A method for route planning of road testing in real-time traffic, a storage medium and an apparatus

Abstract

The application discloses a kind of real-time road condition under the route planning method of road testing, storage medium and device, the method includes the following steps: (1) collection road network information, all passable streets in current road network are abstracted as mixed graph G, and the street that has not been passed is abstracted as the subset E of graph G edge set;(2) find the path p from current position to specified terminal position in graph G, traverse each edge in set E at least once, and find the shortest path;Wherein, current position is set as vertex s, and terminal position is set as vertex t;(3) the path p is converted into the actual driving path of navigation vehicle;Compared with the route planning of traditional navigation officer or driver's personal experience, the application can be calculated in real time according to road network same trip data, can improve the real-time performance and effectiveness of route design, and can also save labor cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer technology, and in particular to a route planning method for road testing under real-time road conditions, a storage medium and a device. BACKGROUND

[0002] Road testing is a commonly used testing method for road wireless signals in the communication industry. In order to improve the testing efficiency, the test personnel generally sit in the car and use professional test instruments to test all roads in the target area. Before the road testing vehicle departs, the vehicle driver will actively design a road testing tour route according to the road network conditions of the region. However, in the actual operation process, unexpected situations such as road restrictions, road closures or traffic jams often occur on urban streets, making the original route planning no longer feasible, and at this time there are still some routes to be tested. It is necessary to redesign the route for the road testing vehicle to tour the remaining roads to be tested and the distance is as small as possible. Traditionally, the driver or navigator needs to rely on their own experience to re-plan the route, and the rationality and timeliness of the new route are difficult to guarantee. SUMMARY

[0003] The purpose of the present application is to provide a route planning method for road testing under real-time road conditions, a storage medium and a device, which can calculate a feasible road testing route in real time according to the passable conditions of the streets in the current road network.

[0004] Technical solution: The present application provides a route planning method for road testing under real-time road conditions, comprising the following steps:

[0005] (1) Collect road network information, abstract all passable streets in the current road network as a mixed graph G, and abstract the streets that have not been passed through as a subset E of the edge set of the graph G;

[0006] (2) Find a path p from the current position to the specified end position in the graph G, traverse each edge in the set E at least once, and find the shortest path; wherein the current position is set as vertex s and the end position is set as vertex t;

[0007] (3) Convert the path p into an actual driving path for the navigation vehicle.

[0008] Further, the step (2) comprises the following steps:

[0009] (21) Encoding conversion, specifically: converting the undirected edges, directed edges and orientation of undirected edges in the edge set E into vector form, and converting the mixed graph into a directed graph D; wherein the undirected edge represents a double street and the directed edge represents a single street; the formula is as follows:

[0010] Let the undirected edges in the edge set E be m1, denoted as set E1, and the directed edges be m2, denoted as set E2; define an integer vector set with a length of 2m1+m2;

[0011] The first m1+m2 elements of each vector are a permutation of the edges in the set E; the last m1 elements are composed of 0-1, and the components take values 0 or 1, indicating two orientations of the undirected edge, i.e., the undirected edges in the set E are given a direction;

[0012] (22) generating N initial individuals x (1) , x (2) , …, x (N) population, specifically: let N be a positive even number, randomly generate N integer vectors with a length of z-1+m2, to obtain a population of N individuals x (1) , x (2) , …, x (N) ;

[0013] Wherein, N represents the size of the population; each vector x (t) , the first m1+m2 elements represent the traversal order of the edges in the edge set E, and are assigned a random permutation of {1, 2, …, m1+m2}, and the last m1 elements represent the random orientation of the undirected edge, and are randomly assigned 0 or 1;

[0014] (23) evolving the population.

[0015] Further, the step (23) comprises the following steps:

[0016] (221) calculating the fitness of the individual, and saving the individual with the minimum fitness as the current optimal solution;

[0017] (222) replacing N / 2 individuals with greater fitness in the population with the current optimal solution;

[0018] (223) replacing each individual in the new population with a better solution in its neighborhood, and if the optimal solution in the new population is better than the current optimal solution, updating the current optimal solution; otherwise, the current optimal solution is still retained;

[0019] (224) setting the time T=T+1, and determining whether the time T reaches a preset upper limit of 2000; if so, returning the current optimal solution, and the first m1+m2 elements of the vector are the path p that traverses all edges in E and has the shortest length; otherwise, the steps (222)-(223) are executed in a loop.

[0020] Further, the step ( 221 ) is specifically: initially setting T=0, and defining the fitness of the individual x (t) : the length of the path from the point s to the point t by traversing the edges in the set E according to the permutation order of the first m1+m2 elements in x (i) , and the distance between each point in the path is the shortest distance in the graph C; wherein, T represents the preset number of time rounds for population evolution.

[0021] Further, the step (222) is specifically: for each individual x in the current population (i） , let the permutation of the first m1+m2 elements be π i , and the 0-1 vector of the last m1 elements be v i , update π i and v i as follows:

[0022] Generate a random number r in the interval [0, 1];

[0023] If r≤0.5, exchange randomly two elements in the permutation π i to get a new permutation π i , and replace v i with a random 0-1 vector of length m1;

[0024] If r>0.5, π i is unchanged, and update v i in a simulated annealing way; visit each element in v i in a random order, let the j-th visited element be v ij , if replacing this element with 1-v ij will make the fitness of individual x (i) smaller, then perform this operation, otherwise, perform this operation with probability 2 -j .

[0025] Further, a storage medium, the storage medium has a computer program stored therein, wherein the computer program is arranged to execute the above method when running.

[0026] Further, an electronic device, comprising a memory and a processor, the memory has a computer program stored therein, and the processor is arranged to run the computer program to execute the above method.

[0027] Advantages: compared with the prior art, the present application has the following remarkable advantages: compared with the traditional route planning relying on the personal experience of the navigator or the driver, the present application can perform real-time calculation according to the road network travel data, can improve the real-time performance and effectiveness of the route design, and can save the labor cost. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a schematic diagram of the principle of the present application;

[0029] Figure 2 is a flowchart of the evolutionary algorithm involved in the present application;

[0030] Figure 3 is a flowchart of the individual updating process in the evolutionary algorithm of the present application. DETAILED DESCRIPTION

[0031] The technical solutions of the present application are further described below with reference to the drawings.

[0032] As Figure 1 shown, the embodiment of the present application provides a route planning method for road testing in real-time road conditions, comprising the following steps:

[0033] (1) Collect road network information, abstract all passable streets in the current road network as a mixed graph G, and abstract the streets that have not been passed through as a subset E of the edge set of graph C;

[0034] (2) Find a path p from the current position to the specified end position in graph G, traverse each edge in set E at least once, and find the shortest path; wherein the current position is set as vertex s and the end position is set as vertex t, comprising the following steps:

[0035] (21) Encoding conversion, specifically: converting the undirected edges, directed edges and directional conversion of undirected edges in edge set E into vector form, and converting the mixed graph into a directed graph D; wherein the undirected edge represents a double street and the directed edge represents a single street; the formula is as follows:

[0036] Let the undirected edges in edge set E be m1, denoted as set E1, and the directed edges be m2, denoted as set E2; define an integer vector set with length 2m1+m2;

[0037] Then the first m1+m2 elements in each vector are a permutation of the edges in set E; the last m1 elements are composed of 0-1, and the component values are 0 or 1, representing the two directions of the undirected edge, i.e. giving direction to the undirected edges in set E;

[0038] ( 22 ) Generate N initial individuals x (1) , x (2) , …, x (N) population, specifically: let N be a positive even number, randomly generate N integer vectors with length 2m1+m2, to obtain a population x (1) , x (2) , …, x (N) consisting of N individuals;

[0039] Wherein, N represents the size of the population; each vector x (t) , the first m1+m2 elements represent the traversal order of the edges in edge set E, and are assigned a random permutation of {1, 2, …, m1+m2}, and the last m1 elements represent the random orientation of the undirected edges, and are randomly assigned 0 or 1;

[0040] (23) Evolution population, comprising the following steps:

[0041] (221) Calculate the fitness of each individual, and save the individual with the minimum fitness as the current optimal solution, specifically: initially let T = 0, define the fitness of individual x (t) as: starting from the point s, traverse the set E according to the permutation order of the first m1+m2 elements in x (t) , and reach the point t, the length of the path is the shortest distance between the points in the graph G; wherein T represents the preset number of time rounds of population evolution.

[0042] (222) Replace N / 2 individuals with greater fitness in the population with the current optimal solution, specifically: for each individual x (t) in the current population, let the permutation corresponding to the first m1+m2 elements be π i , and the 0-1 vector corresponding to the last m1 elements be v i , update π i and v i , and the formula is as follows:

[0043] Generate a random number r in the interval [0, 1];

[0044] If r ≤ 0.5, exchange the random two elements in the permutation π i to obtain a new permutation π i , and replace v i with a random 0-1 vector of length m1;

[0045] If r > 0.5, π i is unchanged, and v i is updated in the simulated annealing manner; randomly access each element in v i , let the jth accessed element be v ij , if replacing this element with 1-v ij will make the fitness of individual x (i) smaller, then perform this operation, otherwise, perform this operation with a probability of 2 -i .

[0046] (223) Replace each individual in the new population with a better solution in its neighborhood, and if the optimal solution in the new population is better than the current optimal solution, update the current optimal solution; otherwise, still keep the current optimal solution.

[0047] (224) Let the time T = T + 1, and judge whether the time T reaches the preset upper limit of time 2000; if so, return the current optimal solution, and the first m1+m2 elements of the vector are the path p that traverses all edges in E and has the shortest length; otherwise, cyclically execute steps (222)-(223).

[0048] (3) Convert the path p into an actual driving path for the navigation vehicle.

[0049] The embodiment of the present application also provides a storage medium, wherein the storage medium stores a computer program, and the computer program is configured to execute the method when the computer program runs.

[0050] The embodiment of the present application also provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to execute the method.

Claims

1. A method for route planning for a drive test in real-time traffic, characterized in that, The method comprises the following steps: (1) Collect road network information, and abstract all passable streets in the current road network as a mixed graph , and abstract streets that have not been passed through as a graph subset of edge set ; (2) Find the picture The path from the current position to the specified destination position. traverse the set Find the shortest path by examining each edge in the graph at least once; where the current position is set as the vertex. The endpoint position is set as the vertex. The steps include: (21) Encoding conversion, specifically: converting undirected edges, directed edges and orientations of undirected edges in the edge set into vector form, converting the mixed graph into a directed graph ; wherein the undirected edge represents a double street, and the directed edge represents a single street; the formula is as follows: Set edge set There is no direction to the edge Items, denoted as sets , with directed edge A set of items. ; Define the length as A set of integer vectors; Then the first of each vector The elements are a set A permutation of the middle edge; after Each element is composed of Composition, component values Or 1, representing the two orientations of an undirected edge, i.e., for the set Assign direction to undirected edges in the array; (22) generating N initial individuals a population, specifically: let be a positive even integer, randomly generate integer vectors of length to obtain a population of individuals ; wherein, denotes the size of the population; each vector , the first elements represent the traversal order of the edges in the edge set, assigned a random permutation of ; the last elements represent the random orientation of the undirected edges, randomly assigned to be either or ; (23) evolving the population; comprising the following steps: (221) calculating the fitness of the individuals, and saving the individual with the minimum fitness as the current optimal solution; (222) The population with higher fitness Each individual is replaced with the current optimal solution; specifically: for each individual in the current population... , assuming The permutations corresponding to the elements are: ,back The corresponding elements vector is ,right and Update the formula as follows: Generation interval Random number within the interval ; If , swap random two elements in permutation to get new permutation , while replacing with random 0-1 vector of length ; If then do not change, update in a simulated annealing fashion; randomly access each element in , let the element accessed the th time be , if replacing this element with will make the fitness of the individual smaller, then do it, otherwise, do it with probability ; (223) replacing each individual in the new population with a better solution in its neighborhood, and if the optimal solution in the new population is better than the current optimal solution, updating the current optimal solution; otherwise, keeping the current optimal solution; (224) Let time Determine the time Has the preset time limit of 2000 been reached? If so, return the current optimal solution and the first vector. The element is the shortest path that iterates through all edges in E. Otherwise, repeat steps (222)-(223); (3) converting the path into an actual travel path of the navigation vehicle.

2. The route planning method in real-time road condition based on road test according to claim 1, wherein, The specific steps (221) are as follows: Initial command Define individual The fitness is: from Starting from point, according to Center front Traverse the collection in the order of the elements. From each side, reach the destination. The length of the path between points, and the distance between points in the path are as described above in the figure. The shortest distance in; where, This indicates the preset time cycle for population evolution.

3. A storage medium, characterized by The storage medium has stored therein a computer program, wherein the computer program is configured to execute the method of any one of claims 1-2 when executed.

4. An electronic device, characterized by comprising: The device comprises a memory and a processor, wherein the memory has stored therein a computer program, and the processor is configured to execute the computer program to execute the method of any one of claims 1-2.

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