CN112710323A - Vehicle-mounted navigation method and device - Google Patents
Vehicle-mounted navigation method and device Download PDFInfo
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- CN112710323A CN112710323A CN202011541372.7A CN202011541372A CN112710323A CN 112710323 A CN112710323 A CN 112710323A CN 202011541372 A CN202011541372 A CN 202011541372A CN 112710323 A CN112710323 A CN 112710323A
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- 238000004590 computer program Methods 0.000 claims description 17
- 238000003860 storage Methods 0.000 claims description 11
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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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The embodiment of the invention provides a vehicle navigation method and a device, wherein the method comprises the following steps: upon detecting a navigation event, determining a plurality of candidate route information for the navigation event; judging whether to automatically select the route of the navigation event; and when the automatic route selection of the navigation event is judged, determining target route information from the candidate route information, and navigating by adopting the target route information. According to the embodiment of the invention, automatic selection of the navigation route is realized, and the target route information is determined from the candidate route information for navigation when the automatic selection of the route is judged, so that the route selection is more efficient and intelligent, the password confirmation link is reduced for the user, and the user experience is improved.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a vehicle navigation method and device.
Background
At present, the following experience problems exist for route selection of a route calculation result in navigation: after the destination navigated by the user is confirmed, route calculation processing needs to be performed, and for the returned route calculation result, the user is required to confirm the selection result for navigation each time, so that the user experience is influenced.
Disclosure of Invention
In view of the above, it is proposed to provide a method and apparatus for in-vehicle navigation overcoming the above problems or at least partially solving the above problems, comprising:
a method of in-vehicle navigation, the method comprising:
upon detecting a navigation event, determining a plurality of candidate route information for the navigation event;
judging whether to automatically select the route of the navigation event;
and when the automatic route selection of the navigation event is judged, determining target route information from the candidate route information, and navigating by adopting the target route information.
Optionally, when determining to perform route automatic selection on the navigation event, determining target route information from the plurality of candidate route information includes:
determining a target selection mode when judging that the automatic route selection is carried out on the navigation event;
and determining target route information from the candidate route information by adopting the target selection mode.
Optionally, when determining to automatically select a route for the navigation event, determining a target selection manner includes:
acquiring judgment information;
when the judgment information is met, determining a first selection mode as a target selection mode; the first selection mode is a mode of automatically selecting a route according to the judgment information;
when the judgment information is not met, determining a second selection mode as a target selection mode; and the second selection mode is a mode of automatically selecting a route by adopting a pre-trained route selection model.
Optionally, the determining, by using the target selection method, target route information from the plurality of candidate route information includes:
and when the target selection mode is a second selection mode, performing model processing on the candidate route information by adopting a pre-trained route selection model to determine the target route information.
Optionally, the method further comprises:
obtaining sample route data and user behavior data for the sample route data;
determining a plurality of feature type information;
and performing model training by combining the sample route data, the user behavior data and a plurality of feature type information to obtain a route selection model.
Optionally, the method further comprises:
acquiring navigation feedback information; the navigation feedback information is user feedback information generated in the process of navigating by adopting the target route information;
and performing model training on the route selection model according to the navigation feedback information.
Optionally, the plurality of feature type information includes a plurality of items:
route label information, label frequency information, route attribute information, own vehicle attribute information and historical selection information.
An apparatus for in-vehicle navigation, the apparatus comprising:
a plurality of candidate route information determination modules for determining a plurality of candidate route information for a navigation event when the navigation event is detected;
the automatic route selection judging module is used for judging whether to automatically select the route of the navigation event;
and the target route information determining module is used for determining target route information from the candidate route information and navigating by adopting the target route information when judging that the route of the navigation event is automatically selected.
A vehicle comprising a processor, a memory and a computer program stored on the memory and operable on the processor, the computer program, when executed by the processor, implementing a method of in-vehicle navigation as described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of in-vehicle navigation as set forth above.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, when a navigation event is detected, a plurality of candidate route information aiming at the navigation event are determined, then whether the navigation event is automatically selected is judged, and when the navigation event is judged to be automatically selected, the target route information is determined from the candidate route information, and the target route information is adopted for navigation, so that the navigation route is automatically selected.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart illustrating steps of a method for vehicle navigation according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of another method for vehicle navigation according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an exemplary vehicle navigation process according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating steps of another method for vehicle navigation according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a model training process in vehicle navigation according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a car navigation device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below. It is to be understood that the embodiments described are only a few embodiments of the present invention, and 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 invention.
Referring to fig. 1, a flowchart illustrating steps of a method for vehicle navigation according to an embodiment of the present invention is shown, which may specifically include the following steps:
the navigation event may be a voice interaction operation triggered by a user for navigation, for example, the user may send a voice interaction request for navigation through the voice interaction operation, and then the vehicle-mounted system may perform navigation processing according to the voice interaction request.
In a vehicle-mounted navigation scene, whether a user triggers a navigation event can be detected through a vehicle-mounted system, and then when the navigation event is detected, a plurality of candidate route information aiming at the navigation event can be determined, so that the plurality of candidate route information can be further adopted for subsequent navigation processing.
In an example, a voice interaction request for navigation by a user may be received, and then a semantic understanding may be performed on the voice interaction request to identify a POI keyword (e.g., a destination keyword), so that destination information for this navigation may be determined from a candidate destination list by searching for a destination, and a route calculation process may be performed to obtain a route candidate list for this navigation (i.e., a navigation event), where the route candidate list may include a plurality of candidate route information.
For example, the destination search processing may be performed by a third-party map application, the destination information for the navigation event may be determined from the list of destinations to be selected, and the route calculation processing may be performed to acquire a plurality of candidate route information for the navigation event.
after determining the plurality of candidate route information, subsequent navigation processing may be performed by determining whether to perform route automatic selection for the navigation event, for example, by determining by an in-vehicle system, to perform route automatic selection for the navigation event.
In an example, whether a route is automatically selected for a navigation event may be preset, and then the vehicle-mounted system may determine whether the route is automatically selected for the navigation event according to the determination condition, for example, by setting a distance threshold, when the distance threshold is exceeded, it may be determined that the route is automatically selected for the navigation event, where the distance threshold may be a minimum value (e.g., 1Km) of a distance from a starting point to a destination of the navigation; when it is detected that the route calculation processing is continuously performed twice for the same destination, it may be determined that the route automatic selection is not performed for the navigation event.
And 103, when judging that the route of the navigation event is automatically selected, determining target route information from the candidate route information, and navigating by adopting the target route information.
In a specific implementation, when the automatic route selection of the navigation event is determined, the target route information may be determined from the multiple candidate route information, and then the target route information may be used for navigation, for example, in the process of automatically selecting the route of the navigation event, the target route information may be obtained by presetting custom information for determination, or the intelligent route selection may be performed by using model processing, so as to automatically select the target route information.
In one example, whether route automatic selection is carried out or not is judged by adopting the vehicle-mounted system according to a navigation event, and when the route automatic selection is judged, target route information can be automatically screened out from a plurality of candidate route information, so that the optimal route automatic selection of vehicle-mounted navigation can be carried out, and a more efficient and intelligent route selection effect is achieved.
In the embodiment of the invention, when a navigation event is detected, a plurality of candidate route information aiming at the navigation event are determined, then whether the navigation event is automatically selected is judged, and when the navigation event is judged to be automatically selected, the target route information is determined from the candidate route information, and the target route information is adopted for navigation, so that the navigation route is automatically selected.
Referring to fig. 2, a flowchart illustrating steps of another vehicle navigation method according to an embodiment of the present invention is shown, which may specifically include the following steps:
in a specific implementation, when it is determined that the route of the navigation event is automatically selected, the target selection mode may be determined, so as to further determine the target route information by using the target selection mode, for example, in the process of automatically selecting the route of the navigation event, the target route information may be obtained by presetting the custom information for determination, or the model processing may be used for intelligent route selection, so as to automatically select the target route information.
In an embodiment of the present invention, step 203 may include the following sub-steps:
substep 11, obtaining judgment information;
in practical applications, the judgment information may be obtained, and the judgment information may be preset self-defined information, such as a self-defined rule or a policy, which may be used for route automatic selection processing based on the judgment information.
Substep 12, when detecting that the judgment information is satisfied, determining a first selection mode as a target selection mode; the first selection mode is a mode of automatically selecting a route according to the judgment information;
in a specific implementation, when it is detected that the judgment information is satisfied, the first selection manner may be determined as a target selection manner, where the first selection manner may be a manner of automatically selecting a route according to the judgment information, for example, the route may be automatically selected based on a custom rule or a policy (i.e., the first selection manner), and then when the policy is hit, the target route information may be automatically selected from a plurality of candidate route information.
In an example, a threshold may be preset for a time difference between the candidate routes as a custom rule, and the route may be automatically selected based on the custom rule, for example, when three candidate routes exist in the candidate route list, if the total time consumption of route 2 and the total time consumption of route 3 are both 20 minutes more than that of route 1, route 1 may be used as the automatically selected route for this navigation; other judgment information may also be preset, which is not limited in the present invention.
Substep 13, when detecting that the judgment information is not satisfied, determining a second selection mode as a target selection mode; and the second selection mode is a mode of automatically selecting a route by adopting a pre-trained route selection model.
In a specific implementation, when it is detected that the determination information is not satisfied, the second selection manner may be determined to be a target selection manner, where the second selection manner may be a manner of automatically selecting a route by using a pre-trained route selection model, for example, in a case of missing a policy in a process of automatically selecting a route based on a custom rule or a policy, target route information may be automatically selected from a plurality of candidate route information by using the pre-trained route selection model.
And 204, determining target route information from the candidate route information by adopting the target selection mode, and navigating by adopting the target route information.
After the target selection mode is determined, the target selection mode may be adopted to determine target route information from a plurality of candidate route information, and then navigation may be performed by adopting the target route information, for example, in the process of automatically selecting a route for a navigation event, route intelligent selection may be performed by the first selection mode or the second selection mode, so as to automatically select the target route information.
In an example, as shown in fig. 3, a voice interaction request (query) for navigation by a user may be received, and then the voice interaction request may be semantically understood to identify a POI keyword (e.g., a destination keyword), so that a destination may be searched, destination information for this navigation may be determined from a to-be-selected destination list, and a route calculation process may be performed to obtain a route candidate list for this navigation (i.e., a navigation event), for example, three candidate routes (i.e., a plurality of candidate route information) may be included in the route candidate list, whether route automatic selection is performed or not may be determined by an in-vehicle system, in the case that route automatic selection is determined, automatic selection may be performed based on a custom rule (i.e., a first selection manner), a target route (i.e., target route information) may be obtained, and in the case that a policy is not hit, and automatically selecting (namely a second selection mode) through a classification model (namely a route selection model) to obtain a target route so as to start navigation according to the target route.
In yet another example, a list of routes may be displayed for the user to navigate according to the route selected by the user in the event that the in-vehicle system determines that automatic selection of a route is not to be made, or the list of routes may be displayed for the user as well when the classification model processing results are not to be automatically selected.
Referring to fig. 4, a flowchart illustrating steps of another vehicle navigation method according to an embodiment of the present invention is shown, which may specifically include the following steps:
in a specific implementation, in the case of adopting the second selection mode, that is, in the case of training the route selection model in advance for automatic route selection, model training may be further performed by obtaining sample route data and user behavior data for the sample route data.
In an example, a plurality of route data (i.e., sample route data) for a specified destination may be obtained through a third-party map application, and raw behavior data (i.e., user behavior data) for a user may be collected through an on-board system, so that subsequent model training may be performed according to the obtained data.
as an example, the plurality of feature type information may include the following items:
route label information, label frequency information, route attribute information, own vehicle attribute information and historical selection information.
In the process of training the model in advance, after the sample route data and the user behavior data for the sample route data are acquired, the model training may be performed by determining a plurality of feature type information to extract route features.
in a specific implementation, model training may be performed in combination with sample route data, user behavior data, and a plurality of feature type information, and a route selection model may be obtained.
In an example, model training may be performed by way of offline learning, and route features may be extracted as follows:
1. route label features
The method includes the steps that a plurality of route data and feature names of routes are obtained for a destination through third-party map application (such as no-speed-walking, major-route-first and the like), a feature group can be extracted according to the feature names, for example, the feature names contained in the routes are marked as 1, the feature names not contained in the routes are marked as 0, then a feature group can be obtained for each route, feature splicing can be carried out on the feature groups of the plurality of route data of the same destination to obtain route label features, the route label features can be represented in an identification-feature value mode, for example, the IDs can be trace _ ID and passage _ ID, the feature values can be features for completing splicing, and the route label features can be stored, for example, the storage IDs can be trace _ ID and use _ ID.
2. Per user selected tag distribution feature
By obtaining user behavior data of sample route data, for a route selected by a user, a feature group can be extracted according to feature names (such as no-speed, major route priority and the like), for example, the feature names included in the route are marked as 1, and the non-included marks are marked as 0, so that label distribution features of a plurality of routes can be obtained, the total times of all labels can be selected according to user statistics, the time period can be set from the first time to the previous time of the route selected by navigation at the current time, and the processed data can be stored through normalization processing of the times of selecting the labels according to the user, for example, the storage ID can be trace _ ID and use _ ID.
3. Route attribute features
By acquiring user behavior data of sample route data, for a route selected by a user, features can be extracted according to feature names (such as the number of traffic lights, the total mileage kilometers and the like), and then route attribute feature groups corresponding to a plurality of routes can be obtained and stored, for example, the storage ID can be trace _ ID.
4. Features of bicycle
By obtaining the user behavior data of the sample route data, for the route selected by the user, features can be extracted according to feature names (such as remaining capacity, weekend or not) so as to obtain a vehicle feature group, and the vehicle feature group can be stored, for example, the storage ID can be trace _ ID or use _ ID.
5. Current destination history selection tag feature
By acquiring user behavior data of sample route data, a feature group can be extracted according to feature names (such as no-speed, major route priority and the like) aiming at a route selected by a user, for example, the feature names included in the route are marked as 1, and the non-included marks are marked as 0, so that historical selection label features of a current destination can be obtained, the total times of all labels can be selected according to the statistics of the user and the destination, the time period can be set from the first time to the previous time of the route selection in the current navigation, and the processed data can be stored through normalization processing of the times of label selection, for example, the storage ID can be trace _ ID, use _ ID and poi _ ID.
In yet another example, the model (i.e., routing model) may be trained offline in the following manner:
1. extracting relevant route features such as route label features, each user selection label distribution feature, route attribute features, own vehicle features, current destination historical selection label features and classification standard answers set for the route selected by the user, such as classification scheme numbers 1, 2 and 3;
2. feature splicing can be carried out on the extracted route features and the classification standard answers;
3. the training/test set may be segmented;
4. through the LigthBGM training model, a classification model file can be obtained, and then the offline training model is completed.
For example, by using the LigthBGM training model, the training model can be based on the gradient lifting tree (GBDT) principle and can adopt a histogram algorithm, so that the training model has high speed and high accuracy, can process large-scale data, supports category attributes and has better interpretability.
and 407, when the target selection mode is the second selection mode, performing model processing on the candidate route information by using a pre-trained route selection model, determining target route information, and performing navigation by using the target route information.
In practical application, in the process of automatically selecting a route in the second selection mode, a pre-trained route selection model can be adopted to perform model processing on a plurality of candidate route information, so that target route information can be determined, and the target route information is adopted for navigation.
In an example, for a plurality of candidate route information, a route selection model may be used to calculate scores by selecting features and combining the selected features, such as route label features, user-selected label distribution features, route attribute features, plan number distribution features of current destination historical selection, and the like, and then target route information may be determined from the candidate route information according to the feature scores, such as taking a route with the highest score as the target route information.
For example, features may be selected from a plurality of candidate route information based on a gradient lifting tree (GBDT) principle, and feature scores may be calculated, and target route information may be determined based on the feature score calculation result, where if (route 1) ═ 2+0.9 ═ 2.9, — (route 2) ═ 1-0.9 ═ 1.9, and f (route 3) ═ xx, (non-automatic selection) ═ xx), then route 1 may be used as the target route information.
In yet another example, as shown in fig. 5, a model is trained in an offline learning manner, sample route data and user behavior data for the sample route data may be obtained, then offline training data or offline test data may be performed by extracting route features to obtain a pre-trained route selection model, and then a received voice interaction request (query) for navigation by a user may be subjected to route intelligent selection through the pre-trained route selection model in a case that it is determined that a route is automatically selected, so that target route information to be navigated may be obtained, and thus, by using multi-feature model training, such as adding a route label of a map operator, traffic information in the route, and information personalized by the user, the degree of intelligence of route selection is enhanced.
In an embodiment of the present invention, the method may further include the following steps:
acquiring navigation feedback information; the navigation feedback information is user feedback information generated in the process of navigating by adopting the target route information; and performing model training on the route selection model according to the navigation feedback information.
In practical application, after route automatic selection is performed on a navigation event, navigation feedback information can be obtained, the navigation feedback information can be user feedback information generated in the process of navigation by adopting target route information, model training can be performed on a route selection model according to the navigation feedback information, so that the route selection model can be continuously trained, user behaviors can be further confirmed by the target route information obtained by automatically selecting the route, and if the user drives a vehicle in the navigation process, the navigation feedback information can be added into the model training, so that the route automatic selection is more efficient and intelligent.
It should be noted that, for simplicity of description, the method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the illustrated order of acts, as some steps may occur in other orders or concurrently in accordance with the embodiments of the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.
Referring to fig. 6, a schematic structural diagram of a vehicle navigation apparatus according to an embodiment of the present invention is shown, which may specifically include the following modules:
a plurality of candidate route information determination modules 601, configured to determine, when a navigation event is detected, a plurality of candidate route information for the navigation event;
a route automatic selection judgment module 602, configured to judge whether to automatically select a route for the navigation event;
and a target route information determining module 603, configured to determine target route information from the multiple candidate route information and perform navigation using the target route information when determining that a route is automatically selected for the navigation event.
In an embodiment of the present invention, the target route information determining module 603 includes:
the target selection mode determining submodule is used for determining a target selection mode when judging that the route of the navigation event is automatically selected;
and the target route information determining submodule is used for determining target route information from the candidate route information in the target selection mode.
In an embodiment of the present invention, the target selection mode determining sub-module includes:
a judgment information acquisition unit for acquiring judgment information;
a first selection mode determination unit, configured to determine that the first selection mode is a target selection mode when it is detected that the determination information is satisfied; the first selection mode is a mode of automatically selecting a route according to the judgment information;
a second selection mode determination unit, configured to determine, when it is detected that the determination information is not satisfied, that the second selection mode is a target selection mode; and the second selection mode is a mode of automatically selecting a route by adopting a pre-trained route selection model.
In an embodiment of the present invention, the target route information determining sub-module includes:
and the target route information determining unit is used for performing model processing on the candidate route information by adopting a pre-trained route selection model when the target selection mode is a second selection mode, and determining the target route information.
In an embodiment of the present invention, the method further includes:
a data acquisition module for acquiring sample route data and user behavior data for the sample route data;
a plurality of feature type information determination modules for determining a plurality of feature type information;
and the route selection model obtaining module is used for carrying out model training by combining the sample route data, the user behavior data and a plurality of characteristic type information to obtain a route selection model.
In an embodiment of the present invention, the method further includes:
the navigation feedback information acquisition module is used for acquiring navigation feedback information; the navigation feedback information is user feedback information generated in the process of navigating by adopting the target route information;
and the model training module is used for carrying out model training on the route selection model according to the navigation feedback information.
In an embodiment of the present invention, the feature type information includes the following items:
route label information, label frequency information, route attribute information, own vehicle attribute information and historical selection information.
In the embodiment of the invention, when a navigation event is detected, a plurality of candidate route information aiming at the navigation event are determined, then whether the navigation event is automatically selected is judged, and when the navigation event is judged to be automatically selected, the target route information is determined from the candidate route information, and the target route information is adopted for navigation, so that the navigation route is automatically selected.
An embodiment of the present invention also provides a vehicle, which may include a processor, a memory, and a computer program stored on the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method for vehicle navigation as described above.
An embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program, when executed by a processor, implementing the method for vehicle navigation as above.
For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method and the device for vehicle navigation provided by the invention are described in detail above, and the principle and the implementation mode of the invention are explained by applying specific examples in the text, and the description of the above examples is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method of vehicle navigation, the method comprising:
upon detecting a navigation event, determining a plurality of candidate route information for the navigation event;
judging whether to automatically select the route of the navigation event;
and when the automatic route selection of the navigation event is judged, determining target route information from the candidate route information, and navigating by adopting the target route information.
2. The method of claim 1, wherein determining target route information from the plurality of candidate route information when determining automatic route selection for the navigation event comprises:
determining a target selection mode when judging that the automatic route selection is carried out on the navigation event;
and determining target route information from the candidate route information by adopting the target selection mode.
3. The method of claim 2, wherein determining a target selection mode when determining automatic route selection for the navigation event comprises:
acquiring judgment information;
when the judgment information is met, determining a first selection mode as a target selection mode; the first selection mode is a mode of automatically selecting a route according to the judgment information;
when the judgment information is not met, determining a second selection mode as a target selection mode; and the second selection mode is a mode of automatically selecting a route by adopting a pre-trained route selection model.
4. The method according to claim 2 or 3, wherein the determining target route information from the candidate route information in the target selection manner includes:
and when the target selection mode is a second selection mode, performing model processing on the candidate route information by adopting a pre-trained route selection model to determine the target route information.
5. The method of claim 4, further comprising:
obtaining sample route data and user behavior data for the sample route data;
determining a plurality of feature type information;
and performing model training by combining the sample route data, the user behavior data and a plurality of feature type information to obtain a route selection model.
6. The method of claim 5, further comprising:
acquiring navigation feedback information; the navigation feedback information is user feedback information generated in the process of navigating by adopting the target route information;
and performing model training on the route selection model according to the navigation feedback information.
7. The method of claim 5, wherein the plurality of feature type information comprises a plurality of:
route label information, label frequency information, route attribute information, own vehicle attribute information and historical selection information.
8. An apparatus for vehicle navigation, the apparatus comprising:
a plurality of candidate route information determination modules for determining a plurality of candidate route information for a navigation event when the navigation event is detected;
the automatic route selection judging module is used for judging whether to automatically select the route of the navigation event;
and the target route information determining module is used for determining target route information from the candidate route information and navigating by adopting the target route information when judging that the route of the navigation event is automatically selected.
9. A vehicle comprising a processor, a memory and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing a method of in-vehicle navigation according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method of in-vehicle navigation according to any one of claims 1 to 7.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105973255A (en) * | 2016-06-17 | 2016-09-28 | 北京奇虎科技有限公司 | Route recommendation method and terminal |
| WO2017015882A1 (en) * | 2015-07-29 | 2017-02-02 | Bayerische Motoren Werke Aktiengesellschaft | Navigation device and navigation method |
| CN107745711A (en) * | 2017-09-05 | 2018-03-02 | 百度在线网络技术（北京）有限公司 | A kind of method and apparatus that route is determined under automatic driving mode |
| CN108182273A (en) * | 2018-01-23 | 2018-06-19 | 成都信达智胜科技有限公司 | Network data processing method based on cloud storage |
| CN108444486A (en) * | 2017-02-16 | 2018-08-24 | 高德信息技术有限公司 | A kind of navigation routine sort method and device |
| CN109115236A (en) * | 2018-07-10 | 2019-01-01 | 上海博泰悦臻电子设备制造有限公司 | Vehicle, vehicle device equipment and its stroke automatic navigation method based on condition triggering |
| CN109154509A (en) * | 2016-05-10 | 2019-01-04 | 微软技术许可有限责任公司 | The user's efficiency improved in route planning using route preferences |
| US20190113353A1 (en) * | 2017-10-12 | 2019-04-18 | Toyota Jidosha Kabushiki Kaisha | Optimizing a Route Selection for a Highly Autonomous Vehicle |
| CN110375760A (en) * | 2019-07-29 | 2019-10-25 | 北京百度网讯科技有限公司 | Route determination method, apparatus, equipment and medium |
| CN110617830A (en) * | 2014-03-04 | 2019-12-27 | 谷歌有限责任公司 | Navigation directions between an automatically determined start point and a selected end point |
| CN110864701A (en) * | 2019-12-17 | 2020-03-06 | 上海博泰悦臻电子设备制造有限公司 | Navigation route recommendation method, vehicle-mounted terminal and computer storage medium |
| US20200378769A1 (en) * | 2017-11-30 | 2020-12-03 | ZICT Technology Co., Ltd | Intelligent route navigation method and device, server, mobile terminal, and storage medium |
| CN112050824A (en) * | 2020-09-17 | 2020-12-08 | 北京百度网讯科技有限公司 | Route planning method, device and system for vehicle navigation and electronic equipment |
-
2020
- 2020-12-23 CN CN202011541372.7A patent/CN112710323B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110617830A (en) * | 2014-03-04 | 2019-12-27 | 谷歌有限责任公司 | Navigation directions between an automatically determined start point and a selected end point |
| WO2017015882A1 (en) * | 2015-07-29 | 2017-02-02 | Bayerische Motoren Werke Aktiengesellschaft | Navigation device and navigation method |
| CN107850447A (en) * | 2015-07-29 | 2018-03-27 | 宝马股份公司 | Guider and air navigation aid |
| CN109154509A (en) * | 2016-05-10 | 2019-01-04 | 微软技术许可有限责任公司 | The user's efficiency improved in route planning using route preferences |
| CN105973255A (en) * | 2016-06-17 | 2016-09-28 | 北京奇虎科技有限公司 | Route recommendation method and terminal |
| CN108444486A (en) * | 2017-02-16 | 2018-08-24 | 高德信息技术有限公司 | A kind of navigation routine sort method and device |
| CN107745711A (en) * | 2017-09-05 | 2018-03-02 | 百度在线网络技术（北京）有限公司 | A kind of method and apparatus that route is determined under automatic driving mode |
| US20190113353A1 (en) * | 2017-10-12 | 2019-04-18 | Toyota Jidosha Kabushiki Kaisha | Optimizing a Route Selection for a Highly Autonomous Vehicle |
| US20200378769A1 (en) * | 2017-11-30 | 2020-12-03 | ZICT Technology Co., Ltd | Intelligent route navigation method and device, server, mobile terminal, and storage medium |
| CN108182273A (en) * | 2018-01-23 | 2018-06-19 | 成都信达智胜科技有限公司 | Network data processing method based on cloud storage |
| CN109115236A (en) * | 2018-07-10 | 2019-01-01 | 上海博泰悦臻电子设备制造有限公司 | Vehicle, vehicle device equipment and its stroke automatic navigation method based on condition triggering |
| CN110375760A (en) * | 2019-07-29 | 2019-10-25 | 北京百度网讯科技有限公司 | Route determination method, apparatus, equipment and medium |
| CN110864701A (en) * | 2019-12-17 | 2020-03-06 | 上海博泰悦臻电子设备制造有限公司 | Navigation route recommendation method, vehicle-mounted terminal and computer storage medium |
| CN112050824A (en) * | 2020-09-17 | 2020-12-08 | 北京百度网讯科技有限公司 | Route planning method, device and system for vehicle navigation and electronic equipment |
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| Publication number | Publication date |
|---|---|
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