CN114812584A - Path planning method, system, storage medium and equipment - Google Patents
Path planning method, system, storage medium and equipment Download PDFInfo
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- CN114812584A CN114812584A CN202110071205.9A CN202110071205A CN114812584A CN 114812584 A CN114812584 A CN 114812584A CN 202110071205 A CN202110071205 A CN 202110071205A CN 114812584 A CN114812584 A CN 114812584A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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Abstract
The application discloses a path planning method, a system, a storage medium and equipment, and belongs to the technical field of map navigation. The path planning method comprises the following steps: carrying out grid segmentation on a regional road network in advance to obtain each grid unit, and establishing a coordinate index corresponding to each grid unit according to a central point coordinate and a boundary point coordinate of each grid unit; counting and storing the running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle; and when the vehicle to be driven requests path planning, planning the driving path of the vehicle to be driven according to the starting and ending point information of the vehicle to be driven and the driving information of each corresponding grid unit. According to the method and the device, grid division is performed, the grid region unit is used as a path planning unit, statistics and storage capacity of data are reduced, and the operation process is simple, convenient, rapid and efficient.
Description
Technical Field
The present application relates to the field of map navigation technologies, and in particular, to a method, a system, a storage medium, and a device for path planning.
Background
In the existing path planning method, conditions such as road planning grade, lane number, road surface laying condition, speed limit and the like are comprehensively considered through a map algorithm, the consideration priority of each factor in the path planning process is obtained, different weight references are given in the path planning process, and the optimal path is calculated by combining the path distance. In the subsequent method improvement, the time consumption estimation can be carried out in the route planning by combining the real-time dynamic traffic condition of the road section, the congestion condition of the road is represented by using different colors, and the user is reminded to avoid the congested road section.
However, in the above-described path planning method, the priority at the time of road planning is fixed. When temporary emergencies such as traffic accidents occur, the original route planning method cannot take the temporary emergencies into consideration, so that the user cannot be reminded to detour, and the navigation experience is poor. The existing path planning method needs to count complex dynamic traffic information, needs to count and calculate larger data volume, and has complex operation process. And for the newly developed path, because the operation cannot be brought in time during path planning, a path planning blind area may exist, and the optimal path cannot be planned.
Disclosure of Invention
The application provides a path planning method, a system, a storage medium and equipment, which are used for solving the problems of huge storage data volume, complex calculation process and high implementation difficulty in the conventional path planning method.
In one technical scheme of the application, a path planning method is provided, which comprises the steps of carrying out grid segmentation on a regional road network in advance to obtain each grid unit, and establishing a coordinate index corresponding to each grid unit according to a central point coordinate and a boundary point coordinate of each grid unit; counting and storing the running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, wherein the running information comprises the starting and ending point information, the path information and the time information corresponding to the path information of each road vehicle in each grid unit; and when the vehicle to be driven requests path planning, planning the driving path of the vehicle to be driven according to the starting and ending point information of the vehicle to be driven and the driving information of each corresponding grid unit.
In one aspect of the present application, a path planning system is provided, including: the road network planning module is used for carrying out grid segmentation on the regional road network in advance to obtain each grid unit, and establishing a coordinate index corresponding to each grid unit according to the center point coordinate and the boundary point coordinate of each grid unit; the big data statistics module is used for counting and storing the running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, wherein the running information comprises the starting and ending point information, the path information and the time information corresponding to the path information of each road vehicle in each grid unit; and the path planning module is used for planning the running path of the vehicle to be run according to the starting and ending point information of the vehicle to be run and the running information of each grid unit corresponding to the starting and ending point information of the vehicle to be run when the vehicle to be run requests path planning.
In another aspect of the present application, a computer-readable storage medium is provided, which stores computer instructions, wherein the computer instructions are operated to execute the path planning method in the first aspect.
In another aspect of the present application, a computer device is provided, which includes a processor and a memory, where the memory stores computer instructions, and the processor operates the computer instructions to execute the path planning method in the first aspect.
The beneficial effect that this application technical scheme can reach is: according to the method and the device, grid division is carried out on the regional road network to obtain grid region units, the grid region units are used as the basis of path planning, data statistics and storage capacity are reduced, the obtained planned path is enabled to better meet the requirements of users, user experience is improved, and meanwhile the calculation pressure of a path planning system is reduced.
Drawings
FIG. 1 is a schematic diagram illustrating the components of an embodiment of the path planning method of the present application;
FIG. 2 is a diagram illustrating an embodiment of a mesh partitioning result of the path planning method of the present application;
fig. 3 is an exemplary diagram of the relationship between each grid unit and the coordinate information;
FIG. 4 is a diagram illustrating an embodiment of a path planning method according to the present application;
fig. 5 is a path information statistical diagram of an embodiment of the path planning method of the present application;
FIG. 6 is a diagram illustrating an embodiment of a same grid co-spotting method in the path planning method of the present application;
FIG. 7 is a diagram illustrating an embodiment of time-consuming prediction in the path planning method of the present application;
FIG. 8 is a schematic diagram illustrating the components of an embodiment of the path planning system of the present application;
fig. 9 is a schematic composition diagram of an embodiment of the path planning system of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings (if any) are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows a specific embodiment of the path planning method of the present application.
In the specific embodiment shown in fig. 1, the path planning method of the present application includes: the process S101 includes the steps that grid segmentation is carried out on a regional road network in advance to obtain each grid unit, and a coordinate index corresponding to each grid unit is established according to a center point coordinate and a boundary point coordinate of each grid unit; the process S102 is that the running information of each road vehicle in each grid unit is counted and stored in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, and the running information comprises the starting and ending point information, the path information and the time information corresponding to the path information of each road vehicle in each grid unit; and a process S103, when the vehicle to be driven requests path planning, planning the driving path of the vehicle to be driven according to the starting and ending point information of the vehicle to be driven and the driving information of each corresponding grid unit.
According to the path planning method, the regional road network is subjected to grid segmentation to obtain each grid unit, the grid units are used as the basis of path planning, the optimal path is avoided being omitted, data statistics and storage capacity are reduced, the obtained planned path is enabled to better meet the user requirements, user experience is improved, and meanwhile the calculation pressure in the path planning process is reduced.
In the specific embodiment shown in fig. 1, the path planning method of the present application includes: the process S101 is to segment the grid of the regional road network in advance to obtain each grid unit, and establish a coordinate index corresponding to each grid unit according to the center point coordinates and the boundary point coordinates of each grid unit.
In this embodiment, each grid unit is obtained by dividing the road network grid. And establishing corresponding indexes of each grid unit according to the coordinates of the central point and the boundary point of each grid unit. For example, the corresponding grid cells are numbered according to the coordinates of the center point and the boundary point of each grid cell, so that the information of each grid cell can be conveniently expressed and referred. Therefore, in the process of information statistics, corresponding grid unit information can be obtained directly by counting the corresponding grid unit numbers.
In a specific embodiment of the present application, the process of performing mesh segmentation on a regional road network in advance to obtain each mesh unit includes: the regional road network is segmented according to the distribution condition of key traffic nodes in the regional road network, each grid unit is obtained, each grid unit comprises the key traffic nodes, and the key nodes comprise urban main road intersections and/or parking lot entrances.
In this specific embodiment, in order to enable the grid units obtained by grid segmentation to embody the actual need of path planning, when the road network grid is segmented, the obtained grid units include key traffic nodes. The key traffic nodes comprise urban main road intersections and/or parking lot entrances and exits.
In a specific example of the application, in the process of grid division of an area, in order to ensure that the urban grid division is uniform, if no main road exists in a certain area, an entrance and an exit of a parking lot of a shopping mall or a supermarket, an entrance and an exit of a parking lot of an office building group or an entrance and an exit of a parking lot of a tourist attraction can be used as key traffic nodes.
In a specific embodiment of the present application, the regional road network is segmented according to the positions of the key traffic nodes in the regional road network, so that the obtained central point of each grid unit is the key traffic node. The key traffic nodes comprise urban main road intersections, parking lot entrances and exits of shopping malls or supermarkets, parking lot entrances and exits of office buildings or parking lot entrances and exits of tourist attractions.
In a specific embodiment of the present application, the process of performing mesh segmentation on the regional road network in advance to obtain each mesh unit further includes: and dividing the regional road network according to the shape of the regional road network to obtain each grid cell, so that the regional road network is covered by each grid cell.
In this embodiment, when the mesh segmentation of the road surface region is performed, the uniformity of each mesh unit is ensured as much as possible. For example, the grid elements are uniformly square or rectangular. In the process of actual graticule mesh segmentation, because receive the influence of factors such as actual road surface region, building, the graticule mesh unit can carry out the graticule mesh that corresponds according to the road surface region's of reality shape and cut apart to guarantee that whole road surface region is covered by each graticule mesh unit after being cut apart.
In a specific embodiment of the present application, the process of performing mesh segmentation on a regional road network in advance to obtain each mesh unit includes: and carrying out grid segmentation on the regional road network according to the range condition of each grid unit to obtain each grid unit, wherein the distance between the boundary and the center of each grid unit is less than a preset distance threshold.
In this embodiment, in order to ensure that the divided grid cells have good accuracy, the distance between the boundary of each grid cell and the center of each grid cell is less than the preset distance threshold. When the preset distance threshold is set, if the preset distance threshold is set to be too small, the number of units for grid segmentation is increased, and the data quantity of the addresses needing to be counted is increased; when the preset distance threshold is set too large, the accuracy of path planning for the vehicle may be reduced. When the preset distance threshold is set, appropriate setting may be performed according to actual equipment conditions and accuracy requirements.
Preferably, the boundaries of each grid element are no more than 660 meters from the center of each grid element.
Fig. 2 shows a specific example of the mesh partitioning result of the path planning method of the present application.
As shown in FIG. 2, number O 1 、O 2 、...、O 25 Respectively representing the corresponding grid units after grid segmentation. Wherein each number is O 1 、O 2 、...、O 25 The coordinate information corresponding to the respective grid units comprises a central point coordinate, a left upper point coordinate, a left lower point coordinate, a right upper point coordinate and a right lower point coordinate. Fig. 3 shows an example of the relationship between each grid cell and the corresponding coordinate information.
In the specific embodiment shown in fig. 1, the path planning method of the present application includes: and S102, counting and storing the running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, wherein the running information comprises the starting and ending point information, the path information and the time information corresponding to the path information of each road vehicle in each grid unit.
In this embodiment, after the road mesh is divided into meshes, the traveling information of each vehicle on each mesh unit needs to be counted according to the coordinate index corresponding to each mesh unit and the traffic information of the vehicle. The traffic information of the vehicle includes information of a starting point and an end point of the vehicle during the running process of the vehicle, information of a route through which the vehicle runs, information of time taken by the vehicle to run, and the like. The method comprises the steps of combining and corresponding communication information of a vehicle and each divided grid unit, and counting running information of the vehicle, wherein the running information of the vehicle comprises starting and ending point information, route information and time information corresponding to the route information in the vehicle and each grid unit. The vehicle format information corresponds to each grid cell.
In a specific embodiment of the present application, the process of counting the driving information of each road vehicle in each grid unit according to the coordinate index of each grid unit and the traffic information of each road vehicle in real time includes: acquiring a grid unit corresponding to a driving track according to the driving track of each road vehicle; and counting the running information of each road vehicle in each grid unit according to the corresponding coordinate index of the grid unit corresponding to the running track.
In this embodiment, the driving information is determined according to each grid cell, wherein the start and end point information includes information of the grid cell where the start and end point is located, and the route information includes information of the grid cell to which the route relates. The driving information of the vehicle is determined according to each grid unit. The starting and ending point information is the number information of the grid unit corresponding to the starting point and the ending point of the running vehicle, and the path information corresponds to the information of the grid unit passed by the running vehicle in the running process.
Fig. 4 shows a specific example of the path planning method of the present application.
Fig. 5 shows a specific example of a path information statistical chart of the path planning method of the present application, where fig. 5 corresponds to fig. 4.
As shown in FIG. 5, wherein O S Grid cell number, O, indicating the origin of the traveling vehicle e And a grid cell number indicating the end point of the traveling vehicle. Wherein the several path lines represent the paths from the starting point O S To the end point O e In the course of (2), several travel paths are possible. Wherein FIG. 5 shows the starting point O S To the end point O e A list of statistically completed formal information.
As shown in fig. 5, at the start point, the grid cell O S To end grid cell O e In the process of (2), the grid unit information passed by the running vehicle is counted. As shown by the route 1 in FIG. 5, the traveling vehicle has a starting point O S To the end point O e In the process of (1), sequentially pass through the grid cells O S 、O 15 、O 10 、O 8 And grid cell O e . In addition, the average elapsed time T1 taken to traverse path 1 is also counted. Similarly, the statistics of grid cell O S As a starting point, grid cell O e The statistical results are shown in fig. 5 for the travel information of the other routes as the end points.
In one example of the application, the big data statistical module adopts the same grid homography method to perform statistics on the starting point information and the end point information of the running vehicle. Fig. 6 shows a specific example of the same grid same-point method. At the starting point grid cell O S In (1), the point S represents the grid cell O S The key traffic nodes of the midpoint, points S1 and S2, respectively, represent other key traffic nodes located within the grid cell; at the end point grid cell O E In (1), point E represents the cell located in the grid cell O E The key traffic nodes of the middle point, point E1 and point E2, respectively, represent other key traffic nodes located within the grid cell. When the driving vehicle starts from the key traffic node S1 or the key traffic node S2, counting and storing starting point information according to the key traffic node S; correspondingly, when the running vehicle finishes running from the key traffic node E1 or the key traffic node E2, the terminal information is counted and stored according to the key traffic node E. That is, the information of S1-E1 and S2-E2 are counted and stored as the result of S-E. The larger the range of grid unit division is, the more the number of traffic nodes represented by key traffic nodes represented by the points in the grid unit is, and the lower the navigation accuracy of grid homography is. Therefore, when grid division is performed, a proper grid division range needs to be controlled, and in practical application, the middle point of each grid unit takes the intersection of the main road as a key traffic nodeAnd the problem of overlarge navigation precision deviation is also avoided.
The starting and ending point information of the running vehicle is counted by the same grid synchronization method, so that when a large number of facility points or path information exist in the grid unit where the starting and ending point is located, the data volume is reduced to a reasonable level, statistics and storage are facilitated, and calculation is simplified.
In one example of the application, the big data statistics module needs to count average consumed time corresponding to a driving route in addition to counting information of a starting point, an end point and a route traveled by the driving vehicle. For example, for route 1 shown in fig. 4 and 5, if the traveling vehicle is the first passing vehicle that travels through route R1, the route is stored as a new route, and the elapsed time for the vehicle to travel through the route is T1; if the traveling vehicle is not the first passing vehicle on the route R1, the average elapsed time on the route is recalculated in conjunction with the average elapsed time before the vehicle passes through the route, and the calculation formula is shown in formula 1:
wherein T1 History of The average time consumed before the vehicle passes through the route is represented, n represents the total number of passing vehicles before the vehicle, and t is the current time consumed when the vehicle passes through the route.
In the specific embodiment shown in fig. 1, the path planning method of the present application includes: and S103, when the vehicle to be driven requests path planning, planning the driving path of the vehicle to be driven according to the starting and ending point information of the vehicle to be driven and the driving information of each corresponding grid unit.
In the specific embodiment, the path planning module plans the path of the vehicle to be driven according to the start and end point information of the vehicle to be driven and the vehicle driving information which is counted and stored in advance and according to the principle of shortest time consumption.
In a specific embodiment of the application, the start and end point information of the vehicle to be driven is matched with the driving information, and if the driving information contains the start and end point information of the vehicle to be driven, the route which is the shortest in time and corresponds to the start and end point of the vehicle to be driven is determined as the planned route.
In this embodiment, if the start and end point information of the vehicle to be traveled for which the route planning is currently performed is included in the start and end point information in the previously counted travel information about the traveling vehicle, the route that is least used is selected from the previously counted and stored travel information and determined as the planned route corresponding to the vehicle to be traveled.
In one example of the present application, as shown in fig. 4, if the start and end points of the vehicle to be driven correspond to the grid unit O S And grid cell O E . When the vehicle running information is counted, the corresponding grid cell O is also counted and stored S Starting from grid cell O E Start and end point information for an end point, grid cell O S To grid cell O E Path information and time-consuming information. Will be derived from all grid cells O S To grid cell O E The route which consumes the least time is selected from the route information to be used as the planned route of the vehicle to be driven.
In a specific embodiment of the application, the start and end point information of the vehicle to be driven is matched with the driving information, and if the driving information does not contain the start and end point information of the vehicle to be driven, the planned path is determined according to a conventional navigation method.
In this embodiment, because of the diversity of traveling of the vehicle to be traveled, if the start and end point information of the vehicle to be traveled, which is currently subjected to the route planning, is not included in the start and end point information in the traveling information about the traveling vehicle, which is counted in advance, the planned route of the vehicle to be traveled is determined according to the conventional navigation method.
In one example of the present application, as shown in fig. 4, for example, the start and end points of the vehicle to be driven correspond to the grid unit O 17 And grid cell O 3 Since there is no relevant grid cell O when the running information of the running vehicle is counted 17 Starting from grid cell O 3 As path information of the destination, now according to the existing conventional navigation methodThe method determines a planned path of the vehicle to be driven.
In a specific embodiment of the present application, the path planning method further includes: and in the running process of the vehicle to be run, updating the starting and ending point information of the vehicle to be run, and planning the path of the vehicle to be run according to the updated starting and ending point information of the vehicle to be run.
In the specific embodiment, the start-stop point information, the route information and the corresponding time information of the current route of the vehicle are recorded in real time during the running process of the vehicle. And the information is fed back to update the stored running information of the running vehicle. For example, as described above, when the information of the traveling vehicle counted in advance does not include the start and end point information of the vehicle to be traveled, the route information, and the time information including the route elapsed time are counted by the route tracking timer module, and fed back to the big data counting module, and the start and end point information, the route information, and the time information including the route elapsed time of the vehicle to be traveled are stored. When the vehicles which accord with the driving information appear, the path planning can be carried out on the vehicle form according to the driving information.
The running information of the running vehicle is collected and subjected to data feedback, and then the running information of the vehicle is stored and updated. Therefore, when a new path appears, the new path can be counted in time, and a shorter path is recommended to the user when the path of the running vehicle is planned.
In a specific embodiment of the application, in the running process of the vehicle to be run, the starting and ending point information of the vehicle to be run is updated, and the path of the vehicle to be run is planned according to the updated starting and ending point information of the vehicle to be run.
In the specific embodiment, the starting point information of the vehicle to be driven is changed at any moment along with the running of the vehicle to be driven, and the optimal path is recommended to the user by counting the starting point information and replanning the path according to the new starting point.
In one example of the present application, as shown in figures 4 and 5,if the starting point of the vehicle to be driven corresponds to the grid unit O S End point corresponds to grid cell O E Where the shortest time consuming path is path R1, as shown in fig. 5. The vehicle to be driven travels along the path R1. When the vehicle to be driven reaches the grid cell O 15 While using grid cell O 15 As a starting point, grid cell O E And replanning the path as an end point. If the original route 1 still consumes the shortest time, the vehicle to be driven continues to drive according to the route R1; if the time consumption of the newly planned route, for example, the route R2 is shorter, the vehicle travels along the newly planned route R2. When the vehicle to be driven reaches the next grid unit, the path is continuously re-planned according to the method.
By updating the starting point information of the vehicle to be driven and continuously re-planning the path of the vehicle to be driven in the driving process, the optimal path and the path with the least time consumption are recommended to the user, and the user experience is improved.
In a specific embodiment of the application, during the running process of a vehicle to be driven, route planning time-consuming prediction is carried out before a key traffic node, and a route which consumes the least time is selected at the key traffic node.
In this embodiment, when predicting the time consumption of the path planning, if the driving route needs to be changed when the path planning is performed at the road node (intersection), the situation that the vehicle approaches the intersection and cannot change the lane occurs. Therefore, the route needs to be planned in advance when the route is a certain distance S from the road node, but this may cause an optional route deviation. In order to ensure the accuracy of the path planning, the time-consuming change of the vehicle of the selectable path in the process of the vehicle running distance S needs to be considered.
Fig. 7 shows a schematic diagram of the time-consuming prediction of the path planning method of the present application.
Assuming that T' is the time consumed by the vehicle on the optional path when the vehicle runs to the intersection, the time consumed by the vehicle when the T is away from the intersection by S, Δ V is the time consumed by the vehicle on the path in unit time, and T is the time consumed by the vehicle on the optional path when the vehicle runs to the intersection, the time consumed by the vehicle on the optional path is T + Δv T, and T is the vehicle speed of S/V.
Fig. 8 is a schematic diagram illustrating a configuration of an embodiment of the path planning system according to the present application.
As shown in fig. 8, the path planning system of the present application includes: a road network grid dividing module 801, configured to perform grid division on a regional road network in advance to obtain each grid unit, and establish a coordinate index corresponding to each grid unit according to a center point coordinate and a boundary point coordinate of each grid unit; the big data statistics module 802 is configured to count and store the driving information of each road vehicle in each grid unit according to the coordinate index of each grid unit and the traffic information of each road vehicle in real time, where the driving information includes start and end point information, path information, and time information corresponding to the path information of each road vehicle in each grid unit; and a path planning module 803, configured to plan a driving path of the vehicle to be driven according to the start-stop point information of the vehicle to be driven and the driving information of each corresponding grid unit when the vehicle to be driven requests path planning.
Through the path planning system, grid segmentation is carried out on the regional road network, each grid unit is obtained, the grid unit is used as the basis of path planning, the optimal path is avoided being omitted, data statistics and storage capacity are reduced, the obtained planned path is enabled to be more in line with the user requirements, user experience is improved, and meanwhile the calculation pressure in the path planning process is reduced.
Fig. 9 shows a specific example of the path planning system of the present application.
As shown in fig. 9, in the path planning system of the present application, a road network grid dividing module is used to perform grid division on a regional road network, and a grid center point and a grid boundary coordinate index of each grid unit are established according to the grid unit center point and the grid boundary coordinate. Wherein, when carrying out the graticule mesh and cutting apart, make each graticule mesh unit as far as possible even, can carry out the segmentation of graticule mesh unit simultaneously according to the regional shape of reality, guarantee that each graticule mesh unit will should cut apart the regional complete coverage. In order to ensure the accuracy of the path planning, the distance between the boundary of each grid cell obtained by the segmentation and the central point is less than a preset distance threshold, and preferably, the distance threshold is 660 meters.
In the big data statistics module, the incidence relation between the starting and ending point of the vehicle passing and the grid unit is counted to obtain the starting and ending point information of the vehicle, wherein the starting and ending point information of the vehicle comprises the grid unit information where the starting and ending point of the vehicle is located. After the start and end point information of the vehicle is determined, all path information meeting the start and end point information and the average time for vehicle passing corresponding to each path information are counted.
Big data storage module, it mainly stores the data of big data statistics module statistics, includes: and storing the start and end point information of the vehicles, the path information corresponding to the start and end point information and the average time consumption information corresponding to each path through which each vehicle passes.
And the path planning module plans the path of the vehicle to be driven according to the starting and ending point information set in the vehicle to be driven. In the path planning module, whether the big data storage module contains the starting and ending point information of the vehicle to be driven is judged at first. And if so, acquiring the least average time-consuming path in the paths corresponding to the start and end point information in the big data storage module, and recommending the path to the user to complete the task of path planning. If the big data storage module does not contain the starting point information and the ending point information of the vehicle to be driven, the big data storage module does not contain the starting point information of the vehicle to be driven, the ending point information of the vehicle to be driven or the starting point information and the ending point information of the vehicle to be driven. At this time, path planning is performed according to a conventional map navigation method.
In the specific example shown in fig. 9, the path planning system of the present application further includes a path tracking timing module, which records path information such as arbitrary start and end point information of the vehicle and time information of the vehicle of this time, and finally feeds back the path information to the big data statistics module to store or update related data. For example, when the start and end point information of the running vehicle is not stored in the big data storage module and the path planning service cannot be provided, the path tracking timing module records the road of the running vehicle planned according to the traditional path. Therefore, when the same start and end point information exists next time, the path planning system can provide path planning service. In addition, through the path tracking timing module, accidents such as car accidents in the driving process of the vehicle can be found and recorded in time and fed back to the big data statistics module, so that the unexpected road sections are avoided when the path of the subsequent vehicle is planned, the final planned path consumes the shortest time, and the user experience is improved.
In one embodiment of the present application, the various constituent modules in the path planning system of the present application may be directly in hardware, in a software module executed by a processor, or in a combination of both.
A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium.
The Processor may be a Central Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), other Programmable logic devices, discrete Gate or transistor logic, discrete hardware components, or any combination thereof. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.
In one embodiment of the present application, a computer-readable storage medium stores computer instructions operable to perform a path planning method as described in any one of the embodiments.
In one embodiment of the present application, a computer device includes a processor and a memory, the memory storing computer instructions, wherein: the processor operates the computer instructions to perform the path planning method described in any of the embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above embodiments are merely examples, which are not intended to limit the scope of the present disclosure, and all equivalent structural changes made by using the contents of the specification and the drawings, or any other related technical fields, are also included in the scope of the present disclosure.
Claims (10)
1. A method of path planning, comprising:
carrying out grid segmentation on a regional road network in advance to obtain each grid unit, and establishing a coordinate index corresponding to each grid unit according to a central point coordinate and a boundary point coordinate of each grid unit;
counting and storing the running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, wherein the running information comprises the starting and ending point information, the path information and the time information corresponding to the path information of each road vehicle in each grid unit;
when the vehicle to be driven requests path planning, planning the driving path of the vehicle to be driven according to the starting and ending point information of the vehicle to be driven and the corresponding driving information of each grid unit.
2. The path planning method according to claim 1, wherein the pre-grid segmentation of the regional road network to obtain each grid unit comprises:
and segmenting the regional road network according to the distribution condition of key traffic nodes in the regional road network to obtain each grid unit, wherein each grid unit comprises the key traffic nodes, and the key nodes comprise urban main road intersections and/or parking lot entrances.
3. The path planning method according to claim 1, wherein the pre-grid segmentation of the regional road network to obtain each grid unit further comprises:
and segmenting the regional road network according to the shape of the regional road network to obtain each grid unit, so that the regional road network is covered by each grid unit.
4. The path planning method according to claim 1, wherein the pre-grid segmentation of the regional road network to obtain each grid unit comprises:
and performing grid segmentation on the regional road network according to the range condition of each grid unit to obtain each grid unit, wherein the distance between the boundary and the center of each grid unit is smaller than a preset distance threshold.
5. The path planning method according to any one of claims 1 to 4, wherein the process of counting the driving information of each vehicle on each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each vehicle comprises:
acquiring a grid unit corresponding to the driving track according to the driving track of each road vehicle;
and counting the running information of each road vehicle in each grid unit according to the corresponding coordinate index of the grid unit corresponding to the running track.
6. The path planning method according to any one of claims 1 to 4, wherein the process of planning the driving path of the vehicle to be driven according to the start and end point information of the vehicle to be driven and the driving information of each corresponding grid unit comprises:
and matching the starting and ending point information of the vehicle to be driven with the driving information, and if the driving information contains the starting and ending point information of the vehicle to be driven, determining the path corresponding to the grid unit with the shortest time consumption corresponding to the starting and ending point information of the vehicle to be driven as a planned path.
7. The path planning method according to any one of claims 1 to 4, further comprising:
and in the running process of the vehicle to be run, updating the starting and ending point information of the vehicle to be run, and planning the path of the vehicle to be run according to the updated starting and ending point information of the vehicle to be run.
8. A path planning system, comprising:
the road network planning module is used for carrying out grid segmentation on the regional road network in advance to obtain each grid unit, and establishing a coordinate index corresponding to each grid unit according to the central point coordinate and the boundary point coordinate of each grid unit;
the big data statistics module is used for counting and storing running information of each road vehicle in each grid unit in real time according to the coordinate index of each grid unit and the traffic information of each road vehicle, wherein the running information comprises starting and ending point information, path information and time information corresponding to the path information of each road vehicle in each grid unit; and
and the path planning module plans the running path of the vehicle to be run according to the starting and ending point information of the vehicle to be run and the running information of each grid unit corresponding to the starting and ending point information of the vehicle to be run when the vehicle to be run requests path planning.
9. A computer readable storage medium storing computer instructions, wherein the computer instructions are operative to perform the path planning method of any of claims 1-7.
10. A computer apparatus comprising a processor and a memory, the memory storing computer instructions, wherein the processor operates the computer instructions to perform the path planning method of any of claims 1-7.
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| CN202110071205.9A CN114812584A (en) | 2021-01-19 | 2021-01-19 | Path planning method, system, storage medium and equipment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115080866A (en) * | 2022-08-22 | 2022-09-20 | 北京中交兴路信息科技有限公司 | Travel path recommendation method and device, storage medium and terminal |
| CN115830896A (en) * | 2022-11-17 | 2023-03-21 | 云控智行科技有限公司 | Lane recommendation method, device and equipment |
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2021
- 2021-01-19 CN CN202110071205.9A patent/CN114812584A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115080866A (en) * | 2022-08-22 | 2022-09-20 | 北京中交兴路信息科技有限公司 | Travel path recommendation method and device, storage medium and terminal |
| CN115830896A (en) * | 2022-11-17 | 2023-03-21 | 云控智行科技有限公司 | Lane recommendation method, device and equipment |
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