CN114780869A

CN114780869A - Riding point recommendation method and device, electronic equipment and medium

Info

Publication number: CN114780869A
Application number: CN202210377250.1A
Authority: CN
Inventors: 张志立; 魏巍; 陈卓; 沈洪顺
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2022-04-11
Filing date: 2022-04-11
Publication date: 2022-07-22

Abstract

The disclosure provides a riding point recommendation method, a riding point recommendation device, electronic equipment and a medium, and relates to the fields of artificial intelligence and traffic, in particular to the fields of intelligent recommendation and automatic driving. The riding point recommendation method comprises the following steps: receiving a riding request of a first user; acquiring first information related to a first user; and determining at least one candidate starting site and at least one candidate ending site based on the first information, wherein the first information comprises at least one historical starting site and corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point and the at least one candidate ending point is selected from a predetermined set of ride-by sites.

Description

Riding point recommendation method and device, electronic equipment and medium

Technical Field

The present disclosure relates to the field of artificial intelligence and traffic technologies, and in particular, to an intelligent recommendation and automatic driving, and more particularly, to a riding point recommendation method, apparatus, electronic device, computer-readable storage medium, and computer program product.

Background

In everyday life, users often have a need for a vehicle, such as calling a vehicle, etc. The user often needs to manually input the start point and the end point to call the vehicle, and such an operation may be time-consuming and inconvenient for the user. It is desirable to have a more intelligent and user-friendly point of ride recommendation method.

Disclosure of Invention

The disclosure provides a riding point recommendation method, a riding point recommendation device, an electronic device, a computer readable storage medium and a computer program product.

According to an aspect of the present disclosure, there is provided a riding point recommendation method including: receiving a riding request of a first user; obtaining first information related to the first user; and determining at least one candidate starting site and at least one candidate ending site based on the first information, wherein the first information comprises at least one historical starting site and corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point and the at least one candidate ending point is selected from a predetermined set of ride-by sites.

According to another aspect of the present disclosure, there is provided a riding point recommending apparatus including: the request receiving unit is used for receiving a riding request of a first user; an information acquisition unit configured to acquire first information related to the first user; and a candidate point determination unit configured to determine at least one candidate starting site and at least one candidate ending site based on first information related to the first user, wherein the first information includes at least one historical starting site and corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point, and the at least one candidate ending point is selected from a predetermined set of bus taking sites.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a point of ride recommendation method in accordance with one or more embodiments of the present disclosure.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform a riding point recommendation method according to one or more embodiments of the present disclosure.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements a point of ride recommendation method according to one or more embodiments of the present disclosure.

According to one or more embodiments of the present disclosure, an entering point and a leaving point may be effectively recommended to a user.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a point of ride recommendation method according to an embodiment of the disclosure;

fig. 3A-3B show schematic diagrams of models to which a point of ride recommendation method according to embodiments of the disclosure may be applied.

Fig. 4 shows a block diagram of a riding point recommendation device according to an embodiment of the present disclosure;

FIG. 5 shows a schematic diagram of an autonomous vehicle according to an embodiment of the disclosure;

FIG. 6 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of embodiments of the present disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, while in some cases they may refer to different instances based on the context of the description.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the point of ride recommendation methods according to the present disclosure.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user

operating client devices

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

The user may receive the point of ride recommendation, etc. using

client devices

101, 102, 103, 104, 105, and/or 106. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the conventional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the databases in response to the commands.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or conventional stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with this disclosure.

A method 200 of point of ride recommendation according to an exemplary embodiment of the present disclosure is described below with reference to fig. 2.

At step S201, receiving a ride request of a first user;

at step S202, first information related to the first user is acquired.

At step S203, at least one candidate starting site and at least one candidate ending site are determined based on the first information, wherein the first information comprises at least one historical starting site and corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point and the at least one candidate ending point is selected from a predetermined set of ride-by sites.

According to the method disclosed by the embodiment of the disclosure, the riding points including the getting-on point and the getting-off point can be effectively recommended for the user.

It is to be understood that although the expression "predetermined" site set is used herein, this does not mean that the site set to which the at least one history start site, the at least one history end site, the at least one candidate start point, and the at least one candidate end point belong is fixed. The expression of a set of predetermined sites may mean that at some particular moment (e.g. the moment of recommending a site to a user or the moment of calling a vehicle by a user), a number of sites available are known (and thus may not require additional input from the user), but in the long term, a "set of predetermined sites" may change constantly as lines change, planning changes, new roads or areas are built, etc. As another example, a "set of predetermined sites" may include more or fewer sites depending on the region or geographic hierarchy in which the user is located or selected (county, district, urban traffic; intra-provincial traffic; cross-provincial traffic even greater, etc.). It is to be understood that the present disclosure is not limited thereto.

According to some embodiments, the first information may further comprise at least one of: the number of available vehicles near the first user, the number of riding users near the first user, and the distance of the first user from each station in the set of riding stations.

For example, getting on a bus at a site with more vehicles nearby may result in faster responses and orders. Also for example, getting on a bus at a site with a smaller number of nearby users may reduce queuing and user waiting times. Therefore, according to the embodiment, one or more of the factors of the number of vehicles, the number of other users and the distance between the stations can be considered, so that the time spent by the users on walking, waiting and the like is reduced, the recommendation effect is more user-friendly, and the user experience is improved.

The various data in the first information may take a variety of suitable data forms including, without limitation, words, numbers, sequences, vectors, and the like. An exemplary form of data in the first information will be described below.

As one non-limiting example, the number of available vehicles or vehicle resource conditions may be represented as a distance vector of each available vehicle from the user: (Car)₀,Car₁,…,Car_r)，Car_jKm, if not available, distance Car_j1, is ═ 1; distance, if available, is a distance value from the customer, such as: car_j3.5. Or, if the distance between the vehicle on which the passenger is seated and the passenger is 0 in the case where the user is already on the vehicle, Car_use＝0。

Taxi queue conditions can be expressed as a distance vector (Per) of all nearby taxi riders at a predetermined distance (e.g., 3km, 5km, 10km … … or set by the user) from the user₀,Per₁,…,Per_s) The highest entry to the queuing system from near to far is set as s individuals (e.g., 20, 30, 50, 100, etc.), which are each a distance Per from the user_j＝*km。

The distance vector of the user from each site can be represented as (Dist)₀,Dist₁,…,Dist_n)，Dist_iIs the distance of the user from site i.

It will be appreciated that the above are merely examples, and that the disclosure is not limited thereto.

According to some embodiments, method 200 may further include obtaining second information for a second user that is co-multiplied with the first user, and wherein the second information includes at least one starting site and a corresponding ending site in the second user's historical itinerary.

And a recommendation effect which is more accurate and more in line with user habits can be obtained according to the historical travel of the fellow passengers. For example, the historical order details of the fellow passenger may be represented as (d 1)₀,d1₁,…,d1_m)，d1_j＝(x₀,x₁,…,x_n) Wherein, a site value x corresponding to the starting point can be set_start1, the station value x corresponding to the destination_end-1 and the remaining stations are set to a different third value, e.g. x_other0, wherein x_start,x_end,x_other∈{x₀,x₁,…,x_n}. For a total of m history records. The fellow passenger (also referred to as a fellow passenger or a fellow passenger, etc.) may be obtained by the user adding a fellow passenger ID, or may be obtained byOther ways to facilitate obtaining (e.g., two users with a friendship or other contact use the taxi service at the same time in a nearby location and proactively or by default confirm the other as their fellow passenger), and the disclosure is not so limited.

According to some embodiments, the first information may further comprise at least one of: a historical order distance of the first user and a user representation of the first user.

The historical order distance may reflect the user's preference or the average value of the user's order. As can be appreciated by those skilled in the art, a user representation may include tag information or user models that can describe various identity attributes or behavioral attributes of the user. For example, the user representation may include behavioral characteristics of the user or the user's identity, region, age, gender, etc. By utilizing such information without violating personal privacy protection, certain ride characteristics of the user can be reflected, and by taking such tags or characteristics into account (e.g., learning the association of particular tag attributes with particular destinations via modeling or neural networks, etc.), the determined start and end points can be made more accurate.

According to some embodiments, the first information may further include a hot end set including sites that satisfy a hot condition for a number of times selected as the end of rides during a predetermined historical period.

Popular sites are, for example, the first n sites with the greatest number, sites selected more than 50, 100, 1000 times a day, etc. By considering popular sites, recent trends (e.g., newly opened shops, exhibitions, vacation popular travel points), etc. can be reflected, making the recommendation more human-based. The Hot site vector may be represented as (Hot)₀,Hot₁,…,Hot_t)，Hot_iIs the distance of the user from the ith hot station;

according to some embodiments, determining at least one candidate starting site and at least one candidate ending site based on first information associated with the first user may comprise: and obtaining at least one candidate starting station and at least one candidate ending station by inputting the first information into a pre-trained riding point recommendation model.

The point of ride recommendation model may be a pre-trained neural network. It will be appreciated that although described herein as a "ride point recommendation model", this does not mean that such a model consists of only a single network model, nor does it mean that all of the parameters need to be input into the same model. For example, a historical start site in the first information may be entered into the first network portion, a historical end site entered into the second network portion, a user representation entered into the third network portion, a distance entered into the fourth network portion … …, and so on, and generated by stitching or other processing. Alternatively, a starting point (or list of starting points) may be generated based on the first network portion, an ending point (or list of ending points) may be generated based on the second network portion, and the first network portion and the second network portion may use the same input, partially the same input (e.g., the distance of the current user from the respective site may be input only as the first network portion, or "hot site information" may be input only as the second network portion, etc.). It will be understood that the above are examples only, and the disclosure is not limited thereto.

According to some embodiments, the at least one historical starting station and the corresponding at least one historical ending station of the first user may be represented as at least one historical travel vector, each historical travel vector having a dimension equal to the number of stations in the set of bus stations, such that the value of each dimension in each historical travel vector corresponds to the state of the respective station in the set of bus stations in the historical travel, respectively, the value of each dimension being a value selected from the group consisting of: a first value indicating that the station is a starting point, a second value indicating that the station is an ending point, and a third value indicating that the station is neither a starting point nor an ending point, and

wherein inputting the first information to a pre-trained ride point recommendation model may include inputting the at least one historical travel vector to the ride point recommendation model.

For example, the historical order details may be in the form of (d)₀,d₁,…,d_m) In which d is_j＝(x₀,x₁,…,x_n). Similarly, a site value x corresponding to the starting point may be set_start1, the station value x corresponding to the destination_end1 and the remaining stations are set to a different third value, e.g., x_other0, wherein x_start,x_end,x_other∈{x₀,x₁,…,x_nAnd there may be a total of m history records. Thus, (x) can be used₀,x₁,…,x_n) Representing the value corresponding to the bus station set vector, where x_iRepresenting the historical values of the corresponding dockable site i out of the total of n available sites. As a specific, non-limiting example, assuming a total of ten stations, with the last trip from station 1 to station 2, the historical trip vector may be [1, -1,0,0,0,0,0,0]. The vector construction mode has small data quantity and reflects the information of all the stations and the stations involved in a single journey, so that the model can conveniently identify the characteristics in the stations and give corresponding recommended stations.

According to some embodiments, method 200 may further include, in response to receiving a determination of a first candidate starting point of the at least one candidate starting point and a first candidate ending point of the at least one candidate ending point, sending a vehicle call request having the first candidate starting point as a ride start point and the first candidate ending point as a ride end point.

According to some embodiments, the method 200 may further comprise: a first candidate start point of the at least one candidate start point and a first candidate end point of the at least one candidate end point are displayed, and in response to determining that a change request for the first candidate start point and the first candidate end point has not been received within a predetermined period of time after the first candidate start point and the first candidate end point are displayed, a vehicle call request is sent having the first candidate start point as a ride start point and the first candidate end point as a ride end point. According to such an embodiment, a smarter (and thus more reduced user input) "auto-fill" mode may be turned on, free from user input or selection from a list, but with the first candidate start point and the first candidate end point automatically filled in to the user and without a negative input, the call request is sent. The first candidate start point and the first candidate end point may be the highest ranked sites of the determined at least one candidate start point and at least one candidate end point, respectively, based on some criteria, where some criteria may include model calculation scores, or may be default or user-selected other criteria (e.g., ranked by distance, ranked by price, ranked by popularity … …, etc.), and it is understood that the disclosure is not limited thereto.

For example, the vehicle call APP may have an automatic mode and a user self-selected mode, and may default to the automatic mode. In the automatic mode, the user starting station and the user destination station can be automatically filled according to the recommendation condition, and in the self-selection mode, the user can be displayed in a list recommendation mode when clicking an input box. It is to be understood that the present disclosure is not limited thereto.

According to some embodiments, the ride request may be a ride request for an autonomous vehicle. With the rapid development of intelligent interaction and automatic driving, great challenges are brought to travel services and automobile consumption, and brand new development opportunities of urban traffic and mobile travel services are brought, so that the popularization of automatic driving can reduce the automobile purchasing willingness of consumers, and people can change from purchasing whole automobiles to purchasing travel services. In the travel service, it is very important to predict user initial site recommendation according to different dimensions so as to improve travel efficiency, improve vehicle resource utilization rate and improve user experience.

According to the embodiment of the disclosure, when the unmanned taxi taking service is used, the initial station and the final station can be recommended more intelligently and accurately, the input operation of the user is reduced as much as possible, the operation of the user is facilitated, and the user experience is improved.

In the related art, the information of the start site, the destination site or the site selected on the map is required to be input through an input box, the interaction is not intelligent enough, and the recommendation strategy recommendation of the start site and the destination site is relatively single. According to one or more embodiments of the present disclosure, vehicle resources can be distributed reasonably in a comprehensive manner according to factors such as user historical orders, user figures, vehicle resources and the like.

Some further non-limiting example embodiments according to the present disclosure are described below in conjunction with fig. 3A-3B.

In such an embodiment, the predicted start point and the predicted end point are obtained by inputting the first information (or a portion thereof) into the

pre-trained models

300 and 350, respectively.

As one example, the starting site recommendation logic may consider historical order details (starting site, ending site), car resources, taxi-taking queuing, distance of the user from each site.

Referring to FIG. 3A, as previously described, inputs to the model 300 may include a ride site set vector (x)₀,x₁,…,x_n) History order details (d)₀,d₁,…,d_m) Available vehicle distance vector (Car)₀,Car₁,…,Car_r) Distance vector (Per) of nearby users₀,Per₁,…,Per_s) Distance vector (Dist) of each station₀,Dist₁,…,Dist_n) One or more of the above. These vectors are input to a pre-trained neural network layer, such as a long-short term memory network (LSTM) or a Deep Neural Network (DNN), to obtain a corresponding result or results, such as a historical order feature d_outAvailable automobile distance characteristic Car_outDistance characteristic Per of nearby users_outDistance feature Dist of each station_out. Where there are multiple results, the multiple results may be concatenated (concat), and a self-attention (self-attention) mechanism may also optionally be used, with the final result being passed through an activation layer (e.g., softmax) to obtain a probability for each site in the list

Thus, it may be recommended to have a probability maximum

As the recommended starting site. In addition, the order of the recommended initial site list can be directly adjusted according to the weight of each variable. For example, the display order of the final station results may be controlled directly by sorting the distance d, by sorting the available Car distance Car, by sorting the nearby user distance Per, or by sorting the station distance features Dist, according to user selection, according to default settings, or according to other regulatory requirements, etc., or according to an alternative embodiment, in an automatic filling mode, the automatically filled stations may be further controlled or modified accordingly.

Table 1 shows effect data before a method and a method of use according to one embodiment of the present disclosure.

TABLE 1

Referring to FIG. 3B, similarly, as previously described, inputs to the model 350 can include a ride site set vector (x)₀,x₁,…,x_n) History order details (d)₀,d₁,…,d_m) Available automobile distance vector (Car)₀,Car₁,…,Car_r) Distance vector (Per) of nearby users₀,Per₁,…,Per_s) And will not be described in detail herein.

The inputs to the model 350 may also include the historical order details of the fellow (d 1)₀,d1₁,…,d1_m) Hot site vector (Hot)₀,Hot₁,…,Hot_t) User gender sex, user age, order value money, and the like.

These vectors are input to a pre-trained neural network layer, such as a long-short term memory network (LSTM) or a Deep Neural Network (DNN), to obtain a corresponding result or results, such as a historical order feature d_outAvailable automobile distance characteristic Car_outDistance characteristic Per of nearby users_outDistance feature of each station Dist_outHot station characteristic Hot_outCo-passenger characteristics d_outAnd so on. In case there are multiple results, the multiple results may be concatenated (concat). It is understood that the user gender sex, the user age, the order value money, and the like may be directly spliced with the extraction result of other vectors without passing through the feature extraction network, as shown in fig. 3B. Thereafter, a self-attention (self-attention) mechanism can optionally be used, and the final result passed through an activation layer (e.g., softmax) to obtain a probability for each site in the list

Thus, a recommendation probability maximum may be recommended

The site j of (2) is used as a recommended destination site, and in addition, the sequence of the recommended destination site list can be directly adjusted according to the weight of each variable. For example, the display order of the final site results may be controlled by sorting directly by distance d, by available Car distance Car, by nearby user distance Per, by site trending Hot, by tags or attributes in the user profile (e.g., gender Sex, Age), by historical order value Money, by historical orders by fellow d1, etc., according to user selection, according to default settings, or according to other regulatory requirements, etc. Alternatively, according to an alternative embodiment, in the auto-fill mode, the auto-fill station may be further controlled or modified accordingly.

Table 2 shows effect data prior to a method and method of use according to one embodiment of the present disclosure.

TABLE 2

According to one or more embodiments, order creation can be performed according to the recommendation or user selection when the user clicks on a call to the vehicle.

According to one or more embodiments of the present disclosure, when using the unmanned vehicle service, the intelligent and accurate recommendation of the initial station and the terminal station is performed, the best effort reduction user input operation is performed, the user operation is facilitated, the user experience is improved, unmanned vehicle resources are distributed reasonably according to factors such as the user historical order image and the unmanned vehicle resources, and the use efficiency of the unmanned vehicle is improved.

A point of ride recommendation device 400 according to an embodiment of the disclosure is now described with reference to fig. 4. The riding point recommending apparatus 400 may include a request receiving unit 401, an information acquiring unit 402, and a candidate point determining unit 403.

The request receiving unit 401 may be configured to receive a riding request of a first user.

The information obtaining unit 402 may be configured to obtain first information related to the first user.

The candidate point determination unit 403 may be configured to determine at least one candidate starting site and at least one candidate ending site based on first information related to the first user, where the first information includes at least one historical starting site and a corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point, and the at least one candidate ending point is selected from a predetermined set of bus stops.

According to the device disclosed by the embodiment of the disclosure, the getting-on point and the getting-off point can be effectively recommended for the user.

According to some embodiments of the disclosure, there is also disclosed an autonomous vehicle comprising a processor configured to control the vehicle to perform a manned mission from a first starting point to a first terminal point in response to a vehicle call request having the first starting point as a ride starting point and the first terminal point as a ride terminal point, wherein the first starting point and the first terminal point are selected from at least one candidate starting point and at least one candidate terminal point determined according to a ride point recommendation method of embodiments of the disclosure, respectively.

Referring to fig. 5, the system 500 includes a motor vehicle 510, a server 520, and one or more communication networks 530 coupling the motor vehicle 510 to the server 520. In embodiments of the present disclosure, the motor vehicle 510 may include a computing device and/or be configured to perform a method in accordance with embodiments of the present disclosure.

Server 520 may run one or more services or software applications that enable execution of, or schedule vehicles according to, the point of ride recommendation methods of the present disclosure. In some embodiments, server 520 may also provide other services or software applications that may include non-virtual environments and virtual environments. In the configuration shown in fig. 5, server 520 may include one or more components that implement the functions performed by server 520. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user of motor vehicle 510 may, in turn, utilize one or more client applications to interact with server 520 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 500.

Server 520 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. Server 520 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, server 520 may run one or more services or software applications that provide the functionality described below.

The computing units in server 520 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. Server 520 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some embodiments, server 520 may include one or more applications to analyze and consolidate data feeds and/or event updates received from motor vehicle 510. The server 520 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of the motor vehicle 510.

Network 530 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. By way of example only, one or more networks 510 may be a satellite communication network, a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (including, e.g., bluetooth, WiFi), and/or any combination of these and other networks.

The system 500 may also include one or more databases 550. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 550 may be used to store information such as audio files and video files. The data repository 550 may reside in various locations. For example, a data store used by server 520 may be local to server 520, or may be remote from server 520 and may communicate with server 520 via a network-based or dedicated connection. The data store 550 may be of different types. In certain embodiments, the data store used by server 520 may be a database, such as a relational database. One or more of these databases may store, update, and retrieve data to and from the database in response to the command.

In some embodiments, one or more of the databases 550 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The motor vehicle 510 may include sensors 511 for sensing the surroundings. The sensors 511 may include one or more of the following sensors: visual cameras, infrared cameras, ultrasonic sensors, millimeter wave radar, and laser radar (LiDAR). Different sensors may provide different detection accuracies and ranges. The camera may be mounted in front of, behind, or otherwise on the vehicle. The visual camera may capture conditions inside and outside the vehicle in real time and present to the driver and/or passengers. In addition, by analyzing the picture captured by the visual camera, information such as traffic light indication, intersection situation, other vehicle running state, and the like can be acquired. The infrared camera can capture objects under night vision conditions. The ultrasonic sensors can be arranged around the vehicle and used for measuring the distance between an object outside the vehicle and the vehicle by utilizing the characteristics of strong ultrasonic directionality and the like. The millimeter wave radar may be installed in front of, behind, or other positions of the vehicle for measuring the distance of an object outside the vehicle from the vehicle using the characteristics of electromagnetic waves. The lidar may be mounted in front of, behind, or otherwise in the vehicle for detecting object edges, shape information, and thus object identification and tracking. The radar apparatus can also measure the velocity change of the vehicle and the moving object due to the doppler effect.

Motor vehicle 510 may also include a communication device 512. The communication device 512 may include a satellite positioning module capable of receiving satellite positioning signals (e.g., beidou, GPS, GLONASS, and GALILEO) from the satellites 541 and generating coordinates based on these signals. The communication device 512 may also include modules for communicating with a mobile communication base station 542, and the mobile communication network may implement any suitable communication technology, such as current or evolving wireless communication technologies (e.g., 5G technologies) like GSM/GPRS, CDMA, LTE, etc. The communication device 512 may also have a Vehicle-to-Vehicle (V2X) module configured to enable Vehicle-to-Vehicle (V2V) communication with other vehicles 543 and Vehicle-to-Infrastructure (V2I) communication with Infrastructure 544, for example. In addition, the communication device 512 may also have a module configured to communicate with a user terminal 545 (including but not limited to a smartphone, tablet, or wearable device such as a watch), for example, via wireless local area network using IEEE802.11 standards or bluetooth. The motor vehicle 510 may also access the server 520 via the network 530 using the communication device 512.

Motor vehicle 510 may also include a control device 513. The control device 513 may include a processor, such as a Central Processing Unit (CPU) or a Graphics Processing Unit (GPU), or other special purpose processor, etc., in communication with various types of computer-readable storage devices or media. The control device 513 may include an autopilot system for automatically controlling various actuators in the vehicle. The autopilot system is configured to control a powertrain 510 (not shown), a steering system, and a braking system, etc., of a motor vehicle, via a plurality of actuators, in response to inputs from a plurality of sensors 511 or other input devices, to control acceleration, steering, and braking, respectively, without human intervention or limited human intervention. Part of the processing functions of the control device 513 may be implemented by cloud computing. For example, some processing may be performed using an onboard processor while other processing may be performed using the computing resources of the cloud. The control device 513 may be configured to control the vehicle according to the method of the present disclosure. Furthermore, the control device 513 may be implemented as one example of a computing device on the motor vehicle side according to the present disclosure.

In the technical scheme of the disclosure, the collection, acquisition, storage, use, processing, transmission, provision, public application and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations, and do not violate the good customs of the public order.

According to an embodiment of the present disclosure, there is also provided an electronic device, a readable storage medium, and a computer program product.

Referring to fig. 6, a block diagram of a structure of an electronic device 600, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 can also be stored. The calculation unit 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Various components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 607 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. The storage unit 608 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and/or a chipset, such as a bluetooth (TM) device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and/or the like.

Computing unit 601 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The computing unit 601 performs the various methods and processes described above, such as the method 200 and its variants. For example, in some embodiments, the method 200, variations thereof, and so forth, may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 600 via the ROM602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by computing unit 601, one or more steps of method 200 and variations thereof described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method 200, variations thereof, and so on in any other suitable manner (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server combining a blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

While embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely illustrative embodiments or examples and that the scope of the invention is not to be limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A riding point recommendation method comprises the following steps:

receiving a riding request of a first user;

acquiring first information related to the first user; and

determining at least one candidate starting site and at least one candidate ending site based on the first information, wherein the first information comprises at least one historical starting site and corresponding at least one historical ending site of the first user, and each of the at least one historical starting site, the at least one historical ending site, the at least one candidate starting point and the at least one candidate ending point is selected from a predetermined set of ride-by sites.

2. The method of claim 1, wherein the first information further comprises at least one of: the number of available vehicles near the first user, the number of riding users near the first user, and the distance of the first user from each station in the set of riding stations.

3. The method of claim 1 or 2, further comprising obtaining second information of a second user that is co-multiplied with the first user, and wherein the second information comprises at least one starting site and a corresponding ending site in the second user's historical trip.

4. The method of any of claims 1-3, wherein the first information further comprises at least one of: a historical order distance of the first user and a user representation of the first user.

5. The method of any of claims 1-4, wherein the first information further comprises a set of trending endpoints comprising sites that meet a trending condition a number of times selected as ride endpoints during a predetermined historical period.

6. The method of any of claims 1-5, wherein determining at least one candidate starting site and at least one candidate ending site based on the first information associated with the first user comprises:

and obtaining at least one candidate starting station and at least one candidate ending station by inputting the first information into a pre-trained riding point recommendation model.

7. The method of claim 6, wherein the at least one historical starting station and the corresponding at least one historical ending station of the first user are represented as at least one historical travel vector, each historical travel vector having a dimension equal to the number of stations in the set of travel stations such that the value of each dimension in each historical travel vector corresponds to a status of a respective station in the set of travel stations in a historical travel, respectively, the value of each dimension being a value selected from the group consisting of: a first value indicating that the station is a starting point, a second value indicating that the station is an ending point, and a third value indicating that the station is neither a starting point nor an ending point, and

wherein inputting the first information into a pre-trained riding point recommendation model comprises inputting the at least one historical travel vector into the riding point recommendation model.

8. The method according to any one of claims 1-7, further including: in response to receiving a determination of a first candidate start point of the at least one candidate start point and a first candidate end point of the at least one candidate end point, transmitting a vehicle call request having the first candidate start point as a ride start point and the first candidate end point as a ride end point.

9. The method of any of claims 1-7, further comprising: displaying a first candidate starting point of the at least one candidate starting point and a first candidate ending point of the at least one candidate ending point, and in response to determining that a change request for the first candidate starting point and the first candidate ending point has not been received within a predetermined period of time after displaying the first candidate starting point and the first candidate ending point, transmitting a vehicle call request having the first candidate starting point as a ride start point and the first candidate ending point as a ride end point.

10. The method of any of claims 1-9, wherein the ride request is a ride request for an autonomous vehicle.

11. A point of ride recommendation device, comprising:

the request receiving unit is used for receiving a riding request of a first user;

an information acquisition unit configured to acquire first information related to the first user; and

a candidate point determination unit configured to determine at least one candidate starting point and at least one candidate ending point based on first information related to the first user, wherein the first information includes at least one historical starting point and at least one corresponding historical ending point of the first user, and each of the at least one historical starting point, the at least one historical ending point, the at least one candidate starting point, and the at least one candidate ending point is selected from a predetermined set of bus taking sites.

12. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-10.

13. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-10.

14. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-10 when executed by a processor.

15. An autonomous vehicle comprising a processor configured to control the vehicle to perform a manned mission from a first starting point to a first ending point in response to a vehicle call request having the first starting point as a ride start point and the first ending point as a ride end point, wherein the first starting point and the first ending point are selected from at least one candidate starting point and at least one candidate ending point, respectively, determined according to the method of any one of claims 1-7.