CN111982145B - Travel path recommendation method, device, equipment and storage medium - Google Patents
Travel path recommendation method, device, equipment and storage medium Download PDFInfo
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- CN111982145B CN111982145B CN202011023748.5A CN202011023748A CN111982145B CN 111982145 B CN111982145 B CN 111982145B CN 202011023748 A CN202011023748 A CN 202011023748A CN 111982145 B CN111982145 B CN 111982145B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3484—Personalized, e.g. from learned user behaviour or user-defined profiles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
Abstract
The present disclosure provides a travel path recommendation method. The invention relates to the field of intelligent transportation and automatic driving, in particular to the field of driving path planning. The travel path recommendation method comprises the following steps: receiving a plurality of driving information from a plurality of vehicles, wherein the driving information comprises driving paths, driving time consumption and driving road condition information of the vehicles; screening a target vehicle from a plurality of vehicles according to the running time consumption and the running road condition information of the vehicles aiming at each running path with the same running starting point and running finishing point; storing the driving path of the target vehicle as a candidate familiar road path in a familiar road data set; and recommending the driving path according to the familiar road data set. The disclosure also provides a travel path recommendation device, equipment and a storage medium.
Description
Technical Field
The present disclosure relates to the field of intelligent transportation and automatic driving, and in particular, to the field of driving path planning, and more particularly, to a driving path recommendation method, apparatus, device, and storage medium.
Background
In intelligent transportation and automatic driving, a method of combining an algorithm based on shortest path search with real-time traffic information is widely used to provide a recommended travel path. However, for travel routes with complex urban road distribution or sudden congestion, the method often cannot provide an optimal travel path.
Disclosure of Invention
In view of the above, the present disclosure provides a method, an apparatus, a device and a storage medium for recommending a driving path.
A first aspect of the present disclosure provides a travel path recommendation method, including:
receiving a plurality of driving information from a plurality of vehicles, wherein the driving information comprises driving paths, driving time consumption and driving road condition information of the vehicles;
aiming at each driving path with the same driving starting point and driving end point, screening out a target vehicle from the plurality of vehicles according to the driving time consumption and the driving road condition information of the vehicles;
storing the driving path of the target vehicle as a candidate familiar road path in a familiar road data set; and
and recommending a driving path according to the familiar road data set.
A second aspect of the present disclosure provides a travel path recommendation method including:
generating travel information in response to the current travel path deviating from the selected recommended path, the travel information including travel path, travel elapsed time, and travel road condition information;
sending the driving information to a familiar road recommendation server; and
and receiving the travel path recommendation from the familiar road recommendation server.
A third aspect of the present disclosure provides a travel path recommendation device including:
the information acquisition module is configured to receive a plurality of pieces of driving information from a plurality of vehicles, wherein the driving information comprises driving paths, driving time consumption and driving road condition information of the vehicles;
the vehicle screening module is configured to screen out target vehicles from the plurality of vehicles according to the running time consumption and the running road condition information of the vehicles aiming at each running path with the same running starting point and running finishing point;
a route storage module configured to store a travel route of the target vehicle as a candidate familiar road route in a familiar road dataset; and
and the path recommendation module is configured to recommend a driving path according to the familiar road data set.
A fourth aspect of the present disclosure provides a travel path recommendation apparatus including:
a memory storing program instructions; and
a processor configured to execute the program instructions to perform a travel path recommendation method provided according to a first aspect of the present disclosure.
A fifth aspect of the present disclosure provides a travel path recommendation device including:
an information generation module configured to generate driving information in response to a deviation of a current driving path from the selected recommended path, the driving information including a driving path, a driving elapsed time, and driving road condition information;
the information feedback module is configured to send the driving information to the familiar road recommendation server; and
and the recommendation receiving module is configured to receive the travel path recommendation from the acquaintance road recommendation server.
A sixth aspect of the present disclosure provides a travel path recommendation apparatus including:
a memory storing program instructions; and
a processor configured to execute the program instructions to perform a travel path recommendation method provided according to a second aspect of the present disclosure.
A seventh aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the travel path recommendation method as described above when executed.
An eighth aspect of the disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements the above method.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an architecture of a system for travel path recommendation according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a travel path recommendation method according to an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow chart of a travel path recommendation method according to another embodiment of the present disclosure;
FIG. 4 schematically illustrates an example process of screening and deriving target vehicles according to an embodiment of this disclosure;
fig. 5 schematically shows an example of calculating road condition similarity according to an embodiment of the present disclosure;
fig. 6 schematically shows a block diagram of a travel path recommendation device according to an embodiment of the present disclosure;
fig. 7 schematically shows a block diagram of a travel path recommendation device according to another embodiment of the present disclosure; and
fig. 8 schematically shows a block diagram of an electronic device adapted to perform travel path recommendation according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
A Path Search Algorithm (Path Search Algorithm) is used to solve the shortest Path problem, and is widely applied to travel Path planning of an urban road network. The addition of the real-time road condition information extraction algorithm provides a better solution for the travel path planning of the urban road network, and the addition of the real-time road condition attribution temporal attribute enables the actual path planning scheme to be more fit with a real road condition scene. Suppose that the starting point of travel in the route planning is S and the end point of travel is E. The route planning from the travel starting point S to the travel end point E is usually based on a shortest path search algorithm, and other auxiliary route solutions represented by less traffic lights are given as an initial screening result. And selecting optimal path solutions according to the result of the primary screening and the real-time road condition information for the selection of the staff. But is not necessarily the shortest time solution to the results of the screening. Taking the selection from the starting point of the trip journey to the international building a of Baidu (Shenzhen) to the western village, the xi xiang, the road, the Hezhen, the Shenzhen city, and the Shenzhen district as the ending point as an example, the results of the general path screening provide three schemes. For example, the scheme I and the scheme II are respectively common urban road selection schemes, the distance is 12-13 kilometers, the scheme III recommends a highway selection scheme, and the distance is 18-19 kilometers. However, if the distribution of urban roads is complicated or an emergency road event is encountered, the situation of the end point B may have many different choices, and if the location of the end point B changes, the recommended route may also change. For example, when the terminal B is changed in an area with a radius of 600 meters of the current terminal B, the recommended route thereof is changed. The third option can easily be replaced by the same type of ordinary road selection as the first and second options, i.e. the expressway selection option is replaced. But for experienced travelers, i.e. for familiar road drivers, the expressway plan which is actually a longer distance is still the better route plan selected by the familiar travelers. In particular, for routes with poor travel experience, such as poor road conditions, although time consuming may be short, it is not necessarily the best travel route for the skilled travelers.
Generally, for the case that urban roads are complex in distribution and sudden road congestion is encountered in a trip journey, a path search algorithm combined with real-time road condition information cannot provide an optimal trip path. In such a case, the familiar road path selected by experienced travelers can often be less time consuming or have a better driving experience. For example, trading off the route selection of a slightly farther trip for less total time overhead of traveling is one of the travel route schemes selected by the expert driver. In some cases, the travel route selected by the acquaintance road driver is provided to the user as the recommended route, which is beneficial to providing better travel experience for the user.
Fig. 1 schematically shows the architecture of a system for travel path recommendation according to an embodiment of the present disclosure. As shown in fig. 1, the system for making travel path recommendations includes a vehicle 110, on which vehicle 110 a vehicle-mounted terminal 120 and a satellite navigation terminal 130 are mounted, and a server apparatus 140 communicatively connected to vehicle-mounted terminal 120. Vehicle 110 may be an autonomous vehicle or a drive-assisted vehicle. Satellite Navigation terminal 130 may locate a position of vehicle 110 by a Global Navigation Satellite System (GNSS) and provide vehicle 110 with a recommended travel path according to the location and an electronic map, for example, a travel path is recommended for vehicle 110 based on a shortest path search algorithm calculation. The in-vehicle terminal 120 may provide the vehicle 110 with an acquaintance path recommendation according to an embodiment of the present disclosure. The in-vehicle terminal 120 may monitor the driving state of the vehicle 110 in real time, and acquire various driving data generated by the vehicle 110 during driving according to the driving state of the vehicle 110. The in-vehicle terminal 120 may transmit the travel information including the travel data to the server apparatus 140. A database 150 is also provided at the server device 140. The server apparatus 140 may update the database 150 according to the received travel information, and may acquire the acquaintance path recommendation scheme from the database 150 and transmit to the in-vehicle terminal 120 for selection by the vehicle 110 while traveling. The server device 130 in the embodiments of the present disclosure may be, for example, a remote computer, and may also be an edge computing platform, a cloud computing platform, and the like.
Fig. 2 schematically shows a flow chart of a travel path recommendation method 200 according to an embodiment of the disclosure. As shown in fig. 2, the travel path recommendation method 200 includes:
in step S210, driving information is generated in response to the current driving path deviating from the selected recommended path, the driving information including a driving path, a driving elapsed time, and driving road condition information.
In step S220, the travel information is transmitted to the rich road recommendation server.
In step S230, a travel route recommendation from the acquaintance road recommendation server is received.
According to an embodiment, a user may select a travel route from a route recommendation scheme provided to the user when preparing to drive a vehicle for travel. According to an embodiment, the path recommendation scheme provided to the user may include at least one driving path calculated based on a shortest path search algorithm provided by the satellite navigation terminal 130. According to an embodiment, the path recommendation scheme provided to the user may further include an acquaintance path recommendation scheme provided by the in-vehicle terminal 120 according to an embodiment of the present disclosure. The embodiment of the present disclosure does not limit this, and may further include a path recommendation scheme provided according to other methods. In some cases, the user may select a recommended route from the provided route recommendation schemes, and drive according to the recommended route completely during driving, without generating the driving information of the vehicle. In other cases, for example, the travel of the user is the travel that the user has traveled, or the user is a driver with a rich driving experience, the user may have a more ideal driving scheme. Therefore, in this case, the user may not select any one of the provided route recommendation schemes, but selects a travel route by himself or herself based on his or her own experience. According to the embodiment of the disclosure, in the case that the user travels according to the self-selected travel route, the travel process of the vehicle driven by the user is monitored and the relevant information is fed back, because the travel route in this case may become an acquainted road route. In addition, there is a possibility that the user selects a recommended route from the provided route recommendations and travels a distance according to the selected recommended route at the start of travel of the driving vehicle, that is, at the travel start point of the trip, and after reaching a certain position in the trip, the user has a more ideal travel plan for the following trip, so the user selects the travel route to drive by himself after the position. In this case, the travel route from the position where the user starts to select the travel route by himself/herself to the travel end point of the final trip may become a familiar route.
According to an embodiment, in step S210, generating the travel information in response to the current travel path deviating from the selected recommended path requires data collection for the two scenarios described above.
The first scenario is that the user selects a route to travel by himself without using any path recommendation scheme. In the scene, data collection is carried out on a vehicle driven by a user in a traveling process from a traveling starting point to a traveling end point of the current trip, so that traveling data of the vehicle is obtained. According to an embodiment, the driving data includes, but is not limited to, a driving start point and a driving end point for marking a driving course that is not driven according to the recommended route, a driving direction of the vehicle, a driving speed, a total elapsed time from the driving start point to the driving end point, and road condition information during driving (i.e., driving road condition information). In the scene, the driving starting point is a trip starting point, and the driving end point is a trip end point. The traveling direction, the traveling speed, and the total elapsed time from the traveling start point to the traveling end point of the vehicle may be acquired by, for example, a plurality of sensors provided on the vehicle. The traffic information in the driving process may be obtained through, for example, real-time traffic information used by the satellite navigation terminal, or may be obtained through, for example, real-time traffic information broadcast of traffic broadcast, which is not limited to this. According to the embodiment, the road condition information in the driving process comprises one or more of congestion degree, congested road sections, congestion length, congestion road names, congestion directions, congestion time, sudden congestion events, traffic light quantity and waiting time of each traffic light.
The second scenario is that the user selects a recommended route to travel, and when the user travels to an intermediate point between a travel starting point and a travel terminal point, the user changes the travel route, and then data collection is performed on a vehicle driven by the user in a travel process from the intermediate point to the travel terminal point so as to obtain travel data of the vehicle. According to an embodiment, in the scene, the driving starting point is a middle point at which the self-selection of the driving path is started, and the driving end point is a travel end point. The method for acquiring the driving data can be referred to the foregoing embodiments, and is not described herein again. The road condition information in the driving process also comprises one or more of congestion degree, congested road sections, congestion length, congested road names, congestion directions, congestion time, sudden congestion events, traffic light quantity and waiting time of each traffic light. It is readily understood that the travel path recorded in the second scenario is a partial travel path relative to the trip.
Next, a total elapsed time from the travel start point to the travel end point is acquired from the recorded travel data, and the time consumed for an abnormal stop in the travel of the vehicle is removed from the total elapsed time to obtain a travel elapsed time. It is readily understood that an abnormal stay does not include a stay due to, for example, a road congestion event. The abnormal stay may include, for example, stays due to other emergencies, such as stays due to refueling the vehicle, stays due to acquaintances, stays due to purchasing behavior, and the like. The elapsed time for the actual travel of the vehicle can be found after the time consumed for the above-described abnormal stay is removed from the total elapsed time.
Next, the obtained travel time is compared with the shortest travel time of the travel route calculated based on the shortest route search algorithm. According to an embodiment, if a route recommendation is also provided according to other methods, the resulting travel elapsed time is compared with the shortest elapsed time of the route recommendation according to other methods. If the obtained travel time is less than the shortest travel time of the travel route calculated based on the shortest route search algorithm, the travel route selected by the user is more optimal, and the travel route may become an acquainted route for recommendation. Therefore, in the case where the obtained travel time is less than the shortest travel time of the travel route calculated based on the shortest route search algorithm, the travel information may be generated from the travel time, the travel route, and the recorded road condition information during travel, and the travel information may be transmitted to an acquaintance recommendation server, such as the server device 140, in step S220.
When the travel time of the travel route is compared, it is necessary to align the travel start point and the travel end point. As described above, for the first scenario, the travel starting point is the travel starting point of the travel trip, and the travel end point is the travel end point of the travel trip, so that the shortest time consumption of the travel route calculated based on the shortest path search algorithm can be directly used in the comparison. Typically the time consuming first recommendation of a recommended path calculated based on a shortest path search algorithm. In the second scenario, the travel starting point is the middle point of the travel route starting to be selected by itself, and the travel end point is the travel end point of the travel route, so that the travel time from the travel starting point to the middle point needs to be removed from the shortest time consumption of the travel route calculated based on the shortest path search algorithm.
The above process realizes real-time monitoring and collection of vehicle data, and the provided driving information can be used for updating the database, such as the database 150, by the acquaintance road recommendation server. Thereby enabling online updating of the database for the familiar road path recommendations.
According to an embodiment, a user may obtain a recommended path from a server for familiarity recommendation. Specifically, when the user prepares for traveling, the user may input the travel starting point and the travel ending point of the travel route into the vehicle-mounted terminal 120, and the vehicle-mounted terminal 120 may further obtain the road condition information at the current time, and the method for obtaining the road condition information may refer to the foregoing embodiment, which is not described herein again. The vehicle-mounted terminal 120 then sends a path recommendation request to the acquaintance recommendation server, as shown in step S225 in fig. 2. When the path recommendation request comprises a travel starting point, a travel end point and road condition information (starting point road condition information) at the current time, the acquaintance road recommendation server selects a proper acquaintance road from the database according to the information included in the path recommendation request. Next, the user may receive a travel route recommendation from the acquaintance road recommendation server through the in-vehicle terminal 120, and may determine whether to travel according to the recommended acquaintance road according to actual conditions.
According to the travel route recommendation method, the familiar route recommendation for the travel route can be obtained, the time consumption of the familiar route is generally shorter than that of travel route calculated based on a shortest path search algorithm, and better travel experience can be provided for users.
Fig. 3 schematically illustrates a flow chart of a travel path recommendation method 300 according to another embodiment of the present disclosure. The travel path recommendation method 300 may be applied to an acquaintance recommendation server. As shown in fig. 3, the travel path recommendation method 300 includes the steps of:
in step S310, a plurality of pieces of travel information including travel paths, travel elapsed times, and travel road condition information of the vehicles are received from the plurality of vehicles.
In step S320, for each travel path having the same travel starting point and travel ending point, a target vehicle is screened out from a plurality of vehicles according to the travel time consumption and the travel road condition information of the vehicles.
In step S330, the travel route of the target vehicle is stored as a candidate rich road route in the rich road data set.
In step S340, a travel route recommendation is made based on the familiar road data set.
Specifically, in step S310, according to an embodiment, the acquaintance road recommendation server receives the travel information transmitted from the different vehicle. According to the foregoing embodiment, when the vehicle does not travel according to any of the recommended routes provided, the travel information is transmitted. The driving path, the driving time consumption and the driving road condition information of the vehicle included in the driving information may refer to the foregoing embodiments, and are not described herein again. The expert road recommendation server may temporarily store the received travel information in a buffer storage area in the memory after receiving the travel information, and process the travel information after receiving a certain amount of travel information. For example, the processing of the travel information may be started when the buffer memory area reaches a preset storage threshold, or the stored travel information may be processed after a preset time period (for example, one week), and the time period may be set according to an update request of the database.
Next, in step S320, the travel information is filtered. The travel routes may be classified according to their travel start and end points. The driving routes having the same driving start point and driving end point are divided into one group. And then, aiming at each group of running paths, screening target vehicles from a plurality of vehicles according to the running time consumption and the running road condition information of the vehicles. The target vehicle has a characteristic that a travel time of the target vehicle is less than a travel time of a candidate rich road route having the same travel start point and the same travel end point and having similar travel road condition information, which has been stored in the database, or a travel route of the target vehicle having the same travel start point and travel end point as the stored candidate rich road route is provided by a rich road driver.
In this case, in step S330, the travel route of the target vehicle may be stored as a candidate acquaintance route in the acquaintance route data set, that is, the travel route of the target vehicle may be provided as a recommended acquaintance route. And screening each group of driving paths obtained by division, and thus obtaining an updated mature road data set.
Next, in step S340, making a travel path recommendation according to the updated acquaintance road data set specifically includes selecting candidate acquaintance road paths from the acquaintance road data set in response to receiving a path recommendation request including a travel start point, a travel end point, and start point road condition information, and making a travel path recommendation using the selected candidate acquaintance road paths. The acquaintance road recommendation server (i.e., the server apparatus 140) may receive a path recommendation request from the vehicle (transmitted from the in-vehicle terminal 120) and select a candidate acquaintance road path from an acquaintance road data set (stored in the database 150) according to the path recommendation request.
According to the embodiment of the disclosure, the acquaintance road data set can be updated online in real time for recommending the acquaintance road paths, so that the acquaintance road path recommendation scheme can be effectively expanded, and the recommended acquaintance road paths are always kept optimal.
FIG. 4 schematically illustrates an example process of screening and deriving a target vehicle according to another embodiment of the present disclosure. As shown in fig. 4, for each driving route having the same driving start point and driving end point, in step S410, the road condition similarity is calculated according to the driving road condition information of the vehicle and the driving road condition information of the candidate frequent roads having the same driving start point and driving end point and stored in the frequent road data set. The road condition similarity is used for representing the similarity between the driving road condition information of the vehicle and the driving road condition information of candidate mature road paths with the same driving starting point and driving ending point, which are stored in the mature road data set. The greater the value of the road condition similarity is, the closer the traveling road condition information is. Next, in step S420, the calculated road condition similarity is compared with a preset first threshold TH 1. The preset first threshold TH1 may be determined according to the similarity degree between the driving road condition information. It is easily understood that the larger the value of the first threshold TH1, the more similar the compared driving road condition information of the vehicle and the driving road condition information of the candidate rich road paths having the same driving start point and driving end point and stored in the rich road data set. If the determination result is yes, that is, if it is determined that the road condition similarity is greater than the preset first threshold TH1, in step S430, the driving time consumption of the vehicle is continuously compared with the driving time consumption ti of the stored candidate familiar road paths, where ti represents the stored candidate familiar road paths in the familiar road data set, and i represents the number of the candidate familiar road paths having the same driving start point and driving end point and having similar driving road condition information determined through the determination in step S420. The purpose of this operation is to determine whether the travel time of the vehicle is the smallest among the stored candidate rich routes having the same travel start point and travel end point and having similar travel road conditions. If the determination result is yes, that is, if the travel time of the vehicle is less than the travel time of the stored candidate familiar road route, the vehicle is screened as the target vehicle in step S440. If the result of the determination is no, that is, in the case where the travel time of the vehicle is greater than or equal to the travel time of the stored candidate rich road route, in step S460, the travel information of the vehicle is discarded. If the result of the determination at step S420 is no, that is, if the road condition similarity is less than or equal to the first threshold TH1, it is continuously determined whether the vehicle is a vehicle driven by a familiar driver at step S450. If the determination result is yes, that is, in the case where it is determined that the vehicle is a vehicle driven by a familiar road driver, it proceeds to step S440 to screen the vehicle as a target vehicle. If the result of the determination is negative, that is, in the case where it is determined that the vehicle is not a vehicle driven by an acquainted road driver, it proceeds to step S460, where the travel information of the vehicle is discarded.
According to the embodiment of the present disclosure, the familiar drivers include a driver who provides a number of travel paths whose travel time consumption is less than the shortest travel time calculated based on the shortest path search algorithm, which is greater than the preset second threshold TH2, and a driver who provides a number of travel paths as candidate familiar paths, which is greater than the preset third threshold TH 3. Specifically, in the initial stage of building the familiarity road data set, data needs to be acquired from a large number of driver subjects that can provide a self-traveling route. Then, not every driver is an experienced driver, that is, not every driver provides a travel route that takes less time to travel than the shortest time for the recommended route calculated based on the shortest path search algorithm. Thus, if data are collected without screening, a great deal of manpower, material resources and financial resources are wasted. Therefore, according to the embodiment of the disclosure, a large number of drivers are screened first to determine the driver in the familiar road, and the determined principle is that if the driving time provided by the driver is less than the number of the shortest time-consuming driving paths calculated based on the shortest path search algorithm and is greater than the preset second threshold TH2, the driver is determined to be the driver in the familiar road. In addition, it is also considered that all the familiar road drivers cannot be found in the stage of establishing the familiar road data set, and therefore, it is also determined that the driver is an familiar road driver if the number of travel paths provided by the driver as candidate familiar road paths in the stage of providing the familiar road paths by using the familiar road data set is greater than a preset third threshold value TH 3. According to the embodiment of the disclosure, the limit of the familiar road driver is applied to the stage of providing the familiar road path by using the familiar road data set, so that more attention can be paid to the driving path provided by the familiar road driver, and the familiar road path can be acquired more accurately.
According to the embodiment of the present disclosure, the reliability of the familiar road route for recommendation is further ensured by determining the travel route that can be stored as the candidate familiar road route through the multi-level filtering of the travel information.
Further, according to the embodiment, in the case where the travel path of the target vehicle is stored in the rich road data set as the candidate rich road path, it is noted that, when the road condition similarity is greater than the preset first threshold TH1 and the travel time of the vehicle is less than the travel time ti of the stored candidate rich road path, storing the travel path of the target vehicle in the rich road data set as the candidate rich road path is actually a replacement operation, that is, replacing the stored candidate rich road path, which takes more travel time than the travel time of the travel path, with the travel path of the vehicle, that is, removing the stored candidate rich road path in the rich road data set.
Further, according to the embodiment, selecting candidate close-road paths from the close-road data set according to the path recommendation request also needs to be selected according to the driving road condition information. According to an embodiment, selecting candidate acquaintance paths from the acquaintance path data set according to the path recommendation request includes screening candidate acquaintance paths having a travel start point and a travel end point identical to the travel start point and the travel end point from the acquaintance path data set, calculating a recommended road condition similarity according to the start point road condition information and the travel road condition information of each of the screened candidate acquaintance paths, and selecting a candidate acquaintance path having the recommended road condition similarity greater than a preset first threshold TH 1.
Fig. 5 schematically shows an example of calculating road condition similarity according to an embodiment of the present disclosure. According to the embodiment, the traffic similarity may be calculated based on calculation of vector similarity, for example, a specific traffic representing the traveling traffic information may be taken as one dimension of the vector. As shown in fig. 5, the congestion degree is further divided into severe congestion, light congestion, and non-congestion according to the specific road condition, and the severe congestion, the light congestion, and the non-congestion are respectively used as a dimension of a vector, and then assigned according to the specific road condition. For example, if the actual road condition is heavy congestion, the value of heavy congestion is set to 1, and the values of both light congestion and non-congestion are set to 0. Therefore, the congestion degree can be converted into a plurality of dimensions of the vector, so that the road condition similarity can be calculated according to the vector similarity calculation method.
The urban road familiar road path searching scheme combined with the real-time road condition is applied to urban road travel path planning, and the selection of the urban road travel scheme can be optimized through the combination of the temporal familiar road attribute and the real-time path, so that a path selection scheme which is better in travel experience and better in accordance with a real scene is provided for travelers.
Fig. 6 schematically shows a block diagram of a travel path recommendation device 600 according to an embodiment of the present disclosure. As shown in fig. 6, the travel path recommending apparatus 600 includes an information obtaining module 610, a vehicle filtering module 620, a path storing module 630, and a path recommending module 640.
According to an embodiment, the information acquisition module 610 is configured to receive a plurality of traveling information including a traveling path, a traveling time, and traveling road condition information of a vehicle from a plurality of vehicles. The vehicle screening module 620 is configured to screen a target vehicle from a plurality of vehicles according to the travel time consumption and the travel road condition information of the vehicles for each travel path having the same travel starting point and travel ending point. The route storage module 630 is configured to store the travel route of the target vehicle as a candidate acquaintance route in an acquaintance route data set. The path recommendation module 640 is configured to make travel path recommendations based on the familiar road data sets.
The specific operations of the above functional modules may be obtained by referring to the operation steps of the travel path recommendation method 300 in the foregoing embodiment, and are not described herein again.
Fig. 7 schematically shows a block diagram of a travel path recommendation device according to another embodiment of the present disclosure. As shown in fig. 7, the travel path recommending apparatus 700 includes an information generating module 710, an information feedback module 720, a request transmitting module 730, and a recommendation receiving module 740.
According to an embodiment, the information generating module 710 is configured to generate the travel information in response to the current travel path deviating from the selected recommended path, the travel information including a travel path, a travel elapsed time, and travel road condition information. The information feedback module 720 is configured to send the travel information to the familiar road recommendation server. The request sending module 730 is configured to send a path recommendation request, where the path recommendation request includes a travel starting point, a travel end point, and starting point road condition information. The recommendation receiving module 740 is configured to receive a travel path recommendation from the acquaintance road recommendation server.
The specific operations of the above functional modules may be obtained by referring to the operation steps of the driving route recommendation method 200 in the foregoing embodiment, and are not described herein again.
Fig. 8 schematically shows a block diagram of an electronic device 800 adapted to perform travel path recommendation according to an embodiment of the present disclosure. The travel path recommendation method according to the embodiment of the present disclosure may be performed using the electronic device shown in fig. 8.
As shown in fig. 8, an electronic device 800 according to an embodiment of the disclosure includes a processor 801 and a memory 802. Processor 801 may perform various suitable actions and processes in accordance with programs or instructions stored in memory 802. The processor 801 may include, for example, a general purpose microprocessor (e.g., CPU), an instruction set processor and/or related chip sets and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
The processor 801 and the memory 802 are connected to each other via a bus. The processor 801 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the memory 802. It is noted that the program may also be stored in one or more storage devices other than the memory 802. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memory devices.
According to an embodiment of the present disclosure, the electronic device 800 may further include an input device 803 and an output device 804, the input device 803 and the output device 804 also being connected to the bus. Further, the electronic device 800 may also include one or more of the following components: an input section including a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section including a hard disk and the like; and a communication section including a network interface card such as a LAN card, a modem, or the like.
An execution subject of the travel path recommendation method according to the embodiment of the present disclosure may be a separate travel path recommendation device or integrated as one module in an existing path recommendation device (e.g., a satellite navigation terminal).
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium and a computer program product. The computer-readable storage medium may be embodied in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer readable storage medium carries one or more programs which, when executed by the processor 801, implement the method according to embodiments of the present disclosure. The computer program product comprises a computer program which, when executed by a processor, may implement the method of any of the embodiments described above.
According to embodiments of the present disclosure, the computer-readable storage medium may be a computer-non-volatile computer-readable storage medium, which may include, for example and without limitation: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.
Claims (16)
1. A travel path recommendation method comprising:
receiving a plurality of driving information from a plurality of vehicles, wherein the driving information comprises driving paths, driving time consumption and driving road condition information of the vehicles;
aiming at each driving path with the same driving starting point and driving end point, screening out a target vehicle from the plurality of vehicles according to the driving time consumption and the driving road condition information of the vehicles;
storing the driving path of the target vehicle as a candidate familiar road path in a familiar road data set; and
recommending a driving path according to the familiar road data set,
wherein, for each driving route with the same driving starting point and driving end point, screening out the target vehicle from the plurality of vehicles according to the driving time consumption and the driving road condition information of the vehicles comprises:
calculating road condition similarity according to the driving road condition information of the vehicle and the driving road condition information of the candidate familiar road paths with the same driving starting point and driving end point, which are stored in the familiar road data set;
comparing the running time of the vehicle with the stored running time of the candidate familiar road path under the condition that the road condition similarity is greater than a preset first threshold value:
and screening the vehicle as a target vehicle when the running time of the vehicle is less than the running time of the stored candidate familiar road path.
2. The method according to claim 1, wherein the screening of the target vehicles from the plurality of vehicles according to the travel time and the travel road condition information of the vehicles for each travel path with the same travel starting point and travel ending point further comprises:
determining whether the vehicle is a vehicle driven by an expert driver when the road condition similarity is less than or equal to the first threshold:
in a case where the vehicle is a vehicle driven by an expert driver, the vehicle is screened as a target vehicle.
3. The method according to claim 2, wherein the familiar road drivers include a driver who provides a number of travel paths whose travel time is less than the shortest travel time calculated based on the shortest path search algorithm, which is greater than a preset second threshold value, and a driver who provides a number of travel paths as candidate familiar road paths, which is greater than a preset third threshold value.
4. The method according to claim 1, wherein, in case the road condition similarity is greater than a preset first threshold and the travel time of the vehicle is less than the travel time of the stored candidate acquaintance road paths, the storing the travel path of the target vehicle as the candidate acquaintance road path in an acquaintance road dataset further comprises removing the stored candidate acquaintance road path.
5. The method of claim 1, wherein the making travel path recommendations from the familiar road data set comprises:
selecting a candidate familiar road from the familiar road data set in response to receiving a path recommendation request including a travel starting point, a travel end point and starting point road condition information; and
and recommending the driving path by using the selected candidate familiar road path.
6. The method of claim 5, wherein said selecting a candidate acquaintance path from the acquaintance data set in response to receiving a path recommendation request including a travel start point, a travel end point, and start point road condition information comprises:
screening candidate mature road paths with the same driving starting points and driving end points as the driving starting points and the driving end points from the mature road data set;
calculating recommended road condition similarity according to the starting point road condition information and the driving road condition information of each candidate familiar road path in the candidate familiar road paths obtained by screening; and
and selecting the candidate mature road paths with the recommended road condition similarity larger than a preset first threshold value.
7. The method as claimed in claim 1, wherein the traveling traffic information includes at least one of congestion degree, congested section, congestion length, congested road name, congestion direction, congestion time, sudden congestion event, traffic light number and waiting time of each traffic light.
8. A travel path recommendation method comprising:
generating driving information in response to the deviation of the current driving path from the selected recommended path, wherein the driving information comprises driving path information, driving time consumption information and driving road condition information;
sending the driving information to a familiar road recommendation server; and
receiving a travel path recommendation from the acquaintance road recommendation server,
wherein the travel path recommendation is made by the method of any one of claims 1 to 7.
9. The method of claim 8, wherein prior to receiving the travel path recommendation from the familial road recommendation server, further comprising:
and sending a path recommendation request, wherein the path recommendation request comprises a travel starting point, a travel end point and starting point road condition information.
10. The method of claim 8, wherein the generating travel information in response to the current travel path deviating from the selected recommended path comprises:
acquiring driving data of the vehicle in response to the deviation of the current driving path from the selected recommended path;
acquiring the driving path, total time consumption and driving road condition information of the vehicle from the driving data;
removing the time consumed by abnormal stop in the vehicle running from the total consumed time to obtain running consumed time;
comparing the travel time with the shortest travel time of the travel path calculated based on the shortest path search algorithm; and
and under the condition that the running time consumption is less than the shortest time consumption, generating the running information according to the running time consumption, the running path and the running road condition information.
11. The method of claim 8, wherein the recommended route further comprises a travel route calculated based on a shortest path search algorithm.
12. A travel path recommendation device comprising:
the information acquisition module is configured to receive a plurality of pieces of driving information from a plurality of vehicles, wherein the driving information comprises driving paths, driving time consumption and driving road condition information of the vehicles;
the vehicle screening module is configured to screen a target vehicle from the plurality of vehicles according to the running time consumption and the running road condition information of the vehicles aiming at each running path with the same running starting point and running ending point;
a route storage module configured to store a travel route of the target vehicle as a candidate familiar road route in a familiar road dataset; and
a path recommendation module configured to recommend a driving path according to the familiar road data set,
wherein the vehicle screening module is specifically configured to:
calculating road condition similarity according to the driving road condition information of the vehicle and the driving road condition information of the candidate familiar road paths with the same driving starting point and driving end point, which are stored in the familiar road data set;
comparing the running time of the vehicle with the stored running time of the candidate familiar road path under the condition that the road condition similarity is greater than a preset first threshold value:
and screening the vehicle as a target vehicle when the running time of the vehicle is less than that of the stored candidate familiar road path.
13. A travel path recommendation apparatus comprising:
a memory storing program instructions; and
a processor configured to execute the program instructions to perform the travel path recommendation method of any of claims 1 to 7.
14. A travel path recommendation device comprising:
the information generating module is configured to generate driving information in response to the deviation of the current driving path from the selected recommended path, wherein the driving information comprises driving path information, driving time consumption information and driving road condition information;
the information feedback module is configured to send the driving information to a familiar road recommendation server; and
a recommendation receiving module configured to receive a travel path recommendation from the acquaintance road recommendation server,
wherein the travel path recommendation is made using the apparatus of claim 12.
15. A travel path recommendation apparatus comprising:
a memory storing program instructions; and
a processor configured to execute the program instructions to perform the travel path recommendation method of any of claims 8 to 11.
16. A computer-readable storage medium storing computer-executable instructions for implementing the travel path recommendation method of any one of claims 1 to 7, or the travel path recommendation method of any one of claims 8 to 11 when executed.
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| CN112866910B (en) * | 2021-01-08 | 2022-09-23 | 腾讯科技（深圳）有限公司 | Method, device and system for recommending starting point route and computer storage medium |
| CN113762583A (en) * | 2021-04-20 | 2021-12-07 | 腾讯科技（深圳）有限公司 | Route planning method, device and equipment and computer readable storage medium |
| CN115171416B (en) * | 2022-08-24 | 2023-05-26 | 禾多科技（北京）有限公司 | Recommended driving information sending method, device, electronic equipment and medium |
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