Driving training coordination management method and system

Abstract

The embodiment of the invention provides a driving training coordination management method and system, and can solve the problems that the simulation scene is single, the simulation of the driving track and state of surrounding vehicles is limited, the demand of considering randomness and reality is difficult to meet, and the development difficulty is large. The method comprises the steps of generating at least one accompanying vehicle simulation instruction under the condition that a target student performs out-road driving simulation practice; sending the accompanying vehicle simulation instruction to other trainee terminals except the target trainee, wherein the accompanying vehicle simulation instruction comprises a vehicle identifier or a user identifier which is displayed on a simulation practice interface and is used for indicating an accompanying target vehicle; and generating a driving state of the accompanying vehicle in the simulation scene based on the driving operation of the other trainees on the accompanying vehicle, so that the target trainee performs simulation driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scene.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular, to a driving training coordination management method and system. Background Art

[0002] During the driving training process, simulators are widely used to provide a safe and controllable learning environment. However, existing driving simulators usually have the following limitations. The current driving simulation environment mainly consists of preset fixed scenarios, which cannot be dynamically adjusted according to the actual needs of trainees and are difficult to effectively cover the ever-changing real driving environment. Most of the other vehicles in existing simulators run according to established trajectories and behavior patterns, lacking sufficient intelligence and randomness. Although this method helps with basic driving skill training, it is relatively limited in cultivating trainees' ability to adapt to the real road environment. Although random events can be introduced into the simulation environment, if these events are completely randomly set, they often do not match the real driving environment, easily leading trainees to form coping habits that do not conform to real driving during the training process, affecting the improvement of actual driving ability. Constructing a highly realistic driving environment involves complex traffic flow modeling, artificial intelligence decision-making, multi-vehicle interaction, and real-time physical simulation technologies. The development cost is high and the technical implementation is difficult, resulting in a low level of intelligence in most driving simulation systems and making it difficult to dynamically simulate real traffic scenarios. Summary of the Invention

[0003] Embodiments of this application provide a driving training coordination management method and system, which can solve the problems of single simulation scenarios, limited simulation of the driving trajectories and states of surrounding vehicles, difficulty in meeting the requirements of both randomness and realism, and high development difficulty.

[0004] The first aspect of the embodiments of this application provides a driving training coordination management method, including:

[0005] When a target trainee conducts off-road driving simulation practice, generating at least one accompanying vehicle simulation instruction;

[0006] Sending the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or user identifier displayed on the simulation practice interface for indicating the vehicle accompanying the target vehicle;

[0007] Generating the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0008] Optionally, it further includes:

[0009] Obtain the driving state of the accompanying vehicle, the simulated scenario information, and the feedback operation of the target trainee on the simulated driving of the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulated scenario, and evaluate the driving ability of the target trainee.

[0010] Optionally, the accompanying vehicle simulation instruction further includes accompanying driving requirements, and the method further includes:

[0011] Evaluate the driving ability of the other trainees who execute the accompanying vehicle simulation instruction based on the matching degree between the driving state of the accompanying vehicle and the accompanying driving requirements.

[0012] Optionally, the accompanying vehicle simulation instruction further includes interactive driving requirements with the accompanying target, and further includes:

[0013] Evaluate the driving ability of the other trainees who execute the accompanying vehicle simulation instruction based on the matching degree between the driving state of the accompanying vehicle and the interactive driving requirements.

[0014] Optionally, it further includes:

[0015] In the case of a collision between the accompanying vehicle and the accompanying target vehicle, predict the damage degrees of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the moment of collision;

[0016] Generate damage pictures of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degrees of the accompanying vehicle and the accompanying target vehicle;

[0017] Display the damage pictures of the accompanying vehicle and the accompanying target vehicle on the target trainee side and the other trainee sides that execute the accompanying vehicle simulation instruction.

[0018] Optionally, it further includes:

[0019] In the case of a collision between the accompanying vehicle and the accompanying target vehicle, predict the damage degrees of the drivers of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the moment of collision;

[0020] Generate damage pictures of the drivers of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degrees of the drivers of the accompanying vehicle and the accompanying target vehicle;

[0021] Display the damage pictures of the drivers of the accompanying vehicle and the accompanying target vehicle on the target trainee side and the other trainee sides that execute the accompanying vehicle simulation instruction.

[0022] Optionally, it further includes:

[0023] Obtain a simulation switching request from the target trainee side or the other trainee sides that execute the accompanying vehicle simulation instruction;

[0024] Perform role conversion on the accompanying vehicle and the accompanying target vehicle based on the simulated handover request.

[0025] The second aspect of the embodiments of the present application provides a driving training coordination management device, including:

[0026] A generation unit, configured to generate at least one accompanying vehicle simulation instruction when a target trainee performs off-road driving simulation practice;

[0027] A sending unit, configured to send the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on the simulation practice interface for indicating an accompanying target vehicle;

[0028] A simulation unit, configured to generate a driving state of the accompanying vehicle in a simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0029] The third aspect of the embodiments of the present application provides an electronic system, including a memory and a processor, where the processor is configured to implement the steps of the above-mentioned driving training coordination management method when executing a computer program stored in the memory.

[0030] The fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is configured to implement the steps of the above-mentioned driving training coordination management method when executed by a processor.

[0031] In summary, the driving training coordination management method provided by the embodiments of the present application generates at least one accompanying vehicle simulation instruction when a target trainee conducts off-road driving simulation practice; sends the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, and the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle; generates the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario. Compared with the traditional AI simulation environment, this method enables the target trainee to face more realistic driving challenges through real interactions among trainees. For example, in urban road driving training, the target trainee may encounter an accompanying vehicle suddenly cutting in or changing lanes controlled by other trainees, and this kind of uncertainty is much more in line with the actual driving situation than the traditional pre-set AI behavior pattern, making the learning experience of trainees closer to reality. By introducing variable behaviors of accompanying vehicles, the target trainee needs to always maintain attention and adjust the driving strategy at any time. For example, in highway driving training, the accompanying trainee can control a vehicle driving slowly ahead, and the target trainee needs to judge whether to maintain a safe distance or overtake safely. This kind of interactive training can significantly improve the driving decision-making ability of trainees on real roads. The system can adjust the behavior pattern of the accompanying vehicle according to the driving level and learning progress of the trainees. For example, for novice drivers, the accompanying vehicle may just maintain a fixed speed to help trainees master basic following skills; while for advanced drivers, the accompanying vehicle may suddenly change lanes or drive side by side at high speed to improve the adaptability of trainees in complex environments. This method improves the interest and interactivity of simulated driving through multi-trainee collaborative training. Trainees not only need to focus on their own driving, but also consider the dynamic behaviors of the accompanying vehicles. This simulation mode helps to improve the teamwork ability and makes the training process more interesting. For example, in some scenarios, the system can design a "driving challenge" that requires the target trainee to complete a safe overtaking task within a specific time, and the accompanying vehicle will also adjust its driving state accordingly, making the training more challenging.

[0032] Correspondingly, the driving training coordination management 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

[0033] Figure 1 It is a schematic flow chart of a possible driving training coordination management method provided by the embodiments of the present application;

[0034] Figure 2 It is a schematic structural block diagram of a possible driving training coordination management device provided by the embodiments of the present application;

[0035] Figure 3 A schematic diagram of the hardware structure of a possible driving training coordination and management device provided in an embodiment of the present application;

[0036] Figure 4 A schematic structural block diagram of a possible electronic system provided in an embodiment of the present application;

[0037] Figure 5 A schematic structural block diagram of a possible computer-readable storage medium provided for an embodiment of the present application. DETAILED DESCRIPTION

[0038] The embodiments of the present application provide a driving training coordination management method and system, which can solve the problems of single simulation scene, limited simulation of surrounding vehicle driving trajectory and status, difficulty in meeting the requirements of balancing randomness and reality, and high development difficulty.

[0039] 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.

[0040] See also Figure 1 , which is a flowchart of a driving training coordination management method provided in an embodiment of the present application, and may specifically include: S110-S130.

[0041] S110, when the target trainee performs an off-road driving simulation exercise, generating at least one accompanying vehicle simulation instruction.

[0042] S120, sending the accompanying vehicle simulation instruction to other trainees except the target trainee, wherein the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle.

[0043] S130. Generate the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs simulated driving feedback operations on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0044] It can be understood that the system automatically generates simulation instructions according to the training tasks of the target trainee, enabling the trainee to face traffic conditions closer to real road conditions during driving training. Other trainees control the accompanying vehicle, allowing the target trainee to face more realistic driving challenges during training, rather than just AI vehicles controlled by fixed programs. Adjust the driving environment based on the feedback of the target trainee to ensure that the complexity and authenticity of traffic conditions are balanced during training, and improve the trainee's adaptability and driving skills.

[0045] Exemplarily, when the target trainee enters the off-road driving simulation training, the system first analyzes the trainee's current training tasks, driving ability, and the complexity of the simulation scenario. Based on this information, the system generates at least one simulation instruction for the accompanying vehicle to ensure that the target trainee can experience driving situations closer to the real traffic environment during training. For example, when the target trainee is performing highway overtaking training, the system may generate an accompanying vehicle that slowly changes lanes ahead to test the target trainee's decision-making ability; or when the target trainee is practicing night driving, the system may set an accompanying vehicle that uses high beams irregularly to examine the trainee's adaptability. The generated simulation instructions for the accompanying vehicle include not only the basic attributes of the vehicle, such as vehicle type, speed, driving direction, etc., but also specific behavior patterns are set according to training requirements, such as sudden braking, lane changing, driving side by side, etc., to ensure the dynamics of the simulation environment.

[0046] Exemplarily, after the simulation instructions for the accompanying vehicle are generated, the system sends these instructions to other trainees other than the target trainee, enabling them to act as the drivers of the accompanying vehicle in the simulation environment. The allocation of the accompanying vehicle can be dynamically adjusted based on factors such as the current level of the trainee, training requirements, and willingness matching. For example, the system can preferentially select trainees with higher experience to control the accompanying vehicle on the highway to simulate a more complex overtaking environment, while beginners can control simple straight or following vehicles to help the target trainee familiarize themselves with basic driving skills. The system will assign the accompanying vehicle to specific trainees through vehicle identification or user identification, enabling them to clearly know the vehicle they need to control in their own simulation training interface, and at the same time clarify the specific requirements of the accompanying task, such as "maintain a speed of 60 km / h and change lanes after 500 meters" or "simulate driving in front of the target trainee and brake slowly". This step not only ensures the real interactivity of the simulated driving, but also improves the collaborative training effect among trainees, enabling trainees to enhance their actual combat ability in a mutually influential driving environment.

[0047] Exemplarily, after other trainees control the accompanying vehicle and perform driving operations, the system will synchronize their operation data to the simulation environment of the target trainee in real time, enabling the driving state of the accompanying vehicle to be dynamically updated. For example, if the vehicle controlled by the accompanying trainee suddenly brakes or changes lanes, the target trainee needs to perform corresponding driving operations according to the actual situation, thereby improving their ability to respond to emergencies. In addition, the system will also monitor the driving trajectory of the accompanying vehicle and ensure the achievement of training objectives through intelligent adjustment. For example, if the target trainee needs to practice avoidance behavior and the accompanying vehicle always maintains a fixed speed, the system can appropriately adjust the speed of the accompanying vehicle to make the target trainee face a more realistic avoidance challenge. For situations where there is no matching trainee to participate in accompanying driving, the system can also automatically generate an intelligent accompanying vehicle based on the AI algorithm, ensuring that the simulation environment always maintains sufficient complexity. For example, during simulated urban road training, the AI can control the accompanying vehicle to perform irregular lane changes to test the target trainee's defensive driving ability.

[0048] Exemplarily, when the target trainee faces the driving state of the accompanying vehicle, they need to make real-time driving decisions, including adjusting vehicle speed, changing lanes to avoid, overtaking strategies, following distance control, etc. The system will record the driving behavior of the target trainee in real time and evaluate whether their reaction meets the requirements of safe driving. For example, when the target trainee encounters the accompanying vehicle suddenly changing lanes, if they can quickly judge and adjust the vehicle speed to avoid safely, the system will give positive feedback; conversely, if the target trainee fails to correctly anticipate and causes an emergency brake or collision, the system will mark the behavior and provide improvement suggestions. In addition, the system can also dynamically adjust the subsequent training content according to the driving style and error situation of the target trainee to help the trainee make up for their weaknesses. For example, if the target trainee repeatedly reacts slowly during emergency brake tests, the system may increase similar training scenarios to ensure that the trainee can respond faster when facing emergencies.

[0049] In summary, the driving training coordination management method provided by the above embodiments generates at least one accompanying vehicle simulation instruction when a target trainee conducts off-road driving simulation practice; sends the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, and the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on the simulation practice interface for indicating an accompanying target vehicle; generates the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario. Compared with the traditional AI simulation environment, this method enables the target trainee to face more realistic driving challenges through real interactions among trainees. For example, in urban road driving training, the target trainee may encounter an accompanying vehicle controlled by other trainees suddenly cutting in or changing lanes, and this kind of uncertainty is much more in line with the actual driving situation than the traditional preset AI behavior pattern, making the learning experience of the trainee closer to reality. By introducing variable accompanying vehicle behaviors, the target trainee needs to always maintain attention and adjust the driving strategy at any time. For example, in highway driving training, the accompanying trainee can control a vehicle driving slowly ahead, and the target trainee needs to judge whether to maintain a safe distance or overtake safely. This kind of interactive training can significantly improve the driving decision-making ability of trainees on real roads. The system can adjust the behavior pattern of the accompanying vehicle according to the driving level and learning progress of the trainees. For example, for novice drivers, the accompanying vehicle may just maintain a fixed speed to help the trainees master basic following skills; while for advanced drivers, the accompanying vehicle may suddenly change lanes or drive side by side at high speed to improve the adaptability of the trainees in complex environments. This method improves the interest and interactivity of simulated driving through multi-trainee collaborative training. Trainees not only need to focus on their own driving, but also consider the dynamic behaviors of the accompanying vehicles. This simulation mode helps to improve the team collaboration ability and makes the training process more interesting. For example, in some scenarios, the system can design a "driving challenge" that requires the target trainee to complete a safe overtaking task within a specific time, and the accompanying vehicle will also adjust its driving state accordingly, making the training more challenging.

[0050] In one embodiment, it further includes:

[0051] Obtain the driving state of the accompanying vehicle, the simulation scenario information, and the target trainee's simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario, and evaluate the driving ability of the target trainee.

[0052] Exemplarily, when the target student is conducting off-road driving simulation training, the system first analyzes its current training task, driving skill level and training goal to dynamically generate at least one accompanying vehicle simulation instruction. The instruction is used to create an accompanying vehicle in the simulation environment and set its behavior mode, such as driving at a fixed speed, changing lanes, overtaking, braking, etc. For example: for overtaking training, the system may generate a slowly moving accompanying vehicle in front to test the decision-making ability of the target student; for lane change avoidance training, the system can set a fast approaching accompanying vehicle from behind to examine whether the target student can accurately predict and make reasonable decisions; for emergency handling, the accompanying vehicle may randomly brake or suddenly change lanes to simulate emergencies in a real driving environment and train the target student's emergency response ability.

[0053] Exemplarily, the system sends the simulation task of the accompanying vehicle to other students, who control the accompanying vehicle to increase the interactivity of the simulation environment. The system assigns a vehicle ID or a user ID to each accompanying vehicle to ensure that the student can clearly identify the vehicle he controls and perform driving operations in the simulation interface. The way to assign tasks includes: matching according to training needs, the system matches suitable accompanying students according to the driving level and training goals of the target students. For example, in highway simulation training, experienced students can play the role of high-speed lane-merging vehicles to improve the actual combat adaptability of the target students; matching according to willingness, students can choose whether to participate in the accompanying vehicle control task, which makes the training process more flexible and improves the participation and interactivity of students. For example, when the target student A is conducting urban road driving training, the system can assign student B to control a accompanying vehicle in the right lane, and let it simulate a sudden lane change at a traffic light intersection to examine A's avoidance reaction.

[0054] Exemplarily, during the process of the accompanying student controlling the accompanying vehicle, the system will synchronize its real-time driving status to the target student's simulation scene, so that the dynamic behavior of the accompanying vehicle interacts with the target student's driving process. The system will transmit the speed, direction, braking, acceleration, lane change and other data of the accompanying vehicle to the training environment of the target student to ensure that the target student feels the changes in real traffic flow. If the training effect of the target student does not meet expectations, such as slow reaction to sudden lane changes, the system can dynamically adjust the behavior pattern of the accompanying vehicle, such as adding more frequent lane changes to increase the difficulty of training. If no student participates in accompanying driving, the system can use AI intelligent driving to control the accompanying vehicle to maintain the complexity and authenticity of the training environment. For example, when the target student C is simulating driving on a rural road, the system allows student D to control a vehicle driving in front and randomly change lanes without setting a turn signal to observe whether C can respond correctly.

[0055] Exemplarily, when the target trainee faces the dynamic changes of the accompanying vehicle and needs to adjust the driving strategy in real time, the system will automatically record the trainee's driving behavior and evaluate whether it meets the safe driving standards. The operation feedback of the target trainee includes: correct avoidance / lane change / braking and other operations; incorrect driving behaviors (such as failure to correctly maintain a safe distance, incorrect overtaking, etc.); emergency handling (such as whether to correctly avoid a vehicle that suddenly changes lanes). During the driving process of the target trainee, the system will further analyze the driving ability of the target trainee in combination with the driving state of the accompanying vehicle and the complexity of the simulated scenario, and provide real-time or post-training feedback. For example, if the target trainee can quickly decelerate and maintain a safe distance when the accompanying vehicle brakes suddenly, a positive evaluation will be given. If the target trainee reacts slowly when facing a vehicle merging into the highway, the system will record the error and provide improvement suggestions. After the training is completed, the system will conduct a comprehensive driving ability assessment based on the driving performance of the target trainee. This assessment includes the following core indicators: safety, evaluating the correct response rate of the target trainee in case of emergencies, such as whether the target trainee brakes or avoids correctly when the accompanying vehicle brakes suddenly; driving fluency, observing whether the driving of the target trainee is smooth, including the smoothness of acceleration, braking, and lane change, and whether there are problems such as excessive sudden braking and incorrect lane change; environmental adaptability, the system will analyze the driving performance of the target trainee in different scenarios (such as highways, urban roads, night, etc.) to judge their adaptability to complex environments; detection of dangerous behaviors, if the target trainee has dangerous driving behaviors (such as frequent sudden braking, incorrect overtaking, etc.), the system will record these behaviors and provide targeted training suggestions. The system can also generate a driving training report, which can include the scores of the target trainee in different training scenarios (such as overtaking training score, avoidance training score, etc.). Error case playback can also be carried out, and the trainee can review their incorrect behaviors after training and learn improvement strategies. Generate personalized training suggestions. For example, for the weak links of the target trainee, the system will recommend additional training content. For example, if the trainee fails many times in the lane change and avoidance test, the system will automatically arrange more similar training. For example, during highway training, target trainee E encountered a situation where the accompanying vehicle suddenly changed lanes, but their reaction time was too long, resulting in the vehicle deviating from the lane. The system marked this error in the driving report and provided lane change and avoidance training suggestions, such as increasing the frequency of observing the rearview mirror and adjusting the lane change timing.

[0056] It is understandable that through multi - trainee collaborative simulation driving, the target trainee can face a traffic environment that is more random and realistic than that of traditional AI vehicles, making the driving training closer to reality. The system can analyze the driving behaviors of trainees in different scenarios, provide a comprehensive driving ability assessment report, help trainees identify their own driving weaknesses and conduct targeted training. The system can dynamically adjust the behavior patterns of the accompanying vehicles according to the driving levels and performances of the trainees, and even recommend subsequent personalized training plans to improve the training effect. In addition to the training experience of the target trainee, the trainees participating in the driving of the accompanying vehicles can also obtain additional driving experience by controlling the accompanying vehicles, making the training process more interactive and efficient.

[0057] In one embodiment, the accompanying vehicle simulation instruction further includes accompanying driving requirements, and the method further includes:

[0058] Evaluating the driving abilities of the other trainees who execute the accompanying vehicle simulation instruction based on the matching degree between the driving state of the accompanying vehicle and the accompanying driving requirements.

[0059] It is understandable that not only the driving abilities of the target trainees are evaluated, but also a driving ability evaluation mechanism for the accompanying trainees can be further introduced, that is, by analyzing the matching degree between the driving state of the accompanying vehicle and the accompanying driving requirements, the driving performances of the trainees participating in the driving of the accompanying vehicle are evaluated. This two - way evaluation system can ensure that the target trainees receive high - quality driving training, while improving the driving skills of the accompanying trainees and optimizing the effectiveness of the entire training system.

[0060] Exemplarily, when the target trainee enters the off - road driving simulation training, the system first analyzes its current training tasks, driving skill levels, and training objectives to dynamically generate at least one accompanying vehicle simulation instruction. The simulation instructions of the accompanying vehicle not only include basic vehicle attributes (such as type, initial position, driving direction, speed, etc.), but also include accompanying driving requirements, that is, the operation specifications that the accompanying vehicle should follow in the training scenario, such as "maintain 60 km / h and change lanes after 500 m" or "apply brakes in a timely manner to give way at the intersection", etc. These driving requirements will be adjusted according to the training objectives of the target trainee. For example, for novice trainees, the accompanying vehicle may be set to drive at a fixed uniform speed, while for trainees with certain driving experience, the accompanying vehicle may perform more challenging operations, such as sudden lane changes, simulating sudden braking of the vehicle in front, etc., to examine the reaction ability of the target trainee. In addition, the system will set task completion conditions, that is, judge whether the driving state of the accompanying vehicle meets the set requirements, such as "the lane change needs to be completed 50 m in front of the target trainee, otherwise the task fails". This way ensures that the behavior of the accompanying driving is not too random, thus affecting the training effect of the target trainee.

[0061] Exemplarily, the system will send the generated accompanying vehicle simulation instructions to other student terminals, so that they can play the role of the accompanying vehicle driver in the simulation environment to enhance the interactivity of the training. In the training interface of the student terminal, the vehicle identification, task objectives and specific accompanying driving requirements of the accompanying vehicle will be clearly displayed to ensure that the student can accurately understand and perform the task. The allocation mechanism of the system is mainly based on the following two methods: matching by experience and matching by willingness. Matching by experience means that the system gives priority to more experienced students to perform complex accompanying driving tasks, such as simulating parallel driving on highways, while less experienced students can perform more basic tasks, such as maintaining a fixed distance. Matching by willingness means that students can actively choose whether to participate in the accompanying driving task, making the training process more flexible and avoiding students who are not suitable for the role from affecting the training quality. For example, in a city road training, the target student needs to practice lane change avoidance. The system can assign a student to drive the accompanying vehicle and change lanes when the target student approaches, thereby simulating dynamic changes in real traffic environments. This task allocation method can ensure that the target student can face real driving challenges that meet the training objectives, while also making the driving behavior of the accompanying student more standardized.

[0062] Exemplarily, when the accompanying student starts driving the accompanying vehicle, the system will continuously monitor its driving status and synchronize the data to the target student's simulation environment in real time, so that the target student can perceive the dynamic changes of the accompanying vehicle. For example, if the accompanying student performs the task of "changing lanes 200m ahead of the target student", the system will track the speed, lane change time, vehicle distance and other information of the accompanying vehicle to ensure that its operation meets the set accompanying driving requirements. If the accompanying student fails to perform as required, such as changing lanes at the wrong location or the speed does not match the requirements, the system will record its wrong operation and evaluate it after training. In addition, in order to ensure that the target student can continue to receive effective training, the system will also adopt an intelligent adjustment mechanism, that is, adjust the driving status of the accompanying vehicle in a timely manner during the driving process of the target student. For example, if the target student fails to correctly avoid the accompanying vehicle many times, the system can adjust the behavior of the accompanying vehicle to make it change lanes more slowly or make actions in advance to help the target student gradually master the response strategy. On the other hand, if there are not enough students to be allocated, the system can use AI autonomous driving to control the accompanying vehicle to ensure the stability and consistency of the training environment.

[0063] Exemplarily, during the training process, the target trainee needs to make real-time driving decisions regarding the driving state of the accompanying vehicle. The system will record all their driving behaviors and evaluate whether they meet the requirements of safe driving. For example, when encountering a sudden lane change by the accompanying vehicle ahead, whether the target trainee can correctly adjust the vehicle speed and safely avoid it; or during highway lane-changing training, whether the target trainee can reasonably predict the driving trajectory of the accompanying vehicle and successfully complete the overtaking operation. For correct operations, the system will give positive feedback, while for incorrect driving behaviors, such as failure to maintain a safe distance or failure to make reasonable avoidance, the system will mark the error and provide improvement suggestions. In addition, the system can generate a training report, detailing the performance of the target trainee in different driving scenarios and recommending subsequent training plans. For example, if the target trainee fails multiple times in avoidance training, the system will arrange additional avoidance exercises to help improve their driving skills.

[0064] Exemplarily, after the training is completed, the system will not only evaluate the driving ability of the target trainee but also evaluate the driving behaviors of the accompanying trainees to ensure that they strictly perform tasks according to the requirements of accompanying driving. The system will conduct accuracy scoring, stability scoring, cooperation scoring, etc. on the performance of the accompanying trainees. For example, the system will analyze whether the accompanying trainees execute instructions accurately (such as changing lanes within the specified vehicle distance). If the accompanying trainees deviate from the set requirements, their scores will be reduced. At the same time, the system will also detect whether the driving behaviors of the accompanying trainees comply with safe driving norms. For example: whether there are unnecessary sudden brakes when changing lanes, whether the vehicle speed is reasonably adjusted, etc. If the driving behaviors of the accompanying trainees are too random or do not meet the task requirements, the system will reduce their training scores and provide improvement suggestions. For example, in an overtaking training scenario, if the task of the accompanying trainee is to "brake to 40 km / h 100 m ahead of the target trainee" but the actual braking position is significantly different from the requirement, the system will reduce their accuracy score. In addition, the system will also evaluate the cooperation of the accompanying trainees, that is, whether their driving behaviors help the target trainee complete the training. For example: if the target trainee is practicing high-speed lane changes and the accompanying trainee fails to provide appropriate overtaking conditions as required, their cooperation score will be reduced. Finally, the system will generate a driving assessment report for the accompanying trainees to help them identify their own driving problems and provide improvement suggestions to optimize the efficiency of the entire simulation training system.

[0065] It can be understood that through the two-way evaluation of the target trainee and the accompanying trainees, the effectiveness of the training is ensured, and trainees can improve their driving ability in a dynamic and changing driving environment. The system can not only evaluate the driving performance of the target trainee but also ensure that the accompanying trainees strictly execute the driving tasks, making the entire simulation training more realistic and interactive. In addition, the personalized training plan of this method can be dynamically adjusted according to the abilities of the target trainee and the accompanying trainees, improving the training quality and helping trainees master key driving skills in a shorter time.

[0066] In one embodiment, the accompanying vehicle simulation instruction further includes the interactive driving requirements with the accompanying target, and further includes:

[0067] Based on the matching degree between the driving state of the accompanying vehicle and the interactive driving requirements, evaluate the driving ability of the other trainees who execute the accompanying vehicle simulation instruction.

[0068] It can be understood that the accompanying vehicle simulation instruction not only includes accompanying driving requirements (such as speed control, lane change, braking, etc.), but also further introduces interactive driving requirements, that is, stipulates the driving interaction rules between the accompanying vehicle and the target trainee, such as "simulate normal overtaking and yield the overtaking lane", "maintain an appropriate distance at intersections", etc. This method evaluates the driving ability of the accompanying trainees who execute the accompanying vehicle simulation instruction by analyzing the matching degree between the driving state of the accompanying vehicle and the interactive driving requirements, ensuring that the training environment is more real and standardized, and improving the driving skills of all trainees.

[0069] In one embodiment, it further includes:

[0070] In the case where the accompanying vehicle collides with the accompanying target vehicle, predict the damage degrees of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the moment of collision;

[0071] Generate damage images of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degrees of the accompanying vehicle and the accompanying target vehicle;

[0072] Display the damage images of the accompanying vehicle and the accompanying target vehicle on the target trainee side and the other trainee side that executes the accompanying vehicle simulation instruction.

[0073] Exemplarily, during the driving training of the target trainee, the system continuously monitors the driving states of the accompanying target vehicle driven by the target trainee and the accompanying vehicle controlled by the accompanying trainee, including their speeds, accelerations, directions, relative distances, braking states, etc., and calculates the relative motion parameters between the two vehicles in real time. Once it detects a collision between the two vehicles, the system immediately records all key data at the moment of collision, including the speeds of the vehicles, relative positions, collision angles, braking forces, vehicle masses, and collision parts, etc. These data will be used as the basis for subsequent damage prediction. For example, in the case where the target trainee changes lanes directly without observing the blind spot, the accompanying vehicle may have a side collision with it. At this time, the system records the lane-changing behavior of the target trainee, the vehicle speed and direction of the accompanying trainee, and calculates the impact force at the moment of collision to determine the severity of the collision.

[0074] Exemplarily, after detecting a collision, the system will immediately use a physical collision simulation model to predict the damage degrees of the accompanying vehicle and the target trainee vehicle. The damage prediction model comprehensively calculates the impact of the collision on the vehicle based on methods such as conservation of momentum, energy absorption calculation, and vehicle structure analysis, and combines with a real traffic accident database for data matching to improve the accuracy of the prediction. For example, the system will judge the damage degree according to the collision speed - if the relative speed is low (such as below 20 km / h), the collision may only cause minor paint scratches or door dents; while if the speed is high (such as a head-on collision above 80 km / h), the system will predict serious damages such as severe deformation of the vehicle head, airbag deployment, and glass breakage. In addition, the system will conduct a more refined analysis based on the vehicle type and collision location. For example, when a small sedan is hit head-on, the front bumper will be severely deformed, while the structure of an SUV is more solid and the relative damage may be less severe. Taking the example of a target trainee making a high-speed lane change and causing a side collision, the system may predict that the front bumper of the accompanying vehicle is dented, the right door of the target trainee vehicle is deformed, and may judge the triggering situation of the airbag.

[0075] Exemplarily, based on the damage prediction results, the system will generate damage images of the accompanying vehicle and the accompanying target vehicle, and perform 3D visualization rendering on the target trainee side and the accompanying trainee side to ensure that the consequences of the accident are intuitively visible. This process involves modeling the damage location, damage degree of the vehicle, and possible triggering situations of the safety system, and presenting them in the form of dynamic images in the simulation training interface. For example, if the collision impact is large, the system may display effects such as a dented front bumper, damaged engine compartment, and broken window glass in the damage image, while if the collision is minor, it may only display details such as vehicle surface dents and scratches. In addition, to increase the immersion, the system may also display scenes such as airbag deployment and damaged headlights. For example, in the case where the target trainee fails to control the following distance and causes a rear-end collision, the system can present the deformation of its front bumper, while showing the damage to the rear bumper of the accompanying vehicle, and may be accompanied by a slight smoke effect to simulate the real consequences of the collision.

[0076] Exemplarily, to ensure that the trainee can understand the cause of the accident, the system will synchronously display the damage pictures at the target trainee's end and the accompanying trainee's end, and provide an accident replay function to help the trainee review the whole process of the accident. The system will show the driving operations of the target trainee and the accompanying trainee within 5 seconds before the collision during the accident replay, including key behaviors such as accelerator, brake, and steering wheel turning, to help the trainee analyze the cause of the accident. For example, if the target trainee fails to brake in time due to inattention and causes a rear-end collision, the system will highlight the time point of the trainee's slow brake reaction during the replay and provide driving suggestions. The system will also automatically generate an accident analysis report, which includes driving evaluations of the target trainee and the accompanying trainee, such as: "The target trainee should fully observe the rearview mirror before changing lanes and ensure safety before operating." "The accompanying trainee can appropriately decelerate when detecting that the target trainee is about to change lanes to reduce the collision risk." In addition, the system may provide additional training suggestions. For example, in the case where the target trainee causes a lane-changing accident due to not observing the rearview mirror, the system will recommend that the trainee conduct more lane-changing safety training to strengthen their observation awareness and driving skills.

[0077] Thus, through collision detection, damage prediction, damage picture generation, and accident replay, the realism of driving training is enhanced, enabling the target trainee to experience the consequences of an accident in a safe simulation environment and improving their driving safety awareness. Through the visual damage pictures, the trainee can more intuitively understand the physical consequences of the collision, strengthen their awareness of the importance of safe driving, and prompt them to be more cautious in future driving.

[0078] According to some embodiments, it further includes:

[0079] In the case of a collision between the accompanying vehicle and the accompanying target vehicle, predicting the damage degree of the drivers of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the moment of the collision;

[0080] Generating damage pictures of the drivers of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degree of the drivers of the accompanying vehicle and the accompanying target vehicle;

[0081] Displaying the damage pictures of the drivers of the accompanying vehicle and the accompanying target vehicle at the target trainee's end and the other trainee's end that executes the accompanying vehicle simulation instruction.

[0082] For example, during the simulated driving training of the target trainee, the system will monitor the driving status of the accompanying target vehicle driven by the target trainee and the accompanying vehicle controlled by the accompanying trainee in real time, including key parameters such as speed, direction, relative distance, and braking status. When a collision between the two vehicles is detected, the system will immediately record the vehicle status at the time of the collision, such as relative speed, collision angle, vehicle type, collision position, collision force, etc., and analyze the impact force of the collision using a physical collision simulation model and a safety device response algorithm. The system first calculates the degree of damage to the vehicle, and then further predicts the impact force and damage to the driver. For example, if the target trainee changes lanes at a speed of 80km / h and collides sideways with the vehicle accompanying the trainee at a speed of 60km / h, the system will analyze the collision impact force and, combined with factors such as the driver's position, steering wheel deviation, seat belt restraint, and seat protection, infer the type and severity of damage that the driver may suffer.

[0083] Exemplarily, during the injury prediction process, the system uses a driver's biomechanical injury prediction model to comprehensively consider the impact of the collision, the force area of ​​the human body, and the intervention of safety equipment, such as whether the airbag pops out, whether the seat belt is tightened urgently, and whether the seat provides sufficient cushioning, etc., to estimate the driver's injury level. For example, in a high-speed frontal collision, the system may predict that the driver's chest is squeezed by the seat belt and the probability of rib fractures is high, while in a low-speed rear-end collision, the system may predict that the driver may suffer a slight cervical strain ("whiplash"). In addition, the system will also combine the real traffic accident database to compare similar collision cases to further optimize the accuracy of injury prediction. For passengers, the system will also make predictions based on the seat belt wearing status, seat position, side airbag protection, etc. For example, if the co-pilot is not wearing a seat belt, the probability of the head hitting the front windshield is high.

[0084] Exemplarily, based on the predicted injury results, the system will generate a driver injury screen and visualize it in 3D dynamic rendering on the target trainee side and the accompanying trainee side. The injury screen includes a visual presentation of the damage to the driver's body parts, such as bruises, fractures, bleeding, airbag ejection and other effects, and dynamically changes in combination with the situation inside the cockpit. For example, in the case of a severe collision, the system may display screens such as airbag ejection, driver body tilt, and arm injury due to steering wheel impact, while in the case of a lighter collision, it may only display the driver's head slightly hitting the seat or steering wheel due to inertia. In addition, the system can adjust the driver's expression and posture according to the degree of injury. For example, when slightly injured, the driver may frown or rub his shoulders, while when seriously injured, the driver may lose consciousness or show obvious pain reactions. This visualization method not only enhances the realism of the training, but also allows trainees to have a more intuitive understanding of the impact of the accident.

[0085] Exemplarily, to ensure that the trainee can understand the process of the accident and the cause of the injury, the system will synchronously display the injury pictures at the target trainee's end and the accompanying trainee's end, and provide an accident playback function to help the trainee review the whole process of the accident. The playback function includes the driving operation records within 5 seconds before the collision, such as whether the target trainee made a wrong lane change and whether the accompanying trainee failed to avoid in time, so that the trainee can analyze the accident liability. In addition, the system will automatically generate an accident analysis report and provide targeted improvement suggestions. For example, if the target trainee caused a serious side collision due to speeding lane change, the system may provide improvement suggestions such as "reduce the lane change speed, observe the rearview mirror, and maintain a sufficient safety distance", and if the accompanying trainee failed to reasonably adjust the vehicle speed when the target trainee changed lanes, the system may prompt "reasonably adjust the vehicle distance and avoid unnecessary emergency braking". At the same time, the system may recommend relevant training scenarios. For example, in the case where the target trainee caused a rear-end collision of the vehicle behind due to excessive hard braking, the system may arrange additional braking control training to improve their driving smoothness.

[0086] It can be understood that by visually displaying the driver's injury situation, the trainee can more intuitively understand the potential harm of traffic accidents to people, improve the awareness of safe driving, and prompt the trainee to drive more carefully. By predicting the degree of the driver's injury, the system can provide targeted safety training for the trainee. For example, if a certain trainee has multiple high-injury prediction results caused by not wearing a seat belt, the system will remind him to develop correct safe driving habits and increase relevant training courses. The trainee can not only see the damage situation of the vehicle, but also directly see the injury pictures of the driver. This immersive experience can enable the trainee to more deeply understand the consequences of wrong driving behaviors and improve the sensitivity to risky driving behaviors. Through accident playback and analysis, the trainee can learn how to judge the accident liability and understand how to avoid the occurrence of similar accidents. For example, in the case of a rear-end collision at night due to the failure to reasonably judge the brake light status of the vehicle in front, the trainee can learn how to adjust the night following vehicle strategy through playback and analysis.

[0087] In one embodiment, it further includes:

[0088] Obtain a simulation switching request from the target trainee's end or the other trainee's end that executes the accompanying vehicle simulation instruction;

[0089] Based on the simulation switching request, perform role conversion on the accompanying vehicle and the accompanying target vehicle.

[0090] Please refer to Figure 2 , an embodiment of the driving training coordination and management device in the embodiment of the present application may include:

[0091] A generating unit 201, configured to generate at least one accompanying vehicle simulation instruction when a target trainee conducts off-road driving simulation practice;

[0092] A sending unit 202, configured to send the accompanying vehicle simulation instruction to other trainee terminals except the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on a simulation practice interface for indicating an accompanying target vehicle.

[0093] A simulation unit 203, configured to generate a driving state of the accompanying vehicle in a simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0094] In summary, for the driving training coordination and management device provided in the above embodiments, when the target trainee conducts off-road driving simulation practice, at least one accompanying vehicle simulation instruction is generated; the accompanying vehicle simulation instruction is sent to other trainee terminals except the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on a simulation practice interface for indicating an accompanying target vehicle; a driving state of the accompanying vehicle is generated in a simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario. Compared with the traditional AI simulation environment, this method enables the target trainee to face more realistic driving challenges through real interactions among trainees. For example, in urban road driving training, the target trainee may encounter an accompanying vehicle controlled by other trainees suddenly cutting in or changing lanes, and this kind of uncertainty is much more in line with the actual driving situation than the traditional preset AI behavior pattern, making the learning experience of the trainee closer to reality. By introducing variable behaviors of the accompanying vehicle, the target trainee needs to always maintain attention and adjust the driving strategy at any time. For example, in highway driving training, the accompanying trainee can control a vehicle driving slowly ahead, and the target trainee needs to judge whether to keep a safe distance or overtake safely. This kind of interactive training can significantly improve the driving decision-making ability of the trainee on real roads. The system can adjust the behavior pattern of the accompanying vehicle according to the driving level and learning progress of the trainee. For example, for a novice, the accompanying vehicle may just maintain a fixed speed to help the trainee master basic following skills; while for an advanced driver, the accompanying vehicle may suddenly change lanes or drive side by side at a high speed to improve the adaptability of the trainee in a complex environment. This method improves the interest and interactivity of simulated driving through multi-trainee collaborative training. The trainee not only needs to focus on their own driving, but also consider the dynamic behaviors of the accompanying vehicle. This simulation mode helps to improve the team collaboration ability and makes the training process more interesting. For example, in some scenarios, the system can design a "driving challenge", requiring the target trainee to complete a safe overtaking task within a specific time, and at the same time the accompanying vehicle will also adjust its driving state accordingly, making the training more challenging.

[0095] AboveFigure 2 The driving training coordination management device in the embodiments of the present application has been described from the perspective of modular functional entities. Below, a detailed description of the driving training coordination management device in the embodiments of the present application will be provided from the perspective of hardware processing. Please refer to Figure 3 An embodiment of the driving training coordination management device 300 in the embodiments of the present application includes:

[0096] 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 through a bus or other means. Among them, Figure 3 Taking connection through a bus as an example.

[0097] Among them, by invoking the operation instructions stored in the memory 304, the processor 303 is used to execute the following steps:

[0098] In the case where a target trainee conducts off-road driving simulation practice, generate at least one accompanying vehicle simulation instruction;

[0099] Send the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee. The accompanying vehicle simulation instruction includes a vehicle identifier or a user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle;

[0100] Generate the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee responds to the driving state of the accompanying vehicle and the simulation scenario to perform a simulated driving feedback operation on the accompanying target vehicle.

[0101] By invoking the operation instructions stored in the memory 304, the processor 303 is further used to execute Figure 1 Any of the corresponding embodiments.

[0102] Please refer to Figure 4 Figure 4 It is a schematic diagram of an embodiment of an electronic system provided by the embodiments of the present application.

[0103] 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:

[0104] In the case where a target trainee conducts off-road driving simulation practice, generate at least one accompanying vehicle simulation instruction; ​

[0105] Send the accompanying vehicle simulation instruction to other trainee terminals except the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle;

[0106] Generate the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0107] In a specific implementation process, when the processor 420 executes the computer program 411, it can implement Figure 1 Any implementation manner in the corresponding embodiment.

[0108] Since the electronic system introduced in this embodiment is the device adopted for implementing a driving training coordination management device in an 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 introduced 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.

[0109] 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.

[0110] 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:

[0111] Generate at least one accompanying vehicle simulation instruction when the target trainee is performing off-road driving simulation practice;

[0112] Send the accompanying vehicle simulation instruction to other trainee terminals except the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle;

[0113] Generate the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

[0114] In a specific implementation process, when the computer program 511 is executed by a processor, it can implement Figure 1 Any implementation manner in the corresponding embodiment.

[0115] It should be noted that in the above embodiments, the descriptions of the various embodiments have their own focuses. For parts not described in detail in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0116] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. 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.) containing computer-usable program code.

[0117] 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 process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes 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 specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0118] 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 implements the specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0119] 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, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0120] 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 asFigure 1 The process in the driving training coordination management method in the corresponding embodiment.

[0121] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are fully or partially generated. 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 a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). 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)).

[0122] 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 may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may 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 displayed or discussed couplings or direct couplings or communication connections to each other may be through some interfaces, and the indirect couplings or communication connections of the devices or units may be in electrical, mechanical, or other forms.

[0124] The units described as separate components may or may not be physically separated, and the components displayed 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.

[0125] In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit, may exist separately as individual physical units, 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 a software functional unit.

[0126] 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 may 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 this technical solution, may 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 may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs.

[0127] 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 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 the various embodiments of the present application.

Claims

1. A driving training coordination management method, characterized in that Including: When a target trainee conducts off-road driving simulation practice, generating at least one accompanying vehicle simulation instruction; Sending the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle; Generating the driving state of the accompanying vehicle in the simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that the target trainee performs a simulated driving feedback operation on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

2. The method according to claim 1, characterized in that Also including: Obtaining the driving state of the accompanying vehicle, simulation scenario information, and the simulated driving feedback operation of the target trainee on the accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario, and evaluating the driving ability of the target trainee.

3. The method according to claim 1, characterized in that The accompanying vehicle simulation instruction further includes accompanying driving requirements, and the method further includes: Evaluating the driving ability of the other trainees who execute the accompanying vehicle simulation instruction based on the matching degree between the driving state of the accompanying vehicle and the accompanying driving requirements.

4. The method according to claim 1, wherein The accompanying vehicle simulation instruction further includes interactive driving requirements with the accompanying target, and further includes: Evaluating the driving ability of the other trainees who execute the accompanying vehicle simulation instruction based on the matching degree between the driving state of the accompanying vehicle and the interactive driving requirements.

5. The method according to any one of claims 1 to 4, characterized in that, Also including: When a collision occurs between the accompanying vehicle and the accompanying target vehicle, predicting the damage degrees of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the collision moment; Generating a damage picture of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degrees of the accompanying vehicle and the accompanying target vehicle; Displaying the damage picture of the accompanying vehicle and the accompanying target vehicle on the target trainee terminal and the other trainee terminals that execute the accompanying vehicle simulation instruction.

6. The method according to any one of claims 1 to 4, characterized in that, Also including: When a collision occurs between the accompanying vehicle and the accompanying target vehicle, predicting the damage degrees of the drivers of the accompanying vehicle and the accompanying target vehicle based on the driving states of the accompanying vehicle and the accompanying target vehicle at the collision moment; Generating a damage picture of the drivers of the accompanying vehicle and the accompanying target vehicle based on the predicted damage degrees of the drivers of the accompanying vehicle and the accompanying target vehicle; Displaying the damage picture of the drivers of the accompanying vehicle and the accompanying target vehicle on the target trainee terminal and the other trainee terminals that execute the accompanying vehicle simulation instruction.

7. The method according to any one of claims 1 to 4, characterized in that, Also including: Obtaining a simulation switching request from the target trainee terminal or the other trainee terminals that execute the accompanying vehicle simulation instruction; Performing a role conversion on the accompanying vehicle and the accompanying target vehicle based on the simulation switching request.

8. A driving training coordination management device, characterized in that, Including: A generating unit, configured to generate at least one accompanying vehicle simulation instruction when a target trainee conducts off-road driving simulation practice; A sending unit, configured to send the accompanying vehicle simulation instruction to other trainee terminals other than the target trainee, where the accompanying vehicle simulation instruction includes a vehicle identifier or user identifier displayed on the simulation practice interface for indicating the accompanying target vehicle; A simulation unit for generating a driving state of an accompanying vehicle in a simulation scenario based on the driving operations of other trainees on the accompanying vehicle, so that a target trainee performs a simulated driving feedback operation on an accompanying target vehicle in response to the driving state of the accompanying vehicle and the simulation scenario.

9. An electronic system, comprising a memory and a processor, characterized in that, When the processor is used to execute the computer program stored in the memory, the steps of the driving training coordination management method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the driving training coordination management method according to any one of claims 1 to 7 are implemented.

Images