Personalized exercise item recommendation method and system for driving test training

Abstract

The invention discloses a personalized exercise item recommendation method and system for driving test training, and relates to the technical field of computer-aided education training, and the method comprises the steps: building a plurality of simulation driving scenes, and enabling students to carry out simulation training in the scenes through employing a VR driving simulator. In the training process, a simulation sensor is activated to collect operation data of a trainee, and an operation data set is formed. And carrying out data mapping, inputting a mapping result into a driving analysis channel, and carrying out response decision analysis and operation behavior analysis. The driving weak links of the trainee are identified, and the personalized exercise items are generated by matching the basic operation skills with the driving scene, so that the driving ability of the trainee is improved in a targeted manner. The technical problem that personalized practice item matching cannot be carried out according to the actual driving performance and operation data of the trainee in an existing driving test training method is solved, and the technical effects of improving the pertinence and effectiveness of driving test training and improving the overall driving ability are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of computer-assisted education and training, and in particular to a method and system for recommending personalized practice items for driving test training. Background Art

[0002] With the development of the transportation industry and the increasing number of motor vehicles, driving training plays a vital role in improving driver skills and ensuring road traffic safety. Traditional driving training methods mainly rely on the instructor's experience and guidance and fixed practice items, which often lack flexible adjustments for individual differences of trainees and make it difficult to comprehensively evaluate and improve trainees' driving abilities. In addition, although driving simulators provide a simulated driving environment to a certain extent, their data collection and analysis methods are relatively basic and cannot capture trainees' operating behaviors and reaction decisions in real time and accurately in different driving situations. As a result, during the training process, it is difficult for trainees' driving weaknesses to be identified and improved in a targeted manner, which affects the training effect and the improvement of driving skills. With the diversification and efficiency of driving training needs, the limitations of traditional methods have gradually emerged, and it is urgent to explore more scientific and accurate driving training methods to improve the quality and effect of driving training and meet the requirements of modern driving training for efficient, accurate and personalized training.

[0003] At present, there is a technical problem in the relevant technologies that the driving test training method cannot match personalized practice items according to the students' actual driving performance and operation data. Summary of the invention

[0004] This application solves the technical problem that the existing driving test training methods cannot match personalized practice items according to the students' actual driving performance and operation data by providing a personalized practice item recommendation method and system for driving test training.

[0005] This application provides a personalized practice project recommendation method for driving test training, including:

[0006] A simulated driving scenario is established, and a VR driving simulator is used to perform scenario simulation training for trainees based on the simulated driving scenario; a simulation sensor of the VR driving simulator is activated, and data for scenario simulation training is collected using the simulation sensor to establish an operation data set; after data mapping between the simulated driving scenario and the operation data set is established, the data mapping result is input into a driving analysis channel to perform driving analysis, and a driving analysis result is generated, wherein the driving analysis includes reaction decision analysis and operation behavior analysis; driving weakness data is extracted according to the driving analysis result, and basic operation skills and driving scenarios are matched using the driving weakness data, and personalized practice items are generated according to the matching result.

[0007] This application also provides a personalized practice project recommendation system for driving test training, including:

[0008] A simulated driving scenario construction module, the simulated driving scenario construction module is used to establish a simulated driving scenario, and use a VR driving simulator to perform scenario simulation training for trainees based on the simulated driving scenario; a data acquisition module, the data acquisition module is used to activate the simulation sensor of the VR driving simulator, use the simulation sensor to collect data for scenario simulation training, and establish an operation data set; a driving analysis module, the driving analysis module is used to establish a data mapping between the simulated driving scenario and the operation data set, and then input the data mapping result into a driving analysis channel for driving analysis to generate a driving analysis result, wherein the driving analysis includes a reaction decision analysis and an operation behavior analysis; a personalized practice item generation module, the personalized practice item generation module is used to extract driving weakness data according to the driving analysis result, and use the driving weakness data to match basic operation skills and driving scenarios, and generate personalized practice items according to the matching results.

[0009] The personalized practice project recommendation method and system for driving test training proposed in this application first establishes multiple simulated driving scenarios and uses a VR driving simulator to allow trainees to conduct simulated training in these scenarios. During the training process, the simulation sensor is activated to collect the trainee's operation data to form an operation data set. The simulated driving scenario and the operation data set are data mapped, and the mapping results are input into the driving analysis channel for reaction decision analysis and operation behavior analysis. According to the driving analysis results, the trainee's driving weaknesses are identified, and personalized practice projects are generated by matching basic operating skills with driving scenarios to improve the trainee's driving ability in a targeted manner, thereby achieving the technical effect of improving the pertinence and effectiveness of driving test training and improving overall driving ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0011] Figure 1 A flowchart of a method for recommending personalized practice items for driving test training provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the structure of a personalized practice item recommendation system for driving test training provided in an embodiment of the present application.

[0013] Description of reference numerals: simulated driving scenario construction module 10 , data collection module 20 , driving analysis module 30 , personalized practice item generation module 40 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The present application embodiment provides a personalized practice item recommendation method for driving test training, such as Figure 1 As shown, the method includes:

[0018] Step S100, establish a simulated driving scenario, and use a VR driving simulator to perform scenario simulation training for students based on the simulated driving scenario. Specifically, use professional software (such as Unity) to combine multiple types of data to build a simulated driving scenario, including multiple road types, surrounding buildings, traffic facilities and different weather conditions; VR driving simulator equipped with high-resolution display, motion tracking system and force feedback control components; after the student enters, start the training, the simulator runs according to the scenario, the student uses the display and audio to feel the environment, and the control components to perform routine and emergency driving operations, and the data is recorded for subsequent analysis and evaluation.

[0019] Step S200, activate the simulation sensor of the VR driving simulator, use the simulation sensor to collect data for scene simulation training, and establish an operation data set. Specifically, the trainee conducts scene simulation training in the VR driving simulator, activates the simulation sensors therein, which include motion capture sensors (which can accurately record the steering wheel rotation angle, speed, direction change, pedal stepping depth, strength, duration, etc.), visual tracking sensors (tracking the direction of sight and focus, reflecting driving attention and observation habits) and environmental perception sensors (collecting environmental and interactive data such as bumpy road vibration, bad weather visibility and trainee response operations), and collaboratively collect data. After classification, coding conversion, and format unification, a comprehensive and detailed operation data set is established as an important basis for subsequent evaluation of driving ability and formulation of personalized plans.

[0020] Step S300, after establishing the data mapping between the simulated driving scenario and the operation data set, the data mapping result is input into the driving analysis channel for driving analysis to generate the driving analysis result, wherein the driving analysis includes reaction decision analysis and operation behavior analysis. Specifically, after establishing the data mapping between the simulated driving scenario and the operation data set and inputting into the driving analysis channel, reaction decision analysis is performed, i.e., the calibrated decision data such as the action selection and time information of the trainee when the scene such as the sudden lane change of the front vehicle is extracted from the data mapping result, and the decision authentication unit searches for similar scene decision data in the operation data set for consistency authentication based on this, such as the judgment of the similarity of the decision and operation of the trainee in the scene of multiple lane changes, and the timeliness authentication of the time from the trainee's discovery of the situation to the operation in the case of emergency braking is performed according to the reaction time required by the safe driving specification, and the decision authentication result is obtained; at the same time, the operation behavior analysis sub-channel sequentially performs the operation execution sequence, interruption, repetition, and excessive operation authentication on the operation data set, and completes the operation behavior authentication by combining the authentication results of each behavior, and finally generates an accurate driving analysis result by synergistically integrating the results of reaction decision and operation behavior analysis, providing a key basis for the subsequent.

[0021] In a possible implementation, after establishing the data mapping between the simulated driving scene and the operation data set, the data mapping result is input into the driving analysis channel for driving analysis to generate a driving analysis result, wherein the driving analysis includes reaction decision analysis and operation behavior analysis, and step S300 further includes step S310, calling the reaction decision subchannel of the driving analysis channel, and extracting the calibration decision data of the simulated driving scene in the data mapping result through the reaction decision subchannel. Specifically, the reaction decision subchannel of the driving analysis channel is called, and the subchannel deeply analyzes the data mapping result to extract the calibration decision data of the simulated driving scene. For example, in the simulated driving scene, when encountering various situations such as traffic light changes, pedestrians crossing the road, and other vehicles suddenly changing lanes or cutting in, the reaction decision subchannel will accurately capture the specific decisions made by the trainee in the scene, such as selecting operation actions such as braking, accelerating through, steering avoidance, and the precise time node of the action. The captured data is the calibration decision data, which constitutes the basic material for the subsequent analysis of the trainee's reaction decision-making ability.

[0022] Step S320, activate the decision authentication unit of the reaction decision subchannel, synchronize the calibration decision data to the decision authentication unit, and use the decision authentication unit to perform decision authentication of the operation data set to establish a decision authentication result. Specifically, activate the decision authentication unit of the reaction decision subchannel, and synchronize the extracted calibration decision data to the decision authentication unit. The decision authentication unit starts to perform decision authentication on the operation data set. In terms of decision consistency authentication, the operation data set will be comprehensively sorted out to find the decision-making situation of the trainees in similar simulated driving scenarios. For example, when encountering pedestrians crossing the road many times, compare whether the trainees have taken the same or similar response decisions, such as whether they all stably choose to brake and decelerate, and the intensity and timing of braking are relatively consistent. If the decision changes greatly, it indicates that the trainee's decision lacks stability and standardization. In terms of decision timeliness authentication, judgment is made based on the time information in the calibration decision data and the pre-set safe driving standard time model. For example, for emergency braking scenarios, the safe driving standard stipulates that a response should be made in a very short time (such as 0.3 to 0.5 seconds) at a specific speed and the braking operation should be started. The decision-making certification unit will check whether the actual time taken by the trainee to make a braking decision meets this standard. If it exceeds the standard time by too much, it means that the trainee has deficiencies in the timeliness of decision-making. Through these two aspects of certification work, the decision-making certification results are finally established, which reflect in detail the accuracy, consistency and timeliness of the trainee in the reaction decision-making process.

[0023] Step S330, performing driving analysis based on the decision authentication results. Specifically, performing driving analysis based on the decision authentication results. The analyst or intelligent analysis system will conduct an in-depth study of the various data indicators in the decision authentication results. If the decision authentication results show that the student's decision consistency in certain scenarios is poor, it may reflect that the student's mastery of driving rules and response strategies is not proficient enough, and the theoretical learning and simulation training of relevant scenarios need to be strengthened. If the decision is not timely enough, it reflects that the student's reaction speed and emergency handling capabilities need to be improved, and reaction speed training and special training for emergency driving scenarios can be carried out in a targeted manner. Through a comprehensive analysis of the decision authentication results, the weaknesses of the student in the driving reaction decision-making link can be accurately identified, thereby providing a key basis for formulating personalized driving training programs to improve the student's overall driving skills.

[0024] In a possible implementation, the decision authentication unit of the reaction decision subchannel is activated, and after the calibration decision data is synchronized to the decision authentication unit, the decision authentication unit is used to perform decision authentication of the operation data set to establish a decision authentication result. Step S320 further includes step S321, using the calibration decision data to perform decision consistency authentication on the operation data set to establish a first decision authentication result. Specifically, the extracted calibration decision data is used to carry out decision consistency authentication on the operation data set. The calibration decision data records various decision behaviors made by the trainee in different simulated driving scenarios, such as whether to choose emergency braking or evasive steering when facing a sudden pedestrian, and the operations taken according to the state of the traffic light when passing through an intersection. When the consistency authentication of the operation data set is performed, the calibration decision data will be compared one by one with the decision records in the same or similar scenarios in the operation data set. For example, if in multiple simulated pedestrian crossing scenes, the calibration decision data shows that the trainee mostly took emergency braking operations, then the actual decision behaviors of the trainee in these scenarios are searched in the operation data set. If it is found that the trainees also performed emergency braking operations in most similar scenarios in the operation data set, and the parameters such as the timing and force of braking are in a relatively similar reasonable range, it indicates that the trainees' decisions in such scenarios are highly consistent; on the contrary, if there are many different decision-making behaviors in the operation data set, such as sometimes braking and sometimes avoiding without obvious rules or rationality, then it means that the decision consistency is low. After a comprehensive and detailed comparison and analysis, the first decision certification result is established, which can clearly reflect the stability and standardization of the trainees' decisions in different driving scenarios.

[0025] Step S322, obtain the operation time of the operation data set, perform decision timeliness authentication according to the operation time, and establish a second decision authentication result. Specifically, obtain the operation time information of the operation data set to perform decision timeliness authentication. The operation time covers the entire time from the student's perception of the change in the driving scene to making the corresponding decision and starting to execute the operation. For example, in the scenario where the vehicle in front suddenly brakes during the simulated driving process, the operation data set will record the time interval from the moment the vehicle starts to brake to the moment the student notices this situation and starts to step on the brake pedal. The operation time is compared with the pre-set standard time model for safe driving, which is formulated based on a large amount of actual driving data and safety specifications, and specifies a reasonable reaction time range for different driving scenarios and operation types. For example, in the above-mentioned scenario where the vehicle in front suddenly brakes, if the standard time model stipulates that the braking reaction should be made within 0.5 seconds at a specific speed, and the student's reaction time in the operation data set is within 0.4 seconds, it indicates that the student's decision timeliness in this scenario is good; conversely, if the student's reaction time exceeds 0.6 seconds, it means that the decision timeliness is insufficient. By analyzing and comparing the operation time in various scenarios in the operation data set, a second decision certification result is established, which can accurately reflect the student's reaction speed and decision-making efficiency during the driving process.

[0026] Step S323, establishing the decision authentication result according to the first decision authentication result and the second decision authentication result. Specifically, a complete decision authentication result is established based on the first decision authentication result and the second decision authentication result. The decision consistency reflected by the first decision authentication result and the decision timeliness reflected by the second decision authentication result are both important dimensions for evaluating the driving decision-making ability of the trainee. By integrating the two, the decision-making performance of the trainee in the simulated driving process can be fully understood. For example, if the first decision authentication result shows that the trainee has a high decision consistency in most scenarios, but the second decision authentication result shows that its decision timeliness is poor, this means that although the trainee can make decisions more standardized, the reaction speed is slow, and it may be necessary to conduct targeted reaction speed training; on the contrary, if the decision consistency is low and the timeliness is not good, then the trainee needs to be comprehensively trained in driving decision rules and reaction ability improvement training. Through such a comprehensive analysis of the two decision authentication results, the established decision authentication result can provide an accurate and comprehensive basis for subsequent driving analysis and personalized training program formulation.

[0027] In a possible implementation, driving analysis is performed according to the decision authentication result, and step S330 further includes step S331, activating the operation behavior analysis subchannel of the reaction decision subchannel, using the operation behavior analysis subchannel to perform operation behavior authentication of the operation data set, and establishing the operation behavior authentication result. Specifically, the operation behavior analysis subchannel of the reaction decision subchannel is activated. The subchannel performs operation behavior authentication on the operation data set. A detailed analysis is carried out for the driving behavior represented by each data point in the operation data set. For example, in terms of the operation execution sequence, it is checked whether the trainee follows the standard process when starting the vehicle, first fastens the seat belt, then steps on the clutch (manual transmission vehicle) or confirms the gear position (automatic transmission vehicle), then ignites and starts, and finally slowly releases the handbrake and lightly steps on the accelerator to start. If there are operation records that violate this sequence in the data, such as starting the vehicle without fastening the seat belt or improper coordination between the gear position and the accelerator when starting, they will be recorded for evaluating the accuracy of the operation execution sequence. In terms of operation interruption identification, the subchannel will search for unreasonable driving behavior interruptions in the operation data set. For example, during normal straight-line driving, suddenly releasing the steering wheel for a long time, or stopping the accelerator for no reason during acceleration and the time exceeds the normal waiting time for a red light or temporary parking, etc., abnormal interruption behaviors, once detected, will become the key basis for evaluating the operation interruption situation. For repeated operation identification, the operation behavior analysis sub-channel will count whether the trainee frequently repeats certain driving operations. For example, meaninglessly switching vehicle gears many times in a short period of time, or repeatedly tapping the brakes without obvious road conditions, etc., unnecessary repeated operation data will be accurately captured and analyzed for their frequency of occurrence and possible impact. In the excessive operation identification link, the analysis sub-channel will determine whether the trainee has performed driving operations beyond the reasonable range based on vehicle performance parameters and normal driving specifications. For example, when turning, the steering wheel turns too much, exceeding the angle range required for safe and efficient turning, or when accelerating, the accelerator is stepped on too deeply, causing the vehicle speed to increase too high instantly, far exceeding the speed allowed by the current road environment and traffic rules, etc., will be regarded as excessive operations and recorded. After comprehensive testing and analysis of all the above aspects, the operational behavior certification results were finally established. The results reflect in detail the standardization, stability and rationality of the trainees' vehicle control during the driving operation.

[0028] Step S332, driving analysis is performed according to the operation behavior authentication result and the decision authentication result. Specifically, after obtaining the operation behavior authentication result and the previous decision authentication result, driving analysis is started. During the analysis process, the various indicators in the operation behavior authentication result and the decision authentication result are first compared. If the operation behavior authentication result shows that the trainee has many errors in the operation execution sequence, and the decision authentication result shows that the trainee performs well in terms of consistency and timeliness of decision-making, it means that although the trainee can make driving decisions in a timely and accurate manner, he lacks proficiency in the vehicle operation process and needs to strengthen the training and practice of basic driving operation specifications. On the contrary, if the decision authentication result shows that the trainee's decision-making ability is weak, for example, hesitant or frequent mistakes in decision-making when facing complex road conditions, and the operation behavior authentication result also shows confusion and irregularity in operation, it indicates that the trainee has a large deficiency in overall driving ability and needs to conduct a comprehensive review of driving theory knowledge, intensive training of simulated driving scenarios, and one-on-one driving guidance to improve his driving skills and ability to cope with various road conditions. Comprehensively analyze the detailed data in the two certification results. For example, in certain specific scenarios, whether the student's decision-making errors are accompanied by specific abnormal operating behaviors, such as dangerous operating behaviors caused by decision-making errors in overtaking scenarios. Through in-depth correlation analysis, we can accurately find out the weak links and root causes of problems in the student's driving process, thereby providing a highly targeted basis for formulating personalized driving training plans, helping students quickly improve their driving level and ensure driving safety.

[0029] In a possible implementation, the operation behavior analysis subchannel of the reaction decision subchannel is activated, and the operation behavior analysis subchannel is used to perform the operation behavior authentication of the operation data set, and the operation behavior authentication result is established. Step S331 further includes step S3311, A: the operation execution sequence of the operation data set is authenticated to establish the first behavior authentication result. Specifically, the operation execution sequence of the operation data set is authenticated. The operation data set contains a series of operation records of the trainee when training in the VR driving simulator. In this authentication process, the trainee's operation sequence will be checked one by one according to the standard driving operation process. For example, at the start of the vehicle, the normal operation sequence should be to fasten the seat belt first, ensure that the gear is in the appropriate position (manual gear is in neutral or first gear, automatic gear is in P gear or D gear), then start the vehicle, release the handbrake after observing the surrounding environment, and then slowly step on the accelerator pedal to start the vehicle smoothly. The authentication system will search the operation data set for various operation data of the trainee at the start to check whether this sequence is strictly followed. If the trainee first steps on the accelerator pedal and then fastens the seat belt or starts the vehicle without correctly shifting the gear, etc., the data will be recorded in detail. After a comprehensive inspection of all parts involving the operation sequence in the entire operation data set, the first behavior certification result is established. This result clearly reflects the accuracy and standardization of the trainees in the driving operation sequence, and provides an important basis for the subsequent evaluation of the trainees' driving operation habits.

[0030] Step S3312, B: Perform operation interruption identification on the operation data set and establish a second behavior authentication result. Specifically, perform operation interruption identification on the operation data set. During driving, some unreasonable operation interruptions may affect driving safety and fluency. For example, when the vehicle is driving normally in a straight line, the steering wheel should maintain a relatively stable operating state, but if the data set shows that the trainee suddenly releases the steering wheel for a long time without special road conditions (such as avoiding obstacles or following traffic signs), this is an operation interruption. Similarly, during acceleration or deceleration, if the trainee stops pedaling for no reason and the time exceeds the range allowed by normal driving logic, such as suddenly not stepping on the accelerator or brake for a long time when driving at high speed, it will also be regarded as an operation interruption. The system will conduct a comprehensive scan of the operation data set, count and analyze all such operation interruptions, determine the frequency, duration, and driving scenario in which they occur, and then establish a second behavior authentication result, which can intuitively show the trainee's operation continuity during driving, and help to discover the trainee's possible lack of concentration or unstable control of driving operations.

[0031] Step S3313, C: Repeated operation identification is performed on the operation data set to establish a third behavior authentication result. Specifically, repeated operation identification is performed on the operation data set. In driving operations, some unnecessary repeated operations may imply that the trainee is not proficient in driving actions or has bad driving habits. For example, in manual transmission vehicle driving, if the data set frequently shows that the trainee switches gears meaninglessly many times in a short period of time, such as switching from second gear to third gear and then quickly switching back to second gear, and there is no corresponding road condition requirement (such as climbing, overtaking, etc.) as a basis, this is a typical repeated operation. For automatic transmission vehicles, repeated and rapid switching between D gear and N gear or frequent light braking while the road condition is stable will also be identified. By deeply mining the operation data set, the type, number, and time interval of these repeated operations are counted, and the third behavior authentication result is established. The result can help analyze whether the trainee can accurately control the driving operation and whether there are redundant actions caused by tension or other reasons, thereby providing a reference for correcting the trainee's driving behavior in a targeted manner.

[0032] Step S3314, D: perform excessive operation identification on the operation data set, and establish the fourth line as the authentication result. Specifically, perform excessive operation identification on the operation data set. Driving operations need to follow certain specifications and limits, and excessive operation may cause the vehicle to lose control or drive unstably. Taking the steering wheel operation as an example, if the data shows that the angle at which the trainee turns the steering wheel far exceeds the reasonable angle range required to complete the turn when turning, it may cause the vehicle to oversteer, which is an excessive operation. In terms of acceleration, if the trainee steps on the accelerator pedal too hard, causing the vehicle to accelerate too high instantly, exceeding the speed allowed by the current road environment and traffic rules, or excessive force is applied when braking, causing the vehicle to stop suddenly and the tires to lock, etc., they are also excessive operations. The system will analyze the operation data set according to the vehicle's performance parameters and normal driving standards, identify all instances of excessive operations, and record the scene in which they occur, the operation amplitude and other information, and establish the fourth line as the authentication result, which can reflect the trainee's mastery of the vehicle's control strength and the ability to control driving risks.

[0033] Step S3315, the operation behavior authentication is completed according to the first behavior authentication result, the second behavior authentication result, the third behavior authentication result, and the fourth behavior authentication result. Specifically, the operation behavior authentication is completed according to the first behavior authentication result, the second behavior authentication result, the third behavior authentication result, and the fourth behavior authentication result. These four behavior authentication results analyze the student's driving operation behavior from different dimensions. The student's operation behavior performance during driving will be comprehensively and systematically evaluated. For example, if the first behavior authentication result shows that there are many errors in the operation sequence, the second behavior authentication result shows that the operation is frequently interrupted, the third behavior authentication result shows a large number of repeated operations, and the fourth behavior authentication result indicates that excessive operations occur from time to time, then it can be judged that the student has more serious problems in driving operation skills and habits, and comprehensive training and correction are required. On the contrary, if the behavior authentication results are relatively good, with only a few slight abnormalities, then special training can be carried out for these specific weak links, thereby effectively improving the student's driving operation level and laying the foundation for them to achieve better results in actual driving or subsequent driving tests.

[0034] Step S400, extract driving weakness data according to the driving analysis results, and use the driving weakness data to match basic operation skills and driving scenarios, and generate personalized practice items according to the matching results. Specifically, after obtaining the driving analysis results, first extract driving weakness data, and extract data such as unstable decision-making at complex intersections, overtime response in emergency scenarios, and wrong operation sequence, interruption, repetition, and excessive operation in operation behavior analysis. Then use the weak data to match basic operation skills with driving scenarios, such as excessive steering wheel operation on curves, matching curve scenarios with steering wheel control skills, untimely decision-making in emergencies, corresponding to relevant scenarios and emergency operation skills, and matching urban road start-stop problems with corresponding scenarios and basic operation skills. Generate personalized practice items based on the matching results, such as constructing a simulated scenario to train speed adjustment operations for highway speed control problems, and setting night scenarios to focus on training observation and decision-making capabilities for weak night driving observation decisions, so as to make up for driving weaknesses and improve skills.

[0035] In a possible implementation, driving weakness data is extracted according to the driving analysis results, and the driving weakness data is used to match basic operating skills and driving scenarios, and personalized practice items are generated according to the matching results. Step S400 further includes step S410, and a monitoring feedback cycle is established according to the personalized practice items. Specifically, the monitoring feedback cycle is constructed according to the generated personalized practice items. The duration of the cycle is determined based on factors such as the complexity of the personalized practice items, the number of items, and the time required for the expected training effect. For example, if the personalized practice items mainly focus on the consolidation of basic operating skills in several specific driving scenarios, such as right-angle turns, side parking, and fixed-point climbing, and the number of exercises set for each item is 10 to 15 times, and it is expected that each exercise plus preparation and summary time is about 15 to 20 minutes, then the entire cycle may be set to 2 to 3 days. During this cycle, the frequency of practice will be clearly specified, such as 3 to 4 exercises per day, and appropriate rest intervals will be set between each exercise to ensure that the trainees have enough energy and attention to devote to training, and it also helps to gradually improve muscle memory and skills.

[0036] Step S420, the trainee's intensive training effect certification is carried out in the monitoring feedback cycle, and certification feedback is generated. Specifically, within the determined monitoring feedback cycle, the trainee's intensive training effect certification work is carried out. Through various sensors and data recording systems set in the VR driving simulator, detailed data of the trainee during each practice is accurately collected. The data covers various contents in the operation data set, such as the steering wheel rotation angle, the accuracy of speed control, the timing and strength of braking operation, etc., and also includes reaction decision data, such as the decision time and correctness of the trainee in the simulated emergency scenario. Compare and analyze the data with the target data set for the personalized practice project. For example, in the side parking project, the target data stipulates that the parallelism error between the vehicle body and the warehouse line should be within a certain range when the vehicle enters the warehouse, and the entry time should be controlled within a specific time. If the actual data of the trainee during the practice exceeds this range, it means that the training effect in this regard has not yet reached the standard. By comparing and evaluating each indicator in the entire personalized practice project one by one, certification feedback is generated, which details the information such as in which aspects the trainee has made progress, in which aspects there are still deficiencies, and the degree of gap with the expected goal.

[0037] Step S430, conduct continued reinforcement exercises for the trainee according to the certification feedback. Specifically, plan the continued reinforcement exercises for the trainee according to the generated certification feedback. If the certification feedback shows that the trainee has achieved the expected goal in the operational skills of a specific driving scenario, such as in the right-angle turn project, the vehicle's driving trajectory, speed control and steering wheel operation all meet the standard requirements, then in the subsequent continued reinforcement exercises, the exercise frequency of the project can be appropriately reduced or some more difficult variant scene exercises can be added, such as right-angle turns on narrow roads or right-angle turns with blocked vision, etc., to further improve the trainee's coping ability and skill proficiency. On the contrary, if the certification feedback shows that the trainee still has a large gap in certain projects, such as in the fixed-point climbing project, the engine often stalls or cannot stop accurately at the designated position, then it is necessary to increase the number of special exercises for these weak links and adjust the methods and focus of the exercises. For example, we can strengthen the training on the coordination between clutch and throttle, increase the fixed-point climbing exercises under different slopes and road conditions, and provide more theoretical explanations and demonstration operations to help students understand and master the correct operating techniques. By continuing to strengthen the exercises in a targeted manner, we can gradually make up for the students' deficiencies and enable them to continuously improve their driving skills until they reach the standard level required by the driving test or achieve a higher level of driving skills improvement goals.

[0038] In a possible implementation, the trainee continues to strengthen the practice according to the certification feedback, and step S430 further includes step S431, judging whether the certification feedback triggers the certification warning. Specifically, the generated certification feedback is analyzed in depth to determine whether it triggers the certification warning. The judgment process needs to be carried out according to a series of pre-set standards and thresholds. For example, when examining the reaction time index of the trainee, if the standard set by the personalized practice project is that the trainee's reaction time should be within 0.5 seconds in a specific emergency scenario, and the certification feedback shows that the trainee's average reaction time in multiple exercises has reached 0.8 seconds, and the number of times exceeding this threshold accounts for more than 30% of the total number of exercises, which may become a factor in triggering the certification warning. For example, in terms of operation accuracy, if in the side parking exercise, the parallelism error standard between the vehicle body and the warehouse line when the vehicle enters the warehouse is set to no more than 10 cm, and the certification feedback shows that the trainee's error exceeds 15 cm in most exercises, and this large error situation continues to occur, it will also be included in the analysis scope of triggering the certification warning. Through detailed comparison and comprehensive evaluation of key indicators in the certification feedback with preset standards, it is determined whether the conditions for triggering the certification warning have been met.

[0039] Step S432, if the authentication feedback triggers the authentication warning, a training abnormality report is generated. Specifically, if the authentication feedback triggers the authentication warning after the above judgment, then the generation program of the training abnormality report is immediately started. The report will comprehensively summarize the various abnormal information that caused the warning to be triggered. The report will list in detail which specific driving skill items have problems, such as the timeliness and force control of the braking operation, or the steering angle control of the steering wheel when driving on a curve. At the same time, the specific performance data of the problem will be presented, such as how many meters the braking distance exceeds the standard value, the angle range of excessive steering wheel rotation when driving on a curve, etc. In addition, the frequency of abnormal situations will be recorded, for example, in 10 simulated driving exercises, the same type of abnormal operation occurred 6 times. In addition, the report will also include the overall performance trend of the trainee throughout the practice process, such as whether the abnormal situation gradually decreases but still does not meet the standard as the number of exercises increases, or whether there is no improvement or even a trend of deterioration, etc., and a complete training abnormality report is constructed through these detailed contents.

[0040] Step S433, report the training exception report as an exception. Specifically, report the generated training exception report as an exception. This is achieved through specific information transmission channels and methods. In the scenario of driving school training, the training exception report will be sent directly to the coach's management terminal. After receiving the report, the coach can check the specific abnormal situation of the students in time to adjust the teaching strategy and give students more targeted guidance. If it is in some intelligent driving test training systems, the training exception report may be pushed to the student's personal account page, and will also be sent to the system administrator. The student can check the report by himself to understand his own problems, and the administrator can analyze the overall training effect and discover possible systemic problems in the teaching process based on the abnormal report data of a large number of students, thereby optimizing and improving the entire training plan and system settings to ensure the improvement of training quality and efficiency.

[0041] The embodiment of the present application adopts the method of establishing multiple simulated driving scenarios and using a VR driving simulator to allow trainees to conduct simulated training in these scenarios. During the training process, the simulated sensor is activated to collect the trainee's operation data to form an operation data set. The simulated driving scenario is mapped with the operation data set, and the mapping result is input into the driving analysis channel for reaction decision analysis and operation behavior analysis. According to the driving analysis results, the trainee's driving weaknesses are identified, and personalized practice items are generated by matching basic operating skills with driving scenarios to improve the trainee's driving ability in a targeted manner, thereby achieving the technical effect of improving the pertinence and effectiveness of driving test training and improving overall driving ability.

[0042] In the above, refer to Figure 1The personalized practice item recommendation method for driving test training according to an embodiment of the present invention is described in detail. Figure 2 A personalized practice item recommendation system for driving test training according to an embodiment of the present invention is described.

[0043] The personalized practice project recommendation system for driving test training according to the embodiment of the present invention is used to solve the technical problem that the existing driving test training method cannot match personalized practice projects according to the actual driving performance and operation data of the trainees, thereby achieving the technical effect of improving the pertinence and effectiveness of driving test training and improving overall driving ability. The personalized practice project recommendation system for driving test training includes: a simulated driving scenario construction module 10, a data acquisition module 20, a driving analysis module 30, and a personalized practice project generation module 40.

[0044] The simulated driving scenario construction module 10 is used to establish a simulated driving scenario and use a VR driving simulator to perform scenario simulation training for trainees based on the simulated driving scenario.

[0045] The data acquisition module 20 is used to activate the simulation sensor of the VR driving simulator, use the simulation sensor to collect data for scene simulation training, and establish an operation data set.

[0046] The driving analysis module 30 is used to establish data mapping between the simulated driving scenario and the operation data set, and then input the data mapping result into the driving analysis channel for driving analysis to generate a driving analysis result, wherein the driving analysis includes reaction decision analysis and operation behavior analysis.

[0047] The personalized practice item generation module 40 is used to extract driving weakness data according to the driving analysis results, and use the driving weakness data to match basic operating skills and driving scenarios, and generate personalized practice items according to the matching results.

[0048] The specific configuration of the driving analysis module 30 will be described in detail below. As described above, after establishing the data mapping between the simulated driving scene and the operation data set, the data mapping result is input into the driving analysis channel for driving analysis to generate a driving analysis result, wherein the driving analysis includes reaction decision analysis and operation behavior analysis, and the driving analysis module 30 further includes: a reaction decision subchannel calling unit, the reaction decision subchannel calling unit is used to call the reaction decision subchannel of the driving analysis channel, and extract the calibration decision data of the simulated driving scene in the data mapping result through the reaction decision subchannel; a decision authentication result construction unit, the decision authentication result construction unit is used to activate the decision authentication unit of the reaction decision subchannel, after synchronizing the calibration decision data to the decision authentication unit, use the decision authentication unit to perform decision authentication of the operation data set, and establish a decision authentication result; a driving analysis unit, the driving analysis unit is used to perform driving analysis according to the decision authentication result.

[0049] Among them, after activating the decision authentication unit of the reaction decision sub-channel and synchronizing the calibration decision data to the decision authentication unit, the decision authentication unit is used to perform decision authentication on the operation data set to establish a decision authentication result. The decision authentication result construction unit further includes: a decision consistency authentication sub-unit, the decision consistency authentication sub-unit is used to perform decision consistency authentication on the operation data set using the calibration decision data to establish a first decision authentication result; a second decision authentication result construction sub-unit, the second decision authentication result construction sub-unit is used to obtain the operation time of the operation data set, perform decision timeliness authentication according to the operation time, and establish a second decision authentication result; a decision authentication result construction sub-unit, the decision authentication result construction sub-unit is used to establish the decision authentication result according to the first decision authentication result and the second decision authentication result.

[0050] Wherein, driving analysis is performed according to the decision authentication result, and the driving analysis unit further includes: a behavior analysis sub-channel activation sub-unit, the behavior analysis sub-channel activation sub-unit is used to activate the operation behavior analysis sub-channel of the reaction decision sub-channel, and use the operation behavior analysis sub-channel to perform operation behavior authentication of the operation data set to establish an operation behavior authentication result; a driving analysis sub-unit, the driving analysis sub-unit is used to perform driving analysis according to the operation behavior authentication result and the decision authentication result.

[0051] Among them, the operation behavior analysis subchannel of the activation reaction decision subchannel is used to perform operation behavior authentication of the operation data set by using the operation behavior analysis subchannel to establish an operation behavior authentication result. The behavior analysis subchannel activation subunit further includes: a first behavior authentication result construction microunit, the first behavior authentication result construction microunit is used to perform operation execution sequence authentication on the operation data set to establish a first behavior authentication result; a second behavior authentication result construction microunit, the second behavior authentication result construction microunit is used to perform operation interruption identification on the operation data set to establish a second behavior authentication result; a third behavior authentication result construction microunit, the third behavior authentication result construction microunit is used to perform repeated operation identification on the operation data set to establish a third behavior authentication result; a fourth behavior authentication result construction microunit, the fourth behavior authentication result construction microunit is used to perform excessive operation identification on the operation data set to establish a fourth behavior authentication result; an operation behavior authentication microunit, the operation behavior authentication microunit is used to complete the operation behavior authentication according to the first behavior authentication result, the second behavior authentication result, the third behavior authentication result and the fourth behavior authentication result.

[0052] The personalized practice item generation module 40 will be described in detail below. It is used to extract driving weakness data according to the driving analysis results, and use the driving weakness data to match basic operating skills and driving scenarios, and generate personalized practice items according to the matching results. The personalized practice item generation module 40 further includes: a monitoring feedback cycle construction unit, which is used to establish a monitoring feedback cycle according to the personalized practice items; a reinforcement training effect certification unit, which is used to perform reinforcement training effect certification of the trainees in the monitoring feedback cycle and generate certification feedback; and a continued reinforcement practice unit, which is used to perform continued reinforcement practice for the trainees according to the certification feedback.

[0053] Among them, the trainees are given continued intensive exercises according to the certification feedback, and the continued intensive exercise unit further includes: a certification warning judgment subunit, the certification warning judgment subunit is used to judge whether the certification feedback triggers the certification warning; a training abnormality report generation subunit, the training abnormality report generation subunit is used to generate a training abnormality report if the certification feedback triggers the certification warning; an abnormality reporting subunit, the abnormality reporting subunit is used to report the training abnormality report as an abnormality.

[0054] The personalized practice item recommendation system for driving test training provided by the embodiment of the present invention can execute the personalized practice item recommendation method for driving test training provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A personalized practice item recommendation method for driving test training, characterized in that: The method comprises: Establishing a simulated driving scenario, and using a VR driving simulator to perform scenario simulation training for trainees based on the simulated driving scenario; activating a simulation sensor of a VR driving simulator, using the simulation sensor to collect data for scene simulation training, and establishing an operation data set; After establishing data mapping between the simulated driving scenario and the operation data set, the data mapping result is input into a driving analysis channel for driving analysis to generate a driving analysis result, wherein the driving analysis includes reaction decision analysis and operation behavior analysis; Driving weakness data is extracted according to the driving analysis results, and basic operating skills and driving scenarios are matched using the driving weakness data, and personalized practice items are generated according to the matching results.

2. The personalized practice item recommendation method for driving test training according to claim 1, characterized in that: The step of inputting the data mapping result into the driving analysis channel for driving analysis includes: calling a reaction decision subchannel of the driving analysis channel, and extracting calibration decision data of the simulated driving scene in the data mapping result through the reaction decision subchannel; Activate the decision authentication unit of the reaction decision subchannel, synchronize the calibration decision data to the decision authentication unit, use the decision authentication unit to perform decision authentication on the operation data set, and establish a decision authentication result; Driving analysis is performed based on the decision authentication result.

3. The personalized practice item recommendation method for driving test training according to claim 2, characterized in that: The step of using the decision authentication unit to perform decision authentication on the operation data set and establishing a decision authentication result includes: Performing decision consistency authentication on the operation data set using the calibration decision data to establish a first decision authentication result; Acquire the operation time of the operation data set, perform decision timeliness authentication according to the operation time, and establish a second decision authentication result; The decision authentication result is established according to the first decision authentication result and the second decision authentication result.

4. The personalized practice item recommendation method for driving test training according to claim 2, characterized in that: The performing driving analysis according to the decision authentication result includes: Activate the operation behavior analysis subchannel of the reaction decision subchannel, use the operation behavior analysis subchannel to perform operation behavior authentication of the operation data set, and establish an operation behavior authentication result; Driving analysis is performed based on the operation behavior authentication result and the decision authentication result.

5. The personalized practice item recommendation method for driving test training according to claim 4, characterized in that: The using the operation behavior analysis subchannel to perform operation behavior authentication of the operation data set includes: A: Authenticate the operation execution order of the operation data set and establish an authentication result in the first line; B: performing operation interruption identification on the operation data set and establishing a second behavior authentication result; C: performing repeated operation identification on the operation data set to establish a third line authentication result; D: performing excessive operation identification on the operation data set and establishing a fourth line authentication result; The operation behavior authentication is completed according to the first behavior authentication result, the second behavior authentication result, the third behavior authentication result, and the fourth behavior authentication result.

6. The personalized practice item recommendation method for driving test training according to claim 1, characterized in that: The method further comprises: Establishing a monitoring and feedback cycle based on the personalized practice program; During the monitoring feedback cycle, the trainees' intensive training effect is certified and certification feedback is generated; Carry out further reinforcement exercises for trainees based on the certification feedback.

7. The personalized practice item recommendation method for driving test training according to claim 6, characterized in that: The method further comprises: Determining whether the authentication feedback triggers an authentication warning; If the authentication feedback triggers the authentication warning, a training anomaly report is generated; The training abnormality report is reported as an abnormality.

8. A personalized practice item recommendation system for driving test training, characterized in that: The system comprises: A simulated driving scenario construction module, wherein the simulated driving scenario construction module is used to establish a simulated driving scenario and use a VR driving simulator to perform scenario simulation training for trainees based on the simulated driving scenario; A data acquisition module, wherein the data acquisition module is used to activate the simulation sensor of the VR driving simulator, use the simulation sensor to collect data for scene simulation training, and establish an operation data set; A driving analysis module, wherein the driving analysis module is used to establish a data mapping between the simulated driving scenario and the operation data set, input the data mapping result into a driving analysis channel for driving analysis, and generate a driving analysis result, wherein the driving analysis includes a reaction decision analysis and an operation behavior analysis; A personalized practice item generation module is used to extract driving weakness data according to the driving analysis results, use the driving weakness data to match basic operating skills with driving scenarios, and generate personalized practice items according to the matching results.

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