Route multi-target coordination control method and system based on left turning speed guidance

Abstract

The invention discloses a path multi-target coordination control method and system based on left-turn speed guidance, and the method comprises the steps: constructing an FVD car-following model based on acceleration attenuation, and carrying out the improvement of the FVD car-following model based on a left-turn car-following condition, and obtaining a left-turn FVD optimization model; a minimm-jerk turning track prediction model is constructed, speed guidance is carried out on the left-turning vehicle through the left-turning FVD optimization model and the minimm-jerk turning track prediction model, and the speed guidance comprises straight-going stage speed guidance and left-turning stage speed guidance; and by taking the maximum steering traffic flow green wave bandwidth and the minimum path carbon emission as optimization objectives, performing multi-objective coordination optimization on the original AM-BAND model and the carbon emission model based on speed guidance. According to the method, left-turn speed guidance is combined with a multi-objective optimization scheme, and meanwhile, a vehicle guidance speed and an intersection signal timing scheme are optimized, so that coordinated control and optimization of a left-turn path are realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban traffic signal coordination, and particularly relates to a multi-objective coordinated control method and system for a path based on left-turn speed guidance. Background Art

[0002] During peak commuting hours in the urban road network, the traffic demand accounts for 10% - 12% of the total daily traffic volume. Due to objective factors such as urban industrial layout and road network distribution, a large number of commuting traffic paths consist of multiple rather than single main lines. Especially for the left-turn traffic flow in the path, due to reasons such as its turning radius and the length of the intersection widening section, it is extremely easy to cause congestion at intersections, greatly affecting the smoothness of urban traffic. On the other hand, vehicle speed guidance is a typical application of advanced driver assistance system (ADAS). By giving the driver a recommended speed, the vehicle can reach the intersection during the green light phase and pass through the intersection without interruption.

[0003] Speed guidance is an effective method to improve the traffic capacity of signalized intersections and reduce stop delay. Domestic and foreign scholars' research on vehicle speed guidance mainly focuses on two aspects: intersection signal coordination and eco-driving. The existing research on signal coordinated control mainly focuses on the combination of vehicle speed guidance and signal timing optimization. Biao et al. developed a collaborative optimization strategy that integrates signal timing and vehicle speed optimization, aiming to improve both signal timing efficiency and vehicle driving speed simultaneously. This strategy includes two parts: roadside traffic signal optimization and in-vehicle speed control. Zhang Jingsi et al. took the main line delay, traffic capacity, number of stops, and sub-road direction delay of a double-cycle intersection as optimization objectives, constructed a multi-objective optimization model for a double-cycle main line under vehicle speed guidance, and proposed a dynamic vehicle speed guidance model considering queue dissipation and phase difference for different traffic flow conditions at upstream intersections.

[0004] Currently, most of the research on urban path speed guidance focuses on the guidance of the straight-through traffic flow in the main line direction. For the guidance of the left-turn traffic flow, it mainly considers aspects such as the influence of left-turn merging and the phase release sequence. For example, Xu Jianmin et al. proposed two phase release methods for the left-turn traffic flow at a T-shaped intersection and established a green wave coordinated control model with the goal of maximizing the two-way green wave bandwidth. Yang Xiaofang et al. considered the influence of left-turn merging vehicles and guided the left-turn and straight-through traffic flows to pass through the main line coordination control maximally by determining the passable time of the main line straight-through vehicles and left-turn merging vehicles. However, this method lacks consideration of the left-turn traffic flow merging time and vehicle queuing problems. Summary of the Invention

[0005] The present invention aims to solve the deficiencies of the prior art and provides the following solutions:

[0006] A multi-objective coordinated control method for a path based on left-turn speed guidance, comprising the following steps:

[0007] Construct an FVD following model based on acceleration decay, and improve the FVD following model based on the left-turn following situation to obtain an optimized left-turn FVD model;

[0008] Construct a minimum-jerk turning trajectory prediction model, and perform speed guidance on the left-turning vehicle through the optimized left-turn FVD model and the minimum-jerk turning trajectory prediction model. The speed guidance includes: straight-line stage speed guidance and left-turn stage speed guidance;

[0009] Taking the maximum green wave bandwidth of the turning traffic flow and the minimum path carbon emissions as the optimization objectives, perform multi-objective coordinated optimization on the original AM-BAND model and the carbon emissions model based on the speed guidance.

[0010] Preferably, the method for obtaining the optimized left-turn FVD model includes:

[0011] Establish an FVD following model based on acceleration decay:

[0012] α loss (t) = γ·lg(Δx n (t)) + ξ

[0013] where α loss (t) represents the acceleration signal attenuation value of the leading vehicle to the following vehicle when the distance between the leading vehicle and the following vehicle at time t is Δx n (t), γ represents the acceleration loss exponential factor, and ξ represents the random error term;

[0014] Improve the FVD following model based on the safe distance between vehicles to obtain an improved FVD following model:

[0015] a n (t) = k[V[Δx n (t)] - v n (t)] + λΔv(t) + α loss (t)

[0016] v n (t + T) = v n (t + a n (t + T)·T

[0017]

[0018] where a n (t) represents the updated following acceleration of the nth following vehicle, k represents the driver sensitivity coefficient, λ represents the feedback coefficient, and V[Δx n(t) represents the speed optimization function considering the safety distance, Δv(t) represents the speed difference between the leading vehicle and the following vehicle, and v n (t) represents the speed of the leading vehicle, and h e represents the safety distance between vehicles, and v max represents the maximum driving speed, τ represents the reaction time of the driver, and L represents the length of the leading vehicle;

[0019] Based on the turning radius and the safety distance, the improved FVD car-following model is further improved to obtain the left-turn FVD optimization model:

[0020]

[0021] V l [Δx n (t)] = V l ×V[Δx n (t)]

[0022]

[0023] Among them, represents the updated car-following acceleration of the nth following vehicle in the left-turn FVD optimization model, and k * represents the driver sensitivity coefficient considering the turning radius, and λ * represents the feedback coefficient considering the turning radius, and V l represents the speed optimization coefficient function at different turning positions, and L d represents the buffer distance for the turning vehicle to decelerate, and x g represents the position of the stop line, and x n represents the position of the nth following vehicle, represents the turning coefficient of the vehicle.

[0024] Preferably, the speed guidance in the straight-ahead stage includes:

[0025]

[0026] Among them, V i * represents the guidance speed in the straight-ahead stage, L0 represents the length of the guidance area, and φ i-1，i represents the phase difference in the coordinated direction between intersections i - 1 and i, and g 直 represents the green light time for straight-ahead, τ i represents the time for clearing the queuing vehicles, and t i represents the time when the vehicle reaches the speed guidance area.

[0027] Preferably, the speed guidance in the left-turn stage includes: speed guidance at the left-turn intersection and speed guidance in the straight-ahead stage after the left-turn ends;

[0028] The left-turn intersection speed guidance includes:

[0029]

[0030] Wherein, represents the guidance speed of the leading vehicle at the left-turn intersection, r represents the turning distance, and t f represents the movement time of the vehicle from the starting position to the ending position;

[0031] The straight-ahead speed guidance after the left turn includes:

[0032]

[0033] Wherein, t i+1 represents the time when the vehicle enters the intersection guidance area, and t s represents the time when the vehicle reaches the intersection exit, V f represents the left-turn guidance speed of the vehicle, represents the vehicle speed during the left turn, X i+1 represents the position of intersection X i+1 represents the position of intersection X i represents the position of intersection X i represents the position of intersection X i，i+1 represents the phase difference of the coordinated direction between intersection i and i + 1.

[0034] Preferably, the method for multi-objective coordinated optimization includes:

[0035] Taking the maximum green-wave bandwidth of the turning traffic flow as the optimization objective, setting several constraint conditions, and optimizing the original AM-BAND model;

[0036] Constructing a carbon emission model, taking the minimum path carbon emission as the optimization objective, setting several constraint conditions, and optimizing the carbon emission model;

[0037] Using the optimized AM-BAND model and the optimized carbon emission model to perform multi-objective coordinated optimization on the vehicle path.

[0038] The present invention also provides a multi-objective coordinated control system for vehicle paths based on left-turn speed guidance. The system applies the method described in any one of the above, and includes: a left-turn model construction module, a speed guidance module, and a multi-objective optimization module;

[0039] The left-turn model construction module is used to construct an FVD car-following model based on acceleration decay, and improve the FVD car-following model based on the left-turn car-following situation to obtain an optimized left-turn FVD model;

[0040] The speed guidance module is used to construct a minimum-jerk turning trajectory prediction model, and speed guidance is provided for left-turning vehicles through the left-turn FVD optimization model and the minimum-jerk turning trajectory prediction model. The speed guidance includes: speed guidance in the straight-ahead stage and speed guidance in the left-turn stage;

[0041] The multi-objective optimization module is used to perform multi-objective coordinated optimization on the original AM-BAND model and the carbon emission model based on the speed guidance, with the maximum green wave bandwidth of the turning traffic flow and the minimum path carbon emission as the optimization objectives.

[0042] Preferably, the working process of the left-turn model construction module includes:

[0043] Establish an FVD car-following model based on acceleration decay:

[0044] α loss (t) = γ·lg(Δx n (t)) + ξ

[0045] where α loss (t) represents the acceleration signal attenuation value of the leading vehicle to the following vehicle when the distance between the leading vehicle and the following vehicle at time t is Δx n (t), γ represents the acceleration loss exponential factor, and ξ represents the random error term;

[0046] Improve the FVD car-following model based on the safe distance between vehicles to obtain an improved FVD car-following model:

[0047] a n (t) = k[V[Δx n (t)] - v n (t)] + λΔv(t) + α loss (t)

[0048] v n (t + T) = v n (t) + a n (t + T)·T

[0049]

[0050] where a n (t) represents the updated car-following acceleration of the nth following vehicle, k represents the driver sensitivity coefficient, λ represents the feedback coefficient, V[Δx n (t)] represents the speed optimization function considering the safe distance, Δv(t) represents the speed difference between the leading vehicle and the following vehicle, v n (t) represents the speed of the leading vehicle, h e represents the safe distance between vehicles, v maxV represents the maximum driving speed, τ represents the driver's reaction time, and L represents the length of the leading vehicle;

[0051] Based on the turning radius and safety distance, the improved FVD following model is further improved to obtain the left-turn FVD optimization model:

[0052]

[0053] V l [Δx n (t)] = V l ×V[Δx n (t)]

[0054]

[0055] Among them, represents the updated following acceleration of the nth following vehicle in the left-turn FVD optimization model, k * represents the driver sensitivity coefficient considering the turning radius, λ * represents the feedback coefficient considering the turning radius, V l represents the speed optimization coefficient function at different turning positions, L d represents the buffer distance for the turning vehicle to decelerate, x g represents the position of the stop line, x n represents the position of the nth following vehicle, represents the turning coefficient of the vehicle.

[0056] Preferably, in the speed guidance module, the speed guidance in the straight-ahead stage includes:

[0057]

[0058] Among them, V i * represents the guidance speed in the straight-ahead stage, L0 represents the length of the guidance area, φ i-1，i represents the phase difference in the coordinated direction between intersections i - 1 and i, g 直 represents the green light time for straight-ahead, τ i represents the queuing vehicle clearance time, t i represents the time when the vehicle reaches the speed guidance area.

[0059] Preferably, in the speed guidance module, the speed guidance in the left-turn stage includes: speed guidance at the left-turn intersection and speed guidance in the straight-ahead stage after the left-turn ends;

[0060] The speed guidance at the left-turn intersection includes:

[0061]

[0062] Among them, represents the guiding speed of the leading vehicle at the left-turn intersection, r represents the turning distance, and t f represents the movement time of the vehicle from the starting position to the ending position;

[0063] The speed guidance in the straight-ahead stage after the left turn includes:

[0064]

[0065] Among them, t i+1 represents the time when the vehicle enters the intersection guidance area, and t s represents the time when the vehicle reaches the intersection exit, V f represents the left-turn guidance speed of the vehicle, represents the vehicle speed during the left turn, X i+1 represents the position of intersection X i+1 of, X i represents the position of intersection X i of, φ i，i+1 represents the phase difference of the coordinated direction between intersections i and i + 1.

[0066] Preferably, the working process of the multi-objective optimization module includes:

[0067] Taking the maximum green wave bandwidth of the turning traffic flow as the optimization objective, setting several constraint conditions, and optimizing the original AM-BAND model;

[0068] Constructing a carbon emission model, taking the minimum path carbon emission as the optimization objective, setting several constraint conditions, and optimizing the carbon emission model;

[0069] Using the optimized AM-BAND model and the optimized carbon emission model to conduct multi-objective coordinated optimization of the vehicle path.

[0070] Compared with the prior art, the beneficial effects of the present invention are:

[0071] (1) Based on the acceleration and deceleration characteristics of the vehicle before the intersection and the fact that the acceleration and deceleration of the left-turn vehicle are affected by the turning radius, the present invention establishes a vehicle following model considering acceleration attenuation by combining the traditional FVD vehicle following model; combining the minimum-jerk principle and the trajectory characteristics of the vehicle at the left-turn intersection, it proposes a vehicle speed guidance for the path including the left-turn direction;

[0072] (2) The present invention combines the left-turn speed guidance with a multi-objective optimization scheme, and proposes a multi-objective optimization model for coordinated control of the left-turn path under speed guidance. Taking the maximum total bandwidth of the intersection and the minimum carbon emission of the path vehicle as the objectives, it simultaneously optimizes the vehicle guidance speed and the signal timing scheme of the intersection, realizing the coordinated control and optimization of the left-turn path.

[0073] (3) In view of the multi-dimensional characteristics of the model decision variables, the present invention constructs a simulation platform using VISSIM software. Taking three consecutive intersections in the Golden Middle Ring area of Pudong New Area, Shanghai as an example, the model is simulated and verified. The results show that compared with the traditional coordinated control model, the total green wave bandwidth of the left-turn coordinated control model under speed guidance increases by 27.78%, and the carbon emissions are reduced by 18.61%; compared with the multi-objective optimization model without speed guidance, the total green wave bandwidth of the left-turn coordinated control under speed guidance increases by 9.52%, and the carbon emissions are reduced by 6.47%. Description of the Drawings

[0074] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0075] Figure 1 Schematic diagram of the method flow for the embodiment of the present invention;

[0076] Figure 2 Schematic diagram of the time headway for the embodiment of the present invention;

[0077] Figure 3 Schematic diagram of the speed guidance during the straight-ahead phase for the embodiment of the present invention;

[0078] Figure 4 Schematic diagram of the left-turn trajectory planning model for the embodiment of the present invention;

[0079] Figure 5 Schematic diagram of the definition of each parameter of the intersection model for the embodiment of the present invention;

[0080] Figure 6 Corresponding diagram of the position and speed of the leading vehicle's driving path during the left turn for the embodiment of the present invention. Detailed Embodiments

[0081] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0082] To make the above objects, features, and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0083] Embodiment 1

[0084] In this embodiment, as Figure 1 shown, a multi-objective coordinated control method for a path based on left-turn speed guidance includes the following steps:

[0085] S1. Construct an FVD car-following model based on acceleration attenuation, and improve the FVD car-following model based on the left-turn car-following situation to obtain an optimized left-turn FVD model.

[0086] The method for obtaining the optimized left-turn FVD model includes:

[0087] In this embodiment, speed guidance is performed for the left-turn vehicle fleet. After entering the guidance area, there are different guidance methods. The acceleration and deceleration of the leading vehicle will inevitably have a certain impact on the speed of the following vehicle, and the driving characteristics change. It is necessary to improve the car-following model in the traditional environment. Referring to the path loss propagation model, an FVD model considering acceleration attenuation is established:

[0088]

[0089] where α loss (t) represents the acceleration signal attenuation value of the leading vehicle to the following vehicle when the distance between the leading vehicle and the following vehicle is Δx n (t) at time t. When the leading vehicle is accelerating, α loss (t)>0, otherwise α loss (t)≤0; x0 represents the minimum car-following distance; γ represents the acceleration loss exponential factor, which is mainly related to the headway, vehicle type and spatial traffic environment. In this embodiment, the headway is mainly selected as the characterization factor. As x0 changes, γ may have different values to characterize the acceleration signal attenuation at different car-following distances; ξ represents the random error term.

[0090] Substitute x0 = 1m into the above formula to establish an FVD car-following model based on acceleration attenuation:

[0091] α loss (t) = γ·lg(Δx n (t)))+ξ.

[0092] Based on the acceleration attenuation model, improve the full velocity difference (FVD) car-following model, and induce the following vehicle and the leading vehicle to form a vehicle queue with the same target speed, so that they can pass through the stop line at the intersection without stopping or with less stopping when the green light is on. Specifically, based on the safe distance between vehicles, improve the FVD car-following model to obtain the improved FVD car-following model:

[0093] a n (t) = k[V[Δx n (t)]-vn (t)] + λΔv(t) + α loss (t)

[0094] v n (t + T) = v n (t) + a n (t + T)·T

[0095]

[0096] where a n (t) represents the updated following acceleration of the nth following vehicle, k represents the driver sensitivity coefficient, taking 0.41 s -1 , λ represents the feedback coefficient, taking 0.3 s -1 , V[Δx n (t)] represents the speed optimization function considering the safety distance, Δv(t) represents the speed difference between the front and rear vehicles, v n (t) represents the speed of the leading vehicle, h e represents the safety distance between vehicles, v max represents the maximum driving speed, τ represents the driver's reaction time, and L represents the length of the leading vehicle.

[0097] In the traditional FVD model, the influence of the turning radius of the turning vehicle at the intersection on the sensitivity coefficients k and λ during the actual following process is not considered. In addition, during the left-turn following process, due to the influence of the turning radius, the turning speed must be optimized and adjusted. The turning vehicle needs to decelerate in advance before reaching the intersection, turn at a reasonable speed, and then follow the vehicle in the corresponding lane in front. After the vehicle passes through the intersection, to prevent excessive acceleration, the optimized speed of the turning vehicle should not change suddenly, but should gradually accelerate on the premise of ensuring driving safety and riding comfort. In order to ensure that the vehicle does not collide with the vehicle in front and the turning speed is as close as possible to the optimized speed, based on the turning radius and the safety distance, the improved FVD following model is further improved to obtain the left-turn FVD optimization model:

[0098]

[0099] V l [Δx n (t)] = V l ×V[Δx n (t)]

[0100]

[0101] where represents the updated following acceleration of the nth following vehicle in the left-turn FVD optimization model, k * represents the driver sensitivity coefficient considering the turning radius, λ *The feedback coefficient considering the turning radius, V l The speed optimization coefficient function located at different turning positions, L d The buffer distance for the deceleration of the turning vehicle, x g The position of the stop line, x n The position of the nth following vehicle, θ represents the turning coefficient of the vehicle, and the larger θ is, the greater the turning speed of the vehicle.

[0102] For the sensitivity coefficient of the turning radius, in this embodiment, referring to the parameters calibrated by Wei Fulu et al. through genetic algorithm iteration, the final value results of each parameter in the model under different turning radii at the signalized intersection are obtained, as shown in Table 1:

[0103] Table 1

[0104] k λ Straight 0.41 0.3 Turning radius / m <![CDATA[k * > <![CDATA[λ * > 40 0.019 0.665 35 0.024 0.715 30 0.029 0.722 25 0.031 0.739 20 0.037 0.752 。

[0105] S2. Construct a minimum-jerk turning trajectory prediction model, and conduct speed guidance for the left-turning vehicle through the left-turn FVD optimization model and the minimum-jerk turning trajectory prediction model. The speed guidance includes: speed guidance in the straight-ahead stage and speed guidance in the left-turn stage.

[0106] According to Figure 2 the time-distance diagram, taking the upstream direction as an example, where the intersection x i is the left-turn direction intersection, the intersections x i-1 and intersection x i+1 are the upstream and downstream straight-ahead direction intersections of the left-turn direction intersection. The traffic flow passing through the intersection during the green light is divided into two categories: the straight-ahead speed traffic flow at intersections i - 1 and i + 1 and the left-turn speed traffic flow at intersection i. Obviously, the guiding speed is affected by the intersection phase difference and the left-turning radius. Therefore, it is very necessary to deduce and analyze the relationship between the speed and the phase difference and the left-turning radius for establishing the speed guidance model, and the speed guidance is also divided into two stages: straight-ahead speed guidance and left-turn speed guidance.

[0107] The straight-ahead stage guidance mainly determines the guiding speed for the straight-ahead leading vehicle according to the arrival time of the vehicle and the phase difference. The following vehicles of the straight-ahead vehicle determine the following acceleration of the following vehicles based on the car-following model with decelerating acceleration on the basis of the leading vehicle; in the left-turn stage, the speed of the leading vehicle travels according to the left-turn leading vehicle guiding speed determined by the minimum-jerk trajectory planning, and then the straight-ahead guiding speed after the left-turn is determined according to the phase difference, while the acceleration of the left-turn following vehicle is determined according to the car-following model under the left-turn speed optimization.

[0108] The straight-ahead stage speed guidance is as Figure 3 shown, guiding the vehicle from intersection x i-1Drive out, because at intersection x i A left turn is required. If driving at the initial speed V, it will arrive during the straight-ahead green light. Therefore, deceleration guidance is required after entering the guidance area. The speed guidance during the straight-ahead phase includes:

[0109]

[0110] Among them, V i * represents the guidance speed during the straight-ahead phase, L0 represents the length of the guidance area, φ i-1，i represents the phase difference in the coordinated direction between intersections i - 1 and i, g 直 represents the green light time for straight-ahead, τ i represents the queuing vehicle clearance time, t i represents the time when the vehicle reaches the speed guidance area; then the acceleration of the following vehicle at this time is as follows:

[0111]

[0112] Among them, k takes the value of 0.41 s -1 , λ takes the value of 0.3 s -1 .

[0113] The speed guidance during the left-turn phase includes: left-turn intersection speed guidance and straight-ahead speed guidance after the left turn ends.

[0114] The left-turn trajectory planning model studied in this embodiment is based on the minimum-jerk principle. This method is mainly used to describe the inertial motion of an object in a two-dimensional plane. Flash and Hogan proved that the smoothness of an object's inertial motion (such as reaching, writing, and drawing tasks, etc.) can be represented by an acceleration function. To ensure the smoothness of the turn, it is represented by minimizing the value of the objective cost function: the sum of the squares of all accelerations in the direction of motion from a given initial position to the end position within a given time. The representation of the function:

[0115]

[0116] Among them, J represents the vector sum of the smooth movement of the end effector from one position to another, t f represents the movement time of the vehicle from the starting position to the end position, x(t) represents the abscissa of the object's position at time t, and y(t) represents the ordinate of the object's position at time t;

[0117] For the convenience of calculation, the solution of the objective cost function is represented by two fifth-order polynomials of time:

[0118] x(t) = a0 + a1t + a2t 2 + a3t3 +a4t 4 +a5t 5

[0119] y(t) = b0 + b1t + b2t 2 +b3t 3 +b4t 4 +b5t 5

[0120] where a i and b i (i = 1, 2, 3, 4, 5) represent constant coefficients.

[0121] The above two fifth-order polynomials contain 12 constant coefficients, which means there are 12 unknowns. Therefore, solving these equations requires 12 boundary conditions. The initial position and end position of the vehicle (coordinates of x(0) and y(0)), velocity vector, and acceleration vector (components of x(t) and y(t)) can provide 12 boundary conditions. Among them, the coordinates of the initial position and end position of the vehicle trajectory can be obtained from the geometric data of the intersection location and its surroundings. The speed and acceleration of the vehicle at the entrance and exit depend on the speed characteristics when entering and leaving.

[0122] Vehicles on the commuting path often appear in the form of a convoy. Determining the guiding speed of the leading vehicle is crucial for determining the speed of the convoy. In this embodiment, the minimum-jerk theory is used to plan the trajectory of the leading vehicle in the left-turning traffic flow to determine the guiding speed of the leading vehicle.

[0123] Traditional trajectory models use the starting and ending positions, speed and acceleration, and the travel time t f ) between these positions as trajectory information and explain that the trajectory of a turning vehicle under free-flow conditions can be described using the minimum-torsion principle. Although the applicability of the minimum-jerk theory to turning vehicles has been proven, it does not consider the influence of intersection geometry; secondly, this model requires t f as input, and this information cannot be obtained. Therefore, this embodiment does not choose to use the travel time t from the starting position to the ending position f , but uses the position and speed information of the intermediate point to estimate the left-turn trajectory, and combines two fifth-order polynomials to estimate the magnitude of the unknown t f , providing the left-turn travel time inside the intersection for the following speed guidance model and improving the accuracy of speed guidance for the left-turning traffic flow.

[0124] The position information of the intermediate point of the left-turn trajectory (minimum speed and minimum speed position) is estimated using the model proposed by Wolfermann et al. The specific structure of the left-turn trajectory planning model is as Figure 4As shown, the input variables are the vehicle type and the geometric state of the intersection (intersection turning angle, curb radius, lateral exit distance, and the definitions of each parameter are as Figure 5 shown), the conditions (position, speed, acceleration) of the left-turn leading vehicle when entering and exiting the intersection, and the minimum speed and the position of the minimum speed are selected in the probability model of the normal distribution estimated by Wolfermann.

[0125] By combining the minimum-jerk theory with the speed prediction model proposed by Wolfermann, the corresponding synchronization equation can be obtained. The specific correspondence between position and position speed is as Figure 6 shown. It can be seen from this that the speed of the left-turn leading vehicle decreases continuously from the initial position to the minimum speed and then increases continuously to reach the corresponding end speed. Therefore, selecting three points, namely the initial position, the minimum speed position, and the end position, for predicting the turning trajectory will be more in line with the actual situation.

[0126] Define the initial position of the trajectory as (describe the three speed position information in pictures):

[0127] x(0) = a0

[0128]

[0129] where x(0), respectively represent the starting point position, speed, and acceleration of the turning vehicle, all of which are known. For the convenience of calculation, the starting point position is set to (0, 0).

[0130] According to the minimum-jerk theory, the end position of the trajectory can be defined as:

[0131]

[0132] where x(t = t f ), respectively represent the position, speed, and acceleration of the left-turn leading vehicle at the known end position.

[0133] Similarly, define the minimum speed and the minimum speed position:

[0134]

[0135]

[0136] where x(t = t m ), respectively represent the position, speed, and acceleration of the left-turn leading vehicle at the minimum speed, and x(t = t m ) and The normal distribution, and randomly select x(t = t m ) and the value of, substitute it into the above formula for calculation.

[0137] Wolfermann's model estimates the minimum speed and the position of the minimum speed of turning vehicles under ideal or free flow conditions based on the historical data of left-turning vehicles, and transforms it into a function model considering the entrance speed and intersection geometric characteristics. Table 2 lists the parameters of the minimum speed (v min ) and the minimum speed position (s min ) model; v min and s min are represented by the normal distribution; the mean (μ) and standard deviation (σ) are modeled as functions of the entrance speed and intersection geometric characteristics.

[0138] Table 2

[0139]

[0140] Since t m and are still unknown, but the vehicle state is at the minimum speed at this time, so we can assume Solve the above formula by simultaneous equations to obtain the turning movement time t f of the turning vehicle. The turning vehicle trajectory obtained according to the minimum-jerk theory considers the left-turn safety speed, so the guiding speed V f of the vehicle in the intersection is obtained on this basis, then the left-turn intersection speed guidance includes:

[0141]

[0142] Among them, represents the guiding speed of the leading vehicle at the left-turn intersection, r represents the turning distance; then the following is the calculation method of the following vehicle acceleration at this time:

[0143] a’ i (t) = k * {V l [Δx n (t)] - v n (t)} + λ * Δv n (t) + α loss (t)

[0144] Among them, k * and λ * are determined according to Table 1.

[0145] The vehicle needs to turn left and then go straight when passing through intersection xi. If the vehicle travels at the left-turn guiding speed Vf, it will not reach at the moment when the green light of the next intersection starts. Therefore, it is necessary to travel at a speed until the start of the next green light phase. The speed guidance for the straight-ahead phase after the left turn includes:

[0146]

[0147] Among them, t i+1 represents the time when the vehicle enters the intersection guiding area, t s represents the time when the vehicle reaches the exit of the intersection, V f represents the left-turn guiding speed of the vehicle, represents the vehicle speed during the left turn, X i+1 represents the position of intersection X i+1 ; X i represents the position of intersection X i ; φ i，i+1 represents the phase difference in the coordinated direction between intersections i and i + 1; then the acceleration of the following vehicle at this time is:

[0148] a” i (t) = k[V[Δx n (t)] - V * + λΔv(t) + α loss (t)

[0149] Among them, the value of k is 0.41 s -1 , and the value of λ is 0.3 s -1 .

[0150] S3. Taking the maximum green-wave bandwidth of the turning traffic flow and the minimum path carbon emissions as the optimization objectives, multi-objective coordinated optimization is carried out on the original AM-BAND model and the carbon emission model based on speed guidance.

[0151] The methods for multi-objective coordinated optimization include:

[0152] Taking the maximum green-wave bandwidth of the turning traffic flow as the optimization objective, setting several constraint conditions, and optimizing the original AM-BAND model; in this embodiment, for the AM-BAND model, the symmetry constraint of the green wave band is cancelled, and at the same time, the constraint of the left and right bandwidth ratios is added to allow different bandwidths between different intersections. Its objective function is:

[0153]

[0154] Among them, B represents the weighted sum of all bandwidths in the up and down directions, i represents the intersection number, represents the green-wave bandwidth weight value of the up (down) direction of the section between intersections for the intersection is the actual traffic volume in the upstream (downstream) direction of the road section, is the saturated traffic volume in the upstream (downstream) direction of the road section, and the power exponent p can take 0, 1, 2; is the intersection S in the upstream (downstream) direction i to intersection S i+1 is the bandwidth of the green wave band, where b i = b′ i + b" i ,

[0155] Due to the imbalance of upstream and downstream traffic volumes in the commuting path, by setting k i to ensure:

[0156]

[0157] where k i represents the ratio of the bandwidth requirements in the downstream direction to the upstream direction, usually the ratio of the traffic volumes in the downstream and upstream directions. Determine the upper and lower limits of each intersection cycle:

[0158]

[0159] where C max and C min represent the upper and lower limits of the signal cycle respectively. By setting interference constraints to ensure that the upstream and downstream directions of the green wave bandwidth do not overlap with the time of the red light signal, reducing the stops and delays caused by the red light signal:

[0160]

[0161] where represents the time difference between the center line of the upstream (downstream) green wave band of intersection S in the coordinated direction and the right (left) edge of the adjacent red light on the left (right); i represents the queue clearance time of the upstream (downstream) in the coordinated direction of intersection S; represents the intersection S i the queue clearance time of the upstream (downstream) in the coordinated direction; represents the intersection S i the red light time of the upstream (downstream) in the coordinated direction.

[0162] Prevent the progress band component in one direction from becoming too large or too small, thus maintaining the balance of the progress band:

[0163]

[0164] Cycle constraint:

[0165]

[0166] where Indicates the travel time of the vehicle from intersection S i to intersection S i+1 ; δ i represents a 0-1 variable used to determine the straight and left-turn phase sequence in the coordinated direction; L i represents the green light duration for left turns in the up (down) direction at intersection S i ; m i represents an integer multiple of the cycle C, i.e., m i = ηC.

[0167] Speed constraint:

[0168]

[0169] Among them, represents the distance from intersection S i (S i+1 ) to intersection S i+1 (S i ); e i , f i represents the lower limit of the vehicle driving speed on the section between intersection S i in the up direction and intersection S i+1 ; represents the upper limit of the vehicle driving speed on the section between intersection S i+1 in the down direction and intersection S i ; g i , h i represents the lower limit of the allowable speed change between adjacent sections in the up direction, represents the upper limit of the allowable speed change between adjacent sections in the down direction.

[0170] Build a carbon emission model, with the minimum path carbon emission as the optimization goal, set several constraint conditions, and optimize the carbon emission model; in this embodiment, in order to achieve a more green and low-carbon travel, this paper designs a carbon emission model to minimize the total carbon emission when the vehicle passes through multiple intersections, and the speed control performance index of each part of the vehicle is evaluated according to the cost function M to calculate the carbon emission rate (the minimum delay variance in each direction):

[0171]

[0172] Among them, w1 represents the weight coefficient of carbon emissions, w2 represents the target speed weight deviation coefficient, w3 represents the braking deviation weight coefficient, which can be set to 1 / 3 first and then optimized and adjusted later; v g represents the guiding speed at different stages.

[0173] FR represents the instantaneous carbon emission rate of the vehicle, and its calculation method is:

[0174]

[0175] VSP = V·(1.1·a + 0.132) + 0.000302·V 3

[0176]

[0177] F brake represents the braking force of the vehicle, and its calculation method is as follows:

[0178] F brake = m·a brake

[0179] where m represents the mass of the vehicle, and a brake represents the acceleration and deceleration of the vehicle.

[0180] To achieve speed guidance, certain constraint conditions need to be added to control the movement of the vehicle. In this embodiment, by setting speed constraints, acceleration and deceleration constraints, and the minimum safe following distance, it is ensured that the speed guidance model can operate reasonably: Speed limit:

[0181] v min ≤ v g ≤ v max

[0182] Acceleration and deceleration limit constraints. By controlling the acceleration and deceleration, the vehicle can approach the target speed as closely as possible while maintaining a safe distance from the vehicle in front. The constraint is expressed as:

[0183]

[0184] where, represents the maximum acceleration, represents the minimum deceleration. Minimum safe following distance limit. The minimum safe distance between the vehicle and the vehicle in front is limited by a function of the vehicle speed:

[0185] x f - x g ≥ α·v g + β

[0186] where xf represents the position of the vehicle in front, xg represents the position of the guided vehicle, β represents the static gap parameter, which is used to determine the minimum distance required for the vehicle to stop, and α represents the dynamic gap parameter, which is used to represent the additional time gap when the speed increases.

[0187] The optimized AM - BAND model and the optimized carbon emission model are used to perform multi - objective coordinated optimization on the vehicle path.

[0188] Embodiment 2

[0189] In this embodiment, a path multi-objective coordination control system based on left-turn speed guidance includes: a left-turn model construction module, a speed guidance module, and a multi-objective optimization module.

[0190] The left-turn model construction module is used to construct an FVD car-following model based on acceleration decay, and improve the FVD car-following model based on the left-turn car-following situation to obtain an optimized left-turn FVD model.

[0191] The working process of the left-turn model construction module includes: establishing an FVD car-following model based on acceleration decay:

[0192] α loss (t) = γ·lg(Δx n (t)) + ξ

[0193] where α loss (t) represents the acceleration signal attenuation value of the leading vehicle to the following vehicle when the distance between the leading vehicle and the following vehicle at time t is Δx n (t), γ represents the acceleration loss exponential factor, and ξ represents the random error term; improving the FVD car-following model based on the safe distance between vehicles to obtain an improved FVD car-following model:

[0194] a n (t) = k[V[Δx n (t)] - v n (t)] + λΔv(t) + α loss (t)

[0195] v n (t + T) = v n (t) + a n (t + T)·T

[0196]

[0197] where a n (t) represents the updated car-following acceleration of the nth following vehicle, k represents the driver sensitivity coefficient, λ represents the feedback coefficient, V[Δx n (t)] represents the speed optimization function considering the safe distance, Δv(t) represents the speed difference between the leading vehicle and the following vehicle, v n (t) represents the speed of the leading vehicle, h e represents the safe distance between vehicles, v max represents the maximum driving speed, τ represents the reaction time of the driver, and L represents the length of the leading vehicle; further improving the improved FVD car-following model based on the turning radius and the safe distance to obtain an optimized left-turn FVD model:

[0198]

[0199] V l [Δx n (t)] = V l ×V[Δx n (t)]

[0200]

[0201] Among them, represents the following - car acceleration after update of the nth following car in the left - turn FVD optimization model, k * represents the driver sensitivity coefficient considering the turning radius, λ * represents the feedback coefficient considering the turning radius, V l represents the speed optimization coefficient function at different turning positions, L d represents the buffer distance for the turning vehicle to decelerate, x g represents the position of the stop line, x n represents the position of the nth following car, represents the turning coefficient of the vehicle.

[0202] The speed guidance module is used to construct a minimum - jerk turning trajectory prediction model, and speed - guide the left - turning vehicle through the left - turn FVD optimization model and the minimum - jerk turning trajectory prediction model. The speed guidance includes: straight - ahead phase speed guidance and left - turn phase speed guidance.

[0203] The straight - ahead phase speed guidance includes:

[0204]

[0205] Among them, V i * represents the guidance speed in the straight - ahead phase, L0 represents the length of the guidance area, φ i-1，i represents the phase difference in the coordinated direction between intersections i - 1 and i, g 直 represents the green - light time for going straight, τ i represents the time for clearing the queuing vehicles, t i represents the time when the vehicle reaches the speed - guidance area.

[0206] The left - turn phase speed guidance includes: left - turn intersection speed guidance and straight - ahead phase speed guidance after the left - turn ends; the left - turn intersection speed guidance includes:

[0207]

[0208] Among them, represents the guidance speed of the leading vehicle at the left - turn intersection, r represents the turning distance, t f represents the movement time of the vehicle from the starting position to the ending position;

[0209] The speed guidance in the straight-ahead stage after the left turn ends includes:

[0210]

[0211] Among them, t i+1 represents the time when the vehicle enters the intersection guidance area, and t s represents the time when the vehicle reaches the intersection exit. V f represents the left-turn guidance speed of the vehicle, represents the vehicle speed during the left turn, X i+1 represents the position of the intersection X i+1 and X i represents the position of the intersection X i and φ i，i+1 represents the phase difference of the coordinated direction between intersections i and i + 1.

[0212] The multi-objective optimization module is used to perform multi-objective coordinated optimization on the original AM-BAND model and the carbon emission model based on the speed guidance with the maximum green wave bandwidth of the turning traffic flow and the minimum path carbon emission as the optimization objectives.

[0213] The working process of the multi-objective optimization module includes: taking the maximum green wave bandwidth of the turning traffic flow as the optimization objective, setting several constraint conditions, and optimizing the original AM-BAND model; constructing a carbon emission model, taking the minimum path carbon emission as the optimization objective, setting several constraint conditions, and optimizing the carbon emission model; using the optimized AM-BAND model and the optimized carbon emission model to perform multi-objective coordinated optimization on the vehicle path.

[0214] The embodiments described above are only descriptions of the preferred embodiments of the present invention, and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A multi-objective coordinated control method for paths based on left-turn speed guidance, characterized in that: The following steps are involved: Constructing a FVD following model based on acceleration attenuation, and improving the FVD following model based on the left-turn following situation to obtain a left-turn FVD optimization model; Constructing a minimum-jerk turning trajectory prediction model, and guiding the speed of the left-turning vehicle through the left-turn FVD optimization model and the minimum-jerk turning trajectory prediction model, wherein the speed guidance includes: speed guidance in the straight-ahead phase and speed guidance in the left-turn phase; Taking the maximum green wave bandwidth of turning traffic and the minimum path carbon emission as the optimization objectives, the original AM-BAND model and the carbon emission model are optimized with multi-objective coordination based on the speed guidance.

2. According to claim 1, a path multi-objective coordinated control method based on left turn speed guidance is characterized in that: The method for obtaining the left-turn FVD optimization model includes: Establishing FVD car-following model based on acceleration attenuation: a loss (t)=γ·lg(Δx n (t)))+ξ Among them, α loss (t) indicates the distance between the front and rear vehicles at time t is Δx n (t) is the acceleration signal attenuation value of the front vehicle to the rear vehicle, γ represents the acceleration loss exponential factor, and ξ represents the random error term; The FVD following model is improved based on the safety distance between vehicles to obtain the improved FVD following model: a n (t)=k[V[Δx n (t)]-v n (t)]+λΔv(t)+α loss (t) v n (t+T)=v n (t)+a n (t+T).T Among them, a n (t) represents the updated following acceleration of the nth following car, k represents the driver sensitivity coefficient, λ represents the feedback coefficient, V[Δx n (t)] represents the speed optimization function considering the safety distance, Δv(t) represents the speed difference between the front and rear vehicles, and v n (t) represents the speed of the preceding vehicle, h e Indicates safe vehicle spacing, v max represents the maximum driving speed, τ represents the driver’s reaction time, and L represents the length of the leading vehicle; Based on the turning radius and the safety distance, the improved FVD following model is further improved to obtain the left turn FVD optimization model: V l [Δx n (t)]=V l ×V[Δx n (t)] in, represents the updated following acceleration of the nth following car in the left-turn FVD optimization model, k * represents the driver sensitivity factor considering the turning radius, λ * Indicates the feedback factor considering the turning radius, V l Represents the speed optimization coefficient function at different turning positions, L d Indicates the buffer distance for turning vehicle deceleration, x g represents the position of the stop line, x n represents the position of the nth following car, Indicates the turning coefficient of the vehicle.

3. According to claim 2, a path multi-objective coordinated control method based on left turn speed guidance is characterized in that: The speed guidance in the straight-ahead phase includes: Among them, V i * represents the guiding speed in the straight-line phase, L0 represents the length of the guiding area, φ i-1，i represents the phase difference in the coordination direction between intersections i-1 and i, g 直 represents the green light time for going straight, τ i represents the time it takes for the queued vehicles to be cleared, t i Indicates the time when the vehicle reaches the speed guidance area.

4. The multi-objective coordinated control method for paths based on left-turn speed guidance according to claim 3 is characterized in that: The speed guidance during the left turn phase includes: speed guidance at the left turn intersection and speed guidance during the straight-ahead phase after the left turn; The left-turn intersection speed guidance includes: Among them, V f * represents the leading vehicle’s guiding speed at the left-turn intersection, r represents the turning distance, and t f Indicates the movement time of the vehicle from the starting position to the end position; The speed guidance in the straight-ahead phase after the left turn includes: Among them, t i+1 Indicates the time when the vehicle enters the intersection guidance area, t s represents the time when the vehicle arrives at the intersection exit, V f Indicates the left turn guidance speed of the vehicle, Indicates the vehicle speed when turning left, X i+1 Indicates intersection X i+1 The position of X i Indicates intersection X i The position of i，i+1 Represents the phase difference in the coordination direction between intersections i and i+1.

5. The multi-objective coordinated control method for paths based on left-turn speed guidance according to claim 1, characterized in that: Methods for multi-objective coordinated optimization include: Taking the maximum green wave bandwidth of turning traffic as the optimization goal, several constraints are set to optimize the original AM-BAND model. Constructing a carbon emission model, taking the path carbon emission minimization as the optimization goal, setting a number of constraints, and optimizing the carbon emission model; The optimized AM-BAND model and the optimized carbon emission model are used to perform multi-objective coordinated optimization of vehicle paths.

6. A path multi-objective coordinated control system based on left-turn speed guidance, the system applying the method described in any one of claims 1 to 5, characterized in that: include: Left-turn model building module, speed guidance module and multi-objective optimization module; The left-turn model building module is used to build a FVD following model based on acceleration attenuation, and improve the FVD following model based on the left-turn following situation to obtain a left-turn FVD optimization model; The speed guidance module is used to construct a minimum-jerk turning trajectory prediction model, and to guide the speed of the left-turning vehicle through the left-turn FVD optimization model and the minimum-jerk turning trajectory prediction model, wherein the speed guidance includes: speed guidance in the straight-ahead phase and speed guidance in the left-turn phase; The multi-objective optimization module is used to perform multi-objective coordinated optimization of the original AM-BAND model and the carbon emission model based on the speed guidance, taking the maximum green wave bandwidth of the turning traffic flow and the minimum path carbon emission as the optimization objectives.

7. A path multi-objective coordinated control system based on left turn speed guidance according to claim 6, characterized in that: The workflow of the left turn model building module includes: Establishing the FVD following model based on acceleration attenuation: a loss (t)=γ·lg(Δx n (t))+ξ Among them, α loss (t) indicates the distance between the front and rear vehicles at time t is Δx n (t) is the acceleration signal attenuation value of the front vehicle to the rear vehicle, γ represents the acceleration loss exponential factor, and ξ represents the random error term; The FVD following model is improved based on the safety distance between vehicles to obtain the improved FVD following model: a n (t)=k[V[Δx n (t)]-v n (t)]+λΔv(t)+α loss (t) v n (t+T)=v n (t)+a n (t+T)·T Among them, a n (t) represents the updated following acceleration of the nth following car, k represents the driver sensitivity coefficient, λ represents the feedback coefficient, V[Δx n (t)] represents the speed optimization function considering the safety distance, Δv(t) represents the speed difference between the front and rear vehicles, and v n (t) represents the speed of the preceding vehicle, h e Indicates safe vehicle spacing, v max represents the maximum driving speed, τ represents the driver’s reaction time, and L represents the length of the leading vehicle; Based on the turning radius and the safety distance, the improved FVD following model is further improved to obtain the left turn FVD optimization model: V l [Δx n (t)]=V l ×V[Δx n (t)] in, represents the updated following acceleration of the nth following car in the left-turn FVD optimization model, k * represents the driver sensitivity factor considering the turning radius, λ * Indicates the feedback factor considering the turning radius, V l Represents the speed optimization coefficient function at different turning positions, L d Indicates the buffer distance for turning vehicle deceleration, x g represents the position of the stop line, x n represents the position of the nth following car, Indicates the turning coefficient of the vehicle.

8. The multi-objective coordinated control system of a path based on left-turn speed guidance according to claim 7, characterized in that: In the speed guidance module, the speed guidance in the straight-ahead phase includes: Among them, V i * represents the guiding speed in the straight-line phase, L0 represents the length of the guiding area, φ i-1，i represents the phase difference in the coordination direction between intersections i-1 and i, g 直 represents the green light time for going straight, τ i represents the time it takes for the queued vehicles to be cleared, t i Indicates the time when the vehicle reaches the speed guidance area.

9. A path multi-objective coordinated control system based on left turn speed guidance according to claim 8, characterized in that: In the speed guidance module, the speed guidance in the left turn stage includes: speed guidance at the left turn intersection and speed guidance in the straight-ahead stage after the left turn; The left-turn intersection speed guidance includes: Among them, V f * represents the leading vehicle’s guiding speed at the left-turn intersection, r represents the turning distance, and t f Indicates the movement time of the vehicle from the starting position to the end position; The speed guidance in the straight-ahead phase after the left turn includes: Among them, t i+1 Indicates the time when the vehicle enters the intersection guidance area, t s represents the time when the vehicle arrives at the intersection exit, V f Indicates the left turn guidance speed of the vehicle, Indicates the vehicle speed when turning left, X i+1 Indicates intersection X i+1 The position of X i Indicates intersection X i The position of i，i+1 Represents the phase difference in the coordination direction between intersections i and i+1.

10. The multi-objective coordinated control system of a path based on left-turn speed guidance according to claim 6, characterized in that: The workflow of the multi-objective optimization module includes: Taking the maximum green wave bandwidth of turning traffic as the optimization goal, several constraints are set to optimize the original AM-BAND model. Constructing a carbon emission model, taking the path carbon emission minimization as the optimization goal, setting a number of constraints, and optimizing the carbon emission model; The optimized AM-BAND model and the optimized carbon emission model are used to perform multi-objective coordinated optimization of vehicle paths.

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