Driving teaching method and device, vehicle and storage medium

Abstract

The application relates to a driving teaching method and device, a vehicle and a storage medium, wherein the method comprises the following steps: uploading a vehicle identifier of a vehicle and an identity identifier of a user to a preset server, determining a vehicle model by using the preset server, matching corresponding multi-user historical feedback information, determining a teaching mode of the vehicle, determining a current teaching stage of the user in the teaching mode based on historical learning data of the user, selecting a target teaching site matched with the current teaching stage, generating a corresponding learning task, guiding the user to perform a corresponding training action, evaluating the training action of the user to obtain an evaluation result, and generating a driving analysis report of the user based on the evaluation result. Therefore, the problem that the driving teaching in the prior art has poor applicability, the automatic driving function or the driving assistance function is difficult to guarantee the driving safety in an extreme road environment, and the driving technology of a novice driver is not conducive to cultivation is solved.

Description

Technical Field

[0001] This application relates to the field of driver assistance teaching technology, and in particular to a driver teaching method, device, vehicle and storage medium. Background Technology

[0002] In related technologies, driving instruction is usually applied to vehicles specifically designed for driver training. When some novice drivers are driving, the differences between their own vehicles and those used in driver training vehicles, as well as their unfamiliarity with different road environments, can cause anxiety and tension. While autonomous driving or driver assistance functions can help drivers cope with road environments, they are limited in some more complex environments, such as intersections without traffic lights, sudden obstacles, and inclement weather. Novice drivers, without having effectively accumulated experience, find it even more difficult to cope with such complex environments, thus increasing the probability of traffic accidents. This situation urgently needs improvement. Summary of the Invention

[0003] This application provides a driving instruction method, device, vehicle, and storage medium to address the problems in related technologies, such as poor applicability of driving instruction, difficulty in ensuring driving safety in extreme road environments due to autonomous driving or driving assistance functions, and the difficulty in cultivating driving skills in novice drivers.

[0004] The first aspect of this application provides a driving instruction method, comprising the following steps: uploading a vehicle identifier and a user identifier to a preset server, using the preset server to determine the vehicle model based on the vehicle identifier, and matching corresponding multi-user historical feedback information based on the model; receiving the model and the multi-user historical feedback information from the preset server, determining the vehicle's instruction mode, and determining the user's current instruction stage under the instruction mode based on the user's historical learning data; responding to the current instruction stage, determining a target instruction location, and generating corresponding learning tasks to guide the user to perform corresponding training actions; acquiring the vehicle's current status data and the user's image data to evaluate the user's training actions, obtaining an evaluation result, and generating a driving analysis report for the user based on the evaluation result.

[0005] Optionally, in one embodiment of this application, after acquiring the current state data of the vehicle and the image data of the user, the method further includes: determining, based on the current state data and the image data, whether the user's training actions meet the preset standard corresponding to the learning task; if the preset standard is not met, determining the user's action error level based on the current state data and the image data; and matching a corresponding error correction reminder action based on the action error level.

[0006] Optionally, in one embodiment of this application, the step of acquiring the current state data of the vehicle and the image data of the user to evaluate the user's training actions and obtain an evaluation result includes: calculating the user's operation accuracy rate based on the current state data, the image data, and the preset standard; evaluating the user's training actions using the operation accuracy rate; and updating the user's historical learning data.

[0007] Optionally, in one embodiment of this application, after acquiring the current state data of the vehicle and the image data of the user, the method further includes: determining whether the vehicle meets preset collision conditions based on the current state data; and controlling the vehicle to perform a preset braking action if it is determined that the vehicle meets the preset collision conditions.

[0008] Optionally, in one embodiment of this application, determining the target teaching site in response to the current teaching stage includes: determining the target teaching site as a vehicle-free road site when the current teaching stage is a first teaching stage; determining the target teaching site as a road site with traffic flow greater than a preset threshold when the current teaching stage is a second teaching stage; determining the target teaching site as an elevated road site when the current teaching stage is a third teaching stage; and confirming the target teaching site as a road site that meets preset mixed conditions when the teaching stage is a fourth teaching stage.

[0009] Optionally, in one embodiment of this application, the method further includes: obtaining feedback information input by the user based on the driving analysis report; uploading the feedback information to the preset server to update the multi-user historical feedback information of the corresponding vehicle model in the preset server, and updating the teaching mode of the corresponding vehicle model based on the updated multi-user historical feedback information.

[0010] A second aspect of this application provides a driving instruction device, comprising: an upload module for uploading a vehicle identifier and a user identifier to a preset server, wherein the preset server determines the vehicle model based on the vehicle identifier and matches corresponding multi-user historical feedback information based on the model; a receiving module for receiving the model and the multi-user historical feedback information from the preset server, determining the vehicle's instruction mode, and determining the user's current instruction stage under the instruction mode based on the user's historical learning data; a generation module for determining a target instruction area in response to the current instruction stage and generating corresponding learning tasks to guide the user to perform corresponding training actions; and an evaluation module for acquiring the vehicle's current status data and the user's image data to evaluate the user's training actions, obtain an evaluation result, and generate a driving analysis report for the user based on the evaluation result.

[0011] Optionally, in one embodiment of this application, it further includes: a first judgment module, configured to determine whether the user's training action meets the preset standard corresponding to the learning task based on the current state data and the image data; a determination module, configured to determine the user's action error level based on the current state data and the image data if the preset standard is not met; and a matching module, configured to match a corresponding error correction reminder action based on the action error level.

[0012] Optionally, in one embodiment of this application, the evaluation module includes: a calculation unit, configured to calculate the user's operation accuracy based on the current state data, the image data, and the preset standard; and an evaluation unit, configured to evaluate the user's training actions using the operation accuracy and update the user's historical learning data.

[0013] Optionally, in one embodiment of this application, it further includes: a second judgment module, used to determine whether the vehicle meets the preset collision conditions based on the current state data; and a control module, used to control the vehicle to perform a preset braking action when it is determined that the vehicle meets the preset collision conditions.

[0014] Optionally, in one embodiment of this application, the generation module includes: a first determining unit, configured to determine that the target teaching site is a vehicle-free road site when the current teaching stage is a first teaching stage; a second determining unit, configured to determine that the target teaching site is a road site with traffic flow greater than a preset threshold when the current teaching stage is a second teaching stage; and to determine that the target teaching site is an elevated road site when the current teaching stage is a third teaching stage; and a third determining unit, configured to confirm that the target teaching site is a road site that meets preset mixed conditions when the teaching stage is a fourth teaching stage.

[0015] Optionally, in one embodiment of this application, it further includes: an acquisition module, used to acquire feedback information input by the user based on the driving analysis report; and an update module, used to upload the feedback information to the preset server to update the multi-user historical feedback information of the corresponding vehicle model in the preset server, and update the teaching mode of the corresponding vehicle model based on the updated multi-user historical feedback information.

[0016] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the driving instruction method as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the driving instruction method as described in the above embodiments.

[0018] A fifth aspect of this application provides a computer program product, including a computer program, which, when executed, is used to implement the driving instruction method described above.

[0019] This application embodiment utilizes vehicle identification to match corresponding vehicle model data from a server. Based on the vehicle model, it identifies multiple user-uploaded feedback information for that model to determine a matching teaching mode. By analyzing the user's learning progress, it determines the user's learning stage within the vehicle-matched teaching mode, enabling the selection of teaching locations and the generation of learning tasks to guide the user in learning the driving content for that stage. Furthermore, by acquiring vehicle status data and extracting image data of the user's driving behavior during learning, it determines whether the user made any errors during training, assessing the user's learning outcomes and generating a driving analysis report. This allows the user to correct errors later, enabling novice drivers to accumulate adaptive driving experience using their own vehicles, reducing their anxiety. Therefore, it solves the problems in related technologies where driving instruction has poor applicability, and autonomous driving or driver assistance functions cannot guarantee driving safety in extreme road environments, hindering the development of novice drivers' driving skills.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a driving instruction method provided according to an embodiment of this application; Figure 2 This is a flowchart of the teaching mode generation stage according to an embodiment of this application; Figure 3 This is a flowchart of the implementation phase of a teaching model provided according to an embodiment of this application; Figure 4 A flowchart of the teaching phase assessment stage according to one embodiment of this application; Figure 5 This is a flowchart of the debriefing and optimization phase according to one embodiment of this application; Figure 6 This is a schematic diagram of the structure of a driving instruction device according to an embodiment of this application; Figure 7 This is a structural schematic diagram of a vehicle provided according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] The following description, with reference to the accompanying drawings, outlines a driving instruction method, apparatus, vehicle, and storage medium according to embodiments of this application. Addressing the issues raised in the background section regarding the poor applicability of driving instruction methods and the difficulty in ensuring driving safety in extreme road conditions with autonomous driving or driver assistance functions, thus hindering the development of novice drivers' skills, this application provides a driving instruction method. This method utilizes the vehicle's identifier to match corresponding vehicle model data from a server. Based on the vehicle model, it identifies multiple user-uploaded feedback information for that model to determine a matching instruction mode. By analyzing the user's learning progress, it determines the user's learning stage within the matching instruction mode, thereby enabling the selection of learning locations and the generation of learning tasks to guide the user in learning the driving content for that stage. Furthermore, by acquiring vehicle status data and extracting image data of the user's driving behavior during learning, it determines whether the user made any errors during training, assessing the user's learning outcomes and generating a driving analysis report. This allows the user to correct errors later, enabling novice drivers to accumulate adaptive driving experience using their own vehicles and reducing their anxiety. This solves the problems of poor applicability of driving instruction in related technologies, the inability of autonomous driving or driving assistance functions to guarantee driving safety in extreme road environments, and the difficulty in cultivating driving skills in novice drivers.

[0024] Specifically, Figure 1 This is a flowchart illustrating a driving instruction method provided in an embodiment of this application.

[0025] like Figure 1 As shown, this driving instruction method includes the following steps: In step S101, the vehicle identifier and the user's identity identifier are uploaded to a preset server so that the preset server can determine the vehicle model based on the vehicle identifier and match the corresponding multi-user historical feedback information based on the model.

[0026] In actual implementation, this application embodiment can rely on vehicles and servers. After authorization, basic vehicle parameters, such as vehicle configuration, structural parameters, and available functions, can be uploaded to the server. The vehicle's identifier (such as VIN code) or some basic vehicle parameters (such as model configuration, body size, steering ratio, wheelbase, track width, etc.) can be used as tags to build a database for each vehicle model on the server. When teaching is required, only the vehicle identifier needs to be uploaded, or the basic parameters of the vehicle can be determined by the identifier as an index. The corresponding vehicle model database can then be determined on the server to obtain more detailed parameter information of the vehicle, such as mechanical structure parameters, power system parameters, electronic card control system parameters, etc.

[0027] Furthermore, this application embodiment can also retrieve user history feedback information uploaded by multiple users driving the same vehicle model from the server's user database, including actual usage experience, problem feedback, and optimization suggestions for the vehicle model in various driving scenarios.

[0028] By integrating more detailed vehicle parameter information and multi-user historical feedback information, in this embodiment of the application, the server can extract key data directly related to vehicle adaptive teaching from the data, such as brake sensitivity coefficient and steering assist characteristic curve, to form a concise and targeted data set.

[0029] To ensure data privacy, taking basic parameters as tags as an example, vehicles can use the AES encryption algorithm to encrypt the data, integrating and encrypting the basic parameters. After data encryption, the data is transmitted to the server via 4G, 5G wireless networks, or Wi-Fi. Upon receiving the data, the server first decrypts it. After integrating the data to obtain the data set, the server can encrypt and transmit it again to the vehicle to complete the data decryption.

[0030] In step S102, the model and multi-user historical feedback information fed back by the preset server are received, the teaching mode of the vehicle is determined, and the current teaching stage of the user in the teaching mode is determined based on the user's historical learning data.

[0031] After receiving detailed data from the server, this embodiment of the application can initially generate a teaching mode based on pre-set parameters, rules, weights, and other parameters. Then, based on changes in the focus of user feedback, the weights of the vehicle's basic parameters are reconfigured and calculated to generate a basic teaching mode. For example, if the user is more concerned about vehicle handling performance, the algorithm will increase the weight of this aspect and recalculate the output. During the calculation, the algorithm will also consider vehicle parameters and user feedback according to different driving scenarios, producing teaching guidance information tailored to each driving scenario.

[0032] After all calculations are completed, the embodiments of this application can output the generated basic teaching mode in a certain format, such as voice prompts, on-screen graphics and text information, etc.

[0033] The teaching mode can include multiple teaching stages, with different learning tasks assigned to different stages, allowing users to gradually accumulate driving experience. To determine the user's current teaching stage, this embodiment can obtain the user's historical learning data, such as historically generated driving analysis reports, to determine whether the user has completed a certain stage's learning task, thereby determining the user's current teaching stage and generating targeted learning tasks.

[0034] In step S103, in response to the current teaching stage, the target teaching venue is determined and a corresponding learning task is generated to guide the user to perform the corresponding training actions.

[0035] Based on the user's current learning stage, this application embodiment can determine a suitable teaching venue for the user's learning and training. For example, if the teaching stage is still the novice teaching stage, the teaching venue will be a teaching venue with a simple and safe driving environment, such as no cars or very little traffic (e.g., urban suburbs).

[0036] When selecting teaching venues, this embodiment can first obtain the current location of the vehicle, and then, based on the venue adaptation requirements of the teaching stage (road type, distance, traffic volume, etc.), select nearby roads that meet the requirements of the teaching scenario, and then sort and display the selection results according to traffic volume, distance, etc.

[0037] After determining the teaching location, this embodiment of the application can generate navigation to guide the user to the teaching location, and generate corresponding learning tasks based on the characteristics of the teaching location and the training objectives of the teaching stage. For example, in the novice teaching stage, in a low-speed driving road scenario, the learning tasks may include following other vehicles, turning, U-turns, etc., to train the novice driver's sense of the car.

[0038] After generating the learning task, the embodiments of this application can generate corresponding teaching instructions and prompts, which can be broadcast in real time or displayed on the screen through voice or other means. For example, in a low-speed driving road scenario, the voice can issue "Please follow the current road at a speed of less than 40km / h", the instrument screen can display the distance value, the steering wheel adjustment angle outline, warning icons, etc., and the central control screen can display the task route map and detailed task text, thereby guiding the user to complete the training action.

[0039] Optionally, in one embodiment of this application, determining the target teaching site in response to the current teaching stage includes: determining the target teaching site as a vehicle-free road site when the current teaching stage is the first teaching stage; determining the target teaching site as a road site with traffic flow greater than a preset threshold when the current teaching stage is the second teaching stage; determining the target teaching site as an elevated road site when the current teaching stage is the third teaching stage; and confirming the target teaching site as a road site that meets preset mixed conditions when the teaching stage is the fourth teaching stage.

[0040] The selection of teaching venues can be determined according to the teaching stage. In the first teaching stage, the target teaching venue is a car-free road to reduce external environmental interference and help users develop a feel for the car and practice standardized operating procedures. In the second teaching stage, the target teaching venue is a road with traffic flow exceeding a preset threshold, i.e., a road with high traffic volume, so that users can develop the ability to maintain following distance, grasp the timing of lane changes, and react to emergencies. In the third teaching stage, the target teaching venue is an elevated road to guide users in learning relevant skills for driving on elevated roads (such as maintaining the prescribed speed, using lanes correctly, and safely merging into and out of ramps). In the fourth teaching stage, the target teaching venue is a road that meets preset mixed conditions to cultivate users' coping abilities in complex road environments such as national highways with mixed vehicle types, narrow village roads / old residential areas, night driving, and congested sections, and to assess whether users can complete the teaching.

[0041] In step S104, the current state data of the vehicle and the image data of the user are acquired to evaluate the user's training actions, obtain the evaluation results, and generate the user's driving analysis report based on the evaluation results.

[0042] This application embodiment can acquire the vehicle's current status data through various on-board sensors distributed in key parts of the vehicle, and collect the user's image data through sensing devices such as cameras. By analyzing the timeline of the status data and image data, this application embodiment can comprehensively determine whether the user's operation meets the standards of the learning task, thereby evaluating the user's learning effect and generating a user driving analysis report.

[0043] This application embodiment can analyze the behavioral patterns exhibited by users in various driving scenarios and finally provide a driving review report, presenting the advantages and disadvantages in the form of charts and graphs, and proposing corresponding improvement measures, so that users can clearly see their driving level, and based on a preset multi-dimensional evaluation system, provide detailed improvement suggestions for each weak link.

[0044] Optionally, in one embodiment of this application, after obtaining the current state data of the vehicle and the image data of the user, the method further includes: determining whether the user's training actions meet the preset standard corresponding to the learning task based on the current state data and the image data; if the preset standard is not met, determining the user's action error level based on the current state data and the image data; and matching the corresponding error correction reminder action based on the action error level.

[0045] In actual implementation, the embodiments of this application can combine current state data and image data to determine whether the user's training actions are consistent with the operation specifications corresponding to the learning task, such as whether the user forgot to use the turn signal when turning, whether the user slowed down in front of the pedestrian crossing, whether the user checked the rearview mirror, and whether the user followed the vehicle too closely.

[0046] In cases of discrepancies, embodiments of this application can determine the user's action error level based on current status data and image data. For example, forgetting to use the turn signal is set to Level 1, and correction can be made through voice reminders. If following too closely is set to Level 2, correction can be made through voice and screen. If the user fails to increase the distance in time, progressive reminders can be given, such as vibration, and vehicle assistance can be automatically provided to reduce the vehicle speed.

[0047] Optionally, in one embodiment of this application, the current state data of the vehicle and the image data of the user are obtained to evaluate the user's training actions and obtain evaluation results, including: calculating the user's operation accuracy rate based on the current state data, image data and preset standard specifications; evaluating the user's training actions using the operation accuracy rate and updating the user's historical learning data.

[0048] This application embodiment can count the number of times the user's operations conform to the specifications after the user completes the learning task, and then calculate the operation accuracy rate. The operation accuracy rate is used for evaluation, and the user's historical learning data is updated to determine whether the user has completed the current teaching stage.

[0049] For example, in the first teaching stage, based on data such as vehicle speed, distance, and steering wheel angle, it is judged whether the operation is in accordance with the standard, and the operation accuracy rate is calculated. The error rate of pressing the accelerator hard / pressing the brake hard at low speed (≤20km / h) is ≤10%, the success rate of lane changing is ≥90%, the driving mileage is greater than 100km, and the accelerator / brake mis-pressing rate, etc., if the standard is met, the second teaching stage is unlocked; if the standard is not met, the training continues to be strengthened. In the second teaching stage, data such as vehicle speed, distance, steering wheel angle, and handling of cutting in are tested to determine whether the training actions meet the standards, and the operation accuracy rate is calculated. Based on the driving skills requirements for roads with heavy traffic, such as following distance, timing of lane changes, and ability to react to emergencies, the third teaching stage is unlocked when the standards are met; otherwise, intensive training continues. In the third teaching stage, the operation is judged to be in accordance with the standard by detecting data such as vehicle speed, distance, steering wheel angle, and merging and exiting ramps. The operation accuracy rate is calculated (the relevant skill standards for driving on ordinary elevated roads, i.e., maintaining the prescribed speed, using the lane correctly, and safely merging and exiting ramps). If the standard is met, the fourth teaching stage is unlocked. If the standard is not met, the training continues to be strengthened. In the fourth teaching stage, the accuracy rate of operation is calculated using data from complex road environments such as national highways with mixed vehicle types, narrow village roads / old residential areas, night driving, and congested road sections.

[0050] Optionally, in one embodiment of this application, after obtaining the current state data of the vehicle and the image data of the user, the method further includes: determining whether the vehicle meets the preset collision conditions based on the current state data; and controlling the vehicle to perform a preset braking action if it is determined that the vehicle meets the preset collision conditions.

[0051] When a vehicle collision is detected, the embodiments of this application can immediately take emergency braking measures, apply maximum braking force, tighten the seat belts, and bring the vehicle to a stop in the shortest distance to reduce the damage caused by the collision.

[0052] Optionally, in one embodiment of this application, the method further includes: obtaining feedback information input by the user based on the driving analysis report; uploading the feedback information to a preset server to update the multi-user historical feedback information of the corresponding vehicle model in the preset server, and updating the teaching mode of the corresponding vehicle model based on the updated multi-user historical feedback information.

[0053] This application embodiment can set up a feedback entry on the display interface of the driving analysis report, allowing users to ask questions or express their opinions on the report content. By regularly following up with novice drivers, this application embodiment can obtain new problems encountered by users in subsequent driving processes. Based on the opinions collected from novice feedback channels and the problems obtained from follow-up visits, the information can be sorted and classified to improve the way teaching scenarios and teaching guidance content are output in a targeted manner, thereby generating a teaching model for this vehicle model and this user.

[0054] Combination Figures 2 to 5 As shown, the working principle of the embodiments of this application will be explained in detail in multiple stages.

[0055] The embodiments of this application can be divided into four stages: teaching model generation stage, teaching model implementation stage, teaching stage evaluation stage, and review and optimization stage.

[0056] like Figure 2 As shown, in the teaching mode generation stage, the embodiments of this application may include the following steps: Step S1: Obtain the vehicle's basic parameters. In this embodiment, the vehicle's basic parameters, such as model configuration and body dimensions, can be obtained based on the vehicle's VIN code.

[0057] Step S2: Data is transmitted to the server to match the vehicle model database and the user database.

[0058] Based on the basic parameters, the embodiments of this application can be encrypted and uploaded to the server, and the corresponding vehicle model database and user database can be matched from the server to obtain detailed vehicle model data and multi-user historical feedback information, thereby generating a highly targeted data set.

[0059] Step S3: Teaching mode based on data feedback from the server.

[0060] Based on the data fed back from the server, this application embodiment can generate a basic teaching mode and optimize the focus based on user feedback, so that the generated teaching mode can better match the characteristics of the vehicle model.

[0061] like Figure 3 As shown, the implementation phase of the teaching model includes the following steps: Step S1: Determine the current teaching stage. This embodiment of the application can assess the user's current teaching stage based on the user's historical learning data.

[0062] Step S2: Determine the target teaching site. Based on the current teaching stage and the vehicle's current location, this embodiment of the application can filter out nearby road sites that meet the requirements of the teaching scenario, and then sort and display the filtered results according to factors such as traffic volume and distance for the user to select.

[0063] Step S3: Generate learning tasks. In this embodiment, corresponding learning tasks can be generated based on the teaching venue and the objectives of the teaching stage.

[0064] Step S4: Obtain the vehicle's current status data and the user's image data to monitor user operations and provide tiered correction prompts for the operations. This embodiment of the application can calculate the user's operation accuracy rate using the current status data and image data, and provide timely reminders when operation errors occur.

[0065] Step S5: Emergency situation triggers active safety actions. When a collision is detected, emergency braking measures can be taken immediately, applying maximum braking force and tightening seat belts to bring the vehicle to a stop in the shortest possible distance, thereby reducing the damage caused by the collision.

[0066] Step S6: Generate a report. After completing this training task, conduct a comprehensive analysis of all data from this driving process, and present the results on the screen in the form of charts and text.

[0067] like Figure 4 As shown, the assessment phase of the teaching process includes the following steps: Step S1: Evaluate the teaching stage and weaknesses based on the user's historical learning data. Before starting driving training, this embodiment of the application can conduct in-depth analysis of the collected operational data based on the user's historical learning data and / or previous driving data, judge whether the user's current driving skills meet a certain level standard, identify weaknesses, and then improve subsequent learning tasks based on the evaluation results to create a personalized teaching model.

[0068] Step S2: First teaching phase completed. In the first teaching phase, the target teaching area is determined to be a car-free road surface. Based on data such as vehicle speed, distance, and steering wheel angle, the operation is judged to meet the standards, and the operation accuracy rate is calculated. The following criteria are met: error rate ≤10% for sudden acceleration / deep braking at low speeds (≤20km / h), lane changing success rate ≥90%, driving mileage greater than 100km, and accelerator / brake mis-pressing rate. Meeting these standards unlocks the second teaching phase; failing to meet them requires continued intensive training.

[0069] Step S3, completion of the second teaching phase. In the second teaching phase, the target teaching site is determined to be a road with traffic flow exceeding a preset threshold. By detecting data such as vehicle speed, distance, steering wheel angle, and handling of lane-cutting situations, it is determined whether the training actions meet the standards, and the operation accuracy rate is calculated. Based on the driving skill requirements for roads with high traffic flow, such as following distance, timing of lane changes, and reaction ability to emergencies, the third teaching phase is unlocked if the standards are met; otherwise, intensive training continues.

[0070] Step S4, completion of the third teaching phase. In the third teaching phase, the target teaching site is determined to be an elevated road. By checking data such as vehicle speed, distance, steering wheel angle, and merging / exiting ramps, it is determined whether the operation conforms to the specifications, and the operation accuracy rate is calculated (the relevant skill standards for driving on ordinary elevated roads, i.e., maintaining the prescribed speed, using the lane correctly, and safely merging / exiting ramps, etc.). If the standard is met, the fourth teaching phase is unlocked; if the standard is not met, intensive training continues.

[0071] Step S5, completion of the fourth teaching stage. In the fourth teaching stage, the target teaching site is determined to be a road surface that meets the preset mixed conditions. By using data from complex road environments such as national highways with mixed vehicle types, narrow village roads / old residential areas, night driving, and congested sections, the operation accuracy is calculated to assess whether the user can complete the teaching.

[0072] Step S6: Complete the training.

[0073] like Figure 5 As shown, the debriefing and optimization phase includes the following steps: Step S1: Generate a report. After each training session, the user's behavioral patterns in various driving scenarios are analyzed, and a report is generated. The report presents the strengths and weaknesses in the form of charts and graphs, and proposes corresponding improvement measures so that the user can clearly see their driving level.

[0074] Step S2: Provide improvement suggestions. Based on the preset multi-dimensional evaluation system, provide detailed improvement suggestions for each weak link, such as attention dimension (determining the user's level of attention concentration through facial features), observation dimension (whether the user observes the rearview mirror, left and right road conditions, etc.), and operation standardization dimension.

[0075] Step S3: Collect user feedback. A feedback entry point is set up on the report display interface, allowing users to ask questions or express their opinions about the report's content. Users are regularly followed up to understand any new issues they encounter during subsequent driving.

[0076] Step S4: Adjust the teaching model. Organize and categorize the opinions collected from feedback channels and the problems identified during follow-ups, and make targeted improvements to the output teaching model.

[0077] The driving instruction method proposed in this application utilizes vehicle identification to match corresponding vehicle model data from a server. It then identifies multiple user-uploaded feedback information for that vehicle model to determine a matching instruction mode. By analyzing the user's learning progress, it determines the user's learning stage within the vehicle-matched instruction mode, enabling the selection of instructional sites and the generation of learning tasks to guide the user in learning the driving content for that stage. Furthermore, by acquiring vehicle status data and extracting image data of the user's driving behavior during learning, it assesses whether errors were made during training, evaluating the user's learning outcomes and generating a driving analysis report. This allows the user to correct errors later, enabling novice drivers to accumulate adaptive driving experience using their own vehicles and reducing their anxiety. This addresses the problems in related technologies where driving instruction has poor applicability, and autonomous driving or driver assistance functions struggle to guarantee driving safety in extreme road conditions, hindering the development of novice drivers' skills.

[0078] Next, the driving instruction device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0079] Figure 6 This is a block diagram of a driving instruction device according to an embodiment of this application.

[0080] like Figure 6 As shown, the driving instruction device 10 includes: Specifically, the upload module 100 is used to upload the vehicle identification and user identification to a preset server, so that the preset server can determine the vehicle model based on the vehicle identification and match the corresponding multi-user historical feedback information based on the model.

[0081] The receiving module 200 is used to receive the model and multi-user historical feedback information from the preset server, determine the teaching mode of the vehicle, and determine the current teaching stage of the user in the teaching mode based on the user's historical learning data.

[0082] The generation module 200 is used to determine the target teaching venue in response to the current teaching stage and generate corresponding learning tasks to guide the user to perform corresponding training actions.

[0083] The evaluation module 300 is used to acquire the current state data of the vehicle and the image data of the user to evaluate the user's training actions, obtain evaluation results, and generate a driving analysis report for the user based on the evaluation results.

[0084] Optionally, in one embodiment of this application, the driving instruction device 10 further includes: The first judgment module is used to determine whether the user's training actions meet the preset standard of the learning task based on the current state data and image data.

[0085] The determination module is used to determine the user's action error level based on the current status data and image data when the preset standard is not met.

[0086] The matching module is used to match corresponding error correction reminder actions based on the error level of the action.

[0087] Optionally, in one embodiment of this application, the evaluation module 300 includes: The calculation unit is used to calculate the user's operation accuracy based on the current status data, image data, and preset standard parameters.

[0088] The evaluation unit is used to assess the user's training actions based on the accuracy of the operation and to update the user's historical learning data.

[0089] Optionally, in one embodiment of this application, the driving instruction device 10 further includes: The second judgment module is used to determine whether the vehicle meets the preset collision conditions based on the current state data.

[0090] The control module is used to control the vehicle to perform a preset braking action when it is determined that the vehicle meets the preset collision conditions.

[0091] Optionally, in one embodiment of this application, the generation module 200 includes: The first determining unit is used to determine the target teaching site as a vehicle-free road site when the current teaching stage is the first teaching stage.

[0092] The second determining unit is used to determine the target teaching site as a road site with traffic flow greater than a preset threshold when the current teaching stage is the second teaching stage.

[0093] Given that the current teaching stage is the third stage, the target teaching site is determined to be the elevated road area.

[0094] The third determination unit is used to confirm that the target teaching site is a road site that meets the preset mixed conditions when the teaching stage is the fourth teaching stage.

[0095] Optionally, in one embodiment of this application, the driving instruction device 10 further includes: The acquisition module is used to acquire feedback information from users based on the driving analysis report.

[0096] The update module is used to upload feedback information to a preset server to update the multi-user historical feedback information of the corresponding vehicle model in the preset server, and to update the teaching mode of the corresponding vehicle model based on the updated multi-user historical feedback information.

[0097] It should be noted that the foregoing explanation of the driving teaching method embodiment also applies to the driving teaching device of this embodiment, and will not be repeated here.

[0098] The driving instruction device proposed in this application can utilize vehicle identification to match corresponding vehicle model data from a server. Based on the vehicle model, it identifies multiple user-uploaded feedback information for that model to determine a matching instruction mode. By analyzing the user's learning progress, it determines the user's learning stage within the matching instruction mode, thereby selecting a learning location and generating learning tasks to guide the user in learning the driving content for that stage. Furthermore, by acquiring vehicle status data and extracting image data of the user's driving behavior during learning, it determines whether the user made any errors during training, evaluating the user's learning outcomes and generating a driving analysis report to help the user correct these errors. This allows novice drivers to accumulate adaptive driving experience using their own vehicles, reducing their anxiety. Therefore, it solves the problems in related technologies where driving instruction has poor applicability, and autonomous driving or driver assistance functions cannot guarantee driving safety in extreme road environments, hindering the development of novice drivers' skills.

[0099] Figure 7 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 701, the processor 702, and the computer program stored on the memory 701 and executable on the processor 702.

[0100] When the processor 702 executes the program, it implements the driving instruction method provided in the above embodiments.

[0101] Furthermore, the vehicle also includes: Communication interface 703 is used for communication between memory 701 and processor 702.

[0102] The memory 701 is used to store computer programs that can run on the processor 702.

[0103] The memory 701 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0104] If the memory 701, processor 702, and communication interface 703 are implemented independently, then the communication interface 703, memory 701, and processor 702 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0105] Optionally, in a specific implementation, if the memory 701, processor 702, and communication interface 703 are integrated on a single chip, then the memory 701, processor 702, and communication interface 703 can communicate with each other through an internal interface.

[0106] The processor 702 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0107] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the driving instruction method described above.

[0108] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the driving instruction method provided in this embodiment of the invention.

[0109] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0111] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0113] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0114] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0115] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0116] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A driving instruction method, characterized in that, Includes the following steps: The vehicle identification and user identification are uploaded to a preset server, so that the preset server can determine the vehicle model based on the vehicle identification and match the corresponding multi-user historical feedback information based on the model. Receive the model and multi-user historical feedback information from the preset server, determine the teaching mode of the vehicle, and determine the current teaching stage of the user under the teaching mode based on the user's historical learning data; In response to the current teaching stage, a target teaching venue is determined, and corresponding learning tasks are generated to guide the user to perform corresponding training actions; The current status data of the vehicle and the image data of the user are acquired to evaluate the user's training actions, obtain evaluation results, and generate a driving analysis report for the user based on the evaluation results.

2. The method according to claim 1, characterized in that, After acquiring the current status data of the vehicle and the image data of the user, the method further includes: Based on the current state data and the image data, it is determined whether the user's training actions meet the preset standard of the learning task; If the preset standard is not met, the user's action error level is determined based on the current state data and the image data. The corresponding error correction reminder action is matched based on the error level of the action.

3. The method according to claim 2, characterized in that, The process of acquiring the current state data of the vehicle and the image data of the user to evaluate the user's training actions and obtain evaluation results includes: The user's operation accuracy is calculated based on the current status data, the image data, and the preset standard. The accuracy of the operation is used to evaluate the user's training actions and update the user's historical learning data.

4. The method according to claim 1, characterized in that, After acquiring the current status data of the vehicle and the image data of the user, the method further includes: Based on the current state data, determine whether the vehicle meets the preset collision conditions; If the vehicle is determined to meet the preset collision conditions, the vehicle is controlled to perform a preset braking action.

5. The method according to claim 1, characterized in that, The response to the current teaching stage, determining the target teaching venue, includes: If the current teaching stage is the first teaching stage, the target teaching site is determined to be a vehicle-free road site; When the current teaching stage is the second teaching stage, the target teaching site is determined to be a road site with traffic flow greater than a preset threshold. In the case that the current teaching stage is the third teaching stage, the target teaching site is determined to be the elevated road site; In the case that the teaching stage is the fourth teaching stage, the target teaching site is indeed a road site that meets the preset mixed conditions.

6. The method according to claim 1, characterized in that, Also includes: Obtain feedback information from the user based on the driving analysis report; The feedback information is uploaded to the preset server to update the multi-user historical feedback information of the corresponding vehicle model in the preset server, and the teaching mode of the corresponding vehicle model is updated based on the updated multi-user historical feedback information.

7. A driving instruction device, characterized in that, include: The upload module is used to upload the vehicle identification and user identification to a preset server, so that the preset server can determine the vehicle model based on the vehicle identification and match the corresponding multi-user historical feedback information based on the model. The receiving module is used to receive the model and the multi-user historical feedback information fed back by the preset server, determine the teaching mode of the vehicle, and determine the current teaching stage of the user in the teaching mode based on the user's historical learning data. The generation module is used to determine the target teaching venue in response to the current teaching stage and generate corresponding learning tasks to guide the user to perform corresponding training actions. The evaluation module is used to acquire the current state data of the vehicle and the image data of the user to evaluate the user's training actions, obtain evaluation results, and generate a driving analysis report for the user based on the evaluation results.

8. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the driving instruction method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the driving instruction method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the driving instruction method as described in any one of claims 1-6.

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