CN110823236B - Path planning method and device, electronic equipment and storage medium - Google Patents
Path planning method and device, electronic equipment and storage medium Download PDFInfo
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- CN110823236B CN110823236B CN201910967526.XA CN201910967526A CN110823236B CN 110823236 B CN110823236 B CN 110823236B CN 201910967526 A CN201910967526 A CN 201910967526A CN 110823236 B CN110823236 B CN 110823236B
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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
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
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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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
Abstract
The application discloses a path planning method, a path planning device, electronic equipment and a storage medium, and relates to the technical field of intelligent traffic. The specific implementation scheme is as follows: receiving a navigation request carrying a starting point and an end point; acquiring optimal paths from the starting point to the end point at each predicted time point in the future according to the starting point and the end point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future; and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point. According to the method and the device, the traffic cost can be simplified into a plurality of samples of future prediction time points from a continuous time function, the algorithm complexity is greatly reduced while the optimal solution is obtained with high probability, the method and the device can be really applied in a production mode, more reasonable routes are provided for users, the accuracy of the obtained target route can be effectively guaranteed, and the navigation efficiency can be effectively improved.
Description
Technical Field
The present application relates to the field of computer application technologies, and in particular, to a method and an apparatus for path planning, an electronic device, and a storage medium.
Background
The path planning aims to solve the problem of calculating the optimal path between starting and ending points on the directed weighted topological graph. For example, the optimal path may be the path with the smallest sum of the path weights between the starting point and the ending point.
In the existing driving navigation field, a topological graph is abstracted from a road network, and the edge right expresses the cost of passing through a road, for example, the passing time of the passing road can be adopted as the cost of the road. When path planning application is carried out on a road network with a large coverage area, such as a state road network, the requirement of millisecond response cannot be met by adopting traditional algorithms, such as Dijkstra and A-star algorithms, in practical engineering application, usually, an offline preprocessing stage is added, starting and ending point pairs are selected according to a certain rule, the optimal path between the starting and ending point pairs is calculated to serve as a cache, and the calculation path is accelerated by utilizing information stored in the cache. The acceleration scheme limits that the edge weight of each road section can only be expressed by scalar, namely the edge weight of the road can only take the passing cost at a certain moment, and the finally planned path is the optimal path cost of the whole network under a snapshot at a certain moment.
In practical application, the road traffic capacity is influenced by a congestion state and a dynamic intersection rule and dynamically changes along with time, namely, the theoretical optimal route is calculated and obtained on the basis that the road traffic cost is expressed as a function based on time, so that a user has a real optimal route under a 'Shangdi view angle'. Therefore, in the existing path planning scheme, the way of acquiring the path by using the road cost in the scalar form is unreasonable, and the real optimal path cannot be accurately acquired.
Disclosure of Invention
The application provides a path planning method, a path planning device, an electronic device and a storage medium, which are used for ensuring the reasonability of a path acquisition mode and improving the accuracy of an acquired optimal path.
The application provides a path planning method, which comprises the following steps:
receiving a navigation request carrying a starting point and an end point;
acquiring optimal paths from the starting point to the end point at each predicted time point according to the starting point, the end point in the navigation request and the traffic cost of each road in a pre-stored road network at each predicted time point in the future;
and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
Further, in the method as described above, obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each of the predicted time points includes:
combining the optimal paths from the starting point to the end point at each predicted time point to form a topological graph;
and acquiring a target path from the starting point to the end point based on time deduction in the topological graph.
Further, in the method as described above, obtaining the target path from the starting point to the ending point based on time deduction in the topological graph includes:
and performing path calculation based on time deduction by adopting a TD-Dijkstra algorithm in the topological graph to obtain a target path from the starting point to the end point.
Further, in the method as described above, before obtaining an optimal path between the starting point and the ending point at each predicted time point according to the starting point and the ending point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future, the method further includes:
calculating the passing cost of each road in the road network at each predicted time point in the future by predicting the future road condition;
and storing the passing cost of each road at each predicted time point in the future in a cache.
The present application further provides a path planning apparatus, including:
the receiving module is used for receiving a navigation request carrying a starting point and an end point;
an optimal path obtaining module, configured to obtain optimal paths at each predicted time point and between the starting point and the destination according to the starting point, the destination in the navigation request, and traffic costs of each road in a pre-stored road network at each predicted time point in the future;
and the target path acquisition module is used for acquiring a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
The present application further provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as any one of above.
The present application also provides a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any of the above.
One embodiment in the above application has the following advantages or benefits: acquiring optimal paths from the starting point to the end point at each predicted time point in the future according to the starting point and the end point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future; and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point. The route planning is realized by referring to the traffic cost of each road at each prediction time point, although the traffic cost of each road at each prediction time point is still a scalar quantity, the optimal target route can be obtained by adopting the traffic costs of each road at a plurality of prediction time points and based on time deduction, the technical problem of unreasonable route acquisition in the prior art is overcome, the traffic cost can be simplified into a plurality of samples of the future prediction time points from a continuous time function, the optimal solution is obtained at a high probability, the algorithm complexity is greatly reduced, the algorithm can be truly applied in a production mode, more reasonable routes are provided for users, the accuracy of the obtained target route can be effectively guaranteed, and the navigation efficiency can be effectively improved.
Further, in the application, a topological graph is formed by combining the optimal paths from the starting point to the end point at each predicted time point; the target path from the starting point to the end point is obtained based on time deduction in the topological graph, the target path from the starting point to the end point can be obtained more intuitively from the topological graph based on time deduction, a very convenient and fast obtaining mode with a very intuitive and effective implementation mode is provided, and meanwhile the accuracy of the obtained target path can be effectively guaranteed.
Furthermore, in the application, the passing cost of each road in the road network at each future prediction time point can be calculated through predicting the future road condition in the offline processing stage; the passing cost of each road at each future prediction time point is stored in the cache, so that the subsequent on-line road calculation process can be accelerated, the road calculation efficiency is improved, the response time to the navigation request is shortened, and the response efficiency is improved.
Other effects of the above-described alternative will be described below with reference to specific embodiments.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a schematic diagram according to a first embodiment of the present application;
fig. 2 is a schematic diagram of the path planning principle of the present application.
FIG. 3 is a schematic diagram according to a second embodiment of the present application;
fig. 4 is a block diagram of an electronic device for implementing a path planning method according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of a path planning method based on time deduction according to a first embodiment of the present application. As shown in fig. 1, the path planning method of this embodiment may specifically include the following steps:
s101, receiving a navigation request carrying a starting point and an end point;
the main execution body of the path planning method of the embodiment is a path planning device, and the path planning device can be applied to a navigation server. Or the path planning apparatus of this embodiment may also be an independent electronic entity or an application adopting software integration, and communicate with the navigation server when in use, so as to implement path planning based on time deduction.
S102, acquiring optimal paths from the starting point to the end point at each predicted time point according to the starting point and the end point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future;
the road network of the embodiment can be a road network with a large coverage area, such as an intercontinental road network. In the embodiment, the traffic cost of each road in the road network at each future prediction time point is prestored, so that the time for path planning is reduced, and the efficiency of path planning is improved.
Each predicted time point in the future of this embodiment may be backward-fetched based on the current time, for example, N predicted time points within 1 hour after the current time may be fetched, and the N predicted time points may be uniformly distributed within 1 hour after the current time, for example, 12 preset time points may be fetched, and one preset time point is set every 5 minutes. Or the plurality of preset time points may be non-uniformly distributed within 1 hour after the current time. The roads in the road network of the embodiment are basic constituent units of the path, and a plurality of roads can be connected end to form the path in series. In this embodiment, the passing cost of each road is the time of passing through the road. For example, the passing cost of a certain road at each predicted time point is the passing time of the road at the preset time point. In addition, in this embodiment, for the passing cost of the road at other time points between two adjacent predicted time points, it may be considered that the passing cost is equal to the passing cost of the corresponding road at the preset time point before the time.
Further optionally, before the step S102, the following steps may be further included:
(1) calculating the passing cost of each road in the road network at each future prediction time point through predicting the future road condition;
(2) and storing the passing cost of each road at each predicted time point in the future in a cache.
The steps (1) and (2) can be regarded as an offline processing stage before path planning, which is used for calculating and pre-storing the passing cost of each road in the road network at each future predicted time point, and the result is used in the subsequent online processing stage to perform path planning.
The traffic cost of each road in the road network at each future prediction time point is calculated by predicting the future road condition, and the traffic cost of each road at each prediction time point can be calculated according to the prediction capability of the path planning device on the future road condition. For example, according to the historical navigation information, whether each road at each predicted time point is congested or not, congestion degrees, road passing time under different congestion degrees and other road condition information can be predicted, the passing cost of each road at each predicted time point is calculated, and the passing cost is stored in a cache for use in a subsequent online stage. It should be noted that, since the calculation result is to support planning of all navigation paths in the entire road network, in this embodiment, it is necessary to obtain the traffic cost of each road at each predicted time point in the entire road network. And, the traffic cost of each road at each predicted time point is also a scalar, i.e., a single value, rather than a variable that changes with time.
In this way, after the path planning device receives the navigation request carrying the starting point and the end point, the path with the minimum passing time between the starting point and the end point at each predicted time point in the future can be obtained according to the starting point and the end point in the navigation request and the passing cost of each road in the road network at each predicted time point in the future, and the path with the minimum passing time between the starting point and the end point at each predicted time point can be used as the corresponding optimal path. For example, in this embodiment, if there are 12 calculated future predicted time points for each current time, the optimal routes from the starting point to the end point at the future 12 predicted time points may be acquired correspondingly here, for example, the route with the shortest transit time may be taken as the optimal route in this embodiment.
S103, obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
In this embodiment, after the optimal path from the starting point to the end point at each predicted time point is obtained, the final optimal path from the starting point to the end point, that is, the target path, is obtained based on time deduction. The time-based deduction of the present embodiment considers that, in the process from the starting point 1 to the end point 2, the time is ahead of the deduction, for example, the optimal path from the starting point 1 to the end point 2 passes through the nodes such as the node A, B, C in the future for 5 minutes; the optimal path from start point 1 to end point 2 for 10 minutes in the future passes through nodes such as node A, E, F, respectively, and so on. However, when the user goes from the starting point 1 to the node a, the time is already deduced backwards, for example, the time may already be deduced to a time of 10 minutes in the future, at this time, the road condition of the road on the road section which is not traveled ahead may have changed, if it is obvious that the optimal path of 5 minutes in the future is not suitable, at this time, the user may start to use a section of the optimal path of 10 minutes in the future from the node a to travel, and so on, based on the time deduction, continue to take a section of the optimal path corresponding to a suitable prediction time point backwards until the end point 2 is reached. The target path between the starting point and the end point obtained through the process is deduced by considering time, so that the obtained target path can be ensured to be the optimal target path in actual use, and the accuracy of the obtained target path can be effectively improved.
For example, the step S103 obtains the target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point, and specifically may include the following steps:
(a) combining the optimal paths from the starting point to the end point at each predicted time point to form a topological graph;
(b) and acquiring a target path from the starting point to the end point based on time deduction in the topological graph.
The steps (a) and (b) are a manner of obtaining the target path in this embodiment, and first, the calculated optimal paths from the starting point to the ending point at each predicted time point are combined together to form a topological graph. Then, a TD-Dijkstra algorithm can be adopted in the topological graph to perform path calculation based on time deduction calculation, and further a target path from the starting point to the end point is obtained.
For example, fig. 2 is a schematic diagram of the path planning principle of the present application. As shown in fig. 2, taking N prediction Time points, which are Time 1, Time 2, … …, and Time N, as an example, according to the technical solution of the above embodiment, an optimal path corresponding to each prediction Time point may be obtained, and as shown in the leftmost side of fig. 2, N optimal paths may be obtained. The N optimal paths are then combined together to form a topology, as shown in the middle topology of fig. 2. The graph covers the theoretical optimal route with a large probability, and then path calculation based on time deduction is carried out in the topological graph by adopting a TD-Dijkstra algorithm, so that a target path from a starting point to an end point is obtained.
Finally, the navigation request can be responded based on the acquired target path, and the passing cost of each road in the road network at each future prediction time point is prestored in the embodiment, so that the subsequent road calculation process can be accelerated, the response time can be shortened, and the response efficiency can be improved.
Although the target path obtained by the path planning method of the embodiment is not theoretically optimal, in actual evaluation, when the granularity of the future time slice segmentation is sufficiently fine by using a plurality of predicted time points, the target path provided by the present application will coincide with the optimal path with a probability of more than 95%. By reasonably slicing the future time and constructing the optimal route sub-road network at each preset time point obtained after each slicing, the scale of the road network finally applying TD-Dijkstra can be greatly reduced, and the performance of the algorithm is improved to the degree of commercialized application.
In addition, although the traffic cost of each road at each prediction time point is still treated as a scalar in the offline treatment stage, the target path finally acquired can be ensured to be covered optimally with high probability by setting a plurality of prediction times. In engineering implementation, the method and the device utilize the topological structure invariance of the CRP algorithm, separate the graph structure in the topological graph from the traffic cost of the road, and design a high-efficiency algorithm with multi-traffic cost parallel compiling to realize the preprocessing requirement. Meanwhile, logic for dynamic road network change increment compiling is achieved based on the CRP region subdivision characteristics, and therefore the data real-time requirement of on-line road calculation is met.
According to the path planning method of the embodiment, the optimal path between each predicted time point and the starting point to the end point is obtained according to the starting point and the end point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future; and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point. The route planning is realized by referring to the traffic cost of each road at each prediction time point, although the traffic cost of each road at each prediction time point is still a scalar quantity, the optimal target route can be obtained by adopting the traffic costs of each road at a plurality of prediction time points and based on time deduction, the technical problem of unreasonable route acquisition in the prior art is overcome, the traffic cost can be simplified into a plurality of samples of the future prediction time points from a continuous time function, the optimal solution is obtained at a high probability, the algorithm complexity is greatly reduced, the algorithm can be truly applied in a production mode, more reasonable routes are provided for users, the accuracy of the obtained target route can be effectively guaranteed, and the navigation efficiency can be effectively improved.
Further, in this embodiment, a topological graph is formed by combining the optimal paths from the starting point to the ending point at each predicted time point; the target path from the starting point to the end point is obtained based on time deduction in the topological graph, the target path from the starting point to the end point can be obtained more intuitively from the topological graph based on time deduction, a very convenient and fast obtaining mode with a very intuitive and effective implementation mode is provided, and meanwhile the accuracy of the obtained target path can be effectively guaranteed.
Furthermore, in this embodiment, the traffic cost of each road in the road network at each future predicted time point can be calculated by predicting the future road condition in the offline processing stage; the passing cost of each road at each future prediction time point is stored in the cache, so that the subsequent on-line road calculation process can be accelerated, the road calculation efficiency is improved, the response time to the navigation request is shortened, and the response efficiency is improved.
Fig. 3 is a structural diagram of a path planning apparatus according to a second embodiment of the present application. As shown in fig. 3, the path planning apparatus 300 of the present embodiment may specifically include:
a receiving module 301, configured to receive a navigation request carrying a start point and an end point;
an optimal path obtaining module 302, configured to obtain optimal paths at each predicted time point and from a start point to an end point according to a start point and an end point in the navigation request and traffic costs of each road in a pre-stored road network at each predicted time point in the future;
and the target path obtaining module 303 is configured to obtain a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
Further optionally, in the path planning apparatus of this embodiment, the target path obtaining module 303 is specifically configured to:
combining the optimal paths from the starting point to the end point at each predicted time point to form a topological graph;
and acquiring a target path from the starting point to the end point based on time deduction in the topological graph.
Further optionally, in the path planning apparatus of this embodiment, the target path obtaining module 303 is specifically configured to:
and (3) adopting a TD-Dijkstra algorithm to calculate a path based on time deduction in the topological graph, and obtaining a target path from the starting point to the end point.
Further optionally, as shown in fig. 3, the path planning apparatus of this embodiment further includes:
the calculation module 304 is configured to calculate the traffic cost of each road in the road network at each future predicted time point by predicting the future road condition;
the storage module 305 is configured to store the passing cost of each road at each predicted time point in the future in the cache.
The path planning apparatus of this embodiment, which uses the module to implement the implementation principle and technical effect of path planning, is the same as the description of the related method embodiment, and reference may be made to the description of the related method embodiment in detail, which is not described herein again.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 4 is a block diagram of an electronic device according to the method of path planning in the embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 4, the electronic apparatus includes: one or more processors 401, memory 402, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 4, one processor 401 is taken as an example.
The memory 402, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules (e.g., modules shown in fig. 3) corresponding to the path planning method in the embodiments of the present application. The processor 401 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 402, that is, implements the path planning method in the above method embodiment.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device for path planning, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 402 may optionally include memory located remotely from the processor 401, which may be connected to the path planning electronics via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the path planning method may further include: an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403 and the output device 404 may be connected by a bus or other means, and fig. 4 illustrates an example of a connection by a bus.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the path-planning electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input device. The output devices 404 may include a display device, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the optimal path from the starting point to the end point at each predicted time point is obtained according to the starting point and the end point in the navigation request and the pre-stored traffic cost of each road in the road network at each predicted time point in the future; and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point. The route planning is realized by referring to the traffic cost of each road at each prediction time point, although the traffic cost of each road at each prediction time point is still a scalar quantity, the optimal target route can be obtained by adopting the traffic costs of each road at a plurality of prediction time points and based on time deduction, the technical problem of unreasonable route acquisition in the prior art is overcome, the traffic cost can be simplified into a plurality of samples of the future prediction time points from a continuous time function, the optimal solution is obtained at a high probability, the algorithm complexity is greatly reduced, the algorithm can be truly applied in a production mode, more reasonable routes are provided for users, the accuracy of the obtained target route can be effectively guaranteed, and the navigation efficiency can be effectively improved.
Further, in the embodiment of the application, a topological graph is formed by combining the optimal paths from the starting point to the end point at each predicted time point; the target path from the starting point to the end point is obtained based on time deduction in the topological graph, the target path from the starting point to the end point can be obtained more intuitively from the topological graph based on time deduction, a very convenient and fast obtaining mode with a very intuitive and effective implementation mode is provided, and meanwhile the accuracy of the obtained target path can be effectively guaranteed.
Furthermore, in the embodiment of the application, the passing cost of each road in the road network at each future predicted time point can be calculated through predicting the future road condition in the offline processing stage; the passing cost of each road at each future prediction time point is stored in the cache, so that the subsequent on-line road calculation process can be accelerated, the road calculation efficiency is improved, the response time to the navigation request is shortened, and the response efficiency is improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of path planning, comprising:
receiving a navigation request carrying a starting point and an end point;
acquiring optimal paths from the starting point to the destination at the predicted time points according to the starting point, the destination and the traffic cost of each road in a pre-stored road network at each predicted time point in the future, wherein each predicted time point in the future is a plurality of preset time points sampled backwards based on the current time;
and obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
2. The method according to claim 1, wherein obtaining a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each of the predicted time points comprises:
combining the optimal paths from the starting point to the end point at each predicted time point to form a topological graph;
and acquiring a target path from the starting point to the end point based on time deduction in the topological graph.
3. The method of claim 2, wherein obtaining the target path from the starting point to the ending point based on time deduction in the topological graph comprises:
and performing path calculation based on time deduction by adopting a TD-Dijkstra algorithm in the topological graph to obtain a target path from the starting point to the end point.
4. The method according to any one of claims 1 to 3, wherein before obtaining the optimal path between the starting point and the ending point at each predicted time point according to the starting point, the ending point in the navigation request and the traffic cost of each road in the pre-stored road network at each predicted time point in the future, the method further comprises:
calculating the passing cost of each road in the road network at each predicted time point in the future by predicting the future road condition;
and storing the passing cost of each road at each predicted time point in the future in a cache.
5. A path planning apparatus, comprising:
the receiving module is used for receiving a navigation request carrying a starting point and an end point;
an optimal path obtaining module, configured to obtain optimal paths at the predicted time points and between the starting point and the destination according to the starting point, the destination in the navigation request, and traffic costs of pre-stored roads in a road network at the predicted time points in the future, where the predicted time points in the future are multiple preset time points taken backward based on the current time;
and the target path acquisition module is used for acquiring a target path from the starting point to the end point based on time deduction according to the optimal path from the starting point to the end point at each predicted time point.
6. The apparatus of claim 5, wherein the target path obtaining module is configured to:
combining the optimal paths from the starting point to the end point at each predicted time point to form a topological graph;
and acquiring a target path from the starting point to the end point based on time deduction in the topological graph.
7. The apparatus of claim 6, wherein the target path obtaining module is configured to:
and performing path calculation based on time deduction by adopting a TD-Dijkstra algorithm in the topological graph to obtain a target path from the starting point to the end point.
8. The apparatus of any of claims 5-7, further comprising:
the calculation module is used for calculating the passing cost of each road in the road network at each future predicted time point through the prediction of the future road condition;
and the storage module is used for storing the passing cost of each road at each future predicted time point in a cache.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-4.
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| CN111854785A (en) * | 2020-07-23 | 2020-10-30 | 中国平安财产保险股份有限公司 | Path planning method based on multi-user demand and related equipment |
| CN112347313A (en) * | 2020-11-10 | 2021-02-09 | 中国科学院、水利部成都山地灾害与环境研究所 | Big data-based location dominance path analysis method |
| CN112529254B (en) * | 2020-11-19 | 2022-11-25 | 歌尔股份有限公司 | Path planning method and device and electronic equipment |
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