Vehicle roadside guidance control method and system under mixed traffic environment at intersection

Abstract

This invention discloses a method and system for controlling vehicle roadside guidance in mixed traffic environments at intersections. The method comprises: establishing a vehicle driving strategy so that vehicles follow the strategy; establishing a signal light control strategy so that the time ratio of traffic lights varies with changes in vehicle penetration and lane saturation; and implementing coordinated control and guidance for vehicles arriving at core traffic areas, enabling them to efficiently pass through the core traffic areas. This invention can provide technical support for improving traffic efficiency, safety, and comfort at intersections, reducing urban road traffic congestion and improving the resilience of urban road networks.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation, and in particular to a vehicle roadside guidance control method and system in a mixed traffic environment at an intersection. Background Art

[0002] As a key component of urban transportation, the traffic flow at intersections is directly impactful on the city's traffic situation. The timing of intersection signal control is crucial to road traffic reliability and effectively prevents traffic jams at intersections. Current urban road intersection control methods in connected traffic environments are primarily divided into two categories: signal-based control and non-signal-based control.

[0003] There are three main signal phase modes used on urban roads. The first phase mode releases oppositely moving straight vehicles first, followed by oppositely moving left-turning vehicles. The second phase mode releases vehicles in all four directions one by one. The third phase mode releases oppositely moving straight and left-turning vehicles simultaneously. Due to complex road traffic conditions, large variations in peak vehicle density, and the current low penetration of CAVs, fixed signal phase control at intersections is difficult to meet the safety and efficiency requirements for traffic flow throughout the intersection. Furthermore, control without signal lights remains at the theoretical research stage and has yet to be truly applied in practice.

[0004] Therefore, in order to solve the problem of safe and efficient vehicle driving under the complex mixed traffic characteristics of urban road networks, a vehicle roadside guidance control method and system in a mixed traffic environment at an intersection is needed, which can provide technical support for the traffic efficiency, safety and comfort of the intersection, reduce urban road traffic congestion and improve the resilience of the urban road network. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to overcome the defects in the prior art and provide a vehicle roadside guidance control method and system in a mixed traffic environment at an intersection, which can provide technical support for the traffic efficiency, safety and comfort of the intersection, reduce urban road traffic congestion and improve the resilience of the urban road network.

[0006] The vehicle roadside guidance control method under mixed traffic conditions at an intersection of the present invention comprises:

[0007] Constructing a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy;

[0008] Construct a traffic light control strategy so that the time ratio of traffic lights changes with changes in vehicle penetration and lane saturation;

[0009] Vehicles arriving at the core traffic area are coordinated and controlled and guided to enable them to pass through the core traffic area efficiently.

[0010] Furthermore, the vehicle driving strategy includes:

[0011] When a vehicle reaches the second stop line, it receives control instructions and enters the designated lane. When the green light comes on, all vehicles in the corresponding phase before the second stop line exit, and vehicles after the second stop line enter the designated lane. The second stop line is s meters away from the intersection ahead.

[0012] The following constraints are imposed on the speed and acceleration of the vehicle that is about to reach the second stop line:

[0013]

[0014] in, and represents the speed and acceleration of the i-th CAV in the x-axis direction, where the positive direction of the x-axis is the direction pointing to the intersection; and represents the maximum speed and acceleration of the i-th CAV in the x-axis direction; TTC i and TTC min They are the actual pre-collision time and the minimum pre-collision time of the vehicle; CAV stands for connected autonomous vehicle;

[0015] The vibration constraint of the vehicle during driving is:

[0016] |jerk i |≤jerk max ;

[0017] Among them, jerk i represents the jitter of the i-th CAV, jerk max Indicates the maximum value of allowed jitter;

[0018] The lateral stability constraint of the vehicle during lane changing is:

[0019] |β i |≤arctan(0.02μg);

[0020] Among them, β i is the deflection rate associated with lateral stability, μ is the tire-road adhesion coefficient, and g is the acceleration due to gravity;

[0021] The following constraints are imposed on the driving distance and speed difference between vehicles:

[0022] Δp i ≥Δp min ,|Δv i |≤Δv max ;

[0023] Where Δp iand Δv i are the position distance and speed difference between the preceding vehicle and the vehicle; Δp min and Δv max are the minimum safe distance and maximum speed difference between vehicles respectively.

[0024] Furthermore, the vehicle driving strategy also includes a road right scheduling scheme:

[0025] The following cyclic processing is performed according to the set detection cycle:

[0026] If the variable lane is a CAV-only road, proceed to step a; if the variable lane is a mixed lane, proceed to step b;

[0027] a. Calculate lane saturation r l , if r l ∈[0, ε1], then C l,1 Accumulate 1, otherwise, go to step a1;

[0028] al. If r l ∈[ε2,1], then C l，2 Accumulate 1; otherwise, C l，1 =C l，2 =0; where ε1 and ε2 are low saturation threshold and high saturation threshold respectively; C l，1 is the low saturation conversion index; C l，2 is the high saturation conversion index;

[0029] b. Calculate lane saturation r l , if r l ≥ε2, then C l，1 Accumulate 1, C l，2 =0; otherwise, C l，1 =C l，2 =0;

[0030] Through the processing of step a or b, parameter C is obtained l =max{C l，1 , C l，2}; If C l =3, then change the middle lane state and make C l，1 =C l，2 =0.

[0031] Further, in step a,

[0032] Among them, V l is the traffic flow of dedicated lane l, p l，cav is the penetration rate of CAVs in the dedicated lane l, h bl is the saturated headway of the dedicated lane; g l，kis the green light duration of the dedicated lane l in the kth cycle;

[0033] In step b,

[0034] Among them, b l , 2 is the number of straight mixed lanes in the lane group; b l , 3 is the number of mixed driving lanes for straight going and right turning in the lane group.

[0035] Furthermore, the signal light control strategy includes:

[0036] Design lane l Green light duration at an intersection g l , k is:

[0037]

[0038] Among them, V l is the traffic flow of lane l, S l is the traffic flow when lane l is saturated, and the signal cycle duration C is:

[0039]

[0040] PHF is the peak hour factor, q i is the peak hour demand, q max Design volume for peak hours; L l Y is the time lost by vehicles in lane l due to starting or failure; l is the flow rate ratio of lane l; r l,des is the expected lane saturation when the road is designed.

[0041] Furthermore, vehicles arriving at the core traffic area are coordinated and controlled and guided, including:

[0042] Build a simplified vehicle model:

[0043]

[0044]

[0045] β=arctan[l r / (l f +l r )tanδ f ];

[0046]

[0047] u(t)=[a x , δ f ] T ;

[0048] in, represents the derivative of the vehicle state variable x, v x is the longitudinal velocity, is the yaw angle, (X g , Y g ) Coordinates of the vehicle's center of gravity, a x Front wheel longitudinal acceleration, δ f is the steering angle, β is the slip angle, l f 、l r is the front and rear wheelbase;

[0049] The entire traffic system at the intersection is regarded as a grand coalition, and a single CAV is regarded as a single-person coalition. A fuzzy coalition is constructed that combines the grand coalition and the single-person coalition.

[0050] Using the fuzzy alliance game method, the decision variable a x and δ f Control is performed and a decision consumption function is optimized so that the CAV obtains good driving performance at signal-controlled intersections; wherein the decision consumption function includes a driving safety consumption function and a passing efficiency consumption function, and the driving safety consumption function includes consumption functions for longitudinal, lateral and lane keeping safety.

[0051] A vehicle roadside guidance control system in a mixed traffic environment at an intersection, comprising a vehicle driving unit, a signal light unit, and a coordinated guidance unit;

[0052] The vehicle driving unit is used to construct a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy;

[0053] The signal light unit is used to construct a signal light control strategy so that the time ratio of the traffic light changes with the change of vehicle penetration rate and lane saturation;

[0054] The cooperative guidance unit is used to perform cooperative control and guidance on vehicles arriving at the core traffic area, so that the vehicles can pass through the core traffic area efficiently.

[0055] Furthermore, the vehicle driving strategy includes:

[0056] When a vehicle reaches the second stop line, it receives control instructions and enters the designated lane. When the green light comes on, all vehicles in the corresponding phase before the second stop line exit, and vehicles after the second stop line enter the designated lane. The second stop line is s meters away from the intersection ahead.

[0057] The following constraints are imposed on the speed and acceleration of the vehicle that is about to reach the second stop line:

[0058]

[0059] in, and represents the speed and acceleration of the i-th CAV in the x-axis direction, where the positive direction of the x-axis is the direction pointing to the intersection; and represents the maximum speed and acceleration of the i-th CAV in the x-axis direction; TTC i and TTC min They are the actual pre-collision time and the minimum pre-collision time of the vehicle; CAV stands for connected autonomous vehicle;

[0060] The vibration constraint of the vehicle during driving is:

[0061] |jerk i |≤jerk max ;

[0062] Among them, jerk i represents the jitter of the i-th CAV, jerk max Indicates the maximum value of allowed jitter;

[0063] The lateral stability constraint of the vehicle during lane changing is:

[0064] |β i |≤arctan(0.02μg);

[0065] Among them, β i is the deflection rate associated with lateral stability, μ is the tire-road adhesion coefficient, and g is the acceleration due to gravity;

[0066] The following constraints are imposed on the driving distance and speed difference between vehicles:

[0067] Δp i ≥Δp min ,|Δv i |≤Δv max ;

[0068] Where Δp i and Δv i are the position distance and speed difference between the preceding vehicle and the vehicle; Δp min and Δv max are the minimum safe distance and maximum speed difference between vehicles respectively.

[0069] Furthermore, the vehicle driving strategy also includes a road right scheduling scheme:

[0070] The following cyclic processing is performed according to the set detection cycle:

[0071] If the variable lane is a CAV-only road, proceed to step a; if the variable lane is a mixed lane, proceed to step b;

[0072] a. Calculate lane saturation r l , if r l ∈[0, ε1], then C l,1 Accumulate 1, otherwise, go to step a1;

[0073] a1. If r l ∈[ε2,1], then C l，2 Accumulate 1; otherwise, C l，1 =C l，2 =0; where ε1 and ε2 are low saturation threshold and high saturation threshold respectively; C l，1 is the low saturation conversion index; C l，2 is the high saturation conversion index;

[0074] b. Calculate lane saturation r l , if r l ≥ε2, then C l，1 Accumulate 1, C l，2 =0; otherwise, C l，1 =C l，2 =0;

[0075] Through the processing of step a or b, parameter C is obtained l =max{C l，1 , C l，2}; If C l =3, then change the middle lane state and make C l，1 =C l，2 =0.

[0076] Furthermore, the signal light control strategy includes:

[0077] Design lane l Green light duration at an intersection g l,k for:

[0078]

[0079] Among them, V l is the traffic flow of lane l, S l is the traffic flow when lane l is saturated, and the signal cycle duration C is:

[0080]

[0081] PHF is the peak hour factor, q i is the peak hour demand, q max Design volume for peak hours; L l Y is the time lost by vehicles in lane l due to starting or failure; l is the flow rate ratio of lane l; r l，desis the expected lane saturation when the road is designed.

[0082] The beneficial effects of the present invention are as follows: the present invention discloses a vehicle roadside guidance control method and system in a mixed traffic environment at an intersection, which establishes a road right switching and adaptive signal light control system. All vehicles are first adjusted at the specified second stop line according to the dispatched road right information and signal light information, and are secondarily adjusted in the core traffic area to improve road traffic efficiency and safety; according to the complex traffic conditions in the city, the road driving strategy is updated and the signal control scheme is optimized, which plays an important role in improving the vehicle traffic capacity of the intersection, reducing traffic congestion and preventing safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0084] Figure 1 Schematic diagram of the side guidance control method of the present invention;

[0085] Figure 2 This is a schematic diagram of an example of a three-lane intersection of the present invention;

[0086] Figure 3 Schematic diagram of vehicle motion trajectory analysis of the present invention. DETAILED DESCRIPTION

[0087] The present invention is further described below with reference to the accompanying drawings, as shown in the drawings:

[0088] This embodiment discloses a method for controlling vehicle roadside guidance in a mixed traffic environment at an intersection, comprising the following steps:

[0089] Constructing a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy;

[0090] Construct a traffic light control strategy so that the time ratio of traffic lights changes with changes in vehicle penetration and lane saturation;

[0091] Vehicles arriving at the core traffic area are coordinated and controlled and guided to enable them to pass through the core traffic area efficiently.

[0092] In this embodiment, a three-lane unprotected left-turn intersection is used as a scenario for expansion. Figure 2As shown. The lanes are six in both directions, and the three lanes are divided into left turn, straight, and straight plus right turn. Taking into account the existence of CAV and HDV vehicles, in the case of mixed lanes and variable lanes, the mixed lanes allow CAV and HDV to pass, and the variable lanes switch between mixed lanes and CAV-only lanes according to the penetration rate and lane saturation. For the design of the stop line, in order to better adjust the driving of CAV and HDV, a second stop line is stipulated, and the vehicle will adjust according to the specified strategy before the second stop line. Utilize the existing intelligent roadside decision-making system (including cooperative intelligent transportation system), whose control range includes the entire traffic system, to statistically predict CAV penetration rate, control CAV vehicles entering the signal range, give driving suggestions to HDV vehicles, control the duration of signal lights, etc. Among them, CAV is a connected autonomous driving vehicle, and HDV is a manually driven vehicle.

[0093] In addition, for the signal light phases that directly affect the traffic efficiency and safety of the intersection, four-phase signal control is adopted in this scenario. Specifically, phase 1 is north-south straight, phase 2 is north-south left turn, phase 3 is east-west straight, and phase 4 is east-west left turn, which is the first mode in the phase control mode. In this phase mode, the intersection has only divergence points and merging points, and there are no conflict points, which is safer. However, compared with the other two phase modes, the four-phase mode has a longer red light waiting period, and the lane group requires a longer travel time, which can easily lead to problems such as increased queue length and wasted travel time.

[0094] Due to the large differences in peak vehicle density and lane saturation at urban intersections, it is not possible to use a fixed signal light duration to handle the traffic conditions at intersections. Therefore, the vehicle driving strategy includes:

[0095] When a vehicle reaches the second stop line, it receives control instructions from the intelligent roadside decision-making system and enters the designated lane. When the green light comes on, all vehicles in the corresponding phase before the second stop line exit, and vehicles behind the second stop line enter the designated lane. The second stop line is s meters from the intersection ahead; s can be set to 10-15 meters depending on actual operating conditions. The stop line at the intersection ahead serves as the first stop line. This second stop line improves traffic efficiency in complex and changing road conditions.

[0096] To ensure that all vehicles in front of the second stop line can leave, the second stop line L is set. i The location is:

[0097]

[0098] Among them, v cross is the speed limit at the intersection, V L,i is the short-term predicted traffic volume for left-turn imports, p l，iis the penetration rate of CAV on the left turn of import (%), a1 is the expected acceleration / deceleration, d v is the vehicle length (m), d hi is the safe distance between CAVs when queuing (m); θ is the steering angle of the left-turning CAV.

[0099] The following constraints are imposed on the speed and acceleration of the vehicle that is about to reach the second stop line:

[0100]

[0101] in, and represents the speed and acceleration of the i-th CAV in the x-axis direction, where the positive direction of the x-axis is the direction pointing to the intersection; and represents the maximum speed and acceleration of the i-th CAV in the x-axis direction; TTC i and TTC min They are the actual pre-collision time and the minimum pre-collision time of the vehicle respectively; CAV stands for connected autonomous vehicle; through the above-mentioned speed and acceleration constraints, the vehicle's driving safety is guaranteed at complex intersections.

[0102] The vibration constraint of the vehicle during driving is:

[0103] |jerk i |≤jerk max ;

[0104] Among them, jerk i represents the jitter of the i-th CAV, jerk max Represents the maximum value of allowed vibration; through the above vibration constraint, the comfort of the vehicle during driving is ensured and the vehicle vibration is reduced.

[0105] The lateral stability constraint of the vehicle during lane changing is:

[0106] |β i |≤arctan(0.02μg);

[0107] Among them, β i is the deflection rate associated with lateral stability, μ is the tire-road adhesion coefficient, and g is the acceleration due to gravity. Through the above stability constraints, the lateral stability of the vehicle during lane changing is guaranteed.

[0108] The following constraints are imposed on the driving distance and speed difference between vehicles:

[0109] Δp i ≥Δp min ,|Δv i |≤Δv max;

[0110] Where Δp i and Δv i are the position distance and speed difference between the preceding vehicle and the vehicle; Δp min and Δv max The minimum safe distance and maximum speed difference between vehicles are respectively. The above spacing and speed difference constraints ensure the implementation of vehicle following behavior and increase traffic efficiency.

[0111] The vehicle driving strategy also includes a road right scheduling plan:

[0112] The following cyclic processing is performed according to the set detection cycle:

[0113] If the variable lane is a CAV-only road, proceed to step a; if the variable lane is a mixed lane, proceed to step b;

[0114] a. Calculate lane saturation r l , if r l ∈[0, ε1], then C l,1 Accumulate 1, otherwise, go to step a1; where lane saturation r l It is the saturation of CAV lanes,

[0115] Among them, V l is the traffic flow of dedicated lane l, p l，cav is the penetration rate of CAVs in the dedicated lane l, h bl is the saturated headway of the dedicated lane; g l，k is the green light duration of the dedicated lane l in the kth cycle;

[0116] a1. If r l ∈[ε2,1], then C l,2 Accumulate 1; otherwise, C l,1 =C l,2 =0; where ε1 and ε2 are low saturation threshold and high saturation threshold respectively; C l,1 is the low saturation conversion index; C l,2 is the high saturation conversion index;

[0117] b. Calculate lane saturation r l , if r l ≥ε2, then C l，1 Accumulate 1, C l，2 =0; otherwise, C l，1 =C l，2 =0; where lane saturation r l is the saturation of the mixed lane,

[0118] Among them, b l，2 is the number of straight mixed lanes in the lane group; b l，3 b is the number of mixed driving lanes for straight driving and right turning in the lane group; l，2 =b l，3 =1;

[0119] Through the processing of step a or b, parameter C is obtained l =max{C l,1 , C l,2}; If C l =3, then change the middle lane state and make C l,1 =C l,2 =0.

[0120] When the phase saturation exceeds 0.9, traffic congestion is likely to occur at the intersection; and when the phase saturation is lower than 0.6, the CAV dedicated lane is not fully utilized. The lane saturation here is equivalent to the phase saturation, indicating that r l When ≥0.9, the lane traffic demand is too high, and it is necessary to reconfigure the lane function, increase the lane saturation flow rate, and reduce r l Avoid traffic jams. At the same time, when r l When ε1 is ≤0.6, the effective utilization rate of CAV lanes is low, and lane functions need to be reconfigured to balance the traffic load of different types of lanes. Therefore, ε1 = 0.6 and ε2 = 0.9.

[0121] Furthermore, when configuring lanes, it is necessary to predict the traffic flow of each lane and the penetration rate of CAVs in the future. To meet the accuracy requirements of the short-term traffic flow prediction model, the time step of the short-term prediction data is generally not less than 5 minutes, which is about 3 signal cycles. In order to avoid repeated predictions within a time step, the time interval for reconfiguring lanes should be no less than one time step of the prediction data. Therefore, the threshold of the conversion coefficient is set to 3, that is, C l,1 =C l,2 =3, the lane group restarts the lane configuration function according to the above road right scheduling plan.

[0122] In this embodiment, while following the above-mentioned vehicle driving strategy, in order to solve the problems of waste of time resources and long red light time of four-phase traffic lights, adaptive traffic lights are designed for different penetration rates and lane saturations, so that the time ratio of traffic lights changes with the changes in penetration rate and lane saturation, thereby increasing the traffic efficiency of the intersection.

[0123] Lane saturation r l for:

[0124]

[0125] Where: b s is the total number of lanes in lane group l.

[0126] It can be seen that the saturation of the lane is related to the green light duration. The longer the green light duration, the higher the saturation r of lane l. l The lower it is, the longer the street light time at other intersections will be. The corresponding relationship between them is:

[0127] b l1，k =g l2,k +g′ l1，k +g′ l2，k ,

[0128] b l2,k =g l1，k +g′ l1，k +g′ l2，k ,

[0129] b′ l1,k =g l1，k +g l2,k +g′ l2，k ,

[0130] b′ l2,k =g l1，k +g l2,k +g′ l2，k ,

[0131] Where g l1，k 、g l2,k , g′ l1,k and g′ l2,k They are the green light durations for going straight and turning left in the north-south direction and the green light durations for going straight and turning left in the east-west direction; the corresponding b l1，k 、b l2,k , b′ l1,k and b′ l2,k They are the length of red lights for going straight and turning left in the north-south direction and the length of red lights for going straight and turning left in the east-west direction.

[0132] It can be seen that if the green light time of one intersection is long, the red light time of other intersections will be long, which may cause congestion in other intersections. Therefore, our signal light control strategy includes:

[0133] Design lane l Green light duration at an intersection g l，k for:

[0134]

[0135] Among them, V l is the traffic flow of lane l, S l is the traffic flow when lane l is saturated, and the signal cycle duration C is:

[0136]

[0137] PHF is the peak hour factor, q i is the peak hour demand, q max Design volume for peak hours; L l is the time lost by vehicles in lane l due to starting or failure; is the flow rate ratio of lane l; r l,des is the expected lane saturation when the road is designed.

[0138] In this embodiment, when vehicles arrive at the core traffic area, even though they follow traffic lights, they still form merging and converging points. To avoid this and improve the traffic efficiency of vehicles in the core traffic area, a collaborative control and guidance strategy based on fuzzy game is proposed.

[0139] In order to reduce the amount of decision-making calculations, a simplified vehicle model is adopted:

[0140]

[0141]

[0142] β=arctan[l r / (l f +l r )tanδ f ],

[0143]

[0144] in, represents the derivative of x, x represents the vehicle state, which includes speed, yaw angle, and horizontal and vertical position; v x is the longitudinal velocity, is the yaw angle, (X g , Y g ) coordinates of the center of gravity, a x Front wheel longitudinal acceleration, δ f is the steering angle, β is the slip angle, l f 、l r is the wheelbase of the front and rear wheels, and the turning radius R r It can be expressed as ρ r represents the path curvature, such as Figure 3 shown.

[0145] ρ r =tanδ f / l;

[0146] Then, the vehicle position G r And the position of the turning circle center C can be expressed as:

[0147]

[0148]

[0149]

[0150]

[0151] Use Gaussian surfaces to represent the underlying risk model:

[0152]

[0153] Among them, the height of the Gaussian surface Λ=c(sv x t p ) 2 , c=c0e κ , c0 is a constant, K∈[-1, 1], s is the length of the predicted path, t p is the prediction time;

[0154] The width of the Gaussian surface σ=(b+c|δ f |)s+d, b is when δ f =0, c is the gain, d=W / 4, W is the vehicle width.

[0155] The fuzzy coalition game method is used to design the decision algorithm for the core access area. The fuzzy coalition game belongs to the cooperative game category, and its goal is to minimize the cost of the coalition through cooperation. In a game problem, there are N players in total, and all players are represented by the set N = [1, 2, ..., n]. Every subset S of N is a coalition, that is, S∈2 n S can be an empty set, that is, S = φ. If S contains only one player, it is called a single-player alliance. If S contains all players, that is, S = N, it is also called a large alliance. The alliance game model is composed of a pair of<N,U,V> , where U represents the set of all players' behavioral decisions, and V represents a characteristic function, with V(φ) = 0. In a typical game problem, characteristic function V is usually represented by a utility function. The goal of each alliance is to maximize the reward value. However, for the decision-making problem of CAVs at unsignalized intersections in this invention, V corresponds to the minimum cost.

[0156] The above is based on the assumption that a player only joins one alliance. In reality, players can distribute their resources to several alliances. This is the fuzzy alliance game, in which players can distribute their resources and join several alliances. It is used to describe the source distribution of participant i in the fuzzy alliance S. Therefore, the participation coefficients of all participants in the fuzzy alliance S are obtained as the following vector:

[0157]

[0158] When all the participation coefficients in the fuzzy alliance S When both are equal to 0 or 1, the fuzzy coalition game problem will degenerate into a traditional coalition game. Specifically, the traditional coalition game is just a special case of the fuzzy coalition game.

[0159] The distribution costs of all participants in the fuzzy alliance S constitute a vector:

[0160] If H S If individual rationality, collective rationality and superadditivity are satisfied, then H S It can be used as a solution to the fuzzy alliance game problem.

[0161] Individual rationality requires that players in a fuzzy coalition achieve a satisfactory consumption or cost that does not exceed the consumption incurred by the same vehicles operating alone:

[0162]

[0163] Among them, e i =[0, 0, ... 1 ... 0] T The i-th element is 1 and the rest are zero.

[0164] Collective rationality requires that the costs of a fuzzy coalition be distributed to all participants simultaneously:

[0165]

[0166] Superadditivity means that the consumption of any fuzzy coalition does not exceed the total consumption of all participants in the fuzzy coalition operating individually with the same participation.

[0167]

[0168] A fuzzy alliance combining the Grand Alliance and the Single Alliance was constructed. g Including all players, that is, the entire transportation system, the single-player alliance is the personal utility of a single CAV. In addition, the fuzzy alliance S g The participation vectors of all participants in are expressed as:

[0169]

[0170] In a single league Each CAV joins two alliances, namely S i and S g,and Assumptions Related to driving aggressive κ, are:

[0171]

[0172] in, κ i represents the driving aggressiveness of the i-th vehicle.

[0173] By x , δ f ), CAVs can achieve good driving performance at signal-controlled intersections.

[0174] In constructing the decision consumption function, the types of driving performance, namely driving safety and passing efficiency, are considered. For HDV / CAVi, the decision consumption function includes the consumption of driving safety. and through efficiency consumption.

[0175]

[0176]

[0177] Cost function of driving safety It consists of three consumption functions, namely the consumption functions of longitudinal, lateral and lane keeping safety, which are respectively Hehe express.

[0178]

[0179] in, and is the weighting coefficient. The value of is related to the driving risk assessment,

[0180] Consumption function of vertical security Set as the collision time between CAV and its preceding vehicle (LV), specifically:

[0181]

[0182]

[0183]

[0184]

[0185] according to If the speed of the CAV is less than that of the LV, the longitudinal safety consumption is ignored.

[0186] Consumption function of horizontal security Set as the time to collision (TTC) between CAVi and its potentially conflicting vehicle j (NVj) at the confluence point (CP), mainly dealing with merging conflicts. The lateral safety cost is expressed as:

[0187]

[0188]

[0189]

[0190]

[0191]

[0192] Where, represents the set of lateral safety costs at all confluences. In the jth CP, the lateral safety cost is described by the second equation, where and are CAV and NV j, respectively, representing the TTC of the j-th CP. i-CPj and Δs NVj-CPj They represent the distance between CAV and the j-th CP and the distance between NVj and the j-th CP, respectively. In addition, ξ is a design parameter consisting of a small positive value to avoid the denominator in the calculation.

[0193] The third part is the consumption of lane keeping safety Set to the lateral distance error Δy between the CAVi position and the target path i and yaw angle error Δφ l , which is derived as:

[0194]

[0195] in, and is the weighting coefficient,

[0196] Finally, the CAVi transmission efficiency is set using the CAVi duration THWi The consumption function is expressed as:

[0197]

[0198]

[0199] In order to reduce the computational complexity of the decision-making algorithm, the weighting coefficient and It is set based on driving risk assessment:

[0200]

[0201]

[0202] Where Γ0 is the safety value of driving risk assessment, ω0 is a fixed weighting coefficient, ω0 = 10. Γ i (NVj) is the driving risk assessment value of NVj to CAVi, Γ NVj (i) is the driving risk assessment value of CAVj to NVj. i (NVj) or Γ Nvj (i) Both are greater than the safety value, and the consumption function of lateral driving safety should be considered.

[0203] In order to ensure the performance of the decision-making algorithm, including driving safety, passing efficiency, riding comfort, control vector and lateral stability, some constraints must be considered. The safety constraints of CAVi are expressed as:

[0204] TTC i ≥TTC min ,|Δy i |≤Δy max ,

[0205] Furthermore, the defined constraints designed into the driving strategy are considered.

[0206] Finally, the above CAVi constraints can be expressed in a compact form as:

[0207]

[0208] This invention constructs two fuzzy alliances, S g and S i , i∈N={1,2,...,n},used for decision making. g In the game, all participants aim to maximize S g The utility of the entire transportation system. In the fuzzy alliance, CAVi tries to pursue the maximum individual utility.

[0209] Based on the above analysis, CAVi must consider both self-utility and collective utility in the decision-making process. These weights are described by the participation vector P in the equation g and P i The decision vector of CAVi is the decision vector of all participants and is written as:

[0210] U=[u 1 ,u2 ,...,u i ,...,u n ] T ,

[0211] The decision consumption function vector of all participants is expressed as:

[0212] V=[V 1 , V 2 ,…,V i ,…,V n ] T ,

[0213] In addition, the fuzzy alliance S can be derived g The decision consumption function is:

[0214]

[0215] Will blur the alliance S i The decision consumption function is expressed as:

[0216]

[0217] Based on the established decision-making cost function and constraints, the CAV decision-making problem at unsignalized intersections is transformed into a fuzzy coalition game, which can be expressed as follows:

[0218]

[0219]

[0220] The above formula describes a multi-level optimization problem with constraints. The high-level optimization maximizes the social utility of the entire transportation system, and the low-level optimization maximizes the individual utility of each CAV. Finally, the decision vector U can be determined using the existing activation set optimization algorithm. * The optimization algorithm uses an existing second-order optimization algorithm, and the formula updates the Hessian estimate of the Lagrangian in each iteration. In the fuzzy joint, the fuzzy Shapley method is used to allocate the utility of each participant, satisfying individual rationality, collective rationality, and superadditivity.

[0221] The present invention also relates to a vehicle roadside guidance control system in a mixed traffic environment at an intersection. The guidance control system corresponds to the guidance control method of the above embodiment and can be understood as a system that implements the above guidance control method. The system includes a vehicle driving unit, a signal light unit, and a coordinated guidance unit.

[0222] The vehicle driving unit is used to construct a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy;

[0223] The signal light unit is used to construct a signal light control strategy so that the time ratio of the traffic light changes with the change of vehicle penetration rate and lane saturation;

[0224] The cooperative guidance unit is used to perform cooperative control and guidance on vehicles arriving at the core traffic area, so that the vehicles can pass through the core traffic area efficiently.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A vehicle roadside guidance control method in a mixed traffic environment at an intersection, characterized by: include: Constructing a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy; The vehicle driving strategy includes: When a vehicle reaches the second stop line, it receives control instructions and enters the designated lane. When the green light comes on, all vehicles in the corresponding phase before the second stop line exit, and vehicles after the second stop line enter the designated lane. The second stop line is s meters away from the intersection ahead. The following constraints are imposed on the speed and acceleration of the vehicle that is about to reach the second stop line: in, and represents the speed and acceleration of the i-th CAV in the x-axis direction, where the positive direction of the x-axis is the direction pointing to the intersection; and represents the maximum speed and acceleration of the i-th CAV in the x-axis direction; TTC i and TTC min They are the actual pre-collision time and the minimum pre-collision time of the vehicle; CAV stands for connected autonomous vehicle; The vibration constraint of the vehicle during driving is: |jerk i |≤jerk max ; Among them, jerk i represents the jitter of the i-th CAV, jerk max Indicates the maximum value of allowed jitter; The lateral stability constraint of the vehicle during lane changing is: |b i |≤arctan(0.02μg); Among them, β i is the deflection rate associated with lateral stability, μ is the tire-road adhesion coefficient, and g is the acceleration due to gravity; The following constraints are imposed on the driving distance and speed difference between vehicles: Δp i ≥Δp min ,|Δv i |≤Δv max ; Where Δp i and Δv i are the position distance and speed difference between the preceding vehicle and the vehicle; Δp min and Δv max are the minimum safe distance and maximum speed difference between vehicles respectively; Construct a traffic light control strategy so that the time ratio of traffic lights changes with changes in vehicle penetration rate and lane saturation; the traffic light control strategy includes: Design lane l Green light duration at an intersection g l,k for: Among them, V l is the traffic flow of lane l, S l is the traffic flow when lane l is saturated, and the signal cycle duration C is: PHF is the peak hour factor, q i is the peak hour demand, q max Design volume for peak hours; L l Y is the time lost by vehicles in lane l due to starting or failure; l is the flow rate ratio of lane l; r l,des is the expected lane saturation when the road is designed; Vehicles arriving at the core traffic area are coordinated and controlled and guided to enable them to pass through the core traffic area efficiently.

2. The vehicle roadside guidance control method in a mixed traffic environment at an intersection according to claim 1, characterized in that: The vehicle driving strategy also includes a road right scheduling plan: The following cyclic processing is performed according to the set detection cycle: If the variable lane is a CAV-only road, proceed to step a; if the variable lane is a mixed lane, proceed to step b; a. Calculate lane saturation r l , if r l ∈[0,ε1], then C l,1 Accumulate 1, otherwise, go to step a1; a1. If r l ∈[ε2,1], then C l,2 Accumulate 1; otherwise, C l,1 =C l,2 =0; where ε1 and ε2 are low saturation threshold and high saturation threshold respectively; C l,1 is the low saturation conversion index; C l,2 is the high saturation conversion index; b. Calculate lane saturation r l , if r l ≥ε2, then C l,1 Accumulate 1, C l,2 =0; otherwise, C l,1 =C l,2 =0; Through the processing of step a or b, parameter C is obtained l =max{C l,1 ,C l,2 }; If C l =3, then change the middle lane state and make C l,1 =C l,2 =0.

3. The vehicle roadside guidance control method under mixed traffic conditions at an intersection according to claim 2, characterized in that: In step a, Among them, V l is the traffic flow of dedicated lane l, p l,cav is the penetration rate of CAVs in the dedicated lane l, h bl is the saturated headway of the dedicated lane; g l,k is the green light duration of the dedicated lane l in the kth cycle; In step b, Among them, b l,2 is the number of straight mixed lanes in the lane group; b l,3 The number of mixed driving lanes for straight driving and right turning in the lane group.

4. The vehicle roadside guidance control method in a mixed traffic environment at an intersection according to claim 1, characterized in that: Coordinated control and guidance of vehicles arriving at the core traffic area, including: Build a simplified vehicle model: β=arctane[l r / (l f +l r )timeδ f ]: u(t)=[a x ,δ f ] T ; in, represents the derivative of the vehicle state variable x, v x is the longitudinal velocity, is the yaw angle, (X g ,Y g ) is the coordinate of the vehicle's center of gravity, a x is the front wheel longitudinal acceleration, δ f is the steering angle, β is the slip angle, l f 、l r is the front and rear wheelbase; The entire traffic system at the intersection is regarded as a grand coalition, and a single CAV is regarded as a single-person coalition. A fuzzy coalition is constructed that combines the grand coalition and the single-person coalition. Using the fuzzy alliance game method, the decision variable a x and δ f Control is performed and a decision consumption function is optimized so that the CAV obtains good driving performance at signal-controlled intersections; wherein the decision consumption function includes a driving safety consumption function and a passing efficiency consumption function, and the driving safety consumption function includes consumption functions for longitudinal, lateral and lane keeping safety.

5. A vehicle roadside guidance control system in a mixed traffic environment at an intersection, characterized by: It includes a vehicle driving unit, a signal light unit and a cooperative guidance unit; The vehicle driving unit is used to construct a vehicle driving strategy so that the vehicle drives according to the vehicle driving strategy; The vehicle driving strategy includes: When a vehicle reaches the second stop line, it receives control instructions and enters the designated lane. When the green light comes on, all vehicles in the corresponding phase before the second stop line exit, and vehicles after the second stop line enter the designated lane. The second stop line is s meters away from the intersection ahead. The following constraints are imposed on the speed and acceleration of the vehicle that is about to reach the second stop line: in, and represents the speed and acceleration of the i-th CAV in the x-axis direction, where the positive direction of the x-axis is the direction pointing to the intersection; and represents the maximum speed and acceleration of the i-th CAV in the x-axis direction; TTC i and TTC min They are the actual pre-collision time and the minimum pre-collision time of the vehicle; CAV stands for connected autonomous vehicle; The vibration constraint of the vehicle during driving is: |jerk i |≤jerk max ; Among them, jerk i represents the jitter of the i-th CAV, jerk max Indicates the maximum value of allowed jitter; The lateral stability constraint of the vehicle during lane changing is: |b i |≤arctan(0.02μg); Among them, β i is the deflection rate associated with lateral stability, μ is the tire-road adhesion coefficient, and g is the acceleration due to gravity; The following constraints are imposed on the driving distance and speed difference between vehicles: Δp i ≥Δp min ,|Δv i |≤Δv max ; Where Δp i and Δv i are the position distance and speed difference between the preceding vehicle and the vehicle; Δp min and Δv max are the minimum safe distance and maximum speed difference between vehicles respectively; The signal light unit is used to construct a signal light control strategy so that the time ratio of the traffic light changes with the change of vehicle penetration rate and lane saturation; the signal light control strategy includes: Design lane l Green light duration at an intersection g l,k for: Among them, V l is the traffic flow of lane l, S l is the traffic flow when lane l is saturated, and the signal cycle duration C is: PHF is the peak hour factor, q i is the peak hour demand, q max Design volume for peak hours; L l Y is the time lost by vehicles in lane l due to starting or failure; l is the flow rate ratio of lane l; r l,des is the expected lane saturation when the road is designed; The cooperative guidance unit is used to perform cooperative control and guidance on vehicles arriving at the core traffic area, so that the vehicles can pass through the core traffic area efficiently.

6. The vehicle roadside guidance control system in a mixed traffic environment at an intersection according to claim 5, characterized in that: The vehicle driving strategy also includes a road right scheduling plan: The following cyclic processing is performed according to the set detection cycle: If the variable lane is a CAV-only road, proceed to step a; if the variable lane is a mixed lane, proceed to step b; a. Calculate lane saturation r l , if r l ∈[0,ε1], then C l,1 Accumulate 1, otherwise, go to step a1; a1. If r l ∈[ε2,1], then C l,2 Accumulate 1; otherwise, C l,1 =C l,2 =0; where ε1 and ε2 are low saturation threshold and high saturation threshold respectively; C l,1 is the low saturation conversion index; C l,2 is the high saturation conversion index; b. Calculate lane saturation r l , if r l ≥ε2, then C l,1 Accumulate 1, C l,2 =0; otherwise, C l,1 =C l,2 =0; Through the processing of step a or b, parameter C is obtained l =max{C l,1 ,C l,2 }; If C l =3, then change the middle lane state and make C l,1 =C l,2 =0.

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