Highway tunnel section car-following modeling method based on driver visual characteristics

Abstract

The application discloses a highway tunnel section vehicle car-following modeling method based on driver visual characteristics, which comprises the following steps: step 1, selecting a highway tunnel section, obtaining driving state data and vehicle related parameters to obtain the car-following state of the vehicle; step 2, constructing a car-following vehicle driver acceleration and deceleration decision behavior model to determine the car-following driving decision behavior under different driving conditions; step 3, constructing a vehicle driver utility optimal function based on a safe car-following distance to obtain a minimum car-following distance based on the current vehicle speed; step 4, correcting the vehicle driver utility optimal function based on the perception speed to obtain a corrected minimum safe car-following distance; step 5, establishing a highway tunnel section vehicle car-following model based on driver visual characteristics; step 6, changing the operation state of a leading vehicle to set a tunnel section simulation traffic scene; and step 7, based on the simulation traffic scene, the car-following vehicle is operated according to the vehicle car-following model to simulate the operation state of the car-following vehicle at t>0.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety in tunnels, and specifically to a method for modeling vehicle following in highway tunnel sections based on driver visual characteristics. Background Technology

[0002] Tunnels, as a crucial component of the road system, exhibit significant differences in driving environment between their interior and exterior due to their unique tubular structure. Although tunnel sections have a lower accident rate in terms of frequency, the severity of accidents is higher, with rear-end collisions being the most common type of traffic accident. Accidents in tunnel sections occur more frequently at tunnel entrances and exits. The drastic changes in light when vehicles enter and exit tunnels cause "visual stun" for drivers, hindering their ability to accurately judge the driving environment ahead. Furthermore, the stronger illuminance in high-altitude areas compared to plains exacerbates the impact of these changes on drivers. Therefore, constructing a car-following model for long highway tunnels based on driver visual characteristics is of significant practical importance in improving traffic flow stability and optimizing speed limits in tunnel sections.

[0003] Car-following models can be categorized into three types: driver stimulus-response models, safe following distance models, and driver psychology and response models. In the driver stimulus-response model, whether a following vehicle accelerates is considered to be determined by the relative speed of the two vehicles and the reaction of the vehicle in front. In the safe following distance model, the following vehicle maintains a safe following speed and distance to avoid a rear-end collision caused by sudden braking of the vehicle in front. However, these models lack consideration of driver visual characteristics and only consider a single driving environment, failing to account for the impact of changes in the driving environment on car-following behavior. Due to the unique tubular structure of highway tunnels, the driving environment inside and outside the tunnel differs significantly, and the impact of these different driving environments on driving conditions and driver vision is not reflected in the above models. Summary of the Invention

[0004] This invention is made to solve the above-mentioned problems, and aims to provide a method for modeling vehicle following in highway tunnel sections based on driver visual characteristics.

[0005] This invention provides a method for modeling vehicle following behavior in highway tunnel sections based on driver visual characteristics, characterized by the following steps: Step 1, selecting a highway tunnel section, acquiring driving status data and relevant vehicle parameters of vehicles traveling in the tunnel section, and calculating the vehicle following status; Step 2, based on the vehicle following status, constructing a model of the driver's acceleration and deceleration decision-making behavior, and determining the following driving decision-making behavior under different driving conditions; Step 3, based on the following driving decision-making behavior, constructing a vehicle driver utility-optimal function based on a safe following distance, and calculating the minimum following distance based on the current vehicle speed; Step 4: Based on the speed perception selection, the driver's acceleration / deceleration decision-making behavior model is used to correct the driver's utility-optimal function, and the corrected minimum safe following distance is calculated. Step 5: Based on the corrected minimum safe following distance, a vehicle following model for highway tunnel sections based on driver visual characteristics is established. Step 6: By changing the running state of the leading vehicle, a simulated traffic scenario for highway tunnel sections is set. Step 7: Based on the simulated traffic scenario for highway tunnel sections, the following vehicles operate according to the vehicle following model for highway tunnel sections based on driver visual characteristics, and the running state of the following vehicles when t>0 is simulated.

[0006] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics provided by this invention may also have the following features: In step 1, driving status data is obtained through video statistics or real vehicle tests. The driving status data includes the speed, acceleration, following distance, road section position, and illuminance of the following vehicle. The vehicle-related parameters include the length and width of the vehicle.

[0007] The vehicle following behavior modeling method for highway tunnel sections based on driver visual characteristics provided by this invention may also have the following feature: In step 2, the acceleration / deceleration decision-making behavior model of the following vehicle driver is a model of the acceleration / deceleration decision-making behavior of the following vehicle driver in a highway tunnel section. The specific construction process of the acceleration / deceleration decision-making behavior model of the following vehicle driver is as follows: Step 2-1, the following state of the vehicle is represented by the ratio of the speed difference between the two vehicles to the following distance of the following vehicle. Then:

[0008] θ=dv / d

[0009] dv = v N+1 -v N

[0010] In the formula, θ represents the driving state of the following vehicle, dv represents the speed difference, and v N+1 To keep up with the speed of other vehicles, v NLet d be the speed of the vehicle ahead and d be the following distance; in step 2-2, construct the driver acceleration / deceleration decision model for different driving states from the perspectives of the following vehicle's speed being lower than the vehicle ahead and higher than the vehicle ahead, respectively. Then we have:

[0011] a=αθ 2 +βθ+γ

[0012] A=αθ 2 +βθ+γ

[0013] In the formula, a is the acceleration of the following vehicle when its speed is lower than that of the vehicle in front, A is the acceleration of the following vehicle when its speed is higher than that of the vehicle in front, and α, β and γ are all constants.

[0014] The vehicle following modeling method for highway tunnel sections based on driver visual characteristics provided by this invention may also have the following features: In step 3, the driver discovers an object ahead and makes a judgment decision during the driving process in the tunnel section, which is specifically divided into three stages: in the first stage, the object ahead appears during the driving process; in the second stage, the driver discovers and judges the object ahead as an obstacle or a vehicle and prepares to deal with it; in the third stage, the driver makes a decision and takes measures to slow down or brake to deal with it in order to ensure driving safety. The total utility of following vehicles during the driving process in the tunnel section is evaluated from the perspectives of time utility and safety utility. The optimal utility function of vehicle driver based on safe following distance is constructed, and the minimum following distance based on the current vehicle speed is calculated. Time utility is reflected by following vehicle speed, and safety utility is reflected by following distance.

[0015] The vehicle following modeling method for highway tunnel sections based on driver visual characteristics provided by this invention may also have the following features: In step 3, the specific process for calculating the minimum following distance is as follows: Step 3-1, to ensure driving safety, the following vehicle speed should meet the following safety conditions:

[0016] d min ≤d

[0017] In the formula, d min Let d be the minimum safe following distance at the current following speed, and d be the following distance of the following vehicle; Step 3-2, the calculation method for the minimum safe following distance at the current following speed is as follows:

[0018] d min =L N+1 -L N +β

[0019] In the formula, L N+1 L is the braking distance of vehicle N+1. NLet β be the braking distance of vehicle N, and β be the distance between vehicle N+1 and vehicle N after vehicle N stops. In step 3-3, when determining the following safety distance, the most dangerous situation is considered, that is, when the vehicle in front is in a state of uniform deceleration braking, the minimum following distance is obtained as follows:

[0020]

[0021] In the formula, β0 is the driver's desired safe distance, v is the speed of the following vehicle before braking, and t r For braking hysteresis time, t a For the time of light adaptation or dark adaptation at the tunnel entrance and exit, A N+1 Let A be the maximum deceleration of car N+1. N Let v be the maximum deceleration of car N; in steps 3-4, when the vehicles are in a following state, the speed difference between the two vehicles is small, therefore v N+1 and v N Treating them as two approximately equal values, and disregarding differences in vehicle deceleration performance, we assume the accelerations of both vehicles are equal, thus obtaining the minimum safe following distance when following another vehicle:

[0022] L m =v N+1 (t r +t a )+β0

[0023] In the formula, L m This represents the minimum safe following distance at the current following speed.

[0024] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics provided by this invention may also have the following features: Step 4 specifically includes the following steps: Step 4-1, during daytime driving, the driving section outside the tunnel is considered daytime driving, while the driving section inside the tunnel is considered nighttime driving. Therefore, the speed of following vehicles in the tunnel section is corrected according to the driver's perceived speed function during the day and night:

[0025] Δv o =v N+1 -f(v N+1 )

[0026] Δv i =v N+1 -p(v N+1 )

[0027] In the formula, Δv o For the driver's speed perception deviation on the road section outside the tunnel, Δv i For the driver's speed perception deviation in the tunnel section, f(v) N+1 Let p(v) be the speed function perceived by the driver on the road section outside the tunnel. N+1Step 4-2: Correct the following vehicle speed in the tunnel section using the speed deviation to obtain the driver's perceived speed.

[0028] V = v N+1 +Δv

[0029] In the formula, V is the corrected theoretical maximum speed limit for long tunnel sections of plateau expressways, and Δv is the driver's speed perception deviation; Step 4-3, calculate the corrected minimum following distance:

[0030] L m =V(t) r +t a )+β0

[0031] In the formula, L m This is the minimum safe following distance at the current following speed after correction.

[0032] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics provided by this invention may also have the following features: In step 5, from the perspective of the local optimum of the following vehicle, considering time utility and safety utility, the modified minimum safe following distance function is used as the decision objective function of the following vehicle driver. The following vehicle driver takes acceleration and deceleration measures to correct the driving speed and following distance towards the optimal state.

[0033] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics provided by this invention may also have the following feature: in step 6, the operating state includes vehicle speed and vehicle position.

[0034] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics provided by this invention may also have the following feature: In step 7, it is assumed that the leading vehicle changes its motion state according to a pre-set scenario, while the following vehicles operate according to the vehicle following model in highway tunnel sections based on driver visual characteristics. The operating state of the following vehicles when t>0 is examined, and the update rule is as follows:

[0035] Vehicle speed: v n+1 (t+Δt)=v n+1 (t)+a n+1 (t)×Δt,n=1,...,N

[0036] Vehicle location:

[0037] In the formula, Δt is the acceleration adjustment time.

[0038] The role and effect of invention

[0039] The method for modeling vehicle following in highway tunnel sections based on driver visual characteristics according to the present invention comprises the following steps: Step 1, selecting a highway tunnel section, acquiring driving state data and relevant vehicle parameters of vehicles traveling in the tunnel section, and calculating the vehicle following state; Step 2, based on the vehicle following state, constructing a model of the driver's acceleration and deceleration decision-making behavior for following vehicles, and determining its following driving decision-making behavior under different driving conditions; Step 3, based on the following driving decision-making behavior, constructing a vehicle driver utility-optimal function based on a safe following distance, and calculating the minimum following distance based on the current vehicle speed; Step 4... Step 5: Based on the speed perception selection driver acceleration / deceleration decision behavior model, the vehicle driver utility optimal function is modified, and the modified minimum safe following distance is calculated; Step 6: Based on the modified minimum safe following distance, a vehicle following model for highway tunnel sections based on driver visual characteristics is established; Step 7: By changing the running state of the leading vehicle, a simulated traffic scenario for highway tunnel sections is set; Step 8: Based on the simulated traffic scenario for highway tunnel sections, the following vehicles run according to the vehicle following model for highway tunnel sections based on driver visual characteristics, and the running state of the following vehicles when t>0 is simulated.

[0040] Therefore, the present invention has the following advantages:

[0041] 1. Based on the minimum safe following distance model, the driver's visual characteristics are introduced to make the newly established traffic flow model closer to reality and better reflect the impact of changes in the driving environment in tunnel sections on vehicle driving status.

[0042] 2. This invention can determine the impact of changes in the driving environment (speed limit, illumination) of tunnel sections on drivers at a microscopic level, as well as the stability of following vehicles and traffic flow on the road section. It can be used to improve microscopic traffic simulation software models and provide a basis for the layout of traffic facilities and the reasonable setting of speed limits.

[0043] 3. Safety assessments can be conducted on highway tunnel sections during the operational phase. Therefore, by constructing a car-following model for extra-long highway tunnels based on driver visual characteristics, it is helpful to improve traffic flow stability in tunnel sections and optimize speed limits in tunnel sections, which has strong practical significance.

[0044] In summary, this invention can not only help assess the stability and variation characteristics of traffic flow in highway tunnel sections, but also further optimize the speed limit value of tunnel sections based on driving characteristics and changes in driver vision, thereby achieving segmented speed limits and a smooth transition of vehicle speed. Attached Figure Description

[0045] Figure 1 This is a vehicle following scenario diagram in an embodiment of the present invention;

[0046] Figure 2 This is a driver dynamic decision-making flowchart based on local utility optimization in an embodiment of the present invention. Detailed Implementation

[0047] To make the technical means, creative features, objectives and effects of this invention easy to understand, the following embodiments, in conjunction with the accompanying drawings, specifically illustrate a method for modeling vehicle following in highway tunnel sections based on driver visual characteristics.

[0048] In this embodiment, a method for modeling vehicle following in highway tunnel sections based on driver visual characteristics is provided.

[0049] The vehicle following modeling method for highway tunnel sections based on driver visual characteristics involved in this embodiment includes the following steps:

[0050] Figure 1 This is a scenario diagram of vehicles following each other in this embodiment.

[0051] like Figure 1 As shown, in step S1, a highway tunnel section is selected, and driving status data and related vehicle parameters of vehicles traveling in the tunnel section are obtained through video statistics or real vehicle tests, and the following status of the vehicles is calculated. The driving status data includes the speed, acceleration, following distance, road section location, and illuminance of the following vehicles, and the related vehicle parameters include the length and width of the vehicles.

[0052] Step S2: Based on the car-following status of the vehicle, construct a model of the acceleration and deceleration decision-making behavior of the driver in the car-following vehicle on the highway tunnel section, and determine its car-following driving decision-making behavior under different driving conditions. The specific construction process is as follows:

[0053] Step S2-1: The following state of a vehicle is represented by the ratio of the speed difference between the two vehicles to the following distance of the following vehicle. Then:

[0054] θ=dv / d

[0055] dv = v N+1 -v N

[0056] In the formula, θ represents the driving state of the following vehicle, dv represents the speed difference, and v N+1 To keep up with the speed of other vehicles, v N d represents the speed of the vehicle ahead, and d represents the following distance.

[0057] Step S2-2 involves constructing driver acceleration / deceleration decision models for different driving states, considering both the following vehicle's speed being lower than and higher than the vehicle in front. The results are as follows:

[0058] a=αθ 2+βθ+γ

[0059] A=αθ 2 +βθ+γ

[0060] In the formula, a is the acceleration of the following vehicle when its speed is lower than that of the vehicle in front, A is the acceleration of the following vehicle when its speed is higher than that of the vehicle in front, and α, β and γ are all constants.

[0061] Step S3: Construct the vehicle driver utility optimal function based on safe following distance according to the car-following driving decision behavior, and calculate the minimum following distance based on the current vehicle speed.

[0062] When a driver encounters an object ahead while driving in a tunnel, the process involves three stages: Stage 1: The object appears ahead; Stage 2: The driver identifies and determines the object to be an obstacle or vehicle and prepares to respond; Stage 3: The driver makes a decision to slow down or brake to ensure driving safety. The overall utility of following vehicles in tunnel driving is evaluated from both time and safety utility perspectives (time utility is reflected by the following vehicle speed, while safety utility is reflected by the following distance). An optimal utility function for the driver based on the safe following distance is constructed, and the minimum following distance based on the current speed is calculated, including the following steps:

[0063] Step S3-1: To ensure driving safety, the following safety conditions must be met when following another vehicle:

[0064] d min ≤d

[0065] In the formula, d min d represents the minimum safe following distance at the current following speed, where d is the following distance of the following vehicle.

[0066] Step S3-2, the method for calculating the minimum safe following distance at the current following speed is as follows:

[0067] d min =L N+1 -L N +β

[0068] In the formula, L N+1 L is the braking distance of vehicle N+1. N Let β be the braking distance of car N, and β be the distance between car N+1 and car N after car N stops.

[0069] Step S3-3: When determining the following safety distance, the most dangerous situation is considered, i.e., when the vehicle in front is in a state of uniform deceleration and braking. The minimum following distance is then obtained as follows:

[0070]

[0071] In the formula, β0 is the driver's desired safe distance, v is the speed of the following vehicle before braking, and t r For braking hysteresis time, t a For the time of light adaptation or dark adaptation at the tunnel entrance and exit, A N+1 Let A be the maximum deceleration of car N+1. N Let N be the maximum deceleration of car N.

[0072] Steps S3-4: When the vehicle is in a following state, the speed difference between the two vehicles is small, therefore v N+1 and v N Treating them as two approximately equal values, and disregarding differences in vehicle deceleration performance, we assume the accelerations of both vehicles are equal, thus obtaining the minimum safe following distance when following another vehicle:

[0073] L m =v N+1 (t r +t a )+β0

[0074] In the formula, L m This represents the minimum safe following distance at the current following speed.

[0075] Step S4: Based on the driver's acceleration / deceleration decision-making behavior model for the vehicle following the vehicle at perceived speed, the optimal utility function for the vehicle driver is modified, and the modified minimum safe following distance is calculated. This specifically includes the following steps:

[0076] Step S4-1: During daytime driving, the road section outside the tunnel is considered daytime driving, while the road section inside the tunnel can be considered nighttime driving. Therefore, the speed of following vehicles in the tunnel section is corrected based on the driver's perceived speed function during the day and night.

[0077] Δv o =v N+1 -f(v N+1 )

[0078] Δv i =v N+1 -p(v N+1 )

[0079] In the formula, Δv o For the driver's speed perception deviation on the road section outside the tunnel, Δv i For the driver's speed perception deviation in the tunnel section, f(v) N+1 Let p(v) be the speed function perceived by the driver on the road section outside the tunnel. N+1 ) represents the speed perceived by the driver on the road section inside the tunnel.

[0080] Step S4-2: Correct the following vehicle speed in the tunnel section using the speed deviation to obtain the driver's perceived speed:

[0081] V = v N+1 +Δv

[0082] In the formula, V is the corrected theoretical maximum speed limit for extra-long tunnel sections of plateau expressways, and Δv is the driver's speed perception deviation.

[0083] Step S4-3: Calculate the corrected minimum following distance:

[0084] L m =V(t) r +t a )+β0

[0085] In the formula, L m This is the minimum safe following distance at the current following speed after correction.

[0086] Step S5: Establish a vehicle following model for highway tunnel sections based on driver visual characteristics, using the corrected minimum safe following distance. From the perspective of local optima for following vehicles, considering time utility and safety utility, the corrected minimum safe following distance function is used as the decision objective function for the following vehicle driver. The following vehicle driver takes acceleration and deceleration measures to correct the driving speed and following distance towards the optimal state.

[0087] Step S6: By changing the operating status of the leading vehicle, a simulated traffic scenario is set for the highway tunnel section. The operating status includes vehicle speed and vehicle position.

[0088] Figure 2 This is a flowchart of the driver's dynamic decision-making process based on local utility optimization in this embodiment.

[0089] like Figure 2 As shown, in step S7, based on the simulated traffic scenario of the highway tunnel section, the following vehicles operate according to the highway tunnel section vehicle following model based on the driver's visual characteristics, and the simulation simulates the running state of the following vehicles when t>0.

[0090] Assuming the lead vehicle changes its motion state according to a pre-set scenario, while the following vehicles operate according to a car-following model based on driver visual characteristics in a highway tunnel section, the operating state of the following vehicles is examined when t>0, and the update rule is as follows:

[0091] Vehicle speed: v n+1 (t+Δt)=v n+1 (t)+a n+1 (t)×Δt,n=1,...,N

[0092] Vehicle location:

[0093] In the formula, Δt is the acceleration adjustment time.

[0094] The role and effect of the embodiments

[0095] According to the vehicle following modeling method for highway tunnel sections based on driver visual characteristics involved in this embodiment, the specific process is as follows: Step 1, select the highway tunnel section, obtain the driving status data and related parameters of vehicles traveling in the tunnel section, and calculate the following state of the vehicles; Step 2, based on the following state of the vehicles, construct a model of the acceleration and deceleration decision-making behavior of the following vehicle driver, and determine its following driving decision-making behavior under different driving conditions; Step 3, based on the following driving decision-making behavior, construct the vehicle driver utility optimal function based on the safe following distance, and calculate the minimum following distance based on the current vehicle speed; Step 4, ... Step 5: Based on the speed perception selection, the driver's acceleration and deceleration decision-making behavior model is used to modify the driver's utility optimal function, and the modified minimum safe following distance is calculated; Step 6: Based on the modified minimum safe following distance, a vehicle following model for highway tunnel sections based on driver visual characteristics is established; Step 7: By changing the running state of the leading vehicle, a simulated traffic scenario for highway tunnel sections is set; Step 8: Based on the simulated traffic scenario for highway tunnel sections, the following vehicles run according to the vehicle following model for highway tunnel sections based on driver visual characteristics, and the running state of the following vehicles when t>0 is simulated.

[0096] Therefore, the above embodiments have the following advantages:

[0097] 1. Based on the minimum safe following distance model, the driver's visual characteristics are introduced to make the newly established traffic flow model closer to reality and better reflect the impact of changes in the driving environment in tunnel sections on vehicle driving status.

[0098] 2. The above embodiments can determine the impact of changes in the driving environment (speed limit, illumination) of tunnel sections on drivers at a microscopic level, as well as the stability of following vehicles and traffic flow on the road section. This can be used to improve microscopic traffic simulation software models and provide a basis for the layout of traffic facilities and the reasonable setting of speed limits.

[0099] 3. Safety assessments can be conducted on highway tunnel sections during the operational phase. Therefore, by constructing a car-following model for extra-long highway tunnels based on driver visual characteristics, it is helpful to improve traffic flow stability in tunnel sections and optimize speed limits in tunnel sections, which has strong practical significance.

[0100] The above embodiments are preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention.

Claims

1. A method for vehicle following modeling in highway tunnel sections based on driver visual characteristics, characterized in that, Includes the following steps: Step 1: Select a highway tunnel section, obtain the driving status data and related parameters of vehicles traveling in the tunnel section, and calculate the following status of the vehicles. Step 2: Based on the following status of the vehicle, construct a model of the acceleration and deceleration decision-making behavior of the following vehicle driver, and determine its following driving decision-making behavior under different driving conditions. Step 3: Construct the optimal utility function for vehicle drivers based on the safe following distance, and calculate the minimum following distance based on the current vehicle speed; Step 4: Based on the perceived speed, select the driver's acceleration and deceleration decision behavior model of the following vehicle to modify the vehicle driver's utility optimal function, and calculate the modified minimum safe following distance; Step 5: Establish a vehicle following model for highway tunnel sections based on driver visual characteristics according to the corrected minimum safe following distance; Step 6: Set up a simulated traffic scenario for the highway tunnel section by changing the operating status of the lead vehicle; Step 7: Based on the simulated traffic scenario of the highway tunnel section, the following vehicles operate according to the highway tunnel section vehicle following model based on the driver's visual characteristics, and the simulation simulates the operating state of the following vehicles when t>0.

2. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 1, the driving status data is obtained through video statistics or real-vehicle testing. The driving status data includes the speed, acceleration, following distance of the following vehicle, road segment location, and illuminance of the vehicle in front. The vehicle-related parameters include the vehicle's length and width.

3. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 2, the acceleration and deceleration decision-making behavior model of the driver of the following vehicle is a model of the acceleration and deceleration decision-making behavior of the driver of the following vehicle in a highway tunnel section. The specific construction process of the acceleration and deceleration decision-making behavior model for drivers of car-following vehicles is as follows: Step 2-1: The following state of the vehicle is represented by the ratio of the speed difference between the two vehicles to the following distance of the following vehicle. Then: θ=dv / d dv=v N+1 -v N In the formula, θ represents the driving state of the following vehicle, dv represents the speed difference, and v N+1 To keep up with the speed of other vehicles, v N d represents the speed of the vehicle ahead, and d represents the following distance. Step 2-2: Construct driver acceleration / deceleration decision models for different driving states, considering both the following vehicle's speed being lower than and higher than the vehicle in front. Then: a=αθ 2 +βθ+γ A=αθ 2 +βθ+γ In the formula, a is the acceleration of the following vehicle when its speed is lower than that of the vehicle in front, A is the acceleration of the following vehicle when its speed is higher than that of the vehicle in front, and α, β and γ are all constants.

4. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 3, while driving through the tunnel, the driver observes an object ahead and makes a judgment and decision. This is divided into three stages: Stage 1: The object appears ahead during driving; Stage 2: The driver observes and judges the object ahead to be an obstacle or a vehicle and prepares to deal with it; Stage 3: The driver makes a decision and takes measures such as slowing down or braking to ensure driving safety. The total utility of following vehicles during driving in tunnel sections is evaluated from two aspects: time utility and safety utility. An optimal utility function for drivers based on safe following distance is constructed, and the minimum following distance based on the current vehicle speed is calculated. The time utility is reflected in the following vehicle speed, and the safety utility is reflected in the following distance.

5. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 4, characterized in that: in, In step 3, the specific process for calculating the minimum following distance is as follows; Step 3-1: To ensure driving safety, the following safety conditions must be met when following another vehicle: d min ≤d In the formula, d min d represents the minimum safe following distance at the current following speed, where d is the following distance of the following vehicle. Step 3-2, the method for calculating the minimum safe following distance at the current following speed is as follows: d min =L N+1 -L N +b In the formula, L N+1 L is the braking distance of vehicle N+1. N Let β be the braking distance of car N, and β be the distance between car N+1 and car N after the vehicles come to a stop. Step 3-3: When determining the safe following distance, the most dangerous situation is considered, i.e., when the vehicle in front is in a state of uniform deceleration and braking. The minimum following distance is then obtained as follows: In the formula, β0 is the driver's desired safe distance, v is the speed of the following vehicle before braking, and t r For braking hysteresis time, t a For the time of light adaptation or dark adaptation at the tunnel entrance and exit, A N+1 Let A be the maximum deceleration of car N+1. N Let N be the maximum deceleration of the vehicle. Steps 3-4: When the vehicle is in a following state, the speed difference between the two vehicles is small, therefore v N+1 and v N Treating them as two approximately equal values, and disregarding differences in vehicle deceleration performance, we assume the accelerations of both vehicles are equal, thus obtaining the minimum safe following distance when following another vehicle: L m =v N+1 (t r +t a )+β0 In the formula, L m This represents the minimum safe following distance at the current following speed.

6. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, Step 4 specifically includes the following steps: Step 4-1: During daytime driving, the road section outside the tunnel is considered daytime driving, while the road section inside the tunnel is considered nighttime driving. Therefore, the speed of vehicles following in the tunnel section is corrected based on the driver's perceived speed function during the day and night. Δv o =v N+1 -f(v N+1 ) Δv i =v N+1 -p(v N+1 ) In the formula, Δv o For the driver's speed perception deviation on the road section outside the tunnel, Δv i For the driver's speed perception deviation in the tunnel section, f(v) N+1 Let p(v) be the speed function perceived by the driver on the road section outside the tunnel. N+1 () represents the speed perceived by the driver on the road section inside the tunnel; Step 4-2: Correct the following vehicle speed in the tunnel section using the speed deviation to obtain the driver's perceived speed: V=v N+1 +Δv In the formula, V is the corrected theoretical maximum speed limit for extra-long tunnel sections of plateau expressways, and Δv is the driver's speed perception deviation value. Step 4-3, calculate the corrected minimum following distance: L m =V(t r +t a )+β0 In the formula, L m This is the minimum safe following distance at the current following speed after correction.

7. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 5, from the perspective of the local optimum of the following vehicle, considering time utility and safety utility, the modified minimum safe following distance function is used as the decision objective function of the following vehicle driver. The following vehicle driver takes acceleration and deceleration measures to correct the driving speed and following distance towards the optimal state.

8. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 6, the operating status includes vehicle speed and vehicle position.

9. The method for vehicle following modeling in highway tunnel sections based on driver visual characteristics according to claim 1, characterized in that: in, In step 7, assuming the leading vehicle changes its motion state according to a pre-set scenario, while the following vehicles operate according to a car-following model for highway tunnel sections based on driver visual characteristics, the operating state of the following vehicles when t>0 is examined, and the update rule is as follows: Vehicle speed: v n+1 (t+Δt)=v n+1 (t)+a n+1 (t)×Δt,n=1,...,N Vehicle location: In the formula, Δt is the acceleration adjustment time.

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