CN113096425A - Dispatching method and system for automatic driving patrol car applied to large scene - Google Patents

Dispatching method and system for automatic driving patrol car applied to large scene Download PDF

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CN113096425A
CN113096425A CN202110335081.0A CN202110335081A CN113096425A CN 113096425 A CN113096425 A CN 113096425A CN 202110335081 A CN202110335081 A CN 202110335081A CN 113096425 A CN113096425 A CN 113096425A
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贾立东
王雷
王更泽
王毅
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Ziqing Zhixing Technology Beijing Co ltd
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Abstract

The invention discloses a scheduling method of an automatic driving patrol car applied to a large-scale scene, which comprises the following steps: step 1, establishing a scene road map model G according to an actual road map of an actual scene, wherein the scene road map model G comprises a vertex and an edge formed by a connecting line segment between the two vertexes, the vertex represents an intersection of each actual road in the actual scene, the edge represents each actual road in the actual scene, and the side length corresponds to the weight of the edge; step2, determining the number N of vehicles required by an actual scene according to the scene road network graph model G; step 3, dividing the actual scene into N areas by adopting different dividing methods, evaluating each dividing result, and selecting an optimal dividing result according to the results; and 4, planning the driving route of the vehicle in each area according to the divided areas. The invention can reasonably configure the number of patrol cars, divide respective task areas and plan patrol routes according to the actual situation of a scene, and can provide a set of scheduling scheme for automatically driving vehicles in scenic spots, factories or similar large-scale scenes.

Description

Dispatching method and system for automatic driving patrol car applied to large scene
Technical Field
The invention relates to the technical field of automatic driving, in particular to a dispatching method and a dispatching system of an automatic driving patrol car applied to a large scene.
Background
In some large-scale scenes, a patrol mechanism is usually needed to realize functions such as security and the like, and the scenes have the characteristic of fixed routes, so that the automatic driving vehicle is increasingly applied. However, the floor space and the total route mileage of a large scene are usually large, and are limited by the running speed and the cruising range of the existing automatic driving vehicle, and if one automatic driving vehicle is used for completing all patrol tasks, the efficiency is low, and the effect is obviously poor.
Disclosure of Invention
It is an object of the present invention to provide a method and system for dispatching autonomous patrol cars for use in large scenes to overcome or at least alleviate at least one of the above-mentioned drawbacks of the prior art.
In order to achieve the purpose, the invention provides a scheduling method of an automatic driving patrol car applied to a large scene, which comprises the following steps:
step 1, establishing a scene road network graph model G according to an actual road map of an actual scene, wherein the scene road network graph model G comprises vertexes and edges formed by connecting line segments between the two vertexes, the vertexes represent intersections of all actual roads in the actual scene, the edges represent all the actual roads in the actual scene, and the weights of the edges represent the lengths of the corresponding actual roads;
step2, determining the number N of vehicles required by an actual scene according to the scene road network graph model G;
step 3, dividing the actual scene into N areas by adopting different dividing methods, evaluating each dividing result, and selecting an optimal dividing result according to the results;
and 4, planning the driving route of the vehicle in each area according to the divided areas.
Further, in step 3, the division results are evaluated by the standard deviation value calculated by equation (4) to minimize the mileage value in each divided area:
Figure BDA0002997225020000021
in the formula, WsumRepresenting the sum of the weights of the edges of the map scene road network map model G,
Figure BDA0002997225020000022
the mileage value in the kth region of the division is represented, and k is 1,2 … … N.
Further, the step 3 adopts a loop division method, and specifically comprises the following steps:
step a1, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
step b1, judging whether the scene road network graph model G has an edge which is not recorded in the step a1, and if the edge is judged to have the edge, indicating that a loop exists, entering the step c 1;
step c1, traversing the former vertex of one of the return edges to the latter vertex of the return edge according to the traversing direction of the step a1, and obtaining a loop;
and d, sequentially processing other return edges judged in the step b1 according to the method provided by the step c1 to obtain all loops in the scene road network graph model G.
Further, step 3 adopts a cutting edge dividing method, and specifically comprises the following steps:
step a2, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
b2, judging whether the scene road network graph model G has a cut edge, if so, entering the step c 2;
step c2, splitting the scene road network graph model G into two possible closed regions by deleting one of the cut edges;
and c, sequentially processing the other cut edges determined in the step b2 according to the method provided by the step c2 to obtain other possible closed areas in the scene road network graph model G.
Further, in step 3, a dividing method combining a loop dividing method and a cutting edge dividing method is adopted, which specifically comprises the following steps:
step a3, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
b3, judging whether the scene road network graph model G has a cut edge, if so, entering the step c 3;
step c3, splitting the scene road network graph model G into two possible closed regions by deleting one of the cut edges, and entering step d3 under the condition that the evaluation value difference of each division result is large;
step d3, determining whether the region with larger evaluation value in step c3 has a return edge, if yes, entering step e 3;
step e3, traversing the previous vertex of one of the return edges to the next vertex of the return edge according to the traversing direction of the step a3, and obtaining a loop;
the other return edges determined in step d3 are processed in sequence according to the method provided in step e3 to obtain all loops in the region with larger evaluation value.
Further, if the number of the divided regions in step 3 still cannot be equal to the required number of vehicles N determined in step2, the following two cases are further processed respectively according to the number of the divided regions:
the first case: if the number of the regions is larger than N, combining adjacent regions with common edges to enable the number of the divided regions to reach N;
the second case: if the number of the regions is less than N, performing secondary splitting on some regions, wherein the splitting method specifically comprises the following steps:
checking whether a return edge exists in the region, if so, indicating that a loop exists in the region, and continuously splitting the region into independent regions; if not, the area cannot be continuously split.
Further, step 4 specifically includes:
setting an initial point for the patrol car in each divided area, and setting patrol counters for each edge, wherein the initialization is 0; starting from an initial point, adding 1 to a counter of each edge when the patrol car passes through one edge; when a vertex is positioned and which side is required to be patrolled next, selecting the side with the smallest counter value in all the rest sides connected with the vertex, and if the counter values in all the rest sides connected with the vertex are equal, selecting the side with the longest previous patrol finishing time and the longest current patrol in all the sides; in the process, if a certain patrol car passes through the public edge, the counter of the corresponding edge of the area where the patrol car is located and the counter of the corresponding edge of the adjacent area are added with 1.
Further, N is calculated by formula (1):
Figure BDA0002997225020000031
wherein the round (#) function represents rounding, WsumAnd the weight sum of each side of the map scene road network model G is represented, X represents the endurance mileage of the automatic driving patrol trolley, and C represents the number of charging times of the trolley every day.
The invention also provides a dispatching system of the automatic driving patrol car applied to a large-scale scene, which comprises the following components:
the model establishing device is used for establishing a scene road map model G according to an actual road map of an actual scene, wherein the scene road map model G comprises vertexes and edges formed by connecting line segments between the two vertexes, the vertexes represent intersections of all actual roads in the actual scene, the edges represent all the actual roads in the actual scene, and the weights of the edges represent the lengths of the corresponding actual roads;
the vehicle number calculating device is used for determining the number N of vehicles required by an actual scene according to the scene road network graph model G;
the region dividing device is used for dividing the actual scene into N regions by adopting different dividing methods, evaluating each dividing result and selecting an optimal dividing result according to the evaluation result;
and the route planning device is used for planning the driving route of the vehicle in each area according to the divided areas.
Further, the area dividing device includes a loop dividing unit and/or a cut edge dividing unit, wherein:
the loop division unit includes:
the vertex searching subunit is used for traversing all the vertices of the scene road map model G by adopting DFS and simultaneously recording edges passed by in the traversal process;
a back edge judgment subunit, configured to judge whether there is an edge that is not recorded in step a1 in the scene road map model G, where the edge is marked as a "back edge", and if it is judged that there is a back edge, it indicates that there is a loop;
the closed region acquisition subunit is used for traversing a front vertex of one return edge to a rear vertex of the return edge according to the traversal direction of the vertex search subunit under the condition that a loop exists, so as to obtain the loop;
the closed region acquisition subunit finishes the processing of other return edges judged by the return edge judgment subunit in sequence to obtain all loops in the scene road network graph model G;
the cutting edge dividing unit includes:
the vertex searching subunit is used for traversing all vertexes of the scene road network graph model G and simultaneously recording edges passed in the traversing process;
a cut edge judging subunit, configured to judge whether a cut edge exists in the scene road network graph model G, and if so, judge that a cut edge exists;
the closed region acquisition subunit is used for dividing the scene road network graph model G into two possible closed regions by deleting one of the cut edges under the condition that the cut edges exist;
and the closed area acquisition subunit sequentially processes other cut edges obtained by judgment of the cut edge judgment subunit to obtain other possible closed areas in the scene road network graph model G.
The invention can reasonably configure the number of patrol cars, divide respective task areas and plan patrol routes according to the actual situation of a scene, and can provide a set of scheduling scheme for automatically driving vehicles in scenic spots, factories or similar large-scale scenes.
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Fig. 1 is a scene road network graph model in the scheduling method according to the embodiment of the present invention.
Fig. 2 is a schematic diagram of a loop and a back edge according to an embodiment of the invention.
Fig. 3 is an exemplary diagram of a cutting edge according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of region division and merging according to an embodiment of the present invention
Fig. 5 is an example of vehicle driving route planning provided by the embodiment of the invention.
Fig. 6 is a flowchart of an automatic driving vehicle scheduling method according to an embodiment of the present invention.
Fig. 7 is a road network map model of a large scene.
Fig. 8 is a DFS traversal result of fig. 7.
Fig. 9 is a result of the region division of fig. 7.
Detailed Description
The invention is described in detail below with reference to the figures and examples.
As shown in fig. 6, the method for scheduling an autonomous driving patrol car applied in a large-scale scene according to the embodiment of the present invention includes:
step 1, establishing a scene road map model G according to an actual road map of an actual scene. The scene road network graph model G is an undirected graph and comprises vertexes and edges formed by connecting line segments between the two vertexes. The vertex represents the intersection of each actual road in the actual scene, the side represents each actual road in the actual scene, and the weight of the side represents the length of the corresponding actual road.
Referring to FIG. 1, FIG. 1 shows a simple road network graph model G, which includes a first vertex V1Second vertex V2And a third vertex V3And respectively represent intersections of actual roads. It also comprises an edge, in particular a first edge E12A second side E13And a third side E23Respectively, representing the actual road. And, each edge is provided with a corresponding weight, such as: first side E12Has a weight of W12Second side E13Has a weight of W13And a third side E23Has a weight of W23And respectively represent the length of the actual road.
And 2, determining the number N of vehicles required by the actual scene.
As a preferred embodiment of step2, it can be calculated by equation (1):
(1)
Figure BDA0002997225020000051
Wsum＝∑Wij (2)
where the round (×) function represents rounding, it can be understood that: under an ideal condition, the scene is divided into N areas, namely the required number of vehicles is obtained, and the mileage of each area is WsumXC, each patrol trolley can run for one day in the area; wsumThe sum of the weights of all sides of the graph scene road network graph model G is obtained by calculation according to the formula (2); x represents the endurance mileage of the automatic driving patrol trolley, and C represents the charging times of the trolley every day, and the value is generally 1 or 2.
It should be noted that other implementations may be selected according to the actual situation, such as: from the perspective of cost, the purchasing budget is B, the price of the patrol car is P, and the required number of vehicles N is represented by formula (3):
Figure BDA0002997225020000061
such implementations are not enumerated here.
Step 3, dividing the actual scene into N areas by adopting different dividing methods, evaluating each dividing result by the standard difference value which is obtained by calculating the minimum mileage value in each divided area according to the formula (4), and selecting the optimal dividing result according to the standard difference value:
Figure BDA0002997225020000062
in the formula (I), the compound is shown in the specification,
Figure BDA0002997225020000063
the mileage value in the kth region of the division is represented, and k is 1,2 … … N. .
By the division mode, the divided N regional mileage values can be enabled
Figure BDA0002997225020000064
Reach the average value W as much as possiblesumand/N, approaching an ideal state. In addition to this, the division result can be provided by equation (5)
Figure BDA0002997225020000065
And
Figure BDA0002997225020000066
the sum of the absolute values of the differences is evaluated:
Figure BDA0002997225020000067
wherein, each divided area should be as close as possible to the closed area surrounded by the loop.
In one embodiment, the method adopts a loop division method, and specifically comprises the following steps:
step a1, traversing all the vertices of the scene roadmap model G of step 1 by using DFS (depth first search), and simultaneously recording the edges passed through during the traversal.
And b1, judging whether the scene road network graph model G has an edge which is not recorded in the step a1, and if the edge is judged to have the edge, indicating that a loop exists, entering the step c 1.
And step c1, traversing the previous vertex of one of the return edges to the next vertex of the return edge according to the traversing direction of the step a1, and obtaining a loop.
And d, sequentially processing other return edges judged in the step b1 according to the method provided by the step c1 to obtain all loops in the scene road network graph model G.
For example: as shown in FIG. 2, according to the DFS traversal method, each vertex has a traversal order V1→V2→V3→V4After the completion of the traversal, all edges are covered with E14Has not been passed through and is therefore a return edge. From one of the vertices V4Begin to trace back to another vertex V in the order of DFS1The path followed is added with E14Then V is1→V2→V3→V4→V1Is a loop.
In another embodiment, the method adopts a cutting edge dividing method, and specifically comprises the following steps:
step a2, traversing all the vertices of the scene roadmap model G of step 1 by using DFS (depth first search), and simultaneously recording the edges passed through during the traversal.
And b2, judging whether the scene road network graph model G has a cut edge, and if so, entering the step c 2. In the scene road network graph model G, if the cut edge is deleted, all the vertices cannot be connected.
And c2, dividing the scene road network graph model G into two possible closed regions by deleting one of the cut edges.
And c, sequentially processing the other cut edges determined in the step b2 according to the method provided by the step c2 to obtain other possible closed areas in the scene road network graph model G.
The method comprises the steps of searching for cut edges in a scene road network graph model G by using a cut edge dividing method, dividing the whole graph model G into completely independent areas according to the cut edges, and reducing coupling among the areas, namely overlapping road sections.
For example: as shown in FIG. 3, E45And E56All are cut edges, delete E45Or E56Thereafter, the original model is divided into 2 unconnected parts. The m cut edges can divide the graph model into m +1 regions. In an actual road network, once the cut edge is interrupted for some reason, the areas where the cut edge is located cannot be communicated, so that the patrol effect is seriously influenced. The area is divided by cutting edges, so that the problems can be effectively avoided. The search for the cut edge may employ the Tarjan algorithm, which is a DFS (depth first search) based algorithm.
In one embodiment, the actual scene is divided into N regions by combining the loop division method and the edge-cutting division method.
That is, when dividing the region, one or a combination of the two methods can be selected according to the actual scene situation and the dividing effect. For an actual scene without a cut edge, only a loop can be used; for practical scenarios where there may be no loops, only cut edges can be used; and for the actual scene with both cut edges and loops, using the loops and the cut edges, and finally evaluating according to the standard deviation of the mileage values of the areas obtained by calculation to select the optimal division result. Such as: the method of step 3 is implemented as follows:
step a3, traversing all vertexes of the scene road map model G in the step 1 by adopting DFS, and simultaneously recording edges passed in the traversing process;
b3, judging whether the scene road network graph model G has a cut edge, if so, entering the step c 3;
step c3, splitting the scene road network graph model G into two possible closed regions by deleting one of the cut edges, and entering step d3 under the condition that the evaluation value difference of each division result is large;
step d3, determining whether the region with larger evaluation value in step c3 has a return edge, if yes, entering step e 3;
step e3, traversing the previous vertex of one of the return edges to the next vertex of the return edge according to the traversing direction of the step a3, and obtaining a loop;
the other return edges determined in step d3 are processed in sequence according to the method provided in step e3 to obtain all loops in the region with larger evaluation value.
In one embodiment, in the case where the number of regions divided by the loop division method or the edge division method in the above embodiment still cannot be equal to the required number of vehicles N determined in step2, the following two cases are further processed in a secondary manner, respectively, according to the number of divided regions:
the first case: if the number of the regions is larger than N, combining adjacent regions with common edges, and enabling the number of the divided regions to reach N. For example: as shown in FIG. 4, E23Is the common boundary between the area A and the area B, and is combined into the inner edge of the area C.
The second case: if the number of the regions is less than N, performing secondary splitting on some regions, wherein the splitting method specifically comprises the following steps:
checking whether a return edge exists in the region, if so, indicating that a loop exists in the region, and continuously splitting the independent region; if the sum of the weights is not greater than the preset weight, the area cannot be continuously split, the number of patrol cars can be reduced, or more than one patrol car is put in the area with the greater sum of the weights.
And 4, planning the driving route of the vehicle in each area according to the divided areas.
In each divided area, an initial point is set for the patrol car, and a patrol counter is set for each edge, and the initialization is 0. The patrol car starts from an initial point, and the counter of each side is increased by 1 every time the patrol car passes through one side. When a vertex is positioned and which side is required to be patrolled next, selecting the side with the smallest counter value in all the other sides connected with the vertex, and if the counter values in all the other sides connected with the vertex are equal, selecting the side with the longest previous patrol finishing time and the longest current patrol finishing time in all the sides. Because a common boundary exists between the areas, communication must be established between the patrol cars to synchronize the counter values of the common edges of the areas, that is, if a patrol car passes through the common edge, the counter values of the corresponding edges of the area where the patrol car is located and the adjacent area should be increased by 1.
As shown in fig. 4, when the autonomous patrol car in the area a is at the peak V1When, the next step has E12And E14Two roads being selectable, but road E12Ratio E14The counter value of (a) is smaller, and thus the patrol car will select E12This road is patrolled. E23Is a public boundary road of the area A and the area B, and when the patrol car in the area B patrols the road, the area A and the area B are opposite to the area E23Counter C of23Will add 1 to become 3.
The rationality of using this route planning strategy is that the patrol car can select a road with a small number of current patrol to patrol, thereby avoiding that some roads are not patrolled for a long time, providing patrol efficiency of public boundaries among regions, and avoiding complex route planning inside regions.
The following describes a method for scheduling an autonomous patrol car, with a road network in a large scene as a specific embodiment.
Step 1, establishing a scene road map model G according to an actual road map of an actual scene: fig. 7 is a road network graph model established according to the large-scale scene. As can be seen from the figure, there are 9 vertices (intersections) and 11 edges (roads), and the weight of each road is its length.
Step2, determining the number N of vehicles required by the actual scene: the sum of the weights of all the edges in the road network graph model is 59, and in this embodiment, assuming that the driving range of the autonomous vehicle is 18, and only one charging is required within one day, the number of required vehicles is N ═ round (59/(18 × 1)) -3.
Step 3, dividing the actual scene into N areas by adopting different dividing methods, evaluating each dividing result by the standard difference value which is obtained by calculating the minimum mileage value in each divided area according to the formula (4), and selecting the optimal dividing result according to the standard difference value:
in both the loop division method and the edge division method, DFS (depth first search) is required for the graph model. DFS is performed for fig. 7 and the result is shown in fig. 8.
As can be seen from the traversal result of FIG. 8, E14、E34And E79Not passed, as a back edge, but according to the Tarjan algorithm, E47For cutting edges, i.e. deleting E47Thereafter, the entire graph model is divided into 2 unconnected segments. According to the loop division method, using E14、E34And E79Three back edges, 4 loops in the graph model can be obtained:
loop 1: v1→V2→V3→V4→V1
Loop 2: v3→V5→V6→V4→V3
Loop 3: v1→V2→V3→V5→V6→V4→V1
Loop 4: v7→V8→V9→V7
The sum of the weights of 4 loops is calculated respectively, and the loop 1 is 20, the loop 2 is 22, the loop 3 is 36, and the loop 4 is 17. Since the number of vehicles is determined to be 3 in Step2 in the previous Step, 3 loops need to be selected from the 4 loops. It is easy to see that loop 3 should be discarded because if loop 3 is selected, the whole graph model is only divided into two parts, i.e. loop 3 and loop 4, and the sum of the weights of loop 3 is 36, so that the standard deviation value of the divided two parts is large and obviously cannot meet the requirement. Therefore, according to the loop division method, the entire graph model should be divided into three regions, loop 1, loop 2, and loop 4. If the edge is divided according to the edge-cutting method, the edge is cut47Dividing the whole graph model into two parts of a loop 3 and a loop 4, wherein the sum of weights of the loop 3 and the loop 4 is 36 and 17 respectively, evaluating by using standard deviation to obtain a standard deviation value of 10.6, and uniformly dividing the graph model into two regions with a theoretical mean value of 29, and two values of 36 and 17All of which are close to or even out of the standard deviation fluctuation range of the mean (29-10.6, 29+10.6), and thus it can be seen that this way of partitioning is not optimal. Therefore, it is necessary to continue to split loop 3 into two parts, loop 1 and loop 2, in conjunction with the loop division method.
In summary, the division result of the entire graph model G is shown in fig. 9, where loop 1 constitutes area a, loop 2 constitutes area B, and loop 4 constitutes area C.
And 4, planning the driving route of the vehicle in each area according to the divided areas:
firstly, determining the starting point of the automatic driving patrol car in each area, wherein V1 is selected as the starting point of the area A, V5 is selected as the starting point of the area B, and V8 is selected as the starting point of the area C.
Then, the counter values of all edges in each area are initialized to 0. As can be seen from FIG. 9, road E34A common boundary road for region A and region B, and road E47A common boundary road for the area a, the area B and the area C. Table 1 shows the simulation of the route planning for the autonomous patrol car in the three regions A, B and C in the graphical model shown in fig. 9.
Table 1 automatic driving prowl car route planning analog table
Figure BDA0002997225020000101
The embodiment of the invention provides a dispatching system of an automatic driving patrol car applied to a large-scale scene, which comprises a model establishing device, a car number calculating device, an area dividing device and a route planning device, wherein the model establishing device comprises a model establishing device, a car number calculating device, a route planning device and a control device, wherein the model establishing device comprises a control module, a control module and a control module, the control module comprises:
the model establishing device is used for establishing a scene road map model G according to an actual road map of an actual scene, wherein the scene road map model G comprises vertexes and edges formed by connecting line segments between the two vertexes, the vertexes represent intersections of all actual roads in the actual scene, the edges represent all the actual roads in the actual scene, and the weights of the edges represent the lengths of the corresponding actual roads.
As shown in figure 1 of the drawings, in which,FIG. 1 shows a simple road network graph model G, which includes a first vertex V1Second vertex V2And a third vertex V3And respectively represent intersections of actual roads. It also comprises an edge, in particular a first edge E12A second side E13And a third side E23Respectively, representing the actual road. And, each edge is provided with a corresponding weight, such as: first side E12Has a weight of W12Second side E13Has a weight of W13And a third side E23Has a weight of W23And respectively represent the length of the actual road.
And the vehicle number calculating device is used for determining the number N of the vehicles required by the actual scene according to the scene road network graph model G.
As a preferred embodiment of the vehicle number calculating device, it can be calculated by equation (1):
Figure BDA0002997225020000111
Wsum＝∑Wij (2)
where the round (×) function represents rounding, it can be understood that: under an ideal condition, the scene is divided into N areas, namely the required number of vehicles is obtained, and the mileage of each area is WsumXC, each patrol trolley can run for one day in the area; wsumThe sum of the weights of all sides of the graph scene road network graph model G is obtained by calculation according to the formula (2); x represents the endurance mileage of the automatic driving patrol trolley, and C represents the charging times of the trolley every day, and the value is generally 1 or 2.
It should be noted that other implementations may be selected according to the actual situation, such as: from the perspective of cost, the purchasing budget is B, the price of the patrol car is P, and the required number of vehicles N is represented by formula (3):
Figure BDA0002997225020000112
such implementations are not enumerated here.
The region dividing device is used for dividing the actual scene into N regions by adopting different dividing methods, evaluating each dividing result and selecting the optimal dividing result according to the result.
And the route planning device is used for planning the driving route of the vehicle in each area according to the divided areas.
In one embodiment, the area dividing apparatus includes a loop dividing unit and/or a cut edge dividing unit and an evaluation unit, wherein:
the evaluation unit is used for evaluating each division result by the standard deviation value which is obtained by calculating the minimized mileage value in each divided area according to the formula (4), and selecting the optimal division result according to the standard deviation value:
Figure BDA0002997225020000121
in the formula (I), the compound is shown in the specification,
Figure BDA0002997225020000122
the mileage value in the kth region of the division is represented, and k is 1,2 … … N. .
By the division mode, the divided N regional mileage values can be enabled
Figure BDA0002997225020000123
Reach the average value W as much as possiblesumand/N, approaching an ideal state.
Wherein, each divided area should be as close as possible to the closed area surrounded by the loop.
The loop dividing unit comprises a vertex searching subunit, a back edge judging subunit and a closed region acquiring subunit, wherein:
and the vertex searching subunit is used for traversing all the vertices of the scene road network graph model G by adopting DFS and simultaneously recording the edges passed in the traversal process.
And the back edge judging subunit is used for judging whether an edge which is not recorded in the step a1 exists in the scene road network graph model G, the edge is marked as a back edge, and if the back edge exists, the existence of a loop is shown.
And the closed region acquisition subunit is used for searching the traversal direction of the subunit according to the vertex under the condition that the loop exists, traversing the front vertex of one return edge to the back vertex of the return edge, and obtaining the loop.
And the closed region acquisition subunit finishes the processing of other return edges judged by the return edge judgment subunit in sequence to obtain all loops in the scene road network graph model G.
For example: as shown in FIG. 2, according to the DFS traversal method, each vertex has a traversal order V1→V2→V3→V4After the completion of the traversal, all edges are covered with E14Has not been passed through and is therefore a return edge. From one of the vertices V4Begin to trace back to another vertex V in the order of DFS1The path followed is added with E14Then V is1→V2→V3→V4→V1Is a loop.
The cutting edge dividing unit comprises a vertex searching subunit, a cutting edge judging subunit and a closed region acquiring subunit, wherein:
and the vertex searching subunit is used for traversing all the vertices of the scene road network graph model G by adopting DFS and simultaneously recording the edges passed in the traversal process.
And the cut edge judging subunit is used for judging whether a cut edge exists in the scene road network graph model G or not, and if so, judging that the cut edge exists.
And the closed region acquisition subunit is used for splitting the scene road network graph model G into two possible closed regions by deleting one of the cut edges under the condition that the cut edges exist.
And the closed area acquisition subunit sequentially processes other cut edges obtained by judgment of the cut edge judgment subunit to obtain other possible closed areas in the scene road network graph model G.
For example: as shown in FIG. 3, E45And E56All are cut edges, delete E45Or E56Then, the original drawingThe model is divided into 2 unconnected parts. The m cut edges can divide the graph model into m +1 regions. In an actual road network, once the cut edge is interrupted for some reason, the areas where the cut edge is located cannot be communicated, so that the patrol effect is seriously influenced. The area is divided by cutting edges, so that the problems can be effectively avoided. The search for the cut edge may employ the Tarjan algorithm, which is a DFS (depth first search) based algorithm.
In one embodiment, if the number of the divided regions by the loop dividing unit and/or the cut edge dividing unit in the above embodiments still cannot be equal to the required number of vehicles N determined by the vehicle number calculating device, the following two cases are performed respectively according to the number of the divided regions:
the first case: if the number of the regions is larger than N, combining adjacent regions with common edges, and enabling the number of the divided regions to reach N. For example: as shown in FIG. 4, E23Is the common boundary between the area A and the area B, and is combined into the inner edge of the area C.
The second case: if the number of the regions is less than N, performing secondary splitting on some regions, wherein the splitting method specifically comprises the following steps:
checking whether a return edge exists in the region, if so, indicating that a loop exists in the region, and continuously splitting the independent region; if the sum of the weights is not greater than the preset weight, the area cannot be continuously split, the number of patrol cars can be reduced, or more than one patrol car is put in the area with the greater sum of the weights.
The route planning device sets an initial point for the patrol car in each divided area, sets a patrol counter for each side, and initializes the patrol counters to 0. The patrol car starts from an initial point, and the counter of each side is increased by 1 every time the patrol car passes through one side. When a vertex is positioned and which side needs to be patrolled next is determined, the side with the smallest counter value in all the other sides connected with the vertex is selected, and if the counter values in all the other sides connected with the vertex are equal, the side with the longest previous patrol completion time and the longest current patrol time in all the sides is selected. Because a common boundary exists between the areas, communication must be established between the patrol cars to synchronize the counter values of the common edges of the areas, that is, if a patrol car passes through the common edge, the counter values of the corresponding edges of the area where the patrol car is located and the adjacent area should be increased by 1.
As shown in fig. 4, when the autonomous patrol car in the area a is at the peak V1When, the next step has E12And E14Two roads being selectable, but road E12Ratio E14The counter value of (a) is smaller, and thus the patrol car will select E12This road is patrolled. E23Is a public boundary road of the area A and the area B, and when the patrol car in the area B patrols the road, the area A and the area B are opposite to the area E23Counter C of23Will add 1 to become 3.
Finally, it should be pointed out that: the above examples are only for illustrating the technical solution of the present invention, and are not limited thereto. Those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may be modified, or some technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (10)

1. A scheduling method of an automatic driving patrol car applied to a large scene is characterized by comprising the following steps:
step 1, establishing a scene road map model G according to an actual road map of an actual scene, wherein the scene road map model G comprises vertexes and edges formed by connecting line segments between the two vertexes, the vertexes represent intersections of all actual roads in the actual scene, the edges represent all the actual roads in the actual scene, and the weights of the edges represent the lengths of the corresponding actual roads;
step2, determining the number N of vehicles required by an actual scene according to the scene road network graph model G;
step 3, dividing the actual scene into N areas by adopting different dividing methods, evaluating each dividing result, and selecting an optimal dividing result according to the results;
and 4, planning the driving route of the vehicle in each area according to the divided areas.
2. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 1, wherein in the step 3, the division results are evaluated by the standard deviation value which is calculated by the formula (4) and minimizes the mileage value in each divided area:
Figure FDA0002997225010000011
in the formula, WsumRepresenting the sum of the weights of the edges of the map scene road network map model G,
Figure FDA0002997225010000012
the mileage value in the kth region of the division is represented, and k is 1,2 … … N.
3. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 1, wherein the step 3 adopts a loop division method, and specifically comprises the following steps:
step a1, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
step b1, judging whether the scene road network graph model G has an edge which is not recorded in the step a1, and if the edge is judged to have the edge, indicating that a loop exists, entering the step c 1;
step c1, traversing the former vertex of one of the return edges to the latter vertex of the return edge according to the traversing direction of the step a1, and obtaining a loop;
and d, sequentially processing other return edges judged and obtained in the step b1 according to the method provided by the step c1 to obtain all loops in the scene road network graph model G.
4. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 1, wherein the step 3 adopts a cutting edge division method, and specifically comprises the following steps:
step a2, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
b2, judging whether the scene road network graph model G has a cut edge, if so, entering the step c 2;
step c2, splitting the scene road network graph model G into two possible closed areas by deleting one of the cut edges;
and according to the method provided by the step c2, sequentially processing the other cut edges obtained by the step b2 to obtain other possible closed areas in the scene road network graph model G.
5. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 1, wherein in the step 3, a dividing method combining a loop dividing method and a cutting edge dividing method is adopted, and the dispatching method specifically comprises the following steps:
step a3, traversing all vertexes of the scene road map model G in the step 1, and simultaneously recording edges passed in the traversing process;
b3, judging whether the scene road network graph model G has a cut edge, if so, entering the step c 3;
step c3, splitting the scene road network graph model G into two possible closed areas by deleting one of the cut edges, and entering step d3 under the condition that the evaluation value difference of each division result is large;
step d3, determining whether the region with larger evaluation value in step c3 has a return edge, if yes, entering step e 3;
step e3, traversing the previous vertex of one of the return edges to the next vertex of the return edge according to the traversing direction of the step a3, and obtaining a loop;
and e, sequentially processing other loops obtained by the judgment of the step d3 according to the method provided by the step e3 to obtain all loops in the area with larger evaluation value.
6. The dispatching method of the automatic driving patrol cars in large scenes according to any one of claims 1 to 5, wherein the number of the divided areas in step 3 still cannot be equal to the required number of vehicles N determined in step2, and then the secondary processing is respectively carried out according to the number of the divided areas according to the following two situations:
the first case: if the number of the regions is larger than N, combining adjacent regions with common edges to enable the number of the divided regions to reach N;
the second case: if the number of the regions is less than N, performing secondary splitting on some regions, wherein the splitting method specifically comprises the following steps:
checking whether a return edge exists in the region, if so, indicating that a loop exists in the region, and continuously splitting the region into independent regions; if not, the area cannot be continuously split.
7. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 6, wherein the step 4 specifically comprises:
setting an initial point for the patrol car in each divided area, and setting patrol counters for each edge, wherein the initialization is 0; starting from an initial point, adding 1 to a counter of each edge when the patrol car passes through one edge; when a vertex is positioned and which side is required to be patrolled next, selecting the side with the smallest counter value in all the other sides connected with the vertex, and if the counter values in all the other sides connected with the vertex are equal, selecting the side with the longest previous patrol finishing time and the longest current patrol time in all the sides; in the process, if a certain patrol car passes through the public edge, the counter of the corresponding edges of the area where the patrol car is located and the adjacent area is increased by 1.
8. The dispatching method of the automatic driving patrol car applied to the large-scale scene as claimed in claim 7, wherein N is calculated by the formula (1):
Figure FDA0002997225010000031
wherein the round (#) function represents rounding, WsumAnd the weight sum of each side of the map scene road network model G is represented, X represents the endurance mileage of the automatic driving patrol trolley, and C represents the charging times of the trolley every day.
9. The utility model provides a dispatch system that is applied to autopilot cruiser under large-scale scene which characterized in that includes:
the model establishing device is used for establishing a scene road map model G according to an actual road map of an actual scene, wherein the scene road map model G comprises vertexes and edges formed by connecting line segments between the two vertexes, the vertexes represent intersections of all actual roads in the actual scene, the edges represent all the actual roads in the actual scene, and the weights of the edges represent the lengths of the corresponding actual roads;
the vehicle number calculating device is used for determining the number N of vehicles required by an actual scene according to the scene road network graph model G;
the region dividing device is used for dividing the actual scene into N regions by adopting different dividing methods, evaluating each dividing result and selecting an optimal dividing result according to the result;
and the route planning device is used for planning the driving route of the vehicle in each area according to the divided areas.
10. The dispatching system of the automatic driving patrol car applied to the large-scale scene as claimed in claim 9, wherein the area dividing means comprises a loop dividing unit and/or a cut edge dividing unit, wherein:
the loop division unit includes:
the vertex searching subunit is used for traversing all the vertices of the scene road map model G by adopting DFS and simultaneously recording edges passed by in the traversal process;
a back edge judgment subunit, configured to judge whether there is an edge that is not recorded in step a1 in the scene road map model G, where the edge is marked as a "back edge", and if it is judged that there is a back edge, it indicates that there is a loop;
the closed region acquisition subunit is used for searching the traversal direction of the subunit according to the vertexes under the condition that a loop exists, traversing the front vertex of one return edge to the back vertex of the return edge, and obtaining the loop;
the closed region acquisition subunit finishes the processing of other return edges judged by the return edge judgment subunit in sequence to obtain all loops in the scene road network graph model G;
the cutting edge dividing unit includes:
the vertex searching subunit is used for traversing all the vertices of the scene road map model G by adopting DFS and simultaneously recording edges passed by in the traversal process;
a cut edge judging subunit, configured to judge whether a cut edge exists in the scene road network graph model G, and if so, judge that a cut edge exists;
the closed region acquisition subunit is used for dividing the scene road network graph model G into two possible closed regions by deleting one of the cut edges under the condition that the cut edges exist;
and the closed area acquisition subunit sequentially processes other cut edges obtained by judgment of the cut edge judgment subunit to obtain other possible closed areas in the scene road network graph model G.
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