A method for coordinated time-space right-of-way priority control of emergency vehicles in a hybrid network environment

Abstract

The present invention constructs a method for coordinated time-space right of way priority control of emergency vehicles in a hybrid networked environment to improve the rescue efficiency of emergency vehicles in a hybrid networked environment while avoiding major delays to social vehicles. First, a vehicle trajectory reconstruction method based on CV refined trajectory data is proposed for networked vehicles and manual vehicles in a hybrid networked environment to estimate the traffic status of each lane; then, a time right of way priority strategy based on schemes such as early red light off and green light extension and a spatial right of way priority strategy based on schemes such as emergency priority lanes and dynamic clearing distance are proposed; finally, an active priority control algorithm is designed based on the model predictive control framework to ensure the priority right of way for emergency vehicles through speed guidance, signal priority control and other methods.

Description

Technical Field

[0001] The present invention relates to a hybrid network environment emergency vehicle time-space right of way collaborative priority control method technology, belonging to the field of intelligent transportation technology. Background Art

[0002] Existing research (CN202311186522.0) provides a method for priority control of emergency vehicles on urban roads in a networked environment, including establishing a functional relationship between the time a vehicle enters and exits a grid and the speed-time of entering an intersection on each trajectory, and proposing a vehicle speed classification method. By planning the driving trajectory of vehicles at intersections and road sections, emergency vehicles can be given priority.

[0003] Another study (CN201610804554.6) provides a signal priority control method for emergency vehicles at intersections based on a two-stage optimization process, including emergency vehicle detection, calculation of vehicle information in each lane in different import directions, calculation of emergency vehicle travel time and queue vehicle dissipation time, and green signal design. According to the current signal status of the intersection, emergency vehicles are given signal priority, the green light time is advanced or delayed, and the delay time of other vehicles is reduced.

[0004] In addition, there is also a study (CN202011069431.5) that provides an intersection priority control method based on the emergency lane, including classifying the queue status, determining the emergency lane opening time, calculating the green light time required to clear the vehicles inside the lane, and establishing a combined optimization model of the emergency lane opening time and the signal priority opening time, so as to form the optimal opening plan and improve the efficiency of emergency rescue.

[0005] At present, there is a certain foundation for the research on emergency rescue priority control scheme, but there are still the following defects:

[0006] First, most existing studies are mainly based on an ideal experimental environment of full connectivity, without considering the non-fully connected state, that is, the mixed environment. In this environment, it is necessary to consider the acquisition or estimation of traffic state information of non-connected vehicles. The development of intelligent transportation systems to a fully connected state will inevitably go through a long period of mixed traffic flow, and currently, there are few reasonable solutions designed for emergency rescue control problems during this period.

[0007] Second, most existing emergency rescue methods are modeled based on historical data and fixed parameters, and cannot be dynamically adjusted to adapt to different scenarios. This makes it impossible for the dispatch system to dynamically fine-tune according to the actual traffic status, limiting the system's adaptability in complex and changing environments, thereby reducing emergency rescue efficiency. Currently, there are few solutions that can achieve dynamic coordination of emergency rescue plans. Summary of the invention

[0008] Aiming at the main defects in the existing emergency rescue priority control scheme research, an innovative emergency rescue priority control method with practical application value is designed. Specifically, the objectives of the present invention mainly include:

[0009] First, fully consider the different network penetration rates in the future, provide a stable emergency rescue priority control solution suitable for mixed environments, and fully consider the problem of obtaining or estimating traffic status information of non-networked vehicles.

[0010] Secondly, the introduction of the adaptive and real-time model predictive control (MPC) method enables the emergency rescue plan to be dynamically and gradually fine-tuned according to the actual traffic conditions, thereby improving the dynamic coordination of the system and improving the efficiency of emergency rescue.

[0011] Technical solution: The complete technical means and methods of the present invention.

[0012] Step 1: Based on V2I technology, the traffic status information of the connected social vehicles (CV) in each lane is obtained when the emergency vehicle (EV) enters the road section, including the connected social vehicle position matrix x CV 、Networked society vehicle speed matrix v CV , signal phase Φ, emergency vehicle position x EV , speed v EV The schematic diagram of the acquisition process is as follows Figure 2 .

[0013] Step 2: Select the coordinated priority strategy of time and space right of way for emergency vehicles. Combined with the signal phase Φ obtained in step 1, taking into account the emergency rescue efficiency and social vehicle delays, the time right of way priority control strategy is implemented according to the execution conditions of early red light off and green light extension; according to the status information of social vehicles and emergency vehicles obtained in step 1, the spatial right of way priority control strategy is implemented.

[0014] 2.1、Time road priority control strategy

[0015] 2.1.1. Red light early off solution determination conditions

[0016] When the following four conditions are met, the red light early off solution is implemented:

[0017] Condition 1: When the emergency rescue vehicle is expected to reach the stop line of the intersection, Φ is in the red light phase;

[0018] Condition 2: The green light duration of all non-emergency rescue phases is greater than the minimum green light time of intersection b (g minjb );

[0019] Condition 3: The compressible red light duration is greater than 0;

[0020] Condition 4: General traffic performance index PI bThe value of is greater than 0.

[0021] The specific red light early breaking scheme design and index calculation method are as follows:

[0022] When the emergency vehicle reaches the stop line of the intersection but the traffic light has not switched from the red light phase to the green light phase, the red light early off control solution can be considered to allow the emergency vehicle to pass through the intersection directly. The prerequisites for using the red light early off solution are as follows:

[0023] g0<Δr b <r i+1 -r i+1min

[0024] Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δr b represents the length of red light time that needs to be shortened in the current phase i, r i+1 represents the duration of the red light in the i+1th phase, r i+1min represents the shortest red light duration of the i+1th phase. The schematic diagram of the red light early off solution is as follows Figure 3 shown.

[0025] According to the Highway Capacity Manual, jb , C b and S ijb Has the following relationship:

[0026]

[0027]

[0028] Among them, λ jb represents the green signal ratio of intersection b in the jth phase, C b represents the signal period of intersection b, S ijb represents the saturated flow rate (veh / h) of the i-th lane of intersection b corresponding to the j-th phase, λ minjb represents the minimum green-to-signal ratio of the jth phase of intersection b, V ijb represents the actual flow rate (veh / h) of the i-th lane of intersection b at the j-th phase, g minjb represents the shortest green light duration of the jth phase at intersection b. Then the early red light off duration Δr b The calculation formula is as follows:

[0029] Δr b =d b =C b -tl b -C b λ minjb +t STI

[0030] tSTI Represents the safe response time interval of emergency rescue vehicles, d b represents the time that intersection b is compressed to allow emergency vehicles to pass first, tl b is the start-up loss time. Assume that the total number of lanes at intersection b is n, and the social vehicle delay d is reduced due to the early red light break in the i-th lane corresponding to the j-th phase of intersection b. ijb , and the shortened delay time obtained by weighted summation The calculation formula is as follows:

[0031]

[0032]

[0033] The weight ω is determined by the urgency of the emergency rescue. phb represents the red light duration of the phase ph when there is no emergency vehicle passing at intersection b under the red light early off scheme. Then, calculate the social vehicle delay on the i-th lane corresponding to the z-th phase at intersection b when there is no signal priority and the total social vehicle delay at intersection b Assuming that the number of phases without emergency signal priority at intersection b is m, the calculation formula is as follows:

[0034]

[0035]

[0036] S izb represents the saturated flow rate (veh / h) of the i-th lane at intersection b at the z-th phase, V izb represents the actual flow rate (veh / h) of the i-th lane at intersection b corresponding to the z-th phase, r pab Represents the red light duration of intersection b in the direction relative to the emergency rescue phase pa under the red light early breaking scheme. General traffic performance index PI of intersection b under the red light early breaking scheme b :

[0037]

[0038] 2.1.2. Green light extension scheme determination conditions

[0039] The green light extension plan is implemented when the following three conditions are met:

[0040] Condition 1: The emergency rescue vehicle is expected to arrive at the intersection stop line when the traffic light is red;

[0041] Condition 2: After the green light extension scheme is adopted, the saturation flow rate of each phase is less than 0.98;

[0042] Condition 3: PIb The value of is greater than 0.

[0043] The specific green light extension scheme design and indicator calculation method are as follows:

[0044] When the emergency vehicle enters the road section with a green light, but reaches the stop line of the intersection, the signal light has switched from green to red (i.e. the remaining green light time is not enough for the emergency vehicle to pass the stop line), the green light extension scheme can be adopted. The prerequisites for adopting the green light extension scheme are as follows:

[0045] g0<Δg b <g i+1 -g i+1min

[0046] Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δg b Indicates the length of green light time that needs to be extended for the current phase i, g i+1 Represents the green light duration of the i+1th phase, g i+1min Represents the shortest green light duration of the i+1th phase. The schematic diagram of the green light extension scheme is as follows Figure 4 As shown:

[0047] The calculation formula for the green light extension time is as follows:

[0048]

[0049] Therefore, the delay time d of emergency vehicles under the green light extension scheme is reduced b is equal to the queuing time of emergency vehicles when the signal priority scheme is not adopted, which can be expressed as:

[0050] d b =Δg b +r phb +tl b

[0051] The delay time d of the i-th lane in the j-th phase of intersection b under the green light extension scheme is reduced ijb And the delay time under the green light extension scheme obtained by weighted summation The calculation formula is as follows:

[0052]

[0053]

[0054] Similarly, calculate the social vehicle delay on the i-th lane corresponding to the z-th phase at intersection b under the green light extension scheme when there is no signal priority: and the total social vehicle delay at intersection b The calculation formula is as follows:

[0055]

[0056]

[0057] Then, based on the above formula, the general traffic performance index PI of intersection b under the green light extension scheme can be calculated: b :

[0058]

[0059] 2.2. Spatial right-of-way priority control

[0060] The present invention proposes a spatial right-of-way priority scheme based on emergency rescue dedicated lanes and dynamic lane clearance, such as Figure 5 The core algorithm flow of the spatial right of way priority scheme is as follows:

[0061]

[0062] Specifically, the spatial right-of-way priority solution can be divided into three key steps: priority lane selection, lane pre-clearance, and spatial right-of-way restoration:

[0063] 2.2.1. Emergency rescue priority lane selection plan

[0064] The lane where the emergency vehicle is located is defined as the emergency rescue priority lane (EV_LANE). The V2X technology is used to notify the social vehicles in front of the emergency vehicle not to enter the priority lane. The present invention uses the lane j with the smallest average speed in the lane space as the emergency rescue priority lane (EV_LANE), and the calculation formula is as follows:

[0065] mind j (A * )=min{d1(A * ),d2(A * ),...,d N (A * )}

[0066] 2.2.2 Lane Pre-clearing Solution

[0067] The area in front of the emergency vehicle is defined as a dynamic clearance area (EV_ZONE), and information is sent to private vehicles traveling in the area through V2V communication technology, requiring them to actively give way to the emergency vehicle under the condition that the conditions for safe lane change are met.

[0068] Taking the starting point of the road as the coordinate origin, lane veh represents the lane where the social vehicle is located, x EV and x VehThey represent the spatial positions of emergency vehicles and social vehicles in the relative coordinate system. Let the length range of EV_ZONE space be S c When the following conditions are met, private vehicles will actively give way to emergency vehicles:

[0069] Lane veh ==EV_LANE,x Veh -x EV c ,Gap tar >minGap

[0070] Among them, Gap tar is the target lane safety distance, and minGap is the vehicle safe lane change condition. When the social vehicle and the emergency vehicle meet the following relative position relationship, the emergency vehicle sends an emergency rescue message to the social vehicle through V2X technology. At this time, the social vehicles in the lanes outside EV_LANE need to drive carefully and are not allowed to enter EV_LANE.

[0071] lane_list[id]! =EV_LANE,pos_list[id]-x EV >0

[0072] 2.2.3. Spatial right-of-way recovery plan

[0073] When the social vehicles and the emergency vehicles meet the following relative position relationship, the driving status of the social vehicles returns to normal and these social vehicles can freely enter and exit EV_LANE:

[0074] x EV -x Veh >0

[0075] Step 3: Based on the position and location information of CV obtained in step 1, the vehicle trajectory is reconstructed for the mixed traffic flow (CV, HV, EV) on the emergency priority lane (EV_LANE) determined in step 2. The process is as follows Figure 6 As shown, the specific steps are as follows:

[0076] 3.1. Preliminary estimation of the position and velocity of HVs that may exist between CV trajectories. The present invention assumes that the space-time domain ( Figure 6 The possible HV estimated velocities extracted from the two known CV trajectories at the upper and lower boundaries (the gray part between the two trajectories in the figure) are uniformly distributed in the time and space domain. HV The possible HV position and speed estimation formulas between the HV trajectories to be reconstructed are as follows:

[0077]

[0078] ​

[0079] Then the estimated position and velocity of the HV closest to the leading CV (when i=1) are:

[0080]

[0081]

[0082] Among them, x n,i , v n,i They represent the possible HV position and speed of the i-th vehicle between the n-1th and nth CV trajectories, respectively, and x n-1 , v n-1 , x n , v n N denotes the known position and velocity of the n-1th and nth CVs respectively. HV That is, the number of possible HVs between the n-1th and nth CV trajectories.

[0083] 3.2. Reconstruct the trajectory of manually driven vehicles.

[0084] 3.2.1. Determine the number of HV tracks N between CV tracks HV On the one hand, the position and speed of the first HV are substituted into the IDM model to calculate the acceleration of the first HV close to the n-1th CV between the n-1th and nth CV trajectories. The expression of IDM is as follows:

[0085]

[0086] Among them, a p (t) is the acceleration of vehicle p at time t, v f is the expected speed, a M is the maximum acceleration, T is the safe headway, b is the absolute value of the comfortable deceleration, s0 is the minimum parking distance, v p (t) is the speed of vehicle p at time t, x p (t) is the position of vehicle p at time t, and δ is the acceleration index.

[0087] On the other hand, according to the speed of the corresponding i-th HV estimated in step 2.1, the estimated acceleration of the HV at each time step division point is calculated again, and the expression is as follows:

[0088]

[0089] At this point, under the constraints of limited vehicles, the absolute error is selected as the judgment criterion, and the number of optimal HV trajectories when the error is minimum at time t is as follows:

[0090]

[0091] In each reconstruction region, that is, the spatiotemporal region between each pair of CV trajectories, the number of possible HV vehicle trajectories is limited, as shown in the following formula:

[0092]

[0093] Among them, [·] represents the rounding function, t i Represents moment i.

[0094] After excluding N that do not meet the above constraints HV Then, the number of optimal vehicle trajectories N at each moment is HV (t) Take the mode and get the optimal number of vehicles between two CV trajectories:

[0095] N HV =mode(N HV (t i ),i=1,2,...)

[0096] 3.2.2. Calculate the position of each HV at each moment, i.e., the trajectory of the HV. HV After determination, the estimated speed of the HV can be updated by substituting it into the formula of step 2.1 and used as one of the inputs of the IDM to re-estimate the inter-vehicle distance Δx of the HV at each moment.

[0097] At any time, the HV needs to maintain a safe distance from the vehicles in front and behind, that is, to meet the following constraints:

[0098]

[0099] Where l represents the average vehicle length. According to the above calculation formula, the estimated value of HV acceleration at the current moment is And IDM can be used to calculate N HV Acceleration of a HV The calculation formula of the acceleration error with HV is:

[0100]

[0101] Then this problem can be transformed into a constrained nonlinear programming problem, and the final vehicle trajectory is:

[0102] x n,i =x n,i-1 -Δx n,i ,i=0,1,...,N HV,n

[0103] Among them, x n,i That is, the space-time trajectory of the i-th possible HV between the n-1th and nth CVs.

[0104] Step 4: Active priority control. The active priority control algorithm designed based on the model predictive control (MPC) framework is used to predict the travel time of the emergency vehicle on the EV_LANE, adjust the signal corresponding to the current lane (signal priority), and issue a recommended speed (speed guidance) to all vehicles in the lane. The specific process of the algorithm is as follows Figure 7 shown.

[0105] 4.1. Travel time prediction. The present invention uses the MPC prediction module to estimate the travel time t of each social vehicle when it reaches the stop line according to the motion trajectory of the vehicle in front of the emergency vehicle on the EV_LANE. i , and then calculate the possible travel time TT of the emergency vehicle EV Considering that the motion state of the vehicle (the leading vehicle) closest to the intersection on the EV_LANE may have multiple possible situations (passing directly without stopping, slowing down to stop, stopping and starting) due to the influence of different signal phases (red light, green light), the above vehicle motion state can be summarized into the following three situations as the traffic lights change:

[0106] Case 1: Passing directly without stopping

[0107] If the leading vehicle in the EV_LANE convoy can pass through the intersection without stopping at the current signal phase, the leading vehicle in the EV_LANE can be considered to be traveling in a free flow state, and the degraded IDM can be used to describe the motion state of the vehicle. The calculation formula for vehicle acceleration is as follows:

[0108]

[0109] Case 2: Slow down to stop

[0110] For the leading vehicle that needs to slow down and stop after queuing, it needs to actively slow down and stop in front of the stop line due to the traffic light. The driving process of the vehicle includes three stages: deceleration and stopping, queuing and waiting, and starting at the green light. In the deceleration and stopping stage, the leading vehicle is equivalent to following the virtual vehicle that is stationary on the stop line of the intersection, and the vehicle driving behavior obeys the IDM until it stops safely in front of the stop line.

[0111] Situation 3: Green light on

[0112] At this time, the first private vehicle in front of the EV_LANE stop line actively accelerates and starts, and the following vehicles gradually start following the front vehicle. At this stage, since the starting lead vehicle is basically not affected by other vehicles, it can be regarded as a free driving state starting from rest.

[0113] 4.2. Based on the travel time prediction result TT EV , use the MPC control module to adjust the signal corresponding to the current lane (signal priority) and issue a recommended speed (speed guidance) to all vehicles in the lane.

[0114] First, the vehicle dynamics equation is used to describe the operating state of the emergency rescue vehicle. The vehicle dynamics equation is as follows:

[0115] V t+1 =V t +a t ×b

[0116]

[0117] Where b represents the sampling time interval (i.e. prediction step length), X t represents the vehicle position at time t, X t+1 represents the vehicle position at time t+1, a t represents the vehicle acceleration at time t, V t represents the vehicle speed at time t; then the operating state space equation of the emergency vehicle at time t is expressed as:

[0118]

[0119] The acceleration of the emergency vehicle is t is the control variable, position X t Speed ​​V t are state variables respectively. Considering that the primary task of emergency rescue is to ensure the efficiency of emergency rescue, the optimization equation of the operating state of the emergency rescue vehicle is:

[0120]

[0121]

[0122] Where t0 represents the initial time of vehicle running state prediction, and the initial speed of the emergency vehicle is close to the free flow speed V free a min and a max are the maximum deceleration and maximum acceleration of the emergency rescue vehicle, t STI is the safe response time interval of the emergency rescue vehicle. t V t ] T With the control variable a t Merge and construct the variable y′, which has the following specific form:

[0123]

[0124] Where c represents the number of predictions, and its specific calculation formula is as follows:

[0125]

[0126] p k is the prediction time domain, q is the control time domain, is the rounding symbol, ΔT represents the maximum control time domain of the emergency vehicle, and p n Represents the last predicted time domain before the emergency rescue vehicle passes the stop line of the intersection. Although EV has V2V and V2I communication functions, it is still a manually driven vehicle in essence and has not reached the level of autonomous driving. Considering the limited precision of manual driving, the control time domain should not be too small. The control time domain of this paper is set to 5s. Combining the variable y′ with the spatial state equation of the emergency vehicle's operating state, the above formula can be converted into a quadratic programming function as shown below:

[0127]

[0128]

[0129] The values ​​of the matrix H′ are:

[0130]

[0131]

[0132] h′∈R (2c+2)×(2c+2)

[0133] y′ min The values ​​are:

[0134] y′ min =[0,…,P r+1 ,0,a min ,…,a min ] T ,y′ min ∈R 1×(3c+2)

[0135] y′ max The values ​​are:

[0136] y′ max =[yy′,a max ,...,a max ] T ,y′ max ∈R 1×(3c+2)

[0137] yy′=[+∞,V free ,...,+∞,V free ] T ,yy′∈R 1×(2c+2)

[0138] The value of the matrix Aeq′ is:

[0139] Aeq′=[A′C′],Aeq′∈R 2c×(3c+2)

[0140]

[0141]

[0142] The values ​​of the matrix beq′ are:

[0143] beq′=[0,0,…,0],beq′∈R 1×2c

[0144] The quadratic programming function is solved using the active set method (ASM) to obtain the state variables [X t V t ] T With the control variable a t , then, based on the state variable [X t V t ] T Determine the corresponding signal priority duration and phase recovery timing, and use the control variable a t Determine the recommended speed V for the emergency vehicle adv , to achieve intersection speed guidance for emergency vehicles:

[0145] V adv =a t ×q

[0146] Beneficial effects:

[0147] 1. A method for coordinated time-space right-of-way priority control of emergency vehicles in a hybrid network environment is constructed to improve the rescue efficiency of emergency vehicles in a hybrid network environment while avoiding major delays to social vehicles.

[0148] 2. A trajectory reconstruction method based on hybrid networked refined CV trajectory data (such as speed, acceleration, and position) is proposed to estimate the traffic status of the emergency priority lane.

[0149] 3. A time right-of-way priority strategy based on early red light breaking and green light extension, as well as a spatial right-of-way priority strategy based on optimal lane and dynamic clearing distance, are proposed.

[0150] 4. An active priority control algorithm is designed based on the model predictive control (MPC) framework, which guarantees the priority right of emergency vehicles through lane guidance, speed guidance, signal priority control, etc., and is more superior than the passive priority method in reducing social vehicle delays. BRIEF DESCRIPTION OF THE DRAWINGS

[0151] Figure 1The figure is a flow chart of a method according to an embodiment of the present invention.

[0152] Figure 2 This is a traffic data collection diagram based on V2I in step 1 of an embodiment of the present invention.

[0153] Figure 3 This is a schematic diagram of the red light early breaking solution in step 2 of an embodiment of the present invention.

[0154] Figure 4 This is a schematic diagram of the green light extension solution in step 2 of an embodiment of the present invention.

[0155] Figure 5 Schematic diagram of the space priority scheme for emergency rescue vehicles in step 2 of an embodiment of the present invention.

[0156] Figure 6 This is a flow chart of the vehicle trajectory reconstruction model in step three of the embodiment of the present invention.

[0157] Figure 7 This is a schematic diagram of the active priority control process in step 4 of an embodiment of the present invention.

[0158] Figure 8 Schematic diagram of SUMO simulation scenario according to an embodiment of the present invention.

[0159] Fig. 9 The vehicle trajectory reconstruction effect of the model of the embodiment of the present invention under different combinations of flow and permeability is analyzed.

[0160] Fig.10 This is a diagram verifying the effect of the model of the embodiment of the present invention on improving emergency rescue efficiency and controlling social vehicle delays. DETAILED DESCRIPTION

[0161] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0162] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance. In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0163] See also Figure 1 The embodiment of the present invention provides a method for coordinated priority control of time-space right of way of emergency vehicles in a hybrid network environment, comprising the steps of:

[0164] Step 1: Based on V2I technology, the traffic status information of the connected social vehicles (CV) in each lane is obtained when the emergency vehicle (EV) enters the road section, including the connected social vehicle position matrix x CV 、Networked society vehicle speed matrix v CV , signal phase Φ, emergency vehicle position x EV , speed v EV The schematic diagram of the acquisition process is as follows Figure 2 .

[0165] Step 2: Select the coordinated priority strategy of time and space right of way for emergency vehicles. Combined with the signal phase Φ obtained in step 1, taking into account the emergency rescue efficiency and social vehicle delays, the time right of way priority control strategy is implemented according to the execution conditions of early red light off and green light extension; according to the status information of social vehicles and emergency vehicles obtained in step 1, the spatial right of way priority control strategy is implemented.

[0166] 2.1、Time road priority control strategy

[0167] 2.1.1. Red light early off solution determination conditions

[0168] When the following four conditions are met, the red light early off solution is implemented:

[0169] Condition 1: When the emergency rescue vehicle is expected to reach the stop line of the intersection, Φ is in the red light phase;

[0170] Condition 2: The green light duration of all non-emergency rescue phases is greater than the minimum green light time of intersection b (g minjb );

[0171] Condition 3: The compressible red light duration is greater than 0;

[0172] Condition 4: General traffic performance index PIb The value of is greater than 0.

[0173] The specific red light early breaking scheme design and index calculation method are as follows:

[0174] When the emergency vehicle reaches the stop line of the intersection but the traffic light has not switched from the red light phase to the green light phase, the red light early off control solution can be considered to allow the emergency vehicle to pass through the intersection directly. The prerequisites for using the red light early off solution are as follows:

[0175] g0<Δr b <r i+1 -r i+1min

[0176] Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δr b represents the length of red light time that needs to be shortened in the current phase i, r i+1 represents the duration of the red light in the i+1th phase, r i+1min represents the shortest red light duration of the i+1th phase. The schematic diagram of the red light early off solution is as follows Figure 3 shown.

[0177] According to the Highway Capacity Manual, jb , C b and S ijb Has the following relationship:

[0178]

[0179]

[0180] Among them, λ jb represents the green signal ratio of intersection b in the jth phase, C b represents the signal period of intersection b, S ijb represents the saturated flow rate (veh / h) of the i-th lane of intersection b corresponding to the j-th phase, λ minjb represents the minimum green-to-signal ratio of the jth phase of intersection b, V ijb represents the actual flow rate (veh / h) of the i-th lane of intersection b at the j-th phase, g minjb represents the shortest green light duration of the jth phase at intersection b. Then the early red light off duration Δr b The calculation formula is as follows:

[0181]

[0182]

[0183] The weight ω is determined by the urgency of the emergency rescue. phbrepresents the red light duration of the phase ph when there is no emergency vehicle passing at intersection b under the red light early off scheme. Then, calculate the social vehicle delay on the i-th lane corresponding to the z-th phase at intersection b when there is no signal priority and the total social vehicle delay at intersection b Assuming that the number of phases without emergency signal priority at intersection b is m, the calculation formula is as follows:

[0184]

[0185]

[0186] S izb represents the saturated flow rate (veh / h) of the i-th lane at intersection b at the z-th phase, V izb represents the actual flow rate (veh / h) of the i-th lane at intersection b corresponding to the z-th phase, r pab Represents the red light duration of intersection b in the direction relative to the emergency rescue phase pa under the red light early breaking scheme. General traffic performance index PI of intersection b under the red light early breaking scheme b :

[0187]

[0188] 2.1.2. Green light extension scheme determination conditions

[0189] The green light extension plan is implemented when the following three conditions are met:

[0190] Condition 1: The emergency rescue vehicle is expected to arrive at the intersection stop line when the traffic light is red;

[0191] Condition 2: After the green light extension scheme is adopted, the saturation flow rate of each phase is less than 0.98;

[0192] Condition 3: PI b The value of is greater than 0.

[0193] The specific green light extension scheme design and indicator calculation method are as follows:

[0194] When the emergency vehicle enters the road section with a green light, but reaches the stop line of the intersection, the signal light has switched from green to red (i.e. the remaining green light time is not enough for the emergency vehicle to pass the stop line), the green light extension scheme can be adopted. The prerequisites for adopting the green light extension scheme are as follows:

[0195] g0<Δg b <g i+1 -g i+1min

[0196] Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δgb Indicates the length of green light time that needs to be extended for the current phase i, g i+1 Represents the green light duration of the i+1th phase, g i+1min Represents the shortest green light duration of the i+1th phase. The schematic diagram of the green light extension scheme is as follows Figure 4 As shown:

[0197] The calculation formula for the green light extension time is as follows:

[0198]

[0199] Therefore, the delay time d of emergency vehicles under the green light extension scheme is reduced b is equal to the queuing time of emergency vehicles when the signal priority scheme is not adopted, which can be expressed as:

[0200] d b =Δg b +r phb +tl b

[0201] The delay time d of the i-th lane in the j-th phase of intersection b under the green light extension scheme is reduced ijb And the delay time under the green light extension scheme obtained by weighted summation The calculation formula is as follows:

[0202]

[0203]

[0204] Similarly, calculate the social vehicle delay on the i-th lane corresponding to the z-th phase at intersection b under the green light extension scheme when there is no signal priority: and the total social vehicle delay at intersection b The calculation formula is as follows:

[0205]

[0206]

[0207] Then, based on the above formula, the general traffic performance index PI of intersection b under the green light extension scheme can be calculated: b :

[0208]

[0209] 2.2. Spatial right-of-way priority control

[0210] The present invention proposes a spatial right of way priority scheme based on emergency rescue dedicated lanes and dynamic lane clearance, as shown in the figure. The core algorithm flow of the spatial right of way priority scheme is shown in Table 1.

[0211] Table 1 Pseudocode of spatial right-of-way priority scheme

[0212]

[0213]

[0214] Specifically, the spatial right-of-way priority solution can be divided into three key steps: priority lane selection, lane pre-clearance, and spatial right-of-way restoration:

[0215] 2.2.1. Emergency rescue priority lane selection plan

[0216] The lane where the emergency vehicle is located is defined as the emergency rescue priority lane (EV_LANE). The V2X technology is used to notify the social vehicles in front of the emergency vehicle not to enter the priority lane. The present invention uses the lane j with the smallest average speed in the lane space as the emergency rescue priority lane (EV_LANE), and the calculation formula is as follows:

[0217] mind j (A * )=min{d1(A * ),d2(A * ),...,d N (A * )}

[0218] 2.2.2 Lane Pre-clearing Solution

[0219] The area in front of the emergency vehicle is defined as a dynamic clearance area (EV_ZONE), and information is sent to private vehicles traveling in the area through V2V communication technology, requiring them to actively give way to the emergency vehicle under the condition that the conditions for safe lane change are met.

[0220] Taking the starting point of the road as the coordinate origin, lane veh represents the lane where the social vehicle is located, x EV and x Veh They represent the spatial positions of emergency vehicles and social vehicles in the relative coordinate system. Let the length range of EV_ZONE space be S c When the following conditions are met, private vehicles will actively give way to emergency vehicles:

[0221] Lane veh ==EV_LANE,x Veh -x EV c ,Gap tar >minGap

[0222] Among them, Gap tar ​is the target lane safety distance, and minGap is the vehicle safe lane change condition. When the social vehicle and the emergency vehicle meet the following relative position relationship, the emergency vehicle sends an emergency rescue message to the social vehicle through V2X technology. At this time, the social vehicles in the lanes outside EV_LANE need to drive carefully and are not allowed to enter EV_LANE.

[0223] lane_list[id]! =EV_LANE,pos_list[id]-x EV >0

[0224] 2.2.3. Spatial right-of-way recovery plan

[0225] When the social vehicles and the emergency vehicles meet the following relative position relationship, the driving status of the social vehicles returns to normal and these social vehicles can freely enter and exit EV_LANE:

[0226] x EV -x Veh >0

[0227] Step 3: Based on the position and location information of CV obtained in step 1, the vehicle trajectory is reconstructed for the mixed traffic flow (CV, HV, EV) on the emergency priority lane (EV_LANE) determined in step 2. The process is as follows Figure 6 As shown, the specific steps are as follows:

[0228] 3.1. Preliminary estimation of the position and velocity of HVs that may exist between CV trajectories. The present invention assumes that the space-time domain ( Figure 6 The possible HV estimated velocities extracted from the two known CV trajectories at the upper and lower boundaries (the gray part between the two trajectories in the figure) are uniformly distributed in the time and space domain. HV The possible HV position and speed estimation formulas between the HV trajectories to be reconstructed are as follows:

[0229]

[0230]

[0231] Then the estimated position and velocity of the HV closest to the leading CV (when i=1) are:

[0232]

[0233]

[0234] Among them, x n,i , v n,i They represent the possible HV position and speed of the i-th vehicle between the n-1th and nth CV trajectories, respectively, and x n-1 , v n-1 , xn , v n N denotes the known position and velocity of the n-1th and nth CVs respectively. HV That is, the number of possible HVs between the n-1th and nth CV trajectories.

[0235] 3.2. Reconstruct the trajectory of manually driven vehicles.

[0236] 3.2.1. Determine the number of HV tracks N between CV tracks HV On the one hand, the position and speed of the first HV are substituted into the IDM model to calculate the acceleration of the first HV close to the n-1th CV between the n-1th and nth CV trajectories. The expression of IDM is as follows:

[0237]

[0238] Among them, a p (t) is the acceleration of vehicle p at time t, v f is the expected speed, a M is the maximum acceleration, T is the safe headway, b is the absolute value of the comfortable deceleration, s0 is the minimum parking distance, v p (t) is the speed of vehicle p at time t, x p (t) is the position of vehicle p at time t, and δ is the acceleration index.

[0239] On the other hand, according to the speed of the corresponding i-th HV estimated in step 2.1, the estimated acceleration of the HV at each time step division point is calculated again, and the expression is as follows:

[0240]

[0241] At this point, under the constraints of limited vehicles, the absolute error is selected as the judgment criterion, and the number of optimal HV trajectories when the error is minimum at time t is as follows:

[0242]

[0243] In each reconstruction region, that is, the spatiotemporal region between each pair of CV trajectories, the number of possible HV vehicle trajectories is limited, as shown in the following formula:

[0244]

[0245] Among them, [·] represents the rounding function, t i Represents moment i.

[0246] After excluding N that do not meet the above constraints HV Then, the number of optimal vehicle trajectories N at each moment is HV(t) Take the mode and get the optimal number of vehicles between two CV trajectories:

[0247] N HV =mode(N HV (t i ),i=1,2,...)

[0248] 3.2.2. Calculate the position of each HV at each moment, i.e., the trajectory of the HV. HV After determination, the estimated speed of the HV can be updated by substituting it into the formula of step 2.1 and used as one of the inputs of the IDM to re-estimate the inter-vehicle distance Δx of the HV at each moment.

[0249] At any time, the HV needs to maintain a safe distance from the vehicles in front and behind, that is, to meet the following constraints:

[0250]

[0251] Where l represents the average vehicle length. According to the above calculation formula, the estimated value of HV acceleration at the current moment is And IDM can be used to calculate N HV Acceleration of a HV The calculation formula of the acceleration error with HV is:

[0252]

[0253] Then this problem can be transformed into a constrained nonlinear programming problem, and the final vehicle trajectory is:

[0254] x n,i =x n,i-1 -Δx n,i ,i=0,1,...,N HV,n

[0255] Among them, x n,i That is, the space-time trajectory of the i-th possible HV between the n-1th and nth CVs.

[0256] Step 4: Active priority control. The active priority control algorithm designed based on the model predictive control (MPC) framework is used to predict the travel time of the emergency vehicle on the EV_LANE, adjust the signal corresponding to the current lane (signal priority), and issue a recommended speed (speed guidance) to all vehicles in the lane. The specific process of the algorithm is as follows Figure 7 shown.

[0257] 4.1. Travel time prediction. The present invention uses the MPC prediction module to estimate the travel time t of each social vehicle when it reaches the stop line according to the motion trajectory of the vehicle in front of the emergency vehicle on the EV_LANE. i, and then calculate the possible travel time TT of the emergency vehicle EV Considering that the motion state of the vehicle (the leading vehicle) closest to the intersection on the EV_LANE may have multiple possible situations (passing directly without stopping, slowing down to stop, stopping and starting) due to the influence of different signal phases (red light, green light), the above vehicle motion state can be summarized into the following three situations as the traffic lights change:

[0258] Case 1: Passing directly without stopping

[0259] If the leading vehicle in the EV_LANE convoy can pass through the intersection without stopping at the current signal phase, the leading vehicle in the EV_LANE can be considered to be traveling in a free flow state, and the degraded IDM can be used to describe the motion state of the vehicle. The calculation formula for vehicle acceleration is as follows:

[0260]

[0261] The meanings of the variables in the formula are consistent with those in the previous IDM formula. i (t) is the acceleration of vehicle i at time t, v i is the speed of vehicle i, v f is the expected speed, A i is the maximum acceleration, δ is the acceleration index;

[0262] Case 2: Slow down to stop

[0263] For the leading vehicle that needs to slow down and stop after queuing, it needs to actively slow down and stop in front of the stop line due to the traffic light. The driving process of the vehicle includes three stages: deceleration and stopping, queuing and waiting, and starting at the green light. In the deceleration and stopping stage, the leading vehicle is equivalent to following the virtual vehicle that is stationary on the stop line of the intersection, and the vehicle driving behavior obeys the IDM until it stops safely in front of the stop line.

[0264] Situation 3: Green light on

[0265] At this time, the first private vehicle in front of the EV_LANE stop line actively accelerates and starts, and the following vehicles gradually start following the front vehicle. At this stage, since the starting lead vehicle is basically not affected by other vehicles, it can be regarded as a free driving state starting from rest.

[0266] 4.2. Based on the travel time prediction result TT EV , use the MPC control module to adjust the signal corresponding to the current lane (signal priority) and issue a recommended speed (speed guidance) to all vehicles in the lane.

[0267] First, the vehicle dynamics equation is used to describe the operating state of the emergency rescue vehicle. The vehicle dynamics equation is as follows:

[0268] V t+1 =V t +a t ×b

[0269]

[0270] Where b represents the sampling time interval (i.e. prediction step length), X t represents the vehicle position at time t, X t+1 represents the vehicle position at time t+1, a t represents the vehicle acceleration at time t, V t represents the vehicle speed at time t; then the operating state space equation of the emergency vehicle at time t is expressed as:

[0271]

[0272] The acceleration of the emergency vehicle is t is the control variable, position X t Speed ​​V t are state variables respectively. Considering that the primary task of emergency rescue is to ensure the efficiency of emergency rescue, the optimization equation of the operating state of the emergency rescue vehicle is:

[0273]

[0274]

[0275] Where t0 represents the initial time of vehicle running state prediction, and the initial speed of the emergency vehicle is close to the free flow speed V free a min and a max are the maximum deceleration and maximum acceleration of the emergency rescue vehicle, t STI is the safe response time interval of the emergency rescue vehicle. t V t ] T With the control variable a t Merge and construct the variable y′, which has the following specific form:

[0276]

[0277] Where c represents the number of predictions, and its specific calculation formula is as follows:

[0278]

[0279] p k is the prediction time domain, q is the control time domain, is the rounding symbol, ΔT represents the maximum control time domain of the emergency vehicle, and pn Represents the last predicted time domain before the emergency rescue vehicle passes the stop line of the intersection. Although EV has V2V and V2I communication functions, it is still a manually driven vehicle in essence and has not reached the level of autonomous driving. Considering the limited precision of manual driving, the control time domain should not be too small. The control time domain of this paper is set to 5s. Combining the variable y′ with the spatial state equation of the emergency vehicle's operating state, the above formula can be converted into a quadratic programming function as shown below:

[0280]

[0281]

[0282] The values ​​of the matrix H′ are:

[0283]

[0284]

[0285] h′∈R (2c+2)×(2c+2)

[0286] y′ min The values ​​are:

[0287] y′ min =[0,…,P r+1 ,0,a min ,…,a min ] T ,y′ min ∈R 1×(3c+2)

[0288] y′ max The values ​​are:

[0289] y′ max =[yy′,a max ,...,a max ] T ,y′ max ∈R 1×(3c+2)

[0290] yy′=[+∞,V free ,...,+∞,V eree ] T ,yy′∈R 1×(2c+2)

[0291] The value of the matrix Aeq′ is:

[0292] Aeq′=[A′C′],Aeq′∈R 2c×(3c+2)

[0293]

[0294]

[0295] The values ​​of the matrix beq′ are:

[0296] beq′=[0,0,…,0],beq′∈R 1×2c

[0297] The quadratic programming function is solved using the active set method (ASM) to obtain the state variables [X t V t ] T With the control variable a t , then, based on the state variable [X t V t ] T Determine the corresponding signal priority duration and phase recovery timing, and use the control variable a t Determine the recommended speed V for the emergency vehicle adv , to achieve intersection speed guidance for emergency vehicles:

[0298] V adv =a t ×q

[0299] Among them, V adv is the recommended speed for emergency vehicles, a t represents the vehicle acceleration at time t, and q represents the control time domain.

[0300] Case Study:

[0301] 1. Test scenario

[0302] In order to verify the effectiveness of the proposed method, the traffic flow simulation of an isolated intersection with a two-way four-lane road was carried out using the SUMO method. Each entrance lane contains a straight-left turn lane (SL) and a straight-right turn lane (SR). The simulation parameters of all vehicles are randomly generated, and a certain percentage is selected to represent the CV (that is, the trajectory can be output by SUMO). The length of the experimental section is 800m. Using the two-phase fixed signal scheme with a cycle of 120s and an average green time commonly used in urban signal intersections, the present invention constructs Figure 8 The two-phase signalized intersection model is shown.

[0303] 2. Simulation Effect

[0304] The simulation step size is set to 0.1s, and the IDM parameter settings for estimating and predicting the microscopic traffic status of the road section are shown in Table 2 below.

[0305] Table 2 IDM parameter settings

[0306]

[0307] Then, this method is compared with the existing passive signal priority strategy and the no priority scheme (baseline). Assuming the CV penetration rate is 50%, the travel time and intersection delay time under different traffic flow density scenarios are calculated. The simulation results are shown in Table 3.

[0308] Table 3 Simulation results under different flow conditions

[0309]

[0310] As the road traffic volume increases, the speed of emergency vehicles decreases, the travel time increases, and the total delay at the intersection increases. From the results in the table, it can be seen that the method proposed in the present invention can more effectively improve the efficiency of emergency rescue. In addition, combined with the total delay of vehicles at the intersection without priority (baseline method), the algorithm proposed in the present invention can better reduce the additional delay caused by signal transition compared with the passive priority algorithm.

[0311] 3. Sensitivity Analysis

[0312] (1) Trajectory reconstruction effect

[0313] Considering that the lane-level spatiotemporal right-of-way collaborative priority control model proposed in this chapter is highly dependent on the number of vehicles and the accuracy of vehicle positions in trajectory reconstruction, this paper studies the impact of CV penetration and traffic volume on the accuracy of trajectory reconstruction. The accuracy of the number of HVs inserted into the trajectory is measured by calculating the Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE); the MAE and the Root Mean Square Error (RMSE) of the reconstructed trajectory at each moment are used to measure the accuracy of the inserted HV position. The calculation formulas for MAE, MAPE, and RMSE are as follows:

[0314]

[0315]

[0316]

[0317] Among them, k represents the number of time steps and Δt represents the time step length. Table 4 shows the HV insertion quantity error and insertion position error under different traffic volume scenarios at 50% CV penetration rate:

[0318] Table 4 Trajectory reconstruction error for different traffic volume scenarios with 30% CV penetration

[0319]

[0320] It can be seen from Table 3 that when the traffic volume is small, the number of vehicles that need to be inserted is too small, and the MAE and MAPE of the number of HV insertions and the MAE and RMSE of the insertion position are relatively high. As the traffic volume increases, the estimation error of the number of HV insertions and the insertion position decreases. As the traffic volume further increases, the MAE, MAPE, and RMSE of the model tend to increase. At this time, congestion has already occurred on the road section in the SUMO simulation. The possible reason is that at a relatively low CV penetration rate, when the number of vehicles inserted between two vehicles is large, the error of trajectory reconstruction increases. Overall, the trajectory reconstruction method proposed in the present invention is relatively stable in the number error and position error of reconstructing HV trajectories under different traffic volumes, and can effectively support the coordinated priority control of the spatiotemporal road rights of emergency vehicles. In addition, the present invention also explores the trajectory reconstruction error of different CV penetration scenarios under a traffic volume of 600veh / h, and the results are shown in Table 5:

[0321] Table 5 Trajectory reconstruction error for 600 veh / h traffic volume and different CV penetration rates

[0322]

[0323] In general, even when the CV penetration rate is relatively low (30%), the method proposed in the present invention can achieve good trajectory reconstruction effects. As the CV penetration rate increases, the error in the number of HV insertions and the error in the insertion position gradually decrease. One possible reason is that as the CV penetration rate increases, the number of known trajectories on the road section increases, the number of vehicles that need to be reconstructed under the same traffic volume decreases, and the difficulty of trajectory reconstruction is reduced, which reduces the errors in the number of HV insertions and position estimation. It can be foreseen that as the CV market penetration rate continues to increase, the model can obtain a more stable trajectory reconstruction effect, thereby ensuring the effectiveness of the EP-MPC algorithm. See examples of trajectory reconstruction results. Fig. 9 :

[0324] (2) Emergency rescue efficiency and intersection delays

[0325] The present invention has proved that the EP-MPC algorithm can effectively improve the emergency rescue efficiency and reduce intersection delays under different traffic volume scenarios at 50% CV penetration rate. In order to further explore the relationship between emergency rescue efficiency, intersection delay and CV penetration rate, the present invention conducted 10 repeated experiments under different penetration rates and different traffic volume scenarios to obtain the average travel time of emergency vehicles passing through the intersection, as well as the average simulation delay error value of the method in this paper and the passive priority method. The heat map drawn is shown in Fig.10 .

[0326] Fig.10In the figure, the travel time of emergency vehicles along the y-axis increases with the increase of traffic volume. Since CV vehicles are manually driven vehicles (without automatic driving functions), it is difficult to directly improve the road capacity, and the ability to improve the efficiency of emergency rescue is limited when the traffic volume is large; the travel time of emergency vehicles along the x-axis shows an overall downward trend with the increase of network penetration rate. When more CV vehicles obey the time-space road right coordinated priority regulation proposed in this paper, the efficiency of emergency rescue can be improved to a certain extent.

[0327] In order to better demonstrate the effectiveness of the proposed method in reducing intersection delays, Figure x(b) also shows the heat map of the delay reduction (i.e., the simulated delay of the proposed method minus the simulated delay of the passive priority method). Along the x-axis, as the CV penetration rate increases, the proposed method can further reduce intersection delays compared to the passive priority method. Along the y-axis, as the traffic volume increases, the improvement of the proposed method in reducing intersection delays compared to the passive priority method is not significant, especially at relatively low CV penetration rates. A reasonable explanation is that when the traffic volume is large, the time and space resources that can be allocated on the road are limited. Therefore, in order to ensure the efficiency of emergency rescue, it may be necessary to sacrifice the traffic efficiency of social vehicles.

[0328] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for coordinated priority control of time-space right of way for emergency vehicles in a hybrid network environment, characterized in that: Includes steps: Step 1: Obtain the traffic status information of the connected social vehicles in each lane starting from the time the emergency vehicle enters the road section, including the connected social vehicle position matrix x CV 、Networked society vehicle speed matrix v CV , signal phase Φ, emergency vehicle position x EV , Emergency vehicle speed v EV ; Step 2: Select the emergency vehicle time-space right-of-way coordination priority strategy, combine the signal phase Φ obtained in step 1, and consider the emergency rescue efficiency and social vehicle delays. According to the execution conditions of early red light off and green light extension, execute the time right-of-way priority control strategy; according to the networked social vehicle position matrix x obtained in step 1 CV 、Networked society vehicle speed matrix v CV , emergency vehicle position x EV , Emergency vehicle speed v EV , implement spatial right-of-way priority control strategy; Step 3: Based on the location x of the connected social vehicle obtained in step 1 CV , speed v CV Information, reconstruct vehicle trajectories for the mixed traffic flow on the emergency priority lane determined in step 2; Step 4: Use the active priority control algorithm designed based on the model predictive control framework to predict the travel time of emergency vehicles in the emergency rescue priority lane, adjust the signal corresponding to the current lane, and issue a recommended speed to all vehicles in the lane.

2. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 1 is characterized in that: In step 2, the time right of way priority control strategy includes: When the following four conditions are met at the same time, the red light early disconnection plan is implemented: Condition 1: When the emergency rescue vehicle is expected to arrive at the stop line of the intersection, the signal phase Φ is the red light phase; Condition 2: The green light duration of all non-emergency rescue phases is greater than the minimum green light time g of intersection b minjb ; Condition 3: The compressible red light duration is greater than 0; Condition 4: General traffic performance index PI b The value of is greater than 0.

3. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 2 is characterized in that: When the following conditions are met, the red light early off solution is implemented: g0<Δr b <r i+1 -r i+1min ; Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δr b represents the length of red light time that needs to be shortened in the current phase i, r i+1 represents the duration of the red light in the i+1th phase, r i+1min Represents the shortest red light duration of the i+1th phase; The green signal ratio λ of intersection b in the jth phase jb , the signal cycle C of intersection b b and the saturated flow S of the i-th lane corresponding to the j-th phase of intersection b ijb With the following relationship: Among them, λ minjb represents the minimum green-to-signal ratio of the jth phase of intersection b, V ijb represents the actual flow of the ith lane at intersection b corresponding to the jth phase, g minjb represents the shortest green light duration of the jth phase at intersection b; then the early red light off duration Δr b The calculation formula is as follows: Δr b =d b =C b -tl b -C b λ minjb +t STI t STI Represents the safe response time interval of emergency rescue vehicles, d b represents the time that intersection b is compressed to allow emergency vehicles to pass first, tl b is the start-up loss time; assuming that the total number of lanes at intersection b is n, the social vehicle delay d reduced by the early red light break in the i-th lane corresponding to the j-th phase of intersection b ijb , and the shortened delay time obtained by weighted summation The calculation formula is as follows: Among them, the weight ω is determined by the urgency of emergency rescue, r phb represents the red light duration of the intersection b when there is no emergency vehicle passing through phase ph under the red light early breaking scheme, represents the total social vehicle delay at intersection b; represents the social vehicle delay on the i-th lane corresponding to the z-th phase when intersection b has no signal priority, and m represents the phase number of intersection b without emergency signal priority; S izb represents the saturated flow of the ith lane at intersection b corresponding to the zth phase, V izb represents the actual flow of the ith lane at intersection b corresponding to the zth phase, r pab represents the red light duration in the direction opposite to the emergency rescue phase pa at intersection b under the red light early breaking scheme; General traffic performance index PI of intersection b under the red light early breaking scheme b :

4. The method for coordinated time-space right-of-way priority control of emergency vehicles in a hybrid network environment according to claim 3 is characterized in that: When the following three conditions are met at the same time, the green light extension plan will be implemented: Condition 1: The emergency rescue vehicle is expected to arrive at the intersection stop line when the traffic light is red; Condition 2: After the green light extension scheme is adopted, the saturation flow rate of each phase is less than 0.98; Condition 3: General traffic performance index PI of intersection b under the green light extension scheme b The value of is greater than 0.

5. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 4 is characterized in that: When the following conditions are met, the green light extension plan is implemented: g0<Δgb<g i+1 -g i+1min Among them, g0 represents the time required for the emergency vehicle to enter the road section and pass the intersection, Δg b Indicates the length of green light time that needs to be extended for the current phase i, g i+1 Represents the green light duration of the i+1th phase, g i+1min Represents the shortest green light duration of the i+1th phase; The calculation formula for the green light extension time is as follows: The delay time d of the i-th lane in the j-th phase of intersection b under the green light extension scheme is reduced ijb And the delay time under the green light extension scheme obtained by weighted summation The calculation formula is as follows: Calculate the social vehicle delay on the i-th lane corresponding to the z-th phase at intersection b under the green light extension scheme when there is no signal priority and the total social vehicle delay at intersection b The calculation formula is as follows: Then, based on the above formula, the general traffic performance index PI of intersection b under the green light extension scheme can be calculated: b :

6. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 5 is characterized in that: In step 2, the spatial right-of-way priority control strategy includes: The lane j with the smallest average speed in lane space is used as the emergency rescue priority lane EV_LANE, and the social vehicles in front of the emergency vehicle are not allowed to enter the priority lane. The calculation formula is as follows: mind j (A * )=min{d1(A * ),d2(A * ),…,d N (A * )}; d lane (A * ) represents the spatial average speed of lane; Part of the area in front of the emergency vehicle is a dynamic clearing area EV_ZONE. Information is sent to social vehicles traveling in this area, requiring them to actively give way to the emergency vehicle under the condition that the safe lane change conditions are met. The calculation method of the safe lane change conditions is as follows: hell veh ==EV_LANE, x Veh -x EV c ,Talk tar >minGap;​ Taking the starting point of the road as the coordinate origin, lane veh represents the lane where the social vehicle is located, x EV and x Veh They represent the spatial positions of emergency vehicles and social vehicles in the relative coordinate system. The length range of EV_ZONE space is S c , Gap tar is the target lane safety distance, and minGap is the vehicle safe lane change condition; When the social vehicle and the emergency vehicle meet the following relative position relationship, the emergency vehicle will issue an emergency rescue message to the social vehicle. At this time, the social vehicles in the lanes other than the emergency rescue priority lane EV_LANE need to drive carefully and are not allowed to enter the emergency rescue priority lane EV_LANE. lane_list[id]!=EV_LANE,pos_list[id]-x EV >0; When the social vehicles and the emergency vehicles meet the following relative position relationship, the driving status of the social vehicles returns to normal, and these social vehicles can freely enter and exit the emergency rescue priority lane EV_LANE: x EV -x Veh >0。 7. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 1 is characterized in that: Step three specifically includes: Preliminary estimation of the position and speed of artificial vehicles HV that may exist between the trajectories of connected social vehicles CV: Assume that the possible HV estimated velocities extracted from the two known CV trajectories at the upper and lower boundaries of the space-time domain are uniformly distributed in the space-time domain. HV The possible HV position and speed estimation formulas between the HV trajectories to be reconstructed are as follows: Then the HV closest to the leading CV, that is, when i=1, the estimated position and velocity are: Among them, x n,i , v n,i They represent the possible HV position and speed of the i-th vehicle between the n-1th and nth CV trajectories, respectively, and x n-1 , v n-1 , x n , v n denote the known positions and velocities of the n-1th and nth CVs, respectively, N HV That is, the number of possible HVs between the n-1th and nth CV trajectories; Reconstructing human-driven vehicle trajectories: Determine the number of HV tracks N between CV tracks HV , substitute the position and velocity of the first HV into the IDM model and calculate the acceleration of the first HV between the n-1th and nth CV trajectories and close to the n-1th CV The expression of IDM is as follows: Among them, a p (t) is the acceleration of vehicle p at time t, v f is the expected speed, a M is the maximum acceleration, T is the safe headway, b is the absolute value of the comfortable deceleration, s0 is the minimum parking distance, v p (t) is the speed of vehicle p at time t, x p (t) is the position of vehicle p at time t, δ is the acceleration index; According to the estimated speed of the corresponding i-th HV, the estimated acceleration of the HV at each time step division point is calculated again, and the expression is as follows: Under the constraints of limited vehicles, the absolute error is selected as the judgment criterion, and the number of optimal HV trajectories when the error is minimum at time t is as follows: In each reconstruction region, that is, the spatiotemporal region between each pair of CV trajectories, the number of possible HV vehicle trajectories is limited, as shown in the following formula: Among them, [·] represents the rounding function, t i represents time i; After excluding N that do not meet the above constraints HV Then, the number of optimal vehicle trajectories N at each moment is HV (t) Take the mode and get the optimal number of vehicles between two CV trajectories: N HV =mode(N HV (t i ),i=1,2,…); Calculate the position of each HV at each moment, that is, the trajectory of the HV. HV After determination, it is substituted into the HV position and speed estimation formula to update the estimated speed of the HV and used as one of the inputs of the IDM to re-estimate the inter-vehicle distance Δx of the HV at each moment; At any time, the HV needs to maintain a safe distance from the vehicles in front and behind, that is, to meet the following constraints: Among them, l represents the average vehicle length. According to the above calculation formula, the estimated value of HV acceleration at the current moment is obtained: And use IDM to calculate N HV Acceleration of a HV The calculation formula of the acceleration error with HV is: The final vehicle trajectory is: x n,i =x n,i-1 -Δx ni ,i=0,1,…,N HV，n Among them, x n,i That is, the space-time trajectory of the i-th possible HV between the n-1th and nth CVs.

8. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 1 is characterized in that: Step 4 specifically includes: Using the MPC prediction module, the travel time t of each social vehicle when it reaches the stop line is estimated based on the movement trajectory of the vehicle in front of the emergency vehicle on the emergency rescue priority lane EV_LANE i , and then calculate the possible travel time TT of the emergency vehicle EV ; The vehicle motion state changes with the traffic lights can be summarized into the following three situations: Case 1: Passing directly without stopping If the leading vehicle in the convoy on the emergency rescue priority lane EV_LANE can pass through the intersection without stopping under the current signal phase, the leading vehicle on the emergency rescue priority lane EV_LANE can be regarded as traveling in a free flow state. The degraded IDM can be used to describe the motion state of the vehicle. The calculation formula of the vehicle acceleration is as follows: In the formula, a i (t) is the acceleration of vehicle i at time t, v i is the speed of vehicle i, v f is the expected speed, A i is the maximum acceleration, δ is the acceleration index; Case 2: Slow down to stop For the leading vehicle that needs to slow down and stop after queuing, it needs to actively slow down and stop in front of the stop line due to the traffic light. The driving process of the vehicle includes three stages: deceleration and stopping, queuing and waiting, and starting with the green light. In the deceleration and stopping stage, the leading vehicle is equivalent to following the virtual vehicle that is stationary on the stop line of the intersection. The vehicle driving behavior obeys the IDM until it stops safely in front of the stop line. Situation 3: Green light on At this time, the first private vehicle in front of the stop line of the emergency rescue priority lane EV_LANE actively accelerates and starts, and the subsequent vehicles gradually start following the front vehicle. At this stage, since the starting lead vehicle is basically not affected by other vehicles, it is regarded as a free driving state starting from a standstill.

9. The hybrid network environment emergency vehicle time-space right-of-way collaborative priority control method according to claim 8 is characterized in that: According to the travel time prediction results TT EV , use the MPC control module to adjust the signal corresponding to the current lane and issue a recommended speed to all vehicles in the lane: First, the vehicle dynamics equation is used to describe the operating state of the emergency rescue vehicle. The vehicle dynamics equation is as follows: In t+1 =V t +a t ×b Where b represents the sampling time interval, X t represents the vehicle position at time t, X t+1 represents the vehicle position at time t+1, a t represents the vehicle acceleration at time t, V t represents the vehicle speed at time t; then the operating state space equation of the emergency vehicle at time t is expressed as: The acceleration of the emergency vehicle is t is the control variable, position X t Speed ​​V t are state variables respectively. Considering that the primary task of emergency rescue is to ensure the efficiency of emergency rescue, the optimization equation of the operating state of the emergency rescue vehicle is: Where t0 represents the initial time of vehicle running state prediction, and the initial speed of the emergency vehicle is close to the free flow speed V free a min and a max are the maximum deceleration and maximum acceleration of the emergency rescue vehicle, t SrI The safety response time interval of the emergency rescue vehicle is a state variable [X t V t ] T With the control variable a t Merge and construct the variable y′, which has the following specific form: Where c represents the number of predictions, and its specific calculation formula is as follows: p k is the prediction time domain, q is the control time domain, is the rounding symbol, ΔT represents the maximum control time domain of the emergency vehicle, and p n Represents the last prediction time domain before the emergency rescue vehicle passes the stop line of the intersection. The control time domain is set to 5s. Combining the variable y′ with the spatial state equation of the emergency vehicle's operating state, the above formula is converted into a quadratic programming function as shown below: The values ​​of the matrix H′ are: h′∈R (2c+2)×(2c+2) y′ min The values ​​are: and' min =[0,…,P r+1 ,0,a min ,…,to min ] T ,and' min ∈R 1×(3c+2) y′ max The values ​​are: and' max =[yy′,a max ,...,to max ] T ,and' max ∈R 1×(3c+2) yy′=[+∞,V free ,…,+∞,V free ] T ,yy′∈R 1×(2c+2) The value of the matrix Aeq′ is: Aeq′=[A′C′],Aeq′∈R 2c×(3c+2) A′∈R 2c×(2c+2) C′∈R 2c×c The values ​​of the matrix beq′ are: beq′=[0,0,...,0],beq′∈R 1×2c The active set method (ASM) is used to solve the quadratic programming function to obtain the state variables [X t V t ] T With the control variable a t , then, based on the state variable [X t V t ] T Determine the corresponding signal priority duration and phase recovery timing, and use the control variable a t Determine the recommended speed V for the emergency vehicle adv , to achieve intersection speed guidance for emergency vehicles: In adv =a t ×q; Among them, V adv is the recommended speed for emergency vehicles, a t represents the vehicle acceleration at time t, and q represents the control time domain.

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