Out-road test driving training simulation scene generation method and system

Abstract

The embodiment of the invention provides an out-road test driving training simulation scene generation method and system, and can solve the problem that the learning efficiency of examinees is affected due to the fact that most systems only provide an error question review function and cannot provide personalized learning path recommendation based on big data analysis. The method comprises the following steps: obtaining a driving theory simulation practice result of a target student, wherein the driving theory simulation practice result comprises wrong question information of driving theory simulation practice; based on the driving knowledge points associated with the error question information, establishing an outer road driving training assessment sub-scene; and loading the outer road driving training assessment sub-scene into a target outer road driving training scene under the condition that an outer road test driving training simulation request of the target student for the target outer road driving training scene is received.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a method and system for generating a simulated scenario for off-road driving training for driving tests. Background Art

[0002] Currently, the test questions for driving test simulation training generally lack pertinence. Most of the current test questions are randomly generated and cannot be adjusted individually according to the actual learning situation of the candidates. The current simulation training system analyzes the candidates' answering situations rather roughly and cannot accurately identify the candidates' knowledge blind spots and automatically recommend corresponding test questions. Most systems only provide a function for reviewing wrong questions and fail to provide personalized learning path recommendations based on big data analysis, which affects the learning efficiency of the candidates. Summary of the Invention

[0003] The embodiments of this application provide a method and system for generating a simulated scenario for off-road driving training for driving tests, which can solve the problem that most systems only provide a function for reviewing wrong questions and fail to provide personalized learning path recommendations based on big data analysis, thus affecting the learning efficiency of the candidates.

[0004] The first aspect of the embodiments of this application provides a method for generating a simulated scenario for off-road driving training for driving tests, including:

[0005] Obtaining the driving theory simulation practice results of a target student, where the driving theory simulation practice results include the wrong question information of the driving theory simulation practice;

[0006] Establishing a sub-scenario for off-road driving training assessment based on the driving knowledge points associated with the wrong question information;

[0007] When receiving an off-road driving training simulation request for a target off-road driving training scenario from the target student, loading the off-road driving training assessment sub-scenario into the target off-road driving training scenario.

[0008] Optionally, it further includes:

[0009] Obtaining the simulated driving operation behaviors of the target student in the off-road driving training assessment sub-scenario;

[0010] When the simulated driving operation behaviors are correct, deleting the wrong question information associated with the off-road driving training assessment sub-scenario from the driving theory simulation practice results, so as not to assess the wrong question information associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice anymore.

[0011] Optionally, it further includes:.

[0012] Obtaining the simulated driving operation behaviors of the target student in the off-road driving training assessment sub-scenario;

[0013] Increase the assessment frequency of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice.

[0014] Optionally, it further includes:

[0015] Obtain the simulated driving operation behavior of the target student in the off-road driving training assessment sub-scenario;

[0016] In the case where the simulated driving operation behavior is a wrong simulated driving operation behavior, analyze the wrong simulated driving operation behavior;

[0017] Conduct a text description based on the operation behavior itself of the wrong simulated driving operation behavior;

[0018] Add the text description of the operation behavior to the options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice.

[0019] Optionally, it further includes:

[0020] Match the text description of the operation behavior itself with the content of the existing options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice;

[0021] In the case where there is no matching option, add the text description of the operation behavior to the options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice, and the driving theory simulation practice is a multiple-choice question.

[0022] Optionally, the driving theory simulation practice result includes a theory simulation practice score, and the method further includes:

[0023] In the case where the wrong question information associated with the off-road driving training assessment sub-scenario is deleted in the driving theory simulation practice result, regenerate the theory simulation practice score of the target student based on the current driving theory simulation practice result.

[0024] Optionally, the driving theory simulation practice result includes the correct question information of the driving theory simulation practice, and the method further includes:

[0025] Establish an off-road driving training assessment verification scenario based on the driving knowledge points associated with the correct question information;

[0026] In the case where a simulation request for off-road driving training for the target off-road driving training scenario from the target student is received, load the off-road driving training assessment verification scenario into the target off-road driving training scenario;

[0027] Obtain the simulated driving operation behavior of the target student in the off-road driving training assessment verification scenario;

[0028] In the case where the simulated driving operation behavior is an incorrect simulated driving operation behavior, analyze the incorrect simulated driving operation behavior;

[0029] Based on the operation behavior itself of the incorrect simulated driving operation behavior, conduct a text description;

[0030] In the driving theory simulation exercise results, modify the correct questions associated with the off-road driving training assessment and verification scenario to incorrect questions, and use the text description of the operation behavior as a new option. The driving theory simulation exercise is a multiple-choice question.

[0031] The second aspect of the embodiments of the present application provides an off-road driving training simulation scenario generation device, including:

[0032] An acquisition unit, configured to acquire the driving theory simulation exercise results of a target student, where the driving theory simulation exercise results include the incorrect question information of the driving theory simulation exercise;

[0033] A simulation unit, configured to establish an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the incorrect question information;

[0034] A loading unit, configured to load the off-road driving training assessment sub-scenario into the target off-road driving training scenario when receiving an off-road driving training simulation request of the target student for the target off-road driving training scenario.

[0035] The third aspect of the embodiments of the present application provides an electronic system, including a memory and a processor. When the processor executes the computer program stored in the memory, the steps of the above-mentioned off-road driving training simulation scenario generation method are implemented.

[0036] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned off-road driving training simulation scenario generation method are implemented.

[0037] In summary, the method for generating an off-road driving training simulation scenario provided by the embodiments of the present application obtains the driving theory simulation practice results of a target student, where the driving theory simulation practice results include the wrong question information of the driving theory simulation practice; establishes an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information; and when receiving an off-road driving training simulation request from the target student for a target off-road driving training scenario, loads the off-road driving training assessment sub-scenario into the target off-road driving training scenario. Thus, the training is no longer randomly assigned, but directly targets the weak knowledge points of the student for intensive training, avoiding ineffective learning. By associating wrong questions with actual driving scenarios, students can master theoretical knowledge in practice and reduce the knowledge forgetting rate. Through the analysis of wrong question records, the system can accurately recommend the required training scenarios and improve the passing rate of the exam. Students can practice emergencies in a real simulation environment, improving their actual driving ability, rather than just memorizing answers. This method can be applied to different regions and different types of driving exam training (such as urban driving, highway driving, mountain driving, etc.) to meet different driving needs.

[0038] Correspondingly, the off-road driving training simulation scenario generation device, electronic system, and computer-readable storage medium provided by the embodiments of the present invention also have the above technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flow chart of a possible method for generating an off-road driving training simulation scenario provided by the embodiments of the present application;

[0040] Figure 2 It is a schematic structural block diagram of a possible off-road driving training simulation scenario generation device provided by the embodiments of the present application;

[0041] Figure 3 It is a schematic hardware structure diagram of a possible off-road driving training simulation scenario generation device provided by the embodiments of the present application;

[0042] Figure 4 It is a schematic structural block diagram of a possible electronic system provided by the embodiments of the present application;

[0043] Figure 5 It is a schematic structural block diagram of a possible computer-readable storage medium provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] The embodiments of the present application provide a method and system for generating an off-road driving training simulation scenario, which can solve the problem that most systems only provide a wrong question review function and fail to provide personalized learning path recommendations based on big data analysis, affecting the learning efficiency of candidates.

[0045] In the description and claims of this application and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that shown or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. Hereinafter, the technical solutions in the embodiments of this application will be described clearly and completely in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0046] Please refer to Figure 1 , which is a flowchart of a method for generating an off-road driving training simulation scenario provided by an embodiment of this application, and specifically may include: S110 - S130.

[0047] S110, obtain the driving theory simulation practice result of the target trainee, and the driving theory simulation practice result includes the wrong question information of the driving theory simulation practice.

[0048] S120, establish an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information.

[0049] S130, when receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario, load the off-road driving training assessment sub-scenario into the target off-road driving training scenario.

[0050] It is understandable that during the driving training process, each trainee will take multiple rounds of driving theory mock exams to test their mastery of knowledge such as road traffic regulations, driving operation skills, and emergency situation response. The system will automatically record the results of each trainee's practice and focus on collecting their wrong answers and questions with a higher error rate to identify the weak points of the trainee in theoretical knowledge. To ensure the accuracy of the analysis, the system will classify the wrong questions and file them according to knowledge points. For example, the wrong questions may involve "incorrect braking method in rainy days", "incorrect switching of high and low beam lights at night", or "incorrect operation of emergency avoidance of obstacles", etc. In addition, based on information such as the frequency of wrong questions and the trainee's answering time, the system will further analyze whether the trainee has cognitive biases in certain driving situations. For example, if a trainee repeatedly answers wrongly "whether it is illegal to stop at the grid line when the red light is on" in multiple exams, it indicates that the trainee has a problem with understanding this rule and needs additional training and reinforcement. The key at this stage lies in the accurate collection and classification of data to ensure that subsequent training can be truly optimized according to the weaknesses of the trainees.

[0051] Exemplarily, after analyzing the wrong question information of the trainee, the system needs to associate the theoretical wrong questions with the actual on-road driving scenarios to achieve an organic combination of knowledge points and actual driving operations. This process involves setting the mapping rules from knowledge points to scenarios. The system will establish a knowledge point - scenario matching database, where each driving knowledge point will correspond to one or more training scenarios. For example, if a trainee wrongly understands the "correct braking method when driving in rainy days" in the theoretical exam, the system will match the "braking test on a slippery road" scenario and add an assessment point of sudden braking in the training. Another example is that if a trainee makes more mistakes in the knowledge point of "using high and low beam lights at night", the system will match the "night driving on suburban roads" scenario and require the trainee to correctly use the lights in the simulated environment. If a trainee makes more mistakes in the questions related to "pedestrians suddenly crossing the road", then add the "sudden appearance of pedestrians on urban roads" test in the training scenario to evaluate the trainee's emergency avoidance ability. During the scenario construction process, the system does not simply load a certain driving environment, but dynamically adjusts the assessment difficulty in combination with the trainee's wrong question data. For example, by adjusting the weather, adding sudden situations, changing road signs, etc., the training can be made more targeted and challenging, so as to achieve the best training effect.

[0052] Exemplarily, when a trainee submits a request for off-road driving training simulation, the system will dynamically retrieve the corresponding off-road driving assessment sub-scenarios based on the trainee's wrong-question information and embed them into the entire training simulation process. The core of this process lies in the intelligent scenario generation and real-time loading technology. The system will preferentially select scenarios highly relevant to the trainee's knowledge blind spots and load them in a random or preset order to test the trainee's response ability. For example, if a trainee has insufficient mastery of the knowledge point of "braking on a slippery road" in the simulated exam, when the trainee is driving for training, the system may randomly trigger an event of "the vehicle in front brakes suddenly" during the driving process and detect whether the trainee can decelerate or brake correctly instead of operating in the wrong way of "stamping on the brakes hard". In addition, the system can adjust the scenario difficulty according to the training effect. For example, at the initial stage, the trainee's reaction is detected in a more lenient way, but in the subsequent stage, sudden situations are added (such as adding oncoming vehicles, shortening the reaction time, etc.) to ensure that the trainee truly masters the driving skill. Similarly, in the night driving assessment, the system may set multiple oncoming vehicle meeting points, dynamically check whether the trainee's lighting operation complies with the regulations, and give real-time feedback or training summary when there are mistakes. During the training process, the system not only provides assessment, but also helps the trainee continuously optimize the driving skill through the dynamic scenario adjustment and real-time feedback mechanism, ultimately improving the passing rate of the driving exam and the level of safe driving.

[0053] In summary, for the off-road driving training simulation scenario generation method provided by the above embodiments, by obtaining the driving theory simulation practice results of the target trainee, the driving theory simulation practice results include the wrong-question information of the driving theory simulation practice; establishing an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong-question information; and loading the off-road driving training assessment sub-scenario into the target off-road driving training scenario when receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario. Thus, the training is no longer randomly assigned, but directly strengthens the training for the trainee's weak knowledge points, avoiding ineffective learning. By associating wrong questions with actual driving scenarios, the trainee can master theoretical knowledge in practice and reduce the knowledge forgetting rate. Through the analysis of wrong-question records, the system can accurately recommend the required training scenarios and improve the passing rate of the exam. The trainee can practice sudden situations in a real simulation environment and improve the actual driving ability, rather than just memorizing the answers. This method can be applied to driving exam training in different regions and different types (such as urban driving, highway driving, mountain driving, etc.) to meet different driving needs.

[0054] In one embodiment, it further includes:

[0055] Obtaining the simulated driving operation behaviors of the target trainee in the off-road driving training assessment sub-scenario;

[0056] When the simulated driving operation behavior is correct, delete the wrong question information associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice results, so that the wrong questions associated with the off-road driving training assessment sub-scenario are no longer assessed in the driving theory simulation practice.

[0057] It can be understood that after the trainee enters the off-road driving training assessment sub-scenario, the system will automatically monitor and record their driving operation behavior to evaluate their performance in the actual driving scenario. This process involves the real-time collection and analysis of multiple key parameters. For example: Steering wheel operation, to judge whether the trainee controls the vehicle in the correct way, such as whether there is oversteering or insufficient steering wheel return during sharp turns; Brake and accelerator control, on slippery roads, in case of emergencies or in front of red lights, the system records the trainee's braking force, braking timing, and whether there are incorrect behaviors such as "slamming on the brakes" or "failing to brake in time"; Lighting use, in night driving scenarios, whether the trainee correctly switches the high and low beams according to regulations, and whether there are problems such as "failing to switch to low beam during oncoming traffic" or "failing to turn on the headlight when entering the tunnel"; Avoidance behavior, when pedestrians or obstacles suddenly appear, whether the trainee can react correctly, such as decelerating to avoid or changing lanes in a timely manner, rather than blindly jerking the steering wheel or braking suddenly, causing the vehicle to lose control. The system will not only evaluate the operation behavior of a single training, but also make a comprehensive judgment based on multiple training data using machine learning algorithms or expert rule models to ensure the stability of the trainee's operation, rather than considering that they have mastered a certain knowledge point just because of a single correct operation.

[0058] Exemplarily, when a trainee demonstrates correct driving operation behaviors in the off-road driving training assessment sub-scenario, and the system confirms that they have mastered the knowledge point after comprehensively evaluating multiple training data, the system will automatically update their driving theory simulation exercise results and delete the wrong question information corresponding to this scenario from the assessment scope to avoid re-examining this knowledge point in subsequent theoretical exams. For the correct operation judgment, a qualified threshold can be set. For example, a trainee needs to correctly brake continuously 3 times in the slippery road surface scenario or correctly use the lights in all 5 night driving tests to be judged as having mastered this knowledge point; record the success rate of the trainee and calculate the stability of their correct operations. If the trainee still makes mistakes occasionally, targeted training will continue until their error rate drops below the set threshold. When the trainee reaches the qualified standard in the off-road training assessment of this knowledge point, the system will automatically modify the wrong question bank of their theoretical simulation exam and remove the wrong questions associated with this knowledge point; in this way, the trainee will no longer be examined on this knowledge point in subsequent theoretical exams, but will shift the training focus to other knowledge points that have not been mastered. If a trainee has already mastered a certain knowledge point in a certain training but makes mistakes again in subsequent training or actual exam simulations, the system will re-add this knowledge point to the wrong question bank and re-load the relevant assessment sub-scenario in subsequent off-road exam training to ensure that the trainee truly masters driving skills. Traditional driving exam systems often cannot distinguish whether a trainee has mastered a certain knowledge point. Therefore, even if a trainee has corrected their mistakes in practice, they still need to repeat answering questions in the theoretical exam. However, this method evaluates through actual operation behaviors. Once a trainee truly masters a certain knowledge point, this knowledge point will be removed from the theoretical exam, making subsequent training more targeted. Traditional driving exams focus on theoretical assessments but cannot guarantee that trainees can correctly apply them during actual driving. However, this method allows trainees to practice through off-road driving simulations and adjusts the content of the theoretical exam based on their performance, thereby strengthening the close combination of theory and practice. Since the system dynamically adjusts the assessment content according to the specific situation of the trainee, the training path for each trainee is personalized, which can maximally meet the learning needs of the trainee, avoid overlearning the knowledge that has been mastered, and at the same time strengthen the training of weak links. This method forms a learning closed-loop of theoretical exam - off-road driving simulation - operation behavior assessment - knowledge point correction - theoretical exam optimization, ensuring that trainees can truly master all necessary driving skills before the exam, thereby effectively improving the passing rate of driving exams and enhancing actual driving safety.

[0059] Exemplarily, assume that trainee A has a poor grasp of the "correct use of high and low beam headlights when meeting oncoming vehicles at night" in the theoretical mock exam and selects the wrong answers multiple times. The system will specifically load the night driving assessment sub-scenario for him during the on-road driving training exam and repeatedly require him to perform high and low beam headlight switching operations during the training. If trainee A correctly uses the lights in 5 consecutive trainings, the system will automatically determine that he has mastered this knowledge point and delete this question from the wrong question bank in the theoretical exam to avoid such questions from appearing again in subsequent theoretical exams. If trainee A still makes mistakes occasionally, the system will continue to load this scenario and strengthen the relevant training until he masters it.

[0060] In one embodiment, it further includes:

[0061] Obtain the simulated driving operation behavior of the target trainee in the on-road driving training assessment sub-scenario;

[0062] Increase the assessment frequency of the wrong questions associated with the on-road driving training assessment sub-scenario in the driving theory mock practice.

[0063] In one embodiment, it further includes:

[0064] Obtain the simulated driving operation behavior of the target trainee in the on-road driving training assessment sub-scenario;

[0065] In the case where the simulated driving operation behavior is a wrong simulated driving operation behavior, analyze the wrong simulated driving operation behavior;

[0066] Based on the operation behavior itself of the wrong simulated driving operation behavior, make a text description;

[0067] Add the text description of the operation behavior to the options of the wrong questions associated with the on-road driving training assessment sub-scenario in the driving theory mock practice.

[0068] It can be understood that in order to further enhance the driving training effect of trainees and the pertinence of theoretical exams, not only the driving operation behavior of trainees needs to be recorded, but also the wrong operation behavior needs to be analyzed, and the wrong behavior needs to be added to the wrong question options in the driving theory mock practice in the form of text description. This method can help trainees identify real wrong driving behaviors in theoretical exams, so as to avoid similar mistakes in future training and actual driving.

[0069] Exemplarily, after the trainee enters the off-road driving training assessment sub-scenario, the system will monitor and record their driving operation behaviors in real time to evaluate the trainee's driving ability in a specific scenario. The core objective of this step is to determine whether the trainee is performing operations according to the correct driving methods and to identify their incorrect driving habits through vehicle sensor data, operation records, and driving behavior analysis. For example: Steering wheel operation, whether there are problems such as oversteering, insufficient straightening, or failure to change lanes in a timely manner; Braking and throttle control, whether there are problems such as "sudden braking" or "insufficient braking" resulting in vehicle out of control; Lighting use, whether the high and low beams are correctly switched during night driving; Response to emergencies, when encountering suddenly appearing pedestrians or obstacles, whether the trainee's reaction complies with safe driving norms. When the trainee's operation complies with the norms, the system will automatically record the correctness of this operation behavior and, according to the foregoing implementation manner, remove the mastered knowledge points from the wrong question bank of the theory exam. However, if the trainee's operation is incorrect, it enters the next analysis process. When the trainee makes an incorrect operation in the training scenario, the system will analyze this error to determine the specific type of the error and the possible driving risks. The key to this process lies in error classification and risk assessment. For example: Error classification: Minor errors, such as slightly late braking but without causing an accident; Medium errors, such as failure to correctly judge the right of way at an intersection, resulting in other vehicles being forced to slow down; Severe errors, such as running a red light, suddenly turning the steering wheel sharply resulting in vehicle out of control. The system evaluates the risks that this incorrect behavior may bring based on driving rules and traffic safety standards and provides corresponding teaching feedback. For example: Incorrect behavior, the trainee fails to switch to the low beam when meeting an oncoming vehicle at night; Error classification, medium error; Risk assessment, it may cause the oncoming driver's line of sight to be blocked, increasing the risk of traffic accidents. Incorrect behavior, the trainee directly slams on the brakes instead of gently stepping on the brakes first when braking suddenly on a slippery road; Error classification, severe error; Risk assessment, it is easy to cause the vehicle to lose control, skid, or even roll over. After completing the analysis and classification of the incorrect behavior, the system enters the next step, describes the incorrect behavior in text and adds it to the theory exam questions. The system will convert the incorrect operation behavior into an easy-to-understand text description for presentation to the trainee in the theory exam. This text description needs to have the key details that can accurately describe the incorrect operation and simulate the real driving situation in the exam, making it easier for the trainee to understand the severity of the error and, if possible, providing the correct operation method to help the trainee establish the correct driving concept. For example, the text description: Incorrect behavior, the trainee fails to switch to the low beam when driving at night. Or, Incorrect behavior, the trainee does not adopt progressive braking when braking on a slippery road but directly slams on the brakes. The system will directly embed the text description of the trainee's incorrect driving behavior into the options of the driving theory simulation exercise, enabling the trainee to more intuitively identify the incorrect operation and select the correct driving method in the subsequent theory exam. The key to this process lies in enhancing the trainee's error awareness and improving the integration of theory and practice.Add the incorrect operation text to the corresponding incorrect question options so that when students encounter such problems in the theoretical exam, they can compare the incorrect descriptions and make the correct choices; set real-case questions: Add a "Case Analysis of Incorrect Driving" question type to the theoretical exam, requiring students to identify the mistakes and choose the correct driving method. Example theoretical exam question: "During night driving, which of the following behaviors is correct?".

[0070] A. Keep the high beam on when meeting an oncoming vehicle at night to see the road conditions clearly

[0071] B. Switch to the low beam in advance when meeting an oncoming vehicle at night to avoid affecting the vision of the driver of the oncoming vehicle

[0072] C. When driving on the highway at night and there are no vehicles ahead, keep the high beam on all the time

[0073] D. Immediately turn off all lights after entering the tunnel to avoid the influence of light reflection on driving

[0074] (Correct answer: B. The incorrect description in option A may be directly taken from the incorrect driving behavior of a certain student).

[0075] Through this method, students can not only correct their driving mistakes in practice, but also repeatedly strengthen the correct driving concepts in the theoretical exam, improving their overall driving ability. Thus, by analyzing the incorrect operation behaviors of students and presenting real incorrect cases in the theoretical exam, students can deeply understand their mistakes in both theoretical learning and practical training and correct their driving habits. Traditional theoretical exams only involve rote memorization of driving rules, while this method allows students to truly understand the danger of incorrect driving behaviors through real-scene feedback and avoid similar mistakes in actual driving. By dynamically adjusting the content of the theoretical exam, students can focus on the knowledge points they have not yet mastered, thereby increasing the passing rate of the driving exam. The incorrect behaviors of each student will be recorded and analyzed, directly affecting the content of their theoretical exam, thus forming a personalized learning path to ensure that each student can receive the most effective training.

[0076] In one embodiment, it further includes:

[0077] Match the text description of the operation behavior itself with the content of the existing options of the incorrect questions associated with the off-road driving training assessment sub-scene in the driving theory simulation exercise;

[0078] In the case where there is no matching option, add the text description of the operation behavior to the options of the incorrect questions associated with the off-road driving training assessment sub-scene in the driving theory simulation exercise, and the driving theory simulation exercise is a multiple-choice question.

[0079] Exemplarily, if the system detects that there is no option in the wrong question option library of the theory exam that matches the trainee's incorrect operation behavior, the description of the incorrect operation behavior will be automatically added to the theory exam question bank to ensure that the trainee can encounter the mistakes they have made in the theory exam. Since it is usually difficult for teachers to ensure the diversity of wrong interference options when setting questions, but the situations of trainees making mistakes may be diverse, it increases the diversity of the test questions. Since the system can automatically identify and update the wrong question options in the driving theory exam, there is no need for manual intervention, and the high adaptability and high pertinence of the theory exam can be maintained for a long time.

[0080] According to some embodiments, the driving theory simulation practice result includes a theory simulation practice score, and the method further includes:

[0081] In the case of deleting the wrong question information associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice result, the theory simulation practice score of the target trainee is regenerated based on the current driving theory simulation practice result.

[0082] Thus, the scoring method of the traditional driving exam system is fixed and cannot be dynamically adjusted, resulting in the situation that even if the trainee has mastered certain knowledge points in actual driving, the theory exam score may still be affected by early mistakes. By deleting the wrong questions of the mastered knowledge points and recalculating the score, the system can more accurately reflect the trainee's real learning progress. In the traditional mode, the trainee may have mastered a certain knowledge point in actual driving training, but still needs to repeatedly practice the same questions in the theory exam. This method can automatically adjust the exam scope, enabling the trainee to focus on learning the knowledge points that have not been mastered, thereby improving the learning efficiency. Since the exam questions of the theory exam are dynamically adjusted, the trainee's score is closer to the real driving ability, making them more confident in the formal exam and improving the passing rate. This method combines the theory exam and actual driving training data to personalize the learning path, ensuring that each trainee can obtain the most suitable training content instead of adopting a one-size-fits-all question bank mode.

[0083] In one embodiment, the driving theory simulation practice result includes the correct question information of the driving theory simulation practice, and the method further includes:

[0084] Establish an off-road driving training assessment verification scenario based on the driving knowledge points associated with the correct question information;

[0085] In the case of receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario, load the off-road driving training assessment verification scenario into the target off-road driving training scenario;

[0086] Obtain the simulated driving operation behavior of the target trainee in the off-road driving training assessment verification scenario;

[0087] In the case where the simulated driving operation behavior is an incorrect simulated driving operation behavior, analyze the incorrect simulated driving operation behavior;

[0088] Based on the operation behavior itself of the incorrect simulated driving operation behavior, conduct a text description;

[0089] In the driving theory simulation exercise results, modify the correct questions associated with the off-road driving training assessment verification scenario to incorrect questions, and use the text description of the operation behavior as a new option. The driving theory simulation exercise is a multiple-choice question.

[0090] It is understandable that in the driving theory simulation practice, even if the trainee answers some questions correctly, it still cannot ensure that they truly master the relevant driving knowledge. Therefore, the system will analyze the trainee's correct-answer information and establish an on-road driving training assessment and verification scenario based on the relevant driving knowledge points. Analyze the trainee's correct-answer data in the theory exam and screen for possible problems with insufficient understanding. If the trainee's correct answers involve "braking distance in rainy days", a "wet road surface emergency braking test" scenario will be generated; if the trainee's correct answers involve "switching between high and low beam lights at night", a "night driving lighting assessment" scenario will be generated; if the trainee's correct answers involve "giving way to pedestrians", a "sudden pedestrian crossing the road" scenario will be generated. The system can adjust the complexity of the scenario according to the trainee's past error situations, such as increasing traffic flow, sudden situations, and road environment changes, to ensure the rigor of the assessment. When the trainee requests on-road driving training, the system will automatically retrieve their correct-answer information in the theory exam and load the corresponding on-road driving training assessment and verification scenario to test whether the trainee truly masters the knowledge point. The trainee enters the "wet road surface emergency braking" verification scenario; the system randomly triggers a sudden situation during driving (such as a vehicle in front suddenly braking); the trainee must adopt the correct braking method (lightly step on the brakes and gradually decelerate), otherwise it will be recorded as an incorrect operation. Trainee B correctly answered "the braking distance needs to be increased in rainy days" in the theory exam, but during on-road driving training, when the system triggered the "wet road surface emergency braking test", the trainee stepped on the brakes hard, causing the vehicle to skid. The system determined that the trainee had insufficient understanding of this knowledge point and entered the next processing flow. The system will monitor the trainee's driving operation behavior in the verification scenario in real time to determine whether they are performing the operation correctly according to the theoretical knowledge. The monitoring content can include the braking method (whether to brake gradually to avoid skidding); the steering wheel operation (whether to correctly adjust the direction to avoid skidding); the use of lights (whether to use high and low beam lights according to traffic rules); emergency avoidance (whether to correctly avoid in case of a sudden situation). If the operation is correct, the verification of this knowledge point passes, and the correct answers in the theory exam remain unchanged; if the operation is incorrect, it enters the next step to analyze the incorrect behavior and modify the correct-answer information in the theory exam. If the trainee's operation behavior in the verification scenario is incorrect, the system will analyze the type of error and generate a text description of the error. For example, incorrect operation: "When braking emergently on a wet road surface, the trainee stepped on the brakes hard, causing the vehicle to skid." Error analysis: "The trainee failed to correctly master the braking skills on a wet road surface and ignored the principle of gradually braking, which is likely to cause the vehicle to lose control." Based on this analysis result, the system enters the next step to modify the theory exam data. The system will convert the trainee's incorrect operation behavior in the verification scenario into a text description and apply it to the multiple-choice questions in the theory exam. Since the trainee failed to perform the operation correctly in the actual driving test, the system believes that their understanding of this knowledge point is still not solid. Therefore, the original correct answers in the theory exam will be marked as wrong questions for subsequent learning and strengthened assessment.The system will automatically add the description of the trainee's incorrect driving behavior as a new option to the multiple-choice questions in the theory test to help trainees identify their own mistakes in the theory test. For example, the original question in the driving theory test:

[0091] Q: "When making an emergency brake on a slippery road surface, what is the correct braking method?"

[0092] A. Stamp on the brake hard to stop the vehicle as soon as possible

[0093] B. Decelerate gradually and step on the brake lightly to avoid skidding

[0094] C. Accelerate through first and then consider braking

[0095] D. As long as the vehicle is equipped with ABS, there is no need to control the braking force

[0096] The trainee originally chose B and the answer was correct, but an incorrect operation occurred during the on-road driving verification. Therefore, the system modified this question and added the description of the trainee's incorrect behavior:

[0097] Or, Q: "When making an emergency brake on a slippery road surface, what is the correct braking method?"

[0098] A. Stamp on the brake hard to stop the vehicle as soon as possible

[0099] B. Decelerate gradually and step on the brake lightly to avoid skidding

[0100] C. Accelerate through first and then consider braking

[0101] D. As long as the vehicle is equipped with ABS, there is no need to control the braking force

[0102] E. Stamp on the brake hard, causing the vehicle to skid (this option is added based on the trainee's real mistake)

[0103] Thus, through the actual driving scenario assessment, even if a trainee selects the correct answer in the theory test, if they still make mistakes during driving, the system will retest their understanding. The system dynamically modifies the wrong questions in the theory test to ensure that trainees focus on learning the knowledge points they have not yet mastered, optimizing the learning path. Automatically adjusting the assessment content based on the trainees' real driving mistakes makes learning more efficient and avoids ineffective training. By combining theory with practice, trainees' driving abilities are strengthened, and the error probability in actual road driving is reduced.

[0104] Please refer to Figure 2 , an embodiment of the on-road test driving training simulation scenario generation device in the embodiment of the present application may include:

[0105] An acquisition unit 201, configured to acquire the driving theory simulation practice results of a target trainee, where the driving theory simulation practice results include the wrong question information of the driving theory simulation practice;

[0106] A simulation unit 202, configured to establish a road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong-question information.

[0107] A loading unit 203, configured to load the road driving training assessment sub-scenario into the target road driving training scenario when receiving an external road test driving training simulation request of the target student for the target road driving training scenario.

[0108] In summary, the external road test driving training simulation scenario generation device provided in the above embodiments establishes an interactive vehicle model based on the contour parameters of different vehicle types, where the interactive vehicle model is a movable vehicle model of other visible road areas for the student during the simulated driving process; a theoretical blind area calculated based on the contour parameters of the interactive vehicle model; monitors the positional relationship between the user's simulated driving vehicle and the interactive vehicle model during the process of the user using a driving simulator or a personal intelligent terminal for driving simulation training, and generates a prompt message when the user's simulated driving vehicle enters the theoretical blind area. Thus, a three-dimensional model is generated using accurate vehicle contour parameters, making each interactive vehicle in the simulation environment as close as possible to the actual vehicle in terms of shape, size, and occlusion situation. It supports the establishment of models for different vehicle types and different vehicle body structures (such as sedans, SUVs, trucks, buses), providing diverse driving scenarios for students. Through ray tracing and geometric occlusion algorithms, the invisible areas caused by the vehicle body structure can be accurately determined, providing a reliable basis for the warning mechanism. Considering the changes in the driver's seat and perspective, the blind area data is dynamically adjusted, making the prompt information more real-time and accurate. For example, during turning, accelerating, or decelerating, the changes in the blind area boundary can be timely reflected in the prompt. A prompt message is generated in a timely manner, warning the driver before entering the blind area of other vehicles, so that the driver can take measures in advance to avoid collisions. For example, during a lane change, if the side blind area of the target vehicle is detected, the system will remind the student "Please confirm before changing lanes", greatly reducing the risk of side collisions. Through real interactive feedback, students can repeatedly experience the danger brought by the blind area in the virtual environment, gradually cultivate the awareness of observing the surrounding environment and judging the blind area risk, and thus improve safety in real driving. This method allows parameter adjustment and function extension according to different user requirements and different driving scenarios, and is applicable to both driving simulators and personal intelligent terminal driving training software.

[0109] Above Figure 2 The external road test driving training simulation scenario generation device in the embodiments of the present application has been described from the perspective of modular functional entities. Next, a detailed description of the external road test driving training simulation scenario generation device in the embodiments of the present application will be given from the perspective of hardware processing. Please refer to Figure 3, an embodiment of the off-road driving training simulation scenario generation device 300 in the embodiments of the present application includes:

[0110] An input device 301, an output device 302, a processor 303, and a memory 304. Among them, the number of processors 303 can be one or more. Figure 3 Taking one processor 303 as an example. In some embodiments of the present application, the input device 301, the output device 302, the processor 303, and the memory 304 can be connected by a bus or other means. Among them, Figure 3 Taking connection by bus as an example.

[0111] Among them, by invoking the operation instructions stored in the memory 304, the processor 303 is configured to perform the following steps:

[0112] Obtain the driving theory simulation practice result of the target trainee, where the driving theory simulation practice result includes the wrong question information of the driving theory simulation practice;

[0113] Establish an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information;

[0114] When receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario, load the off-road driving training assessment sub-scenario into the target off-road driving training scenario.

[0115] By invoking the operation instructions stored in the memory 304, the processor 303 is further configured to execute Figure 1 Any one of the corresponding embodiments.

[0116] Please refer to Figure 4 , Figure 4 It is a schematic diagram of an embodiment of the electronic system provided by the embodiments of the present application.

[0117] As Figure 4 shown, the embodiments of the present application provide an electronic system, including a memory 410, a processor 420, and a computer program 411 stored on the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0118] Obtain the driving theory simulation practice result of the target trainee, where the driving theory simulation practice result includes the wrong question information of the driving theory simulation practice;

[0119] Establish an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information;

[0120] When receiving the on-road driving training simulation request of the target trainee for the target on-road driving training scenario, load the on-road driving training assessment sub-scenario into the target on-road driving training scenario.

[0121] In the specific implementation process, when the processor 420 executes the computer program 411, it can implement Figure 1 any one of the corresponding embodiments.

[0122] Since the electronic system introduced in this embodiment is the device adopted by an on-road driving training simulation scenario generation device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic system in this embodiment. Therefore, the specific implementation of how this electronic system implements the method in the embodiment of the present application will not be described in detail here. As long as the device adopted by those skilled in the art to implement the method in the embodiment of the present application belongs to the scope protected by the present application.

[0123] Please refer to Figure 5 , Figure 5 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present application.

[0124] As Figure 5 shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the following steps are implemented:

[0125] Obtain the driving theory simulation practice result of the target trainee, where the driving theory simulation practice result includes the wrong question information of the driving theory simulation practice;

[0126] Based on the driving knowledge points associated with the wrong question information, establish an on-road driving training assessment sub-scenario;

[0127] When receiving the on-road driving training simulation request of the target trainee for the target on-road driving training scenario, load the on-road driving training assessment sub-scenario into the target on-road driving training scenario.

[0128] In the specific implementation process, when the computer program 511 is executed by a processor, it can implement Figure 1 any one of the corresponding embodiments.

[0129] It should be noted that in the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0131] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0132] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks.

[0134] The embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the processes in Figure 1 the method for generating an external road test driving training simulation scenario in the corresponding embodiment.

[0135] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, they implement all or part of the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)).

[0136] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0137] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the couplings, direct couplings, or communication connections shown or discussed with each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, in each embodiment of the present application, each functional unit can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0140] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0141] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for generating a simulated scenario for off-road driving training, characterized in that, Including: Obtaining the driving theory simulation practice results of the target trainee, where the driving theory simulation practice results include the wrong question information of the driving theory simulation practice; Establishing an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information; When receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario, loading the off-road driving training assessment sub-scenario into the target off-road driving training scenario.

2. The method according to claim 1, wherein Also including: Obtaining the simulated driving operation behavior of the target trainee in the off-road driving training assessment sub-scenario; When the simulated driving operation behavior is correct, deleting the wrong question information associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice results, so as not to assess the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice anymore.

3. The method according to claim 1, wherein Also including: Obtaining the simulated driving operation behavior of the target trainee in the off-road driving training assessment sub-scenario; Increasing the assessment frequency of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice.

4. The method according to claim 1, wherein Also including: Obtaining the simulated driving operation behavior of the target trainee in the off-road driving training assessment sub-scenario; When the simulated driving operation behavior is a wrong simulated driving operation behavior, analyzing the wrong simulated driving operation behavior; Making a text description based on the operation behavior itself of the wrong simulated driving operation behavior; Adding the text description of the operation behavior to the options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice.

5. The method according to claim 4, wherein Also including: Matching the text description of the operation behavior itself with the content of the existing options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice; When there is no matching option, adding the text description of the operation behavior to the options of the wrong questions associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice, and the driving theory simulation practice is a multiple-choice question.

6. The method according to claim 2, characterized in that The driving theory simulation practice results include a theory simulation practice score, and the method also includes: When deleting the wrong question information associated with the off-road driving training assessment sub-scenario in the driving theory simulation practice results, regenerating the theory simulation practice score of the target trainee based on the current driving theory simulation practice results.

7. The method according to any one of claims 1 to 6, characterized in that, The driving theory simulation practice results include the correct question information of the driving theory simulation practice, and the method also includes: Establishing an off-road driving training assessment verification scenario based on the driving knowledge points associated with the correct question information; When receiving the off-road driving training simulation request of the target trainee for the target off-road driving training scenario, loading the off-road driving training assessment verification scenario into the target off-road driving training scenario; Obtaining the simulated driving operation behavior of the target trainee in the off-road driving training assessment verification scenario; When the simulated driving operation behavior is a wrong simulated driving operation behavior, analyzing the wrong simulated driving operation behavior; Making a text description based on the operation behavior itself of the wrong simulated driving operation behavior; In the driving theory simulation practice results, modify the questions associated with the off-road driving training assessment verification scenario to wrong questions, and use the text description of the operation behavior as a new option. The driving theory simulation practice is a multiple-choice question.

8. An off-road driving training simulation scenario generation device, characterized in that, Including: An acquisition unit for acquiring the driving theory simulation practice results of a target student, where the driving theory simulation practice results include the wrong question information of the driving theory simulation practice; A simulation unit for establishing an off-road driving training assessment sub-scenario based on the driving knowledge points associated with the wrong question information; A loading unit for loading the off-road driving training assessment sub-scenario into the target off-road driving training scenario when receiving an off-road driving training simulation request of the target student for the target off-road driving training scenario.

9. An electronic system, comprising a memory and a processor, characterized in that, The processor is used to implement the steps of the off-road driving training simulation scenario generation method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by the processor, implements the steps of the off-road driving training simulation scenario generation method as described in any one of claims 1 to 7.

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