parameter learning rate of the vehicle driver; When the decision time t ranges from 1 to ∞, the dynamic heterogeneous bounded rationality parameter β of the bicycle b (t) will be b Reduced to β b , while the vehicle dynamics heterogeneous bounded rationality parameter β v (t) will be v Reduced to β v , the parameter α b , β b , γ b , α v , β v , γ v Perform maximum likelihood estimation.

7. The heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to claim 1 is characterized in that: In step D, simulation is performed based on the constructed heterogeneous bounded rational bicycle-vehicle interaction decision model, including embedding the established bounded rational decision model into the bicycle and vehicle motion dynamics model for simulation, using the natural data of bicycle-vehicle interaction collected through field observation, calibrating the bounded rational parameters in the dynamic game model through maximum likelihood estimation, and comparing the simulation results with the observation results to verify the model effect.

8. A heterogeneous bounded rational bicycle and vehicle interaction decision quantification system, characterized by: A method for implementing a heterogeneous bounded rational bicycle-vehicle interaction decision quantification method according to any one of claims 1 to 7, comprising: a trajectory collection module, a model building module, a rational parameter analysis module, and a comparison and verification module; The trajectory collection module is used to obtain the trajectories of bicycles and vehicles in the unsignalized intersection area; The model building module is used to consider risk costs and delay costs, construct the profit functions of bicycles and vehicles under different action strategy combinations, establish a two-person non-zero and non-cooperative dynamic game model, and then solve the game model. The action strategies of bicycles and vehicles include acceleration, constant speed, and deceleration; The rational parameter analysis module is used to introduce dynamic heterogeneous bounded rational parameters of bicycles and vehicles through quantum response equilibrium, quantify the bounded rationality of the interaction between bicycles and vehicles and the changes in their rationality during the dynamic game process, and estimate the bounded rational parameters using maximum likelihood; The comparison and verification module is used to simulate the constructed heterogeneous bounded rational bicycle and vehicle interaction decision model and compare the model effect with the actual observation value.

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