Emergency vehicle pre-emptying method suitable for large-flow highway traffic cloud control

Abstract

The invention discloses an emergency vehicle pre-emptying method suitable for large-flow highway traffic cloud control, and relates to the technical field of highway management and control. The method comprises the steps that a frame of an emergency lane pre-emptying double-layer model is adopted, a lane changing vehicle and the position of each step length of the lane changing vehicle are obtained through a lower-layer model and transmitted to an upper-layer model, and then the upper-layer model is solved to obtain the lane changing starting position of a vehicle in front of the emergency vehicle. A lower-layer model of the double-layer model obtains an optimal lane changing track by optimizing the lane changing track of the front vehicle to achieve the minimum cost of the space of the lane where the emergency vehicle is located as soon as possible; the lower-layer model is solved based on an improved A * algorithm so as to improve the calculation efficiency; the upper-layer model serves as a bounded convex function to obtain an optimal solution of the double-layer model. In this way, in the process that the emergency vehicle does not change the lane and runs at the speed close to the expected speed, the passing efficiency loss of the front vehicle is reduced to the minimum through lane changing, and the influence of the emergency vehicle on front traffic is minimum.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway management and control, and in particular relates to an emergency vehicle pre-clearing method suitable for cloud control of traffic on high-volume highways. Background Art

[0002] As special vehicles with priority on the road, emergency vehicles send emergency avoidance signals by honking their horns. However, on highways with high traffic volume, traditional avoidance methods such as changing lanes and pulling over often increase the travel time of emergency vehicles due to untimely avoidance, affecting their priority passage effect and reducing the speed of the avoidance vehicle.

[0003] With the increasing complexity and vehicle density of modern transportation systems, traditional emergency vehicle avoidance methods can no longer meet actual needs. These methods mainly rely on the driver's reaction and judgment. On high-traffic highways, drivers may not react in time for various reasons, resulting in obstruction of emergency vehicles and increased rescue time. At the same time, the passage of emergency vehicles often causes the speed of vehicles ahead to drop significantly, further reducing the overall road operation efficiency.

[0004] For example, Chinese patent CN110164155A provides a system for emergency stopping and warning rear vehicles on highways, including an emergency stop vehicle, a hazard warning flasher on the parked vehicle, a navigation positioning system, a first ECU electrically connected to the hazard warning flasher and the navigation positioning system, and a first wireless transceiver module electrically connected to the first ECU. It also includes a second wireless transceiver module wirelessly connected to the first wireless transceiver module, and a regional network electrically connected to the second wireless transceiver module. It also includes a rear vehicle located behind the parked vehicle, and the rear vehicle includes a third wireless transceiver module wirelessly connected to the second wireless transceiver module, a second ECU electrically connected to the third wireless transceiver module, and a vehicle sound output device electrically connected to the second ECU. The driver does not need to risk setting warning signs, and the safety of all vehicles behind can be ensured, so that early warning is achieved, the alarm efficiency is high, the safety factor is large, it can adapt to various environments, and it is not affected by climate change.

[0005] With the continuous development of intelligent transportation systems (ITS) and vehicle-to-everything (C-V2X) systems, how to effectively use advanced technologies to optimize the passage of emergency vehicles has become a hot topic of research. The vehicle-to-everything (V2V) and vehicle-to-infrastructure (V2I) communication enables real-time information exchange between vehicles and the road environment. Although current research has made significant progress, the efficient passage of emergency vehicles still faces many challenges in the environment of high-traffic highways. Traditional avoidance methods are difficult to guarantee the priority passage of emergency vehicles in the face of complex traffic conditions.

[0006] Therefore, there is an urgent need for an emergency vehicle pre-clearance model based on cloud control technology to provide a new solution for the priority passage of emergency vehicles, so as to solve the problems of obstructed emergency vehicle travel, increased rescue time, and the passage of emergency vehicles often causing a significant decrease in the speed of vehicles ahead, further reducing the overall road operation efficiency. Summary of the invention

[0007] The purpose of the present invention is to provide an emergency vehicle pre-clearance method suitable for cloud-controlled traffic on high-volume highways. Through real-time data processing and dynamic control of the vehicle-road cooperative system, the road ahead of the emergency vehicle can be cleared in advance, ensuring the barrier-free passage of the emergency vehicle, thus solving the existing problems.

[0008] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0009] The present invention is a method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic, comprising the following steps:

[0010] Step 1: Establish the lower model P1 of the emergency lane pre-clearance double-layer model, and obtain the position of each vehicle that needs to change lanes in front of the emergency vehicle and the corresponding step length of the vehicle changing lanes and the shortest step length required for lane changing through the lower model P1;

[0011] Step 2: Establish the lower layer model P2 of the emergency lane pre-clearance double-layer model, and obtain the optimal lane change start position through the upper layer model P2;

[0012] Step 3: Set constraints based on the efficiency of emergency vehicles and the safety of all vehicles;

[0013] Step 4: Based on the termination condition, use the improved A* algorithm to solve the lower model P1;

[0014] Step 5: Solve the upper model P2 based on the convex optimization method.

[0015] Furthermore, in step 1, the lower layer model P1 of the emergency lane pre-clearing double-layer model is:

[0016] Get the minimum lane-changing cost for the vehicle ahead to make a lane for the emergency vehicle;

[0017] When the total cost is minimized, the position of each vehicle that needs to change lanes corresponding to the step length and the shortest step length required for lane change are calculated as follows:

[0018]

[0019] Where:

[0020] c is the lane-changing cost;

[0021] i is the number of the vehicle ahead of the emergency vehicle;

[0022] N is the total number of vehicles ahead of the emergency vehicle;

[0023] v e is the expected speed of emergency vehicles;

[0024] L is the emergency avoidance starting position;

[0025] k is the step size;

[0026] K is the maximum step size;

[0027] is the location of the emergency vehicle when the step length is k;

[0028] is the position of vehicle i when the step length is k;

[0029] is the cost of vehicle i moving from its position at step k-1 to its position at step k.

[0030] Furthermore, the lower layer model P2 of the emergency lane pre-clearing double-layer model in step 2 is:

[0031] Cost-adding the time it takes for vehicles ahead of the emergency vehicle to clear a lane;

[0032] The ratio of the distance between the emergency vehicle and the vehicle ahead to the distance between the two vehicles excluding the safety distance is used as a weight to obtain the starting position of the lane change avoidance when minimizing interference to the emergency vehicle.

[0033] The calculation method is:

[0034]

[0035] Where:

[0036] C – emergency vehicle travel costs;

[0037] τ — weight;

[0038] R * ——The minimum cost of the upper model output;

[0039] Δt——unit step time;

[0040] D i,e ——The minimum safe lane-changing distance between the emergency vehicle and vehicle i;

[0041] v i,k ——The speed of vehicle i at step length k.

[0042] Furthermore, the constraints set in step 3 based on the efficiency of the emergency vehicle and the safety of all vehicles are:

[0043] S1: Constraints for emergency vehicles not to change lanes:

[0044]

[0045] In the formula ——the ordinate of the emergency vehicle at step length k;

[0046] ——the vertical coordinate of the emergency vehicle when the step length is 0;

[0047] S2: Constraints for emergency vehicles to travel at close to the desired speed:

[0048]

[0049] In the formula ——The horizontal coordinate of the emergency vehicle when the step length is k+1;

[0050] ——The horizontal coordinate of the emergency vehicle when the step length is k;

[0051] S3: Final state after lane change: There are no other vehicles in the lane where the emergency vehicle is located:

[0052]

[0053] In the formula ——The vertical coordinate of the emergency vehicle when the step length is k;

[0054] S4: All cars meet the minimum safety distance constraint:

[0055]

[0056] Where D i,e ——The minimum lane-changing safety distance between the emergency vehicle and vehicle i;

[0057] D i,j ——The minimum safe lane-changing distance between vehicle i and vehicle j;

[0058] S5: Different vehicle trajectories do not conflict with each other:

[0059]

[0060] In the formula ——The position of vehicle i at step length k;

[0061] ——The position of vehicle j at step size k.

[0062] Furthermore, in step 4, based on the termination condition, the method for solving the lower model P1 using the improved A* algorithm is:

[0063] Set the termination condition: If the vehicles in the lane where the emergency vehicle is located are cleared, terminate the algorithm;

[0064] Selection of grid size: select a lane width of 3.5m in the longitudinal direction as h, select 30m as w based on the length of the vehicle and the safe distance between vehicles during driving, and grid the vehicle with a size of 3.5×30m;

[0065] Grid identification: Set the X-axis and Y-axis according to the vehicle's driving direction and the road, and obtain the corresponding coordinates (x, y) of the grid;

[0066] Grid information encoding: whether there is a vehicle in the grid is stored by matrixing the grid map;

[0067] Use the improved A* algorithm to find the shortest path.

[0068] Furthermore, when the improved A* algorithm is used to obtain the shortest path in step 4:

[0069] Define the cost function f(n) based on Manhattan distance:

[0070] f(k)=g(k)+h(k) Formula (4-13)

[0071]

[0072] Where n k ——The number of vehicles ahead of the emergency vehicle in the lane where the emergency vehicle is located when the step length is k;

[0073] ——The cost spent by vehicle i in step k to k+1.

[0074] Grid identification: Set the X-axis and Y-axis according to the vehicle's driving direction and the road, and obtain the corresponding coordinates (x, y) of the grid;

[0075] Furthermore, the grid identification method is:

[0076] The X-axis is along the direction of vehicle travel, and the right direction is defined as the positive direction. The Y-axis is perpendicular to the road direction, and the upward direction is defined as the positive direction. According to the road conditions, the lower left corner of the road is taken as the zero point.

[0077] Among them, right is the direction of vehicle travel; upward is the direction from lane 0 to lane 1;

[0078] For roads transformed into the index coordinate system, each grid has fixed x and y. Any grid can be represented by (x, y). The specific value is represented by the coordinates of the lower left corner. Since this road has a fixed width and number of lanes, the y-axis has an upper limit.

[0079] Furthermore, the information encoding method of the grid is:

[0080] If there is a vehicle, it is marked as 1, and if there is no vehicle, it is marked as 0. The grid is stored whether there is a vehicle through a matrix method;

[0081] After matrixing, when selecting a path for lane change, if it is 1, it means there is a vehicle in the grid, and lane change is not allowed as it is an obstacle; if it is 0, it means there is no vehicle in the grid, and lane change is allowed.

[0082] Furthermore, the steps of using the improved A* algorithm to obtain the shortest path include:

[0083] Step S01: define the initial state and target state, and the cost of each move;

[0084] Step S02: define a cost function f(k)=g(k)+h(k) based on Manhattan distance;

[0085] Step S03: Calculate the cost of all movable directions, and select the node with the smallest cost F as the current node;

[0086] Step S04: Determine whether the lane where the emergency vehicle is located is cleared. If the lane where the emergency vehicle is located is cleared, terminate the algorithm; otherwise, continue to step S03.

[0087] Furthermore, the method for solving the upper model P2 based on the convex optimization method in step 5 is:

[0088] SS1: weighted by the distance between the vehicle in front of the emergency vehicle and the emergency vehicle;

[0089]

[0090] Where D i,e - the minimum safe distance between the emergency vehicle and the vehicle in front of it;

[0091] To avoid singular points, L ratio (v e -v i,k )·Δt·k+D i,e Much larger;

[0092] SS2: Calculated:

[0093]

[0094] The value range of L is:

[0095] L∈[(v e -v i,k )kΔt+D i,e ,L R ] Formula (4-18)

[0096] Where L R - length of the road;

[0097] It can be clearly seen that It is a quadratic univariate function, strictly convex and monotonically increasing, so S is a strictly convex function of L;

[0098] SS3: Substitute L into S(L) and calculate:

[0099]

[0100] It can be shown that S is bounded, so the upper function M2 is a bounded convex function, and there is an optimal solution L * .

[0101] The present invention has the following beneficial effects:

[0102] The present invention sets the framework of the emergency lane pre-clearing double-layer model, obtains the position of the lane-changing vehicle and each step length through the lower model and transmits it to the upper model, and then solves the upper model to obtain the position where the vehicle in front of the emergency vehicle starts to change lanes, that is, how far away from the emergency vehicle is the lane change. The lower model of the double-layer model aims to optimize the lane-changing trajectory of the front vehicle to minimize the cost of freeing up the space in the lane where the emergency vehicle is located as soon as possible, and obtains the optimal lane-changing trajectory; the upper model aims to minimize the time cost of the front vehicle, and solves the shortest distance between the emergency vehicle and the front vehicle under the condition that the emergency vehicle travels at a speed close to the expected speed. The termination condition of the A* algorithm is set to improve the calculation efficiency; the upper model is used as a bounded convex function, and the solution is based on the convex optimization method; and finally the optimal solution of the double-layer model is obtained. This solves the problem that when the emergency vehicle does not change lanes and travels at a speed close to the expected speed, the front vehicle minimizes the loss of traffic efficiency by changing lanes, so that the impact of the emergency vehicle on the traffic ahead is minimized.

[0103] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0105] Figure 1 This is a schematic diagram of the structure of the double-layer model for pre-clearing the emergency lane of the present invention;

[0106] Figure 2 Schematic diagram of raster map conversion of the present invention (where a) is the actual problem and b) is raster mapping);

[0107] Figure 3 This is a schematic diagram of the grid map marking of the present invention;

[0108] Figure 4 A schematic diagram of the conversion between the grid map and the matrix information coding of the present invention;

[0109] Figure 5 The present invention uses the improved A* algorithm to solve the flow chart;

[0110] Figure 6 A schematic diagram of the grid map process of searching the path of the Dijkstra algorithm of the present invention;

[0111] Figure 7 A schematic diagram of the grid map process of searching paths by the BFS algorithm of the present invention. DETAILED DESCRIPTION

[0112] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0113] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0114] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0115] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0116] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0117] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0118] The present invention is a method for pre-clearing emergency vehicles suitable for cloud-controlled traffic on high-volume highways. The model collects and analyzes road and vehicle information in real time, plans in advance and instructs the vehicles ahead to avoid, and provides an unobstructed driving path for emergency vehicles. The present invention not only helps to improve the traffic efficiency of emergency vehicles and shorten rescue time, but also provides a new research direction for the development of intelligent transportation systems. In the study of the lane change guidance method for emergency vehicles, since the avoidance of the vehicles ahead of the emergency vehicle has a significant impact on the traffic efficiency of the vehicles ahead, the objectives of the present invention are divided into two:

[0119] 1) The emergency vehicle does not change lanes and travels at a speed close to the desired speed, i.e. the operating cost of the emergency vehicle is minimized;

[0120] 2) The traffic efficiency of the vehicle ahead is highest when it avoids the emergency vehicle, that is, the lane-changing cost of the vehicle ahead of the emergency vehicle is minimized.

[0121] An emergency vehicle pre-clearance method applicable to cloud control of high-volume highway traffic, comprising:

[0122] According to the above two objectives, a two-layer model for emergency lane pre-clearance is established. The structure of the model is as follows: Figure 1As shown in the figure, first input the expected speed of the emergency vehicle and the speed, position, sorting number and other vehicle information of the vehicle in front, use the improved A* algorithm to solve the lower model P1, obtain the vehicles that need to change lanes and their lane changing trajectories, that is, the lane changing order, and output the vehicle number, position, speed, and the arrival position of the lane changing vehicle at each step. The result is input to the upper model P2, and the upper model is solved by the convex optimization method. The optimal solution L of P2 is obtained. * Return to the lower model and iterate back to the upper model for convergence test. Finally, after verification, output the position of each step of all vehicles in front and the distance to the emergency vehicle to obtain the optimal lane change trajectory.

[0123] As an embodiment provided by the present invention, preferably, the lower layer model is established:

[0124] The lower model of the double-layer model of emergency lane pre-clearance is P1 (as shown in formula (4-1) to formula (4-3)). The goal is to optimize the lane-changing trajectory of the vehicle in front of the emergency vehicle so that the emergency vehicle has the least interference with the vehicle in front of it, that is, the lane-changing cost of the vehicle in front to make a lane for the emergency vehicle is minimized. The specific model is to minimize the total cost required for each vehicle in front after clearing a lane, and to find the position of each vehicle that needs to change lanes and each step length (i.e., lane-changing trajectory) and the shortest step length required for lane changing when the total cost is minimized.

[0125]

[0126] Where c is the lane-changing cost;

[0127] i – the number of the vehicle ahead of the emergency vehicle;

[0128] N – the total number of vehicles ahead of the emergency vehicle;

[0129] v e — the expected speed of emergency vehicles;

[0130] L——Emergency avoidance starting position;

[0131] k – step length;

[0132] K——maximum step length;

[0133] ——The location of the emergency vehicle when the step length is k;

[0134] ——The location of vehicle i when the step length is k;

[0135] ——The cost of vehicle i moving from its position at step k-1 to its position at step k.

[0136] As an embodiment provided by the present invention, preferably, the upper layer model is established:

[0137] The upper model of the emergency lane pre-clearance double-layer model is P2 (as shown in formulas (4-4) to (4-7)). The goal is to obtain an optimal starting position for lane change avoidance so that the front vehicle will have the least interference with the emergency vehicle during emergency avoidance, that is, the driving cost of the emergency vehicle is minimized. The specific model takes into account the difficult problem to be solved and establishes a better solution model.

[0138] The interference of an emergency vehicle to the vehicle in front of it depends on the time, position and complexity of the corresponding trajectory when the lane is cleared. Therefore, the upper model adds the cost of the time vacated by the vehicle in front of the emergency vehicle, and uses the ratio of the distance between the emergency vehicle and the vehicle in front to the distance between the two vehicles excluding the safety distance as the weight to minimize the interference to the emergency vehicle.

[0139]

[0140] Where C is the travel cost of emergency vehicles;

[0141] τ — weight;

[0142] R * ——The minimum cost of the upper model output;

[0143] Δt——unit step time;

[0144] D i,e ——The minimum safe lane-changing distance between the emergency vehicle and vehicle i;

[0145] v i,k ——The speed of vehicle i at step length k.

[0146] As an embodiment provided by the present invention, preferably, the constraint condition is determined as follows:

[0147] The constraint conditions of the present invention are determined mainly from two aspects: the efficiency of emergency vehicles and the safety of all vehicles.

[0148] 1) Consider the efficiency of emergency vehicles

[0149] In the process of emergency vehicles passing quickly, the traffic efficiency of emergency vehicles is an important prerequisite for quickly transporting rescue personnel to the accident site in the shortest time, so the traffic efficiency of emergency vehicles needs to be considered as follows:

[0150] ① Emergency vehicles do not change lanes

[0151] The scenario of the present invention only considers the situation of one road section. In an emergency rescue, the emergency vehicle needs to pass through such road sections many times and encounters such scenarios many times. Although the time saved in one scenario due to the emergency vehicle not changing lanes is not long, it will save a lot of time in the whole process of the emergency vehicle's driving. And the time when the emergency vehicle arrives at the scene is crucial for rescue. Therefore, a high level of priority is provided for the emergency vehicle, and the constraints of the emergency vehicle not changing lanes are as follows:

[0152]

[0153] In the formula ——the ordinate of the emergency vehicle at step length k;

[0154] ——The vertical coordinate of the emergency vehicle when the step size is 0.

[0155] ② Emergency vehicles are constrained to travel at speeds close to the expected speed

[0156] The ultimate goal of the emergency vehicle's rapid passage is that the emergency vehicle passes through the road section at the maximum speed allowed by road conditions. Therefore, the emergency vehicle must travel at a speed close to the expected speed to achieve the ultimate goal. The constraints are as follows:

[0157]

[0158] In the formula ——The horizontal coordinate of the emergency vehicle when the step length is k+1;

[0159] ——The horizontal coordinate of the emergency vehicle when the step length is k.

[0160] ③ The final state after lane change is that there are no other vehicles in the lane where the emergency vehicle is located.

[0161] After the lane change is completed and before the emergency vehicle passes, there must be no vehicle within the distance L* in front of the emergency vehicle in the lane where the emergency vehicle is located, so that the emergency vehicle can pass quickly. The constraints are as follows:

[0162]

[0163] In the formula ——The vertical coordinate of the emergency vehicle when the step length is k.

[0164] 2) Consider the safety aspects of all vehicles

[0165] Road safety is an important prerequisite for emergency rescue, so all vehicles on the road, especially during the lane change guidance process, must not have safety hazards. Therefore, considering the safety of all vehicles, the constraints are determined as follows:

[0166] ① All cars meet the minimum safety distance constraint

[0167] During the lane change guidance process, all vehicles need to meet the lane change safety distance constraints as follows:

[0168]

[0169] Where D i,e ——The minimum lane-changing safety distance between the emergency vehicle and vehicle i;

[0170] D i,j ——The minimum safe lane-changing distance between vehicle i and vehicle j.

[0171] ② Different vehicle trajectories do not conflict with each other

[0172] During the lane change guidance process, many vehicles need to change lanes at the same time or at different times. The order of vehicle lane changes affects the lane change trajectory of the vehicles. To ensure the safety of the vehicles, the lane change or driving trajectories of different vehicles cannot conflict with each other, thereby avoiding safety accidents. The non-conflicting constraints of different vehicle trajectories are as follows:

[0173]

[0174] In the formula ——The position of vehicle i at step length k;

[0175] ——The position of vehicle j at step size k.

[0176] As an embodiment provided by the present invention, preferably, the solution of the emergency lane pre-clearing double-layer model: the solution of the emergency lane pre-clearing double-layer model can be solved for the upper and lower layers respectively. The essence of solving the lower layer model is to solve the trajectory optimization during the lane changing process. By obtaining the initial state information of all vehicles, according to the lane changing guidance strategy and technical objectives of avoiding emergency vehicles, the final state information of the vehicle after changing lanes is obtained, and the optimal lane changing sequence from the initial state to the final state, that is, the lane changing trajectory, is found. The solution to the optimal path is usually the A* algorithm, so based on the A* algorithm, the A* algorithm is improved based on the emergency lane pre-clearing strategy, and the lower layer model is solved. The upper layer model is used as a convex function. The upper layer model is solved by the convex optimization method through the solution result of the lower layer model, so as to obtain the optimal solution of the emergency lane pre-clearing double layer model.

[0177] As an embodiment provided by the present invention, preferably, the lower layer model based on the improved A* algorithm is solved:

[0178] When solving the optimal trajectory, the grid map method is usually used for solving it. The grid map divides the map into uniform squares and expresses the optimal path by moving each square. The grid map method has the advantages of intuitive expression, easy operation and strong robustness.

[0179] In the scenario studied by the present invention, there are multiple possibilities for the target state when the emergency lane is cleared. If the A* algorithm is used, all target states need to be enumerated, each target state needs to be solved separately, and then compared to obtain the optimal solution. This method affects the efficiency of the calculation, so an improved A* algorithm is proposed for the scenario studied by the present invention. By adding a termination condition: if the vehicle in the lane where the emergency vehicle is located is cleared, the algorithm is terminated. By setting the termination condition, the enumeration of all target states is reduced, and the optimal path can be obtained by only finding a certain target state.

[0180] As an embodiment provided by the present invention, preferably, the following steps are required to solve the lower layer model:

[0181] Rasterization is easy to use and highly efficient, so the path information on the road is discretized using the grid map method. There will be a certain error when obtaining map information after the raster map. This error is related to the resolution of the map. In order to reduce this error, the size of the grid must be reduced. The smaller the grid size, the higher the accuracy of the map, and the lower the error. Therefore, a large grid size will lead to low map accuracy and affect the accuracy of the path result; a small grid size will lead to an increase in grids, and the search speed will slow down when searching for the optimal path.

[0182] As an embodiment provided by the present invention, preferably, Figure 2 As shown, the present invention selects a lane width of 3.5m as h in the longitudinal direction, selects 30m as w based on the length of the vehicle and the safe distance between vehicles during driving, and finally selects a size of 3.5×30m for rasterization. It can effectively distinguish that there are vehicles in the grid, and also ensure that there is only one vehicle in a grid. It can accurately determine the grid where each vehicle is located, so that the optimal path can be obtained more accurately under the condition of the highest accuracy.

[0183] As an embodiment provided by the present invention, preferably, Figure 3As shown, the grid map method is used to describe the situation of vehicles on the road, so grid identification is more necessary. The rectangular coordinate system method and the serial number method have become commonly used methods for grid identification. The rectangular coordinate system method is selected for identification. The direction along which the vehicle is traveling is the X-axis, and the right direction (i.e., the direction of vehicle travel) is defined as the positive direction. The direction perpendicular to the road is the Y-axis, and the upward direction (i.e., the direction from lane 0 to lane 1) is defined as the positive direction. According to the road conditions, the lower left corner of the road is taken as the zero point. For the road that has been transformed into the index coordinate system, each grid has fixed x and y, so any grid can be represented by (x, y), and the specific value is represented by the coordinates of the lower left corner, for example Figure 3 Vehicle A can be represented as (3,2). And because this road has a fixed width and number of lanes, the y-axis has an upper bound.

[0184] As an embodiment provided by the present invention, preferably, Figure 4 As shown in the figure, after the grid map is completed, the information of each grid needs to be encoded. The grid corresponds to the array one by one, and the grid can be stored by matrixing whether there is a vehicle. If there is a vehicle, it is marked as 1, and if there is no vehicle, it is marked as 0. In this way, the location of all vehicles on the road can be clearly identified through matrixing. After matrixing, when changing lanes and selecting a path, if it is 1, it means that there is a vehicle in the grid, and the lane cannot be changed as an obstacle; if it is 0, it means that there is no vehicle in the grid, and the lane can be changed. Information encoding in this way can matrix the grid map, and then perform data processing to better obtain the optimal path.

[0185] As an embodiment provided by the present invention, preferably, Figure 5 As shown, the improved A* algorithm can find the shortest path. The specific flow chart is as follows Figure 5 As shown:

[0186] Step S01: define the initial state and target state, and the cost of each move;

[0187] Step S02: define a cost function f(k)=g(k)+h(k) based on Manhattan distance;

[0188] Step S03: Calculate the cost of all movable directions, and select the node with the smallest cost F as the current node;

[0189] Step S04: Determine whether the lane where the emergency vehicle is located is cleared. If the lane where the emergency vehicle is located is cleared, terminate the algorithm; otherwise, continue to step S03.

[0190] The improved A* algorithm is a method with a hidden target state, so the cost function f(n) is redefined based on the Manhattan distance as follows:

[0191] f(k)=g(k)+h(k) (4-13)

[0192]

[0193] Where n k ——The number of vehicles ahead of the emergency vehicle in the lane where the emergency vehicle is located when the step length is k;

[0194] ——The cost spent by vehicle i in step k to k+1.

[0195] As an embodiment provided by the present invention, preferably, the upper model is solved based on the convex optimization method, and the upper model is solved, and the process is as follows:

[0196] 1) Weighted by the distance between the vehicle in front of the emergency vehicle and the emergency vehicle;

[0197]

[0198] Where D i,e ——The minimum safe distance between an emergency vehicle and the vehicle in front of it.

[0199] To avoid singular points, L ratio (v e -v i,k )·Δt·k+D i,e Much larger;

[0200] 2) Calculate and obtain:

[0201]

[0202] The value range of L is:

[0203] L∈[(v e -v i,k )kΔt+D i,e ,L R ](4-18)

[0204] Where L R ——The length of the road.

[0205] It can be clearly seen that It is a quadratic univariate function, strictly convex and monotonically increasing, so S is a strictly convex function of L;

[0206] 3) Substitute L into S(L) and calculate:

[0207]

[0208] It can be shown that S is bounded, so the upper function M2 is a bounded convex function, and there is an optimal solution L* .

[0209] As an embodiment provided by the present invention, preferably, the device information of the SIM card is updated according to the operator information required by the customer through the SIM card management module. The SIM card management module configures the SIM card information and supports the remote cloud user's mobile phone customized APP to write the SIM card information. The user can also find an operator to purchase a package by himself, and write the SIM card information of the purchased package into the SIM card and synchronize it to the remote remote management. The remote cloud can provide users with traffic management, operator usage status, and recommend vehicles to use operator networks and other functions, flexibly switch operator networks, and provide a reliable wireless data path backup for T-BOX and vehicles at the lowest cost; when the customer wants to change the operator, the new SIM card IMSI, ICCID and other information are set through the mobile phone customized APP, and the vehicle sends the SIM card information provided by the mobile phone customized APP to the NAD of T-BOX through CAN / USB / Ethernet. SIM-SDK module, NAD's SIM-SDK module updates SIM card information through APDU messages. After the SIM-SDK module turns on / off the flight mode, NAD's Modem uses the new SIM card IMSI and ICCID to register the network. After successful registration, it provides data communication services for T-BOX and vehicle computers; by flexibly switching operator networks, it realizes reliable transmission and backup of network data, and provides technical support for emergency vehicle pre-clearance of cloud-controlled traffic on high-traffic highways.

[0210] A method for pre-clearing emergency vehicles suitable for cloud control of high-volume highway traffic, such as Figure 6-7 As shown in the figure, the grid map flow diagram of the Dijkstra algorithm search path, the grid map flow diagram of the BFS algorithm search path, and the framework of the emergency lane pre-clearing double-layer model. The position of the lane-changing vehicle and each step length is obtained through the lower model and transmitted to the upper model. Then the upper model is solved to obtain the position of the vehicle in front of the emergency vehicle starting to change lanes, that is, how far away from the emergency vehicle is the lane change. The lower model of the double-layer model aims to optimize the lane-changing trajectory of the front vehicle to minimize the cost of the space in the lane where the emergency vehicle is located as soon as possible, and obtain the optimal lane-changing trajectory; the upper model aims to minimize the time cost of the front vehicle and solves the shortest distance between the emergency vehicle and the front vehicle under the condition that the emergency vehicle travels at a speed close to the expected speed. In the solution process, the lower model uses the improved A* algorithm to solve, and adds the termination condition of the A* algorithm to improve the calculation efficiency; the upper model is a bounded convex function, and the convex optimization method is used to solve; finally, the optimal solution of the double-layer model is obtained. This solves the problem that when the emergency vehicle does not change lanes and travels at a speed close to the expected speed, the vehicle ahead can minimize the loss of traffic efficiency by changing lanes, thereby minimizing the impact of the emergency vehicle on the traffic ahead.

[0211] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0212] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for pre-clearing emergency vehicles for cloud control of high-volume highway traffic, characterized in that: The following steps are involved: Step 1: Establish the lower model P1 of the emergency lane pre-clearance double-layer model, and obtain the position of each vehicle that needs to change lanes in front of the emergency vehicle and the corresponding step length of the vehicle changing lanes and the shortest step length required for lane changing through the lower model P1; Step 2: Establish the lower layer model P2 of the emergency lane pre-clearance double-layer model, and obtain the optimal lane change start position through the upper layer model P2; Step 3: Set constraints based on the efficiency of emergency vehicles and the safety of all vehicles; Step 4: Based on the termination condition, use the improved A* algorithm to solve the lower model P1; Step 5: Solve the upper model P2 based on the convex optimization method.

2. According to claim 1, a method for pre-clearing emergency vehicles suitable for cloud control of high-volume highway traffic, characterized in that: In step 1, the lower layer model P1 of the emergency lane pre-clearance double-layer model is: Get the minimum lane-changing cost for the vehicle ahead to make a lane for the emergency vehicle; When the total cost is minimized, the position of each vehicle that needs to change lanes corresponding to the step length and the shortest step length required for lane change are calculated as follows: Where: c is the lane-changing cost; i is the number of the vehicle ahead of the emergency vehicle; N is the total number of vehicles ahead of the emergency vehicle; v e is the expected speed of emergency vehicles; L is the emergency avoidance starting position; k is the step size; K is the maximum step length; is the location of the emergency vehicle when the step length is k; is the position of vehicle i when the step length is k; is the cost of vehicle i moving from its position at step k-1 to its position at step k.

3. The method for pre-clearing emergency vehicles suitable for cloud control of high-volume highway traffic according to claim 2 is characterized in that: The lower layer model P2 of the emergency lane pre-clearance double-layer model in step 2 is: Cost-adding the time it takes for vehicles ahead of the emergency vehicle to clear a lane; The ratio of the distance between the emergency vehicle and the vehicle ahead to the distance between the two vehicles excluding the safety distance is used as a weight to obtain the starting position of the lane change avoidance when minimizing interference to the emergency vehicle. The calculation method is: Where: C – emergency vehicle travel costs; τ — weight; R * ——The minimum cost of the upper model output; Δt——the time of unit step; D i,e ——The minimum safe lane-changing distance between the emergency vehicle and vehicle i; v i,k ——The speed of vehicle i at step length k.

4. The method for pre-clearing emergency vehicles suitable for cloud control of high-volume highway traffic according to claim 1 is characterized in that: The constraints set in step 3 based on the efficiency of the emergency vehicle and the safety of all vehicles are: S1: Constraints for emergency vehicles not to change lanes: In the formula ——the ordinate of the emergency vehicle at step length k; ——the vertical coordinate of the emergency vehicle when the step length is 0; S2: Constraints for emergency vehicles to travel at close to the desired speed: In the formula ——The horizontal coordinate of the emergency vehicle when the step length is k+1; ——The horizontal coordinate of the emergency vehicle when the step length is k; S3: Final state after lane change: There are no other vehicles in the lane where the emergency vehicle is located: In the formula ——The vertical coordinate of the emergency vehicle when the step length is k; S4: All cars meet the minimum safety distance constraint: Where D i,e ——The minimum lane-changing safety distance between the emergency vehicle and vehicle i; D i,j ——The minimum safe lane-changing distance between vehicle i and vehicle j; S5: Different vehicle trajectories do not conflict with each other: In the formula ——The position of vehicle i at step length k; ——The position of vehicle j at step size k.

5. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 3 is characterized in that: In step 4, based on the termination condition, the method of solving the lower model P1 using the improved A* algorithm is: Set the termination condition: If the vehicles in the lane where the emergency vehicle is located are cleared, terminate the algorithm; Selection of grid size: select a lane width of 3.5m in the longitudinal direction as h, select 30m as w based on the length of the vehicle and the safe distance between vehicles during driving, and grid the vehicle with a size of 3.5×30m; Grid identification: Set the X-axis and Y-axis according to the vehicle's driving direction and the road, and obtain the corresponding coordinates (x, y) of the grid; Grid information encoding: whether there is a vehicle in the grid is stored by matrixing the grid map; Use the improved A* algorithm to find the shortest path.

6. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 5 is characterized in that: When the improved A* algorithm is used to obtain the shortest path in step 4: Define the cost function f(n) based on Manhattan distance: f(k)=g(k)+h(k) Formula (4-13) Where n k ——The number of vehicles ahead of the emergency vehicle in the lane where the emergency vehicle is located when the step length is k; ——The cost spent by vehicle i in step k to k+1; Grid identification: Set the X-axis and Y-axis according to the vehicle's direction of travel and the road, and obtain the corresponding coordinates (x, y) of the grid.

7. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 5 is characterized in that: The identification method of the grid is: The X-axis is along the direction of vehicle travel, and the right direction is defined as the positive direction. The Y-axis is perpendicular to the road direction, and the upward direction is defined as the positive direction. According to the road conditions, the lower left corner of the road is taken as the zero point. Among them, right is the direction of vehicle travel; upward is the direction from lane 0 to lane 1.

8. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 5 is characterized in that: The information encoding method of the grid is: If there is a vehicle, it is marked as 1, and if there is no vehicle, it is marked as 0. The grid is stored whether there is a vehicle through a matrix method; After matrixing, when selecting a path for lane change, if it is 1, it means there is a vehicle in the grid, which is an obstacle and cannot change lanes; If it is 0, it means there is no vehicle in the grid and lane change is possible.

9. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 5 is characterized in that: The steps to find the shortest path using the improved A* algorithm include: Step S01: define the initial state and target state, and the cost of each move; Step S02: define a cost function f(k)=g(k)+h(k) based on Manhattan distance; Step S03: Calculate the cost of all movable directions, and select the node with the smallest cost F as the current node; Step S04: Determine whether the lane where the emergency vehicle is located is cleared. If the lane where the emergency vehicle is located is cleared, terminate the algorithm; otherwise, continue to step S03.

10. The method for pre-clearing emergency vehicles applicable to cloud control of high-volume highway traffic according to claim 1, characterized in that: The method for solving the upper model P2 based on the convex optimization method in step 5 is: SS1: weighted by the distance between the vehicle in front of the emergency vehicle and the emergency vehicle; Where D i,e - the minimum safe distance between the emergency vehicle and the vehicle in front of it; SS2: Calculated: The value range of L is: L∈[(v e -v i,k )kΔt+D i,e ,L R ] Formula (4-18) where L R - length of the road; SS3: Substitute L into S(L) and calculate: Then the upper function M2 is a bounded convex function, and there is an optimal solution L * .

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