Emergency vehicle priority passage methods, systems and applications in merging areas

Abstract

This invention belongs to the field of transportation management technology, specifically relating to a method, system, and application for prioritizing emergency vehicle merging at ramp merging areas. The specific technical solution involves: first, applying a dynamic iterative grouping algorithm to group vehicles within the cooperative control area and enumerating possible vehicle merging orders; then, constructing an optimization function and using an iterative algorithm to select the optimal vehicle merging sequence; finally, controlling the longitudinal following of manually driven vehicles within the control area according to the optimal merging order. This method, while ensuring priority passage for emergency vehicles at ramp merging areas, also considers the overall traffic flow efficiency and fuel economy, providing an effective solution for prioritizing emergency vehicle merging at ramp merging areas under mixed traffic flow conditions.

Description

Technical Field

[0001] This invention belongs to the field of transportation management technology, specifically relating to the method, system and application of priority passage for emergency vehicles in merging ramp areas. Background Technology

[0002] In traditional traffic management, priority passage for emergency vehicles in merging ramp areas is typically controlled through coercive measures such as traffic lights and physical barriers. While these measures ensure the right-of-way for emergency vehicles, they inevitably lead to a significant reduction in the efficiency of other vehicles and may even cause localized traffic congestion. Furthermore, current merging decision-making methods for merging ramps suffer from high computational complexity, particularly mathematical optimization methods, while reinforcement learning methods, although more adaptable, exhibit poor interpretability and high data dependency. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a priority passage control method for emergency vehicle ramp merging areas based on dynamic iterative grouping. First, a dynamic iterative grouping algorithm is applied to group vehicles within the cooperative control area and enumerate possible vehicle merging orders. Then, an optimization function is constructed and an iterative algorithm is used to select the optimal vehicle merging sequence. Finally, the longitudinal following of manually driven vehicles within the control area is controlled according to the optimal merging order.

[0004] To achieve the above-mentioned objectives, this invention discloses a method for priority passage in the merging area of ​​an emergency vehicle ramp, characterized in that: the outermost lane of the main road within the merging area of ​​the ramp is designated as a dedicated lane for CAVs, which allows connected autonomous vehicles and emergency vehicles to pass; and the dedicated lane of the main road and the area on the ramp at a distance L from the merging point are designated as a cooperative control zone.

[0005] Once the emergency vehicle enters the collaborative control zone, optimization should begin following these steps:

[0006] S1. Grouping vehicles in the cooperative control area based on dynamic iterative grouping algorithm: Preliminary grouping of vehicles in the cooperative control area is formed by distinguishing lanes and vehicle types, further grouping is performed according to the initial headway threshold, and the final vehicle grouping is formed by iterative calculation through dynamic adjustment of the headway threshold. All possible vehicle merging sequences of vehicle groups are enumerated.

[0007] S2. Construct an optimization model and transform 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 filter the vehicle merging sequence based on the condition that the manually driven vehicles are out of control. 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.

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

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

[0010] Preferably, 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 continuously relative to the preceding vehicle, while manually driven vehicles are grouped independently relative to the preceding vehicle;

[0013] S13. Grouping vehicles on the main road and ramps based on headway: Set an initial threshold for safe headway for vehicles in the same lane. If the number of groups M ≤ the maximum number of groups M max If the grouping is successful, the grouping is complete; otherwise, the headway grouping threshold will be gradually increased by 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 Then the grouping is complete.

[0014] Preferably, in step S13, the initial threshold for safe time interval is 1.5 seconds, and the step size 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 The expected time for emergency vehicles to arrive at the merging point; t assign_i Let t be the expected time for vehicle i to arrive at the merging point; n is the total number of vehicles; t min_i ω1, ω2, ω3 are the shortest time for all vehicles i to reach the merging point, i.e., the time required for a vehicle to accelerate to its maximum speed and then maintain that maximum speed to reach the merging point; ω1, ω2, ω3 are the weight parameters of the objective function J1.

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

[0019]

[0020] Among them, v min_iv is the minimum speed of vehicle i; max_i a is the maximum speed of vehicle i; min_i a is the maximum deceleration of vehicle i; max_i Let i be 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 vehicle i and vehicle 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 located in the coordinated control zone on the main road and ramps, respectively.

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

[0031] t min =t0+t1+t2

[0032]

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

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

[0035]

[0036] Among them, a i(t) represents 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 the simulation control; v i (t) represents the velocity of vehicle i at time t, v i (t+T0) represents the velocity of vehicle i at time t+T0; x i (t) represents the distance between vehicle i and the merging point at time t, x i (t+T0) represents the distance between vehicle i and the merging point at time t+T0;

[0040] The vehicle's terminal constraint is as follows, requiring the vehicle to arrive at the expected time t. assign_i Upon arrival at the merging point of the ramp, the acceleration is 0.

[0041]

[0042] Accordingly, the present invention also discloses a computer device / equipment / system, including 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] Accordingly, the present invention also discloses a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0044] Accordingly, the present invention also discloses a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method.

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

[0046] The method of this invention effectively reduces the complexity of enumerating the merging vehicle order through a dynamic iterative grouping algorithm, thereby improving computational efficiency. It selects the optimal vehicle order by minimizing the objective function, reducing the negative impact on other vehicles while ensuring priority passage for emergency vehicles. Furthermore, it optimizes the speed of CAV vehicles by minimizing acceleration changes, thus reducing energy consumption. This method, while guaranteeing priority passage for emergency vehicles in the merging ramp area, also considers the overall traffic flow efficiency and fuel economy, providing an effective solution for prioritizing emergency vehicle merging ramp passage under mixed traffic flow conditions. Attached Figure Description

[0047] Figure 1This is a schematic diagram illustrating the priority passage scenario for emergency vehicles in the merging area of ​​the ramps according to the present invention.

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

[0049] Figure 3 This is a flowchart of the emergency vehicle ramp merging zone priority passage control method of the present invention, where n represents the merging order of the nth vehicle;

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

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

[0052] Figure 6 This is a schematic diagram of the grouping results by vehicle type in this invention;

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

[0054] Figure 8 This invention provides an enumeration of the vehicle merging order based on grouping. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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 stands for Connected Autonomous Vehicle, EMV for Emergency Vehicle, and HDV for Manually Driven Vehicle.

[0057] like Figure 2 , 3 As shown, this invention discloses a priority passage control method for emergency vehicle ramp merging areas based on dynamic iterative grouping. It studies the priority passage control problem of emergency vehicle ramp merging areas under mixed traffic flow conditions, considering connected autonomous driving lanes. The research scenario is an urban expressway ramp merging area, where the outermost lane of the main road is a dedicated lane for CAVs, allowing both CAVs and emergency vehicles to pass. It is assumed that lane changing is not allowed in the outermost lane of the main road within the ramp merging area, and the cooperative control area is the main road CAV lane and the area on the ramp with a distance L from the merging point. By optimizing the merging sequence of vehicles numbered V∈{V1,V2,V3,…} within the control area, the optimal merging sequence that satisfies the priority passage requirement of emergency vehicles is obtained, and the speed of CAVs within the control area is adjusted accordingly. A schematic diagram is shown below. Figure 1 As shown.

[0058] Once the emergency vehicle enters the collaborative control zone, optimization should begin following these steps:

[0059] S1. Grouping vehicles in the cooperative control area based on dynamic iterative grouping algorithm: Preliminary grouping of vehicles in the cooperative control area is formed by distinguishing lanes and vehicle types. Further grouping is carried out according to the initial headway threshold. The final vehicle grouping is formed by iterative calculation through dynamic adjustment of the headway threshold. All possible vehicle merging sequences of vehicle groups are enumerated, which simplifies 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 reducing the search space, while ensuring that the grouping scheme can adapt to the complex scenario of mixed traffic flow with different types of vehicles. Its core idea is to use vehicle type and headway between vehicles as grouping conditions, and to plan vehicles in the same group as a whole. This allows the algorithm to find the suboptimal vehicle merging order within a subset of the search space, rather than traversing all vehicle merging orders. This dynamic adaptive grouping mechanism can minimize computational complexity while ensuring a relatively optimal grouping scheme. The flowchart of the specific algorithm is shown below. Figure 2 As shown.

[0061] S2. Construct an optimization model and transform 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 filter the vehicle merging sequence based on the condition that the manually driven vehicles are out of control. 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 expected arrival time of the corresponding vehicles as the basis for further control.

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

[0063] Using the above method, the originally complex problem of priority passage control of emergency vehicle ramp merging area under 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 cooperative 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. Since manually driven vehicles are uncontrolled in the mixed traffic flow environment, 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, while 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. Grouping vehicles on the main road and ramps based on headway: Set an initial threshold for safe headway for vehicles in the same lane. If the number of groups M ≤ the maximum number of groups M max If the grouping is successful, the grouping is complete; otherwise, the headway grouping threshold will be gradually increased by 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 Then the grouping is completed. In a preferred embodiment, in step S13, the initial threshold for safe time distance is set to 1.5 seconds, the step size T2 is 0.1 seconds, and the maximum number of groups is set to 6 groups / 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 in total, the algorithm's time complexity is optimized from factorial level (O(n!)) of traditional full permutations to grouping and combination level (O(c!·n)), effectively avoiding the exponential growth of computing resources. This is especially beneficial in high-density traffic scenarios where the number of vehicles n is large, significantly saving computation time.

[0071] To improve the passage efficiency of emergency vehicles in ramp merging areas, this invention proposes a vehicle merging order optimization algorithm. By transforming the constructed optimization model into an iterative algorithm, the expected arrival time of each vehicle 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 arrival time of emergency vehicles at the merging point, the maximum expected arrival time of all vehicles at the merging point, and the sum of the differences between the expected arrival times of all vehicles and the fastest arrival time. These three parts are weighted to obtain the objective function J1. The first part of objective function J1 ensures priority passage for emergency vehicles, while the second and third parts ensure the shortest passage time for all vehicles.

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

[0073]

[0074] Among them, t assign_emv The expected time for emergency vehicles to arrive at the merging point; t assign_iLet t be the expected time for vehicle i to arrive at the merging point; n is the total number of vehicles; t min_i ω1, ω2, ω3 are the shortest time for all vehicles i to reach the merging point, i.e., the time required for a vehicle to accelerate to its maximum speed and then maintain that 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 for objective function J1 are as follows:

[0076]

[0077] Among them, v min_i v is the minimum speed of vehicle i; max_i a is the maximum speed of vehicle i; min_i a is the maximum deceleration of vehicle i; max_i Let be 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 vehicle i and vehicle i+1 are adjacent vehicles in the same lane, in order to avoid a collision when following each other, the following constraint must be satisfied: if both vehicles are main road vehicles or both are ramp vehicles, then the expected arrival times of the two vehicles at the merging point must maintain a minimum interval Δt1.

[0081]

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

[0083] When vehicles i and j are adjacent vehicles in different lanes, to avoid a collision during merging, the following constraint must be satisfied: if one vehicle is a main road vehicle and the other is a ramp vehicle, then the expected arrival times of the two vehicles at the merging point must maintain a minimum interval Δt2.

[0084]

[0085] or,

[0086]

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

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

[0089] t min =t0+t1+t2

[0090]

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

[0092] Since the method of this invention considers the priority passage scenario of emergency vehicle ramp merging area, where lane changing is not allowed in the outermost lane of the main road, there is no overtaking or lane changing situation, and only longitudinal control of the vehicle is required. For the longitudinal control problem, the optimization objective is to minimize the change in vehicle acceleration (the square of the vehicle's 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 motion, achieve smooth vehicle control, and improve the vehicle's fuel economy.

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

[0094]

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

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

[0097]

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

[0099] The vehicle's terminal constraint is as follows, requiring the vehicle to arrive at the expected time t. assign_i Upon reaching the merging point of the ramp, the acceleration is 0:

[0100]

[0101] The vehicle motion planning of this invention is a convex quadratic programming problem with all constraints being linear expressions. It can be solved directly by calling the optimize module of the SciPy library, which not only guarantees 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 following is an example of a typical scenario for explanation.

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

[0105] (1) First, perform initial grouping according to vehicle location, such as Figure 5 As shown, vehicles on the main road are numbered as Group1 {CAV1, CAV2, EMV, CAV3}, and vehicles on the ramps are numbered as Group2 {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, vehicles on the main road are numbered as one group, Group1 {CAV1, CAV2, EMV, CAV3}, and vehicles on the ramps are numbered as two groups: Group2 {CAV4} and Group3 {HDV1, CAV5}.

[0107] (3) Finally, dynamic iterative grouping is performed based on the headway, such as... Figure 7 As shown, vehicles on the main road are grouped into two groups: Group1{CAV1} and Group2{CAV2, EMV, CAV3}, and vehicles on the ramps are grouped into three groups: Group3{CAV4}, Group4{HDV1}, and Group4{CAV5}, until the grouping results meet the maximum grouping limit, at which point the grouping ends.

[0108] This dynamic grouping mechanism not only satisfies the reality of uncontrollable HDV in mixed traffic flow scenarios, but also maintains the continuity of similar vehicle travel and reduces the intermingling of traffic in the ramp merging zone.

[0109] After a dynamic iterative grouping algorithm, the 7 vehicles were divided into 5 groups. Since overtaking is not allowed in the same lane, the merging order between vehicles in the same lane is fixed, and vehicles in the same group are treated as a single unit during sorting. The following lists the merging order of all vehicles by group, and the passage order of the grouped vehicles is used as the passage order of all vehicles, as follows: Figure 8 As shown. Compared to the case where each vehicle is independent, this method reduces the number of possible passage order combinations from 7! (5040) to 10. Research indicates that, if the grouping threshold is chosen appropriately, the suboptimal vehicle merging order found by the grouping-based strategy has a high probability of approaching the globally optimal vehicle merging order.

[0110] Example 2: Solving the vehicle merging order optimization algorithm

[0111] The vehicle merging order optimization algorithm of this invention is an optimization problem. However, given the vehicle merging order, 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 (Order) passing through the ramp merging zone, and the shortest arrival time (t) of the vehicle corresponding to that merging order. min Given a list P of HDV vehicle numbers, the algorithm outputs: a Flag indicating whether the vehicle merging order 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] Where t(0) assign t(g) represents the initial expected time for the vehicles to arrive at the merging point. assign t(g) represents the expected arrival time of the vehicle at the merging point after the g-th update; flag(g) is a binary variable, calculated as follows, indicating the comparison between the expected arrival times of the vehicle at the merging point after the g-th update and after the (g-1)-th update. If there exists a t(g) for vehicle i... assign_i Less than t(g-1) assign_i If the condition is met, 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 the process and, for each vehicle in the merging sequence, apply different vehicle intervals based on the lane (main road / ramp) it belongs to, and calculate t(0). assign ;

[0120] (2) Then, updates are made based on the initial expected arrival time. Since HDV vehicles are different from CAV vehicles and are not controlled by vehicle networking technology, they will not arrive according to the calculated t. assign To perform the merging, the expected arrival time of each HDV needs to be updated to its shortest arrival time t. min Simultaneously, updating each HDV requires updating the expected arrival time of the vehicles following it in the merge order, thus obtaining t(g) for each update. assign , then t(g) assign and t(g-1) assign Compare and determine t(g). assign Are there any HDV vehicles in the process whose expected arrival time is less than t(g-1)? assign If the expected arrival time of the vehicle exists, it 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, calculate the objective function value J1 for the vehicle merging order that meets the requirements, and return: Flag representing whether the vehicle merging order meets the requirements, objective function value J1, and expected arrival time t of the vehicle. assign .

[0122] Specifically, the shortest arrival time of HDV vehicles needs to be updated throughout the algorithm based on the expected arrival time of the vehicle ahead in the same lane. This is because when the vehicle ahead in the same lane decelerates, the HDV will decelerate according to the car-following model, causing its shortest arrival time at the merging point to change. Therefore, the shortest arrival time of HDVs must be continuously updated to accurately reflect their actual movement state in the traffic flow.

[0123] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, alterations, substitutions, or variations made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention shall fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for prioritizing passage in the merging area of ​​emergency vehicle ramps, characterized in that: The outermost lane of the main road within the merging area of ​​the ramp is designated as a dedicated CAV lane, which allows connected autonomous vehicles and emergency vehicles to pass. The dedicated CAV lane on the main road and the area on the ramp that is a distance L from the merging point are designated as a cooperative control zone. Once the emergency vehicle enters the collaborative control zone, optimization should begin following these steps: S1. Grouping vehicles in the cooperative control area based on dynamic iterative grouping algorithm: Preliminary grouping of vehicles in the cooperative control area is formed by distinguishing lanes and vehicle types, further grouping is performed according to the initial headway threshold, and the final vehicle grouping is formed by iterative calculation through dynamic adjustment of the headway threshold. All possible vehicle merging sequences of vehicle groups are enumerated. S2. Construct an optimization model and transform 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 filter the vehicle merging sequence based on the condition that the manually driven vehicles are out of control. 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. S3. Control the connected autonomous vehicles in the cooperative control area according to the optimal merging order: Based on the expected time for the vehicles to arrive at the merging point, optimize the vehicle speed value at each time step with the goal of minimizing acceleration change, thereby controlling the connected autonomous vehicles in the cooperative control area. In step S2, the objective function of the vehicle merging order optimization algorithm is... as follows: ， in, The expected time for emergency vehicles to arrive at the merging point; Let be the expected time for vehicle i to arrive at the merging point; n is the total number of vehicles. The shortest time for all vehicles i to reach the merging point is the time required for a vehicle to accelerate to its maximum speed and then maintain that maximum speed to reach the merging point. For the objective function Weight parameters; objective function The constraints are as follows: ， ， in, Let be the minimum speed of vehicle i; The maximum speed of vehicle i; Let be the maximum deceleration of vehicle i; Let i be the maximum acceleration of vehicle i; Ensure vehicle The expected time 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 arrival times of the two vehicles at the merging point must maintain the minimum interval. ; ， When vehicle i and vehicle j are adjacent vehicles in different lanes, the expected arrival times of the two vehicles at the merging point must maintain a minimum interval. ; ， or, ， and These represent the total number of vehicles located within the coordinated control zone on the main road and ramps, respectively.

2. The method for prioritizing passage in the merging area of ​​emergency vehicle ramps according to claim 1, characterized in that: The vehicle types include connected autonomous vehicles, emergency vehicles, and manually driven vehicles.

3. The method for prioritizing passage in the merging area of ​​emergency vehicle ramps according to claim 2, characterized in that: Step S1 includes: 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 continuously relative to the preceding vehicle, while manually driven vehicles are grouped independently relative to the preceding vehicle; S13. Grouping vehicles on the main road and ramps based on headway: Set an initial threshold for safe headway for vehicles in the same lane. If the number of groups M ≤ the maximum number of groups M max If the grouping is successful, the grouping is complete; otherwise, the headway grouping threshold will be gradually increased by 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 Then the grouping is complete.

4. The method for prioritizing passage in the merging area of ​​emergency vehicle ramps according to claim 3, characterized in that: In step S13, the initial threshold for safe time interval is 1.5 seconds, and the step size T2 is 0.1 seconds.

5. The method for prioritizing passage in the merging area of ​​emergency vehicle ramps according to claim 4, characterized in that: In step S2, the formula for calculating the shortest time for a vehicle to reach the merging point is as follows: ， ， ， ， in, The time when the vehicle enters the cooperative control zone; The time during which the vehicle accelerates at its maximum acceleration. The time during which the vehicle continues to travel at a constant speed after reaching its maximum speed; The initial speed of the vehicle; This is the vehicle's maximum acceleration; The distance between the vehicle's current position and the merging point; This is the vehicle's maximum speed.

6. The method for prioritizing passage in the merging area of ​​emergency vehicle ramps according to claim 5, characterized in that: In step S3, the objective function is to minimize the change in vehicle acceleration during the process of the vehicle traveling from the current moment to the merging point. The calculation formula is as follows: ， in, Let be the acceleration of vehicle i at time t; objective function The constraints are as follows: ， ， ， ， in, This refers to the time step of the simulation control. Let be the speed of vehicle i at time t. Let be the speed of vehicle i at time t+T0; Let be the distance between vehicle i and the merging point at time t. Let be the distance between vehicle i and the merging point at time t+T0; The vehicle's terminal constraint is as follows, requiring the vehicle to arrive at the expected time. Upon arrival at the merging point of the ramp, the acceleration is 0. ， 。 7. A computer device, characterized in that: It includes 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 as claimed in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: It stores a computer program / instruction thereon, which, when executed by a processor, implements the steps of the method as described in any one of claims 1-6.

9. A computer program product, characterized in that: Includes a computer program / instructions that, when executed by a processor, implement the steps of the method as described in any one of claims 1-6.

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