Safe driving simulation training method and system

Abstract

The invention relates to the technical field of intelligent training systems, and discloses a safe driving simulation training method and system, and the method comprises the following steps: 1, starting online safety simulation training examination software through an intelligent driving simulation cabin; 2, a driver logs in the online safety simulation training examination software and selects a training or examination mode; 3, in the selected mode, selecting a driving scene to carry out simulated driving training; 4, in the simulated driving training process, operation data of a driver are collected in real time through the sensor, and real-time voice prompt is provided through the software based on the operation data and the current scene; 5, after simulation driving training is completed, assessment is carried out; and step 6, outputting the evaluation result and the improvement suggestion to the driver. According to the invention, diversified and personalized training and examination can be realized, and the efficiency and quality of safe driving training can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent training system, and particularly relates to a safe driving simulation training method and system. BACKGROUND

[0002] In recent years, the urban public transportation system has been continuously improved, and public transportation tools such as taxis and buses play an increasingly important role in the daily commuting of citizens. The operation safety thereof is directly related to the safety of life and property of the general public and social stability. Accidents caused by human factors such as non-standard operation of drivers and weak safety driving consciousness not only cause irreparable personnel casualties and property losses, but also have a negative impact on the efficiency of urban traffic operation and damage the overall image of the public transportation industry. In the face of this severe challenge, the traditional safety training method has been insufficient, and it is urgent to innovate the training mode through technical means, establish a more systematic and scientific driver safety training system, and fundamentally improve the professional quality and emergency disposal ability of the driver group, so as to effectively curb the frequency of traffic accidents and ensure the safe and stable operation of the urban traffic system.

[0003] In the development of the prior art, driving simulation training as a new training method has been gradually applied in the field of professional driver training. The driving simulation systems currently on the market are usually based on virtual reality technology and provide simulated driving environments for students through hardware simulation of the cockpit and software scene rendering. Such systems can simulate various road conditions and weather conditions, allowing students to train basic driving operations and perform specific scenario exercises in a virtual environment. However, the existing systems still have obvious limitations in technical implementation. Most systems can only provide standardized preset scenarios and lack accurate restoration of real road environments, making it difficult to meet the individual training needs of different operating routes. At the same time, the data collection dimension of existing systems is relatively single, mainly focusing on vehicle control operation data, and the monitoring of key safety indicators such as driver physiological state and attention distribution is not perfect, resulting in an incomplete training evaluation system that cannot truly reflect the comprehensive safety quality of drivers.

[0004] Further analysis shows that the main technical difficulties faced by existing driving simulation training systems are reflected in three aspects: first, the scene authenticity and adaptability are insufficient. Most existing systems use a general scene library and lack high-precision digital twin scenes that match the characteristics of real operating routes, which cannot provide specialized training for drivers at specific route risk points. Second, the intelligent level of the training process is limited. The system cannot dynamically adjust the training content and difficulty according to the real-time performance of the students, and lacks the ability to plan personalized training paths based on multi-source data fusion. Third, the evaluation and feedback mechanism is not perfect. Existing systems can only provide basic evaluation of operation standardization and cannot deeply analyze the safety awareness behind the operation, making it difficult to provide targeted improvement suggestions and long-term training planning. These technical bottlenecks seriously restrict the further improvement of driving simulation training effectiveness, and there is an urgent need to develop a new generation of intelligent safe driving simulation training solution to break through existing limitations through technological innovation and build a more scientific, efficient and practical driver safety training system. SUMMARY

[0005] The present application aims to provide a safe driving simulation training method and system to solve the technical problems of single training scene and disconnection with actual driving environment in traditional driving training, which can realize diversified and personalized training and examination, and help to improve the efficiency and quality of safe driving training.

[0006] To achieve the above-mentioned purpose, the present application provides the following basic scheme.

[0007] Scheme one A safe driving simulation training method, comprising the following steps: Step 1, starting online safe simulation training and examination software through an intelligent driving simulation cabin, wherein the intelligent driving simulation cabin includes a high-simulation driving module, a steering wheel, a seat, a foot pedal, a display screen and a sound system, and is configured with sensors for real-time collection of driver operation data and driver physiological data; Step 2, the driver logs in the online safe simulation training and examination software and selects a training or examination mode; the online safe simulation training and examination software provides a high-precision digital twin driving scene based on real data of a target operating route as a training option; Step 3, selecting a driving scene for simulation driving training under the selected mode; Step 4, during the simulation driving training process, the sensor is used to collect the driver's operation data in real time, and based on the operation data and the current scene, the software provides real-time voice prompts; Step 5, after completing the simulation driving training, performing examination and evaluation, wherein the examination and evaluation includes comparing the driver's operation with the preset standard operation, generating an evaluation result and targeted improvement suggestions; Step 6: output the evaluation results and improvement suggestions to the driver.

[0008] Scheme two A safe driving simulation training system for performing a safe driving simulation training method as described in Scheme one, comprising an intelligent driving simulation cabin and a server; the intelligent driving simulation cabin is in communication connection with the server for bidirectional data transmission; The intelligent driving simulation cabin comprises a high-simulation driving module, a steering wheel, a seat, a foot pedal, a display screen and a sound system, and is configured with sensors for real-time collection of driver operation data; The server is deployed with online safe simulation training examination software, and the online safe simulation training examination software comprises the following modules: A scene management module for generating and loading a plurality of predefined driving training scenes; A data receiving and processing module for receiving and processing driver operation data from the sensors; A real-time voice prompt engine for generating and playing voice prompts based on the operation data and the current scene state; An evaluation feedback module for generating evaluation results and targeted improvement suggestions based on the comparison of the operation data with the preset standard operation.

[0009] The working principle and advantages of the present application are: The safe driving simulation training method and system of the present application can simulate various driving environments and intelligently evaluate the training effect, which helps to improve the efficiency and quality of safe driving training. The focus is on: This scheme combines specific steps to build a complete and highly integrated technical solution, effectively solving the core problem of single training scene and disconnection with actual driving environment in traditional driving training. This scheme is not simply a stack of hardware devices and software functions, but through the key feature of "high-precision digital twin driving scene", the training content is deeply bound with the "target operating route real data" of the actual work of the driver. This means that the driver is not practicing in a general and idealized virtual road, but in a simulation environment that highly restores the daily driving route, traffic flow characteristics and even accident black spots. This training scene based on real data greatly improves the relevance and practicality of training, enabling drivers to familiarize themselves with and adapt to the specific road conditions and potential risks they will face in future actual work, thereby improving the training effect at the source, which is unmatched by traditional simulation training methods that are disconnected from specific operating environments.

[0010] Furthermore, the process design of this solution forms a closed loop from data collection and real-time interaction to evaluation and feedback. The digital twin scenario provides a meaningful context for data collection, the collected multidimensional operational and physiological data lays a solid foundation for generating targeted evaluation and improvement suggestions, and the final evaluation results can provide a basis for optimizing training priorities within the digital twin scenario. This interconnected and self-optimizing process design makes the entire training method an organic whole, rather than a simple patchwork of isolated functional modules. It can dynamically adapt to the personalized training needs of different drivers and different routes, thereby improving the overall efficiency and quality of safe driving training. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of an embodiment of a safe driving simulation training method and system according to the present invention. Detailed Implementation

[0012] The following detailed explanation illustrates the specific implementation methods: The basic implementation examples are as follows: Figure 1 As shown: A safe driving simulation training method includes the following steps: Step 1: Activate the online safety simulation training and assessment software through the intelligent driving simulator.

[0013] The intelligent driving simulator includes a highly realistic driving module, steering wheel, seat, pedals, display screen and audio system, and is equipped with sensors to collect driver operation data and driver physiological data in real time.

[0014] The steering wheel is equipped with a force feedback device to simulate steering resistance and feedback force under different road conditions. For example, when driving on a bumpy road or cornering at high speed in a digital twin scenario, the torque motor will simulate the corresponding resistance and vibration, enhancing the immersive experience. The seat is an ergonomic seat with height, fore-aft, and backrest angle adjustments. The pedals, including the accelerator and brake pedals, are full-size and equipped with linear displacement and pressure sensors to accurately collect pedal travel and force. The data is uploaded in real time to the online safety simulation training and assessment software via the driving module.

[0015] The display screen is composed of three or more high-definition LCD screens spliced ​​together at a specific angle, and is driven by the graphics processing unit in the driving module to present a seamless and wide driving field of vision.

[0016] The audio system plays corresponding vehicle driving noises, ambient sounds (such as wind and rain sounds, other vehicle horns) and system voice prompts according to the instructions of the scene engine.

[0017] In addition to the sensors integrated in the steering wheel and foot pedals, the simulation cabin is also equipped with sensors for collecting the physiological state of the driver, including an eye tracker and a heart rate monitor. The eye tracker is installed above the display screen and is used to continuously track the driver's gaze point position, gaze duration, and blink frequency to determine their attention distribution and fatigue state. The heart rate monitor can be integrated into the steering wheel grip or worn as a bracelet and is used to monitor the heart rate variability of the driver when responding to unexpected situations, serving as an auxiliary indicator for assessing their psychological load and stress response.

[0018] Before starting the training, a preliminary step needs to be performed: checking the intelligent driving simulation cabin, including appearance inspection, function inspection, and sensor calibration, to ensure that the equipment is functioning properly.

[0019] In this embodiment, the following operations are specifically included: confirming that all power and data lines are securely connected; starting the system self-checking program to check whether the steering wheel rotation is smooth, the foot pedal return is sensitive, the display screen has no dead pixels, and the sound is normally produced; and periodically calibrating the steering wheel angle sensor, eye tracker, and other devices according to the equipment guidelines every month to ensure the accuracy of data collection.

[0020] The online safety simulation training and assessment software is deployed on a cloud server and uses a B / S architecture, accessed through a browser, while the intelligent driving simulation cabin uses a C / S architecture and runs a client program. The two systems interact through the server to achieve hybrid architecture integration. The online safety simulation training and assessment software specifically includes the following modules: A scene management module for generating and loading multiple predefined driving training scenarios; A data receiving and processing module for receiving and processing driver operation data from the sensors; A real-time voice prompt engine for generating and playing voice prompts based on the operation data and the current scenario state; An evaluation feedback module for generating evaluation results and targeted improvement suggestions based on the comparison of the operation data with the preset standard operation.

[0021] Step 2: The driver logs in to the online safety simulation training and assessment software and selects the training or assessment mode.

[0022] The online safety simulation training and assessment software provides high-precision digital twin driving scenarios based on real data from target operating routes as training options.

[0023] Specifically, in this embodiment, the online safety simulation training and examination software constructs a driving scene by using geographic information system (GIS) map data, historical traffic flow data of the target operating line, and known accident black spot information, using a 3D modeling tool (such as 3D MAX) to perform three-dimensional fine modeling to generate a virtual environment containing precise slopes, curves, signs, and buildings. The driving scene includes an urban road scene, a suburban road scene, a special area scene, and a bad weather scene, wherein the bad weather scene includes a rain simulation scene with configurable road slipperiness and visibility parameters. For example, in the rain simulation scene, the administrator can set the road friction coefficient to simulate the slipperiness and adjust the fog density to change the visibility.

[0024] Step 3, in the selected mode, a driving scene is selected for simulation driving training; Specifically, in this embodiment, the driver can log in to the software using a personal account. After the driver selects "training mode" or "examination mode" in the interface, the software presents a list of driving scenes that can be selected. The driver can move the cursor by turning the steering wheel and confirm by pressing the brake pedal to select a specific digital twin scene for this training (for example, "XX Road Bus Line Peak Scene").

[0025] Step 4, during the simulation driving training, the sensor collects the driver's operation data in real time, and based on the operation data and the current scene, the software provides real-time voice prompts. The real-time voice prompts are automatically triggered based on the driver's operation and the scene, including at least one of over-speed reminder, turn reminder, pedestrian crossing reminder, and intersection speed reduction reminder.

[0026] Specifically, in this embodiment, after entering the scene, the driver begins simulation driving. During this period, the data receiving and processing module of the software continuously receives operation data (steering wheel angle, pedal travel / force, etc.) and physiological data (eye movement, heart rate) from the simulation cabin sensors. These data are sent to the real-time voice prompt engine of the server in real time. The engine has a pre-installed rule base that will make logical judgments based on the current operation data and the context information of the scene (such as current vehicle speed, distance to the next intersection), and automatically trigger the corresponding voice prompts. For example, when the system detects that the vehicle is traveling at 65 km / h on a road with a speed limit of 60 km / h, it immediately plays the voice "You have exceeded the speed limit, please slow down" through the sound system; when the vehicle approaches a pedestrian crossing and no significant deceleration is detected by the system, it will prompt "Please slow down and observe the pedestrian crossing ahead".

[0027] Step 5, after completing the simulation driving training, the examination and evaluation are performed, wherein the examination and evaluation include comparing the driver's operation with the pre-set standard operation, generating an evaluation result and targeted improvement suggestions.

[0028] The evaluation includes at least one of a basic operation evaluation, a safety specification evaluation, a route and speed limit execution evaluation, and an emergency handling evaluation, wherein the emergency handling evaluation simulates a vehicle tire burst, brake failure, or engine failure scenario.

[0029] Specifically, in this embodiment, when the driver completes the driving of the preset route or triggers the termination condition (such as a serious accident), the training or evaluation ends. At this time, the evaluation feedback module of the software starts to work. The module has a multi-dimensional evaluation system built in, including a basic operation evaluation, a safety specification evaluation, a route and speed limit execution evaluation, and an emergency handling evaluation. The module compares the operation data sequence of the driver in the whole process with the preset standard operation model. For example, in the emergency handling evaluation, if the system simulates a vehicle tire burst scenario, the evaluation module checks whether the driver first holds the steering wheel steadily, whether the driver uses the point brake method to slow down slowly, and records the reaction time. Finally, the module generates a quantitative evaluation result.

[0030] Optionally, the evaluation feedback module also has a scoring algorithm embedded in it. For example: Stability score: analyze the first derivative of the brake pedal pressure with respect to time. If the absolute value frequently exceeds the threshold, it is determined as “hard braking” and the score is deducted.

[0031] Risk prediction score: 200 meters before passing the accident black spot, check whether the driver has a gaze point moving to the risk area, and whether the driver has a “preliminary action” of moving the foot from the accelerator pedal to the brake pedal. No action is deducted.

[0032] Emergency handling score: in the tire burst event, check whether the steering wheel angle variance increases sharply within 1 second after the event is triggered (vehicle out of control), and whether it tends to be stable (successfully stable control) within the next 3 seconds.

[0033] Step 6, output the evaluation result and improvement suggestion to the driver.

[0034] The improvement suggestion is displayed in the form of text and graphics through an operation feedback interface, including operation difference comparison, problem analysis, and learning resource links.

[0035] Specifically, in this embodiment, after the evaluation is completed, a structured evaluation report is immediately presented on the display screen. The report lists in detail the scores of the driver in each evaluation item through text and graphics, and visually displays the differences between the driver's operation (such as brake pedal force curve) and the standard operation through video playback or curve superposition. For each deduction item, the report not only analyzes the problem causes, but also provides improvement suggestions and directly links to related theoretical learning materials or special training scene links.

[0036] Step 7: Based on historical training data and examination results, the software dynamically adjusts subsequent training scenarios and difficulty to achieve personalized training path optimization.

[0037] Specifically, based on the current and historical training data and examination results, the algorithm dynamically adjusts the recommended subsequent training scenarios and difficulty for the driver, thereby achieving optimization of the personalized training path. For example, for a driver who has repeatedly failed in "rainy day corner braking", the "wet road handling" series of scenarios are prioritized in the subsequent training plan.

[0038] The embodiment also provides a safe driving simulation training system for executing the safe driving simulation training method as described above, comprising an intelligent driving simulation cabin and a server; the intelligent driving simulation cabin is in communication connection with the server for bidirectional data transmission; The intelligent driving simulation cabin comprises a high-simulation driving module, a steering wheel, a seat, a foot pedal, a display screen and a sound system, and is configured with sensors for real-time collection of driver operation data; The server is deployed with online safe simulation training examination software, and the online safe simulation training examination software comprises the following modules: A scenario management module for generating and loading a plurality of predefined driving training scenarios; A data receiving and processing module for receiving and processing driver operation data from the sensors; A real-time voice prompt engine for generating and playing voice prompts based on the operation data and the current scenario state; An evaluation feedback module for generating evaluation results and targeted improvement suggestions according to the comparison between the operation data and the preset standard operation.

[0039] The safe driving simulation training method and system provided by the embodiment can simulate various driving environments and intelligently evaluate the training effect, which helps to improve the efficiency and quality of safe driving training.

[0040] The above-mentioned are only embodiments of the present application, and the common knowledge of specific structures and characteristics in the scheme is not described in detail, and the ordinary skilled person in the art knows all the ordinary technical knowledge in the technical field of the application before the application date or the priority date, can know all the prior art in the field, and has the ability to apply conventional experimental means before that date, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary skilled person in the art can perfect and implement the present scheme under the guidance of the present application, and the ordinary

Claims

1. A safe driving simulation training method characterized by, The method comprises the following steps: Step 1, starting the online safety simulation training and examination software through the intelligent driving simulation cabin, wherein the intelligent driving simulation cabin comprises a high-simulation driving module, a steering wheel, a seat, a foot pedal, a display screen and a sound system, and is configured with sensors for real-time collection of driver operation data and driver physiological data; Step 2, the driver logs in the online safety simulation training and examination software and selects a training or examination mode; the online safety simulation training and examination software provides a high-precision digital twin driving scene based on real data of a target operating line as a training option; Step 3, in the selected mode, a driving scene is selected for simulation driving training; Step 4, during the simulation driving training, the operation data of the driver are collected in real time through the sensors, and real-time voice prompts are provided through the software based on the operation data and the current scene; Step 5, after the simulation driving training is completed, an examination and evaluation are carried out, wherein the examination and evaluation comprises comparing the driver operation with preset standard operation, generating an evaluation result and targeted improvement suggestions; Step 6, outputting the evaluation result and improvement suggestions to the driver.

2. The method of claim 1, wherein, The steering wheel is equipped with a force feedback device for simulating the steering resistance and feedback force under different road conditions, and the seat is an ergonomic seat with height, forward and backward and backrest angle adjustment functions; the foot pedal comprises an accelerator pedal and a brake pedal.

3. The method of claim 1, wherein, It further comprises step 7: based on historical training data and examination results, the software dynamically adjusts subsequent training scenes and difficulties to realize individualized training path optimization.

4. The method of claim 1, wherein, The driving scene comprises an urban road scene, a suburban road scene, a special area scene and a severe weather scene, wherein the severe weather scene comprises a rain simulation scene with configurable road wetness and visibility parameters.

5. The method of claim 1, wherein, The examination and evaluation comprises at least one of basic operation examination, safety specification examination, route and speed limit execution examination and emergency handling examination, wherein the emergency handling examination simulates vehicle tire burst, brake failure or engine failure scenes.

6. The method of claim 1, wherein, The real-time voice prompt is automatically triggered based on the driver operation and the scene condition, and comprises at least one of overspeed reminding, turning reminding, pedestrian crossing reminding and intersection deceleration reminding.

7. The method of claim 1, wherein, The improvement suggestions are displayed in the form of graphics and texts through an operation feedback interface, including operation difference comparison, problem analysis and learning resource link.

8. The method of claim 1, wherein, In step 1, a preliminary step of checking the intelligent driving simulation cabin is further included, comprising appearance inspection, function inspection and sensor calibration to ensure normal operation of the equipment.

9. The method of claim 1, wherein, The online safety simulation training and examination software adopts B / S architecture and is accessed through a browser, while the intelligent driving simulation cabin adopts C / S architecture and runs a client program, and the two are connected through a server for data interaction, realizing hybrid architecture integration.

10. A safe driving simulation training system characterized by, A safety driving simulation training method according to any one of claims 1-9, comprising an intelligent driving simulation cabin and a server; the intelligent driving simulation cabin is in communication connection with the server for bidirectional data transmission; The intelligent driving simulation cabin comprises a high-simulation driving module, a steering wheel, a seat, a foot pedal, a display screen and a sound system, and is provided with sensors for collecting driver operation data in real time; The server is deployed with online safety simulation training and examination software, which comprises the following modules: A scene management module for generating and loading multiple predefined driving training scenes; A data receiving and processing module for receiving and processing driver operation data from the sensors; A real-time voice prompt engine for generating and playing voice prompts based on the operation data and the current scene state; An evaluation feedback module for generating evaluation results and targeted improvement suggestions based on the comparison of the operation data with preset standard operations.

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