Emergency vehicle ramp confluence zone priority passing method and system and application

Abstract

The invention belongs to the technical field of traffic transportation management, and particularly relates to an emergency vehicle ramp confluence zone priority passing method and system and application. According to the specific technical scheme, firstly, a dynamic iteration grouping algorithm is applied to group vehicles in a cooperative control area and enumerate a possible vehicle merging sequence, then an optimization function is constructed, and an optimal vehicle merging sequence is selected by using an iteration algorithm; and finally, longitudinal following of the manually-driven vehicle in the control area is controlled according to the optimal merging sequence. According to the method, on the premise that priority passing of the emergency vehicles in the ramp confluence area is guaranteed, the passing efficiency and the fuel economy of the whole traffic flow are considered, and an effective solution is provided for priority passing of the emergency vehicles in the ramp confluence area under the condition of mixed traffic flow.

Description

Technical Field

[0001] The present invention belongs to the technical field of transportation management, and in particular relates to a method, system and application for giving priority to emergency vehicles in a ramp merging area. Background Art

[0002] In traditional traffic management, emergency vehicle priority control in ramp merging areas typically relies on coercive measures, such as traffic light control and physical barriers. While these control measures can guarantee priority for emergency vehicles, they inevitably lead to a significant reduction in the efficiency of public vehicle traffic and may even cause localized traffic congestion. Furthermore, current methods for merging ramps based on mathematical optimization suffer from high computational complexity, while reinforcement learning methods, while more adaptable, suffer from poor interpretability and high data dependency. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides a method for controlling priority passage in the ramp merging area of ​​emergency vehicles based on dynamic iterative grouping. First, a dynamic iterative grouping algorithm is applied to group the vehicles in the collaborative control area and enumerate the possible vehicle merging orders. Then, an optimization function is constructed and the optimal vehicle merging sequence is selected using an iterative algorithm. Finally, the longitudinal following of manually driven vehicles in the control area is controlled according to the optimal merging order.

[0004] To achieve the above-mentioned object, the present invention discloses a method for giving emergency vehicles priority passage in a ramp merging area, characterized in that: the outermost lane of the main road within the ramp merging area is determined as a CAV-only lane, wherein the CAV-only lane allows the passage of connected autonomous vehicles and emergency vehicles; and the main road CAV-only lane and an area on the ramp with a length L from the merging point are determined as a collaborative control area;

[0005] When the emergency vehicle enters the coordinated control area, the optimization process begins as follows:

[0006] S1. Group vehicles within the collaborative control area based on a dynamic iterative grouping algorithm: Initial vehicle groups are formed by distinguishing lanes and vehicle types. Further grouping is performed based on the initial headway threshold. Final vehicle groups are formed by iterative calculations using dynamically adjusted headway thresholds. All possible vehicle merging sequences for the vehicle groups are enumerated.

[0007] S2. Build an optimization model and convert it into an iterative algorithm to select the optimal vehicle merging sequence: Calculate the expected time for vehicles to arrive at the merging point and screen the vehicle merging sequence based on the uncontrolled driving conditions of manually driven vehicles. With the goal of minimizing the expected time for the emergency vehicle to arrive at the merging point and the expected time for all vehicles to arrive at the merging point, select the vehicle merging sequence with the smallest objective function value.

[0008] S3. Control the connected autonomous vehicles in the collaborative control area according to the optimal merging order: Based on the expected time for the vehicles to reach the merging point, with the goal of minimizing the acceleration change, optimize the vehicle speed value at each time step to control the connected autonomous vehicles in the collaborative control area.

[0009] Preferably, the vehicle types include connected autonomous driving vehicles, emergency vehicles and manually driven vehicles.

[0010] Preferably, the step S1 includes:

[0011] S11. Classify vehicles in the coordinated control area into main road vehicles and ramp vehicles according to lane type, and sort the vehicles according to their distance from the merging point;

[0012] S12. Grouping based on vehicle type: Connected autonomous vehicles and emergency vehicles are grouped consecutively relative to the preceding vehicle, and manually driven vehicles are grouped independently relative to the preceding vehicle;

[0013] S13, based on the headway time, group the main road and ramp vehicles: set the initial threshold of the safe headway time for vehicles in the same lane, if the number of groups M ≤ the maximum number of groups M max , the grouping is completed; otherwise, the headway grouping threshold will be gradually increased with a step size of T2, and the vehicles will be regrouped through iterative optimization until the number of groups M ≤ the maximum number of groups M max , the grouping is completed.

[0014] Preferably, in step S13, the initial threshold of the safety time interval is 1.5 seconds, and the step length T2 is 0.1 seconds.

[0015] Preferably, in step S2, the objective function J1 of the vehicle merging order optimization algorithm is as follows:

[0016]

[0017] Among them, t assign_emv is the expected time for the emergency vehicle to arrive at the merging point; t assign_i is the expected time for vehicle i to reach the merging point; n is the total number of vehicles; t min_i is the shortest time for all vehicles i to reach the merging point, that is, the time required for the vehicle to accelerate to the maximum speed and then maintain the maximum speed to reach the merging point; ω1, ω2, ω3 are the weight parameters of the objective function J1;

[0018] The constraints of the objective function J1 are as follows:

[0019]

[0020] Among them, v min_iis the minimum speed of vehicle i; v max_i is the maximum speed of vehicle i; a min_i is the maximum deceleration of vehicle i; a max_i is the maximum acceleration of vehicle i;

[0021] Ensure that the expected time for vehicle i to reach the merging point is not less than the shortest time to reach the merging point;

[0022]

[0023] When vehicle i and vehicle i+1 are adjacent vehicles in the same lane, the expected time for the two vehicles to reach the merging point must maintain a minimum interval Δt1;

[0024]

[0025] When vehicles i and j are adjacent vehicles in different lanes, the expected time for the two vehicles to reach the merging point must maintain a minimum interval Δt2;

[0026]

[0027] or,

[0028]

[0029] n1 and n2 are the total number of vehicles in the collaborative control area on the main road and ramp, respectively.

[0030] Preferably, in step S2, the shortest time for a vehicle to reach the merging point is calculated as follows:

[0031] t min =t0+t1+t2

[0032]

[0033] Where t0 is the time when the vehicle enters the cooperative control area; t1 is the time when the vehicle accelerates at the maximum acceleration; t2 is the time when the vehicle continues to travel at a constant speed after reaching the maximum speed; v0 is the initial speed of the vehicle; a max is the maximum acceleration of the vehicle; x0 is the distance between the current position of the vehicle and the merging point; v max is the maximum speed of the vehicle.

[0034] Preferably, in step S3, the objective function J2 is to minimize the change in vehicle acceleration from the current moment to the merging point, and the calculation formula is as follows:

[0035]

[0036] Among them, a i(t) is the acceleration of vehicle i at time t;

[0037] The constraints of the objective function J2 are as follows:

[0038]

[0039] Where T0 is the time step of simulation control; v i (t) is the speed of vehicle i at time t, v i (t+T0) is the speed of vehicle i at time t+T0; x i (t) is the distance between vehicle i and the merging point at time t, x i (t+T0) is the distance between vehicle i and the merging point at time t+T0;

[0040] The terminal constraints of the vehicle are as follows: the vehicle is required to arrive at the expected time t assign_i Arriving at the ramp merging point, with the acceleration at arrival being 0;

[0041]

[0042] Correspondingly, the present invention also discloses a computer device / equipment / system, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0043] Correspondingly, the present invention also discloses a computer-readable storage medium having a computer program / instruction stored thereon, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0044] Correspondingly, the present invention also discloses a computer program product, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The method of the present invention effectively reduces the complexity of enumerating the order of merging vehicles through a dynamic iterative grouping algorithm, improving computational efficiency. It selects the optimal vehicle order by minimizing the objective function, reducing the negative impact on social vehicles while ensuring priority passage for emergency vehicles. It also optimizes the speed of CAV vehicles with the goal of minimizing acceleration change, reducing energy consumption. While ensuring priority passage for emergency vehicles in ramp merging areas, this method takes into account the overall traffic flow efficiency and fuel economy, providing an effective solution for emergency vehicle ramp merging areas under mixed traffic flow conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1Schematic diagram of a scenario in which emergency vehicles have priority access to a ramp merging area according to the present invention;

[0048] Figure 2 This is a flow chart of the dynamic iterative grouping algorithm of the present invention;

[0049] Figure 3 This is a flow chart of the emergency vehicle ramp merging area priority control method of the present invention, in which n represents the merging order of the nth vehicle;

[0050] Figure 4 This is an example scene diagram of the grouping algorithm of the present invention;

[0051] Figure 5 This is a schematic diagram of the grouping results by vehicle position according to the present invention;

[0052] Figure 6 This is a schematic diagram of the grouping results by vehicle type according to the present invention;

[0053] Figure 7 This is a schematic diagram of the iterative grouping results according to the dynamic headway of the present invention;

[0054] Figure 8 The present invention enumerates the order of vehicle merging based on grouping. DETAILED DESCRIPTION

[0055] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0056] It should be noted that CAV is a connected autonomous vehicle, EMV is an emergency vehicle, and HDV is a manually driven vehicle.

[0057] like Figure 2 、 3 As shown, the present invention discloses a method for controlling the priority passage of emergency vehicles in ramp merging areas based on dynamic iterative grouping, and studies the problem of controlling the priority passage of emergency vehicles in ramp merging areas considering networked autonomous driving lanes under mixed traffic flow conditions. The research scenario is the ramp merging area of ​​an urban expressway, in which the outermost lane of the main road is a CAV lane, allowing CAV vehicles and emergency vehicles to pass. Assume that lane changes are not allowed in the outermost lane of the main road in the ramp merging area, and the collaborative control area is the area on the main road CAV lane and the ramp with a length of L from the merging point. By optimizing the merging order of vehicles numbered V∈{V1,V2,V3,…} in the control area, the optimal merging sequence that meets the priority passage requirements of emergency vehicles is obtained, and the speed of CAV vehicles in the control area is adjusted accordingly, as shown in the schematic diagram. Figure 1 shown.

[0058] When the emergency vehicle enters the coordinated control area, the optimization process begins as follows:

[0059] S1. Grouping vehicles within the collaborative control area based on a dynamic iterative grouping algorithm: Initial grouping of vehicles within the collaborative control area is formed by distinguishing lanes and vehicle types. Further grouping is performed based on the initial headway threshold. Final vehicle grouping is formed by iterative calculation using dynamically adjusted headway thresholds. All possible vehicle merging sequences for the vehicle groupings are enumerated, simplifying the complexity of enumerating the order of merging vehicles.

[0060] The dynamic iterative grouping optimization method proposed in this invention aims to improve computational efficiency by reasonably narrowing the search space, while ensuring that the grouping scheme can adapt to the complex scenario of different types of vehicles mixed in mixed traffic flow. The core idea is to use vehicle type and headway between vehicles as grouping conditions, and plan the vehicles in the same group as a whole, so that the algorithm can find the suboptimal vehicle merging order in a subset of the search space, rather than traversing all vehicle merging orders. This dynamic and adaptive grouping mechanism can reduce computational complexity as much as possible while ensuring a better grouping scheme. The flowchart of the specific algorithm is as follows. Figure 2 shown.

[0061] S2. Build an optimization model and convert it into an iterative algorithm to select the optimal vehicle merging sequence: Calculate the expected time for vehicles to arrive at the merging point, and screen the vehicle merging sequence based on the uncontrolled driving conditions of manually driven vehicles. With the goal of minimizing the expected time for emergency vehicles to arrive at the merging point and the expected time for all vehicles to arrive at the merging point, select the vehicle merging sequence with the smallest corresponding objective function value, and use the corresponding expected vehicle arrival time as the basis for further control.

[0062] S3. Control the connected autonomous vehicles in the collaborative control area according to the optimal merging order: Based on the expected time for the vehicles to reach the merging point, with the goal of minimizing the acceleration change, optimize the vehicle speed value at each time step to control the connected autonomous vehicles in the collaborative control area.

[0063] Through the above method, the original problem of emergency vehicle ramp merging area priority control under complex mixed traffic flow conditions is transformed into a problem of optimal selection through objective function value in a finite number of enumerations.

[0064] In a further embodiment, step S1 includes:

[0065] S11. Classify vehicles in the coordinated control area into main road vehicles and ramp vehicles based on lane type. Sort vehicles by distance from the merging point. Because manually driven vehicles in mixed traffic flow environments are uncontrolled, further grouping based on vehicle type is required.

[0066] S12. Grouping based on vehicle type: Connected autonomous vehicles and emergency vehicles with cooperative driving capabilities are grouped continuously relative to the preceding vehicle, and manually driven vehicles are grouped independently relative to the preceding vehicle, as shown in Table 1.

[0067] Table 1 Grouping logic based on vehicle type

[0068]

[0069] S13, based on the headway time, group the main road and ramp vehicles: set the initial threshold of the safe headway time for vehicles in the same lane, if the number of groups M ≤ the maximum number of groups M max , the grouping is completed; otherwise, the headway grouping threshold will be gradually increased with a step size of T2, and the vehicles will be regrouped through iterative optimization until the number of groups M ≤ the maximum number of groups M max In a preferred embodiment, in step S13, the initial threshold of the safe headway is set to 1.5 seconds, the step length T2 is 0.1 seconds, and the maximum number of groups is set to 6 groups per lane.

[0070] This dynamic iterative grouping algorithm searches for a suboptimal vehicle merging order within a subset of the search space. If the maximum number of groups is c, and there are n vehicles, the algorithm's time complexity is optimized from the factorial level of a traditional full permutation algorithm (O(n!)) to the group-combination level (O(c! n)). This effectively avoids the exponential growth of computing resources, significantly reducing computational time when the number of vehicles n is large in high-density traffic scenarios.

[0071] To improve the efficiency of emergency vehicles in ramp merging areas, the present invention proposes a vehicle merging order optimization algorithm. By converting the constructed optimization model into an iterative algorithm, the expected time for each vehicle to arrive at the merging point is calculated, thereby determining the optimal vehicle merging order. The objective function of the vehicle merging order optimization algorithm consists of three parts: the expected time for the emergency vehicle to arrive at the merging point, the maximum expected time for all vehicles to arrive at the merging point, and the sum of the difference between the expected time for all vehicles to arrive at the merging point and the fastest arrival time. The three are weighted to obtain the objective function J1, where the first part of the objective function J1 ensures priority passage for emergency vehicles, and the second and third parts ensure that all vehicles have the shortest passing time.

[0072] The objective function J1 is expressed as follows:

[0073]

[0074] Among them, t assign_emv is the expected time for the emergency vehicle to arrive at the merging point; t assign_iis the expected time for vehicle i to reach the merging point; n is the total number of vehicles; t min_i is the shortest time for all vehicles i to reach the merging point, that is, the time required for the vehicle to accelerate to the maximum speed and then maintain the maximum speed to reach the merging point; ω1, ω2, ω3 are the weight parameters of the objective function J1;

[0075] The vehicle speed and acceleration constraints of the objective function J1 are as follows:

[0076]

[0077] Among them, v min_i is the minimum speed of vehicle i; v max_i is the maximum speed of vehicle i; a min_i is the maximum deceleration of vehicle i; a max_i is the maximum acceleration of vehicle i.

[0078] Ensure that the expected time for vehicle i to reach the merging point is not less than the shortest time to reach the merging point:

[0079]

[0080] When vehicles i and i+1 are adjacent vehicles in the same lane, the following constraints must be met to avoid collisions during following. That is, if both vehicles are on the main road or on the ramp, the expected time between the two vehicles at the merging point must maintain a minimum interval Δt1:

[0081]

[0082] Where Δt1 is the minimum time interval that needs to be maintained between two adjacent vehicles in the same lane; i and i+1 represent vehicles on the main road or ramp; and n is the total number of vehicles on the main road or ramp within the control area.

[0083] When vehicles i and j are adjacent vehicles in different lanes, the following constraints must be met to avoid collisions when merging. That is, when one vehicle is a main road vehicle and the other is a ramp vehicle, the expected time between the two vehicles at the merging point must maintain a minimum interval Δt2:

[0084]

[0085] or,

[0086]

[0087] Among them, Δt2 is the minimum time interval that two vehicles in different lanes need to maintain; n1 and n2 are the total number of vehicles in the coordinated control area on the main road and ramp, respectively.

[0088] In a further embodiment, the shortest time for a vehicle to reach the merging point is calculated as follows:

[0089] t min =t0+t1+t2

[0090]

[0091] Where t0 is the time when the vehicle enters the cooperative control area; t1 is the time when the vehicle accelerates at the maximum acceleration; t2 is the time when the vehicle continues to travel at a constant speed after reaching the maximum speed; v0 is the initial speed of the vehicle; a max is the maximum acceleration of the vehicle; x0 is the distance between the current position of the vehicle and the merging point; v max is the maximum speed of the vehicle.

[0092] Since the outermost lane of the main road in the emergency vehicle ramp merging area priority passage scenario considered by the method of the present invention does not allow lane changes, there is no need for overtaking or lane changing for the vehicle, and only longitudinal control of the vehicle is required. Regarding the longitudinal control problem of the vehicle, the optimization goal is to minimize the change in vehicle acceleration (the square of the vehicle acceleration) during the process of the vehicle traveling from the current moment to the merging point. This objective function can reduce the frequent acceleration / deceleration behavior of the vehicle, reduce the jerk value of the vehicle's longitudinal movement, achieve smooth control of the vehicle, and improve the vehicle's fuel economy.

[0093] The objective function J2 is to minimize the change in vehicle acceleration from the current moment to the merging point. The calculation formula is as follows:

[0094]

[0095] Among them, a i (t) is the acceleration of vehicle i at time t.

[0096] The vehicle dynamics constraints of the objective function J2 are as follows:

[0097]

[0098] Where T0 is the time step of simulation control; v i (t) is the speed of vehicle i at time t, v i (t+T0) is the speed of vehicle i at time t+T0; x i (t) is the distance between vehicle i and the merging point at time t, x i (t+T0) is the distance between vehicle i and the merging point at time t+T0.

[0099] The terminal constraints of the vehicle are as follows: the vehicle is required to arrive at the expected time t assign_i Arriving at the ramp merging point, with an acceleration of 0:

[0100]

[0101] The vehicle motion planning of the present invention is a convex quadratic programming problem whose constraints are all linear expressions. It can be solved directly by calling the optimize module of the SciPy library, which not only ensures the global optimality of the solution but also meets the real-time computing requirements of vehicle control.

[0102] Example 1 Dynamic Iterative Grouping Algorithm

[0103] To further explain the grouping process and the advantages of using dynamic iterative grouping, Figure 4 The typical scenario shown is used as an example for explanation.

[0104] The initial state of the example scenario is as follows: there are 7 vehicles in total, including 5 CAVs, 1 EMV, and 1 HDV, and the vehicles are dynamically iteratively grouped.

[0105] (1) First, the vehicles are initially grouped according to their positions, such as Figure 5 As shown, the main road vehicles are grouped into Group 1 {CAV1, CAV2, EMV, CAV3}, and the ramp vehicles are grouped into Group 2 {CAV4, HDV1, CAV5};

[0106] (2) Then group the vehicle types according to the grouping rules shown in Table 1, such as Figure 6 As shown, the main road vehicles are organized into one group, Group 1 {CAV1, CAV2, EMV, CAV3}, and the ramp vehicles are organized into two groups: Group 2 {CAV4} and Group 3 {HDV1, CAV5};

[0107] (3) Finally, dynamic iterative grouping is performed based on the headway, such as Figure 7 As shown, the main road vehicles are divided into two groups: Group 1 {CAV1}, Group 2 {CAV2, EMV, CAV3}, and the ramp vehicles are divided into three groups: Group 3 {CAV4}, Group 4 {HDV1}, Group 4 {CAV5}. The grouping is completed until the grouping result meets the maximum grouping limit.

[0108] This dynamic grouping mechanism not only meets the reality of the uncontrollable HDV in mixed traffic flow scenarios, but also maintains the continuity of the driving of similar vehicles and reduces the interlaced traffic in the ramp merging area.

[0109] After the dynamic iterative grouping algorithm, the seven vehicles were divided into five groups. Since vehicles in the same lane cannot overtake, the merging order between vehicles in the same lane is fixed, and the vehicles in the same group are considered as a whole when sorting. The following lists the merging order of all vehicles in groups, and the passing order of the grouped vehicles is used as the passing order of all vehicles, as shown below. Figure 8 Compared to the case where each vehicle is independent, this approach reduces the number of possible vehicle merging sequences that need to be enumerated from 7! (5040) to 10. Research has shown that if the grouping threshold is appropriately chosen, the suboptimal vehicle merging sequence found by the grouping-based strategy is highly likely to be close to the global optimal vehicle merging sequence.

[0110] Example 2 Vehicle Merging Order Optimization Algorithm Solution

[0111] The vehicle merging order optimization algorithm of the present invention is an optimization problem. However, if the merging order of the vehicles is given, the above optimization problem can be solved by a simple iterative algorithm, as shown in Table 2. Algorithm 1 is an iterative algorithm for calculating the objective function value and vehicle arrival time corresponding to a certain vehicle merging order. The input of the algorithm includes: a certain vehicle order passing through the ramp merging area, the shortest vehicle arrival time t corresponding to the merging order, and the vehicle arrival time t corresponding to the vehicle merging order. min The output of the algorithm includes: a Flag indicating whether the merging order of the vehicles meets the requirements, the objective function value J1, and the expected arrival time t of each vehicle at the merging point. assign .

[0112] Table 2 Iterative calculation of vehicle arrival time

[0113]

[0114]

[0115]

[0116] Among them, t(0) assign is the expected time for the initial vehicle to reach the merging point; t(g) assign is the expected time for the g-th updated vehicle to arrive at the merging point; flag(g) is a binary variable, and the calculation formula is as follows, which represents the comparison of the expected time for the g-th and g-1-th vehicles to arrive at the merging point. If there is a t(g) for vehicle i assign_i Less than t(g-1) assign_i , then flag(g) is 0, otherwise it is 1.

[0117]

[0118] The algorithm can be divided into three computational stages:

[0119] (1) First, the expected arrival time t of the vehicle assign Initialize and use different vehicle intervals for each vehicle in the vehicle merging order according to the lane (main road / ramp) to calculate t(0) assign ;

[0120] (2) Then, the expected arrival time is updated based on the initial expected arrival time. Since HDV vehicles are different from CAV vehicles and are not controlled by the Internet of Vehicles technology, they will not be updated according to the calculated t assign To merge, the expected arrival time of each HDV needs to be updated to its shortest arrival time t min At the same time, each update of an HDV requires updating the expected arrival time of the vehicles behind it in the vehicle merging order, and obtaining t(g) for each update assign , and then t(g) assign and t(g-1) assign Compare and judge t(g) assign Is there any HDV vehicle whose expected arrival time is less than t(g-1)? assign The expected arrival time of the vehicle in the , if it exists, means that the merging order of the HDV vehicle should be advanced, and the merging order of the vehicle does not meet the requirements;

[0121] (3) Finally, the objective function value J1 is calculated for the vehicle merging order that meets the requirements, and the following is returned: the Flag representing whether the vehicle merging order meets the requirements, the objective function value J1, and the expected arrival time t of the vehicle. assign .

[0122] In particular, the HDV's minimum arrival time must be updated throughout the algorithm based on the expected arrival time of the preceding vehicle in the same lane. This is because when the preceding vehicle in the same lane decelerates, the HDV will also decelerate according to the car-following model, causing its minimum arrival time to change. Therefore, the HDV's minimum arrival time must be continuously updated to accurately reflect its actual movement within the traffic flow.

[0123] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various deformations, modifications, and substitutions made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. A method for giving emergency vehicles priority in ramp merging areas, characterized by: The outermost lane of the main road within the ramp merging area is designated as a CAV lane, which allows the passage of connected autonomous vehicles and emergency vehicles. The CAV lane of the main road and the area on the ramp with a length L from the merging point are designated as the collaborative control area. When the emergency vehicle enters the coordinated control area, the optimization process begins as follows: S1. Group vehicles within the collaborative control area based on a dynamic iterative grouping algorithm: Initial vehicle groups are formed by distinguishing lanes and vehicle types. Further grouping is performed based on the initial headway threshold. Final vehicle groups are formed by iterative calculations using dynamically adjusted headway thresholds. All possible vehicle merging sequences for the vehicle groups are enumerated. S2. Build an optimization model and convert it into an iterative algorithm to select the optimal vehicle merging sequence: Calculate the expected time for vehicles to arrive at the merging point and screen the vehicle merging sequence based on the uncontrolled driving conditions of manually driven vehicles. With the goal of minimizing the expected time for the emergency vehicle to arrive at the merging point and the expected time for all vehicles to arrive at the merging point, select the vehicle merging sequence with the smallest objective function value. S3. Control the connected autonomous vehicles in the collaborative control area according to the optimal merging order: Based on the expected time for the vehicles to reach the merging point, with the goal of minimizing the acceleration change, optimize the vehicle speed value at each time step to control the connected autonomous vehicles in the collaborative control area.

2. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 1, characterized in that: The vehicle types include connected autonomous vehicles, emergency vehicles and manually driven vehicles.

3. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 2, characterized in that: The step S1 comprises: S11. Classify vehicles in the coordinated control area into main road vehicles and ramp vehicles according to lane type, and sort the vehicles according to their distance from the merging point; S12. Grouping based on vehicle type: Connected autonomous vehicles and emergency vehicles are grouped consecutively relative to the preceding vehicle, and manually driven vehicles are grouped independently relative to the preceding vehicle; S13, based on the headway time, group the main road and ramp vehicles: set the initial threshold of the safe headway time for vehicles in the same lane, if the number of groups M ≤ the maximum number of groups M max , the grouping is completed; otherwise, the headway grouping threshold will be gradually increased with a step size of T2, and the vehicles will be regrouped through iterative optimization until the number of groups M ≤ the maximum number of groups M max , the grouping is completed.

4. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 3, characterized in that: In step S13, the initial threshold of the safety time interval is 1.5 seconds, and the step length T2 is 0.1 seconds.

5. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 3, characterized in that: In step S2, the objective function J1 of the vehicle merging order optimization algorithm is as follows: Among them, t assign_emv is the expected time for the emergency vehicle to arrive at the merging point; t assign_i is the expected time for vehicle i to reach the merging point; n is the total number of vehicles; t min_i is the shortest time for all vehicles i to reach the merging point, that is, the time required for the vehicle to accelerate to the maximum speed and then maintain the maximum speed to reach the merging point; ω1, ω2, ω3 are the weight parameters of the objective function J1; The constraints of the objective function J1 are as follows: Among them, v min_i is the minimum speed of vehicle i; v max_i is the maximum speed of vehicle i; a min_i is the maximum deceleration of vehicle i; a max_i is the maximum acceleration of vehicle i; Ensure that the expected time for vehicle i to reach the merging point is not less than the shortest time to reach the merging point; When vehicle i and vehicle i+1 are adjacent vehicles in the same lane, the expected time for the two vehicles to reach the merging point must maintain a minimum interval Δt1; When vehicles i and j are adjacent vehicles in different lanes, the expected time for the two vehicles to reach the merging point must maintain a minimum interval Δt2; or, n1 and n2 are the total number of vehicles in the collaborative control area on the main road and ramp, respectively.

6. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 5, characterized in that: In step S2, the shortest time for a vehicle to reach the merging point is calculated as follows: t min =t0+t1+t2 Where t0 is the time when the vehicle enters the cooperative control area; t1 is the time when the vehicle accelerates at the maximum acceleration; t2 is the time when the vehicle continues to travel at a constant speed after reaching the maximum speed; v0 is the initial speed of the vehicle; a max is the maximum acceleration of the vehicle; x0 is the distance between the current position of the vehicle and the merging point; v max is the maximum speed of the vehicle.

7. The method for giving emergency vehicles priority passage in a ramp merging area according to claim 6, characterized in that: In step S3, the objective function J2 is to minimize the change in vehicle acceleration from the current moment to the merging point, and the calculation formula is as follows: Among them, a i (t) is the acceleration of vehicle i at time t; The constraints of the objective function J2 are as follows: Where T0 is the time step of simulation control; v i (t) is the speed of vehicle i at time t, v i (t+T0) is the speed of vehicle i at time t+T0; x i (t) is the distance between vehicle i and the merging point at time t, x i (t+T0) is the distance between vehicle i and the merging point at time t+T0; The terminal constraints of the vehicle are as follows: the vehicle is required to arrive at the expected time t assign_i Arriving at the ramp merging point, with the acceleration at arrival being 0; 8. A computer device / equipment / system, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program / instruction is stored thereon, and when the computer program / instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that: The method comprises a computer program / instruction, which implements the steps of the method according to any one of claims 1 to 7 when the computer program / instruction is executed by a processor.

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