CN114295144A - DIKW-based vehicle path planning method - Google Patents
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Abstract
The invention provides a DIKW-based vehicle path planning method, which comprises the following specific steps: obtaining the typed resources of a driver, and constructing a DIKW model of the driver according to the typed resources; planning a path according to a starting point and an end point to obtain a plurality of initial paths; acquiring initial path road information, and constructing an initial path DIKW model; the initial path DIKW model screens the initial path according to the intention of the driver DIKW model to obtain an optimal path, the typed resources of the driver are collected, the intention contained in the initial path is extracted, the planned initial path is screened according to the intention of the driver when path planning is carried out, the optimal path is obtained, the obtained optimal path can meet the requirement of the driver, and compared with the traditional path planning, the method only considers the number of traffic lights, the congestion degree, the mileage and other factors, and is more humanized, and the multi-level driving requirement of the driver can be met.
Description
Technical Field
The invention relates to the technical field of path planning, in particular to a DIKW-based vehicle path planning method.
Background
With the continuous improvement of living standards of people, most families purchase automobiles as short-distance transportation vehicles, drivers need to plan driving paths of the vehicles in the driving process of the vehicles, the existing vehicle path planning technology basically plans the paths by collecting information such as road vehicle speed, vehicle quantity, pedestrian information, traffic lights and the like, however, the information belongs to some objective information, the path planning does not consider factors and intentions of the drivers, for example, when the drivers are new hands, the drivers hope to drive fewer vehicles or pedestrians on the paths, or the drivers hope to turn fewer turns on the driving paths due to physical causes, and the subjective factors cannot be considered by the existing path planning technology, so that the finally planned paths do not meet the requirements of the drivers.
Everyone has data (data), information, knowledge and Wisdom (Wisdom) that belong to oneself, represents self to the cognition and understanding of outside objective thing, can obtain DIKW map system through modeling data, information, knowledge and Wisdom, has contained individual detailed data in the DIKW map, how to combine the DIKW to the problem that needs solve at present in the path planning.
Disclosure of Invention
In view of the above, the invention provides a DIKW-based vehicle path planning method, which can ensure that the acquired path meets the requirements of a driver by constructing a DIKW model of the driver and an initial path DIKW model and planning the path based on the intention of the driver.
The technical scheme of the invention is realized as follows:
the DIKW-based vehicle path planning method comprises the following steps:
s1, obtaining typed resources of a driver, and constructing a DIKW model of the driver according to the typed resources;
step S2, planning a path according to the starting point and the end point to obtain a plurality of initial paths;
s3, acquiring initial path road information and constructing an initial path DIKW model;
and S4, screening the initial path according to the intention of the DIKW model of the driver by the initial path DIKW model to obtain an optimal path.
Preferably, the driver DIKW model and the initial path DIKW model each include a data model, an information model, and a knowledge model, and the contents of the data model, the information model, and the knowledge model may be obtained by mutual conversion.
Preferably, the DIKW model of the driver further comprises an intention model, wherein the intention model comprises obvious intention and potential intention, the obvious intention is obtained by a request initiated by the driver actively, and the potential intention is obtained by combining and converting the contents of the data model, the information model and the knowledge model.
Preferably, the typed resources consist of driver identity information on the internet, search records, historical driving records, and preference settings on other vehicles.
Preferably, the specific step of step S1 includes:
step S11, acquiring information entry, browsing records, historical vehicle planning paths and interest preferences of a driver in the Internet, and forming typed resources, wherein the typed resources comprise data resources, information resources and knowledge resources;
step S12, extracting the request actively initiated by the driver as an obvious intention, and obtaining a potential intention according to the combination and conversion of data resources, information resources and knowledge resources;
and step S13, constructing a DIKW model of the driver according to the typed resources, the obvious intention and the potential intention.
Preferably, in step S2, a route is planned according to the starting point and the ending point by using conventional navigation software, and a route that all vehicles can reach the ending point from the starting point is obtained as an initial route.
Preferably, the specific step of step S3 is: road information transmitted by a road side acquisition terminal, a traffic light system and an unmanned automobile networking system is obtained and mapped into typed resources, the typed resources comprise data resources, information resources and knowledge resources, and an initial path DIKW model is constructed for each initial path according to the typed resources.
Preferably, the specific step of step S4 includes:
step S41, extracting obvious intentions or potential intentions in a DIKW model of a driver, wherein the priority of the obvious intentions is higher than that of the potential intentions;
step S42, converting the obvious intention or the potential intention to obtain data, information and knowledge related to the intention;
s43, searching an initial path of content corresponding to data, information and knowledge related to the intention by using an initial path DIKW model;
and step S44, outputting the searched initial path as an optimal path.
Preferably, the method further includes step S5, feeding back the optimal path to the driver, and displaying the intention content corresponding to the optimal path.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a DIKW-based vehicle path planning method, which comprises the steps of respectively constructing a DIKW model of a driver and a DIKW model of an initial path according to typed resources and road information of the driver, obtaining a plurality of initial paths according to a starting point and a key point, comparing roads contained in each initial path in the DIKW model of the initial path according to the intention of the driver contained in the DIKW model of the driver, and outputting the initial path which meets the intention of the driver as an optimal path so as to ensure that the planned path meets the requirement of the driver.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only preferred embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive exercise.
FIG. 1 is a flow chart of a DIKW based vehicle path planning method of the present invention;
FIG. 2 is a flowchart of step S1 of the DIKW based vehicle path planning method of the present invention;
fig. 3 is a flowchart of step S4 of the DIKW-based vehicle path planning method according to the present invention.
Detailed Description
For a better understanding of the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described with reference to the accompanying drawings.
Referring to fig. 1 to 3, the DIKW-based vehicle path planning method provided by the present invention includes the following steps:
s1, obtaining typed resources of a driver, and constructing a DIKW model of the driver according to the typed resources;
step S2, planning a path according to the starting point and the end point to obtain a plurality of initial paths;
s3, acquiring initial path road information and constructing an initial path DIKW model;
s4, screening the initial path according to the intention of the DIKW model of the driver by the initial path DIKW model to obtain an optimal path;
and step S5, feeding back the optimal path to the driver, and displaying the intention content corresponding to the optimal path.
The invention provides a DIKW-based vehicle path planning, which is based on the traditional path planning and combines the intention of a driver to plan so that the finally obtained path can meet the requirement of the driver, and for the driver, the intention considered by the path planning is obtained by carrying out DIKW modeling on the driver, after the typed resources of the driver are obtained, the DIKW model of the driver is obtained by modeling, the DIKW model comprises Data (Data), Information (Information), Knowledge (Knowledge) and wisdom (wisdom), the contents of all parts in the DIKW model can be mutually converted, after the DIKW model of the driver is constructed, the traditional path planning can be carried out according to the current starting point of the driver and the expected terminal point, the path planning at the moment can obtain all the routes from the starting point to the terminal point, finally, a plurality of paths are output and serve as initial paths, more noise paths are contained in the initial paths, the initial paths need to be screened, road information of the initial paths is firstly obtained, an initial path DIKW model is correspondingly constructed, the DIKW model comprises two parts at the moment, the first part is a driver DIKW model, the second part is the initial path DIKW model, corresponding information can be stored in the driver DIKW model and the initial path DIKW model, the intention of a driver is contained in the driver DIKW model, corresponding mapping indications can be displayed in the initial path DIKW model according to the intention of the driver, the initial paths can be screened according to the intention of the driver DIKW model through the initial path DIKW model, the initial paths with the intention met contents are searched, and the initial paths are output as optimal paths.
The traditional path planning only considers the parameters of whether a road is congested, whether a traffic accident occurs, the driving mileage and the like generally, the considered content basically belongs to objective data, and the subjective feeling of a driver is not considered, such as the mileage of a planned path, but more vehicles and pedestrians exist on the road, for some novice drivers, the drivers want to avoid the road with more vehicles as much as possible when driving the vehicles, so the traditional path planning method is not suitable for the requirements of the drivers.
Because the road network is developed at present, a plurality of optimal paths may be obtained aiming at the same intention of a driver, and most of the intentions of the drivers are more than one, so that after the optimal paths are obtained, the optimal paths can be fed back, for example, the optimal paths are fed back to an intelligent terminal of the driver or a vehicle-mounted navigation system, and the intention can be correspondingly displayed on each optimal path, so that the driver can reasonably select one of the paths to drive the vehicle.
Preferably, the driver DIKW model and the initial path DIKW model each include a data model, an information model, and a knowledge model, contents of the data model, the information model, and the knowledge model may be converted into each other, the driver DIKW model further includes an intention model, the intention model includes an obvious intention obtained by a request actively initiated by the driver and a potential intention obtained by combining and converting contents of the data model, the information model, and the knowledge model, and the typed resources are composed of driver identity information on the internet, search records, historical driving records, and preference settings on other vehicles.
The DIKW models comprise three basic models which are respectively a data model, an information model and a knowledge model, in addition, the DIKW model of a driver is also provided with an intention model, the intention model is the key of the DIKW model of the driver, the intention model can be obtained through combination and conversion among the data model, the information model and the knowledge model, the intention model comprises obvious intentions and potential intentions, and the obvious intentions are contents actively initiated before path planning of the driver, for example, the driver initiates a path which is expected to be planned to watch scenic spots before the path planning, the initial path DIKW model obtains the path containing scenic spots according to the data model, the information model and the knowledge model in the initial path DIKW model, and the path is screened out to be used as an optimal path; when no obvious intention exists in the DIKW model of the driver, the DIKW model of the driver can combine and convert the data model, the information model and the knowledge model in the DIKW model of the driver to obtain the potential intention of the driver, for example, the date of issuing the driver license of the driver is within one year, the driver can be correspondingly judged to be the information that the driver is possible to be a novice hand through the data, the probability of traffic accidents on the path expected to be planned by the driver is smaller or the number of vehicles is smaller according to the information and the cognition and personality condition of the driver, and the initial path DIKW model can correspondingly search the initial path with fewer vehicles as the optimal path to output according to the potential intention.
The type resource of the driver can be obtained by identity information, search records, historical driving records and preference setting classes on other vehicles, which are reserved on the internet by the driver, including the age, sex, driving age, personality and the like of the driver, and the historical driving records of the driver can be converted into data, information and knowledge, for example, the driving records of the driver pass through a coastal highway for many times, and the route is not a common starting and ending route of the driver, so that the condition that the driver prefers to watch scenery on the way can be judged.
Preferably, the specific step of step S1 includes:
step S11, acquiring information entry, browsing records, historical vehicle planning paths and interest preferences of a driver in the Internet, and forming typed resources, wherein the typed resources comprise data resources, information resources and knowledge resources;
step S12, extracting the request actively initiated by the driver as an obvious intention, and obtaining a potential intention according to the combination and conversion of data resources, information resources and knowledge resources;
and step S13, constructing a DIKW model of the driver according to the typed resources, the obvious intention and the potential intention.
After obtaining the typed resources of the driver, a DIKW model of the driver can be constructed according to the typed resources and the intention of the driver, wherein the intention of the driver is divided into an obvious intention and a potential intention, the obvious intention is directly initiated by the driver, and the potential intention is obtained by combining and converting the data resources, the information resources and the knowledge resources of the driver.
Preferably, in step S2, a route is planned according to the starting point and the ending point by using conventional navigation software, and a route that all vehicles can reach the ending point from the starting point is obtained as an initial route.
The initial path can be planned by using conventional navigation software, and after the starting point and the end point are input in the navigation software, all paths from the starting point to the end point are output as the initial path.
Preferably, the specific step of step S3 is: road information transmitted by a road side acquisition terminal, a traffic light system and an unmanned automobile networking system is obtained and mapped into typed resources, the typed resources comprise data resources, information resources and knowledge resources, and an initial path DIKW model is constructed for each initial path according to the typed resources.
The initial path DIKW model needs to be modeled, modeling data needs to be acquired, for each initial path, a corresponding DIKW model needs to be established, initial path road information can be acquired through a road side acquisition terminal for acquiring pedestrians and vehicles coming and going, the number of the vehicles, the speed of the pedestrians, the weather condition, the road surface flatness and the like are acquired, a traffic light system can send switching rules and waiting time of intersection traffic lights, an unmanned automobile networking system can send vehicle information on the road surface in real time, and after the information is mapped into a type resource, the initial DIKW model can be established according to the type resource.
Preferably, the specific step of step S4 includes:
step S41, extracting obvious intentions or potential intentions in a DIKW model of a driver, wherein the priority of the obvious intentions is higher than that of the potential intentions;
step S42, converting the obvious intention or the potential intention to obtain data, information and knowledge related to the intention;
s43, searching an initial path of content corresponding to data, information and knowledge related to the intention by using an initial path DIKW model;
and step S44, outputting the searched initial path as an optimal path.
After the initial path DIKW model is built, the initial path can be screened, firstly, intentions in the driver DIKW model are analyzed, wherein the intentions include obvious intentions and potential intentions, the priority of the obvious intentions is higher than that of the potential intentions, if a driver actively initiates a request for path planning before path planning, the initiated request is the obvious intentions, for example, the driver just finishes the vehicle recently and expects less dust on the planned path or the road surface is relatively flat and has no water puddle, so data, information and knowledge related to the intentions can be converted according to the obvious intentions, then the initial path DIKW model searches for the content corresponding to the data, information and knowledge related to the intentions, searches for the initial path conforming to the intentions to be output as the optimal path, for example, searches for a road with lower dust concentration and relatively flat road surface, the final output optimal path can perfectly meet the requirements of the driver.
And if the driver does not initiate a corresponding request, namely no obvious intention exists, the potential intention can be obtained through conversion and combination according to the relevant information of the driver, the potential intention is converted, and then the initial path DIKW model is used for searching and screening to obtain the optimal path.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. The DIKW-based vehicle path planning method is characterized by comprising the following steps of:
s1, obtaining typed resources of a driver, and constructing a DIKW model of the driver according to the typed resources;
step S2, planning a path according to the starting point and the end point to obtain a plurality of initial paths;
s3, acquiring initial path road information and constructing an initial path DIKW model;
and S4, screening the initial path according to the intention of the DIKW model of the driver by the initial path DIKW model to obtain an optimal path.
2. The DIKW-based vehicle path planning method of claim 1, wherein the DIKW model of the driver and the DIKW model of the initial path each include a data model, an information model and a knowledge model, and the contents of the data model, the information model and the knowledge model are transformed with each other.
3. The DIKW-based vehicle path planning method of claim 2, wherein the driver DIKW model further comprises an intent model, the intent model comprises obvious intentions obtained from requests actively initiated by the driver and potential intentions obtained from content integration and transformation of data models, information models and knowledge models.
4. The DIKW-based vehicle path planning method of claim 3, wherein the typed resources consist of driver identity information on the Internet, search records, historical driving records, and preference settings on other vehicles.
5. The DIKW-based vehicle path planning method of claim 4, wherein the specific steps of step S1 include:
step S11, acquiring information entry, browsing records, historical vehicle planning paths and interest preferences of a driver in the Internet, and forming typed resources, wherein the typed resources comprise data resources, information resources and knowledge resources;
step S12, extracting the request actively initiated by the driver as an obvious intention, and obtaining a potential intention according to the combination and conversion of data resources, information resources and knowledge resources;
and step S13, constructing a DIKW model of the driver according to the typed resources, the obvious intention and the potential intention.
6. The DIKW-based vehicle path planning method of claim 1, wherein the step S2 adopts conventional navigation software to plan the path according to the starting point and the ending point, and obtains the path from the starting point to the ending point of all vehicles as the initial path.
7. The DIKW-based vehicle path planning method of claim 1, wherein the step S3 comprises the following steps: road information transmitted by a road side acquisition terminal, a traffic light system and an unmanned automobile networking system is obtained and mapped into typed resources, the typed resources comprise data resources, information resources and knowledge resources, and an initial path DIKW model is constructed for each initial path according to the typed resources.
8. The DIKW-based vehicle path planning method of claim 1, wherein the specific steps of step S4 include:
step S41, extracting obvious intentions or potential intentions in a DIKW model of a driver, wherein the priority of the obvious intentions is higher than that of the potential intentions;
step S42, converting the obvious intention or the potential intention to obtain data, information and knowledge related to the intention;
s43, searching an initial path of content corresponding to data, information and knowledge related to the intention by using an initial path DIKW model;
and step S44, outputting the searched initial path as an optimal path.
9. The DIKW-based vehicle path planning method of claim 1, further comprising step S5, feeding back the optimal path to the driver, and displaying the intention content corresponding to the optimal path.
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