CN111985662A

CN111985662A - Network car booking method and device, electronic equipment and storage medium

Info

Publication number: CN111985662A
Application number: CN202010616996.4A
Authority: CN
Inventors: 朱贝
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2020-06-30
Filing date: 2020-06-30
Publication date: 2020-11-24
Anticipated expiration: 2040-06-30
Also published as: CN111985662B

Abstract

The application discloses a network taxi booking method, a network taxi booking device, electronic equipment and a storage medium, and relates to the technical field of automatic driving, intelligent transportation, big data and intelligent search. The scheme is as follows: obtaining a car booking request, and obtaining a first candidate vehicle according to the car booking request, wherein the car booking request at least comprises a boarding position of a car booking user; acquiring first trajectory data of a first candidate vehicle; acquiring a driving path from the first candidate vehicle to the boarding position according to the first track data and the boarding position; acquiring the driving time of a first candidate vehicle to a boarding position according to a driving path; and selecting a target vehicle from the first candidate vehicles according to the driving time, and issuing a car booking order to the target vehicle. According to the method and the device, the first track data of the first candidate vehicle are adopted, the driving path from the first candidate vehicle to the boarding position is obtained, and the order is sent based on the real driving time, so that the accuracy and the efficiency in the network car booking process are improved.

Description

Network car booking method and device, electronic equipment and storage medium

Technical Field

Embodiments of the present application relate generally to the field of data processing technology, and more particularly to the fields of autopilot, smart traffic, big data, and smart search technology.

Background

In recent years, internet car booking has become a common way of travel. The travel mode of network car booking is adopted, and great convenience can be brought to users who are on business trip, do not have private cars or do not allow driving by the current body. Therefore, how to improve the accuracy and efficiency in the network car booking process has become one of important research directions.

Disclosure of Invention

The application provides a network taxi appointment method, a network taxi appointment device, electronic equipment and a storage medium.

According to a first aspect, there is provided a network taxi appointment method, comprising:

obtaining a car booking request, and obtaining a first candidate vehicle according to the car booking request, wherein the car booking request at least comprises a boarding position of a car booking user;

acquiring first trajectory data of the first candidate vehicle;

acquiring a driving path from the first candidate vehicle to the getting-on position according to the first track data and the getting-on position;

acquiring the driving time of the first candidate vehicle to the getting-on position according to the driving path; and

and selecting a target vehicle from the first candidate vehicles according to the running time, and issuing a car appointment order to the target vehicle.

According to a second aspect, there is provided a network car booking device comprising:

the request obtaining module is used for obtaining a car booking request and obtaining a first candidate vehicle according to the car booking request, wherein the car booking request at least comprises a getting-on position of a car booking user;

a trajectory acquisition module for acquiring first trajectory data of the first candidate vehicle;

the route acquisition module is used for acquiring a driving route from the first candidate vehicle to the boarding position according to the first track data and the boarding position;

the time acquisition module is used for acquiring the driving time of the first candidate vehicle from the driving path to the loading position; and

and the order issuing module is used for selecting a target vehicle from the first candidate vehicles according to the running time and issuing a car booking order to the target vehicle.

According to a third aspect, there is provided 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 network taxi appointment method of the first aspect of the present application.

According to a fourth aspect, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the network taxi appointment method of the first aspect of the present application.

The embodiment provided by the application at least has the following beneficial technical effects:

according to the network vehicle booking method provided by the embodiment of the application, the vehicle booking request is obtained, the first candidate vehicle and the first track data of the first candidate vehicle are obtained according to the vehicle booking request, then the driving route of the first candidate vehicle to the vehicle loading position and the driving time of the first candidate vehicle to the vehicle loading position according to the driving route are obtained according to the first track data and the vehicle loading position, the target vehicle is selected from the first candidate vehicle according to the driving time, and the vehicle booking order is issued to the target vehicle, so that the order of the network vehicle booking is sent. Therefore, the driving route from the first candidate vehicle to the boarding position can be acquired through the first track data of the first candidate vehicle, and the order is sent based on the real driving time, so that the acquired driving route and the driving time are more accurate, matched target vehicles can be screened out more quickly and accurately, the accuracy and the efficiency of the network car booking process are improved, and the user experience is improved.

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

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

FIG. 1 is a schematic diagram according to a first embodiment of the present application;

FIG. 2 is a schematic diagram according to a second embodiment of the present application;

FIG. 3 is a schematic illustration according to a third embodiment of the present application;

FIG. 4 is a schematic illustration of a trace map formed from first trace data;

FIG. 5 is a schematic illustration of another trace map formed from first trace data;

FIG. 6 is a schematic diagram of second track data obtained by removing abnormal positions from first track data;

FIG. 7 is a schematic illustration according to a fourth embodiment of the present application;

FIG. 8 is a schematic view of a road segment and the start-stop position of the road segment;

FIG. 9 is a schematic illustration of a travel path of a first candidate vehicle;

FIG. 10 is a schematic illustration according to a fifth embodiment of the present application;

FIG. 11 is a schematic illustration according to a sixth embodiment of the present application;

FIG. 12 is a schematic illustration of a vehicle screening area;

FIG. 13 is a schematic illustration of a selection of a third candidate vehicle within the vehicle screening area;

FIG. 14 is a schematic illustration according to a seventh embodiment of the present application;

FIG. 15 is a schematic diagram of a driver client, a passenger client and a plurality of modules;

FIG. 16 is a schematic diagram of a car booking network;

fig. 17 is a block diagram of a network car booking device for implementing the network car booking method according to the embodiment of the present application;

FIG. 18 is a block diagram of a network car booking device for implementing the network car booking method of the embodiments of the present application;

fig. 19 is a block diagram of a network car booking electronic device for implementing an embodiment of the present application.

Detailed Description

The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. 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 and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

A network car booking method, an apparatus, an electronic device, and a storage medium according to embodiments of the present application are described below with reference to the accompanying drawings.

Fig. 1 is a schematic diagram according to a first embodiment of the present application. It should be noted that the main execution body of the network car booking method of the embodiment is a network car booking device, and the network car booking device may specifically be a hardware device, or software in the hardware device, or the like. The hardware devices are, for example, terminal devices, servers, and the like. As shown in fig. 1, the network taxi appointment method provided in this embodiment includes the following steps:

s101, obtaining a car booking request, and obtaining a first candidate car according to the car booking request, wherein the car booking request at least comprises a boarding position of a car booking user.

The taxi appointment request at least comprises the boarding position of the taxi appointment user, and can also comprise information such as a destination input by the taxi appointment user. For example, the taxi appointment request includes information that the boarding location of the user is address a and the destination is address b.

In the embodiment of the application, the user can issue the car booking request in modes of clicking the car booking control in the application program with the car booking function installed on the user terminal such as a mobile phone and the like, inputting voice information and the like. Accordingly, after a vehicle appointment request issued by a user is detected, a first candidate vehicle can be acquired according to the vehicle appointment request.

Alternatively, when attempting to acquire the car-booking request, the position of the target operation performed on the main interface by the user may be acquired, and when the position of the target operation is detected to be the operation area of the car-booking control, it may be determined that the car-booking request is detected.

The application having the car-booking function installed on the user terminal may be a car-booking application only used for network car booking, or may be another application integrating the network car-booking function, such as a map navigation application.

The car-booking request may be an instant car-booking request or a timed car-booking request, and if the car-booking request is the timed car-booking request, the car-booking request at least includes the boarding position and the reserved time of the car-booking user.

Further, after the car booking request is obtained, the car booking request can be analyzed, the getting-on position of the car booking user is at least extracted, and then the vehicles meeting the requirements are screened out from all the vehicles to be carried as first candidate vehicles according to the information such as the getting-on position of the car booking user.

S102, first track data of the first candidate vehicle are obtained.

The first trajectory data refers to a data set including a positioning timestamp, longitude and latitude, speed, direction and altitude.

Optionally, the first trajectory data of the first candidate vehicle may be acquired by a positioning system. For example, the first trajectory data may be acquired by a Positioning System such as a Bei Dou Navigation Satellite System (BDS), a Global Positioning System (GPS), or a GLONASS Satellite Navigation System (GLONASS).

S103, acquiring a driving path from the first candidate vehicle to the boarding position according to the first track data and the boarding position.

When attempting to acquire a travel route, the travel route may be further limited, and for example, the travel route may be defined as one of a plurality of travel routes that avoid the west gate bridge.

In the embodiment of the application, after the first trajectory data and the boarding position are obtained, the driving path of the first candidate vehicle from the first candidate vehicle to the boarding position can be obtained in multiple ways. For example, the travel route along which the first candidate vehicle travels to the boarding location may be acquired by a Dijkstra (Dij) algorithm, a florode (Floyd) algorithm, an a Star (a Star) algorithm, and the like.

And S104, acquiring the running time of the first candidate vehicle running to the boarding position according to the running path.

Alternatively, the driving time of the first candidate vehicle to the boarding location according to the driving route may be acquired by calling an Application Programming Interface (API). Any third-party map can be accessed through the map calling API, and the estimated driving time in the third-party map according to the driving path and the getting-on position is read. The map API is an API for embedding a map into a web page by changing Java Script (JS for short) or the like into a language.

And S105, selecting a target vehicle from the first candidate vehicles according to the running time, and issuing a vehicle-booking order to the target vehicle.

As a possible implementation manner, after the travel time is obtained, the travel time may be sorted to select a target vehicle from the first candidate vehicles, and then the order of booking the vehicle is sent to the target vehicle.

Alternatively, the travel times may be sorted in ascending order, and the first candidate vehicle ranked first may be selected as the target vehicle.

Optionally, the driving time may be sorted in an ascending order, a first candidate vehicle within a preset sorting range is selected, then the first candidate vehicle within the preset sorting range is recommended to the car booking user, and the car booking user selects a final target vehicle from the first candidate vehicle. For example, the first 3 first candidate vehicles may be recommended to the vehicle-booking user, and the vehicle-booking user selects a final target vehicle from the 3 first candidate vehicles according to an actual demand.

Further, after the target vehicle is determined, the car appointment order may be issued to the target vehicle through a mobile network such as a fourth Generation mobile communication technology (4th Generation, abbreviated as 4G) or a fifth Generation mobile communication technology (5th Generation, abbreviated as 5G).

In the present application, when trying to obtain a driving route from the first candidate vehicle to the boarding position based on the first trajectory data and the boarding position, the first trajectory data may be subjected to processing such as deviation correction and road network matching to obtain the driving route.

As a possible implementation manner, as shown in fig. 2, on the basis of the foregoing embodiment, the process of acquiring the travel path of the first candidate vehicle to the boarding position in the foregoing step S103 specifically includes the following steps:

s201, correcting the first track data to obtain second track data.

It should be noted that, in practical applications, in the process of acquiring the first trajectory data of the first candidate vehicle by the positioning system, the positioning signal is often blocked due to environmental factors such as standing in a high-rise building, so that the positioning coordinate drifts. Therefore, after the first trajectory data is acquired, more accurate second trajectory data of the first candidate vehicle can be acquired through deviation rectification processing.

As a possible implementation manner, as shown in fig. 3, on the basis of the foregoing embodiment, the process of performing deviation rectification on the first trajectory data in step S201 to obtain the second trajectory data specifically includes the following steps:

s301, identifying the abnormal position where the drift occurs in the first track data.

Alternatively, an abnormal position in the first trajectory data, which is largely different from the lateral or longitudinal distance of the adjacent point, may be identified.

Alternatively, a trajectory map may be formed from the first trajectory data and spike or unusually dense locations identified from the trajectory map.

For example, as shown in FIG. 4, a trace map may be formed from the first trace data and peak locations 11-1 and 11-3 and anomalous dense locations 11-2, that is, anomalous locations where drift occurred 11-1, 11-2 and 11-3, are identified from the trace map.

Alternatively, an abnormal position in the first trajectory data that does not comply with the normal driving law may be identified based on the road network data.

For example, as shown in fig. 5, based on the road network data, it is recognized that the tracks from 12-1 to 12-4 in the first track data deviate from the roads, the positioning coordinates of 12-1 to 12-4 are matched with the road network data, and the vehicle track displayed in the first track data is acquired to be currently driven on the non-roads, so that four abnormal positions where drift occurs, 12-1 to 12-4, can be recognized.

S302, removing abnormal positions from the first track data to obtain second track data.

In the embodiment of the application, the abnormal position is removed from the first track data through the road binding processing, and the obvious noise point in the first track data is bound to the road, so that the real track of the first candidate vehicle, namely the second track data, is obtained.

For example, as shown in fig. 6, 4 abnormal positions 13-1 to 13-4 are obtained, and a perpendicular line is made between a straight line in which the 4 abnormal positions are located and the road 15 to obtain the drop-feet 14-1 to 14-4, so that the track 16 formed by the drop-feet 14-1 to 14-4 is the real track of the first candidate vehicle, that is, the second track data.

S202, acquiring the current driving direction and the current position of the first candidate vehicle according to the second track data and the road network data.

As a possible implementation manner, as shown in fig. 7, based on the foregoing embodiment, the process of obtaining the driving direction and the current position of the first candidate vehicle according to the second trajectory data and the road network data in the step S202 specifically includes the following steps:

s401, matching the second track data with road network data to obtain a road section where the first candidate vehicle runs currently and a start-stop position of the road section.

The road network data comprises road sections (links) and nodes, the road sections comprise information such as road names, road grades and speed limits, and the nodes comprise information such as road intersections and figure outline points.

In this embodiment of the application, after the second trajectory data is acquired, the second trajectory data may be matched with the road network data to determine a road segment currently driven by the first candidate vehicle, and then read a start-stop position of the road segment.

For example, as shown in FIG. 8, current road 17 is comprised of road segment 17-1, road segment 17-2, and node 17-3. After the second trajectory data is obtained, the second trajectory data may be matched with road network data to determine that the road segment currently driven by the first candidate vehicle is 17-2, and then the start-stop positions 18-1 and 18-2 of the road segment are read.

S402, determining the running direction of the first candidate vehicle according to the starting and stopping positions.

For example, obtaining the start-stop positions 18-1 and 18-2 of the road segment shown in FIG. 8 may determine that the driving direction of the first candidate vehicle is from 18-1 to 18-2, i.e., from west to east.

And S403, determining the position of the first candidate vehicle according to the last position point in the second track data.

In this embodiment of the application, each location point in the second trajectory data may be mapped onto the road segment from the first location point in the second trajectory data to obtain a mapping location of the first candidate vehicle on the road segment, and the mapping location corresponding to the last location point may be used as the location of the first candidate vehicle.

And S203, acquiring a driving path of the first candidate vehicle by taking the position as a starting point and the getting-on position as an end point according to the driving direction and the road network data.

The road network data comprises road information of different levels and different urban areas in each city, the route of each road can be three-dimensional and dynamic, and automatic measurement of the length, the angle and the like of the road is realized. In general, in an area with roads as boundaries obtained from road network data, each area is a closed-loop area formed by intersecting a plurality of roads. Wherein the link information may include attribute information of the link.

When the position of the first candidate vehicle and the boarding position of the car-booking user coincide with each other, the acquired travel route of the first candidate vehicle is also different for different travel directions.

For example, as shown in fig. 9, the getting-on position 19 of the car booking user a and the position 20 of the first candidate vehicle are acquired, and if the traveling direction of the first candidate vehicle is from east to west, the traveling path of the first candidate vehicle is a traveling path 21-1; if the traveling direction of the first candidate vehicle is from west to east, the traveling path of the first candidate vehicle is the traveling path 21-2.

According to the network vehicle booking method provided by the embodiment of the application, the first track data can be corrected to obtain the second track data, the current driving direction and the current position of the first candidate vehicle are obtained according to the second track data and the road network data, the current position is taken as the starting point, the vehicle getting-on position is taken as the end point, and the driving path of the first candidate vehicle is obtained according to the driving direction and the road network data. Therefore, the real track data, namely the second track data, of the first candidate vehicle can be obtained through deviation rectifying processing, the matched running track is determined according to different running directions, the obtained running track of the first candidate vehicle is ensured to be more accurate, and the accuracy and the efficiency in the network vehicle booking process are further improved.

In the present application, when an attempt is made to select a target vehicle from the first candidate vehicles, the target vehicle may be determined based on the travel time, or the target vehicle may be determined based on the travel time and evaluation information of at least one dimension. The following is explained for determining the target vehicle from the travel time and the evaluation information of at least one other dimension.

As a possible implementation manner, as shown in fig. 10, on the basis of the foregoing embodiment, the process of selecting the target vehicle from the first candidate vehicles in step S105 specifically includes the following steps:

s501, screening the first candidate vehicles according to the running time to obtain second target candidate vehicles with the running time less than the set time.

Wherein, the setting time can be set according to the actual situation.

Alternatively, a uniform set time, for example, 5 minutes, 10 minutes, or the like, may be set in advance.

Alternatively, the preference selection of the car-booking user may be extracted from the car-booking request, so that the set time in the preference selection of the user is used as the final set time.

For example, if the user is far away from the desired boarding location or needs to perform simple sorting to go to the boarding location, a long setting time, for example, 7 minutes, may be set in the car-booking application. Accordingly, 7 minutes may be used as the final set time; if the user is close to the desired boarding location or the purpose of the car-booking user for making a network car-booking is urgent, a short setup time, for example, 3 minutes, may be set in the car-booking application. Accordingly, 3 minutes may be used as the final set time.

And S502, obtaining at least one dimension of evaluation information of the driver corresponding to the second candidate vehicle.

The evaluation information refers to evaluation information such as personal information of a driver corresponding to the second candidate vehicle, a historical driving behavior score, a violation record, and a historical service score.

S503, selecting a target vehicle from the second candidate vehicles according to the running time and the evaluation information of at least one dimension.

As a possible implementation manner, the first candidate vehicle may be filtered according to the driving time to obtain the second candidate vehicle. Alternatively, the travel times may be sorted in an ascending order, and the first candidate vehicle within the preset sorting range may be selected as the second candidate vehicle. Further, second candidate vehicles within the preset sequencing range are screened again according to the evaluation information of at least one dimension, so that the target vehicle is selected.

For example, the travel times may be sorted in an ascending order, the first candidate vehicle ranked in the first three may be selected as the second candidate vehicle, and then the first three second candidate vehicles may be re-screened according to the historical service score, and the second candidate vehicle having the historical service score greater than 4.9 may be used as the target vehicle.

According to the network vehicle booking method provided by the embodiment of the application, the first candidate vehicle can be screened according to the running time so as to obtain the second target candidate vehicle of which the running time is less than the set time, then the evaluation information of at least one dimension of the driver corresponding to the second candidate vehicle is obtained, and the target vehicle is selected from the second candidate vehicle according to the running time and the evaluation information of at least one dimension. Therefore, the first candidate vehicle can be screened twice according to the running time and the evaluation information of at least one dimension, so that the target vehicle meeting the vehicle booking user requirement can be determined according to the evaluation information with richer content on the basis of ensuring that the target vehicle capable of being quickly connected and driven can be obtained, the accuracy and the efficiency of the network vehicle booking process are further improved, and the user experience is improved.

FIG. 11 is a schematic illustration according to a sixth embodiment of the present application. As shown in fig. 11, on the basis of the foregoing embodiment, the network taxi appointment method provided by this embodiment includes the following steps:

s601, obtaining a car booking request, and obtaining a first candidate vehicle according to the car booking request.

The taxi booking request at least comprises a boarding position of a taxi booking user and can also comprise taxi using demand information of the taxi booking user; the car use requirement information refers to the information such as the car type selected by the car booking user, the boarding time and whether car sharing is allowed or not.

For example, the taxi appointment request includes the user's boarding location as address a, destination as address b, and information of calling both the economy-type vehicle and the luxury-type vehicle, boarding time of 10 o' clock, and no allowance for carpooling.

And S602, generating a vehicle screening area according to the getting-on position.

In the embodiment of the application, after the vehicle loading position is obtained, the vehicle screening area can be generated in various modes.

For example, as shown in fig. 12, a circle is drawn with the upper vehicle position 22 as the center and 3 kilometers as the radius, and the range covered by the drawn circle is the vehicle screening area 23.

S603, obtaining the positioning information of the third candidate vehicle, and selecting the third candidate vehicle with the positioning information in the vehicle screening area as the first candidate vehicle.

For example, on the basis of fig. 12, as shown in fig. 13, the vehicles 24-1 to 24-4, the location information of the vehicles 24-1 and 24-2 is in the vehicle screening area 23, and then the vehicles 24-1 and 24-2 may be taken as the first candidate vehicles.

S604, first track data of the first candidate vehicle is obtained.

It should be noted that, in order to shorten the time consumption of the first trajectory data acquisition process, the range of the first trajectory data may be limited in advance.

Alternatively, the trajectory data of a preset duration may be selected from the current time onward as the first trajectory data. The preset time period may be set according to actual conditions, for example, 5 minutes, 10 minutes, and the like.

Alternatively, the trajectory data within the preset distance may be selected forward from the current position as the first trajectory data. The preset distance may be set according to an actual situation, for example, 2 km, 3 km, and the like.

And S605, identifying the abnormal position where the drift occurs in the first track data.

S606, removing the abnormal position from the first track data to obtain second track data.

And S607, matching the second track data with the road network data to obtain the road section currently driven by the first candidate vehicle and the starting and stopping positions of the road section.

And S608, determining the running direction of the first candidate vehicle according to the starting and stopping positions.

And S609, determining the position of the first candidate vehicle according to the last position point in the second track data.

And S610, acquiring a driving path of the first candidate vehicle by taking the position as a starting point and the getting-on position as an end point according to the driving direction and the road network data.

And S611, acquiring the running time of the first candidate vehicle running to the boarding position according to the running path.

As a possible implementation mode, any third-party map can be accessed by calling a map API, and the estimated driving time in the third-party map according to the position and the getting-on position is read.

As another possible implementation manner, as shown in fig. 14, the method specifically includes the following steps:

and S701, acquiring road condition information of road sections contained in the driving path.

The traffic information may include the number of traffic lights, congestion degree, whether a traffic accident occurs, and the like.

For example, the driving path comprises two road sections, namely a road section A and a road section B, and the number of the traffic signal lamps in the road section A is 1, the congestion degree is serious congestion, and no traffic accident occurs at present; and acquiring that the number of the traffic signal lamps in the road section B is 3, the congestion degree is serious congestion, and no traffic accident occurs at present.

And S702, predicting the running time according to the road condition information of the included road sections.

Alternatively, the driving time may be corrected based on the road condition information of all the road sections on the basis of the driving time prediction from the start-stop position. For example, for every traffic light included, the predicted travel time is increased by 30 seconds, and if the congestion degree is severe congestion, the predicted travel time is increased by 2 minutes, and if the congestion degree is severe congestion, the predicted travel time is increased by 5 minutes.

And S612, screening the first candidate vehicles according to the running time to obtain second target candidate vehicles with the running time less than the set time.

And S613, acquiring at least one dimension of evaluation information of the driver corresponding to the second candidate vehicle.

And S614, selecting a target vehicle from the second candidate vehicles according to the running time and the evaluation information of at least one dimension, and issuing a car appointment order to the target vehicle.

It should be noted that, for the descriptions of steps S601 to S610 and S612 to S614, reference may be made to the relevant descriptions in the above embodiments, and details are not repeated here.

It should be noted that the network car booking method provided by the application can be applied to various scenes related to the network car booking field.

For an automatic driving application scenario, optionally, the passenger vehicle to be loaded may be an unmanned vehicle, at this time, after the vehicle appointment request is obtained, the first candidate vehicle and the first trajectory data thereof may be obtained by combining an intelligent search technology, a driving path of the first candidate vehicle to the vehicle-loading position is obtained according to the first trajectory data and the vehicle-loading position, and then the driving time is predicted according to the road condition information by combining the intelligent driving technology. Further, in combination with a big data processing technology, a target vehicle is determined according to the running time and evaluation information of at least one dimension of the vehicle to be carried, such as the accumulated running mileage of the vehicle, the recognition result of whether the vehicle is maintained regularly, and the like, and a car booking order is issued to the target vehicle, after receiving the car booking order, the unmanned vehicle can run to the getting-on position of the network car booking user according to a running strategy. Therefore, the order can be sent based on the real running time of the unmanned vehicle, and the accuracy and the efficiency of the network car booking process are improved.

It should be noted that, as shown in fig. 15, in the embodiment of the present application, a driver of a to-be-loaded passenger vehicle uploads track data of the driver in real time through a driver-side client (e.g., a car appointment application program in a mobile phone, etc.), and updates attribute information (e.g., information related to a vehicle type of a vehicle currently driven by the driver, whether the vehicle is currently empty, etc.) to a server in time, and at the same time, a car appointment user initiates a car appointment through a passenger-side client (e.g., a car appointment application program in a mobile phone, etc.) through the driver-side client, and accordingly, after receiving a car appointment request of the car appointment user, a dispatch service module may select a driver most suitable for driving based on a search result of driver attribute information, driver real-time location data, candidate drivers in a designated area, a driving route planning result and driving time of the vehicle driven by the candidate drivers, and evaluation information of the driver, and issue a car appointment order to, the driving route from the vehicle driven by the candidate driver to the upper parking position can be obtained through adopting the track data of the candidate vehicle, and the order is sent based on the real driving time, so that the obtained driving route and the driving time are more accurate, the matched target vehicle can be screened out more quickly and accurately, the accuracy and the efficiency of the network vehicle reservation process are improved, and the user experience is improved.

In practical application, based on the network car booking method provided by the application, a car booking user, a vehicle to be carried, a network car booking device serving as an execution subject and a traffic management cloud platform can build a car booking network as shown in fig. 16. The network taxi appointment device can communicate with at least one taxi appointment user and at least one to-be-carried bus, acquires the real driving time of the to-be-carried bus by combining the inquiry result of the traffic management cloud platform and the historical data and the real-time data of the to-be-carried bus after acquiring a taxi appointment request sent by the taxi appointment user, and then dispatches a bill to the determined target bus according to the acquired driving time, so that the acquired driving path and the driving time are more accurate, the matched target bus can be screened out more quickly and accurately, the accuracy and the efficiency in the process of taxi appointment are improved, and the user experience is improved.

Corresponding to the network car booking methods provided in the above embodiments, an embodiment of the present application further provides a network car booking device, and since the network car booking device provided in the embodiment of the present application corresponds to the network car booking methods provided in the above embodiments, embodiments of the network car booking method are also applicable to the network car booking device provided in this embodiment, and detailed description is not given in this embodiment. Fig. 17 to 18 are schematic structural views of a network car booking device according to an embodiment of the present application.

As shown in fig. 17, the network car booking device 1000 includes: a request acquisition module 100, a trajectory acquisition module 200, a path acquisition module 300, a time acquisition module 400, and an order placement module 500. Wherein:

a request obtaining module 100, configured to obtain a car booking request, and obtain a first candidate vehicle according to the car booking request, where the car booking request at least includes a boarding location of a car booking user;

a trajectory acquisition module 200 configured to acquire first trajectory data of the first candidate vehicle;

a route obtaining module 300, configured to obtain, according to the first trajectory data and the boarding position, a driving route along which the first candidate vehicle drives to the boarding position;

a time obtaining module 400, configured to obtain a driving time for the first candidate vehicle to drive to the boarding position according to the driving route; and

and the order issuing module 500 is configured to select a target vehicle from the first candidate vehicles according to the driving time, and issue a car appointment order to the target vehicle.

In an embodiment of the present application, as shown in fig. 18, the path obtaining module 300 in fig. 17 includes: the data deviation rectifying unit 310 is configured to rectify the first trajectory data to obtain second trajectory data; an obtaining unit 320, configured to obtain a current driving direction and a current location of the first candidate vehicle according to the second trajectory data and the road network data; a path obtaining unit 330, configured to obtain the driving path of the first candidate vehicle according to the driving direction and the road network data, with the located position as a starting point and the parking place as an end point.

In the embodiment of the present application, as shown in fig. 18, the obtaining unit 320 in fig. 17 includes: the matching subunit 321 is configured to match the second trajectory data with the road network data, and obtain a road segment currently driven by the first candidate vehicle and a start-stop position of the road segment; a direction determining subunit 322, configured to determine a driving direction of the first candidate vehicle according to the start-stop position; a position determining subunit 323, configured to determine, according to the last position point in the second trajectory data, the position of the first candidate vehicle.

In the embodiment of the present application, as shown in fig. 18, the obtaining unit 320 in fig. 17 further includes: a mapping subunit 324, configured to map, after acquiring the road segment, each location point in the second trajectory data onto the road segment starting from the first location point in the second trajectory data, so as to acquire a mapping location of the first candidate vehicle on the road segment.

In an embodiment of the present application, the location of the first candidate vehicle is the mapping location corresponding to the last location point.

In the embodiment of the present application, as shown in fig. 18, the data skew correction unit 310 in fig. 17 includes: an abnormality identification subunit 311, configured to identify an abnormal position where a drift occurs in the first trajectory data; and an exception culling unit 312, configured to cull the exception location from the first trajectory data to obtain the second trajectory data.

In an embodiment of the present application, as shown in fig. 18, the order placing module 500 in fig. 17 includes: a first screening unit 510, configured to screen the first candidate vehicle according to the travel time to obtain a second target candidate vehicle of which the travel time is less than a set time; an information obtaining unit 520, configured to obtain evaluation information of at least one dimension of a driver corresponding to the second candidate vehicle; and a first selecting unit 530, configured to select the target vehicle from the second candidate vehicles according to the travel time and the evaluation information of the at least one dimension.

In the embodiment of the present application, as shown in fig. 18, the time obtaining module 400 in fig. 17 includes: a road condition obtaining unit 410, configured to obtain road condition information of a road segment included in the driving path; and a time prediction unit 420, configured to predict the driving time according to the road condition information of the included road segment.

In the embodiment of the present application, as shown in fig. 17, the trajectory obtaining module 200 is configured to select, from the current time forward, trajectory data of a preset duration as the first trajectory data.

In an embodiment of the present application, as shown in fig. 18, the request obtaining module 100 in fig. 17 includes: the second screening unit 110 is configured to screen a third candidate vehicle from the multiple vehicles in the order receiving state according to the vehicle demand information; the area generating unit 120 is configured to generate a vehicle screening area according to the boarding position; and a second selecting unit 130, configured to obtain location information of the third candidate vehicle, and select the third candidate vehicle whose location information is in the vehicle screening area as the first candidate vehicle.

According to the network vehicle booking device provided by the embodiment of the application, the booking request is obtained, the first candidate vehicle and the first track data of the first candidate vehicle are obtained according to the booking request, then the driving route of the first candidate vehicle to the upper vehicle position and the driving time of the first candidate vehicle to the upper vehicle position according to the driving route are obtained according to the first track data and the upper vehicle position, the target vehicle is selected from the first candidate vehicle according to the driving time, and the booking order is issued to the target vehicle, so that the order of the network vehicle booking is sent. Therefore, the driving route from the first candidate vehicle to the boarding position can be acquired through the first track data of the first candidate vehicle, and the order is sent based on the real driving time, so that the acquired driving route and the driving time are more accurate, matched target vehicles can be screened out more quickly and accurately, the accuracy and the efficiency of the network car booking process are improved, and the user experience is improved.

According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.

Fig. 19 is a block diagram of a network car booking electronic device according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate 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 meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 19, the electronic apparatus includes: one or more processors 1100, a memory 1200, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output device (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 19 illustrates an example of a processor 1100.

The memory 1200 is a non-transitory computer readable storage medium provided herein. The storage stores instructions executable by at least one processor, so that the at least one processor executes the network taxi appointment method provided by the application. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the network taxi appointment method provided by the present application.

The memory 1200 is a non-transitory computer readable storage medium, and can be used for storing non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the network taxi appointment method in the embodiment of the present application (for example, the request acquisition module 100, the trajectory acquisition module 200, the route acquisition module 300, the time acquisition module 400, and the order issuing module 500 shown in fig. 17). The processor 1100 executes various functional applications of the server and data processing by executing the non-transitory software programs, instructions and modules stored in the memory 1200, so as to implement the network taxi appointment method in the above method embodiment.

The memory 1200 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the positioning electronic device, and the like. Further, the memory 1200 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 1200 may optionally include memory located remotely from processor 1100, which may be connected to a location electronics device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The network car booking electronic device may further include: an input device 1300 and an output device 1400. The processor 1100, the memory 1200, the input device 1300, and the output device 1400 may be connected by a bus or other means, and fig. 19 illustrates the connection by a bus.

The input device 1300 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the pointing electronic device, such as a touch screen, keypad, mouse, track pad, touch pad, pointer stick, one or more mouse buttons, track ball, joystick, or other input device. The output device 1400 may include a display device, an auxiliary lighting device (e.g., an LED), a haptic feedback device (e.g., a vibration motor), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), 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.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

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 can 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.

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 application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations, and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A network car booking method comprises the following steps:

acquiring first trajectory data of the first candidate vehicle;

and selecting a target vehicle from the first candidate vehicles according to the running time, and issuing a car booking order to the target vehicle.

2. The network vehicle appointment method according to claim 1, wherein the obtaining of the travel path of the first candidate vehicle to the boarding location according to the first trajectory data and the boarding location comprises:

correcting the first track data to obtain second track data;

acquiring the current driving direction and the current position of the first candidate vehicle according to the second track data and the road network data;

and acquiring the driving path of the first candidate vehicle according to the driving direction and the road network data by taking the position as a starting point and the getting-on position as an end point.

3. The network vehicle booking method of claim 2, wherein the obtaining of the driving direction and the current position of the first candidate vehicle according to the second trajectory data and the road network data comprises:

matching the second track data with the road network data to obtain a road section on which the first candidate vehicle runs currently and a start-stop position of the road section;

determining the driving direction of the first candidate vehicle according to the starting and stopping positions;

and determining the position of the first candidate vehicle according to the last position point in the second track data.

4. The network vehicle appointment method according to claim 3, wherein after the obtaining of the road section currently traveled by the candidate vehicle, further comprising:

mapping each position point in the second trajectory data onto the road segment starting from a first position point in the second trajectory data to obtain a mapped position of the first candidate vehicle on the road segment.

5. The network car appointment method of claim 4, wherein the location of the first candidate vehicle is the mapped location corresponding to the last location point.

6. A network car appointment method according to any one of claims 2-5, wherein said deskewing the first trajectory data to obtain second trajectory data comprises:

identifying an abnormal position where drift occurs in the first track data; and

and removing the abnormal position from the first track data to obtain the second track data.

7. The network vehicle appointment method according to claim 1, wherein the selecting a target vehicle from the first candidate vehicles according to the travel time comprises:

screening the first candidate vehicles according to the running time to obtain second target candidate vehicles of which the running time is less than the set time;

obtaining at least one dimension of evaluation information of a driver corresponding to the second candidate vehicle; and

and selecting the target vehicle from the second candidate vehicles according to the running time and the evaluation information of the at least one dimension.

8. The network vehicle appointment method according to any one of claims 1 to 5 and 7, wherein the obtaining of the travel time of the first candidate vehicle to travel to the boarding location according to the travel path comprises:

acquiring road condition information of road sections contained in the driving path; and

and predicting the driving time according to the road condition information of the contained road sections.

9. The network vehicle appointment method according to any one of claims 1-5, wherein the obtaining of the first trajectory data of the first candidate vehicle comprises:

and selecting track data with preset duration from the current moment forward as the first track data.

10. The network vehicle booking method of any one of claims 1 to 5, wherein the vehicle booking request further comprises vehicle usage demand information of the vehicle booking user, and the obtaining a first candidate vehicle according to the vehicle booking request comprises:

screening out a third candidate vehicle from the plurality of vehicles in the order receiving state according to the vehicle using demand information;

generating a vehicle screening area according to the boarding position; and

and acquiring the positioning information of the third candidate vehicle, and selecting the third candidate vehicle of which the positioning information is in the vehicle screening area as the first candidate vehicle.

11. A network car booking device, comprising:

the request obtaining module is used for obtaining a car booking request and obtaining a first candidate vehicle according to the car booking request, wherein the car booking request at least comprises a boarding position of a car booking user;

the time acquisition module is used for acquiring the driving time of the first candidate vehicle to the getting-on position according to the driving path; and

12. The network car booking device of claim 11, wherein the path acquisition module comprises:

the data deviation rectifying unit is used for rectifying the first track data to obtain second track data;

the acquisition unit is used for acquiring the current driving direction and the current position of the first candidate vehicle according to the second track data and the road network data;

and the route acquisition unit is used for acquiring the driving route of the first candidate vehicle according to the driving direction and the road network data by taking the position as a starting point and the getting-on position as an end point.

13. The network car booking device of claim 12, wherein the obtaining unit comprises:

the matching subunit is used for matching the second track data with the road network data to acquire a road section on which the first candidate vehicle runs currently and a start-stop position of the road section;

a direction determining subunit, configured to determine a driving direction of the first candidate vehicle according to the start-stop position;

and the position determining subunit is used for determining the position of the first candidate vehicle according to the last position point in the second track data.

14. The network car booking device of claim 13, further comprising:

a mapping subunit, configured to, after acquiring the road segment, map each location point in the second trajectory data onto the road segment starting from a first location point in the second trajectory data to acquire a mapping location of the first candidate vehicle on the road segment.

15. The network car appointment apparatus of claim 14 wherein the location of the first candidate vehicle is the mapped location corresponding to the last location point.

16. The network car booking device according to any one of claims 12 to 15, wherein the data deviation rectifying unit comprises:

an abnormality identification subunit, configured to identify an abnormal position where drift occurs in the first trajectory data; and

and the abnormal removing unit is used for removing the abnormal position from the first track data to obtain the second track data.

17. The network car booking device of claim 11, wherein the order placing module comprises:

the first screening unit is used for screening the first candidate vehicle according to the running time so as to obtain a second target candidate vehicle of which the running time is less than the set time;

an information acquisition unit configured to acquire evaluation information of at least one dimension of a driver corresponding to the second candidate vehicle; and

and the first selecting unit is used for selecting the target vehicle from the second candidate vehicles according to the running time and the evaluation information of the at least one dimension.

18. The network car appointment apparatus according to any one of claims 11-15, 17 wherein the time acquisition module comprises:

a road condition obtaining unit, configured to obtain road condition information of a road segment included in the driving path; and

and the time prediction unit is used for predicting the driving time according to the road condition information of the included road section.

19. The network car booking device according to any one of claims 11 to 15, wherein the trajectory acquisition module is configured to select trajectory data of a preset duration from the current time onward as the first trajectory data.

20. The network car booking device of any one of claims 11 to 15, wherein the car booking request further comprises car demand information of the car booking user, and the request obtaining module comprises:

the second screening unit is used for screening a third candidate vehicle from the plurality of vehicles in the order receiving state according to the vehicle using demand information;

the region generating unit is used for generating a vehicle screening region according to the boarding position; and

and the second selection unit is used for acquiring the positioning information of the third candidate vehicle and selecting the third candidate vehicle of which the positioning information is in the vehicle screening area as the first candidate vehicle.

21. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

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

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