Out-road test driving exercise management method and system

Abstract

The embodiment of the invention provides an out-road test driving exercise management method and system, and can solve the problems that the driving time allocated to each student is limited, the complexity of driving road sections and scenes of different students may be different, the learning effects may be uneven, part of students may only exercise simple road sections, and the learning efficiency is poor. And the whole learning quality is influenced because the user is not familiar with complex road condition processing. The method comprises the steps of obtaining map data and traffic sign data of a target training path before a trainee performs out-road driving training; based on the map data and the traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each trainee in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with main typical driving knowledge points; and sending student information corresponding to the training sub-path to the coach client before the training sub-path is reached.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and system for managing driving practice in an off-road test. Background Art

[0002] During the off-road test training, students take turns driving, and each student is allocated limited driving time, which makes it impossible for them to fully familiarize themselves with the entire route of the off-road test and deal with various driving scenarios. As a result, students are not fully trained in certain specific sections or complex driving tasks (such as merging and turning), and may not be confident or have poor skills during the test and actual driving. The sections and scenes driven by different students may have different levels of complexity, and the learning effects may be uneven. Some students may only practice simple sections and fail to become familiar with complex road conditions, affecting the overall learning quality. Summary of the invention

[0003] The embodiments of the present application provide a method and system for managing driving practice in an off-road test, which can solve the problem that due to the limited driving time allocated to each student, they are unable to fully familiarize themselves with the entire route of the off-road test and deal with various driving scenarios. The complexity of the road sections and scenes driven by different students may vary, and the learning effects may be uneven. Some students may only practice simple sections but fail to become familiar with the handling of complex road conditions, affecting the overall learning quality.

[0004] A first aspect of an embodiment of the present application provides a method for managing off-road test driving practice, comprising:

[0005] Before students conduct off-road driving training, obtain map data and traffic sign data of the target training route;

[0006] Allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points;

[0007] Before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coaching client.

[0008] Optionally, it also includes:

[0009] Before students start off-road driving training, they can obtain the current road condition data of the target training route collected in the cloud;

[0010] Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each trainee in the same training vehicle, so that each training sub-path tends to have a road condition associated with main typical driving knowledge points.

[0011] Optionally, also include:

[0012] Before reaching the target training sub-path, the target training sub-path and subsequent training sub-paths are merged and dynamically adjusted based on the current road condition data, so that the remaining training sub-paths after adjustment tend to have road conditions associated with major typical driving knowledge points.

[0013] Optionally, also include:

[0014] Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information;

[0015] Determine each student's weak theoretical knowledge points based on the historical information;

[0016] Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding theoretical weak knowledge points of the student.

[0017] Optionally, also include:

[0018] Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information;

[0019] Determine each student's weak theoretical knowledge points based on the historical information;

[0020] Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the theoretical weak knowledge point of the corresponding student.

[0021] Optionally, also include:

[0022] Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee;

[0023] Determine each learner's weak driving knowledge points based on the historical information;

[0024] Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding student's weak driving knowledge points.

[0025] Optionally, also include:

[0026] Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee;

[0027] Determine each learner's weak driving knowledge points based on the historical information;

[0028] Based on the current road condition data, a training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the corresponding student's weak driving knowledge point.

[0029] A second aspect of the embodiment of the present application provides a device for managing off-road test driving practice, comprising:

[0030] An acquisition unit, used to acquire map data and traffic sign data of a target training route before the trainee conducts off-road driving training;

[0031] an allocating unit, configured to allocate a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have a road condition associated with a main typical driving knowledge point;

[0032] The prompting unit is used to send the trainee information corresponding to the training sub-path to the coaching client before reaching the training sub-path.

[0033] A third aspect of an embodiment of the present application provides an electronic system, including a memory and a processor, wherein the processor is configured to implement the steps of the above-mentioned off-road test driving practice management method when executing a computer program stored in the memory.

[0034] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned off-road test driving practice management method.

[0035] In summary, the off-road test driving practice management method provided by the embodiment of the present application is to obtain the map data and traffic sign data of the target training path before the trainee conducts off-road driving training; based on the map data and traffic sign data of the target training path, the training sub-path corresponding to the driving training is allocated to each trainee in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the main typical driving knowledge points; before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coach client. Through the balanced allocation of sub-paths, trainees can be exposed to a variety of typical driving scenarios within a limited training time, avoiding technical shortcomings caused by one-sided training content. The tasks of each sub-path are clear and relatively independent, and trainees do not need to repeatedly drive invalid sections, and the training focus is highlighted. The allocation algorithm ensures that the driving time and task difficulty of the trainees are roughly the same, avoiding the influence of individual trainees on the training effect due to unreasonable path allocation. The coach receives prompt information in real time and can provide advance guidance for the upcoming driving tasks.

[0036] Correspondingly, the off-road test driving practice management device, electronic system and computer-readable storage medium provided in the embodiments of the present invention also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flowchart of a possible off-road test driving practice management method provided in an embodiment of the present application;

[0038] Figure 2 A schematic structural block diagram of a possible off-road test driving practice management device provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of the hardware structure of a possible off-road test driving practice management device provided in an embodiment of the present application;

[0040] Figure 4 A schematic structural block diagram of a possible electronic system provided in an embodiment of the present application;

[0041] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided for an embodiment of the present application. DETAILED DESCRIPTION

[0042] The embodiments of the present application provide a method and system for managing driving practice in an off-road test, which can solve the problem that due to the limited driving time allocated to each student, they are unable to fully familiarize themselves with the entire route of the off-road test and deal with various driving scenarios. The complexity of the road sections and scenes driven by different students may vary, and the learning effects may be uneven. Some students may only practice simple sections but fail to become familiar with the handling of complex road conditions, affecting the overall learning quality.

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0044] See also Figure 1 , which is a flow chart of a method for managing off-road test driving practice provided in an embodiment of the present application, and may specifically include: S110-S130.

[0045] S110, before the trainee performs off-road driving training, map data and traffic sign data of a target training route are obtained.

[0046] S120, allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points.

[0047] S130, sending the trainee information corresponding to the training sub-path to the coaching client before reaching the training sub-path.

[0048] It is understandable that the solution optimizes the allocation of training paths and reasonably distributes various typical driving scenarios in off-road driving training to all students in the same training vehicle, thereby improving the coverage and balance of training. This process is based on the following core principles: Target training path decomposition, decomposing the entire training path, identifying the characteristics of each section of the road based on map data and traffic sign data, such as: lane merging, highway entrances, turns, traffic lights, roundabouts and other typical driving knowledge points. Fair distribution and balanced coverage, each student's driving time and task allocation are balanced to ensure that each student can be exposed to different types of driving scenarios within a limited time, avoiding only training simple sections or repeating certain scenes. Real-time guidance and task tracking, before each student arrives at the assigned sub-path, the system sends the sub-path and corresponding student information to the coach, and the coach can guide the student in time according to the characteristics of the road section to improve the training effect.

[0049] Exemplarily, the system obtains detailed geographic information of the target training path through a navigation platform or other map interface, including road morphology (such as straight roads, turns, ramps), traffic signs (such as speed limits, stop and give way) and dynamic traffic information (such as traffic lights, traffic flow). Parse the map data and mark the key driving tasks of each sub-path, such as: merging (two lanes merging or highway entrances and exits), turning (right-angle turns or roundabouts), and complex traffic sign areas (such as school areas and areas with dense traffic lights). Divide the target training path into several training sub-paths, each sub-path covers one or two typical driving knowledge points, and ensure that the length of each sub-path is moderate for easy training. Sub-paths are assigned to students based on the following principles: Balance principle, ensuring that the number of sub-paths and driving time exposed to each student are equal. Coverage principle, giving priority to ensuring that each student can be exposed to different typical driving knowledge points. Random adjustment, on the premise of meeting the above principles, random adjustment is used to avoid students always training similar sections. When the vehicle approaches the starting point of a sub-path, the system sends the current student's name, sub-path characteristics and related driving tasks to the coach's client (for example, "There is a merging section ahead, please perform the merging operation"). The coach provides targeted guidance to the student based on the prompt information, such as reminding the key points of merging in advance and controlling the turning speed. Therefore, through the balanced allocation of sub-paths, students can be exposed to a variety of typical driving scenarios within a limited training time, avoiding technical shortcomings caused by one-sided training content. For example, student A trained high-speed merging and turning skills. Student B was exposed to roundabout driving and complex traffic light operations. Student C practiced ramp starting and straight road speed limit driving. The tasks of each sub-path are clear and relatively independent, so students do not need to repeatedly drive invalid sections, and the training focus is highlighted. The allocation algorithm ensures that the driving time and task difficulty of students are roughly the same, avoiding the influence of individual students on the training effect due to unreasonable path allocation. For example, if the path allocation is uneven, student A may only drive a simple straight road, while student B faces complex merging and roundabouts, resulting in a significant difference in learning effect between the two. The instructor receives prompt information in real time and can provide advance guidance for upcoming driving tasks, such as reminding the driver to slow down before turning and check the rearview mirror before changing lanes.

[0050] In summary, the off-road test driving practice management method provided in the above embodiment obtains the map data and traffic sign data of the target training path before the trainee conducts off-road driving training; based on the map data and traffic sign data of the target training path, the training sub-path corresponding to the driving training is allocated to each trainee in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the main typical driving knowledge points; before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coach client. Through the balanced allocation of sub-paths, trainees can be exposed to a variety of typical driving scenarios within a limited training time, avoiding technical shortcomings caused by one-sided training content. The tasks of each sub-path are clear and relatively independent, and trainees do not need to repeatedly drive invalid sections, and the training focus is highlighted. The allocation algorithm ensures that the driving time and task difficulty of the trainees are roughly the same, avoiding the influence of individual trainees on the training effect due to unreasonable path allocation. The coach receives prompt information in real time and can provide advance guidance for the upcoming driving tasks.

[0051] In one embodiment, it also includes:

[0052] Before students start off-road driving training, they can obtain the current road condition data of the target training route collected in the cloud;

[0053] Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each trainee in the same training vehicle, so that each training sub-path tends to have a road condition associated with main typical driving knowledge points.

[0054] It is understandable that when dynamic traffic data is added, the system is not only based on preset maps and traffic sign data, but also needs to collect real-time current traffic data provided by the cloud (such as traffic flow, road construction, sudden traffic incidents, etc.), and dynamically adjust the allocation of training sub-paths based on this information. This can further improve the rationality and adaptability of training, ensuring that each student can experience representative complex scenarios in actual driving.

[0055] Exemplarily, the system obtains real-time traffic data of the target training path from the cloud, and identifies the current driving characteristics of each sub-path, such as traffic volume, congestion, construction areas, traffic accidents, etc. The complexity of the training path is dynamically classified, and each sub-path is marked in real time as "simple road conditions", "medium-complex road conditions", and "complex road conditions". The sub-paths assigned to the trainees are adjusted according to the real-time traffic data, and the trainees are given priority in assigning sub-paths that are related to typical driving knowledge points and currently have actual driving complexity. Avoid wasting time for trainees because some sections of the road temporarily become simple straight roads or are closed and cannot be trained. Ensure that all trainees can still be exposed to different typical driving knowledge points after dynamic adjustments, and avoid one-sided training content for trainees due to real-time traffic adjustments.

[0056] Exemplarily, the system obtains the current road condition data of the target path through the navigation service interface (such as the real-time map API), including: traffic flow, judging the busyness of the road by traffic density. Traffic events, such as closed sections due to construction and accidents. Dynamic traffic control, such as adding traffic lights and temporary speed limit areas. Based on the road condition data, the sub-paths in the target path are reclassified, for example, sub-path 1: the current traffic flow is high, involving merging and traffic interspersed, marked as "complex road conditions". Sub-path 2: closed due to construction, marked as "unavailable". Sub-path 3: low traffic flow, suitable for practicing basic straight road driving. The current complexity of the sub-path can be calculated by combining the following factors: road condition signs, such as traffic lights, merging, and highway entrances. Real-time traffic flow, high traffic increases complexity. Temporary events, such as construction or accidents, reduce availability. Eliminate unavailable paths: such as sub-paths that are completely inaccessible due to construction closures or accidents. Prioritize complex paths, for example, student A is assigned to sections with merging operations, and student B is assigned to complex traffic light areas. If the original planned path becomes too simple (such as a straight road with low traffic), it will be replaced with an alternate path. Before each student approaches a sub-path, the system sends updated sub-path information to the coach client, including: sub-path characteristics (road condition description, typical driving tasks). Real-time traffic prompts (such as "The current road section is busy, please guide students to observe and merge in advance"). Dynamic adjustment reasons (such as "The original planned road section has been replaced with an alternate path due to construction"). Therefore, by adjusting the training path through real-time traffic data, students can always be exposed to real and representative driving scenarios, avoiding training deviations caused by uncontrollable road conditions (such as construction, accidents). Dynamic adjustment ensures that all students can get balanced training opportunities under any circumstances, avoiding insufficient training for some students due to temporary changes in road conditions. Students are exposed to complex road conditions that change in real time during training, which helps improve their ability to deal with emergencies and lay a more solid foundation for examinations and actual driving. The coach can adjust the teaching strategy according to the current road conditions in real time based on the dynamic prompt information, such as reminding students of the best operating methods for dealing with busy roads, to improve teaching efficiency.

[0057] In one embodiment, it also includes:

[0058] Before reaching the target training sub-path, the target training sub-path and subsequent training sub-paths are merged and dynamically adjusted based on the current road condition data, so that the remaining training sub-paths after adjustment tend to have road conditions associated with major typical driving knowledge points.

[0059] It is understandable that since real-time traffic data may cause changes in the characteristics of certain sub-paths (for example, a simple section becomes a complex section or completely impassable), by dynamically merging sub-paths, it is possible to avoid students from repeatedly driving simple sections or interrupting training due to the impassability of individual sections. The adjustment goal of the dynamically merged paths is to optimize the utilization of the remaining paths and ensure that students are exposed to enough driving knowledge points. Before reaching a target training sub-path, based on the current traffic data, the sub-path is merged with its subsequent sub-paths, and the training plan is readjusted according to the overall characteristics of the merge. Prioritize sub-paths containing typical driving knowledge points and ensure the diversity of student training content.

[0060] Exemplarily, the system obtains the road condition data of the current sub-path and its subsequent paths, including: the current traffic flow; whether there are temporary traffic events (construction, accidents, etc.); whether the typical driving knowledge point characteristics of the road conditions are still valid. If any of the following conditions are met, the merging logic is started: the characteristics of the current sub-path are repeated with the characteristics of the subsequent sub-path; the characteristics of the current sub-path have become invalid (such as closure due to construction); the subsequent sub-path can provide more challenging driving scenarios. Adjacent sub-paths are merged first, and the system merges the target sub-path with the adjacent subsequent path according to the actual road conditions to form a new training segment. After merging, the knowledge points are re-divided to ensure that the merged new sub-path still contains key driving tasks (such as merging, turning, and starting on a slope). Typical scenarios are allocated first. In the adjusted training plan, scenarios related to the test and with actual complexity are allocated to students first. For the merged path and the unmerged path, they are re-divided into multiple new sub-paths and allocated according to the following rules: to ensure that all students are exposed to a balanced amount of knowledge points and if some sections are eliminated due to insufficient complexity, then an alternative path is selected to replace them. As a result, dynamic merging avoids wasting time due to individual sections being impassable or repeated driving of simple sections, thereby improving training efficiency. After merging the paths, reallocation ensures that each student is exposed to a balanced driving task and covers key knowledge points, avoiding the monotony of learning content due to changes in road conditions. In complex or sudden road conditions, by merging and adjusting the paths, the training process can still proceed smoothly, and students can experience the scene changes that may be encountered in real driving. The dynamic prompts on the coach side make teaching more accurate. The coach can adjust the teaching focus in real time according to the characteristics of the new sub-paths, thereby improving the students' learning efficiency and test pass rate.

[0061] In one embodiment, it also includes:

[0062] Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information;

[0063] Determine each student's weak theoretical knowledge points based on the historical information;

[0064] Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding theoretical weak knowledge points of the student.

[0065] It is understandable that by combining the historical data of each student's driving theory test or simulation test, the weak driving theory knowledge points are identified, and then the allocation of the target training path is adjusted according to these weak points, so that students can train weak knowledge points in practice in a targeted manner, improve their learning effect and test pass rate. From the student's driving theory test scores or simulation test data, extract knowledge points with high error rates (such as traffic signs, road priority rules, special road conditions, etc.). Based on the individual characteristics of the students, form a "theoretical weak point portrait" for each student. Analyze the map data and traffic sign data of the target training path, and mark typical driving knowledge points (such as turning rules, speed limit signs, lane merging operations, etc.). Associate these driving knowledge points with the student's weak knowledge points to form a mapping of the path and the student's knowledge points. According to each student's theoretical weak points, assign corresponding training sub-paths to ensure that the student strengthens the understanding and operation ability of weak knowledge points in practice.

[0066] Exemplarily, historical records are obtained from the student's theoretical examination platform or simulation practice system, including: wrong question records, specific weak knowledge points (such as "meeting courtesy rules"); wrong question frequency, the number of errors or error rates of each knowledge point; weak point analysis, generate a list of weak knowledge points for each student based on the wrong question records. For example, student A: insufficient understanding of "roundabout driving rules" and "traffic light meaning". Student B: insufficient understanding of "lane merging rules" and "speed limit signs". Parse the map data and traffic sign data of the target training path, and mark the main driving knowledge points of each sub-path. For example: Sub-path 1: straight road driving, speed limit sign. Sub-path 2: high-speed merging area, merging rules. Sub-path 3: complex traffic light area, signal light meaning and response. Sub-path 4: roundabout area, roundabout driving rules. Establish a mapping relationship between the knowledge points of the target path and the weak knowledge points of the student. For example: student A needs to practice "roundabout driving rules" and is assigned sub-path 4. Student B needs to practice "lane merging rules" and is assigned sub-path 2. Ensure that the assigned paths meet the following conditions at the same time: each student's driving time is roughly equal; each student's weak knowledge points are fully covered; the student's training tasks are of moderate difficulty to avoid excessive concentration on a complex knowledge point. Therefore, by allocating paths based on weak points, each student's training content is more targeted and avoids repeated training of knowledge points that have already been mastered. Through the dynamic allocation algorithm, the students' training time and task volume are balanced, and each student receives personalized training content. Students experience and strengthen weak knowledge points in practice, which helps to transform theoretical knowledge into practical driving skills, improve driving ability and pass rate of the test.

[0067] In one embodiment, it also includes:

[0068] Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information;

[0069] Determine each student's weak theoretical knowledge points based on the historical information;

[0070] Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the theoretical weak knowledge point of the corresponding student.

[0071] It is understandable that the allocation of training sub-paths is dynamically adjusted in combination with the student's theoretical weaknesses and the real-time traffic data of the target path to ensure that the student can focus on strengthening the theoretical weaknesses in practice while adapting to the dynamic changes in the actual driving environment. The historical data of the student's driving theory simulation test or formal test can be obtained, and the wrong questions or knowledge points with high error frequency can be analyzed to form a personalized weak point portrait of the student. The current traffic flow, dynamic traffic events (such as construction, traffic accidents, etc.) and road characteristics data of the target path are obtained from the cloud to provide support for dynamic adjustment. The driving knowledge points in the target path are matched with the student's weak knowledge points, and the training sub-paths are dynamically allocated in combination with the real-time road conditions to ensure the pertinence and effectiveness of the training content for each student.

[0072] Exemplarily, the following contents are extracted from the driving theory test or simulation test records: incorrect question categories (such as sign recognition, priority rules), knowledge points with high error frequency, and points of loss of points in complex scenes in the simulation test (such as dynamic lane selection, speed limit sign recognition). Generate a personalized knowledge point portrait for each student. For example, student A: the weak points are "lane merging rules" and "speed limit signs". Student B: the weak points are "traffic light priority" and "hill start". The cloud or navigation service platform provides real-time road condition data for the target training path, including: traffic flow, identification of busy or unobstructed sections; temporary events, such as construction, accidents, and road closures; road section characteristics, such as slope changes, traffic light distribution, roundabouts, etc. The path characteristic labels are adjusted in real time according to the road conditions. For example, sub-path 1: normal straight road, no complex scenes; sub-path 2: high traffic flow, suitable for practicing lane merging rules; sub-path 3: temporary construction, impassable. According to the map data of the target path, the driving knowledge points are preliminarily matched with the student's weak points. For example, sub-path 2 (parallel line section): assigned to student A; sub-path 4 (ramp start): assigned to student B. If the real-time road conditions cause some path characteristics to change (such as a simple scene becomes a complex scene), readjust the sub-path allocation. Merge unavailable paths (such as construction sections). Replace low-complexity paths with backup paths (such as backup paths contain characteristics related to weak knowledge points). Therefore, combined with the student's weaknesses and real-time road conditions, the path allocation is dynamically adjusted to ensure that students can strengthen their theoretical weaknesses and adapt to the actual driving environment during training. By dynamically adjusting the path, we avoid repeating simple training or interrupting training due to road conditions, ensuring the maximum use of time and resources. Students strengthen their weak theoretical knowledge in practice, especially training under real complex road conditions, which helps to improve the pass rate of the test and actual driving skills. Even if construction or emergencies occur in the target path, the training process can still proceed smoothly through dynamic adjustment and backup path mechanisms.

[0073] According to some embodiments, further comprising:

[0074] Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee;

[0075] Determine each learner's weak driving knowledge points based on the historical information;

[0076] Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding student's weak driving knowledge points.

[0077] According to some embodiments, further comprising:

[0078] Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee;

[0079] Determine each learner's weak driving knowledge points based on the historical information;

[0080] Based on the current road condition data, a training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the corresponding student's weak driving knowledge point.

[0081] See also Figure 2 , an embodiment of the on-road test driving practice management device in the embodiment of the present application may include:

[0082] The acquisition unit 201 is used to acquire the map data and traffic sign data of the target training route before the trainee performs the off-road driving training;

[0083] An allocating unit 202 is configured to allocate a training sub-path corresponding to driving training in the target training path to each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with main typical driving knowledge points;

[0084] The prompting unit 203 is used to send the trainee information corresponding to the training sub-path to the coaching client before reaching the training sub-path.

[0085] In summary, the off-road test driving practice management device provided in the above embodiment obtains the map data and traffic sign data of the target training path before the trainee conducts off-road driving training; based on the map data and traffic sign data of the target training path, the training sub-path corresponding to the driving training is allocated to each trainee in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the main typical driving knowledge points; before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coach client. Through the balanced allocation of sub-paths, trainees can be exposed to a variety of typical driving scenarios within a limited training time, avoiding technical shortcomings caused by one-sided training content. The tasks of each sub-path are clear and relatively independent, and trainees do not need to repeatedly drive invalid sections, and the training focus is highlighted. The allocation algorithm ensures that the driving time and task difficulty of the trainees are roughly the same, avoiding the influence of individual trainees on the training effect due to unreasonable path allocation. The coach receives prompt information in real time and can provide advance guidance for the upcoming driving tasks.

[0086] above Figure 2 The off-road test driving practice management device in the embodiment of the present application is described from the perspective of modular functional entities. The off-road test driving practice management device in the embodiment of the present application is described in detail from the perspective of hardware processing. Figure 3 An embodiment of the off-road test driving practice management device 300 in the embodiment of the present application includes:

[0087] An input device 301, an output device 302, a processor 303 and a memory 304, wherein the number of the processor 303 can be one or more. Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 may be connected via a bus or other means, wherein: Figure 3 The example of connecting through bus is taken in the following.

[0088] Wherein, by calling the operation instruction stored in the memory 304, the processor 303 is used to perform the following steps:

[0089] Before students conduct off-road driving training, obtain map data and traffic sign data of the target training route;

[0090] Allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points;

[0091] Before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coaching client.

[0092] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any method in the corresponding embodiment.

[0093] See also Figure 4 , Figure 4 A schematic diagram of an electronic system according to an embodiment of the present application.

[0094] like Figure 4 As shown, an embodiment of the present application provides an electronic system, including a memory 410, a processor 420, and a computer program 411 stored in the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0095] Before students conduct off-road driving training, obtain map data and traffic sign data of the target training route;

[0096] Allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points;

[0097] Before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coaching client.

[0098] In the specific implementation process, when the processor 420 executes the computer program 411, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0099] Since the electronic system introduced in this embodiment is a device used to implement an off-road test driving practice management device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic system of this embodiment and its various variations. Therefore, how the electronic system implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0100] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present application.

[0101] like Figure 5 As 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:

[0102] Before students conduct off-road driving training, obtain map data and traffic sign data of the target training route;

[0103] Allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points;

[0104] Before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coaching client.

[0105] In the specific implementation process, when the computer program 511 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0106] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0107] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0108] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0111] The present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device is caused to execute the following Figure 1 This corresponds to the process in the off-road test driving practice management method in the embodiment.

[0112] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0113] 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 aforementioned method embodiments and will not be repeated here.

[0114] In the 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0115] 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 on 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.

[0116] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the 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 is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0118] As described 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for managing driving practice in an off-road test, characterized in that: include: Before students conduct off-road driving training, obtain map data and traffic sign data of the target training route; Allocating a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have road conditions associated with major typical driving knowledge points; Before reaching the training sub-path, the trainee information corresponding to the training sub-path is sent to the coaching client.

2. The method according to claim 1, characterized in that Also includes: Before students start off-road driving training, they can obtain the current road condition data of the target training route collected in the cloud; Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each trainee in the same training vehicle, so that each training sub-path tends to have a road condition associated with main typical driving knowledge points.

3. The method according to claim 2, characterized in that Also includes: Before reaching the target training sub-path, the target training sub-path and subsequent training sub-paths are merged and dynamically adjusted based on the current road condition data, so that the remaining training sub-paths after adjustment tend to have road conditions associated with major typical driving knowledge points.

4. The method according to claim 1, characterized in that Also includes: Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information; Determine each student's weak theoretical knowledge points based on the historical information; Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding theoretical weak knowledge points of the student.

5. The method according to claim 2, characterized in that Also includes: Before students start off-road driving training, obtain each student's driving theory simulation or driving theory test history information; Determine each student's weak theoretical knowledge points based on the historical information; Based on the current road condition data, the training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the theoretical weak knowledge point of the corresponding student.

6. The method according to claim 1, characterized in that Also includes: Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee; Determine each learner's weak driving knowledge points based on the historical information; Based on the map data and traffic sign data of the target training path, a training sub-path corresponding to driving training is allocated to each student in the same training vehicle in the target training path, so that each training sub-path tends to have road conditions associated with the corresponding student's weak driving knowledge points.

7. The method according to claim 2, characterized in that Also includes: Before the trainees conduct off-road driving training, obtain the off-road driving training history information of each trainee; Determine each learner's weak driving knowledge points based on the historical information; Based on the current road condition data, a training sub-path corresponding to the driving training is dynamically adjusted in the target training path for each student in the same training vehicle, so that each training sub-path tends to have a road condition associated with the corresponding student's weak driving knowledge point.

8. A device for managing driving practice for off-road test, characterized in that: include: An acquisition unit, used to acquire map data and traffic sign data of a target training route before the trainee conducts off-road driving training; an allocating unit, configured to allocate a training sub-path corresponding to driving training in the target training path for each trainee in the same training vehicle based on the map data and traffic sign data of the target training path, so that each training sub-path tends to have a road condition associated with a main typical driving knowledge point; The prompting unit is used to send the trainee information corresponding to the training sub-path to the coaching client before reaching the training sub-path.

9. An electronic system, comprising a memory and a processor, characterized in that: The processor is configured to implement the steps of the off-road test driving practice management method according to 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: When the computer program is executed by a processor, the steps of the off-road test driving practice management method according to any one of claims 1 to 7 are implemented.

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