CN118069782A

CN118069782A - Driving guidance method, driving guidance device, computer device, and storage medium

Info

Publication number: CN118069782A
Application number: CN202410042726.5A
Authority: CN
Inventors: 张振
Original assignee: Wuhan Lotus Cars Co Ltd
Current assignee: Wuhan Lotus Cars Co Ltd
Priority date: 2024-01-11
Filing date: 2024-01-11
Publication date: 2024-05-24

Abstract

The application relates to a driving instruction method, a driving instruction device, a computer device and a storage medium. The method comprises the following steps: acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from driving operation of a target object; according to the actual operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained; acquiring the problem of the target object, and constructing prompt information according to the problem and the analysis result; inputting the prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on a large language model. By adopting the method, deeper communication and solution can be carried out, and more information which the target object wants to know can be obtained.

Description

Driving guidance method, driving guidance device, computer device, and storage medium

Technical Field

The present application relates to the technical field of driving analysis, and in particular, to a driving instruction method, apparatus, computer device, storage medium, and computer program product.

Background

With the development of intelligent cabin technology, an actual operation driving analysis technology appears, and the technology collects actual operation driving data of a user, and feeds back an actual operation driving report to the user through analysis of the actual operation driving data so that the user can know own driving level more clearly, and can train own driving skills in a targeted manner.

In the conventional technology, a terminal is used to analyze driving data of a user so as to generate a graphic driving report or a text driving report of the user.

However, the traditional driving report is obtained by analyzing the real driving data of the user based on a unified report template, and the user can only acquire the content presented by the report template, so that the acquired information amount is limited.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a driving instruction method, apparatus, computer device, and computer-readable storage medium capable of performing deep driving content analysis.

In a first aspect, the present application provides a driving coaching method. The method comprises the following steps:

acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from driving operation of a target object;

According to the actual operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained;

acquiring the problem of the target object, and constructing prompt information according to the problem and the analysis result;

Inputting the prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on a large language model.

In one embodiment, the analysis results include a first analysis result and a second analysis result, and the analyzing the driving operation of the target object according to the real operation driving data and the standard driving data to obtain the analysis results includes:

Analyzing according to the data of the key driving operation in the actual driving data and the data of the key driving operation in the standard driving data to obtain a first analysis result; and analyzing the real operation driving data to obtain a second analysis result.

In one embodiment, the analyzing the data according to the key driving operation in the actual driving data and the data according to the key driving operation in the standard driving data to obtain a first analysis result includes:

Analyzing according to the actual curve driving time in the actual operation driving data and the standard curve driving time in the standard driving data, and determining an over-curve time analysis result; analyzing according to the actual curve position in the actual operation driving data and the standard curve position in the standard driving data, and determining an over-curve position analysis result; determining a bending speed analysis result according to the actual bending driving time, the bending path and the standard bending speed in the standard driving data; determining an excessive bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data; and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

In one embodiment, the analyzing according to the actual curve position in the actual operating driving data and the standard curve position in the standard driving data to determine the analysis result of the over-curve position includes:

Determining the distance between the actual bending coordinate and the standard bending coordinate according to the actual bending coordinate in the actual operation driving data and the standard bending coordinate in the standard driving data to obtain a bending coordinate offset distance; determining the distance between the actual yielding coordinates and the standard yielding coordinates according to the actual yielding coordinates in the actual operation driving data and the standard yielding coordinates in the standard driving data to obtain the yielding coordinate offset distance; and analyzing the bending coordinate offset distance, the bending coordinate offset distance and the offset direction to obtain an overbending position analysis result.

In one embodiment, the analyzing the actual operating driving data to obtain the second analysis result includes:

Obtaining the maximum driving speed and driving time from the actual operation driving data, and calculating the average speed according to the driving time and driving distance; calculating the variance of the electric door position in the driving process according to the electric door positions in the actual operation driving data; calculating a brake position variance in the driving process according to the brake pedal positions in the actual operation driving data; and obtaining a second analysis result according to the maximum driving speed, the average speed, the driving time, the electric door position variance and the brake position variance.

In one embodiment, the constructing the prompt message according to the problem and the analysis result includes:

Acquiring driving advice for the driving operation according to the analysis result; generating driving behavior summary content of the target object based on the analysis result and the driving advice; and constructing prompt information according to the questions and the summary content of the driving behaviors.

In one embodiment, the problem of obtaining the target object includes:

detecting voice data of a target object, and performing text recognition on the detected voice data to obtain a problem;

the method further comprises the steps of: and converting the reply information into voice for playing.

In a second aspect, the application further provides a driving guidance device. The device comprises:

The data acquisition module is used for acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from driving operation of a target object;

The data analysis module is used for analyzing the driving operation of the target object according to the actual operation driving data and the standard driving data to obtain an analysis result;

The prompt information construction module is used for acquiring the problem of the target object and constructing prompt information according to the problem and the analysis result;

the problem reply module is used for inputting the prompt information into the driving instruction big model and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on a large language model.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

Acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from driving operation of a target object; according to the actual operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained; acquiring the problem of the target object, and constructing prompt information according to the problem and the analysis result; inputting the prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on a large language model.

In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

The driving instruction method, the driving instruction device, the computer equipment and the storage medium are used for acquiring the real operation driving data and the standard driving data aiming at the same road section; according to the real operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained; thus, the driving operation of the target object is analyzed based on the standard driving data, and deeper analysis content can be obtained; acquiring a problem of a target object, and constructing prompt information according to the problem and an analysis result; the prompt information constructed in this way aims at the problem proposed by the target object, and is helpful for the driving instruction big model to output more targeted reply information; inputting prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction big model generates the reply information according to the prompt information, so that the accuracy of the reply information can be improved. According to the method, the driving operation of the target object is analyzed according to the actual operation driving data and the standard driving data, so that a deeper analysis result can be obtained, and the problem of the target object and the prompt information constructed by the analysis result are conducive to outputting more targeted reply information by the driving instruction large model; in addition, through the man-machine interaction mode, deeper communication and solution can be carried out, information which more target objects want to know can be obtained, and through the interaction mode, the effectiveness of information touch can be greatly improved.

Drawings

FIG. 1 is an application environment diagram of a driving coaching method in one embodiment;

FIG. 2 is a flow chart of a driving coaching method in one embodiment;

FIG. 3 is a flow chart of a driving coaching method according to another embodiment;

FIG. 4 is a block diagram of a driving direction device in one embodiment;

Fig. 5 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.

The driving guidance method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. Wherein the computer device 102 communicates with the data acquisition terminal 104 via a network. The data storage system may store data that the terminal 102 needs to process. The data storage system may be integrated on the computer device 102 or may be located on a cloud or other network server. The data acquisition end 104 acquires actual operation driving data of the target object; the computer device 102 acquires real operation driving data and standard driving data for the same road section; the real operation driving data can be track data of a simulated racing car with the wire control turned down in a cabin authorized by a user; the real operation driving data is derived from driving operation of a target object; the computer device 102 analyzes the driving operation of the target object according to the real operation driving data and the standard driving data to obtain an analysis result; the computer device 102 acquires the problem of the target object and constructs prompt information according to the problem and the analysis result; the computer device 102 inputs the prompt information to the driving direction big model and outputs the reply information through the driving direction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on the large language model. The computer device 102 may be a terminal or a server, and the terminal may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, where the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device and the like, and the server can be realized by a stand-alone server or a server cluster formed by a plurality of servers. The data acquisition end 104 may be a data acquisition chip or a device composed of data acquisition chips.

In one embodiment, as shown in fig. 2, a driving instruction method is provided, and the method is applied to the computer device 102 in fig. 1, and is described as an example, and includes the following steps:

Step 202, acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from the driving operation of the target object.

The road section may be a real road section or a virtual road section, for example, the road section may be an examination road section for driving license examination, a preset driving road section, a racing road section, or a virtual road section for racing games, etc. The real-time operation driving data refers to driving data generated by driving operation of a target object on a target road section, and the driving data can comprise vehicle position data, steering data, valve pedal pressure data, brake pedal position data, real-time speed and the like of each data acquisition time. The standard driving data is driving data generated by a relatively excellent driving operation on a target link, and for example, when the target link is a track, the standard driving data is driving data generated by a driver performing a driving operation on the track by a racer who obtains a first score; for example, in a case where the target road section is a driver license test road section, the standard driving data is driving data generated by a test result of a driver performing a driving operation on the road section.

It should be noted that the real operation driving data and the standard driving data refer to driving data generated by driving operation on the same road section.

Specifically, the actual operation driving data may be obtained by data acquisition of the driving operation of the target object, the standard driving data may be obtained by screening from the historically acquired actual operation driving data, and the most available driving data constructed according to the theoretical driving operation is also used as the standard driving data.

In some embodiments, the road section is a virtual road section, the target object performs driving operation in the online steering simulation intelligent cabin, real operation driving data of the target object are collected through a data collecting device in the intelligent cabin, the intelligent cabin has data processing capability, and after the data collection is completed, a micro processor in the intelligent cabin analyzes the real operation data of the target object and constructs prompt information according to analysis results and problems of the target object.

In some embodiments, the road section is a real road section, the target object performs driving operation at the driving position, the target object performs data acquisition on driving operation, electric valve position and brake position of the steering wheel of the vehicle through sensors on the vehicle, the position information of the vehicle is acquired through a positioning system of the vehicle, the acquired real operation driving data is transmitted to the computer equipment through the whole vehicle controller, the computer equipment analyzes the real operation driving data, and prompt information is constructed according to analysis results and problems of the target object.

And 204, analyzing the driving operation of the target object according to the actual driving data and the standard driving data to obtain an analysis result.

The analysis results comprise a first analysis result and a second analysis result. The first analysis result is a result obtained by comparing the actual operation driving data with the standard driving data. The second analysis result is a result obtained by analyzing the actual operation driving data itself.

Specifically, the computer equipment analyzes driving operation of a target object according to real operation driving data and standard driving data to obtain a first analysis result; the computer equipment analyzes the real operation driving data to obtain a second analysis result.

And 206, acquiring the problem of the target object, and constructing prompt information according to the problem and the analysis result.

When the actual operation of the target object is finished, the target object can interact with the vehicle-mounted voice interaction device in real time according to the driving operation of the target object, and the problem of the target object is the content that the target object wants to know and read according to the driving operation of the target object, for example, the problem of the target object can be "please score my current driving operation", "please generate a driving report for me", or "please provide advice for my driving", etc. The prompt information (prompt) is a text or instruction that provides input to the driving direction model to guide it to generate a specific output, and includes a question text paragraph provided by the target object, road segment information of the target road segment, and analysis contents of the driving operation of the target object.

In some embodiments, the problem of the target object may be text or voice, and when the problem of the target object is text, the computer device obtains the text of the target object, identifies the text to obtain the problem of the target object, and constructs prompt information according to the problem and the analysis result; when the problem of the target object is voice, the computer equipment carries out text recognition on the detected voice data to obtain the problem of the target object, and prompt information is constructed according to the problem and an analysis result.

Step 208, inputting prompt information into the driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on the large language model.

The driving instruction big model is obtained by carrying out migration learning on driving knowledge of the big language model, and in driving instruction scenes of different road sections, the driving knowledge is different and is theoretical driving knowledge matched with the driving purpose of the target road section. For example, in the scene of a road section of a race, the driving purpose is to reach the end in the shortest time, so the driving knowledge for performing the migration training should be theoretical driving knowledge conforming to the driving purpose of the road section of the race; in the driver license examination section, the driving purpose is to pass the examination, so the driving knowledge for migration training should be the theoretical driving knowledge conforming to the driving purpose of the driver license examination section.

It should be noted that the driving instruction big model replies information according to the prompt information, so that the more specific and comprehensive the analysis content in the prompt information is, the more accurate the reply information of the driving instruction big model aiming at the problem of the target object is. In addition, the driving instruction large model can be trained offline and network connection can be omitted, so that the processing of the real operation driving data aiming at the target object and the interaction between the target object and the driving instruction model can be carried out locally, and the safety of the real operation driving data of the target object can be ensured.

As an example, the large language model may be, but is not limited to, large language model Meta AI (lalama), baichuan large model (Baichuan), chatGLM model, and the like.

As an example, the training data for the transfer learning may be { "how to perform better track driving": "racetrack driving requires extensive training, including understanding of the racetrack, training of driving skills, adjustment of the mind, etc., and much time is required for racetrack training, the most important of which is the powerful coordination of throttle, steering and brakes" }.

Specifically, the computer device inputs the prompt information into the driving instruction big model, processes the reply information fed back by the driving instruction big model, and displays the reply information to the target object in at least one expression form of language, text and chart.

In the driving instruction method, the actual operation driving data and the standard driving data aiming at the same road section are obtained; according to the real operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained; thus, the driving operation of the target object is analyzed based on the standard driving data, and deeper analysis content can be obtained; acquiring a problem of a target object, and constructing prompt information according to the problem and an analysis result; the prompt information constructed in this way aims at the problem proposed by the target object, and is helpful for the driving instruction big model to output more targeted reply information; inputting prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction big model generates the reply information according to the prompt information, so that the accuracy of the reply information can be improved. According to the method, the driving operation of the target object is analyzed according to the actual operation driving data and the standard driving data, so that a deeper analysis result can be obtained, and the problem of the target object and the prompt information constructed by the analysis result are conducive to outputting more targeted reply information by the driving instruction large model; in addition, through the man-machine interaction mode, deeper communication and solution can be carried out, information which more target objects want to know can be obtained, and through the interaction mode, the effectiveness of information touch can be greatly improved.

In one embodiment, the analysis results include a first analysis result and a second analysis result, and according to the real operation driving data and the standard driving data, the driving operation of the target object is analyzed to obtain the analysis results, including:

The key driving data is an operation which can represent the driving level of the driver, such as an over-bending operation, a braking operation, a steering operation, a reversing operation or the like. The first analysis result is a difference analysis result from the standard driving data, which is obtained by comparing and analyzing the actual driving data and the standard driving data, and may include a driving operation difference of the curve during the curve driving, for example, a distance between the actual bending position and the standard bending position, an error magnitude between the actual bending speed and the standard bending speed, and an error magnitude between the actual bending angle and the standard bending angle. The second analysis result is obtained by analyzing according to the actual operation driving data, and specifically may include a maximum driving speed, an average speed, a driving time, a valve position variance, a brake position variance, and the like of the target object on the target road section.

Specifically, the computer equipment performs comparative analysis on the key operation of the target object according to the key driving operation data in the actual driving data and the key driving operation data in the standard driving data to obtain a first analysis result; and analyzing the real operation driving data to obtain a second analysis result.

In some embodiments, the road section is a virtual road section, the target object performs driving operation in the intelligent cabin, and the micro processor in the intelligent cabin performs comparative analysis on the key operation of the target object according to the key driving operation data in the real operation driving data and the key driving operation in the standard driving data to obtain a first analysis result; and analyzing the real operation driving data to obtain a second analysis result.

In this embodiment, the analysis result not only analyzes the actual driving data of the target object, but also compares and analyzes the key driving operation of the actual driving data with the key driving operation of the standard driving data, so that the obtained analysis result is helpful for deep interpretation of the driving operation of the target object, so that the target object can be aware of the driving operation of the target object in a deep manner.

In one embodiment, the analyzing according to the data of the key driving operation in the actual driving data and the data of the key driving operation in the standard driving data to obtain the first analysis result includes:

Analyzing according to the actual curve driving time in the actual operation driving data and the standard curve driving time in the standard driving data, and determining a curve passing time analysis result; analyzing according to the actual curve position in the actual operation driving data and the standard curve position in the standard driving data, and determining an analysis result of the over-curve position; determining a bending speed analysis result according to the actual driving time of the curve, the curve path and the standard bending speed in the standard driving data; determining an over-bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data; and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

In an actual driving operation, the driving operation of the curve is an important index for evaluating the driving level, so that in the process of analyzing the driving operation, a key analysis of the driving operation of the curve is required.

The evaluation index of the curve operation can comprise a bending time, a bending speed, a bending position, a bending speed, a bending steering angle, a bending electric door position and a bending brake position. The bending time refers to the time required for passing through a bend, the average position of the bending speed finger passing through the bend, the starting position of the bending operation of the bending position finger, the ending position of the bending operation of the bending position finger, the speed of the bending speed finger at the bending position, the steering angle of the steering wheel at the bending position finger, the electric door position at the moment when the bending electric door position fingers at the bending position and the braking position at the moment when the bending braking position fingers at the bending position.

Specifically, the computer equipment compares the actual curve driving time in the actual operation driving data with the standard curve driving time in the standard driving data to obtain a curve driving time difference between the actual curve driving time and the standard curve driving time, and determines a curve passing time analysis result according to the curve driving time difference; according to the bending-in position and the bending-out position in the actual bending position in the actual operation driving data and the bending-in position and the bending-out position of the standard bending position in the standard driving data, obtaining a bending-in offset distance and a bending-out offset distance, and determining an over-bending position analysis result according to the bending-in offset distance and the bending-out offset distance; determining the actual bending speed according to the actual driving time of the curve and the curve path; comparing and analyzing the actual over-bending speed with the standard over-bending speed in the standard driving data, and determining an over-bending speed analysis result; determining an over-bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data; and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

In this embodiment, the comparison analysis is performed on the driving data corresponding to the overbending operation of the target object and the overbending operation in the standard driving data, so that the overbending driving operation of the target object can be further analyzed, the deep interpretation of the overbending driving operation of the target object by the driving instruction large model is facilitated, and the problem raised by the target object can be more accurately and specifically replied.

In one embodiment, analyzing according to the actual curve position in the actual operation driving data and the standard curve position in the standard driving data, determining the over-curve position analysis result comprises:

According to the actual bending coordinate in the actual operation driving data and the standard bending coordinate in the standard driving data, determining the distance between the actual bending coordinate and the standard bending coordinate to obtain the bending coordinate offset distance; according to the actual yielding coordinates in the actual operation driving data and the standard yielding coordinates in the standard driving data, determining the distance between the actual yielding coordinates and the standard yielding coordinates to obtain the yielding coordinate offset distance; and analyzing the offset distance of the in-bending coordinate, the offset distance of the out-bending coordinate and the offset direction to obtain an analysis result of the over-bending position.

In this embodiment, the computer device performs a comparative analysis on the actual curve position of the actual operating data of the target object and the standard curve position of the standard operating data to obtain the in-curve coordinate offset distance, the out-curve coordinate offset distance and the offset direction, and obtains the over-curve position analysis result according to the in-curve coordinate offset distance, the out-curve coordinate offset distance and the offset direction, so that the driving guidance large model is facilitated to perform deeper interpretation on the in-and-out curve position of the target object.

In one embodiment, analyzing the real operation driving data to obtain a second analysis result includes:

Obtaining the maximum driving speed and driving time from the actual operation driving data, and calculating the average speed according to the driving time and driving distance; according to the position of each electric door in the actual operation driving data, calculating the variance of the position of the electric door in the driving process; according to the positions of the brake pedals in the actual operation driving data, calculating a brake position variance in the driving process; and obtaining a second analysis result according to the maximum driving speed, the average speed, the driving time, the valve position variance and the brake position variance.

The electric door position and the brake pedal position may be represented by specific values, for example, the electric door and the brake pedal are represented by a value 0 in a natural state, and the values of the electric door position and the brake pedal position increase as the electric door and the brake pedal position move down.

Specifically, the computer equipment acquires the maximum driving speed and driving time from the actual operation driving data, and calculates the average speed by dividing the driving distance by the driving time; according to the electric gate positions in the actual operation driving data, calculating an average value of the electric gate positions, and calculating an electric gate position variance in the driving process according to the electric gate positions and the average value of the electric gate positions; according to the positions of the brake pedals in the actual operation driving data, calculating the average value of the positions of the brake pedals, and calculating the variance of the brake positions in the driving process according to the positions of the brake pedals and the average value of the positions of the brake pedals; and obtaining a second analysis result according to the maximum driving speed, the average speed, the driving time, the valve position variance and the brake position variance.

As an example, in a scenario where the road segment is a race road segment, the target user may repeatedly drive on the race road segment, and the second analysis result may further include a single turn speed, a single turn time, a single turn shortest time, a single turn optimal brake position variance, and the like for a single drive on the race road segment.

In this embodiment, the real performance data of the target user is obtained by analyzing the real-time operation data of the target user, and the second analysis result can be obtained according to each real performance data, so that a more specific and comprehensive driving report can be generated for the target object according to the second analysis result.

In one embodiment, constructing the hint information based on the questions and the analysis results includes:

acquiring driving advice for driving operation according to the analysis result; generating driving behavior summary content of the target object based on the analysis result and the driving advice; and constructing prompt information according to the summary content of the problems and the driving behaviors.

The driving behavior summary content comprises a horizontal ranking of a user, an operation report, a bending strategy, a curve analysis result and the like. And in particular to a road segment scene of the race, the horizontal ranking can provide horizontal reference for the target user, so that the cognition of the target user on the driving level of the target user is improved. The operation report is an analysis report of the overall driving operation of the target object and comprises regular statistical data such as average speed, highest speed, single-turn time and the like. The overbending strategy is an overbending operation suggestion generated for the target object according to the overbending operation of the target object and the standard overbending operation, and is beneficial to improving the overbending operation skill of the target object. The curve analysis result is based on standard bending operation, and the bending operation deficiency analysis is performed on the bending operation of the target object, so that the target object is helped to better recognize the deficiency of the self operation.

In the embodiment, driving advice of the target object is obtained according to the analysis result, and driving behavior summary content of the target object is generated based on the analysis result and the driving advice; and constructing prompt information according to the summary content of the problems and the driving behaviors. The prompt information constructed in this way is helpful for the driving instruction big model to provide more targeted driving advice, thereby being helpful for improving the driving skill of the target object.

In one embodiment, the problem of obtaining a target object includes: detecting voice data of a target object, and performing text recognition on the detected voice data to obtain a problem; the method further comprises the steps of: and converting the reply information into voice for playing.

In this embodiment, by performing text conversion on the voice data of the target object and performing voice conversion on the reply information, voice interaction between the target object and the driving instruction big model can be performed, so that interaction between the target user and the driving instruction big model is more convenient.

Along with the enrichment of the use scene of the user in the intelligent cabin scene, the cabin itself has a good sound and sensor system, and the use scene of the cabin is expanded through the wire steering and the fusion of the end-side large model. Along with the maturity and the landing of the drive-by-wire steering wheel, the restriction of the understanding coupling of the steering wheel and the wheel object of the steering system in the past is relieved, and the convenience of the physical steering wheel serving as a racing game simulator is greatly improved. The steering wheel and the steering mechanism of the traditional automobile are all connected by mechanical parts all the time, and the steering by wire of the traditional electric automobile realizes the mechanical separation of the steering wheel and the steering execution system, so that the intelligent cabin driving scene is simulated by using the steering by wire to better play. Based on the steer-by-wire system, the track simulator and the collection of simulator data can be realized in the cabin, and richer virtual track driving experience and track report interpretation experience can be provided for users by means of semantic understanding and data analysis capability of the large model. Conventional off-line-based track driving is dangerous, and a common user can start gradually to track driving only after off-line professional track teaching training, the learning time period is longer, and conventional track reports are usually presented in the form of a data chart, lack, cannot be intuitively understood and cannot continue deep interactive interpretation and analysis. With the increasing of the vehicle computing power and the increasing of the model compression quantization technology, the vehicle computing power can also well support the relevant large model application deployment, the mode based on the off-line large model reasoning at the end side is a good landing mode at present, the model carries out real-time calculation at the vehicle end, and the voice interaction data of the user is not uploaded to the cloud, so that the privacy of the user is protected, and the user in the vehicle can carry out off-line interaction service with the virtual coach large model in the vehicle.

In one embodiment, as shown in fig. 3, the driving coaching method is applied to virtual racetrack driving, comprising the steps of:

obtaining optimal driving data of a virtual track, and carrying out data acquisition on the virtual driving data of a target object to obtain real operation driving data; according to the optimal driving data and the optimal driving data, the track driving operation of the target object is analyzed, and a track analysis result is obtained; constructing a promt according to the analysis result of the track and the problem of the target object; in the man-machine interaction process, inputting prompt information into a track teaching big model, and giving out real-time track analysis through the track teaching big model; the course teaching big model is obtained by performing course knowledge transfer learning on the big language model.

In one embodiment, the driving coaching method is applied to virtual racetrack driving, comprising the steps of:

acquiring track data of a simulated racing car with a wire controlled steering direction in a cabin authorized by a target object and real operation driving data; the real-operation driving data comprise the position and angle of an in-turn and an out-turn of a target object in the simulated track, the position and angle of the in-turn, the position data of an electric valve pedal, the position data of a brake pedal and the like; the track data includes environmental simulation data of the track, including fixed coordinates of a departure point, fixed coordinates of an end point, and the like.

Screening the authorized real operation driving data to obtain key driving operation, wherein the key driving operation can be curve driving operation; the method comprises the steps of manually calibrating standard driving data on a track in advance to obtain coordinates of a curve entering and a curve exiting on the standard driving data, calculating to obtain a curve entering coordinate offset distance and a curve exiting coordinate offset distance according to actual curve entering coordinates and actual curve exiting coordinates in the actual driving data, calculating time of a target object at a curve and corresponding curve specific index data comprising curve entering speed, curve entering coordinates, curve entering steering angles, electric gate positions, brake pedal positions and the like, and analyzing curve operation of the target object according to curve index data, curve entering coordinate offset distance and curve exiting coordinate offset distance to obtain a first analysis result;

The obtained actual operation driving data are arranged, the actual performance data of the user are screened out, the actual performance data comprise, but are not limited to, maximum driving speed, average speed, driving time, valve position variance and brake position variance, and the actual performance data are analyzed to obtain a second analysis result.

Constructing a driving guidance large model, and performing fine tuning on the large model in the aspect of the track by manually collecting a small amount of dialogue problems in the aspect of the conventional track, wherein the understanding of the large model on the terms in the track data is mainly solved; the strategy of using transfer learning (SFT, abbreviated as supervised fine tuning) is to transfer and learn the course knowledge with large model bases of open source (this base includes but is not limited to large language model Meta AI (lalama), large-scale hundred models (Baichuan), chatGLM model, etc.), for example we construct transferred training data such as: { "how to make better racetrack drive": the method is characterized in that 'track driving needs multiple exercises, including understanding of the track, training of driving skills, adjustment of heart states and the like, the track driving needs much time for track exercise, wherein the most important is the coordination of the power of accelerator, steering and brakes', and by constructing relevant track data, a large model has the output capability of track professional terms, and the fine-tuned large model can better understand and output questions and answers of the track.

The method comprises the steps of carrying out horizontal division and ranking on a target object in advance according to a racing evaluation strategy by using a first analysis result and a second analysis result, carrying out calculation on the performance of the target object at a track marking point in advance and constructing track suggestions with improved corresponding positions, carrying out script calculation on the pre-processed result data to form dynamic promt in a natural language description mode, inputting the dynamic promt into a driving guidance big model, enabling the driving guidance big model to understand the problems of the target object and output analysis contents of a corresponding track report, and after the promt is constructed, for example, the constructed promt comprises an overbending strategy for each bending angle of the track, a real-time target object horizontal division strategy, a specific analysis result in a curve of a user and the like, and carrying out real-time communication with the driving guidance big model by dynamically constructing the analysis result into the promt in advance.

In one embodiment, the intelligent cabin computing platform and the steering-by-wire racetrack analog front end are used for data acquisition of driving data to obtain real-operation driving data.

In one embodiment, the driving directions large model is deployed offline using an offline large model back-end host.

In one embodiment, a driving coaching system is provided that includes:

The intelligent cabin computing platform and the steer-by-wire device are used for collecting real operation driving data of a target object and sending the real operation driving data to the off-line large model rear end host;

The off-line large model back end host is used for analyzing the driving operation of the target object according to the real operation driving data and the standard driving data to obtain a first analysis result; constructing prompt information based on the first analysis result and the problem of the target object; in the human-computer interaction process, the problem of the target object and the prompt information are input into the driving guidance model together, and the reply information is output; the driving instruction model is obtained by carrying out migration learning of driving knowledge on the large language model.

As an example, the intelligent cockpit computing platform is 8155, and the offline large model back-end host is an X86 offline large model back-end host.

In one embodiment, the driving instruction method includes the steps of:

Acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from the driving operation of the target object.

Further, analyzing according to the actual curve driving time in the actual operation driving data and the standard curve driving time in the standard driving data, and determining an over-curve time analysis result; according to the actual bending coordinate in the actual operation driving data and the standard bending coordinate in the standard driving data, determining the distance between the actual bending coordinate and the standard bending coordinate to obtain the bending coordinate offset distance; according to the actual yielding coordinates in the actual operation driving data and the standard yielding coordinates in the standard driving data, determining the distance between the actual yielding coordinates and the standard yielding coordinates to obtain the yielding coordinate offset distance; and analyzing the offset distance of the in-bending coordinate, the offset distance of the out-bending coordinate and the offset direction to obtain an analysis result of the over-bending position. Determining a bending speed analysis result according to the actual driving time of the curve, the curve path and the standard bending speed in the standard driving data; determining an over-bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data; and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

After a first analysis result is obtained, obtaining the maximum driving speed and driving time from the real operation driving data, and calculating the average speed according to the driving time and driving distance; according to the position of each electric door in the actual operation driving data, calculating the variance of the position of the electric door in the driving process; according to the positions of the brake pedals in the actual operation driving data, calculating a brake position variance in the driving process; and obtaining a second analysis result according to the maximum driving speed, the average speed, the driving time, the valve position variance and the brake position variance.

After the second analysis result is obtained, detecting the voice data of the target object, and carrying out text recognition on the detected voice data to obtain a problem; constructing prompt information according to the problems and the analysis result; the analysis results comprise a first analysis result and a second analysis result; inputting prompt information into a driving instruction big model, outputting reply information through the driving instruction big model, and converting the reply information into voice for playing; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on the large language model.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a driving instruction device for realizing the driving instruction method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in one or more embodiments of the driving instruction device provided below may refer to the limitation of the driving instruction method hereinabove, and will not be repeated here.

In one embodiment, as shown in fig. 4, there is provided a driving instruction apparatus including: a data acquisition module 302, a data analysis module 304, a prompt message construction module 306, and a question-reply module 308, wherein:

the data acquisition module 302 is configured to acquire real operation driving data and standard driving data for the same road section; the real operation driving data is derived from driving operation of a target object;

the data analysis module 304 is configured to analyze the driving operation of the target object according to the real operation driving data and the standard driving data, so as to obtain an analysis result;

The prompt information construction module 306 is configured to obtain a problem of the target object, and construct prompt information according to the problem and the analysis result;

The problem reply module 308 is configured to input the prompt information to the driving direction big model, and output reply information through the driving direction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on the large language model.

In one embodiment, the analysis results include a first analysis result and a second analysis result, and the data analysis module 304 is further configured to:

In one embodiment, the data analysis module 304 is further configured to:

Analyzing according to the actual curve driving time in the actual operation driving data and the standard curve driving time in the standard driving data, and determining a curve passing time analysis result;

Analyzing according to the actual curve position in the actual operation driving data and the standard curve position in the standard driving data, and determining an analysis result of the over-curve position; determining a bending speed analysis result according to the actual driving time of the curve, the curve path and the standard bending speed in the standard driving data; determining an over-bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data; and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

In one embodiment, the data analysis module 304 is further configured to:

In one embodiment, the hint information building module 306 is further configured to:

In one embodiment, the hint information building module 306:

In one embodiment, the issue reply module 308 is further configured to:

And converting the reply information into voice for playing.

The respective modules in the above-described driving direction apparatus may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store data required for driving coaching. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a driving coaching method.

It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:

Acquiring real operation driving data and standard driving data aiming at the same road section; the real operation driving data is derived from driving operation of a target object; according to the real operation driving data and the standard driving data, the driving operation of the target object is analyzed, and an analysis result is obtained; acquiring a problem of a target object, and constructing prompt information according to the problem and an analysis result; inputting prompt information into a driving instruction big model, and outputting reply information through the driving instruction big model; the driving instruction large model is obtained by carrying out migration learning of driving knowledge on the large language model.

In one embodiment, the processor when executing the computer program further performs the steps of:

Detecting voice data of a target object, and performing text recognition on the detected voice data to obtain a problem; and converting the reply information into voice for playing.

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

In one embodiment, the computer program when executed by the processor further performs the steps of:

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magneto-resistive random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (PHASE CHANGE Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. A driving coaching method, the method comprising:

2. The method according to claim 1, wherein the analysis results include a first analysis result and a second analysis result, and the analyzing the driving operation of the target object according to the real operation driving data and the standard driving data to obtain the analysis result includes:

Analyzing according to the data of the key driving operation in the actual driving data and the data of the key driving operation in the standard driving data to obtain a first analysis result;

and analyzing the real operation driving data to obtain a second analysis result.

3. The method according to claim 2, wherein the analyzing according to the data of the key driving operation in the actual driving data and the data of the key driving operation in the standard driving data to obtain the first analysis result includes:

Analyzing according to the actual curve driving time in the actual operation driving data and the standard curve driving time in the standard driving data, and determining an over-curve time analysis result;

Analyzing according to the actual curve position in the actual operation driving data and the standard curve position in the standard driving data, and determining an over-curve position analysis result;

Determining a bending speed analysis result according to the actual bending driving time, the bending path and the standard bending speed in the standard driving data;

Determining an excessive bending index analysis result according to the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the real operation driving data and the bending speed, the bending steering angle, the bending electric door position and the bending brake position in the standard driving data;

and analyzing the overbending driving operation of the target object based on at least one of the overbending time analysis result, the overbending position analysis result, the overbending speed analysis result and the overbending index analysis result to obtain a first analysis result.

4. A method according to claim 3, wherein said analyzing based on the actual curve position in said real driving data and the standard curve position in said standard driving data to determine the result of the over-curve position analysis comprises:

Determining the distance between the actual bending coordinate and the standard bending coordinate according to the actual bending coordinate in the actual operation driving data and the standard bending coordinate in the standard driving data to obtain a bending coordinate offset distance;

determining the distance between the actual yielding coordinates and the standard yielding coordinates according to the actual yielding coordinates in the actual operation driving data and the standard yielding coordinates in the standard driving data to obtain the yielding coordinate offset distance;

and analyzing the bending coordinate offset distance, the bending coordinate offset distance and the offset direction to obtain an overbending position analysis result.

5. The method according to claim 2, wherein the analyzing the real operation driving data itself to obtain a second analysis result includes:

obtaining the maximum driving speed and driving time from the actual operation driving data, and calculating the average speed according to the driving time and driving distance;

calculating the variance of the electric door position in the driving process according to the electric door positions in the actual operation driving data;

Calculating a brake position variance in the driving process according to the brake pedal positions in the actual operation driving data;

And obtaining a second analysis result according to the maximum driving speed, the average speed, the driving time, the electric door position variance and the brake position variance.

6. The method of claim 1, wherein constructing a hint from the question and the analysis result comprises:

acquiring driving advice for the driving operation according to the analysis result;

Generating driving behavior summary content of the target object based on the analysis result and the driving advice;

And constructing prompt information according to the questions and the summary content of the driving behaviors.

7. The method according to any one of claims 1 to 6, wherein the problem of acquiring the target object comprises:

the method further comprises the steps of:

And converting the reply information into voice for playing.

8. A driving coaching device, the device comprising:

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

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