CN112686461B

CN112686461B - Riding information processing method and device, computer equipment and storage medium

Info

Publication number: CN112686461B
Application number: CN202110011730.1A
Authority: CN
Inventors: 赵鹏; 王登峰; 石迎春
Original assignee: Nanjing Leading Technology Co Ltd
Current assignee: Nanjing Leading Technology Co Ltd
Priority date: 2021-01-06
Filing date: 2021-01-06
Publication date: 2022-08-26
Anticipated expiration: 2041-01-06
Also published as: CN112686461A

Abstract

The embodiment of the invention discloses a riding information processing method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring associated riding information of a target user; the associated riding information comprises a current user position and a target arrival position of the target user, an automatic driving area, a current automatic driving vehicle getting-on position and a current automatic driving vehicle getting-off position; determining a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area and the current automatic driving vehicle getting-on position; and determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position. The technical scheme of the embodiment of the invention can improve the intelligence and the efficiency of the riding mode of the automatic driving vehicle, thereby improving the unit yield of the automatic driving vehicle.

Description

Riding information processing method and device, computer equipment and storage medium

Technical Field

The embodiment of the invention relates to the technical field of automatic driving, in particular to a riding information processing method and device, computer equipment and a storage medium.

Background

An automatic driving vehicle is also called as an unmanned vehicle, a computer driving vehicle or a wheeled mobile robot, and is an intelligent vehicle which realizes unmanned driving through a computer system. With the popularization of the autonomous vehicles, the autonomous vehicles can be used as taxis or public transportation means. The autonomous vehicle may travel a route within a prescribed autonomous driving area based on the current location and the destination, and may automatically travel according to the generated travel route.

Currently, a ride experience service for a user is enabled for an autonomous vehicle. If a user wants to experience and use the automatic driving vehicle, the user needs to reserve the vehicle in advance and fill in a series of related information in advance, such as a user name, a user contact way, a user identification number and the like, and the user needs to arrive at a specified boarding position by himself to take a car. The riding rule of the automatic driving vehicle reduces the unit rate of the automatic driving vehicle, and is not beneficial to promoting the popularization of the riding experience service of the automatic driving vehicle.

Disclosure of Invention

The embodiment of the invention provides a riding information processing method, a riding information processing device, computer equipment and a storage medium, which are used for improving the intelligence and the efficiency of a riding mode of an automatic driving vehicle and further improving the unit yield of the automatic driving vehicle.

In a first aspect, an embodiment of the present invention provides a riding information processing method, including:

acquiring associated riding information of a target user; the associated riding information comprises the current user position and the target arrival position of the target user, an automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle;

determining a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area and the current automatic driving vehicle getting-on position;

and determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position.

In a second aspect, an embodiment of the present invention further provides a riding information processing apparatus, including:

the associated riding information acquisition module is used for acquiring associated riding information of a target user; the associated riding information comprises a current user position and a target arrival position of the target user, an automatic driving area, a current automatic driving vehicle getting-on position and a current automatic driving vehicle getting-off position;

the target automatic driving vehicle getting-on position determining module is used for determining a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area and the current automatic driving vehicle getting-on position;

and the target automatic driving vehicle getting-off position determining module is used for determining the target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position.

In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:

one or more processors;

storage means for storing one or more programs;

when the one or more programs are executed by the one or more processors, the one or more processors implement the riding information processing method provided by any embodiment of the invention.

In a fourth aspect, an embodiment of the present invention further provides a computer storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the riding information processing method provided in any embodiment of the present invention.

According to the embodiment of the invention, the current user position of the target user, the target arrival position, the automatic driving area, the current getting-on position of the automatic driving vehicle, the current getting-off position of the automatic driving vehicle and other relevant riding information of the target user are obtained, so that the getting-on position of the target automatic driving vehicle matched with the target user is determined according to the current user position, the automatic driving area and the current getting-on position of the automatic driving vehicle, and the getting-off position of the target automatic driving vehicle matched with the target user is determined according to the target arrival position and the current getting-off position of the automatic driving vehicle.

Drawings

Fig. 1 is a flowchart of a riding information processing method according to an embodiment of the present invention;

fig. 2 is a flowchart of a riding information processing method according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a riding information processing system according to a second embodiment of the present invention;

fig. 4 is a schematic flow chart of a riding information processing method according to a second embodiment of the present invention;

fig. 5 is a schematic diagram of a riding information processing device according to a third embodiment of the present invention;

fig. 6 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention.

It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but could have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.

The terms "first" and "second," and the like in the description and claims of embodiments of the invention and in the drawings, are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not set forth for a listed step or element but may include steps or elements not listed.

Example one

Fig. 1 is a flowchart of a riding information processing method according to an embodiment of the present invention, where this embodiment is applicable to a case where a user is provided with an automated driving vehicle riding service without a reservation operation, and the method may be executed by a riding information processing apparatus, which may be implemented by software and/or hardware and may be generally integrated in a computer device, which may be a server device or a terminal device or the like installed with a background operation system of an automated driving vehicle, and is used in cooperation with a client for providing an automated driving vehicle riding function. Accordingly, as shown in fig. 1, the method comprises the following operations:

s110, obtaining relevant riding information of a target user; the associated riding information comprises a current user position and a target arrival position of the target user, an automatic driving area, a current automatic driving vehicle getting-on position and a current automatic driving vehicle getting-off position.

The target user can be a user with a riding demand. The current user location may be a geographic location where the target user is currently located, the target arrival location may be a destination geographic location that the target user needs to arrive at, and the autonomous driving area may be an area in the city of the target user for defining a driving area of the autonomous vehicle. The current automatic driving vehicle getting-on position can be a current available getting-on position of the automatic driving vehicle determined by the automatic driving vehicle background operation system for a target user, and the current automatic driving vehicle getting-off position can be a current available getting-off position of the automatic driving vehicle determined by the automatic driving vehicle background operation system for the target user. It is understood that the number of current autonomous vehicle boarding locations and current autonomous vehicle disembarking locations may be plural.

In the embodiment of the invention, the background operation system of the automatic driving vehicles is mainly used for managing and controlling the respective automatic driving vehicles, and meanwhile, the corresponding automatic driving vehicles can be distributed to the users according to the riding requirements of the users, or the getting-on position and the getting-off position of the users are designated, and the like, so that the perfect riding service of the automatic driving vehicles is provided for the users.

Accordingly, the background operation system of the automatic driving vehicle firstly determines a target user with a riding demand before providing riding service of the automatic driving vehicle for the user. Optionally, when the target user has a riding demand, the riding demand can be sent in an online calling manner. It should be noted that the target user may send the riding demand of the ordinary vehicle in a manner of calling the vehicle online, for example, in a manner of calling the network for appointment, and the like. The target user can also send the riding requirements of the automatic driving vehicle in an online calling mode, for example, the riding requirements of the automatic driving vehicle are sent through online calling software special for the automatic driving vehicle. That is, the network appointment car-calling software and the automatic driving car-calling software can be used as client software for providing the automatic driving car-taking function. The embodiment of the invention does not limit the specific type and mode of sending the riding demand by the target user. It should be noted that, when the target user sends the riding demand of the autonomous vehicle through the dedicated online taxi-calling software of the autonomous vehicle, the current method for reserving the autonomous vehicle does not need to specify the reserved vehicle type or fill in a series of related information in advance, and other complex reservation operations, and the riding demand of the target user only needs to be input in the same way as the online calling network taxi reservation method. For example, only information such as a departure point, a destination, or a passing point may be input.

Correspondingly, after the target user sends the riding demand, the automatic driving vehicle background operation system can acquire the riding demand of the target user in real time, and acquire the current user position and the target arrival position of the target user, the automatic driving area, the current getting-on position of the automatic driving vehicle, the current getting-off position of the automatic driving vehicle and other associated riding information according to the riding demand of the target user.

And S120, determining the getting-on position of the target automatic driving vehicle matched with the target user according to the current user position, the automatic driving area and the current getting-on position of the automatic driving vehicle.

The target autonomous vehicle pick-up location may be a final autonomous vehicle pick-up location determined for the target user, where the target user may merge with and pick-up the autonomous vehicle at the target autonomous vehicle pick-up location.

It should be noted that, in consideration of safety of automatic driving, the automatic driving vehicle needs to travel in a planned area, that is, an automatic driving area, so as to avoid affecting urban traffic or causing traffic accidents. It will be appreciated that planned autopilot zones vary from city to city. A plurality of getting-on positions and getting-off positions corresponding to the autonomous vehicles are set in the autonomous driving area, and may also be referred to as a getting-on point and a getting-off point. When a user needs to get on the vehicle, the user must get on the vehicle at one of the vehicle getting-on points, and the user cannot randomly specify the vehicle getting-on position. Meanwhile, when the automatically-driven vehicle travels to a destination in the automatically-driven area, the vehicle also needs to get off at one of the get-off points, and the get-off position cannot be designated at will. In the same automatic driving area, a plurality of automatic driving vehicles can be included, and the corresponding upper vehicle point and the lower vehicle point of each automatic driving vehicle are the same.

Correspondingly, the automatic driving vehicle background operation system can determine the getting-on position of the target automatic driving vehicle matched with the target user from the getting-on positions of the current automatic driving vehicles according to the current user position, the automatic driving area and the getting-on positions of the current automatic driving vehicles. Optionally, the getting-on position of the target automatic driving vehicle may be a getting-on position of the automatic driving vehicle closest to the target user in the current getting-on positions of the automatic driving vehicles, so as to reduce the distance from the current user position to the getting-on position of the target automatic driving vehicle for the target user to the maximum extent, thereby saving the walking time of the target user and improving the user experience.

And S130, determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position.

The target autonomous vehicle alighting position may be a final autonomous vehicle alighting position determined for a target user, and the target user may disembark from the autonomous vehicle at the target autonomous vehicle alighting position.

Correspondingly, the automatic driving vehicle background operation system can also determine a target automatic driving vehicle getting-off position matched with a target user from the current automatic driving vehicle getting-off positions according to the target arrival position and the current automatic driving vehicle getting-off positions. Optionally, the target automatic-driving vehicle getting-off position may be a position of the automatic-driving vehicle getting-off closest to the target arrival position among the current positions of the automatic-driving vehicles getting-on, so as to reduce the distance from the target user walking from the target position of the automatic-driving vehicle getting-off to the target arrival position to the maximum extent, thereby saving walking time of the target user and improving user experience.

Therefore, in the riding information processing method provided by the embodiment of the invention, the automatic driving vehicle background operation system can intelligently determine the matched target automatic driving vehicle getting-on position and target automatic driving vehicle getting-off position for the target user according to the acquired associated riding information of the target user, so that the complex operation that the user needs to reserve the automatic driving vehicle in advance is avoided, the walking time of the target user is saved, the intelligence and the high efficiency of the riding mode of the automatic driving vehicle are improved, and the increase of the unit rate of the automatic driving vehicle is further promoted.

Example two

Fig. 2 is a flowchart of a riding information processing method according to a second embodiment of the present invention, which is embodied on the basis of the second embodiment, and in this embodiment, various specific alternative implementations of obtaining the associated riding information of the target user, determining the getting-on position of the target autonomous vehicle, and determining the getting-off position of the target autonomous vehicle are provided. Correspondingly, as shown in fig. 2, the method of the present embodiment may include:

and S210, obtaining the riding order information and the vehicle dynamic adjustment information of the target user.

The taking order information may be order information issued by a target user on line through car calling software, optionally, the car calling software may be car calling software for general network car booking or special car calling software for automatically driving a vehicle, as long as the taking service can be provided for the user, and the embodiment of the invention does not limit the specific application type of providing the taking order information. The vehicle dynamic adjustment information may be information that the background operation system of the autonomous vehicle may dynamically adjust the autonomous vehicle, where the dynamically adjusted information may include, but is not limited to, an autonomous driving area, an getting-on position of the autonomous vehicle, a getting-off position of the autonomous vehicle, and the like, as long as the information related to the autonomous vehicle can be dynamically adjusted, and the specific information content included in the vehicle dynamic adjustment information is not limited in the embodiment of the present invention.

In the embodiment of the invention, the target user can send the riding order information through corresponding taxi calling software, and the automatic driving vehicle background operation system can be in butt joint with each taxi calling software through a related interface so as to obtain the riding order information of the target user. Meanwhile, the automatic driving vehicle background operation system can also acquire vehicle dynamic adjustment information. For example, the background operation system of the autonomous vehicle may obtain relevant information for determining the autonomous driving area from the local, or obtain information such as the getting-on position and the getting-off position of the dynamically adjusted autonomous vehicle from the big data server.

S220, determining the current user position and the target arrival position according to the riding order information.

It will be appreciated that the destination user will typically specify the destination arrival location when issuing the ride order information. Meanwhile, the application receiving the riding order information can also obtain the current user position of the target user. Therefore, the automatic driving vehicle background operation system can determine the current user position and the target arrival position according to the riding order information.

And S230, determining the automatic driving area, the current automatic driving vehicle getting-on position and the current automatic driving vehicle getting-off position according to the vehicle dynamic adjustment information.

Accordingly, the automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle all belong to the information that the automatic driving vehicle can dynamically adjust and configure. Therefore, the background operation system of the automatic driving vehicle can determine the automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle which need to be dynamically adjusted according to the dynamic adjustment information of the vehicle.

In an optional embodiment of the present invention, the determining the automatic driving area, the current automatic driving vehicle getting-on position and the current automatic driving vehicle getting-off position according to the vehicle dynamic adjustment information may include: acquiring traffic control data and area planning data matched with the current user position according to the vehicle dynamic adjustment information; determining the autopilot zone from the traffic control data and the zone planning data; acquiring an updated automatic driving vehicle getting-on position and an updated automatic driving vehicle getting-off position matched with the target user according to the vehicle dynamic adjustment information; determining the getting-on position of the current automatic driving vehicle according to the updated getting-on position of the automatic driving vehicle; and determining the current automatic driving vehicle getting-off position according to the updated automatic driving vehicle getting-off position.

The traffic control data may be data of a control measure for controlling the passage of vehicles and people on a traffic road section in an urban area where the current user position of the target user is located. The regional planning data may be defined according to government-related policies and may be used for particular regions of travel of the autonomous vehicle. The updated autonomous vehicle boarding position may be a latest autonomous vehicle boarding position obtained by real-time dynamic adjustment, and the updated autonomous vehicle disembarking position may be a latest autonomous vehicle disembarking position obtained by real-time dynamic adjustment.

Specifically, the automatic driving vehicle background operation system may obtain traffic control data and area planning data matched with the current user position according to the vehicle dynamic adjustment information, so as to determine the automatic driving area according to the traffic control data and the area planning data. For example, the background operating system of the autonomous vehicle may determine a reference autonomous region from the region planning data and determine a driving prohibition region from the reference autonomous region according to the traffic control data, thereby determining a final autonomous region based on the reference autonomous region. It will be appreciated that in general, the update period for the autopilot zone will also be relatively long if the traffic control data does not change.

Correspondingly, the automatic driving vehicle background operation system can also acquire an updated automatic driving vehicle getting-on position matched with the target user and an updated automatic driving vehicle getting-off position according to the vehicle dynamic adjustment information, so as to determine the current automatic driving vehicle getting-on position according to the updated automatic driving vehicle getting-on position and determine the current automatic driving vehicle getting-off position according to the updated automatic driving vehicle getting-off position. For example, after the background operation system of the autonomous vehicle receives the latest updated on-board position of the autonomous vehicle, the current on-board position of the autonomous vehicle is compared with the currently stored on-board position of the current autonomous vehicle. If the updated getting-on position of the automatic driving vehicle is the same as the current getting-on position of the automatic driving vehicle, the current getting-on position of the automatic driving vehicle can not be adjusted; otherwise, the current autonomous vehicle boarding location may be updated with the updated autonomous vehicle boarding location to determine a latest autonomous vehicle boarding location as the current autonomous vehicle boarding location. Similarly, after the background operation system of the automatic driving vehicle receives the latest updated getting-off position of the automatic driving vehicle, the latest updated getting-off position of the automatic driving vehicle is compared with the current stored getting-off position of the automatic driving vehicle. If the updated automatic driving vehicle getting-off position is the same as the current automatic driving vehicle getting-off position, the current automatic driving vehicle getting-off position is not adjusted; otherwise, the current driverless vehicle alighting position may be updated with the updated driverless vehicle alighting position to determine the latest driverless vehicle alighting position as the current driverless vehicle alighting position.

In an optional embodiment of the present invention, the obtaining of the updated getting-on position of the autonomous vehicle and the updated getting-off position of the autonomous vehicle matched with the target user according to the vehicle dynamic adjustment information may include: obtaining the getting-on position of the updated automatic driving vehicle according to a getting-on position dynamic determination model included in the vehicle dynamic adjustment information; and obtaining the updated automatic driving vehicle getting-off position according to the getting-off position dynamic determination model included in the vehicle dynamic adjustment information.

The getting-on position dynamic determination model can be used for determining the getting-on position of the updated automatic driving vehicle, and the getting-off position dynamic determination model can be used for determining the getting-off position of the updated automatic driving vehicle. It should be noted that the dynamic getting-on position determining model and the dynamic getting-off position determining model may be stored in a big data server of a third party, and the big data server may obtain an updated getting-on position of the autonomous vehicle and an updated getting-off position of the autonomous vehicle according to the dynamic getting-on position determining model and the dynamic getting-off position determining model. Correspondingly, the automatic driving vehicle background operation system can interactively communicate with the big data server to obtain the updated getting-on position of the automatic driving vehicle and the updated getting-off position of the automatic driving vehicle. Or, the getting-on position dynamic determination model and the getting-off position dynamic determination model may also be stored in the background operation system of the autonomous vehicle, and the background operation system of the autonomous vehicle generates and updates the getting-on position of the autonomous vehicle and updates the getting-off position of the autonomous vehicle itself.

In an optional embodiment of the present invention, the getting-on position dynamic determination model may include:

A(lng,lat)＝η*B(lng,lat)+α*C(lng,lat)+β*D+γ*T1+δ*T2+λW+μH

η+α+β+γ+δ+λ+μ＝1

the getting-off position dynamic determination model may include:

D(lng,lat)＝ρ*F(lng,lat)+θ*H(lng,lat)+τ*T3+λW+μH+σJρ+θ+τ+λ+μ+σ＝1

wherein A (lng, lat) represents the updated automatically-driven vehicle getting-on position, B (lng, lat) represents the historical automatically-driven vehicle getting-on position matched by the target user, C (lng, lat) represents the current automatically-driven vehicle getting-on position of the automatically-driven area, D represents the current road congestion factor, T1 represents the time when the target user walks to the automatically-driven vehicle getting-on position, T2 represents the time when the automatically-driven vehicle reaches the automatically-driven vehicle getting-on position, W represents the weather factor, H represents the road condition factor, D (lng, lat) represents the updated automatically-driven vehicle getting-off position, F (lng, lat) represents the historical automatically-driven vehicle getting-off position matched by the target user, H (lng, lat) represents the current automatically-driven vehicle position of the automatically-driven area, T3 represents the time when the target user walks to the automatically-driven vehicle getting-off position, indicating a distance between the target arrival position and the current autonomous vehicle alighting position, lng indicating a longitude, lat indicating a latitude, and η, α, β, γ, δ, λ, μ, ρ, θ, τ, and σ indicating coefficients.

It should be noted that, in the boarding position dynamic determination model, the boarding position of the autonomous vehicle corresponding to each order getting-off of the target user represents the frequent behavior of the target user, the proportion of the factor of the boarding position of the historical autonomous vehicle matched by the target user is large, and the coefficient η may be relatively larger, for example, 0.5. The factor of the current getting-on position of the autonomous vehicle in the autonomous region indicates the currently set getting-on point, and the factor α may be a small value, such as 0.05, as a reference value. The road congestion factor mainly considers whether the current automatic driving vehicle is suitable for driving to the getting-on position of the current automatic driving vehicle, and the coefficient beta can be 0.05. The time the target user walks to the current location on board the autonomous vehicle represents an important experience for the target user. If the walking time is too long, the experience effect of the target user on the automatic driving is greatly influenced, the single rate of the automatic driving is reduced, and therefore the coefficient gamma can be 0.2. The time for the autonomous vehicle to reach the current location on board the autonomous vehicle represents the time required for the autonomous vehicle to drive to the current location on board the autonomous vehicle. If the time for driving to the getting-on position of the current automatic driving vehicle is too long, the experience effect of a target user on automatic driving is also influenced, the automatic driving unit rate is reduced, and therefore the coefficient delta can be 0.1. Factors such as weather factors, road condition factors and time periods (early and late peaks) can be added into the model sea for dynamically determining the getting-on position.

In the model for dynamically determining the getting-off position, the main focus is the historical automatic driving vehicle getting-off position matched with the target user. Since the autonomous vehicle must travel within a fixed autonomous region, the purpose of determining the updated exit location of the autonomous vehicle is to make the exit point of the target user within the autonomous region as close as possible to the actual target arrival location. Meanwhile, when the distance between the target arrival position and the current automatic driving vehicle getting-off position is too large, the experience effect of the target user on the automatic driving vehicle is also reduced. For example, the coefficient ρ occupied by the factor of the getting-off position of the historical driverless vehicle matched with the target user may be 0.5, the coefficient θ occupied by the factor of the current driverless vehicle getting-off position in the driverless area may be 0.1, the coefficient τ occupied by the factor of the time when the target user walks to the driverless vehicle getting-off position may be 0.2, and the coefficient σ occupied by the factor of the distance between the target arrival position and the driverless vehicle getting-off position may be 0.2. Similarly, factors such as weather factors, road condition factors and time periods (early and late peaks) can also be added into the get-off position dynamic determination model.

The weather factor is a property specific to the autonomous vehicle. For example, when snow covers a road, the sight to the lane is blocked, but a vehicle camera, a distance sensing probe and the like need to rely on the lane to find the road. The poor weather such as heavy snow, heavy rain, heavy fog and sand storm can obstruct the field of vision of camera, and the light beam that laser sensor sent can be reflected by snowflake, thinks snowflake is the barrier. Although radar is not subject to such weather interference, it cannot estimate the shape of an object required by the autonomous vehicle computer, and cannot determine what object is. The value of λ is thus dynamically adjusted according to weather conditions. The road condition with the emphasis on road condition factors is different from the road congestion condition, and mainly refers to the number of sidewalks, schools, crossroads, traffic lights and the like. Autonomous vehicles cannot avoid human drivers who do not comply with traffic regulations, and such drivers stop side-by-side or directly in front of other vehicles. There are many pedestrians in schools or sidewalks, and the convergence of vehicles in all directions at the intersection can also affect the driving of the automatic driving vehicles. Therefore, the coefficient μmay be 0.05.

It is understood that the coefficients η, α, β, γ, δ, λ, μ, ρ, θ, τ, and σ may be dynamically adjusted, and corresponding factors may be added or reduced according to actual requirements. But each model needs to guarantee that the sum of all coefficients in the model is 1.

It should be noted that the getting-on position and the getting-off position of the autonomous vehicle corresponding to different users are different. Therefore, for each user, a unique ID (Identity Document) identifier may be assigned to each user, for example, the mobile phone number of each user is used as its corresponding ID identifier, and the information of the getting-on position and the getting-off position of the autonomous vehicle corresponding to each user is stored according to the ID identifier of each user.

It should be noted that, in the initial operation, a fixed getting-on position and a fixed getting-off position of the autonomous vehicle may be set in advance. With the continuous experience of the user on the automatic driving riding service, the automatic driving vehicle background operation system can collect data of the getting-on position and the getting-off position of the automatic driving vehicle of the user in real time, establish the getting-on position dynamic determination model and the getting-off position dynamic determination model according to the data collected and obtained in real time, and adjust the current getting-on position and the getting-off position of the automatic driving vehicle, so that the user is better served, the intelligence and the high efficiency of the riding mode of the automatic driving vehicle are improved, and the unit forming rate of the automatic driving vehicle is further promoted.

And S240, judging whether the target user meets the automatic driving riding condition or not according to the current user position and the automatic driving area, if so, executing S250, and otherwise, executing S290.

The autonomous driving vehicle taking conditions may be used to determine whether the target user is suitable for receiving autonomous vehicle taking services.

It can be understood that the riding order information sent by the target user may not be suitable for receiving the automatic driving vehicle riding service due to the influence of factors such as distance. Therefore, the automatic driving vehicle background operation system can judge whether the target user meets the automatic driving riding condition according to the current user position and the automatic driving area. If the target user is determined to meet the automatic driving riding conditions, an automatic driving vehicle can be distributed for the target user and automatic driving vehicle riding service is provided; otherwise, the common network car appointment riding service is directly provided for the target user.

In an alternative embodiment of the present invention, the autonomous driving ride conditions may include: the current user position belongs to the automatic driving area range; or the current user position does not belong to the automatic driving area range, and an automatic driving vehicle exists in a proximity area range corresponding to the current user position.

The adjacent area range may be an area range set according to actual requirements, and for example, the adjacent area range may be a circular area range with a radius of 3 kilometers and a current user position as a center of a circle. The radius of the circular area range may be set according to actual requirements, and the embodiment of the present invention does not limit the specific value of the radius of the circular area range.

Alternatively, the autonomous ride condition may be determined in a number of ways. For example, the first type of autonomous driving ride condition may be: the current user position falls within the autopilot zone. That is, when the target user is located within the autonomous driving area, the autonomous vehicle riding service may be accepted. The second type of autonomous driving ride condition may be: the current user position does not belong to the automatic driving area range, but the automatic driving vehicle exists in the adjacent area range corresponding to the current user position, and at the moment, the automatic driving vehicle riding service can be conveniently provided for the target user.

And S250, determining a first target walking path according to the current user position and the boarding positions of the current automatic driving vehicles.

And S260, determining the getting-on position of the target automatic driving vehicle according to the first target walking path.

The first target pedestrian path may be the shortest one of the pedestrian paths between the boarding positions of the respective current autonomous vehicles and the current user position. The walk path is a path where the target user walks from a current user position to a current boarding position of the autonomous vehicle, or a path where the target user walks from a disembarking position of the autonomous vehicle to a target arrival position.

Before determining the boarding position of the target automatic driving vehicle, the automatic driving vehicle background operation system can firstly calculate walking paths from the current user position to the boarding positions of the current automatic driving vehicles by the target user, and selects the shortest walking path as a first target walking path so as to take the boarding position of the automatic driving vehicle corresponding to the first target walking path as the boarding position of the target automatic driving vehicle. Optionally, the autonomous vehicle background operation system may determine the first target pedestrian path using a pedestrian shortest path algorithm.

After determining the getting-on position of the target autonomous vehicle, the background operating system of the autonomous vehicle may send the first target walking path to a corresponding application client (e.g., an online car calling application or an electronic map) of the target user, so that the target user walks from the current user position to the getting-on position of the target autonomous vehicle to wait for getting-on according to the navigation information of the first target walking path. Correspondingly, the automatic driving vehicle background operation system can also indicate one automatic driving vehicle, such as an automatic driving vehicle which is closest to the boarding position of the target automatic driving vehicle and is in an idle state, to automatically drive to the boarding position of the target automatic driving vehicle, and wait for the target user to board.

It should be noted that, if the background operation system of the autonomous vehicle obtains the riding order information of the target user through the taxi-calling software of the general network taxi appointment, the background operation system of the autonomous vehicle can display the first target walking path and the boarding position of the target autonomous vehicle for the target user after determining the boarding position of the target autonomous vehicle, and remind the target user whether to need the autonomous vehicle riding service by using a message reminding manner such as a pop-up frame. If the user selects to require the automatic driving vehicle riding service, the automatic driving vehicle background operation system can continue the subsequent operation; otherwise, the automatic driving vehicle background operation system stops subsequent operations and stops providing the automatic driving vehicle riding service for the target user.

And S270, determining a second target walking path according to the target arrival position and the getting-off position of each automatic driving vehicle.

And S280, determining the getting-off position of the target automatic driving vehicle according to the second target walking path.

The second target walking path may be the shortest walking path among the walking paths between the getting-off position and the target arrival position of each current autonomous vehicle.

It can be understood that the background operation system of the automatic driving vehicle needs to arrive at one of the automatic driving vehicle getting-off positions to stop the automatic driving vehicle, and the target user can get off at the getting-off position of the automatic driving vehicle to finish the riding route of the automatic driving vehicle. Similarly, the background operation system of the automatic driving vehicle may first calculate walking paths between the get-off position of each current automatic driving vehicle and the target arrival position, select the shortest walking path as the second target walking path, and use the get-off position of the automatic driving vehicle corresponding to the second target walking path as the get-off position of the target automatic driving vehicle. The automatic driving vehicle can automatically run according to the second target walking path, stop at the getting-off position of the target automatic driving vehicle after running, wait for the target user to get off and complete the taking service of the automatic driving vehicle. Optionally, the autonomous vehicle background operation system may determine the second target pedestrian path using a pedestrian shortest path algorithm.

And S290, refusing to provide the automatic driving vehicle riding service for the target user.

Correspondingly, if the target user is judged not to meet the automatic driving taking condition according to the current user position of the target user and the automatic driving area, if the current user position does not belong to the automatic driving area range and the automatic driving vehicle does not exist in the adjacent area range corresponding to the current user position, the automatic driving vehicle background operation system can refuse to provide the automatic driving vehicle taking service for the target user.

Fig. 3 is a schematic structural diagram of a riding information processing system according to a second embodiment of the present invention, and fig. 4 is a schematic flow diagram of a riding information processing method according to a second embodiment of the present invention. In one specific example, as shown in fig. 3 and 4, the autonomous vehicle back-office operation system may be referred to simply as an operation back office, and the autonomous driving area is illustratively an autonomous driving fence or an autonomous driving operation fence. Correspondingly, the operation background can provide the automatic driving vehicle taking service by combining with passenger ordering software (such as common network car appointment online calling software) and a big data server.

Specifically, the riding information processing system can comprise passenger ordering software which can comprise a map display module and an automatic driving experience prompting module. The map display module can display the current position of a passenger, and when the passenger ordering information is detected to meet the automatic driving ordering condition, the automatic driving fence and a Point of Interest (POI) Point are displayed, wherein the POI Point can specifically comprise a current available boarding Point and a current alighting Point. Meanwhile, the map display module can also display the planned path from the current position of the passenger to the boarding point of the passenger. The planned path may be presented to the passenger in the form of a dashed line. The planning path is determined by an operation background according to a walking shortest path algorithm, the walking shortest path algorithm is based on combining the shortest straight line between two points in mathematics, and the optimal path is finally determined and displayed to passengers by taking actual road conditions such as whether the road is unobstructed, whether the road is congested and whether the road is suitable for walking and other factors as coefficients.

Correspondingly, when the passenger enters the ordering software and orders, the operation background can acquire ordering information and acquire the automatic driving fence, the boarding point and the alighting point. Specifically, the operation background sets coordinates of the top points of the automatic driving fence on a map, and a closed area formed by the top points is used as the automatic driving fence. One or more points are then selected within the autopilot pen to be set as a boarding point and a disembarking point. Optionally, the information of the getting-on point and the getting-off point may include, but is not limited to: vehicle point number, vehicle point name, longitude and latitude, etc. Taking the boarding point as an example for specific explanation, the operation background transmits the currently set boarding point to the big data server, and receives the updated boarding point which is transmitted by the big data server and is obtained through calculation of the boarding position dynamic determination model. If the current set boarding point is the same as the updated boarding point, no adjustment is made; otherwise, dynamically adjusting the currently set boarding point to the updated boarding point. Similarly, the same processing can be performed on the get-off point.

After the operation background acquires the information of the automatic driving fence, the boarding point and the alighting point, if the boarding point positioned by a passenger is determined to be in the automatic driving operation fence area or the current position of the alighting is determined to be in the automatic driving operation fence area, or the alighting point of the passenger is determined not to be in the automatic driving operation area fence, but an automatic driving vehicle is in the vicinity of three kilometers, the automatic driving operation fence area and the boarding point and the alighting point are displayed for taking a bus, and the automatic driving operation fence area and the boarding point and the alighting point are automatically adsorbed to the nearest boarding point. And planning the path, the required time and the arrival time of the automatic driving vehicle from the passenger to the nearest boarding point at the operation background, and popping up an automatic driving popup window for the passenger through ordering software to prompt whether the passenger experiences automatic driving. The passenger may select "experience" or "not experience" at the autopilot popup. If the passenger selects 'experience', the operation background provides the automatic driving vehicle taking service for the passenger; otherwise, entering a non-automatic driving taxi taking process to provide common network taxi appointment taking service for passengers.

The getting-on and getting-off points are set by the operation background, and the initial points of the getting-on and getting-off points are determined according to the road conditions acquired manually and actually. As the number of passengers ordering increases, the operation background continuously collects the getting-on and getting-off points in the passenger ordering data and transmits the getting-on and getting-off points to the big data server. The big data dynamically determines a model according to the established boarding position, dynamically adjusts the boarding position in the automatic driving fence, and dynamically determines a model according to the established alighting position, and dynamically adjusts the alighting position in the automatic driving fence.

By adopting the technical scheme, the getting-on position and the getting-off position of the automatic driving vehicle are dynamically adjusted by utilizing the dynamic getting-on position determining model and the dynamic getting-off position determining model, and the getting-on position and the getting-off position of the target automatic driving vehicle are planned for the target user when the target user is determined to meet the automatic driving conditions, so that the intelligence and the efficiency of the automatic driving vehicle taking mode can be improved, and the ordering rate of the automatic driving vehicle is further improved.

It should be noted that any permutation and combination between the technical features in the above embodiments also belong to the scope of the present invention.

EXAMPLE III

Fig. 5 is a schematic diagram of a riding information processing device according to a third embodiment of the present invention, and as shown in fig. 5, the device includes: associated ride information acquisition module 310, target autonomous vehicle getting-on position determination module 320, target autonomous vehicle getting-off position determination module 330, wherein:

the associated riding information acquiring module 310 is configured to acquire associated riding information of a target user; the associated riding information comprises the current user position and the target arrival position of the target user, an automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle;

a target automatic driving vehicle getting-on position determination module 320, configured to determine a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area, and the current automatic driving vehicle getting-on position;

and a target autonomous vehicle alighting position determining module 330, configured to determine a target autonomous vehicle alighting position matched with the target user according to the target arrival position and the current autonomous vehicle alighting position.

Optionally, the associated riding information obtaining module 310 is specifically configured to: obtaining riding order information and vehicle dynamic adjustment information of the target user; determining the current user position and the target arrival position according to the riding order information; and determining the automatic driving area, the getting-on position of the current automatic driving vehicle and the getting-off position of the current automatic driving vehicle according to the vehicle dynamic adjustment information.

Optionally, the associated riding information obtaining module 310 is specifically configured to: acquiring traffic control data and area planning data matched with the current user position according to the vehicle dynamic adjustment information; determining the autopilot zone from the traffic control data and the zone planning data; acquiring an updated automatic driving vehicle getting-on position and an updated automatic driving vehicle getting-off position matched with the target user according to the vehicle dynamic adjustment information; determining the getting-on position of the current automatic driving vehicle according to the updated getting-on position of the automatic driving vehicle; and determining the current automatic driving vehicle getting-off position according to the updated automatic driving vehicle getting-off position.

Optionally, the associated riding information obtaining module 310 is specifically configured to: obtaining the getting-on position of the updated automatic driving vehicle according to a getting-on position dynamic determination model included in the vehicle dynamic adjustment information; and obtaining the updated automatic driving vehicle getting-off position according to the getting-off position dynamic determination model included in the vehicle dynamic adjustment information.

Optionally, the getting-on position dynamic determination model includes:

A(lng,lat)＝η*B(lng,lat)+α*C(lng,lat)+β*D+γ*T1+δ*T2+λW+μH

η+α+β+γ+δ+λ+μ＝1

the model for dynamically determining the getting-off position comprises the following steps:

D(lng,lat)＝ρ*F(lng,lat)+θ*H(lng,lat)+τ*T3+λW+μH+σJρ+θ+τ+λ+μ+σ＝1

wherein A (lng, lat) represents the updated automatically-driven vehicle getting-on position, B (lng, lat) represents the historical automatically-driven vehicle getting-on position matched by the target user, C (lng, lat) represents the current automatically-driven vehicle getting-on position of the automatically-driven area, D represents the current road congestion factor, T1 represents the time when the target user walks to the automatically-driven vehicle getting-on position, T2 represents the time when the automatically-driven vehicle reaches the automatically-driven vehicle getting-on position, W represents the weather factor, H represents the road condition factor, D (lng, lat) represents the updated automatically-driven vehicle getting-off position, F (lng, lat) represents the historical automatically-driven vehicle getting-off position matched by the target user, H (lng, lat) represents the current automatically-driven vehicle position of the automatically-driven area, T3 represents the time when the target user walks to the automatically-driven vehicle getting-off position, indicating a distance between the target arrival position and the automated vehicle alighting position, lng indicating a longitude, lat indicating a latitude, and η, α, β, γ, δ, λ, μ, ρ, θ, τ, and σ indicating coefficients.

Optionally, the target automatic driving vehicle getting-on position determining module 320 is specifically configured to: if the target user meets the automatic driving riding condition according to the current user position and the automatic driving area, determining a first target walking path according to the current user position and the getting-on position of each current automatic driving vehicle; determining a boarding location of the target autonomous vehicle based on the first target walking path; wherein the autonomous driving ride conditions comprise: the current user position belongs to the automatic driving area range; or the current user position does not belong to the automatic driving area range, and an automatic driving vehicle exists in a near area range corresponding to the current user position.

Optionally, the target automatic driving vehicle getting-off position determining module 330 is specifically configured to: determining a second target walking path according to the target arrival position and each current automatic driving vehicle getting-off position; and determining the getting-off position of the target automatic driving vehicle according to the second target walking path.

The riding information processing device can execute the riding information processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For details of the riding information processing method provided in any embodiment of the present invention, reference may be made to the technical details not described in detail in this embodiment.

Since the above-described riding information processing device is a device capable of executing the riding information processing method in the embodiment of the present invention, based on the riding information processing method described in the embodiment of the present invention, a person skilled in the art can understand a specific implementation manner of the riding information processing device in the embodiment and various modifications thereof, and therefore, a detailed description of how the riding information processing device implements the riding information processing method in the embodiment of the present invention is omitted here. The device used by a person skilled in the art to implement the method for processing riding information in the embodiment of the present invention is within the scope of the present application.

Example four

Fig. 6 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 6 illustrates a block diagram of a computer device 412 suitable for use in implementing embodiments of the present invention. The computer device 412 shown in FIG. 6 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.

As shown in FIG. 6, computer device 412 is in the form of a general purpose computing device. Components of computer device 412 may include, but are not limited to: one or more processors 416, a storage device 428, and a bus 418 that couples the various system components including the storage device 428 and the processors 416.

Bus 418 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

Computer device 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 412 and includes both volatile and nonvolatile media, removable and non-removable media.

Storage 428 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 430 and/or cache Memory 432. The computer device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk-Read Only Memory (CD-ROM), a Digital Video disk (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Storage 428 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program 436 having a set (at least one) of program modules 426 may be stored, for example, in storage 428, such program modules 426 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination may comprise an implementation of a network environment. Program modules 426 generally perform the functions and/or methodologies of embodiments of the invention as described herein.

The computer device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, camera, display 424, etc.), with one or more devices that enable a user to interact with the computer device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 412 to communicate with one or more other computing devices. Such communication may be through an Input/Output (I/O) interface 422. Also, computer device 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network, such as the internet) through Network adapter 420. As shown, network adapter 420 communicates with the other modules of computer device 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 412, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.

The processor 416 executes various functional applications and data processing by executing programs stored in the storage device 428, for example, implementing the riding information processing method provided by the above-described embodiment of the present invention.

That is, the processing unit implements, when executing the program: acquiring associated riding information of a target user; the associated riding information comprises the current user position and the target arrival position of the target user, an automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle; determining a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area and the current automatic driving vehicle getting-on position; and determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position.

EXAMPLE five

Fifth, an embodiment of the present invention further provides a computer storage medium storing a computer program, where the computer program is executed by a computer processor to perform a riding information processing method according to any one of the above embodiments of the present invention: acquiring associated riding information of a target user; the associated riding information comprises the current user position and the target arrival position of the target user, an automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle; determining a target automatic driving vehicle getting-on position matched with the target user according to the current user position, the automatic driving area and the current automatic driving vehicle getting-on position; and determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position.

Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having 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. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A riding information processing method is characterized by comprising the following steps:

determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position;

wherein the current automatic driving vehicle getting-on position is determined by the updated automatic driving vehicle getting-on position, the updated automatic driving vehicle getting-on position is determined according to a dynamic vehicle getting-on position determination model in the vehicle dynamic adjustment information, the current automatic driving vehicle getting-off position is determined by the updated automatic driving vehicle getting-off position, and the updated automatic driving vehicle getting-off position is determined according to a dynamic vehicle getting-off position determination model in the vehicle dynamic adjustment information;

wherein the model parameters of the getting-on position dynamic determination model comprise: the target user matched historical automatic driving vehicle getting-on position, the current automatic driving vehicle getting-on position of the automatic driving area, the current road congestion factor, the time when the target user walks to the automatic driving vehicle getting-on position, the time when the automatic driving vehicle arrives at the automatic driving vehicle getting-on position, the weather factor and the road condition factor; the model parameters of the get-off position dynamic determination model comprise: the target user's matched historical autonomous vehicle alighting location, the current autonomous vehicle alighting location of the autonomous region, the time the target user walks to the autonomous vehicle alighting location, and the distance between the target arrival location and the autonomous vehicle alighting location.

2. The method of claim 1, wherein the obtaining of the associated ride information of the target user comprises:

obtaining riding order information and vehicle dynamic adjustment information of the target user;

determining the current user position and the target arrival position according to the riding order information;

and determining the automatic driving area, the getting-on position of the current automatic driving vehicle and the getting-off position of the current automatic driving vehicle according to the vehicle dynamic adjustment information.

3. The method of claim 2, wherein determining the autonomous driving zone, the current autonomous vehicle pick-up location, and the current autonomous vehicle drop-off location from the vehicle dynamic adjustment information comprises:

acquiring traffic control data and area planning data matched with the current user position according to the vehicle dynamic adjustment information;

determining the autopilot zone from the traffic control data and the zone planning data;

acquiring an updated automatic driving vehicle getting-on position and an updated automatic driving vehicle getting-off position matched with the target user according to the vehicle dynamic adjustment information;

determining the getting-on position of the current automatic driving vehicle according to the updated getting-on position of the automatic driving vehicle;

and determining the current automatic driving vehicle getting-off position according to the updated automatic driving vehicle getting-off position.

4. The method of claim 3, wherein obtaining an updated autonomous vehicle pick-up location and an updated autonomous vehicle drop-off location that match the target user based on the vehicle dynamic adjustment information comprises:

obtaining the getting-on position of the updated automatic driving vehicle according to a getting-on position dynamic determination model included in the vehicle dynamic adjustment information;

and obtaining the updated automatic driving vehicle getting-off position according to the getting-off position dynamic determination model included in the vehicle dynamic adjustment information.

5. The method of claim 4, wherein the dynamically determined boarding location model comprises:

A(lng，lat)＝η*B(lng，lat)+α*C(lng，lat)+β*D+γ*T1+δ*T2+λW+μH

η+α+β+γ+δ+λ+μ＝1

D(lng，lat)＝ρ*F(lng，lat)+θ*H(lng，lat)+τ*T3+λW+μH+σJ

ρ+θ+τ+λ+μ+σ＝1

wherein A (lng, lat) represents the updated automatically-driven vehicle getting-on position, B (lng, lat) represents the historical automatically-driven vehicle getting-on position matched by the target user, C (lng, lat) represents the current automatically-driven vehicle getting-on position of the automatically-driven area, D represents the current road congestion factor, T1 represents the time when the target user walks to the automatically-driven vehicle getting-on position, T2 represents the time when the automatically-driven vehicle reaches the automatically-driven vehicle getting-on position, W represents the weather factor, H represents the road condition factor, D (lng, lat) represents the updated automatically-driven vehicle getting-off position, F (lng, lat) represents the historical automatically-driven vehicle getting-off position matched by the target user, H (lng, lat) represents the current automatically-driven vehicle position of the automatically-driven area, T3 represents the time when the target user walks to the automatically-driven vehicle getting-off position, j denotes a distance between the target arrival position and the automated vehicle alighting position, lng denotes a longitude, lat denotes a latitude, and η, α, β, γ, δ, λ, μ, ρ, θ, τ, and σ denote coefficients.

6. The method of claim 1, wherein determining a target autonomous vehicle pick-up location matching the target user based on the current user location, the autonomous region, and the autonomous vehicle pick-up location comprises:

if the target user meets the automatic driving riding condition according to the current user position and the automatic driving area, determining a first target walking path according to the current user position and the getting-on position of each current automatic driving vehicle;

determining a boarding location of the target autonomous vehicle based on the first target walking path;

wherein the autonomous driving ride conditions comprise:

the current user position belongs to the automatic driving area range; or

The current user position does not belong to the automatic driving area range, and an automatic driving vehicle exists in a close area range corresponding to the current user position.

7. The method of claim 1, wherein determining a target autonomous vehicle alighting location matching the target user based on the target arrival location and the current autonomous vehicle alighting location comprises:

determining a second target walking path according to the target arrival position and each current automatic driving vehicle getting-off position;

and determining the getting-off position of the target automatic driving vehicle according to the second target walking path.

8. A riding information processing device, comprising:

the associated riding information acquisition module is used for acquiring associated riding information of the target user; the associated riding information comprises the current user position and the target arrival position of the target user, an automatic driving area, the current getting-on position of the automatic driving vehicle and the current getting-off position of the automatic driving vehicle;

the target automatic driving vehicle getting-off position determining module is used for determining a target automatic driving vehicle getting-off position matched with the target user according to the target arrival position and the current automatic driving vehicle getting-off position;

wherein the model parameters of the getting-on position dynamic determination model comprise: the target user matched historical automatic driving vehicle getting-on position, the current automatic driving vehicle getting-on position of the automatic driving area, the current road congestion factor, the time when the target user walks to the automatic driving vehicle getting-on position, the time when the automatic driving vehicle reaches the automatic driving vehicle getting-on position, the weather factor and the road condition factor; the model parameters of the get-off position dynamic determination model comprise: the target user's matched historical autonomous vehicle alighting location, the current autonomous vehicle alighting location of the autonomous region, the time the target user walks to the autonomous vehicle alighting location, and the distance between the target arrival location and the autonomous vehicle alighting location.

9. A computer device, characterized in that the computer device comprises:

one or more processors;

storage means for storing one or more programs;

when executed by the one or more processors, the one or more programs cause the one or more processors to implement a ride information processing method according to any of claims 1-7.

10. A computer storage medium on which a computer program is stored, characterized in that the program, when executed by a processor, implements a ride information processing method according to any of claims 1 to 7.