Training auxiliary method and system for fast forward driving 180-degree U-turn driving of police service vehicle

Abstract

The invention discloses a police service vehicle rapid forward driving 180-degree U-turn driving training auxiliary method and system, and belongs to the field of police service vehicle special driving skill training. The method comprises the following steps: acquiring expert and coach successful operation data under different training condition parameters, and establishing a data model based on the data; obtaining training parameters of the current training task; according to the training parameters and the data model, a target operation time sequence is determined, and a synchronous operation prompt signal is sent to the trained driver through a signal prompt module; and recording the actual operation time sequence of the trained driver, comparing the actual operation time sequence with the target operation time sequence, and generating a training evaluation report containing deviation information. Through a data driving mode, a complex driving skill is deconstructed into a measurable, guidable and evaluable scientific process, personalized, standardized and intelligent assistance of special driving training is realized, and the training efficiency, the safety and the actual combat conversion rate are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of special driving of police vehicles, and in particular relates to a method and system for assisting training of a police vehicle to make a 180-degree U-turn while driving forward quickly. Background Art

[0002] A rapid 180-degree U-turn is a critical driving skill required by police officers during urgent pursuits, interceptions, or evasive maneuvers. This technique requires the driver to precisely coordinate the accelerator, handbrake, and steering wheel to quickly complete a 180-degree turn while the vehicle is moving at high speed, changing direction in the shortest possible time.

[0003] The existing skill training method mainly has the following serious defects: High reliance on experience and lack of quantitative standards: Traditional training relies entirely on the instructor's oral instruction and empirical judgment. There are no unified, precise quantitative standards for key maneuvers such as when to release the throttle, when to apply the parking brake, how much pressure to apply, and how much to steer. Training results vary from person to person, making them difficult to replicate and scale, resulting in long training cycles and low efficiency.

[0004] Poor adaptability and inability to personalize training: Traditional, one-size-fits-all teaching methods fail to account for the impact of various variables on operation, such as vehicle types (e.g., differences in center of gravity and wheelbase between sedans and SUVs), tire condition (new or old, tire pressure), road conditions (dry or wet), and the driver's individual reaction speed. When the training environment or vehicle changes, the trainee's acquired "point sense" or "muscle memory" often loses effectiveness, making it difficult to translate training results into practical capabilities in all weather and road conditions.

[0005] High training costs and significant safety risks: These extreme driving maneuvers cause significant damage to the vehicle, particularly the handbrake system, tires, and suspension. Frequent training leads to high maintenance costs. Furthermore, operational errors can lead to loss of vehicle control, posing a potential threat to the safety of both the driver and the instructor, and increasing the risk of occupational diseases such as chronic injuries to the lumbar and cervical spine.

[0006] CN119920142A discloses an intelligent driver training assistance method and system, primarily targeting on-road driving school exams, assisting students by "memorizing points." However, this existing technology has significant limitations: First, its application scenario is low-speed, programmed driving school training, which cannot meet the high-speed, dynamic, and highly confrontational requirements of special police driving. Second, its teaching logic remains superficially based on "stimulus-response," failing to guide students to develop a deep understanding of vehicle dynamics, resulting in poor skill transferability. Finally, it focuses solely on steering timing, ignoring quantitative guidance for the coordinated operation of multiple systems such as the throttle and brakes, failing to meet the complex requirements of special driving.

[0007] Therefore, there is an urgent need for a new special driving training assistance technology that can overcome the above-mentioned defects and achieve standardization, personalization, safety and intelligence. Summary of the Invention

[0008] Purpose of the invention: The purpose of the present invention is to address the deficiencies in the existing technology and provide a method and system for assisting police vehicle driving training to quickly turn around 180 degrees, so as to solve the problems existing in the background technology such as dependence on experience, lack of quantitative standards, poor adaptability and high risk of training.

[0009] Technical solution: The method for assisting police vehicle driving training to quickly turn around 180 degrees includes the following steps: S1: Acquire successful operation data of an expert trainer under different training condition parameters, wherein the successful operation data includes vehicle state data synchronized with a series of key operation moments, and establish a data model based on the successful operation data, wherein the data model represents a correspondence between the training condition parameters and an optimal timing of the series of key operation moments; S2: Get the training parameters corresponding to the current training task; S3: determining a target operation sequence including a series of target operation moments based on the training parameters obtained in step S2 and the data model established in step S1, and sending an operation prompt signal synchronized with the target operation sequence to the trained driver through a signal prompt module according to the target operation sequence; S4: Recording the actual operation timing of the trained driver, and comparing the actual operation timing with the target operation timing, and generating a training evaluation report including deviation information between the two.

[0010] To further improve the above technical solution, the series of key operation moments in step S1 include: the throttle release and refueling moments monitored by the speed sensor; the handbrake application and release moments monitored by the handbrake sensor; the steering wheel rotation and return moments monitored by the steering wheel sensor; The vehicle status data synchronized with the series of key operation moments include: vehicle model, weight, handbrake status, vehicle posture data, tire status, speed, and steering wheel rotation angle collected by vehicle status sensors; and ground status and site environment detected by environmental sensors.

[0011] Furthermore, the data model also includes the influence weight of each training condition parameter corresponding to the time interval of the key operation moment relative to the reference zero moment, and uses the influence weight to adjust the generation of the target operation timing; the training condition parameters include: vehicle model information, tire status information, ground status information of the training site and one or more of the target training speed.

[0012] Furthermore, data collection and preprocessing: The sensor module collects multiple sets of raw data from the expert instructor practicing fast forward 180-degree U-turn driving, and cleans the raw data to remove outliers and erroneous data to form a successful sample data set; Feature extraction and modeling: extracting key features from the successful sample data set, the key features including the distribution of time intervals between key operation moments and the reference zero moment under different training condition parameter combinations; and the weight of the influence of each training condition parameter on the time interval, and constructing the data model based on the key features; Model selection and training: select the model type, initialize the model parameters, select the loss function and use the optimization algorithm for training; Model evaluation and optimization: Evaluate model performance using metrics like mean squared error, mean absolute error, or R² score and perform cross-validation to optimize the model for the data.

[0013] Furthermore, the signal prompt module sends an operation prompt signal through a sound signal, a light signal and / or a vibration signal, wherein: the sound signal uses sounds of different frequencies and tones to prompt each operation moment; the light signal prompts each operation moment through indicator lights of different colors; the vibration signal prompts each operation moment through a vibration device on the steering wheel or seat.

[0014] Furthermore, the method further comprises at least one of the following steps: Skill level assessment step: based on the training evaluation report generated in the evaluation feedback step, comparing the completion time and steering accuracy contained in the report with a preset skill level standard, thereby determining and outputting a corresponding special driving operation skill level; Simulator data configuration step: associating the training evaluation report with the corresponding parameters to be trained and storing them in a structured manner for configuring a driving training simulator; wherein the configuration includes: adapting the steering wheel power assistance and handbrake force feedback according to the vehicle model information in the parameters to be trained, and setting the road friction coefficient or slope according to the ground state information in the parameters to be trained.

[0015] Accordingly, the present invention also provides a system for implementing the above method, comprising: a sensor module configured to collect a series of key operation moments when the vehicle performs a rapid forward 180° U-turn maneuver and vehicle status data synchronized with the moments; A parameter setting module is configured to set training parameters corresponding to the current training task, wherein the training parameters include vehicle type, tire condition, site environment, ground condition and speed; a data processing and storage module, wherein a data model established based on successful operation data of an expert trainer is stored, wherein the data model represents the correspondence between different training condition parameters and the optimal timing of the series of key operation moments; the data and storage module is configured to: receive the training parameters input by the parameter setting module; determine the target operation timing and control the signal prompt module based on the training parameters and the data model; and generate a training evaluation report based on the actual operation timing of the trainee driver collected by the sensor module; a signal prompt module, connected to the data processing and storage module, and configured to send an operation prompt signal to the driver; The display module is connected to the data processing and storage module and is configured to display the training parameters, operation time prompts and training effect evaluation reports.

[0016] Furthermore, the series of key operation moments include the throttle release moment T1, the handbrake application moment T2, the steering wheel turning moment T3, the steering wheel return moment T4, the handbrake release moment T5 and the refueling moment T6, and the vehicle status data includes vehicle model, weight, handbrake status, tire status, speed, and steering wheel rotation angle; and the data processing and storage module is further configured to control the signal prompt module to send prompt signals in sequence at moments T1 to T6 in the target operation sequence.

[0017] Furthermore, the data model also includes the influence weight of each training condition parameter corresponding to the time interval of the key operation moment relative to the reference zero moment, and uses the influence weight to adjust the generation of the target operation timing; the training condition parameters include: vehicle model, driver weight, tire condition, site environment, ground condition and at least one of speed.

[0018] Furthermore, the data processing and storage module is also connected to a real-time data transmission and remote monitoring module, an operation skill recommendation and demonstration module, and a simulation scene generation and switching module; The real-time data transmission and remote monitoring module includes a wireless communication unit and a coach terminal, and the data processing and storage module sends training data to the coach terminal through the wireless communication unit and receives guidance information from the coach terminal; The simulation scene generation and switching module includes a graphics rendering engine and a virtual reality device interface, which is used to construct a three-dimensional driving scene model according to the training parameters and output it to the virtual reality device; The operation skill recommendation and demonstration module includes a driving skill knowledge base, which is used to filter matching skill information from the knowledge base according to the training parameters and display the information through the display module.

[0019] Beneficial effects: Compared with the existing technology, the advantages of the present invention are: the present invention overcomes the fundamental defect of traditional training relying on subjective experience by obtaining the successful operation data of expert coaches under different training conditions and parameters, and establishing a data model. The data model establishes a precise mathematical correspondence between multi-dimensional training parameters such as vehicle type, tire condition, road friction coefficient, and the optimal timing of a series of key operating moments such as throttle reduction, handbrake application, and steering. Therefore, when the trainee inputs specific training parameters, the system can determine a target operation timing that is highly matched with the current conditions, rather than providing a fixed, universal guidance. This data-driven dynamic guidance enables the training program to adapt to various changing objective conditions, fundamentally improving the scientific nature and pertinence of the training.

[0020] Since all guidance and evaluation are based on a unified data model, the present invention provides a set of objective and reproducible standards for special driving training. The synchronous operation prompt signal issued by the signal prompt module ensures that the instruction standards received by each trainee are consistent. More importantly, by recording the actual operation timing and comparing it with the target timing, a training evaluation report containing deviation information is generated, so that the evaluation of training effects no longer relies on the subjective judgment of the instructor. The evaluation report can quantify the advance or lag of each operation with millisecond accuracy, providing reliable data support for skill assessment, problem diagnosis and targeted improvement.

[0021] This invention utilizes a multimodal signaling solution comprised of sound, light, and / or vibration signals. In the high-stress, high-noise environment of specialized driving training, single prompts (such as sound alone) can easily be overlooked. By combining auditory, visual, and tactile sensory channels, this system ensures that operational instructions are received reliably and unambiguously by the trainee driver, significantly reducing operational errors caused by unclear instructions and ensuring a smooth and safe training process.

[0022] The invention features a refined data processing pipeline, encompassing data preprocessing, feature extraction and modeling, and training effectiveness evaluation. Data preprocessing removes outliers, ensuring data quality for the model input. Feature extraction analyzes the distribution patterns and influence weights of time intervals, ensuring that the constructed data model goes beyond simple time series reproduction and instead deeply reflects the inherent laws of vehicle dynamics, ensuring accurate predictions. Furthermore, the application of quantified training data for driver skill level assessment or simulator data configuration significantly expands the invention's application value, enabling its findings to directly contribute to the development of industry standards and related technical equipment.

[0023] In summary, the present invention successfully transforms a complex, experience-based driving skill into a scientific, rigorous, and efficient engineering training process by constructing a complete technical closed loop from data collection and model training to real-time guidance and quantitative evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Schematic diagram of the functional module architecture of the training assistance system in an embodiment of the present invention.

[0025] Figure 2 Schematic diagram of the workflow of the training assistance method in an embodiment of the present invention.

[0026] Figure 3 Schematic diagram of the data processing and analysis process in an embodiment of the present invention.

[0027] Figure 4 Schematic diagram of the system expansion function module in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the embodiments.

[0029] Example 1: This example provides a method for assisting police vehicle driving training by making a 180° U-turn at high speed, which specifically includes the following steps: S1: Obtain successful operation data from expert coaches under different training conditions and parameters. The successful operation data includes vehicle status data synchronized with a series of key operation moments. A data model is established based on the successful operation data. The data model represents the correspondence between the training condition parameters and the optimal timing of a series of key operation moments. S2: Get the training parameters corresponding to the current training task; S3: Determine a target operation timing based on the training parameters obtained in step S2 and the data model established in step S1, and send an operation prompt signal synchronized with the target operation timing to the trainee driver through the signal prompt module according to the target operation timing; S4: The actual operation timing of the trained driver is recorded through the sensor module, and the actual operation timing is compared with the target operation timing to generate a training evaluation report containing deviation information between the two.

[0030] In step S1, the successful operation data of the expert coach is obtained and a data model is established (as shown in Table 1).

[0031] Table 1: Mathematical model The process of building the data model (as shown in Table 1) includes: Data Collection and Preprocessing: One or more experienced police driving instructors (i.e., expert instructors) were selected to drive a police vehicle equipped with a complete sensor suite and perform multiple rapid 180° U-turns on a professional training ground covering a variety of typical road conditions. During this process, the sensor module operated throughout, accurately recording the T0, T1, T2, T3, T4, T5, and T6 times of each maneuver, as well as various variable information. Sample data from successful rapid 180° U-turns was screened and stored in the device database, accumulating a large amount of successful data under various variable conditions, including: Key operating moments: throttle release (T1) and accelerator pedal release (T6): captured by a speed sensor connected to the vehicle's OBD interface or a sensor that directly monitors the accelerator pedal's displacement; handbrake application (T2) and handbrake release (T5): accurately recorded by a pressure sensor installed at the bottom of the handbrake lever. When the pressure reaches or falls below the preset rear wheel locking / release threshold, T2 and T5 are determined respectively; steering wheel turning (T3) and steering wheel return (T4): recorded by the angle sensor inside the steering wheel. When the turning angle exceeds or returns to a small initial threshold, the corresponding moment is determined.

[0032] Synchronized vehicle status data: basic vehicle information: vehicle model (sedan), weight (including occupants and equipment), etc. are input as static parameters; vehicle posture data: the vehicle's yaw rate and sideslip angle are collected in real time through the gyroscope and accelerometer; tire status: tire pressure is obtained through the tire pressure monitoring system (TPMS), combined with the pre-entered tire wear degree (such as new tire, semi-worn tire, aged tire); speed: recorded in real time by the speed sensor.

[0033] Environmental data: Ground conditions: Using an on-board camera combined with an image recognition algorithm or a dedicated ground material recognition sensor, the vehicle determines whether the road surface is dry asphalt (friction coefficient approximately 0.8), slippery road surface (friction coefficient approximately 0.3), or gravel road surface; site environment: such as a ramp (slope 0°-15°).

[0034] The collected raw data is cleaned to remove outliers and erroneous data, such as incorrect time records due to sensor failure and variable values that obviously deviate from the reasonable range, to ensure data accuracy.

[0035] Feature extraction and modeling: With the start of the car's movement as time T0, multiple sensors are used to accurately detect and record the time nodes of releasing the throttle (time T1), applying the handbrake (time T2), turning the steering wheel (time T3), straightening the steering wheel (time T4), releasing the handbrake (time T5), and refueling (time T6). At the same time, information about the training vehicle (model, weight, handbrake status), tire status (wear, tire pressure), ground status (friction coefficient, ground slope), and other variables such as the driver is collected. Then, through the operation of expert drivers, the successful sample data of the fast forward 180° U-turn driving technique is stored in the device, and finally a successful U-turn sample database is formed.

[0036] Key features were extracted from the processed database of successful U-turn samples, such as the distribution patterns of the time intervals between T1 and T0, T2 and T0, T3 and T0, T4 and T0, T5 and T0, and T6 and T0 under different vehicle models, tire conditions, and site conditions, as well as the weights of the influence of each variable on the time intervals (as shown in Table 2). This was used to construct a data model for successful U-turn operations. This model was used to predict the reasonable ranges of T1, T2, T3, T4, T5, and T6 times under different parameter combinations, providing precise guidance for training.

[0037] Table 2 The influence weight of training parameters on operation time interval Model selection and training: Select the model type, initialize the model parameters, choose an appropriate loss function (such as mean squared error), and use an optimization algorithm (such as gradient descent, SMO algorithm) for training.

[0038] Model evaluation and optimization: Use metrics such as mean squared error (MSE), mean absolute error (MAE), and R² score to evaluate model performance and perform cross-validation to optimize and finalize the data model.

[0039] In step S2, when the police use the system, they first input the type of vehicle they are driving, the condition of the tires, the selected venue and the ground condition through the parameter setting module, and set parameters such as the training speed.

[0040] In step S3, model application and feedback: the model is applied to driving training. The system retrieves the corresponding historical successful sample data from the database based on the selected parameters. When the trainee releases the accelerator (T0), the device starts the auxiliary training function. Based on the established data model, at the calculated times T1, T2, T3, T4, T5, and T6, the signal prompt module sends signals for applying the handbrake, turning the direction, returning the direction, releasing the handbrake, and refueling, guiding the trainee to perform the correct operation. At the same time, In step S4, training effectiveness is evaluated: the system uses a sensor module to record the officer's training data in real time, including the T0, T1, T2, T3, T4, T5, and T6 times for each maneuver, as well as various variables. After each maneuver, the officer's performance, completion status, and any errors are displayed. After training, the actual maneuver times are compared with the target maneuver sequence, and deviations are quantitatively analyzed. Training effectiveness is evaluated from multiple perspectives, including accuracy and stability. A training performance report is generated, providing a basis for subsequent targeted reinforcement training. This report, displayed intuitively on the display module shown in Table 3, includes deviation information for each maneuver (e.g., "Instantaneous speed at T1 (42 km / h), assessed as good"), vehicle status, overall performance score, and the assessment of the officer's special driving skill level based on completion time and steering accuracy (e.g., 180° deviation from the target).

[0041] Table 3: Example of display module contents The training assistance method provided by the present invention can be used in the following applications: 1. Quantified training data can serve as a basis for developing industry standards. For example, defining "special driving skill levels" (elementary / intermediate / advanced) corresponding to different steering accuracy (error ≤ 10° / 5° / 3°) and completion time (≤ 12 seconds / 9 seconds / 6 seconds); 2. Quantified training data can provide a basis for the development of police vehicle driving training simulators. For example, VR technology can be used to simulate different environmental parameters, vehicle model adaptation, preset models such as sedans, SUVs, and vans, and automatically adjust steering wheel power assistance and handbrake force feedback; environmental simulation can switch between dry / wet road surfaces (friction coefficient 0.8-0.3) and ramps (slope 0°-15°) and other scenarios. Trainees learn force control under different conditions through pressure-sensing pedals.

[0042] 3. The data collected by the sensors provides a basis for subsequent modifications to police vehicles. For example, based on the lateral pressure collected during a vehicle drift, adjustable hydraulic seats are designed to provide lateral support; seat belts with neck pretensioners are installed, ensuring that the forward movement of the head during an emergency stop is ≤8cm (in compliance with ECE R16 safety standards); and tires made of wear-resistant materials are developed to extend tire life.

[0043] 4. Improve efficiency and shorten training cycles. A multi-sensor fusion system (such as gyroscopes, accelerometers, and wheel speed sensors) is introduced to collect real-time vehicle posture data (sideslip angle, yaw rate, and tire slip rate). This allows the establishment of a "Special Driving Operation Quantitative Standard Database" to address the issue of "ambiguous teaching standards due to differences in instructor feel" in traditional training. For example, the handbrake application timing error is ≤ ±0.1 seconds (compared to ±0.5 seconds for traditional methods); and the time fluctuation for completing a 180° U-turn is ≤ ±1.2 seconds (compared to ±3.8 seconds for traditional methods).

[0044] 5. Parameter variables are considered relatively comprehensively, providing quantitative guidance on throttle / handbrake / steering force control, and achieving coordinated training in powertrain operation. This allows trainees to understand the underlying operational principles from a mechanical perspective, eliminating the potential for failure after a vehicle change, and promoting skill transfer.

[0045] 6. Based on comprehensive consideration of various parameter variables, the coach system generates standard historical data for the police officers being trained, enhancing the cultivation of standardized driving skills. This is conducive to the difficulty for coaches to unify teaching methods, and students will not be affected when changing variables such as vehicles, environments, and venues.

[0046] 7. This invention transforms "experiential special training" into a scientific system integrating "data, cognition, and physics." Its potential value lies not only in improving individual skills but also in opening up a new field of "computable driving ability." This invention deconstructs complex driving skills into a combination of measurable and optimizable parameters, laying the foundation for the subsequent design of auxiliary training methods for other types of special police driving skills (such as quick starts, rapid slalom, high-speed tire blowouts, and avoiding crowding and bumping).

[0047] Example 2: Reference Figures 1 to 3 This embodiment is used to provide a system for implementing the method in Example 1. The system mainly includes: a sensor module, a parameter setting module, a signal prompt module, a data processing and storage module, and a display module.

[0048] The sensor module is responsible for comprehensively and accurately collecting all relevant dynamic information of the vehicle during the process of performing a rapid forward 180° turn operation, realizing the basis of data-driven training; the parameter setting module sets the parameters of the auxiliary training equipment, such as selecting different parameter combinations such as vehicle model, tire status, site environment, ground status, speed, etc., in order to carry out targeted training; the data processing and storage module is responsible for receiving the data collected by the sensor module, and storing, analyzing and processing it, building a data model for successful U-turn operations, and storing historical successful sample data; the signal prompt module can send signals in the form of sound, light, vibration, etc. During training, according to historical successful data, it starts recording from time T0, and gives T1 to reduce the throttle, T2 to apply the handbrake, T3 to turn the steering wheel, T4 to return to the correct direction, T5 to release the handbrake, and T6 to refuel at the corresponding time to assist the participating police officers in operating; the display module intuitively displays the working status of the equipment, current training parameters, operation time prompts, training results (such as Figure 1 ) and other information to facilitate coaches and trained police officers to understand the training situation in real time.

[0049] The sensor module specifically includes: Speed sensor: By connecting to the vehicle's on-board diagnostic system (OBD) interface or installing the sensor directly on the drive shaft, the vehicle's speed is monitored in real time, providing data for determining the time to de-fuel (T1) and refuel (T6).

[0050] The handbrake sensor is preferably a high-precision pressure sensor or displacement sensor installed at the bottom of the handbrake lever. It monitors the force or displacement of the lever and accurately captures the moment the handbrake is applied (T2) and released (T5) when it reaches or falls below the preset rear wheel lock / release threshold.

[0051] Steering wheel sensor: An angle sensor integrated inside the steering wheel, used to monitor the steering wheel's rotation angle and angular velocity in real time, thereby determining the time to turn the steering wheel (T3) and the time to return the steering wheel to the center position (T4).

[0052] Vehicle Status Sensors: This sensor suite includes information such as vehicle model and engine speed, read via the OBD interface, and an additional inertial measurement unit (IMU, consisting of a gyroscope and accelerometer) for real-time acquisition of key vehicle attitude data, including sideslip angle and yaw rate. It also integrates a tire pressure monitoring system (TPMS) and tread depth detection to provide comprehensive tire status information.

[0053] Environmental sensors: These primarily consist of a camera mounted on the vehicle's exterior and a ground material recognition sensor. The camera captures images of the site environment to aid in determining site type. The ground material recognition sensor analyzes the reflection characteristics of electromagnetic or sound waves to accurately determine whether the surface is dry asphalt, slippery concrete, or muddy, and quantifies this information as a coefficient of friction.

[0054] Driver collector is used to collect information such as driver's height, weight, blood pressure, heart rate, dominant hand, reaction time, etc.

[0055] The parameter setting module is one of the system's human-computer interaction interfaces, typically integrated into the display module's touchscreen. Using intuitive menu options and sliders, it allows trainees or instructors to quickly and easily select and enter training parameters for the current mission, such as vehicle type, tire condition, field type, surface conditions, and target training speed, before training begins.

[0056] The signal prompt module is the system's "command output," responsible for transmitting the operational instructions calculated by the data processing module to the trainee in real time in a clear, unambiguous manner. To adapt to the high-pressure and high-noise training environment, this module uses a multimodal prompting solution, including audio, lighting, and vibration prompting units.

[0057] The sound prompt unit is used to generate sound signals: it is connected to the car audio or to the headphones worn by the driver via Bluetooth, and emits preset sound signals of different frequencies and tones according to different operation times. For example, at time T0, it emits a long "di dong" sound to remind you to reduce the throttle, at time T1, it emits a "di" sound to remind you to apply the handbrake, at time T2, it emits a "click" sound to remind you to turn the steering wheel, at time T3, it emits a "ding" sound to remind you to apply the handbrake, at time T4, it emits a "dong" sound to return the steering wheel to the center, at time T5, it emits a "beep" sound to release the handbrake, and at time T6, it emits a "bo" sound to accelerate and drive. The sound is loud enough and not harsh, ensuring that the trained police officers can hear it clearly even in the noisy environment inside the car.

[0058] The lighting prompt unit is used to generate light signals: a group of LED indicator lights of different colors are installed on the driver's instrument panel or head-up display (HUD), and different operating instructions are corresponded by flashing different colors. For example, the red indicator light is on at T0, the orange indicator light is on at T1, the yellow indicator light is on at T2, the green indicator light is on at T3, the cyan indicator light is on at T4, the blue indicator light is on at T5, and the purple indicator light is on at T6. The light flashing frequency is moderate and cooperates with the sound signal to enhance the prompt effect.

[0059] The vibration prompt unit is used to generate vibration signals: micro-vibrators are integrated inside the steering wheel and driver's seat to provide the driver with the most direct operation prompts by generating tactile feedback at specific moments, including: slight vibration of the accelerator pedal at T1, slight vibration of the handbrake at T2, slight vibration of the steering wheel at T3, slight vibration of the steering wheel again at T4, slight vibration of the handbrake at T5, and slight vibration of the accelerator pedal at T6. Tactile feedback assists trained police officers in accurately grasping the operation timing, which is especially suitable for noisy environments or situations where the driver is insensitive to sound or light.

[0060] The display module is the system's primary visual information output window, typically a high-resolution onboard touchscreen. It not only handles the functions of the parameter setting module but also provides a detailed training effect evaluation report after training, using a combination of charts, graphs, and text, for easy review and analysis by trainees and coaches.

[0061] Reference Figure 2 As shown, the entire system workflow is divided into two phases. In the "training data acquisition phase," the instructor demonstrates vehicle operation, the sensor module collects data, and the data processing and storage module selects excellent samples and stores them in the database. In the "training phase," the trainee first enters training parameters through the parameter setting module. Next, the data processing and storage module retrieves historical data based on the parameters and builds / calls a data model. When the officer begins training, the system activates auxiliary functions, and the signal prompt module issues guidance signals based on the timing calculated by the model. Simultaneously, the sensor module records the officer's training data in real time. Finally, the data processing module performs analysis and comparison, and the display module displays the final training results and analysis results.

[0062] Through the above-mentioned method and system, and through the precise coordination of various modules, a complex driving skill training process can be deconstructed and reshaped into an intelligent, scientific closed-loop process that is perceptible, calculable, guideable, and evaluable.

[0063] Example 3: Reference Figure 3 The data processing and storage module is the core intelligent hub of the entire training assistance system. It is usually implemented by a high-performance, vibration-resistant on-board industrial computer or embedded system. It is responsible for executing the entire processing flow from raw data to the final evaluation report. The internal functional logic of this module can be divided into the following collaborative units: 1. Raw data acquisition unit This unit serves as the data input source of the module and continuously receives the raw data stream collected by the sensor module, which contains all the dynamic information of the expert instructor or trained police officer during the execution of the 180° turn operation.

[0064] 2. Data preprocessing unit This unit is responsible for cleaning and purifying raw data, including removing erroneous time records caused by sensor failures and outliers that significantly deviate from the reasonable range, to ensure the quality of data input into the core model. After processing by this module, the raw data is transformed into a clean and reliable success sample dataset.

[0065] 3. Feature extraction and modeling unit This unit is responsible for extracting knowledge from massive amounts of data and building predictive capabilities. The specific implementation details are as follows: Database Design and Storage: To efficiently manage and analyze massive amounts of data, a relational database is used internally for data organization. Multiple carefully designed tables are linked via primary and foreign keys to form a complete and standardized data structure. These tables include: Vehicle Basic Information Table, which stores static information such as vehicle model and weight; Tire Condition Information Table, which stores information such as tire pressure and wear; Driver Information Table, which stores personalized information such as the instructor's and student's height, weight, and reaction time; Site Surface Condition Information Table, which stores environmental information such as site type, slope, and road friction coefficient; and Training Record Table, a core data table that records detailed time-series data such as T0, T1, T2, T3, T4, T5, and T6 for each operation, as well as speed curves and vehicle posture curves. This structured storage approach greatly facilitates subsequent complex data queries and in-depth analysis.

[0066] Feature extraction: Before modeling, this unit will query and extract key features from the database. These features mainly include the distribution pattern of time intervals between key operation moments under different conditions, and the influence weight of each training condition parameter on the time interval.

[0067] Model training includes: (1) model selection, selecting the appropriate model type (such as gradient boosting decision tree, neural network, etc.) according to task requirements; (2) feature and target definition, taking various variables in historical successful sample data (such as vehicle model, weight, tire condition, ground condition, speed, etc.) as the input features of the model, and taking the time interval sequence corresponding to the successful operation moment (i.e., T1-T0, T2-T0, T3-T0, T4-T0, T5-T0, T6-T0) as the target output of the model; (3) training and tuning, using these input features and target outputs to train the model, and continuously adjusting the model parameters (such as learning rate, tree depth, etc.) in the process. The goal is to enable it to accurately predict the reasonable T1, T2, T3, T4, T5, and T6 moments under different input conditions, so as to ultimately achieve the purpose of precise assisted driving.

[0068] 4. Training effect evaluation unit This unit is responsible for objectively and quantitatively evaluating the training effectiveness of trained police officers. It accurately compares the real-time recorded training data of trained police officers with the optimal operating time range predicted by the data model based on current training conditions, quantitatively analyzes the deviation of each operating time, and generates a detailed training performance report.

[0069] The data processing and storage module realizes a complete closed loop from raw data input to structured storage, intelligent analysis, model building, and final quantitative evaluation through a series of closely connected functional units such as internal database design, data preprocessing, feature extraction, model training and effect evaluation.

[0070] Example 4: Figure 4 As shown, in order to further improve the training effect and the practicality of the system, this system can also integrate the following expansion function modules: Real-time data transmission and remote monitoring: The system is equipped with a 4G / 5G wireless communication unit. Training data is encoded in real time and sent to a cloud server or the instructor's terminal. Without being in the vehicle, the instructor can use dedicated software on the terminal to visually monitor the trainee's operating parameters using charts and graphs. The instructor can also send guidance via text or voice messages, which the system receives and provides instant feedback to the trainee, enabling efficient remote learning.

[0071] Simulation Scenario Generation and Switching Module: When integrated with a driving simulator, this system drives a graphics rendering engine. Based on the site and ground conditions input by the parameter setting module, the system generates realistic 3D driving scenarios in real time, including different roads, obstacles, and weather effects. These scenarios are presented to the driver via VR glasses or an AR projector. The driver's actions are reflected in the virtual scene in real time, achieving highly immersive safety training.

[0072] The system includes a built-in driving tips knowledge base, containing text-based tips tailored to different vehicle types and road conditions (e.g., "On slippery roads, release the parking brake gently to prevent secondary slipping") and expert-recorded demonstration videos. Once a trainee enters their training parameters, the system automatically selects and recommends matching tips and videos for pre-training or post-training review.

[0073] As described above, although the present invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the present invention itself. Various changes may be made to it in form and detail without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for assisting police vehicle driving training to quickly turn around 180°, characterized in that: The following steps are involved: S1: Acquire successful operation data of an expert trainer under different training condition parameters, wherein the successful operation data includes vehicle state data synchronized with a series of key operation moments, and establish a data model based on the successful operation data, wherein the data model represents a correspondence between the training condition parameters and an optimal timing of the series of key operation moments; S2: Get the training parameters corresponding to the current training task; S3: determining a target operation sequence including a series of target operation moments based on the training parameters obtained in step S2 and the data model established in step S1, and sending an operation prompt signal synchronized with the target operation sequence to the trained driver through a signal prompt module according to the target operation sequence; S4: Recording the actual operation timing of the trained driver, and comparing the actual operation timing with the target operation timing, and generating a training evaluation report including deviation information between the two.

2. The method for assisting police vehicle rapid forward 180° U-turn driving training according to claim 1 is characterized in that: A series of key operation moments in step S1 include: The time of refueling (T1) and refueling (T6) monitored by the speed sensor; The handbrake application moment (T2) and handbrake release moment (T5) monitored by the handbrake sensor; The steering wheel turning moment (T3) and the steering wheel returning moment (T4) monitored by the steering wheel sensor; The vehicle status data synchronized with the series of key operation moments includes: Vehicle type, weight, handbrake status, vehicle posture data, tire status, speed, and steering wheel angle collected by vehicle status sensors; Ground conditions and site environment detected by environmental sensors.

3. The method for assisting police vehicle rapid forward 180° U-turn driving training according to claim 1 or 2, characterized in that: The data model further includes an influence weight of each training condition parameter corresponding to the time interval between the key operation moment and the reference zero moment, and uses the influence weight to adjust the generation of the target operation timing; The training condition parameters include: one or more of vehicle model information, tire condition information, ground condition information of the training ground, and target training speed.

4. The method for assisting police vehicle rapid forward 180° U-turn driving training according to claim 3 is characterized in that: The data model construction process includes: Data acquisition and preprocessing: The sensor module collects multiple sets of raw data from expert coaches practicing fast forward 180-degree U-turns. The raw data is cleaned to remove outliers and erroneous data, forming a successful sample data set. Feature extraction and modeling: extracting key features from the successful sample data set, the key features including the distribution of time intervals between key operation moments and the reference zero moment under different training condition parameter combinations; and the weight of the influence of each training condition parameter on the time interval, and constructing the data model based on the key features; Model selection and training: select the model type, initialize the model parameters, select the loss function and use the optimization algorithm for training; Model evaluation and optimization: Evaluate model performance using metrics like mean squared error, mean absolute error, or R² score and perform cross-validation to optimize the model for the data.

5. The method for assisting police vehicle driving training in making a 180° U-turn while driving forward quickly according to claim 1, characterized in that: The signal prompt module sends an operation prompt signal through a sound signal, a light signal and / or a vibration signal, wherein: The sound signal uses sounds of different frequencies and tones to prompt each operation moment; The light signal prompts each operation moment through indicator lights of different colors; The vibration signal prompts each operation moment through the vibration device on the steering wheel or the seat.

6. The method for assisting police vehicle driving training in making a 180° U-turn while driving forward quickly according to claim 4, characterized in that: The method further comprises at least one of the following steps: Skill level assessment step: based on the training evaluation report generated in the evaluation feedback step, comparing the completion time and steering accuracy contained in the report with a preset skill level standard to determine and output a corresponding special driving operation skill level; Simulator data configuration step: associating the training evaluation report with the corresponding parameters to be trained and storing them in a structured manner for configuring a driving training simulator; wherein the configuration includes: adapting the steering wheel power assistance and handbrake force feedback according to the vehicle model information in the parameters to be trained, and setting the road friction coefficient or slope according to the ground state information in the parameters to be trained.

7. A system for implementing the police vehicle rapid forward 180° turn driving assistance training method according to claim 1, characterized in that: include: a sensor module configured to collect a series of key operation moments when the vehicle performs a rapid forward 180° U-turn maneuver and vehicle status data synchronized with the moments; A parameter setting module is configured to set training parameters corresponding to the current training task, wherein the training parameters include vehicle type, tire condition, site environment, ground condition and speed; A data processing and storage module stores a data model established based on successful operation data of an expert coach, wherein the data model represents the correspondence between different training condition parameters and the optimal timing of the series of key operation moments; the data and storage module is configured to: receive the training parameters input by the parameter setting module; determine the target operation timing based on the training parameters and the data model and control the signal prompt module; generating a training evaluation report based on the actual operation timing of the trained driver collected by the sensor module; a signal prompt module, connected to the data processing and storage module, and configured to send an operation prompt signal to the driver; The display module is connected to the data processing and storage module and is configured to display the training parameters, operation time prompts and training effect evaluation reports.

8. The system according to claim 7, characterized in that include: The series of key operation moments include the throttle release moment (T1), the handbrake application moment (T2), the steering wheel rotation moment (T3), the steering wheel return moment (T4), the handbrake release moment (T5) and the refueling moment (T6), and the vehicle status data includes the vehicle model, weight, handbrake status, tire status, speed and steering wheel rotation angle; The data processing and storage module is further configured to control the signal prompt module to send prompt signals in sequence at times T1 to T6 in the target operation sequence.

9. The system according to claim 7, wherein: The data model further includes an influence weight of each training condition parameter corresponding to the time interval between the key operation moment and the reference zero moment, and uses the influence weight to adjust the generation of the target operation timing; The training condition parameters include: at least one of vehicle type, driver weight, tire condition, site environment, ground condition and speed.

10. The system according to claim 7, wherein: The data processing and storage module is also connected to a real-time data transmission and remote monitoring module, an operation skill recommendation and demonstration module, and a simulation scene generation and switching module; The real-time data transmission and remote monitoring module includes a wireless communication unit and a coach terminal, and the data processing and storage module sends the training data to the coach terminal through the wireless communication unit and receives guidance information from the coach terminal; The simulation scene generation and switching module includes a graphics rendering engine and a virtual reality device interface, which is used to construct a three-dimensional driving scene model according to the training parameters and output it to the virtual reality device; The operation skill recommendation and demonstration module includes a driving skill knowledge base, which is used to filter matching skill information from the knowledge base according to the training parameters and display the information through the display module.

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