Teaching method for automatically upgrading two-subject disassembly

Abstract

The invention relates to the technical field of intelligent driving training, discloses a teaching method for disassembling and automatically upgrading two subjects, and aims to solve the problems that existing driving training skill points are scattered, personalized adaptation is lacked and linkage with actual driving is insufficient. According to the technical scheme, the method is characterized in that a core project of a subject II is disassembled into independent basic skill points, and a progressive training path is constructed according to an easy-to-difficult sequence; acquiring vehicle, environment and operation three-dimensional data through vehicle-mounted OBD (On-Board Diagnostic) equipment, an environment sensor and a cockpit camera; after comprehensive analysis and multi-dimensional standard scoring, a result and an upgrading rule are input into a language model inference engine, and a next skill point to be practiced or a strengthening suggestion is dynamically output; and after completing all skill point training, the trainees carry out complete process integration. According to the invention, refined and personalized teaching is realized, the skill mastering efficiency and the actual driving suitability of trainees are improved, the standardization difficulty of a driving training mechanism is reduced, and the system is suitable for various driving training scenes.

Description

Technical Field

[0001] This invention relates to the field of motor vehicle driver training technology, specifically to a teaching method for breaking down the second subject of driving test, and in particular, a driving skills teaching method that dynamically adapts and automatically upgrades based on the student's learning progress and skill level. Background Technology

[0002] In driving skills training, Subject 2 is the core component of basic driving skills training, directly determining the safety and compliance of the student's subsequent road driving. In general, Subject 2 instruction revolves around five core skills: reversing into a parking space, parallel parking, driving on a curved road, making a right-angle turn, and hill start. With the popularization of intelligent driving training technology, various assisted teaching systems based on positioning and sensing technologies are gradually replacing some of the work of human instructors, becoming the mainstream trend in the industry.

[0003] The closest existing technical solution to this invention is "A Driving Training System for Subject Two". The core structure of this system includes a Differential Global Positioning System (GPS) positioning module and a central processing module. The core function of the differential GPS positioning module is to acquire the vehicle's location and heading information, and send these two types of information to the central processing module in real time. The central processing module pre-loads a driving training course model, updates the vehicle model's state based on the received vehicle location and heading information, and ultimately determines whether the vehicle has crossed the line based on the vehicle model's state, thus providing feedback to the student on their driving performance and improving teaching quality and efficiency.

[0004] However, existing technical solutions still have significant technical shortcomings, making it difficult to meet the refined and personalized needs of driver training: First, skills teaching lacks systematic breakdown and progressive design. Existing systems only focus on basic compliance judgments such as "whether the line is crossed," failing to break down each item in Subject 2 into independent and logically structured skill points. This forces students to deal with multiple operational points simultaneously, making it impossible to master skills step by step according to cognitive patterns, resulting in high learning difficulty and low efficiency. Second, the teaching scenarios are disconnected from actual road driving. Existing technologies are only designed around compliance training in the examination venue, without considering the transfer of skill points to driving on public roads. Even if students pass Subject 2, they still find it difficult to adapt to complex scenarios on real roads. Third, there is a lack of dynamic adaptation and personalized adjustment mechanisms. The training content and difficulty of existing systems are fixed, failing to optimize training paths based on students' real-time practice performance and skill mastery. This easily leads to a one-size-fits-all problem where students with weak foundations feel frustrated due to excessive difficulty, or skilled students experience inefficiency due to repetitive practice.

[0005] In summary, existing technologies have not yet solved core technical problems in driving test subject 2 instruction, such as fragmented skill points, lack of progressive training paths, disconnect between instruction and actual driving, and insufficient dynamic adaptation. These issues hinder the improvement of teaching effectiveness and practicality in the intelligent driver training industry. Therefore, developing a driving test subject 2 instruction method that constructs progressive training paths through refined skill point breakdown and dynamically and automatically upgrades based on student performance has become a key demand for technological upgrading in the current driver training industry. Summary of the Invention

[0006] This invention aims to overcome the technical shortcomings of existing driving test subject 2 instruction, such as fragmented skill points, lack of progressive training paths, disconnect between instruction and actual road driving, and insufficient dynamic adaptation. It provides a teaching method for automatically upgrading the breakdown of driving test subject 2. By meticulously breaking down the five core items of driving test subject 2—reversing into a parking space, parallel parking, curved driving, right-angle turns, and hill start—into independent and clearly defined basic skill points, a scientifically progressive training path from easy to difficult is constructed. Combined with multi-dimensional student practice data collection and analysis, the training content and difficulty are dynamically adjusted based on the student's real-time skill mastery and practice performance, achieving automatic upgrading of the training path. Simultaneously, the connection between skill points and actual road driving scenarios is strengthened, helping students solidly master each core skill, avoiding the inefficiency or frustration caused by a one-size-fits-all teaching approach. Ultimately, this improves the refinement and personalization of driving test subject 2 instruction, cultivating students' safe operating abilities and adaptability to real-world driving.

[0007] To achieve the above objectives, the technical solution of this invention is: a teaching method for automatically upgrading the breakdown of Subject 2, comprising the following steps: The five core items of driving test part two—reversing into a parking space, parallel parking, curved driving, right-angle turning, and hill start—are broken down into independent basic skill points, and a progressive training path is constructed according to the cognitive law from easy to difficult. The vehicle-mounted OBD device, environmental sensors, and cockpit camera collect multi-dimensional data in real time during the student's practice process. The data includes vehicle operating status data, environmental data, and student operation behavior data. The collected multi-dimensional practice data is comprehensively analyzed, and the student's performance in practicing the current skill points is scored according to the preset scoring criteria to generate the practice results; The results of this exercise and the preset upgrade rules are input into the language model inference engine, which then determines whether the student has met the upgrade requirements for the current skill point. If the upgrade conditions are met, the language model inference engine outputs the next skill point to be practiced, and the student enters the training of that skill point; if the upgrade conditions are not met, the current skill point is maintained and training continues or targeted reinforcement practice suggestions are output. After students complete all the basic skill training for a core project, they will enter the complete process integration training for that core project, and finally complete the skill mastery of all projects in Subject 2.

[0008] Furthermore, the breakdown of each core project is as follows: Hill start and stop: This can be broken down into three skills: hill start and stop, parallel parking, parallel parking and fixed-point parking. Right-angle turn: This can be broken down into three skill points: parallel parking, parallel parking and fixed-point parking, and straightening the car after exiting the parking space. Parallel parking: Break it down into five skill points: entering the parking space with the left hand fully turned on, entering the parking space with the centering point, the entire process of exiting the parking space, entering the project area, and entering the parking space with the right hand fully turned on. Reversing into a parking space can be broken down into five skills: driving out of the parking space and reversing in parallel, driving out of the parking space and reversing in slightly to the left, driving out of the parking space and reversing in slightly to the right, turning right to reverse into the parking space and straightening the wheel, and driving out of the parking space on the right and turning right to reverse into the parking space. Curved driving: Break it down into curve decomposition skill points.

[0009] Furthermore, the vehicle operating status data collected by the on-board OBD device includes gear status, throttle status, brake status, clutch status, and vehicle speed data. Environmental data collected by environmental sensors includes distance data between the vehicle and the edge line, and location data of site markers; The cockpit camera collects data on the trainee's operational behavior, including steering wheel angle and timing.

[0010] Furthermore, the preset scoring criteria include at least three dimensions: operational accuracy, operational standardization, and operational stability. Operational accuracy is scored based on the deviation between the vehicle and the target position; operational standardization is scored based on the degree to which the operation steps conform to the standard procedure; and operational stability is scored based on the frequency of vehicle speed fluctuations and direction adjustments.

[0011] Furthermore, the preset upgrade rules include: if the student's current skill point practice score is not lower than the preset qualified threshold, and the student meets the threshold requirement for 2-3 consecutive practice sessions, the student is considered to have met the upgrade conditions.

[0012] Furthermore, the language model inference engine preloads the correlation data of skill points in Subject 2 and the data on the student's skill improvement patterns, and can combine the results of this practice with the upgrade rules to output the next training skill point that is suitable for the student's current skill level.

[0013] Furthermore, the progressive training path also includes a design that connects skill points with actual road driving scenarios, with the training objectives of each basic skill point corresponding to similar operational needs in actual road driving.

[0014] Furthermore, the training sequence for the three skills corresponding to hill start and stop is as follows: first train hill start and stop, then train parallel parking, and finally train parallel parking and fixed-point parking. Among them, the hill start and stop skill focuses on mastering the semi-clutch state of the vehicle, the parallel parking skill focuses on controlling the distance between the vehicle and the right side line by 30 centimeters, and the parallel parking and fixed-point parking skill focuses on the precise parking operation of aligning the front of the vehicle with the marker line.

[0015] Furthermore, the targeted reinforcement practice suggestions are generated by the language model inference engine based on the error types and weaknesses in the student's practice data, including operation point prompts and practice frequency suggestions.

[0016] The present invention, by adopting the above technical solution, has at least the following beneficial effects: This invention meticulously breaks down the five core items of Driving Test Part 2 (reversing into a parking space, parallel parking, driving on a curved road, making a right-angle turn, and hill start) into independent and clearly defined basic skill points, and constructs a progressive training path according to the cognitive principle of from easy to difficult. This design avoids the cognitive burden of trainees dealing with multiple operational points simultaneously, helping them gradually build muscle memory and driving confidence. For example, the hill start skill point, through step-by-step training, such as mastering the semi-clutch state, 30-centimeter distance control, and precise alignment with the marker line, allows complex items to be mastered one by one, thus enabling rapid and solid skill acquisition.

[0017] This invention collects multi-dimensional practice data through in-vehicle OBD devices, environmental sensors, and cockpit cameras. Combined with a comprehensive scoring standard that assesses operational accuracy, standardization, and stability, it objectively reflects the learner's skill mastery. The language model inference engine automatically determines whether the learner meets the upgrade criteria based on their practice results and upgrade rules, and outputs the next training skill points or provides targeted reinforcement practice suggestions. This mechanism allows learners with weak foundations to receive sufficient targeted training, while avoiding frustration for skilled learners due to repetitive and inefficient practice, significantly improving teaching efficiency and personalization.

[0018] This invention designs a progressive training path that connects to real-world driving scenarios, ensuring that the training objectives for each basic skill point correspond to the operational needs in actual roads. For example, the skill point for parallel parking directly adapts to real-world parallel parking scenarios, while the skill point for precise parking corresponds to requirements such as parking at intersections. This design overcomes the limitations of existing technologies that only focus on exam compliance, enabling trainees not only to pass the driving test (Part 2) smoothly but also to acquire the operational capabilities to adapt to real-world roads, fundamentally improving driving safety.

[0019] This invention reduces inconsistencies in teaching standards caused by manual intervention through a closed-loop process of automatic data collection, scoring, and upgrade assessment. The language model inference engine pre-loads data on the correlation between skill points in Subject 2 and the patterns of student skill improvement, ensuring the scientific rigor and consistency of training path planning. This not only improves the stability of teaching quality but also reduces the labor costs for driving training institutions, promoting the large-scale application of intelligent driving training.

[0020] When learners fail to meet the upgrade requirements, the language model inference engine generates operation tips and practice frequency suggestions based on the error types and weaknesses in the learner's practice data, helping learners to accurately address their shortcomings. Through a multi-dimensional comprehensive scoring mechanism and continuous practice upgrade rules, the impact of single, accidental operational errors on the scoring results is avoided, making teaching feedback more objective and instructive, further accelerating learners' skill improvement. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings used in the description of the embodiment or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this embodiment. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of the teaching method of the present invention; Figure 2 This is a flowchart of the system processing of the present invention. Detailed Implementation

[0023] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this embodiment. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this embodiment as detailed in the appended claims.

[0024] like Figure 1As shown, this embodiment provides a teaching method for automatically upgrading the breakdown of driving test subject 2, including the following steps: The five core items of driving test subject 2—reversing into a parking space, parallel parking, curved driving, right-angle turning, and hill start—are broken down into independent basic skill points, and a progressive training path is constructed according to the cognitive pattern from easy to difficult; multi-dimensional data of the student's practice process is collected in real time through the vehicle's OBD device, environmental sensors, and cockpit camera, including vehicle operating status data, environmental data, and student operation behavior data; the collected multi-dimensional practice data is comprehensively analyzed, and the student is scored according to a preset scoring standard. The current skill point practice performance is scored, generating the practice result. This practice result, along with preset upgrade rules, is input into the language model inference engine, which determines whether the student has met the upgrade requirements for the current skill point. If the upgrade requirements are met, the language model inference engine outputs the next skill point to practice, and the student begins training for that skill point. If the upgrade requirements are not met, the current skill point continues to be trained, or targeted reinforcement practice suggestions are output. After the student completes training for all the basic skill points corresponding to a core project, they enter the complete process integration training for that core project, ultimately achieving mastery of all skills in Subject Two.

[0025] As one implementation method, the breakdown of each core item in this embodiment is as follows: Hill start and stop: broken down into three skill points: hill start and stop, parallel parking, parallel parking, and fixed-point parking; Right angle turn: broken down into three skill points: parallel parking, parallel parking, fixed-point parking, and straightening after exiting the parking space; Parallel parking: broken down into five skill points: turning the steering wheel fully to the left upon entering the parking space, straightening the steering wheel upon entering the parking space, the entire process of exiting the parking space, entering the parking space, and turning the steering wheel fully to the right upon entering the parking space; Reversing into a parking space: broken down into five skill points: parallel reversing in after exiting the parking space, reversing in slightly to the left after exiting the parking space, reversing in slightly to the right after exiting the parking space, straightening the steering wheel when reversing in after exiting the parking space, and right exiting and right reversing with the steering wheel fully to the right; Curved driving: broken down into curve decomposition skill points. The breakdown logic is based on the principle of focusing on a single skill. Each skill point corresponds to only one core operation, avoiding learning confusion caused by the superposition of multiple operation points. For example, after the breakdown of parallel parking, students do not need to pay attention to "entry angle", "righting time" and "exit route" at the same time. They can overcome key difficulties one by one. This not only conforms to the cognitive pattern of students, but also makes it easier for the system to accurately assess the mastery of each operation link, providing a foundation for personalized training.

[0026] As one implementation method, in this embodiment, the vehicle operating status data collected by the on-board OBD device includes gear position, throttle position, brake position, clutch position, and vehicle speed data; the environmental data collected by the environmental sensors includes distance data between the vehicle and the edge line and location data of the site markers; and the student's operational behavior data collected by the cockpit camera includes steering wheel operation angle and operation timing data. The data collected by these three types of devices form a three-dimensional data system based on the vehicle, environment, and operation, covering the core dimensions required for skill assessment. For example, the clutch status data collected by the OBD device is directly related to the hill start half-clutch control ability, the distance data from the environmental sensors accurately reflects the compliance of parking on the side of the road, and the operation timing data collected by the camera can determine the timeliness of turning the steering wheel fully / returning to center. This avoids the one-sidedness of single-data assessment, ensures the objective presentation of the student's operational performance, and provides reliable data support for subsequent scoring and dynamic adjustment.

[0027] As one implementation method, the preset scoring criteria in this embodiment include at least three dimensions: operational accuracy, operational standardization, and operational stability. Operational accuracy is scored based on the deviation between the vehicle and the target position; operational standardization is scored based on the degree to which the operational steps conform to the standard procedure; and operational stability is scored based on the frequency of vehicle speed fluctuations and direction adjustments. The scoring criteria balance "compliance of results" and "standardization of process." For example, operational accuracy focuses on "whether the vehicle stops at the designated position," operational standardization focuses on "whether the 'signal-observe-operate' procedure is followed," and operational stability focuses on "whether there is frequent rapid acceleration or sharp steering while driving." This approach breaks through the single scoring logic of existing technologies that only judge "whether the line is crossed," and can comprehensively assess the quality of the trainee's skill mastery, guiding trainees to develop standardized and stable operating habits that are suitable for actual road driving requirements.

[0028] As one implementation method, the preset upgrade rules in this embodiment include: if the student's current skill point practice score is not lower than a preset passing threshold, and the student meets the threshold requirement for 2-3 consecutive practice sessions, the student is considered to have met the upgrade conditions. Setting the "continuous passing" requirement is to eliminate interference from accidental successes and ensure that the student develops stable muscle memory and operational logic for the current skill point. The passing threshold can be flexibly adjusted according to the difficulty of the skill point. For example, the threshold for hill start and parking can be lower than that for parallel parking and fixed-point parking. This avoids students blindly upgrading before mastering the skill, preventing frustration in subsequent training due to weak foundations, while ensuring the gradual and scientific nature of the training path.

[0029] As one implementation method, the language model inference engine described in this embodiment preloads data on the correlation between skill points in driving test subject 2 and data on student skill improvement patterns. It can combine the results of the current practice with the upgrade rules to output the next training skill point tailored to the student's current skill level. The skill point correlation data clearly defines the sequential dependency logic of different skill points (e.g., mastering "parallel parking" before advancing to "fixed-point parking"). The student skill improvement pattern data is based on a summary of massive amounts of driver training data, adapting to the learning pace of students with different skill levels (e.g., students with weak foundations need to strengthen "semi-clutch control"). This enables intelligent dynamic planning of training paths, avoiding a fixed, "one-size-fits-all" training sequence, allowing each student to obtain a training plan tailored to their own level, thus improving learning efficiency.

[0030] As one implementation method, the progressive training path described in this embodiment also includes a design that connects skill points with actual road driving scenarios. The training objective of each basic skill point corresponds to the same operational requirements in actual road driving. The skill point design is not limited to the examination scenario. For example, the "parking on the side of the road" skill point directly corresponds to the temporary parking operation in actual roads, "returning to the correct position after exiting the parking space" corresponds to the direction correction after turning at an intersection, and "precise parking" corresponds to the parking requirements at traffic light intersections and zebra crossings. This can break the limitation of existing technology that is "training for the exam" and achieve a seamless connection between the skills of Subject 2 and actual road driving, helping students quickly adapt to the real driving environment and improve driving safety and practicality.

[0031] As one implementation method, the training sequence for the three skill points corresponding to hill start and stop in this embodiment is as follows: first train hill start and stop, then train parallel parking, and finally train parallel parking and stop-point parking. Specifically, the hill start and stop skill point focuses on mastering the vehicle's semi-clutch state, the parallel parking skill point focuses on controlling the 30-centimeter distance between the vehicle and the right lane line, and the parallel parking and stop-point parking skill points focus on precise parking operations where the front of the vehicle aligns with the marker line. The training sequence follows a "from basic to complex" logic. Semi-clutch control is the core foundation of the hill project, 30-centimeter distance control is crucial for compliance, and precise parking is the ultimate goal of the project. Each skill point has a clear training focus, avoiding the simultaneous overlap of multiple goals, which reduces the learning difficulty of the hill project and allows trainees to gradually overcome core difficulties. For example, first mastering semi-clutch control to avoid the risk of rolling back, then practicing distance control, and finally achieving precise stop-point parking, significantly improving trainees' learning confidence and project pass rate.

[0032] As one implementation method, the targeted reinforcement practice suggestions described in this embodiment are generated by the language model inference engine based on the error types and weaknesses in the student's practice data, including operation point prompts and practice frequency suggestions. The language model inference engine performs in-depth analysis of the practice data, such as identifying specific problems such as "unstable semi-clutch control" and "turning the steering wheel to full lock too late," and outputs targeted operation prompts such as "slowly release the clutch and feel the vehicle vibration" and "turn the steering wheel to full lock immediately after seeing the reference point." At the same time, it suggests 3-5 reinforcement training sessions based on the degree of weakness, which allows students to clearly identify their shortcomings and areas for improvement, avoid blindly repeating practice, achieve "precise remediation," and significantly improve training efficiency and skill improvement speed.

[0033] As a further implementation method, taking the hill start and stop test for a manual transmission vehicle as an example, the training execution process of this invention is specifically explained: The three skill points of this test correspond to the three core operational points that trainees need to master: First, mastering the vehicle's semi-clutch state to ensure a smooth start after hill start and avoid rolling back; second, maintaining a 30-centimeter distance between the vehicle and the right lane line to meet the compliance requirements of the test; third, accurately judging when to align the front of the vehicle with the marker line to achieve a fixed-point stop. During training, the system guides trainees to practice in the order of difficulty: "hill start and stop, parallel parking, parallel parking, and fixed-point parking." After completing 2-3 consecutive qualified practices for each skill point, the system automatically advances to the next skill point. Once all three skill points are trained, trainees enter the integrated training of the complete hill start and stop process. Through the integration of skill points, trainees can quickly master the complete operational logic of this test.

[0034] like Figure 2 As shown in the diagram, this is a system processing flowchart of the present invention, used to explain in detail the execution logic of data flow and skill recommendation during the teaching process. The diagram includes four core parts: a data acquisition module, a data processing module, a decision-making module, and an output module. The data acquisition module consists of an onboard OBD device, environmental sensors, and a cockpit camera, synchronously collecting real-time data during student practice. The data processing module receives the collected multi-dimensional data, performs comprehensive analysis, and scores the student's current skill point based on preset scoring standards, generating the practice result. The core of the decision-making module is a language model inference engine, which receives the practice score output by the data processing module, loads preset upgrade rules, and performs logical judgment based on pre-stored data on the correlation between skill points in Subject 2 and data on student skill improvement patterns. The output module outputs the next skill point to be practiced based on the judgment result of the language model inference engine, forming a complete teaching closed loop from data acquisition, analysis and scoring, model judgment to skill output.

[0035] This embodiment is based on skill point decomposition and achieves dynamic training upgrades through multi-dimensional data collection and comprehensive scoring, fully implementing the teaching objectives of refined, personalized, and practical driving test subject two. The core logic lies in breaking down complex projects into independent skill points, combining three-dimensional data collection and multi-dimensional scoring, and using a language model inference engine to achieve intelligent adaptation of training paths. This not only solves the problems of fragmented skill points and lack of progressiveness in existing technologies, but also achieves a connection between teaching and actual road driving.

[0036] Throughout the implementation process, the various technical features worked synergistically: skill point breakdown provided the foundation for refined teaching, multi-device data collection ensured the objectivity of the assessment, comprehensive scoring standards guided standardized operations, dynamic upgrade rules ensured solid skill mastery, and the language model inference engine enabled personalized adaptation. Specific implementation cases and system processing flows, exemplified by ramp parking and starting, fully validated the operability and effectiveness of the technical solution.

[0037] The technical solution in this embodiment not only improves the efficiency and quality of driving test preparation, but also cultivates students' standardized and stable driving habits, reduces the labor costs and teaching standardization difficulties of driving training institutions, and provides reliable technical support for the large-scale and high-quality development of the intelligent driving training industry.

[0038] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A teaching method for automatically upgrading the breakdown of Subject 2, characterized in that: Includes the following steps: The five core items of driving test part two—reversing into a parking space, parallel parking, curved driving, right-angle turning, and hill start—are broken down into independent basic skill points, and a progressive training path is constructed according to the cognitive law from easy to difficult. The vehicle-mounted OBD device, environmental sensors, and cockpit camera collect multi-dimensional data in real time during the student's practice process. The data includes vehicle operating status data, environmental data, and student operation behavior data. The collected multi-dimensional practice data is comprehensively analyzed, and the student's performance in practicing the current skill points is scored according to the preset scoring criteria to generate the practice results; The results of this exercise and the preset upgrade rules are input into the language model inference engine, which then determines whether the student has met the upgrade requirements for the current skill point. If the upgrade conditions are met, the language model inference engine outputs the next skill point to be practiced, and the student enters the training of that skill point; If the upgrade requirements are not met, maintain your current skill points and continue training or provide targeted improvement practice suggestions; After students complete all the basic skill training for a core project, they will enter the complete process integration training for that core project, and finally complete the skill mastery of all projects in Subject 2.

2. The method according to claim 1, characterized in that: The breakdown of each core project is as follows: Hill start and stop: This can be broken down into three skills: hill start and stop, parallel parking, parallel parking and fixed-point parking. Right-angle turn: This can be broken down into three skill points: parallel parking, parallel parking and fixed-point parking, and straightening the car after exiting the parking space. Parallel parking: Break it down into five skill points: entering the parking space with the left hand fully turned on, entering the parking space with the centering point, the entire process of exiting the parking space, entering the project area, and entering the parking space with the right hand fully turned on. Reversing into a parking space can be broken down into five skills: driving out of the parking space and reversing in parallel, driving out of the parking space and reversing in slightly to the left, driving out of the parking space and reversing in slightly to the right, turning right to reverse into the parking space and straightening the wheel, and driving out of the parking space on the right and turning right to reverse into the parking space. Curved driving: Break it down into curve decomposition skill points.

3. The method according to claim 1, characterized in that: The vehicle operating status data collected by the on-board OBD device includes gear status, throttle status, brake status, clutch status, and vehicle speed data. Environmental data collected by environmental sensors includes distance data between the vehicle and the edge line, and location data of site markers; The cockpit camera collects data on the trainee's operational behavior, including steering wheel angle and timing.

4. The method according to claim 1, characterized in that: The preset scoring criteria include at least three dimensions: operational accuracy, operational standardization, and operational stability. Operational accuracy is scored based on the deviation between the vehicle and the target position; operational standardization is scored based on the degree to which the operation steps conform to the standard procedure; and operational stability is scored based on the frequency of vehicle speed fluctuations and direction adjustments.

5. The method according to claim 1, characterized in that: The preset upgrade rules include: if a student's current skill point practice score is not lower than the preset qualified threshold, and the student meets the threshold requirement for 2-3 consecutive practice sessions, the student is considered to have met the upgrade conditions.

6. The method according to claim 1, characterized in that: The language model inference engine preloads data on the correlation between skill points in Subject 2 and data on the student's skill improvement patterns. It can combine the results of this practice with the upgrade rules to output the next training skill point that is suitable for the student's current skill level.

7. The method according to claim 1, characterized in that: The progressive training path also includes a design that connects skill points with actual road driving scenarios, with the training objectives of each basic skill point corresponding to similar operational needs in actual road driving.

8. The method according to claim 2, characterized in that: The training sequence for the three skills corresponding to hill start and stop is as follows: first train hill start and stop, then train parallel parking, and finally train parallel parking and fixed-point parking. Among them, the hill start and stop skill focuses on mastering the semi-clutch state of the vehicle, the parallel parking skill focuses on controlling the distance between the vehicle and the right side line by 30 centimeters, and the parallel parking and fixed-point parking skill focuses on the precise parking operation of aligning the front of the vehicle with the marker line.

9. The method according to any one of claims 1 to 8, characterized in that: The targeted reinforcement practice suggestions are generated by the language model inference engine based on the error types and weaknesses in the student's practice data, including operation tips and practice frequency suggestions.

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