Driver real-time state-aware car following behavior intelligent modeling method and system

Abstract

The application discloses a kind of driver real-time state perception's car-following behavior intelligent modeling method and system, specifically including the following steps: (1) data acquisition, the driving data of vehicle in actual road is collected, the data needed to be acquired include the speed of current vehicle and front vehicle, position change information and the change information of relative distance;(2) data screening and processing, the data obtained in step (1) are screened, and the vehicle historical data with following characteristics on a road are selected;(3) the corresponding simulation environment is constructed in Matlab by method;(4) by the following state transformation method, the real driving situation of driver is simulated by constantly changing state in Matlab simulation environment.The method considers human factors in car-following model, so as to ensure the accuracy of car-following.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic flow research, and particularly relates to a driver real-time state perception car-following behavior intelligent modeling method and system. BACKGROUND

[0002] In recent years, modeling of car-following behavior has been a research hotspot in the field of traffic flow theory, and car-following problem has become increasingly important in traffic engineering and safety research. The development of car-following theory is to simulate the motion of vehicles following each other without overtaking on a single lane. As a pillar of traffic flow theory, by analyzing and predicting vehicle acceleration, the car-following behavior can be modeled to quantify the longitudinal interaction between cars, so as to understand the characteristics of traffic flow and reveal the internal mechanism of traffic congestion and other traffic phenomena. The human factors of drivers directly affect the driving trajectory, so it is quite important to consider human factors in the simulation process. The vehicle driving behavior of drivers is composed of mechanical control of vehicles and random human factors of drivers, and these random human factors include reaction delay, error of distance perception, etc. The traditional car-following model oversimplifies the consideration of human factors and largely ignores the human factors of drivers. However, in the simulation of traffic in large cities, especially in the construction of intelligent transportation systems, the research on human factors of drivers is very important, and it is also necessary to model the human factors in the car-following motion of vehicles. SUMMARY

[0003] The present application proposes a driver real-time state perception car-following behavior intelligent modeling method and system to solve the problem of lack of human factors in the design of traditional car-following model, and adds the consideration of human factors in the car-following model, so as to ensure the accuracy of car-following.

[0004] In order to solve the above technical problems, the technical scheme of the present application is as follows: a driver real-time state perception car-following behavior intelligent modeling method and system, specifically comprising the following steps:

[0005] Step (1) data acquisition, used for initialization of simulation parameters and comparison of results. The driving data of vehicles in actual roads are collected, and the data to be acquired include the speed, position change information and relative distance change information of the current vehicle and the front vehicle.

[0006] Step (2) data screening and processing, screening the historical data of vehicles with following characteristics on a road from the data acquired in step (1).

[0007] Specifically, it comprises:

[0008] 1) Select the long section and the long follow-up time of the vehicle driving data, the road condition is normal, the traffic is moderate, no congestion event occurs, the weather condition in the selected section is good, the best vision is clear, and the vehicle driving environment in the section is general traffic environment, and the proportion of large vehicles is not more than 30%.

[0009] 2) When selecting data, the following vehicle and the followed vehicle are regarded as a whole and are processed as a following unit.

[0010] 3) In the process of selecting the vehicle, the following vehicle and the followed vehicle are always in the same lane, and only the changes of the speed, position and distance of the vehicle are involved in the following process, without lane changing behavior and overtaking behavior.

[0011] 4) When the distance between the vehicle and the front vehicle is too large (the distance is more than 2.5 times the speed limit value), it is considered that there is no following behavior, and at this time it is regarded as free acceleration behavior under the speed limit condition.

[0012] Step (3) builds a corresponding simulation environment in Matlab by the method. Initial simulation data is input, and the initial state of the driver is set according to the initial simulation data. If the initial distance is greater than 40 meters, the initial state is set to a long-distance following attention concentration state, and if the initial distance is less than 40 meters, the initial state is set to a short-distance following attention concentration state.

[0013] Step (4) simulates the real driving situation of the driver and verifies the model effect in the Matlab simulation environment by continuously changing the state through the following state transformation method.

[0014] On the other hand, the application also discloses a driver real-time state perception following behavior intelligent modeling system, comprising:

[0015] A data acquisition base station is used to collect the driving data of the vehicle in the actual road, and the data to be obtained includes the speed, position change information and relative distance change information of the current vehicle and the front vehicle;

[0016] A data screening module is used to screen effective data in the obtained data, including following behavior data of the distance between the front and rear vehicles within the effective following distance and free acceleration behavior data under the highway speed limit condition; a Matlab simulator is used to build a corresponding simulation environment. Initial simulation data is input, and the initial state of the driver is set according to the initial simulation data;

[0017] A model evaluation module is used to simulate the real driving situation of the driver in the Matlab simulation environment by continuously changing the state, and then the model effect is verified.

[0018] In another aspect, the present application also discloses a computer device, comprising a processor and a computer readable storage medium; the processor is suitable for executing a computer program;

[0019] The computer readable storage medium has a computer program stored therein, and the computer program is executed by the processor to implement the intelligent modeling method for the car-following behavior of the driver in real-time state perception.

[0020] In another aspect, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is suitable for being loaded and executed by a processor to implement the intelligent modeling method for the car-following behavior of the driver in real-time state perception.

[0021] In another aspect, the present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the intelligent modeling method for the car-following behavior of the driver in real-time state perception.

[0022] The present application has the following characteristics and beneficial effects:

[0023] According to the above technical solution, the driving process of the driver is divided into different states according to the distance of the car-following distance and whether the driver's attention is concentrated or not, the car-following state is taken as a discretization condition, the continuous motion process of the vehicle is taken as a continuous condition, and the car-following process is modeled as a segmented continuous process by using a state machine. The results show that the technical solution can more effectively simulate the real driving process and solve the problem that the traditional car-following model has a large deviation from the real driving situation in simulation. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0025] Figure 1 The present application is a kind of driver real-time state perception car-following behavior intelligent modeling method and system logic framework flow chart.

[0026] Figure 2 The state change diagram in the present embodiment. DETAILED DESCRIPTION

[0027] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0028] In the description of the present application, it needs to be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element indicated must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only for description purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" and the like can be explicitly or implicitly included one or more. In the description of the present application, unless otherwise stated, the meaning of "a plurality of" is two or more.

[0029] In the description of the present application, it needs to be understood that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood through specific circumstances.

[0030] Embodiment 1

[0031] The embodiment provides a driver real-time state perception intelligent modeling method for following behavior,

[0032] In the embodiment, on the basis of long following distance and short following distance, the following eight states are created to distinguish the following state of the driver:

[0033] 1. Long distance following attention: indicating normal driving when the following distance is long.

[0034] 2. Long distance following distraction: indicating that the driver is somewhat distracted and difficult to maintain attention when the following distance is long.

[0035] 3. Long distance following short reaction time: indicating normal reaction delay when the following distance is long.

[0036] 4. Long distance following long reaction time: indicating that the reaction time becomes longer because of distraction and the like when the following distance is long.

[0037] 5. Short distance following attention: indicating normal driving when the following distance is short.

[0038] 6. Short distance following distraction: indicates that the following distance is short, and the driver is somewhat distracted, and it is difficult to maintain attention.

[0039] 7. Short distance following short reaction time: indicates that the following distance is short, and the normal reaction delay.

[0040] 8. Short distance following long reaction time: indicates that the following distance is short, and the reaction time becomes longer due to distraction and other conditions.

[0041] The method is based on short distance following and long distance following, and is combined with other states respectively. Each state transition process is a response to the change of one or more parameters in the model. Taking short distance following as an example, when the estimation error with the preceding vehicle exceeds the threshold value, the state of the driver will be converted from the short distance following attention concentration state to the short distance short reaction time state. When the driving time in the attention concentration time exceeds a certain time (in this paper, the length of time in which the driver can concentrate attention is set to 8 seconds), the state will be converted from the short distance following attention concentration state to the short distance distraction state.

[0042] Specifically, as shown in Figure 1 , the method comprises the following steps:

[0043] Step (1) data acquisition, for initialization of simulation parameters and result comparison. The driving data of vehicles in actual roads are collected, and the data to be acquired include the speed, position change information and relative distance change information of the current vehicle and the preceding vehicle.

[0044] Step (2) data screening, in the acquired data, the effective data are screened, including the following behavior data of the distance between the two vehicles within the effective following distance, the free acceleration behavior data under the highway speed limit condition (the distance between the following vehicle and the followed vehicle is too large) and other information.

[0045] In this embodiment, the data screening comprises:

[0046] 1) Select the driving data of vehicles with longer road sections and longer following time, the road conditions are normal, the traffic flow is moderate, no congestion event occurs, the weather conditions are good in the selected road section, the best vision is clear, and the vehicle driving environment in the road section is a general traffic environment, and the proportion of large vehicles does not exceed 30%.

[0047] 2) When selecting data, the following vehicle and the followed vehicle are regarded as a whole, and the data are processed as a following unit.

[0048] 3) In the process of selecting the driving of vehicles, the following vehicle and the followed vehicle are always on the same lane, and only the changes of vehicle speed, position and distance are involved in the following process, without lane changing behavior and overtaking behavior.

[0049] 4) When the vehicle is too far from the front vehicle (the distance is more than 2.5 times the speed limit value), it is considered that there is no car following behavior, and at this time it is considered to be free acceleration behavior under the speed limit condition.

[0050] Step (3) is to build a corresponding simulation environment in Matlab. Input the initial simulation data, set the initial state of the driver according to the initial simulation data, if the initial distance is greater than s, s = 40 meters in this embodiment, set the initial state to long distance following attention state, if the initial distance is less than 40 meters, set the initial state to short distance following attention state.

[0051] Step (4) is to simulate the real driving situation of the driver and verify the model effect by continuously changing the state in the Matlab simulation environment through the following state transformation method.

[0052] Specifically, as shown in Figure 2 , the state transformation method in this embodiment is as follows:

[0053] 1) In the short distance following attention state, when the estimation error of the position of the front vehicle is greater than k1, the driver needs to react, k1 = 1 m in this embodiment, at this time because t < 8, the state of the driver is changed to short distance following short reaction time, the estimated distance of the front vehicle is updated, and the timer t = 0 is reset.

[0054] That is, when , let t = 0,

[0055] Wherein, the calculation process of is as follows:

[0056] First, calculate the estimated acceleration:

[0057]

[0058] In the formula, represents the start of the i-th reaction of the vehicle n, represents the end of the reaction, and the speed difference through represents the duration of the i-th reaction. When the first reaction starts, the value of the estimated acceleration of the front vehicle adopts the value input at zero time, and after that, the calculation of the estimated acceleration is calculated by dividing the speed difference of the previous reaction time by the reaction duration.

[0059] Through the calculation of the estimated acceleration of the front vehicle, the estimated speed of the front vehicle can be calculated.

[0060]

[0061] Through the estimated speed of the front vehicle, the calculation equation of the estimated position can be obtained:

[0062]

[0063] 2) In the short distance following attention concentrated state, the estimation error of the front vehicle position is within the threshold range, and the driver has not reacted for a long time. At this time, because of the limited attention, the driver cannot concentrate for a long time, and when t >= 8, the driver switches to the short distance following distraction state.

[0064] The transition is made when .

[0065] 3) When the driver is in the short distance following short reaction time state, after making a corresponding reaction to the change, the driver will return to the short distance following attention concentrated state.

[0066] The transition is made when t >= 0.5, t = 0,

[0067] 4) In the short distance following distraction state, the estimation error of the front vehicle position is greater than k1, and the driver state switches to the short distance following long reaction time state. Within the limited reaction time, the driver makes a corresponding reaction, and the timer t = 0 is updated.

[0068] The transition is made when , t = 0,

[0069] 5) In the short distance following long reaction time state, after making a reaction to the change, the attention will be concentrated again, and the driver will return to the short distance following attention concentrated state.

[0070] The transition is made when t >= 0.5, t = 0,

[0071] 6) When the distance to the front vehicle is too large (greater than 40 meters), the driver has difficulty perceiving small distance errors, and the threshold value for estimating the distance error needs to be updated. At this time, the threshold value will be changed to k2, and in this embodiment, k2 = 2.5 m. Then the state is set to the long distance following attention concentrated state.

[0072] The transition is made when x n - x n-1 >= s, t = 0,

[0073] 7) When the distance to the front vehicle is small (less than 40 meters), the sensitivity to the distance increases, and the threshold value for estimating the distance error needs to be updated. The state is set to the short distance following attention concentrated state.

[0074] The transition is made when x n - x n-1 < s, t = 0,

[0075] 8) In the long distance following attention concentrated state, when the estimation error of the front vehicle position is greater than k1, the driver needs to make a response, at this time because t<8, the driver state changes to long distance following short reaction time, the estimated distance of the front vehicle is updated, and the timer t=0 is reset.

[0076] When , let t=0,

[0077] 9) In the long distance following attention concentrated state, the estimation error of the front vehicle position is always within the threshold range within the time when attention can be concentrated, and the driver has not made a response, at this time because attention is limited and cannot concentrate attention for a long time, when t> =8, the driver changes to the long distance following distraction state.

[0078] When , the change is made.

[0079] 10) When the driver is in the long distance following short reaction time state, after making a corresponding response to the change, the driver will return to the long distance following attention concentrated state.

[0080] When t≥0.5, let t=0,

[0081] 11) In the long distance following distraction state, when the estimation error of the front vehicle position is greater than k1, the driver state changes to the long distance following long reaction time, and a corresponding response is made within the limited reaction time, and the timer t=0 is updated.

[0082] When , let t=0,

[0083] 12) In the long distance following long reaction time state, after making a response to the change, the attention will be concentrated again, and the long distance following attention concentrated state will be returned.

[0084] When t≥0.5, let t=0,

[0085] To verify the effect of the above method, the state machine is used to model the state change of the driver, and the experimental results are analyzed and evaluated.

[0086] Specifically, the application uses two well-known car-following models, Intelligent Driver Model (IDM) and Full Velocity Difference model (FVD), for extension and comparison. The two models have fewer parameters, are widely used, and have fewer additional influencing factors, so they can well test the improvement of the method of adding human factors on the simulation efficiency of the driver's driving trajectory.

[0087] The model uses the public NGSIM (Next Generation Simulation) trajectory data set as experimental data, and the NGSIM data set is a traffic data set developed by the United States Federal Highway Administration for research and simulation of traffic flow. The data set mainly contains the running trajectory data of vehicles on the highway, which can be used for research on traffic flow simulation, behavior analysis, and vehicle interaction. The data with long following time and obvious following characteristics are extracted from the data set as the source of experimental data.

[0088] First, the spacing change data of the original IDM and FVD are recorded and compared with the actual data, then the human factors are added to the model, and by comparing the spacing error and root mean square error (RMSE), it is found that compared with the original simulation, the simulation result after adding human factors is obviously close to the actual data curve, the spacing error has decreased obviously during driving, making the spacing more realistic, and the simulation effect has been obviously improved.

[0089] Embodiment 2

[0090] The embodiment discloses a driver real-time state perception car-following behavior intelligent modeling system, comprising:

[0091] A data acquisition base station is used to collect driving data of vehicles in an actual road, and the data to be acquired includes speed, position change information and relative spacing change information of the current vehicle and the front vehicle;

[0092] A data screening module is used to screen effective data in the acquired data, including car-following behavior data within the effective following distance between the front and rear vehicles, and free acceleration behavior data under highway speed limit conditions; a Matlab simulator is used to build a corresponding simulation environment. Initial simulation data is input, and the initial state of the driver is set according to the initial simulation data;

[0093] A model evaluation module is used to simulate the real driving situation of the driver in the Matlab simulation environment by continuously changing the state, and then the model effect is verified.

[0094] In this embodiment, the specific working process details of each module refer to the process in Embodiment 1, which will not be repeated here.

[0095] It can be understood that the above-mentioned modules can be combined into one or several other units respectively or entirely, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can achieve the same operation without affecting the implementation of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions, and the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit in actual application.

[0096] In other embodiments of the present application, the construction system can also include other units, and these functions can also be assisted by other units in actual application, and can be realized by cooperation of multiple units.

[0097] According to another embodiment of the present application, the construction method of the embodiments of the present application can be implemented by running the computer program capable of executing the above-mentioned steps on a general computing device such as a computer including processing elements and storage elements such as a Central Processing Unit (CPU), a Random Access Memory (RAM), a Read Only Memory (ROM) and the like.

[0098] The computer program (including program codes) of each step involved in the corresponding method described in Embodiment 1 can be recorded on, for example, a computer readable recording medium, and loaded into the above-mentioned computing device through the computer readable recording medium, and run therein, to construct the system described in the present embodiment, and to implement the construction method of the embodiments of the present application.

[0099] Embodiment 3

[0100] The present embodiment also discloses a computer device, including a processor and a computer readable storage medium; the processor is suitable for executing a computer program;

[0101] The computer readable storage medium has a computer program stored therein, and the computer program is executed by the processor.

[0102] Specifically, the processor is configured to execute the following process:

[0103] Step 1, collecting driving data of vehicles in actual roads, the collected driving data including speed, position change information and relative distance change information of the current vehicle and the front vehicle;

[0104] Step 2, according to the preset screening condition, the vehicle historical data with car following behavior on a road is selected from the driving data;

[0105] Step 3, according to the screened vehicle historical data, the corresponding simulation environment is constructed in Matlab, the initial simulation data is input, and the initial state of the driver is set according to the initial simulation data;

[0106] Step 4, by state transformation, the real driving situation of the driver is simulated in the Matlab simulation environment and the model effect is verified.

[0107] Embodiment 4

[0108] The embodiment discloses a computer readable storage medium, and the computer readable storage medium stores a computer program. It can be understood that the computer readable storage medium herein can include a built-in storage medium in an electronic device, and of course can include an expansion storage medium supported by the electronic device. The computer readable storage medium provides a storage space, and the storage space stores a processing system of the electronic device.

[0109] And in the storage space, one or more instructions suitable for being loaded and executed by the processor are also stored, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory; optionally, it can also be at least one computer readable storage medium located away from the aforementioned processor

[0110] Specifically, the computer program is suitable for being loaded and executed by the processor to perform the following process:

[0111] Step 1, collecting driving data of vehicles in an actual road, and the collected driving data includes speed, position change information and relative distance change information of the current vehicle and the front vehicle;

[0112] Step 2, according to the preset screening condition, the vehicle historical data with car following behavior on a road is selected from the driving data;

[0113] Step 3, according to the screened vehicle historical data, the corresponding simulation environment is constructed in Matlab, the initial simulation data is input, and the initial state of the driver is set according to the initial simulation data;

[0114] Step 4, by state transformation, the real driving situation of the driver is simulated in the Matlab simulation environment and the model effect is verified.

[0115] Embodiment 5

[0116] The embodiment discloses a computer program product, the computer program product comprises a computer program, the computer program is executed by a processor, the computer instruction is stored in a computer readable storage medium, the processor of an electronic device reads the computer instruction from the computer readable storage medium, and the processor executes the computer instruction, so that the electronic device executes:

[0117] Step 1, collecting driving data of vehicles in actual roads, the collected driving data comprising speed, position change information and relative distance change information of the current vehicle and the front vehicle;

[0118] Step 2, selecting vehicle historical data with car following behavior on a road from the driving data according to a preset screening condition;

[0119] Step 3, constructing a corresponding simulation environment in Matlab according to the selected vehicle historical data, inputting initial simulation data, and setting an initial state of a driver according to the initial simulation data;

[0120] Step 4, simulating the real driving condition of the driver in the Matlab simulation environment through state transformation and verifying the model effect.

[0121] The embodiments of the present application are described in detail in combination with the drawings, but the present application is not limited to the described embodiments. For those skilled in the art, various changes, modifications, replacements and variations of the embodiments including components can be made without departing from the principles and spirits of the present application, and still fall within the protection scope of the present application.

Claims

1. A method for intelligent modeling of car-following behavior with real-time driver state awareness, characterized in that, The method comprises the following steps: Step 1, collecting driving data of a vehicle in an actual road, wherein the collected driving data comprises speed, position change information and relative distance change information of a current vehicle and a front vehicle; Step 2, selecting vehicle historical data with car-following behavior on a road from the driving data according to a preset screening condition; Step 3, constructing a corresponding simulation environment in Matlab according to the selected vehicle historical data, inputting initial simulation data, setting an initial state of a driver according to the initial simulation data, wherein the initial state is defined as: if an initial distance is greater than a preset distance threshold s, the initial state is set as a long-distance car-following attention concentration state; if the initial distance is less than s, the initial state is set as a short-distance car-following attention concentration state; Step 4, simulating a real driving condition of the driver in the Matlab simulation environment and verifying a model effect by state transformation, wherein a timer t at the start of simulation is set as 0; The state transformation method comprises: In the short-distance car-following attention concentration state, when an estimation error of a position of the front vehicle is greater than or equal to a threshold k1, the driver needs to make a response, at this time, because t<8, the state of the driver is changed to a short-distance car-following short reaction time, the estimated distance of the front vehicle is updated, and the timer t is reset as 0; In the short-distance car-following attention concentration state, the estimation error of the position of the front vehicle is always within the threshold k1 within the time of attention concentration, and the driver has not made a response, at this time, because of limited attention, the driver cannot concentrate attention for a long time, when t>=8, the driver is changed to a short-distance car-following distraction state; After the driver makes a corresponding response in the short-distance car-following short reaction time state, the driver returns to the short-distance car-following attention concentration state; In the short-distance car-following distraction state, when the estimation error of the position of the front vehicle is greater than or equal to the threshold k1, the state of the driver is changed to a short-distance car-following long reaction time, and the driver makes a corresponding response within a limited reaction time, and the timer t is reset as 0; After the driver makes a response in the short-distance car-following long reaction time state, the driver concentrates attention again and returns to the short-distance car-following attention concentration state; When the distance from the front vehicle is greater than or equal to s, the driver cannot feel a small distance error, the threshold of the distance estimation error needs to be updated, at this time, the threshold is changed to k2, and then the state is set as a long-distance car-following attention concentration state; When the distance from the front vehicle is less than s, the sensitivity to the distance is increased, the threshold k1 of the distance estimation error needs to be updated, and the state is set as a short-distance car-following attention concentration state; In the long-distance car-following attention concentration state, when the estimation error of the position of the front vehicle is greater than or equal to the threshold k2, the driver needs to make a response, at this time, because t<8, the state of the driver is changed to a long-distance car-following short reaction time, the estimated distance of the front vehicle is updated, and the timer t is reset as 0; In the long-distance following attention concentrated state, the estimation error of the front vehicle position is within the threshold k1 range, and the driver has not reacted for a long time. At this time, because of the limited attention, the driver cannot concentrate for a long time, and when t>=8, the driver switches to the long-distance following distraction state; After the driver makes a corresponding reaction in the long-distance following short reaction time state, the driver will return to the long-distance following attention concentrated state; In the long-distance following distraction state, when the estimation error of the front vehicle position is greater than or equal to the threshold k2, the driver state switches to the long-distance following long reaction time state, and the driver makes a corresponding reaction within the limited reaction time to update the timer t=0; After the driver makes a corresponding reaction in the long-distance following long reaction time state, the driver will return to the long-distance following attention concentrated state.

2. The intelligent modeling method of car-following behavior of real-time driver state awareness according to claim 1, characterized in that, In step 2, the car-following vehicle and the followed vehicle are regarded as a whole and are processed as a car-following unit when the driving data is screened.

3. The intelligent modeling method of car-following behavior of real-time driver state awareness according to claim 1, characterized in that, The screening conditions include: The car-following vehicle and the followed vehicle are always in the same lane, and only the changes in the vehicle speed, position, and distance are involved in the following process, without lane changing behavior and overtaking behavior; A threshold value of the distance between the current vehicle and the front vehicle is set, and if the distance between the current vehicle and the front vehicle exceeds the threshold value, it is considered that there is no car-following behavior, and at this time, the current vehicle is regarded as performing free acceleration behavior under the speed limit condition.

4. The intelligent modeling method of following behavior of driver real-time state awareness according to claim 3, characterized in that, The threshold value of the distance between the current vehicle and the front vehicle is 2.5 times the current road speed limit value.

5. The method of claim 1, wherein, The state transformation method includes: In the short-distance following attention concentrated state, when the estimation error of the front vehicle position is greater than or equal to the threshold k1, the driver needs to make a reaction. At this time, because t<8, the driver state switches to the short-distance following short reaction time, the estimated distance of the front vehicle is updated, and the timer t=0 is reset, That is, when the vehicle n is allowed to where, is the estimated position of the vehicle in front of vehicle n, x n-1 is the actual position of the vehicle in front of vehicle n, t denotes the value of the timer; In the short-distance following attention concentrated state, the estimation error of the front vehicle position is within the threshold k1 range for a long time, and the driver has not reacted for a long time. At this time, because of the limited attention, the driver cannot concentrate for a long time, and when t>=8, the driver switches to the short-distance following distraction state, i.e. when the conversion is performed; After the driver makes a corresponding reaction in the short-distance following short reaction time state, the driver will return to the short-distance following attention concentrated state, i.e. when t≥0.5, let where, is the estimated speed of the vehicle in front of vehicle n, v n-1 is the actual speed of the vehicle in front of vehicle n, t denotes the value of the timer; In the short-distance following distraction state, when the estimation error of the front vehicle position is greater than or equal to the threshold k1, the driver state switches to the short-distance following long reaction time state, and the driver makes a corresponding reaction within the limited reaction time to update the timer t=0, That is when the driver In the short-distance following state with long reaction time, the driver's attention will be refocused after reacting to the change, and the driver will return to the short-distance following attention-focused state. That is, when t≥0.5, let When the distance from the preceding vehicle is greater than s meters, the driver has difficulty in perceiving a small distance error, and the threshold value for updating the distance estimation error needs to be updated, at which point the threshold value will become k2, and then the state is set to the attention concentration state under the long following distance, i.e. when x n -x n-1 ≥ s, let When the distance to the vehicle in front is less than s meters, the sensitivity to the distance is increased, the threshold value k1 for updating the distance estimation error is required, the state is set to the short following distance attention concentration state, i.e. when x n - x n-1 < s, let In the long distance following state, when the estimation error of the position of the vehicle in front is greater than or equal to the threshold k2, the driver needs to react, at this time because t < 8, the state of the driver changes to long distance following short reaction time, the estimated distance of the vehicle in front is updated, and the timer t = 0 is reset. That is when season During long-distance following while maintaining focused attention, if the position estimation error of the vehicle ahead remains within the threshold k1 during the period of focused attention, and the driver does not react, then due to limited attention span, the driver cannot maintain focus for an extended period. When t>=8, the driver transitions to a distracted state during long-distance following. i.e. when the conversion is performed; After the driver makes a corresponding reaction in the long-distance following short reaction time state, the driver will return to the long-distance following attention concentrated state, That is, when t≥0.5, let In the long-distance following distraction state, the estimation error of the position of the preceding vehicle is greater than the threshold k1, the driver state is converted to the long-distance following long reaction time, and the corresponding reaction is made within the limited reaction time to update the timer t=0, That is when season When following a vehicle at a long distance with a long reaction time, attention will refocus after reacting to changes, and you will return to a state of focused attention during long-distance following. i.e. when t≥0.5, let 6. The driver real-time state-aware car following behavior intelligent modeling method of claim 5, wherein, The calculation method of the estimated acceleration: where denotes the estimated acceleration of the vehicle in front of vehicle n, v n-1 denotes the actual speed of the vehicle in front of vehicle n, v denotes the start of the i-th reaction of vehicle n, denotes the end of the reaction, by denotes the duration of the i-th reaction, t denotes the value of the timer; a n-1 (0) denotes that the value of the estimated acceleration of the vehicle in front of vehicle n at the start of the first reaction takes the value input at time zero, and that for each subsequent calculation of the estimated acceleration, the speed difference of the previous reaction time is divided by the duration of the reaction.

7. The method of claim 6, wherein, The estimation method of the front vehicle speed: Through the calculation of the estimated acceleration of the front vehicle, the estimated speed of the front vehicle can be calculated: wherein denotes the estimated speed of the vehicle in front of vehicle n, v n-1 denotes the actual speed of the vehicle in front of vehicle n, denotes the estimated acceleration of the vehicle in front of vehicle n, t denotes the value of the timer, denotes the end of the i-th reaction of vehicle n.

8. The method of claim 7, wherein, The estimation method of the front vehicle position: Through the estimated speed of the front vehicle, the calculation equation of the estimated position can be obtained: wherein represents the estimated position of the vehicle in front of vehicle n, x n-1 represents the actual position of the vehicle in front of vehicle n, represents the estimated speed of the vehicle in front of vehicle n, t represents the value of the timer, represents the start of the i-th reaction of vehicle n.

9. A driver real-time state-aware car following behavior intelligent modeling system, characterized in that, The driver real-time state-aware car-following behavior intelligent modeling method according to any one of claims 1-8 comprises: a data acquisition base station for acquiring driving data of a vehicle on an actual road, and the data to be acquired include speed, position change information and relative distance change information of the current vehicle and a front vehicle; a data screening module for screening effective data from the acquired data, including car-following behavior data when the distance between the front and rear vehicles is within an effective car-following distance and free acceleration behavior data under highway speed limit conditions; a Matlab simulator for building a corresponding simulation environment, inputting initial simulation data and setting an initial state of a driver according to the initial simulation data; and a model evaluation module for simulating real driving conditions of the driver in the Matlab simulation environment through continuous state change, and verifying the model effect. A computer readable storage medium and a processor are included; the processor is adapted to execute a computer program; The computer readable storage medium stores the computer program, and the computer program is adapted to be loaded and executed by the processor to implement the driver real-time state-aware car-following behavior intelligent modeling method according to any one of claims 1-8.

10. A computer device, comprising: The computer readable storage medium stores the computer program, and the computer program is adapted to be loaded and executed by the processor to implement the driver real-time state-aware car-following behavior intelligent modeling method according to any one of claims 1-8. The computer program product comprises a computer program, and the computer program is executed by the processor to implement the driver real-time state-aware car-following behavior intelligent modeling method according to any one of claims 1-8.

11. A computer readable storage medium, characterized in that, ​ 12. A computer program product, characterised in that, ​

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