CN114119301A

CN114119301A - Self-learning vehicle processing method and device based on shared vehicle

Info

Publication number: CN114119301A
Application number: CN202111296034.6A
Authority: CN
Inventors: 马军
Original assignee: Alipay Hangzhou Information Technology Co Ltd
Current assignee: Alipay Hangzhou Information Technology Co Ltd
Priority date: 2021-11-03
Filing date: 2021-11-03
Publication date: 2022-03-01

Abstract

The embodiment of the specification provides a shared vehicle-based self-learning vehicle processing method and device, wherein the shared vehicle-based self-learning vehicle processing method comprises the following steps: the method comprises the steps of obtaining driving positioning data of a shared vehicle driven by a vehicle learning user for the driving subject of the vehicle learning; the driving positioning data is collected based on an identification component of the shared vehicle and a communication component of a self-learning vehicle driving area; generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving equipment configured by the shared vehicle; and determining the completion degree of the self-learning vehicle for the self-learning vehicle driving subject of the vehicle-learning user based on the self-learning vehicle driving data.

Description

Self-learning vehicle processing method and device based on shared vehicle

Technical Field

The present invention relates to the field of data processing technologies, and in particular, to a shared vehicle-based self-learning vehicle processing method and device.

Background

With the increasing quantity of motor vehicles, the requirement for mastering the driving skills of the motor vehicles is rising, so that the motor vehicle driving training mode in the self-learning and direct-examination mode is well known by more and more vehicle learning users. A user who carries out motor vehicle driving training in a self-learning direct-examination mode needs to additionally mount a self-learning vehicle according to requirements, and obtains motor vehicle driving certificates under the condition that the performances are qualified through an autonomous learning driving technology and taking motor vehicle driving examinations.

Disclosure of Invention

One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing method, including: the method comprises the steps of obtaining driving positioning data of a shared vehicle driven by a vehicle learning user for the driving subject of the vehicle learning; the driving location data is collected based on an identification component of the shared vehicle and a communication component of a self-learned vehicle driving area. And generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving equipment configured by the shared vehicle. And determining the completion degree of the self-learning vehicle for the self-learning vehicle driving subject of the vehicle-learning user based on the self-learning vehicle driving data.

One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing apparatus, including: the positioning data acquisition module is configured to acquire driving positioning data of a shared vehicle driven by a school bus user for the driving subject of the school bus; the driving location data is collected based on an identification component of the shared vehicle and a communication component of a self-learned vehicle driving area. A driving data generation module configured to generate self-learned vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving devices of the shared vehicle configuration. A completion determination module configured to determine a self-learned vehicle completion of the vehicle learning user for the self-learned vehicle driving subject based on the self-learned vehicle driving data.

One or more embodiments of the present specification provide a shared vehicle based self-learning vehicle processing apparatus, comprising: a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to: the method comprises the steps of obtaining driving positioning data of a shared vehicle driven by a vehicle learning user for the driving subject of the vehicle learning; the driving location data is collected based on an identification component of the shared vehicle and a communication component of a self-learned vehicle driving area. And generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving equipment configured by the shared vehicle. And determining the completion degree of the self-learning vehicle for the self-learning vehicle driving subject of the vehicle-learning user based on the self-learning vehicle driving data.

One or more embodiments of the present specification provide a storage medium storing computer-executable instructions that, when executed by a processor, implement the following: the method comprises the steps of obtaining driving positioning data of a shared vehicle driven by a vehicle learning user for the driving subject of the vehicle learning; the driving location data is collected based on an identification component of the shared vehicle and a communication component of a self-learned vehicle driving area. And generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving equipment configured by the shared vehicle. And determining the completion degree of the self-learning vehicle for the self-learning vehicle driving subject of the vehicle-learning user based on the self-learning vehicle driving data.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present specification, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise;

FIG. 1 is a flowchart illustrating a shared vehicle-based self-learning vehicle processing method according to one or more embodiments of the present disclosure;

FIG. 2 is a flowchart of a shared vehicle-based self-learning vehicle processing method applied to a driving yard scenario, according to one or more embodiments of the present disclosure;

FIG. 3 is a flowchart of a shared vehicle-based self-learning vehicle processing method applied to a driving training road segment scenario, according to one or more embodiments of the present disclosure;

FIG. 4 is a schematic diagram of a shared vehicle based self-learning vehicle processing device provided in one or more embodiments of the present disclosure;

FIG. 5 is a schematic structural diagram of a shared vehicle-based self-learning vehicle processing device according to one or more embodiments of the present disclosure.

Detailed Description

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments described herein without making any inventive step shall fall within the scope of protection of this document.

The embodiment of the self-learning vehicle processing method based on the shared vehicle provided by the specification comprises the following steps:

referring to fig. 1, which shows a processing flow chart of a shared vehicle-based self-learning vehicle processing method provided by the present embodiment, referring to fig. 2, which shows a processing flow chart of a shared vehicle-based self-learning vehicle processing method applied to a driving field scene provided by the present embodiment, referring to fig. 3, which shows a processing flow chart of a shared vehicle-based self-learning vehicle processing method applied to a driving training road segment scene provided by the present embodiment.

Referring to fig. 1, the self-learning vehicle processing method based on shared vehicles provided in this embodiment specifically includes steps S102 to S108.

Step S102, obtaining driving positioning data of a shared vehicle driven by a school bus user for the driving subject of the school bus.

The method for processing the self-learned vehicle based on the shared vehicle provided by this embodiment collects the driving location data of the shared vehicle driven by the vehicle-learning user for the driving subject of the self-learned vehicle by means of the matching of the identification component configured for the shared vehicle and the communication component configured for the driving area of the self-learned vehicle, and then combines the collected driving location data with the auxiliary driving data of the auxiliary driving device configured for the shared vehicle to construct the self-learned vehicle driving data of the vehicle-learning user, on this basis, the self-learned vehicle completion degree of the vehicle-learning user is determined according to the self-learned vehicle driving data, the actual driving state and the actual completion degree of the vehicle-learning user in the self-learned vehicle process are accurately judged by the self-driving data, more personalized vehicle-learning service is provided for the vehicle-learning user, a broader driving learning platform is provided for the vehicle-learning user by utilizing resource integration, the vehicle-learning cost pressure of the vehicle-learning user is reduced, and the diversified requirements of the vehicle-learning user are met, the driving learning time is more flexible and more abundant for the learner-driven vehicle user, the driving voucher obtaining rate of the learner-driven vehicle user is improved, the user experience of the learner-driven vehicle user is improved, meanwhile, the driving safety of the learner-driven vehicle user and a trainer is guaranteed, and the safety risk of the learner-driven vehicle user and the trainer is reduced.

The vehicle learning users in the embodiment comprise users who execute vehicle learning tasks and apply driving vouchers in a self-learning and direct-learning mode.

In practical application, after a vehicle learning user determines a shared vehicle driven by the vehicle learning user, a self-learning driving test admission qualification application can be carried out, after the self-learning driving test admission qualification is obtained, a vehicle management mechanism issues a vehicle certificate identifier (such as a temporary license plate formulated by the vehicle management mechanism), a driving area, a driving time period, a driving speed and the like are limited in the vehicle certificate identifier, and the vehicle certificate identifier is displayed through an identifier component (such as a license plate display component) of the shared vehicle; optionally, the vehicle learning user obtains a vehicle credential identifier issued by the vehicle management mechanism after applying for the self-learning driving test admission qualification and obtaining the self-learning driving test admission qualification, and the vehicle credential identifier is displayed through the identifier component of the shared vehicle.

The above-mentioned vehicle learning user can perform the self-learning driving examination admission qualification application, the shared vehicle can also perform the self-learning driving examination admission qualification application, after the shared vehicle obtains the self-learning driving examination admission qualification, an auxiliary driving device can be installed for the shared vehicle, for example, a control device for vehicle auxiliary control is installed in a secondary driving, wherein an IoT component can be configured for the control device to perform identity verification, specifically, the configuration form of the IoT component can be a camera installed in a primary driving position and a secondary driving position, namely, a camera inside the vehicle and a camera outside the vehicle, an external camera can scan the identification of the self-learning vehicle to unlock or start the vehicle, and the external camera can be used as the basis for searching the shared vehicle to determine the shared vehicle reserved for the shared vehicle, an internal camera can be used for the situation that a coach is in the primary driving position and the user of the self-learning vehicle is in the secondary driving position, the method comprises the steps that identity images of a vehicle learning user and a coach are collected to identify and verify identities, driving safety in the self-learning vehicle process is guaranteed, the coach can drive and control auxiliary driving equipment, safety of the vehicle learning is guaranteed, integrated execution of self-learning vehicle service is achieved, and more convenient and better self-learning vehicle service is provided for the vehicle learning user; further, after the shared vehicle obtains self-learning driving test admission eligibility, an identification component may also be configured for the shared vehicle.

Before the self-learning direct-examination, the vehicle-learning user can make a reservation by a coach, and after a proper coach is selected, the vehicle voucher identification and the coach establish a reservation association relationship to strengthen the assistant driving responsibility of the coach, so that the coach can better assist the vehicle-learning user to complete the self-learning driving examination and obtain a driving voucher; optionally, the vehicle credential identifier and the trainee user have a one-to-one corresponding binding relationship, and a reservation association relationship between the vehicle credential identifier and the trainee is established after the trainee user makes a reservation by the trainee.

On the deployment of a self-learning vehicle driving area for self-learning driving study, coordinate positioning can be carried out on the self-learning vehicle driving area, grid division is carried out by utilizing positioned coordinate parameters, corresponding communication components (such as a signal transmitting unit or a signal receiving unit) are deployed to all the divided grid nodes, and driving positioning data (such as driving position data of a shared vehicle) of the shared vehicle are acquired by an identification component configured by the shared vehicle in the self-learning vehicle driving area through signal interaction with the communication components of the grid nodes in the self-learning vehicle driving area; the self-learning vehicle driving area comprises a driving area in a field and a specified road section, and the specified road section is designated as a driving training road section.

Optionally, the self-learning vehicle driving area obtains a communication component corresponding to deployment of each grid node after grid division is performed on the self-learning vehicle driving area, and the identification component performs signal interaction with the communication component deployed by the grid nodes in the self-learning vehicle driving area to acquire driving positioning data.

The driving positioning data are collected on the basis of the identification component of the shared vehicle and the communication component of the self-learning vehicle driving area, and the driving positioning data can be obtained in the process that a vehicle learning user drives the shared vehicle and also can be obtained after the vehicle learning user completes a certain self-learning vehicle exercise task; specifically, the driving positioning data are collected in the following manner:

reading the component identification carried by the signal in the detected signal sent by the communication component which carries out signal interaction with the identification component;

and determining the driving positioning data according to the read component identification and the position information of the communication component to which the component identification belongs.

For example, when the shared vehicle runs through the communication assembly in the self-learning vehicle driving area, the communication assembly in the self-learning vehicle driving area collects the license plate display assembly of the shared vehicle to send out a communication signal, reads the license plate identification carried in the communication signal, and determines the driving position data of the shared vehicle according to the license plate identification and the position information of the communication assembly to which the license plate identification belongs.

On the basis, if the self-learning vehicle task of the vehicle learning user completes a certain task progress or reaches a preset condition threshold value, the vehicle learning user can apply for participating in the self-learning driving test, after the qualification of the self-learning driving test is obtained and the driving certificate issued by the vehicle management organization is obtained, the vehicle certificate identification issued to the vehicle learning user is recovered and used as the vehicle certificate identification of other vehicle learning users who apply for the admittance qualification of the self-learning driving test, so that the cyclic utilization of the vehicle certificate identification resources is realized, and the resource utilization rate is improved; optionally, the vehicle learning user obtains the driving certificate issued by the vehicle management organization through self-learning driving test, and then the vehicle certificate identification issued to the vehicle learning user is recovered.

It should be noted that, in this embodiment, before obtaining the driving location data of the shared vehicle driven by the trainee user for the driving subject of the trainee, the trainee user and/or the trainer may further apply for a trainee identifier, and perform identity recognition and verification by using the self-learning vehicle identifier collected by the IoT component configured in the shared vehicle and the identity images of the trainee user and the trainer, so as to start the shared vehicle, specifically, the process of identity recognition and verification includes: calling an IoT component of the shared vehicle configuration to acquire a self-learning vehicle identification and identity images of a learning vehicle user and a coach; the method comprises the steps that self-learning vehicle relation detection is carried out on the basis of self-learning vehicle identification and identity images of a vehicle learning user and a coach; if the vehicle self-learning identification passes the detection, determining a vehicle self-learning task of the shared vehicle according to the reserved order bound by the vehicle self-learning identification; and issuing a self-learning vehicle instruction to auxiliary driving equipment configured for the shared vehicle based on the self-learning vehicle task so as to start a self-learning vehicle mode of the shared vehicle.

The self-learning vehicle identifier is generated after a vehicle learning user makes a reservation request and performs reservation processing on the reservation request, and specifically, the reservation processing process includes: after receiving an appointment request submitted by a user terminal of a vehicle learning user through accessing a self-learning vehicle service, returning a self-learning vehicle resource set to the user terminal of the vehicle learning user; according to a trainee selected by a trainee in the self-learning vehicle resource set, a driving field matched with the position of the trainee is screened from candidate driving fields contained in the self-learning vehicle resource set, a self-learning vehicle identifier corresponding to the reservation request is created, and the self-learning vehicle identifier is synchronized to the trainee and/or the trainee, so that the trainee and/or the trainee can use shared vehicles in the driving field obtained through matching to perform driving training based on the self-learning vehicle identifier.

And step S104, generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and the auxiliary driving data of the auxiliary driving equipment configured by the shared vehicle.

As described above, the shared vehicle is provided with the assistant driving device, and the coach can perform driving operation on the assistant driving device to generate assistant driving data, optionally, the assistant driving data is generated after the coach performs driving operation on the assistant driving device; and the authority priority of the coach for the control authority of the shared vehicle through the auxiliary driving device is higher than the authority priority of the school bus user for the control authority of the shared vehicle.

Specifically, the authority priority of the coach for controlling the shared vehicle by operating the auxiliary driving device is set to be higher than the authority priority of the school bus user for controlling the shared vehicle, namely: under the condition that the coach carries out the control of shared vehicle through controlling supplementary driving equipment, the school bus user is not effective to controlling of shared vehicle to avoid the unskilled emergence that leads to the accident of school bus user's driving technique, guarantee school bus user and coach's driving safety improves school bus user's retention rate, reduces school bus user's psychological pressure, promotes the passing rate of learning by oneself and driving and examining.

In a specific implementation process, in order to detect the driving state of the trainee user, ensure the driving safety of the trainee user and the coach, know the vehicle-learning completion degree of the trainee user, and obtain the driving data of the trainee, so as to ensure that the self-learning completion degree of the trainee user reaches a certain level and then timely obtain the qualification of self-learning driving test, specifically, in an optional implementation manner provided by this embodiment, in the process of generating the vehicle-learning driving data of the trainee user according to the driving positioning data and the auxiliary driving data of the auxiliary driving device configured for the shared vehicle, the following operations are performed:

determining a driving track of the shared vehicle according to the driving positioning data, and determining a driving state of the driving track according to the driving positioning data and the auxiliary driving data; the driving state comprises a self-driving state and an auxiliary driving state;

and determining a self-driving track of which the driving state is the self-driving state in the driving tracks, and taking self-driving data corresponding to the self-driving track as the self-learning vehicle driving data.

In practical application, during the process that the shared vehicle is driven, on one hand, a trainee user can manipulate the shared vehicle, and on the other hand, a trainer can manipulate the shared vehicle by manipulating the auxiliary driving device, during the process, a driving track formed by the trainee user manipulating the shared vehicle can be taken as a self-driving track, while a driving track formed by the trainer manipulating the shared vehicle by manipulating the auxiliary driving device cannot be taken as a self-driving track, that is: the driving track formed by the learner-driven user in the self-driving state of the shared vehicle corresponds to self-driving data, similarly, the driving track formed by the trainer in the auxiliary driving state of the auxiliary driving shared vehicle corresponds to auxiliary driving data, and the self-learning driving data comprises the self-driving data and/or the auxiliary driving data.

And S106, determining the self-learning vehicle completion degree of the vehicle learning user aiming at the self-learning vehicle driving subject based on the self-learning vehicle driving data.

On the basis that the self-learning vehicle driving data of the vehicle learning user is generated according to the driving positioning data and the auxiliary driving data of the auxiliary driving equipment configured by the shared vehicle, the self-learning vehicle completion degree of the vehicle learning user for the self-learning vehicle driving subject is determined according to the self-learning vehicle driving data.

In specific implementation, in order to improve the determination efficiency of the self-learning vehicle completion degree, the driving tracks of the shared vehicles are determined according to the driving positioning data, the driving states of the driving tracks are determined according to the driving positioning data and the auxiliary driving data, the self-driving tracks in the driving tracks in which the driving states are self-driving states are determined, the self-driving data corresponding to the self-driving tracks are used as the driving data of the self-learning vehicle, the driving data corresponding to the driving tracks of the driving subjects and the driving tracks to be completed by the driving subjects can be used for training and learning aiming at the driving matching model, and the trained driving matching model is generated so as to determine the self-learning vehicle completion degree of the learner-learning vehicle users aiming at the driving subjects of the self-learning vehicle.

In an optional implementation manner provided by this embodiment, in the process of determining the completion degree of the learner-driven vehicle user for the driving subject of the learner-driven vehicle based on the self-learned vehicle driving data, the following operations are performed:

inputting the self-driving data into a pre-trained driving matching model for driving subject matching and driving completion degree calculation, and outputting the driving subjects matched with the self-driving data and the driving completion degree of the driving subjects;

detecting whether the driving subjects output by the driving matching model are consistent with the self-learning vehicle driving subjects, and if so, determining the driving completion degree as the self-learning vehicle completion degree; if not, determining that the self-learning vehicle completion degree is 0.

For example, the self-driving data of the learner-driven vehicle user u is input into a pre-trained driving matching model for driving subject matching and driving completion calculation, the driving completion of the driving subject "subject two" (for example, the driving subjects include subject two and subject three) output from the driving data matching and the driving completion of the driving subject "subject two" calculated are 80%, and the driving subject "subject two" output by the model is exactly consistent with the driving subject "subject two" of the self-learning vehicle, so that the completion of the self-learning vehicle is determined to be 80%.

In addition, in addition to the implementation manner of determining the completion degree of the self-learned vehicle driving subject by using the pre-trained driving matching model, the driving track of the shared vehicle is determined according to the driving positioning data, the driving state of the driving track is determined according to the driving positioning data and the auxiliary driving data, the self-driving track in which the driving state is the self-driving state in the driving track is determined, and the driving parameters contained in the self-driving data can be directly compared with the reference driving parameters contained in the driving standard data of the self-learned vehicle driving subject on the basis of taking the self-driving data corresponding to the self-driving track as the driving data of the self-learned vehicle.

and comparing the driving parameters contained in the self-driving data with the reference driving parameters contained in the driving standard data of the self-learning vehicle driving subject to determine the self-learning vehicle completion degree of the vehicle learning user for the self-learning vehicle driving subject.

For example, in the driving learning process of the driving subject "subject two", the learner-driven vehicle user z determines that the degree of completion of the self-learning vehicle by the learner-driven vehicle user with respect to the driving subject "subject two" is 0 if the driving parameter "parking time in reverse" included in the self-driving data is less than the reference driving parameter "parking time in reverse" included in the driving standard data of the self-learning vehicle driving subject "subject two" and the reference driving parameter "parking time in reverse" included in the driving standard data of the self-learning vehicle driving subject "subject two" is not met.

Furthermore, in order to improve the perception degree of the learner-driven vehicle user and provide driving force for the achievement of the self-learning vehicle completion degree of the learner-driven vehicle user, the learner-driven vehicle user can be reminded under the condition that the self-learning vehicle completion degree is lower than a preset completion degree threshold value on the basis of comparing the driving parameters contained in the self-driving data with the reference driving parameters contained in the driving standard data of the self-learning vehicle driving subject and determining the self-learning vehicle completion degree of the learner-driven vehicle user aiming at the self-learning vehicle driving subject.

In an optional implementation manner provided by the embodiment, in the process of detecting the self-learning vehicle completion degree of the vehicle learning user, the following operations are performed:

and under the condition that the self-learning vehicle completion degree is lower than a preset completion degree threshold value, generating a driving guide prompt according to the driving parameters and the reference driving parameters, and displaying the driving guide prompt through the auxiliary driving equipment.

According to the above example, the vehicle learning completion degree of the vehicle learning user z for the driving subject "subject two" of the self-learning vehicle is 0 and is lower than the preset completion degree threshold value by 90%, and the driving guide prompt is generated within 4 minutes according to the driving parameter "backing-up and warehousing time of 5 minutes" and the reference driving parameter "backing-up and warehousing time of 4 minutes", and is displayed on the auxiliary driving equipment, so that the vehicle learning user can know the learning progress of the user in time, and can make adjustment in time, and the obtaining efficiency of the driving certificate is improved.

In addition, in order to prevent the sharing vehicle from not being in the driving area of the self-learning vehicle when the completion degree of the self-learning vehicle of the vehicle learning user is detected to be lower than the preset completion degree threshold value, so that the illegal driving behaviors of the vehicle learning user are caused, and potential safety hazards are caused to the vehicle learning user and a trainer, whether the sharing vehicle is in the driving area of the self-learning vehicle or not can be judged when the completion degree of the self-learning vehicle of the vehicle learning user is detected to be lower than the preset completion degree threshold value. Specifically, in an optional implementation manner provided by this embodiment, in the process of detecting the self-learning vehicle completion degree of the vehicle learning user, the following operations are performed:

under the condition that the self-learning vehicle completion degree is lower than a preset completion degree threshold value, judging whether the shared vehicle is in the driving area of the self-learning vehicle;

if yes, no treatment is carried out;

if not, a permission freezing instruction is sent to the auxiliary driving equipment, so that the driving permission of the trainee and the auxiliary control permission of the auxiliary driving equipment are frozen after the auxiliary driving equipment or a vehicle terminal connected with the auxiliary driving equipment executes the permission freezing instruction.

It should be noted that, in the process that the shared vehicle is driven, violation driving behaviors may occur, such as exceeding a driving area, exceeding a driving speed threshold value, and the like, and for this reason, in order to restrict the driving behavior of the trainee user, and meanwhile, enhance the assistant driving responsibility of the trainer, and ensure the driving safety, violation processing may be performed on the trainer who establishes an association relationship with the vehicle credential identification of the shared vehicle within the violation time on the basis that the identification component of the shared vehicle displays the vehicle credential identification issued by the vehicle management authority and the vehicle credential identification has a reservation association relationship with the trainer.

In an optional implementation manner provided by this embodiment, in the case that the shared vehicle has a violation driving behavior, the following steps are performed:

after the violation processing request of the shared vehicle is obtained, inquiring a vehicle certificate identifier displayed by the shared vehicle in the violation time carried by the violation processing request;

and determining a coach establishing a reservation association relation with the vehicle certificate identification at the violation time as a violation processing object.

The method is characterized in that when the shared vehicle is detected to have illegal driving behaviors or traffic violation behaviors, early warning reminding can be performed through a voice component configured for the shared vehicle, and when early warning responses are not detected, the driving authority of the vehicle learning user and the auxiliary control authority of the auxiliary driving device are frozen to wait for unfreezing processing.

In addition, the scheme can also be used for a self-learning vehicle service platform, the self-learning vehicle service platform is in interactive communication with auxiliary driving equipment configured by shared vehicles, a channel for checking the self-learning vehicle completion progress is provided for vehicle learning users, and the vehicle learning users can timely know the self-learning vehicle completion progress through the self-learning vehicle service platform so as to improve the completion efficiency of self-learning vehicle tasks.

The following further describes the self-learning vehicle processing method based on the shared vehicle provided by the present embodiment by taking the application of the self-learning vehicle processing method based on the shared vehicle provided by the present embodiment in a driving site scene as an example, and referring to fig. 2, the self-learning vehicle processing method based on the shared vehicle applied in the driving site scene specifically includes the following steps.

Step S202, driving positioning data of a shared vehicle driven by a school bus user in a driving field for the driving subject of the school bus are acquired.

The driving positioning data are collected based on an identification component of the shared vehicle and a communication component of the self-learning vehicle driving area.

And step S204, determining the driving track of the shared vehicle according to the driving positioning data, and determining the driving state of the driving track according to the driving positioning data and the auxiliary driving data.

Wherein the driving state comprises a self-driving state and an auxiliary driving state;

and step S206, determining a self-driving track of which the driving state is the self-driving state in the driving tracks, and taking self-driving data corresponding to the self-driving track as self-learning vehicle driving data.

And S208, inputting the self-driving data into a pre-trained driving matching model to perform driving subject matching and driving completion degree calculation, and outputting the driving subjects matched with the self-driving data and the driving completion degree of the driving subjects.

Step S210, detecting whether the driving subjects output by the driving matching model are consistent with the driving subjects of the self-learning vehicle;

if yes, executing step S212 to determine the driving completion degree as the vehicle learning completion degree;

if not, executing step S214, and determining that the self-learning vehicle completion degree is 0.

Step S216, judging whether the shared vehicle is in the self-learning vehicle driving area or not under the condition that the self-learning vehicle completion degree is lower than a preset completion degree threshold value;

if yes, no treatment is carried out;

if not, go to step S218.

And step S218, sending a permission freezing instruction to the auxiliary driving device so as to freeze the driving permission of the vehicle learning user and the auxiliary control permission of the auxiliary driving device after the auxiliary driving device or the vehicle terminal connected with the auxiliary driving device executes the permission freezing instruction.

The following further describes the shared vehicle-based self-learning vehicle processing method provided in this embodiment by taking an application of the shared vehicle-based self-learning vehicle processing method provided in this embodiment in a driving training road segment scene as an example, and referring to fig. 3, the shared vehicle-based self-learning vehicle processing method applied in the driving training road segment scene specifically includes the following steps.

Step S302, driving positioning data of a shared vehicle driven by a school bus user on a driving training section for the driving subject of the school bus are acquired.

Step S304, generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and the auxiliary driving data of the auxiliary driving equipment configured by the shared vehicle.

And S306, comparing the driving parameters contained in the self-driving data with the reference driving parameters contained in the driving standard data of the self-learning vehicle driving subject to determine the completion degree of the self-learning vehicle of the vehicle learning user for the self-learning vehicle driving subject.

Step S308, detecting whether the completion degree of the self-learning vehicle is lower than a preset completion degree threshold value;

if yes, go to step S310 to step S314;

if not, no processing is carried out.

And step S310, generating a driving guide prompt according to the driving parameters and the reference driving parameters, and displaying the driving guide prompt through the auxiliary driving equipment.

Step S312, after the violation processing request of the shared vehicle is obtained, the vehicle certificate identification displayed by the violation time carried by the violation processing request of the shared vehicle is inquired.

And step S314, determining a coach who establishes a reservation association relation with the vehicle certificate identification at the violation time as a violation processing object.

In summary, in the self-learning vehicle processing method based on the shared vehicle provided in this embodiment, first, driving positioning data of the shared vehicle driven by the vehicle learning user for the driving subject of the self-learning vehicle is obtained, a driving track of the shared vehicle is determined according to the driving positioning data, a driving state of the driving track is determined according to the driving positioning data and the auxiliary driving data, a self-driving track in which the driving state is the self-driving state in the driving track is determined, and self-driving data corresponding to the self-driving track is used as the driving data of the self-learning vehicle;

secondly, inputting self-driving data into a pre-trained driving matching model for driving subject matching and driving completion degree calculation, outputting the driving subjects matched with the self-driving data and the driving completion degree of the driving subjects, detecting whether the driving subjects output by the driving matching model are consistent with the driving subjects of the self-learning vehicle or not, and if so, determining the driving completion degree as the completion degree of the self-learning vehicle; if not, determining that the self-learning vehicle completion degree is 0; or comparing the driving parameters contained in the self-driving data with the reference driving parameters contained in the driving standard data of the self-learning vehicle driving subject to determine the self-learning vehicle completion degree of the vehicle learning user for the self-learning vehicle driving subject;

finally, under the condition that the self-learning vehicle completion degree is lower than a preset completion degree threshold value, generating a driving guide prompt according to the driving parameters and the reference driving parameters, displaying the driving guide prompt through auxiliary driving equipment, and meanwhile judging whether the shared vehicle is in a self-learning vehicle driving area or not; if not, an authority freezing instruction is sent to the auxiliary driving device, so that after the auxiliary driving device or a vehicle terminal connected with the auxiliary driving device executes the authority freezing instruction, the driving authority of the vehicle learning user and the auxiliary control authority of the auxiliary driving device are frozen, in the process, after the violation processing request of the shared vehicle is obtained, a vehicle certificate identifier displayed by the shared vehicle at the violation time carried by the violation processing request is inquired, and a trainer establishing a reservation association relation with the vehicle certificate identifier at the violation time is determined to be used as a violation processing object.

The actual driving state and the actual completion degree of the learner-driven vehicle user in the vehicle self-learning process are accurately judged through self-driving data, more personalized vehicle self-learning service is provided for the learner-driven vehicle user, a wider driving learning platform is provided for the learner-driven vehicle user by utilizing resource integration, the cost pressure of the learner-driven vehicle user is reduced, the diversified requirements of the learner-driven vehicle user are met, more flexible and more abundant driving learning time is provided for the learner-driven vehicle user, the driving voucher obtaining rate of the learner-driven vehicle user is improved, the user experience of the learner-driven vehicle user is improved, meanwhile, the driving safety of the learner-driven vehicle user and a coach is guaranteed, and the safety risk of the learner-driven vehicle user and the coach is reduced.

The embodiment of the self-learning vehicle processing device based on the shared vehicle provided by the specification is as follows:

in the embodiment, a shared vehicle-based self-learning vehicle processing method is provided, and correspondingly, a shared vehicle-based self-learning vehicle processing device is also provided, which is described below with reference to the accompanying drawings.

Referring to fig. 4, a schematic diagram of a shared vehicle-based self-learning vehicle processing device provided by the embodiment is shown.

Since the device embodiments correspond to the method embodiments, the description is relatively simple, and the relevant portions may refer to the corresponding description of the method embodiments provided above. The device embodiments described below are merely illustrative.

The embodiment provides a self-learning vehicle processing device based on shared vehicles, which comprises:

a positioning data acquisition module 402 configured to acquire driving positioning data of a shared vehicle driven by a trainee user for a driver of the own trainee; the driving positioning data is collected based on an identification component of the shared vehicle and a communication component of a self-learning vehicle driving area;

a driving data generation module 404 configured to generate self-learned vehicle driving data of the vehicle learning user from the driving location data and auxiliary driving data of auxiliary driving devices of the shared vehicle configuration;

a completion determination module 406 configured to determine a self-learned vehicle completion of the vehicle learning user for the self-learned vehicle driving subject based on the self-learned vehicle driving data.

The embodiment of the self-learning vehicle processing equipment based on the shared vehicle provided by the specification is as follows:

corresponding to the shared vehicle-based self-learning vehicle processing method described above, based on the same technical concept, one or more embodiments of the present specification further provide a shared vehicle-based self-learning vehicle processing apparatus for performing the shared vehicle-based self-learning vehicle processing method provided above, and fig. 5 is a schematic structural diagram of a shared vehicle-based self-learning vehicle processing apparatus provided in one or more embodiments of the present specification.

The embodiment provides a self-learning vehicle processing device based on a shared vehicle, which comprises:

as shown in FIG. 5, the shared vehicle based self-learning vehicle processing device may vary significantly due to configuration or performance, and may include one or more processors 501 and memory 502, where the memory 502 may have one or more stored applications or data stored therein. Memory 502 may be, among other things, transient or persistent storage. The application stored in the memory 502 may include one or more modules (not shown), each of which may include a series of computer-executable instructions in a shared vehicle-based, self-learning vehicle processing device. Still further, the processor 501 may be configured to communicate with the memory 502 to execute a series of computer executable instructions in the memory 502 on a shared vehicle based, self-learning vehicle processing device. The shared vehicle-based, self-learning vehicle processing apparatus may also include one or more power sources 503, one or more wired or wireless network interfaces 504, one or more input/output interfaces 505, one or more keyboards 506, and the like.

In one particular embodiment, the shared vehicle-based, self-learning vehicle processing apparatus includes a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the shared vehicle-based, self-learning vehicle processing apparatus, and being configured for execution by the one or more processors the one or more programs including computer-executable instructions for:

the method comprises the steps of obtaining driving positioning data of a shared vehicle driven by a vehicle learning user for the driving subject of the vehicle learning; the driving positioning data is collected based on an identification component of the shared vehicle and a communication component of a self-learning vehicle driving area;

generating self-learning vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving equipment configured by the shared vehicle;

and determining the completion degree of the self-learning vehicle for the self-learning vehicle driving subject of the vehicle-learning user based on the self-learning vehicle driving data.

An embodiment of a storage medium provided in this specification is as follows:

on the basis of the same technical concept, one or more embodiments of the present specification further provide a storage medium corresponding to the self-learning vehicle processing method based on the shared vehicle described above.

The storage medium provided in this embodiment is used to store computer-executable instructions, and when the computer-executable instructions are executed by the processor, the following processes are implemented:

It should be noted that the embodiment related to the storage medium in this specification and the embodiment related to the shared vehicle-based self-learning vehicle processing method in this specification are based on the same inventive concept, and therefore, for specific implementation of this embodiment, reference may be made to implementation of the foregoing corresponding method, and repeated details are not repeated.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

In the 30 s of the 20 th century, improvements in a technology could clearly be distinguished between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: the ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in multiple software and/or hardware when implementing the embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The description has been presented with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the description. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. One or more embodiments of the specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only an example of this document and is not intended to limit this document. Various modifications and changes may occur to those skilled in the art from this document. Any modifications, equivalents, improvements, etc. which come within the spirit and principle of the disclosure are intended to be included within the scope of the claims of this document.

Claims

1. A self-learning vehicle processing method based on shared vehicles comprises the following steps:

2. The shared vehicle-based self-learning vehicle processing method of claim 1, the generating self-learning vehicle driving data of the trainee user from the driving location data and auxiliary driving data of auxiliary driving devices configured by the shared vehicle, comprising:

3. The shared vehicle based self-learning vehicle processing method of claim 2, the determining the self-learning vehicle completion of the learning vehicle user for the self-learning vehicle driving subject based on the self-learning vehicle driving data, comprising:

4. The shared vehicle based self-learning vehicle processing method of claim 2, the determining the self-learning vehicle completion of the learning vehicle user for the self-learning vehicle driving subject based on the self-learning vehicle driving data, comprising:

5. The shared vehicle-based self-learning vehicle processing method of claim 4, further comprising:

6. The self-learning vehicle processing method based on shared vehicles according to claim 1, wherein the self-learning vehicle driving area obtains the communication components corresponding to the deployment of each grid node after grid division, and the identification component performs signal interaction with the communication components deployed by the grid nodes in the self-learning vehicle driving area to acquire driving positioning data.

7. The self-learning vehicle processing method based on shared vehicles of claim 1, wherein the driving positioning data is collected by the following method:

8. The shared vehicle-based self-learning vehicle processing method of claim 1, the assistant driving data being generated after driving maneuvers are performed on the assistant driving device according to a coach;

and the authority priority of the coach for the control authority of the shared vehicle through the auxiliary driving device is higher than the authority priority of the school bus user for the control authority of the shared vehicle.

9. The shared vehicle-based self-learning vehicle processing method of claim 1, further comprising:

10. The self-learning vehicle processing method based on the shared vehicle as claimed in claim 1, wherein the vehicle learning user obtains a vehicle credential identifier issued by a vehicle management authority after applying for and obtaining the self-learning driving-study admission eligibility, and the vehicle credential identifier is displayed through an identifier component of the shared vehicle;

the vehicle voucher identification and the vehicle learning user have a one-to-one corresponding binding relationship, and a reservation association relationship between the vehicle voucher identification and a coach is established after the vehicle learning user makes a reservation by the coach.

11. The shared vehicle based self-learning vehicle processing method of claim 10, wherein the vehicle learning user is given vehicle credential identification to the vehicle learning user to be recovered after the vehicle learning user passes the self-learning driving test and obtains the driving credential issued by the vehicle administration authority.

12. The shared vehicle-based self-learning vehicle processing method of claim 10, further comprising:

13. A shared vehicle based self-learning vehicle processing device comprising:

the positioning data acquisition module is configured to acquire driving positioning data of a shared vehicle driven by a school bus user for the driving subject of the school bus; the driving positioning data is collected based on an identification component of the shared vehicle and a communication component of a self-learning vehicle driving area;

a driving data generation module configured to generate self-learned vehicle driving data of the vehicle learning user according to the driving positioning data and auxiliary driving data of auxiliary driving devices of the shared vehicle configuration;

a completion determination module configured to determine a self-learned vehicle completion of the vehicle learning user for the self-learned vehicle driving subject based on the self-learned vehicle driving data.

14. A shared vehicle based self-learning vehicle processing device comprising:

a processor; and a memory configured to store computer-executable instructions that, when executed, cause the processor to:

15. A storage medium storing computer-executable instructions that when executed by a processor implement the following: