CN117537834A - Map navigation method and device based on driving preference - Google Patents
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Abstract
The application relates to the technical field of map navigation, and provides a map navigation method and device based on driving preference. The method comprises the following steps: acquiring historical data of a current vehicle; the historical data comprises historical driving time and historical driving track; analyzing the historical data by using a machine learning algorithm, and determining preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the current vehicle on the characteristics of each route; receiving a navigation instruction; and planning a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter, and outputting the navigation path to the user terminal. According to the method and the device for determining the preference parameters of the route characteristics, the preference parameters of the route characteristics can reflect the driving preference of the user, the preference parameters are added into the planning of the navigation path, the fit degree of the navigation path and the driving preference of the user can be further improved, the generated navigation path can further meet the user requirements, and further user experience is improved.
Description
Technical Field
The present disclosure relates to the field of map navigation technologies, and in particular, to a map navigation method and apparatus based on driving preference.
Background
At present, the design of a vehicle-mounted map navigation system generally takes the shortest time or the shortest distance as a planning target to generate a navigation scheme, and the general navigation scheme is widely applied, but practically ignores the actual demands of users, options manually set by the users generally only have limited route recommendation conditions such as high-speed priority, charging cost and the like, ignores the more personalized preferences of the users, and finally the output route navigation scheme is difficult to meet the personalized demands of the users.
Therefore, how to provide a solution to the above technical problem is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of this, the embodiments of the present application provide a map navigation method and apparatus based on driving preference, so as to solve the problem that the prior art cannot meet the personalized requirements of users.
In a first aspect of an embodiment of the present application, there is provided a map navigation method based on driving preference, including:
acquiring historical data of a current vehicle; the historical data comprises historical driving time and historical driving track;
analyzing the historical data by using a machine learning algorithm, and determining preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the current vehicle on the characteristics of each route;
receiving a navigation instruction;
and planning a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter, and outputting the navigation path to the user terminal.
In a second aspect of the embodiments of the present application, there is provided a map navigation apparatus based on driving preference, including:
the historical data acquisition module is used for acquiring the historical data of the current vehicle; the historical data comprises historical driving time and historical driving track;
the preference analysis module is used for analyzing the historical data by utilizing a machine learning algorithm and determining preference parameters of the route characteristics; the preference parameter is used for representing the preference degree of the current vehicle on the characteristics of each route;
the navigation instruction receiving module is used for receiving the navigation instruction;
and the navigation planning module is used for planning a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter and outputting the navigation path to the user terminal.
In a third aspect of the embodiments of the present application, there is provided an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
Compared with the prior art, the beneficial effects of the embodiment of the application at least comprise: according to the method and the device for planning the navigation route, the preference parameters of the route characteristics are determined through analysis of the historical data, and then the navigation route corresponding to the navigation instruction is planned based on the preference parameters.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following description will briefly introduce the drawings that are needed in the embodiments or the description of the prior art, it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a map navigation method based on driving preference according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an initial planned path of a navigation map according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a revised planned path of a navigational map according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a map navigation device based on driving preference according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
A map navigation method and apparatus based on driving preference according to embodiments of the present application will be described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present application. The application scenario may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a server 104, and a network 105.
The first terminal device 101 may be hardware or software. When the first terminal device 101 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 104, including but not limited to car set systems, smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the first terminal apparatus 101 is software, it may be installed in the electronic apparatus as above. The first terminal device 101 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited in this embodiment of the present application. Further, various applications, such as a data processing application, an instant messaging tool, social platform software, a search class application, a shopping class application, and the like, may be installed on the first terminal device 101.
The second terminal device 102 may be hardware or software. When the second terminal device 102 is hardware, it may be a variety of electronic devices having a display screen and supporting communication with the server 104, including but not limited to car systems, smart phones, tablets, laptop and desktop computers, and the like; when the second terminal device 102 is software, it may be installed in the electronic device as above. The second terminal device 102 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited in this embodiment of the present application. Further, various applications may be installed on the second terminal device 102, such as a data processing application, an instant messaging tool, social platform software, a search class application, a shopping class application, and the like.
The third terminal device 103 may be hardware or software. When the third terminal device 103 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 104, including but not limited to car set systems, smart phones, tablet computers, laptop portable computers, desktop computers, and the like; when the third terminal device 103 is software, it may be installed in the electronic device as above. The third terminal device 103 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited in this embodiment of the present application. Further, various applications, such as a data processing application, an instant messaging tool, social platform software, a search class application, a shopping class application, and the like, may be installed on the third terminal device 103.
The server 104 may be a server that provides various services, for example, a background server that receives a request transmitted from a terminal device with which communication connection is established, and the background server may perform processing such as receiving and analyzing the request transmitted from the terminal device and generate a processing result. The server 104 may be a server, a server cluster formed by a plurality of servers, or a cloud computing service center, which is not limited in this embodiment of the present application.
The server 104 may be hardware or software. When the server 104 is hardware, it may be various electronic devices that provide various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103. When the server 104 is software, it may be a plurality of software or software modules providing various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103, or may be a single software or software module providing various services to the first terminal device 101, the second terminal device 102, and the third terminal device 103, which is not limited in this embodiment of the present application.
The network 105 may be a wired network using coaxial cable, twisted pair and optical fiber connection, or may be a wireless network that can implement interconnection of various communication devices without wiring, for example, bluetooth (Bluetooth), near field communication (Near Field Communication, NFC), infrared (Infrared), etc., which is not limited in the embodiment of the present application.
It should be noted that the specific types, numbers and combinations of the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105 may be adjusted according to the actual requirements of the application scenario, which is not limited in the embodiment of the present application.
Fig. 2 is a flow chart of a map navigation method based on driving preference according to an embodiment of the present application. The map navigation method based on driving preference of fig. 2 may be performed by the first terminal device or the second terminal device or the third terminal device or the server of fig. 1, and the user terminal in the present embodiment may be implemented by the first terminal device or the second terminal device or the third terminal device of fig. 1. As shown in fig. 2, the map navigation method includes:
s201: acquiring historical data of a current vehicle; the historical data comprises historical driving time and historical driving track;
s202: analyzing the historical data by using a machine learning algorithm, and determining preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the current vehicle on the characteristics of each route;
s203: receiving a navigation instruction;
s204: and planning a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter, and outputting the navigation path to the user terminal.
It will be appreciated that the current vehicle's historical data includes actual historical travel tracks and historical travel times corresponding to each of the historical travel tracks, the historical data detailing each of the user's historical driving behaviors that can reflect the user's driving preferences, i.e., the degree of preference for different route characteristics, to characterize whether the user is inclined to certain route characteristics, or is exclusive of certain route characteristics, etc. The preference parameter may be implemented by analyzing the historical data by using a machine learning algorithm in step S202, and different machine learning algorithms may be selected for different line characteristics, specifically, according to the actual situation.
After determining the preference parameter in steps S201-S202, if a navigation instruction is received, a navigation path more conforming to the driving preference of the user is planned for the navigation instruction based on the preference parameter, where the navigation path is different from a conventional navigation path generated by a general navigation algorithm, and in this embodiment, the navigation path is more compatible with the driving preference of the user, so that the user has better adaptability and more comfortable experience when traveling with the navigation path.
It can be understood that, when the step S201 is performed after the current historical data of the vehicle is obtained, that is, the execution subject of the method of the embodiment provides the user with an explicit user protocol or privacy policy, and explains in detail the purpose, type and use of the data mobile phone, the user needs to explicitly agree to and authorize the system to collect their personal data, and confirm the authorization to the execution subject by clicking the corresponding authorization intention, and then execute the action of the step S201.
Specifically, the historical data collected in step S201 includes historical driving data and historical driving tracks, and may further include user search data, collection locations, and the like, where the data sources of the historical data include vehicle sensors, smart phone applications, on-board cameras, and the like, where the vehicle sensors include GNSS (Global Navigation Satellite System ), accelerometers, brake sensors, and the like, and are used to obtain driving data such as position, speed, acceleration, and brake of the vehicle; the intelligent mobile phone is similar to a vehicle sensor in application, and can be supplemented when the data of the vehicle sensor is missing or obviously has errors; the vehicle-mounted camera is used for acquiring driving data such as road conditions, traffic signals and the like. It will be understood that, according to these raw driving data, a historical driving time and a historical driving track can be further determined, where the historical driving track refers to a position change track of the vehicle and a driving speed change condition, and the historical driving time corresponds to the position change track, and includes a driving time period, a departure time, an end time, a stay time in the process, and so on. Besides the smart phone application and the vehicle navigation system, search data, collection places and the like input by a user can be also included in the historical data.
After the historical data is obtained, data preprocessing is firstly carried out, wherein the data preprocessing comprises the steps of processing missing values, abnormal values and data standardization so as to ensure consistency and usability of the historical data. The data preprocessing may also include integrating data from different sources, such as correlating data from vehicle sensors with data from smart phone applications to obtain more comprehensive user behavior information.
Then, step S202 is executed, where the history data is analyzed by using a machine learning algorithm to determine preference parameters of route features, and it can be understood that different history data itself shows different route features, and the occurrence frequency of each route feature in the history data reflects the driving preference of the user. Step S202 thus analyzes the history data using a machine learning algorithm, and before determining the preference parameters of the route characteristics, further includes:
marking the historical data with labels conforming to the route characteristics based on the route characteristics;
a corresponding step S202 is a process of analyzing the history data by using a machine learning algorithm to determine preference parameters of the route characteristics, including:
and analyzing the historical data marked with the labels by using a machine learning algorithm to determine preference parameters of the route characteristics.
In particular, the route characteristics include one or more of traffic flow, road type, road grade, landscape type, transit stop type. The traffic flow can be an actual traffic flow value or a plurality of grades divided according to the traffic flow value, such as smooth, congestion, machine congestion and the like; the road types include expressways, primary roads, secondary roads and the like; the road gradient is used for representing gradient fluctuation conditions of the road, such as overall smoothness, mountain climbing, fluctuation, high fluctuation change frequency, larger fluctuation amplitude and the like; the landscape type is used to characterize a specific type and a landscape level of a landscape of a road, such as a beautiful landscape road, a conventional landscape road, a non-landscape and a poor hygienic environment road, etc.; the parking point position where the vehicle stays in a certain area for more than the preset time is used as a parked point, and the corresponding types, namely the parked point type, comprise shopping areas, restaurant areas, public parks, industrial parks, residential areas and the like. It will be appreciated that, in addition to the above detailed description, route characteristics may be added or modified according to actual needs or user selections, such as departure time, travel time period, travel date selection, etc., which are not limited herein.
Further, the historical data is analyzed to determine preference parameters of route features, where the preference parameters of route features refer to preference degrees of route features, for example, if a certain route feature is not marked in the historical data or a record of a certain route feature is less marked, the preference degree of a user on the route feature may be reflected to be lower. From a mathematical perspective, preference parameters refer to the probability of predicting whether a user will receive the route characteristics, based on which specific driving habits of the user can be derived, such as whether he is inclined to a expressway, whether he is inclined to a scenic route, whether he is inclined to avoid peak traffic, etc. The specific preference parameters may be implemented by analyzing the historical data by specific machine learning algorithms, where alternative machine learning algorithms include supervised learning, unsupervised learning, deep learning, classification algorithms, clustering algorithms, or regression algorithms, selecting an appropriate machine learning method based on different questions and data types.
Specifically, the process of analyzing the history data by using a machine learning algorithm to determine preference parameters of route characteristics includes:
and analyzing the historical data by utilizing a decision tree algorithm or a regression analysis algorithm, and determining a preference parameter when the route characteristics are any preset driving scene, wherein the preset driving scene comprises one or more of road sections with different traffic flows, road sections with different road types, road sections with different road gradients and road sections with different landscape types.
Alternatively, the process of analyzing the history data by using a machine learning algorithm to determine preference parameters of the route characteristics includes:
the historical data is analyzed by using a cluster analysis algorithm to determine preference parameters for the route characteristic of any stop-point type, including one or more of shopping areas, restaurant areas, public parks, industrial parks, and residential areas.
Or, a process of analyzing the history data by using a machine learning algorithm to determine preference parameters of the route characteristics, including:
the historical data is analyzed using a deep learning algorithm to determine preference parameters for a plurality of route characteristics including a plurality of traffic flow, road type, road grade, landscape type, and transit stop type.
The decision tree algorithm is a supervised learning algorithm for classification and regression, and when a decision tree is constructed, nodes of the decision tree comprise various features, such as departure time, destination, line features and the like, and complete decision trees are generated by analyzing historical data so as to predict user route selection under different navigation instructions, such as predicting whether a user is more inclined to avoid a route in a peak period or is more inclined to a expressway and the like.
Similarly, the regression analysis algorithm is used to establish a relationship between the input characteristics and the target variables for prediction, and in this embodiment, the regression analysis algorithm may be used to predict the preference of the user for the characteristics of the specific route, including the preference for different traffic flows, different road types, and so on.
Further, the cluster analysis algorithm is an unsupervised learning algorithm for grouping data points into clusters with similar characteristics. In this embodiment, the cluster analysis algorithm may be used to identify the interest point preference of the user, determine the historical warp stop points according to the historical form data, and divide all the historical warp stop points into different warp stop point types, thereby determining the preference degree of the user for each warp stop point type.
Further, the deep learning algorithm may be used to process complex nonlinear relationships, and is suitable for relatively complex user data analysis tasks, and the provincial large learning model corresponding to the deep learning algorithm may learn advanced features of data through a multi-layer neural network so as to better capture personalized preferences of a user, and in this embodiment, may be used to determine preference parameters of multiple line features based on historical data, so as to determine whether the user tends to select a specific route.
It will be appreciated that machine learning algorithms are typically implemented in the form of a data analysis model whose processing stages include: data preparation, feature engineering, model establishment, model evaluation and optimization, model application and the like.
Data preparation, which means that the system needs to prepare a dataset for training the model before constructing the data analysis model. This data set includes historical data, historical navigation records, and user personal data such as departure locations, destinations, navigation routes, time stamps, and user preference characteristics (e.g., whether peak hours are avoided, whether scenic routes are preferred, etc.).
Feature engineering is the process of converting raw data into features that can be understood by machine learning algorithms. In this embodiment, the features may include the following: a historical navigation record of the user, including a departure place, a destination, a route and the like; personalized preferences of the user, such as whether to avoid peak hours, whether to select scenic routes. Time characteristics such as travel time, day of week, etc.; geographic features such as geographic coordinates of a start point and an end point, traffic flow information of a road section, and the like. These features will be used to train a data analysis model to understand the navigation preferences of the user.
Model building, the construction of data analysis models typically relies on machine learning algorithms such as decision trees, random forests, logistic regression, neural networks, etc. The choice of model depends on the task and the data. When building a model, the system will train the model using historical form data so that the model can predict the navigation preferences of the user in different situations. The output of the model will be one or more predicted values representing the user's routing propensity under certain circumstances. For example, the model may output probabilities of navigation options that the user is more likely to select (e.g., route avoiding congestion or scenic route).
Model evaluation and optimization once the model is built, the system needs to evaluate it. This typically involves retaining a portion of the historical data as a validation set for evaluating the performance of the model. The performance evaluation of the model may use various metrics such as accuracy, recall, F1 score, etc. If the performance of the model is not ideal, the system may optimize the model by improving the feature engineering, trying different algorithms, adding more training data, etc. Further, with the application of the method of this embodiment, the historical data of the user may be continuously collected, the historical data and the preference predicted value output by the data analysis model at that time are updated to the training data set of the user, and the data analysis model is optimized again by using the training data set.
Model application once model training and optimization is complete, the present embodiment will apply a data analysis model to the method of the present embodiment. When the user inputs a navigation instruction including a departure place and a destination, step S204 generates personalized navigation advice using the preference parameters output by the data analysis model to satisfy the navigation preference of the user.
Through these steps, the method of the present embodiment can analyze the personal data of the user using a machine learning algorithm to understand its preferences and behavior patterns. This will provide a basis for the navigation system to generate customized navigation routes according to the user's personalized needs. Continued improvements and optimizations of the algorithms will enable the system to more accurately understand the user's preferences, providing better navigation advice.
Furthermore, besides analyzing the basic historical data to obtain the preference parameters, the preference parameters can be determined directly according to direct selection of the preference options by the user, for example, the user manually inputs a preference selection instruction through a navigation interface of the user terminal, for example, the favorite road type is expressway or scenic route, the region is avoided as much as possible, and common destinations such as home, company and the like. The specific method of the embodiment further comprises the following steps: and receiving a preference selection instruction corresponding to the route characteristics, and generating preference parameters for determining the route characteristics according to the preference selection instruction.
Finally, when a navigation instruction is received, when a navigation path is planned on a navigation map based on preference parameters, the actions of preprocessing comprise determining destination and departure information in the navigation instruction; monitoring the current real-time position; detailed data of the navigation map including road conditions, traffic flow, and the like are acquired. After preprocessing is finished, planning a navigation path, wherein the planning of the specific navigation path has at least two ideas, one is to correct the path cost of each planned path on a navigation map, the correction is carried out along with the preference degree, then the planning of the navigation path is carried out based on the corrected path cost, the other is to firstly obtain a preliminary navigation path which does not consider preference parameters in a conventional navigation algorithm, then judge whether the preliminary navigation path meets the preference parameters, and carry out path correction on the part which does not meet the preference parameters in the preliminary navigation path, thereby obtaining the final navigation path.
Specifically, the process of planning a navigation path corresponding to a navigation instruction on a navigation map based on preference parameters includes:
correcting the path cost of each planned path on the navigation map based on the preference parameters;
and planning a navigation path corresponding to the navigation instruction based on the corrected navigation map.
Or, based on the preference parameter, planning a navigation path corresponding to the navigation instruction on the navigation map, including:
planning a preliminary navigation path corresponding to the navigation instruction on the navigation map;
analyzing whether each preliminary navigation path meets preference parameters;
and correcting the preliminary navigation path which does not meet the preference parameters based on the navigation map so as to obtain the navigation path which meets the preference parameters.
For example, on the navigation map shown in fig. 3, where the S point is the start point of the navigation command, the D point is the end point of the navigation command, the potential route points existing on the navigation map include P1, P2, P3, P4 and P5, and the possible planned paths include S-P1, S-P2, P1-P3, P2-P3, P3-P4, P3-P5 and P4-D, P-D, respectively, where the initial path cost is based on the driving distance, and it is assumed that the initial path costs of the several planned paths are 2, 3, 6, 4, 5 and 6. According to the first planning idea, the path cost is corrected based on the preference parameters, generally, the higher the preference degree is, the larger the preference parameters are, the smaller the corrected path cost is, the preference parameters corresponding to each planning path are determined according to the route characteristics of the planning paths, the path cost is corrected according to the preference parameters, and particularly, the reciprocal of the preference parameters is multiplied by the original initial path cost. Assuming that the corrected path costs are sequentially 2, 3, 1.5, 2, 5, 6, 3 and 5, as shown in fig. 4, a navigation path is planned according to the corrected path costs, the planning target is that the path costs are the least comprehensively, and the finally obtained navigation path is S-P1-P3-P4-D. According to the second planning idea, firstly, a conventional preliminary navigation path is obtained according to the minimum comprehensive initial path cost as a target, then whether the preliminary navigation path meets the preference parameters is judged, whether a judgment standard exists when each preliminary navigation path meets the preference parameters or not is analyzed, namely, whether the preference parameters are smaller than the preset standard values of the preference parameters or not is judged, and if yes, the preference parameters are considered not to be met. For example, for traffic flow, the preset reference value is 1, the preference parameter of traffic smoothness is 1.5, the preference parameter of serious traffic jam is 0.6, and if a section of planned path P3-P5 in the preliminary navigation path has the road characteristic of serious traffic jam, the preliminary navigation path does not meet the preference parameter and needs to be corrected, the planned path P3-P5 with the road characteristic of serious traffic jam is replaced by other planned paths, and finally the navigation path S-P1-P3-P4-D meeting the preference parameter is obtained. It can be appreciated that the actual navigation map is far more complex than the example of fig. 3, and the specific navigation path planning based on the preference parameter needs to be adjusted according to the actual navigation map situation, which is not described herein.
The specific path planning algorithm for planning the navigation path or the preliminary navigation path corresponding to the navigation instruction can be selected from various path planning algorithms, such as an a-star algorithm and dijkstra, which are commonly used in the present embodiment, and are not limited herein.
As the vehicle travels, the executing body of the method of the embodiment can determine the position, speed, direction, etc. of the user using the vehicle sensor and the satellite navigation system, and also receive real-time traffic information including traffic flow, accident report, construction work, etc. Based on this information, the method of the present embodiment may also adjust the navigation route to select the best link, avoiding congestion and traffic delays.
The method of the embodiment also allows receiving feedback suggestions of the user and continuously updating various data, including history data, preference parameters, map data of a navigation map and the like, and continuously optimizes and improves various algorithm parameters in the method based on the feedback suggestions and the continuously updated various data, so that the prediction accuracy of the user requirements is further improved.
According to the method, the preference parameters of the route characteristics are determined through analysis of the historical data, and then the navigation path corresponding to the navigation instruction is planned based on the preference parameters, and because the preference parameters of the route characteristics are obtained based on the historical data, the driving preference of a user can be reflected, the preference parameters are added into the planning of the navigation path, the fit degree of the navigation path and the driving preference of the user can be further improved, the generated navigation path can further meet the user requirements, and further user experience is improved.
Any combination of the above optional solutions may be adopted to form an optional embodiment of the present application, which is not described herein in detail. It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
The following are device embodiments of the present application, which may be used to perform method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.
Fig. 5 is a schematic diagram of a map navigation apparatus based on driving preference according to an embodiment of the present application.
As shown in fig. 5, the driving preference-based map navigation apparatus includes:
a historical data acquisition module 501, configured to acquire historical data of a current vehicle; the historical data comprises historical driving time and historical driving track;
the preference analysis module 502 is configured to analyze the historical data by using a machine learning algorithm to determine preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the current vehicle on the characteristics of each route;
a navigation instruction receiving module 503, configured to receive a navigation instruction;
the navigation planning module 504 is configured to plan a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter, and output the navigation path to the user terminal.
According to the device, the preference parameters of the route characteristics are determined through analysis of the historical data, and then the navigation path corresponding to the navigation instruction is planned based on the preference parameters, and because the preference parameters of the route characteristics are obtained based on the historical data, the driving preference of a user can be reflected, the preference parameters are added into the planning of the navigation path, the fit degree of the navigation path and the driving preference of the user can be further improved, the generated navigation path can further meet the user requirements, and further user experience is improved.
In an exemplary embodiment, the preference analysis module 502 uses a machine learning algorithm to analyze the historical data and, prior to determining the preference parameters for the route characteristics, is further configured to:
marking the historical data with labels conforming to the route characteristics based on the route characteristics;
a process for analyzing historical data using a machine learning algorithm to determine preference parameters for route characteristics, comprising:
and analyzing the historical data marked with the labels by using a machine learning algorithm to determine preference parameters of the route characteristics.
In an exemplary embodiment, the preference analysis module 502 analyzes the historical data using a machine learning algorithm, a process of determining preference parameters for route characteristics, comprising:
and analyzing the historical data by utilizing a decision tree algorithm or a regression analysis algorithm, and determining a preference parameter when the route characteristics are any preset driving scene, wherein the preset driving scene comprises one or more of road sections with different traffic flows, road sections with different road types, road sections with different road gradients and road sections with different landscape types.
In an exemplary embodiment, the preference analysis module 502 analyzes the historical data using a machine learning algorithm, a process of determining preference parameters for route characteristics, comprising:
the historical data is analyzed by using a cluster analysis algorithm to determine preference parameters for the route characteristic of any stop-point type, including one or more of shopping areas, restaurant areas, public parks, industrial parks, and residential areas.
In an exemplary embodiment, the preference analysis module 502 analyzes the historical data using a machine learning algorithm, a process of determining preference parameters for route characteristics, comprising:
the historical data is analyzed using a deep learning algorithm to determine preference parameters for a plurality of route characteristics including a plurality of traffic flow, road type, road grade, landscape type, and transit stop type.
In an exemplary embodiment, preference analysis module 502 is further configured to:
and receiving a preference selection instruction corresponding to the route characteristics, and generating preference parameters for determining the route characteristics according to the preference selection instruction.
In an exemplary embodiment, the navigation planning module 504 plans a navigation path corresponding to the navigation instruction on the navigation map based on the preference parameter, including:
correcting the path cost of each planned path on the navigation map based on the preference parameters;
and planning a navigation path corresponding to the navigation instruction based on the corrected navigation map.
In an exemplary embodiment, the navigation planning module 504 plans, based on the preference parameter, a navigation path corresponding to the navigation instruction on a navigation map, including:
planning a preliminary navigation path corresponding to the navigation instruction on the navigation map;
analyzing whether each preliminary navigation path meets preference parameters;
and correcting the preliminary navigation path which does not meet the preference parameters based on the navigation map so as to obtain the navigation path which meets the preference parameters.
Fig. 6 is a schematic diagram of an electronic device 6 provided in an embodiment of the present application. As shown in fig. 6, the electronic device 6 of this embodiment includes: a processor 601, a memory 602 and a computer program 603 stored in the memory 602 and executable on the processor 601. The steps of the various method embodiments described above are implemented by the processor 601 when executing the computer program 603. Alternatively, the processor 601, when executing the computer program 603, performs the functions of the modules/units of the apparatus embodiments described above.
The electronic device 6 may be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The electronic device 6 may include, but is not limited to, a processor 601 and a memory 602. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the electronic device 6 and is not limiting of the electronic device 6 and may include more or fewer components than shown, or different components.
The processor 601 may be a central processing unit (Central Processing Unit, CPU) or other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
The memory 602 may be an internal storage unit of the electronic device 6, for example, a hard disk or a memory of the electronic device 6. The memory 602 may also be an external storage device of the electronic device 6, for example, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the electronic device 6. The memory 602 may also include both internal and external storage units of the electronic device 6. The memory 602 is used to store computer programs and other programs and data required by the electronic device.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium, such as a computer readable storage medium. Based on such understanding, the present application implements all or part of the flow in the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a readable storage medium, where the computer program may implement the steps of the method embodiments described above when executed by a processor. The computer program may comprise computer program code, which may be in source code form, object code form, executable file or in some intermediate form, etc. The readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting thereof; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A map navigation method based on driving preference, comprising:
acquiring historical data of a current vehicle; the historical data comprises historical driving time and historical driving track;
analyzing the historical data by using a machine learning algorithm to determine preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the vehicle on each route characteristic;
receiving a navigation instruction;
and planning a navigation path corresponding to the navigation instruction on a navigation map based on the preference parameter, and outputting the navigation path to a user terminal.
2. The method of claim 1, wherein prior to analyzing the historical data using a machine learning algorithm to determine preference parameters for route characteristics, further comprising:
marking the historical data with labels conforming to each route characteristic based on the route characteristic;
a process for analyzing the historical data using a machine learning algorithm to determine preference parameters for route characteristics, comprising:
and analyzing the historical data marked with the labels by using a machine learning algorithm to determine preference parameters of the route characteristics.
3. The method of claim 1, wherein analyzing the historical data using a machine learning algorithm to determine preference parameters for route characteristics comprises:
and analyzing the historical data by utilizing a decision tree algorithm or a regression analysis algorithm, and determining a preference parameter when the route characteristics are any preset driving scene, wherein the preset driving scene comprises one or more of road sections with different traffic flows, road sections with different road types, road sections with different road gradients and road sections with different landscape types.
4. The method of claim 1, wherein analyzing the historical data using a machine learning algorithm to determine preference parameters for route characteristics comprises:
and analyzing the historical data by using a cluster analysis algorithm, and determining preference parameters when the route characteristics are of any stop type.
5. The method of claim 1, wherein analyzing the historical data using a machine learning algorithm to determine preference parameters for route characteristics comprises:
the historical data is analyzed using a deep learning algorithm to determine preference parameters for a plurality of route characteristics including a plurality of traffic flow, road type, road grade, landscape type, and transit stop type.
6. The method as recited in claim 1, further comprising:
and receiving a preference selection instruction corresponding to the route characteristics, and generating the preference parameters for determining the route characteristics according to the preference selection instruction.
7. The method according to any one of claims 1 to 6, wherein the process of planning a navigation path corresponding to the navigation instruction on a navigation map based on the preference parameter comprises:
correcting the path cost of each planned path on the navigation map based on the preference parameters;
and planning a navigation path corresponding to the navigation instruction based on the corrected navigation map.
8. The method according to any one of claims 1 to 6, wherein the process of planning a navigation path corresponding to the navigation instruction on a navigation map based on the preference parameter comprises:
planning a preliminary navigation path corresponding to the navigation instruction on a navigation map;
analyzing whether each preliminary navigation path meets the preference parameters;
and correcting the preliminary navigation path which does not meet the preference parameter based on the navigation map so as to obtain a navigation path which meets the preference parameter.
9. A map navigation apparatus based on driving preference, characterized by comprising:
the historical data acquisition module is used for acquiring the historical data of the current vehicle; the historical data comprises historical driving time and historical driving track;
the preference analysis module is used for analyzing the historical data by utilizing a machine learning algorithm and determining preference parameters of route characteristics; the preference parameter is used for representing the preference degree of the vehicle on each route characteristic;
the navigation instruction receiving module is used for receiving the navigation instruction;
and the navigation planning module is used for planning a navigation path corresponding to the navigation instruction on a navigation map based on the preference parameter and outputting the navigation path to the user terminal.
10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 8 when the computer program is executed.
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| CN202311414085.3A CN117537834A (en) | 2023-10-27 | 2023-10-27 | Map navigation method and device based on driving preference |
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| CN202311414085.3A CN117537834A (en) | 2023-10-27 | 2023-10-27 | Map navigation method and device based on driving preference |
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