All-terrain vehicle and multi-age-mode intelligent safety control method, equipment, medium and product thereof

Abstract

The invention discloses an all-terrain vehicle and a multi-age-mode intelligent safety control method and device thereof, a medium and a product, and relates to the field of vehicle control, and the method comprises the steps: building a people number and age recognition model based on a YOLOv5s network model; inputting the driver area image into a people number and age identification model to obtain a people number and age identification result; determining a main driving face detection frame based on the people number and age identification result, and taking the age corresponding to the main driving face detection frame as the age of the driver; determining a driving mode based on the driver age; a control instruction of the all-terrain vehicle is generated based on the driving mode, and intelligent safety control over the all-terrain vehicle is completed through the control instruction. According to the method, the driving mode of the vehicle can be automatically adjusted according to the identification result, so that the safety and the driving experience in the driving process are improved, and safe and intelligent driving management is realized.

Description

Technical Field

[0001] The present application relates to the field of vehicle control, and in particular to an all-terrain vehicle and a multi-age mode intelligent safety control method, device, medium and product thereof. Background Art

[0002] Most ATV safety systems currently only support the safety needs of adult drivers and lack differentiated support for drivers of different ages. This situation leads to limitations when dealing with children, teenagers and elderly drivers, which may not meet their specific safety and driving experience needs.

[0003] In particular, insufficient consideration of the safety and driving experience needs of children, teenagers and elderly drivers may lead to driving discomfort and increased safety risks, thus affecting the overall user experience of all-terrain vehicles and there is a problem of insufficient balance between driving experience and safety.

[0004] Traditional ATV systems have limited real-time status monitoring and feedback capabilities for drivers, and are unable to dynamically adjust vehicle parameters to cope with different physiological and psychological characteristics of drivers. This static human-machine interface limitation may lead to insufficient response to the driver's status, thus affecting driving safety and comfort.

[0005] In summary, in the safety control of all-terrain vehicles, in response to the safety needs of drivers and passengers, traditional systems often lack refined control for drivers of different age groups, and there are challenges in age recognition and driving mode adaptability. Summary of the invention

[0006] The purpose of this application is to provide an all-terrain vehicle and its multi-age mode intelligent safety control method, equipment, medium and product, which can automatically adjust the vehicle's driving mode according to the recognition results, thereby improving the safety and driving experience during driving, and realizing safe and intelligent driving management.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a multi-age mode intelligent safety control method for an all-terrain vehicle, comprising:

[0009] Acquire driver area image;

[0010] Build a model for age recognition based on the YOLOv5s network model;

[0011] Inputting the driver area image into the headcount age recognition model to obtain a headcount age recognition result; the headcount age recognition result includes the face detection frame coordinates and the age corresponding to the face detection frame;

[0012] Determine a main driver's face detection frame based on the age recognition result of the number of people, and use the age corresponding to the main driver's face detection frame as the driver's age;

[0013] Determine a driving mode based on the driver's age; the driving modes include: child mode, teenager mode, youth mode, adult mode and elderly mode;

[0014] A control command for the all-terrain vehicle is generated based on the driving mode, and the control command is used to complete intelligent safety control of the all-terrain vehicle.

[0015] Optionally, a person age recognition model is constructed based on the YOLOv5s network model, specifically including:

[0016] Acquire a sample data set; the sample data set includes a training set and a test set; the training set and the test set each include a plurality of training sample pairs; each training sample pair includes a driver area historical image and annotated data corresponding to the driver area image; the annotated data includes: a face detection frame coordinate and an age corresponding to the face detection frame;

[0017] Based on the YOLOv5s network model, use the Model class to create an initial model;

[0018] The initial model is iteratively trained using a sample data set until a set condition is met, and the trained initial model is used as a model for identifying the age of a person.

[0019] Optionally, determining the main driver's face detection frame based on the age recognition result of the number of people specifically includes:

[0020] Determining the number of face detection frames in the age recognition result of the number of people;

[0021] When the number of face detection frames is 1, this face detection frame is used as the main driver face detection frame;

[0022] When the number of face detection frames is greater than 1, obtain the binocular point cloud distance collection of all pixels in each face detection frame based on the face detection frame coordinates;

[0023] The average distance of the binocular point cloud distance collection is determined, and the face detection frame with the smallest average distance is determined as the main driver's face detection frame.

[0024] In a second aspect, the present application provides an all-terrain vehicle, comprising:

[0025] An image acquisition device, used for acquiring an image of the driver area;

[0026] The vehicle terminal is connected to the image acquisition device and is implanted with a headcount age recognition model, and is used to obtain a headcount age recognition result based on the driver area image using the headcount age recognition model; the headcount age recognition result includes the coordinates of a face detection frame and the age corresponding to the face detection frame;

[0027] A vehicle controller connected to the vehicle-mounted terminal, used to determine a driving mode based on the age recognition result of the number of people, and used to generate a control instruction of the all-terrain vehicle based on the driving mode; the driving modes include: child mode, juvenile mode, youth mode, adult mode and elderly mode;

[0028] A motor controller, connected to the vehicle controller, for generating a motor parameter control instruction based on the control instruction;

[0029] The motor is connected to the motor controller and is used to execute the motor parameter control instruction.

[0030] Optionally, the vehicle controller includes: a child lock system;

[0031] The states of the child lock system include: a driving-not-allowed state, a driving-allowed state, and a safety protection state.

[0032] Optionally, when the driving mode is the child mode, the child lock system is turned on, and the state of the child lock system is that driving is not allowed;

[0033] The motor parameter control instruction generated by the motor controller is a motor power-off instruction.

[0034] Optionally, the child lock system includes an electronic screen;

[0035] When the driving mode is the juvenile mode, the child lock system is turned on, and the state of the child lock system is a safety protection state;

[0036] In the safety protection state, the electronic screen displays the words "adult confirmation required" and generates a human-computer interaction interface including a confirmation button and a negative button accordingly;

[0037] When the vehicle controller receives feedback information from the confirmation button, the state of the child lock system is changed to a driving permission state;

[0038] When the vehicle controller receives the feedback information of the negative button, the state of the child lock system is changed to a driving permission state.

[0039] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-age mode intelligent safety control method for an all-terrain vehicle as described in any one of the above-mentioned methods.

[0040] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for multi-age mode intelligent safety control of an all-terrain vehicle.

[0041] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for multi-age mode intelligent safety control of all-terrain vehicles.

[0042] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0043] The present application provides an all-terrain vehicle and its multi-age mode intelligent safety control method, equipment, medium and product, introduces the YOLOv5 network model for face target detection and age recognition, realizes dynamic recognition and position confirmation of the main driver, and can more efficiently identify the driver's status and perform safety control through real-time monitoring and data processing compared to the static human-computer interaction interface of the traditional system. Determining the driving mode according to the age of the main driver can balance driving responsiveness and safety, and improve safety and driving experience during driving. In addition, this dynamic adjustment can better adapt to the actual needs of the driver and the driving environment. Compared with the traditional fixed parameter setting, it has significant advantages and can achieve safe and intelligent driving management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A schematic flow chart of a multi-age mode intelligent safety control method for an all-terrain vehicle provided in one embodiment of the present application;

[0046] Figure 2 A flowchart of an implementation method of a multi-age mode intelligent safety control method for an all-terrain vehicle provided by another embodiment of the present application;

[0047] Figure 3A diagram of a multi-age mode intelligent safety control architecture for an all-terrain vehicle provided in another embodiment of the present application;

[0048] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0051] The present application embodiment provides a multi-age mode intelligent safety control method for an all-terrain vehicle, such as Figure 1 As shown, a multi-age mode intelligent safety control method for an all-terrain vehicle is provided. The method is executed by a computer device, specifically, it can be executed by a computer device such as a terminal or a server alone, or it can be executed by a terminal and a server together. In the embodiment of the present application, the method is applied to a vehicle-mounted terminal as an example for explanation, and includes the following steps 101 to 106. Among them:

[0052] Step 101: Acquire a driver area image.

[0053] Step 102: Build a person age recognition model based on the YOLOv5s network model.

[0054] Step 103: Input the driver area image into the headcount age recognition model to obtain the headcount age recognition result. The headcount age recognition result includes the face detection frame coordinates and the age corresponding to the face detection frame.

[0055] Step 104: Determine the main driver's face detection frame based on the age recognition results, and use the age corresponding to the main driver's face detection frame as the driver's age.

[0056] Step 105: Determine a driving mode based on the driver's age. The driving modes include: child mode, teenager mode, youth mode, adult mode, and elderly mode.

[0057] Step 106: Generate control instructions for the all-terrain vehicle based on the driving mode, and use the control instructions to complete intelligent safety control of the all-terrain vehicle.

[0058] In another exemplary embodiment of the present application, in order to further improve the accuracy of number and age detection, the implementation process of the above step 102 may include:

[0059] Step 1021: Obtain a sample data set. The sample data set includes a training set and a test set. The training set and the test set each include a plurality of training sample pairs. Each training sample pair includes a driver area historical image and annotated data corresponding to the driver area image. The annotated data includes: a face detection frame coordinate and an age corresponding to the face detection frame.

[0060] Step 1022: Based on the YOLOv5s network model, use the Model class to create an initial model.

[0061] Step 1023: The initial model is iteratively trained using the sample data set until the set conditions are met, and the trained initial model is used as the age recognition model for the number of people.

[0062] In another exemplary embodiment of the present application, in order to further improve the accuracy of number and age detection, the implementation process of the above step 104 includes:

[0063] Step 1041: Determine the number of face detection frames in the age recognition result. For example, the number of face detection frames is n.

[0064] Step 1042: When the number of face detection frames is 1, this face detection frame is used as the main driver's face detection frame.

[0065] Step 1043: When the number of face detection frames is greater than 1, obtain the binocular point cloud distance collection of all pixels in each face detection frame based on the face detection frame coordinates. For example, each face detection frame is numbered {id_0, id_1...id_n}, and each face detection frame has a corresponding lower left coordinate (xleft_n, ydown_n) and upper right coordinate (xright_n, yup_n). Then the binocular point cloud distance collection of all pixels in each face detection frame is expressed as: d_n = {d(xleft_n, ydown_n)...d(xright_n, yup_n)}. The average distance of the binocular point cloud distance collection calculated is recorded as dmean_n.

[0066] Step 1044: Determine the average distance of the binocular point cloud distance collection, and determine the face detection frame with the smallest average distance as the main driver's face detection frame.

[0067] Based on the same inventive concept, the embodiment of the present application also provides an all-terrain vehicle for implementing the multi-age mode intelligent safety control method for an all-terrain vehicle. The implementation scheme for solving the problem provided by the all-terrain vehicle is similar to the implementation scheme described in the above method, so the specific limitations in one or more all-terrain vehicle embodiments provided below can refer to the limitations of the multi-age mode intelligent safety control method for an all-terrain vehicle, and will not be repeated here.

[0068] In an exemplary embodiment, an all-terrain vehicle is provided, comprising: an image acquisition device, a vehicle-mounted terminal, a vehicle controller, a motor controller, and a motor.

[0069] The image acquisition device is used to acquire the driver area image. For example, the image acquisition device used is a binocular camera.

[0070] The vehicle terminal (Electronic Control Unit, ECU) is connected to the image acquisition device. The vehicle terminal is implanted with a headcount age recognition model, which is mainly used to use the headcount age recognition model to obtain headcount age recognition results based on the driver area image. The headcount age recognition results include the coordinates of the face detection frame and the age corresponding to the face detection frame.

[0071] The vehicle control unit (VCU) is connected to the vehicle terminal and is mainly used to determine the driving mode based on the age recognition results of the people, and to generate control instructions for the all-terrain vehicle based on the driving mode. The driving modes include: child mode, teenager mode, youth mode, adult mode and elderly mode.

[0072] The motor controller is connected to the vehicle controller, and is mainly used to generate motor parameter control instructions based on control instructions.

[0073] The motor is connected to the motor controller, which is mainly used to execute motor parameter control instructions.

[0074] In another exemplary embodiment of the present application, the vehicle controller includes: a child lock system. The status of the child lock system includes: a driving-not-allowed state, a driving-allowed state, and a safety protection state.

[0075] When the driving mode is child mode, the child lock system is turned on and the status of the child lock system is not allowed to drive.

[0076] The motor parameter control instruction generated by the motor controller is a motor power-off instruction.

[0077] Further, the child lock system may also include an electronic screen.

[0078] When the driving mode is the juvenile mode, the child lock system is turned on and the status of the child lock system is the safety protection status.

[0079] In the safety protection state, the electronic screen displays the words "adult confirmation is required" and generates a human-computer interaction interface including a confirmation button and a negative button. In addition, the confirmation button and the negative button can also be a key unit external to the electronic screen.

[0080] When the vehicle controller receives the feedback information from the confirmation button, the state of the child lock system is changed to the driving allowed state.

[0081] When the vehicle controller receives the feedback information of the negative button, the state of the child lock system is changed to the driving permission state.

[0082] In another exemplary embodiment of the present application, Figure 2 and Figure 3 Taking the implementation process shown in the figure as an example, the multi-age mode intelligent safety control process of the all-terrain vehicle provided by the present application is described. Specifically:

[0083] Step 1: Camera installation and image collection.

[0084] Step 1.1, camera installation and field of view setting:

[0085] Install a binocular camera at the front of the vehicle to ensure that the image of the driver's area can be clearly captured. Adjust the camera's viewing angle to ensure that the driver and passengers can be fully captured.

[0086] Step 1.2, image collection:

[0087] Get the real-time image frames captured by the camera. Transmit and save the images to the cloud server, and create a data set for the collected images.

[0088] Step 2: Train the age recognition model in the cloud, that is, the training process is carried out on the cloud server.

[0089] Step 2.1, Initialization settings:

[0090] Set the total number of training epochs to 300, the batch size to 64, and the initial weights to opt.weights.

[0091] Use a random seed to initialize the random number generator to ensure the repeatability of the training process.

[0092] Determine the number of categories and model parameter settings.

[0093] Step 2.2: Create the initial model:

[0094] Create an initial model using the Model class based on the YOLOv5s network model.

[0095] Step 2.3, verify the image size:

[0096] Calculate the maximum step size of the initial model and ensure that the input image size is a multiple of the maximum step size to accommodate the input requirements of the initial model.

[0097] Step 2.4, select optimizer:

[0098] Select the adam optimizer according to the value of opt.adam, set parameters such as learning rate (lr) and momentum (momentum), and add the parameter group to the optimizer.

[0099] Step 2.5, load the pre-trained model weights:

[0100] Use torch.load to load the checkpoint of the pre-trained model into ckpt.

[0101] Convert the loaded initial model parameters (i.e., the checkpoint of the pre-trained model) to float type and load it into the current model (i.e., the initial model) to ensure compatibility with the current model.

[0102] If the checkpoint contains the optimizer state, the optimizer state and the best performance indicators are loaded.

[0103] Step 2.6, set the training start:

[0104] Set the starting epoch to epoch+1 recorded in the checkpoint, and continue training from where the last training ended.

[0105] Step 2.7, learning rate scheduler:

[0106] Use the cosine annealing scheduler to adjust the learning rate according to the current epoch.

[0107] Step 2.8, create a data loader:

[0108] Create a data loader and batch load the previously collected image dataset for training, set the number of processes for parallel data loading to 2, and set data shuffling.

[0109] Step 2.9, training loop:

[0110] From the beginning to the end of the epoch, each epoch performs the following operations:

[0111] (1) Set the initial model to training mode and enable dropout and batch normalization.

[0112] (2) Calculate the weight of the category weights as needed and reselect the indexes in the dataset for weighted sampling.

[0113] (3) Initialize a tensor to store the average loss.

[0114] (4) Print key training information, such as epoch, GPU memory usage, loss, etc.

[0115] (5) Traverse each batch of data in the training data loader:

[0116] (6) Adjust the learning rate and momentum as needed.

[0117] (7) Preprocess the input image and pass it to the initial model for forward propagation to obtain the predicted value.

[0118] (8) Calculate the loss between the model prediction value and the target label.

[0119] (9) Perform backpropagation and perform gradient updates of the optimizer based on the accumulated gradient steps.

[0120] (10) Update and print average loss, GPU memory usage, and other training metrics.

[0121] (11) Update the learning rate scheduler.

[0122] (12) Calculate the average average precision (mAP).

[0123] (13) Update the properties of the initial model and the moving average parameter EMA.

[0124] (14) Determine whether it is the last epoch. If it is not the last epoch, perform model testing to evaluate the performance of the initial model after training.

[0125] (15) The best performance index is updated according to the fitness of the trained initial model, and the checkpoint of the trained initial model is saved to obtain a model for age recognition of people.

[0126] (16) Convert the saved age recognition model from a .pth model to a .onnx model, and then from a .onnx model to a .rknn model. Deploy the converted model to the lightweight computing board of the ECU.

[0127] Step 3: Driver identification and age inference, i.e. YOLOv5 age recognition.

[0128] Step 3.1: Number detection:

[0129] Initialize RKNNAPI in ECU and initialize the operating environment.

[0130] Initialize an AgeEstimator object and pass in the specified .rknn model path.

[0131] Use OpenCV to read image frames from a camera or other source. If the image is successfully read, call the .rknn model for inference. Inference outputs the face detection frame coordinates and the corresponding age.

[0132] Traverse each face detection frame coordinate and extract the coordinates of each detected face detection frame.

[0133] Returns a list Agelist. Each element in the list Agelist is a dictionary containing a 'box' key. The value corresponding to the 'box' key is a tuple containing the coordinates of the face detection box (x1, y1, x2, y2).

[0134] According to the face detection frames and age predictions output by the population age recognition model, the length of Agelist is calculated to obtain the number of face detection frames, which represents the number of passengers detected.

[0135] Step 3.2: Determine the location and age group of the primary driver:

[0136] If the number of detected face detection frames is 1, it is considered that the person corresponding to the detected face detection frame is the main driver.

[0137] If the detected face detection frame data is greater than 1, then obtain the binocular point cloud distance collection d_n = {d(xleft_n, ydown_n)...d(xright_n, yup_n)} of all pixels in each face detection frame, calculate the average distance dmean_n of the collection, and the face detection frame with the smallest average distance dmean_n is judged as the face detection frame of the main driver, that is, the main driver's face detection frame.

[0138] Step 4: Multi-age system mode determination and driving mode and motor parameter adjustment.

[0139] Step 4.1: Determine the driving mode based on the age in the detection result.

[0140] Step 4.2, driving mode and motor parameter adjustment.

[0141] (1) Driving mode classification and settings:

[0142] According to the age group of the main driver, determine the driving mode to be adopted: child mode (0-8 years old), teenager mode (8-12 years old), youth mode (12-18 years old), adult mode (18-60 years old) and elderly mode (60+ years old).

[0143] (2) Motor parameter adjustment.

[0144] 1) Children mode (0-8 years old):

[0145] A. Startup control:

[0146] Child lock system on: The VCU's child lock system receives the system on signal to ensure that children cannot accidentally start the vehicle. When the child lock system is on, the external child lock screen will light up to show the child lock system status.

[0147] Children are not allowed to start the vehicle: The child lock system is adjusted to the driving-not-allowed state, and the motor controller controls the motor to cut off power to prevent the possibility of children driving the vehicle under any circumstances. The external child lock screen displays the words "not allowed to drive" according to the VCU child lock system command.

[0148] B. Motor parameter control:

[0149] Motor controller power off: When the child lock system does not allow driving, the motor controller immediately controls the motor to power off to ensure that the vehicle stops running. The motor controller controls the motor output voltage to 0V, that is, V_motor=0, V_motor is the output voltage of the motor.

[0150] 2) Teenager Mode (8-12 years old):

[0151] A. Startup control:

[0152] Child lock system is on: The child lock system of the VCU receives the on signal and sets the child lock system to a safety protection state. When the child lock system is on, the external child lock screen (i.e., the electronic screen) will light up to display the child lock system status. The external child lock screen displays the words "adult confirmation required" according to the child lock system command. The two buttons have confirmation and denial respectively. If the user presses the confirmation button, the command is uploaded to the VCU, the child lock system changes to the driving allowed state, and the command is sent to display the words "driving allowed". If the user presses the denial button, the command is uploaded to the VCU, the child lock system changes to the driving not allowed state, and the command is sent to display the words "driving not allowed".

[0153] B. Adult confirmation is required: An adult must go through additional safety start controls to start the vehicle, ensuring that the driving process is safe and controllable.

[0154] C. Motor parameter control:

[0155] Child lock system status confirmation: Determine whether driving is allowed based on the child lock system status.

[0156] In the safety protection state, the motor controller controls the motor to cut off the power to ensure the vehicle stops safely. In the driving not allowed state, if the adult confirms that driving is not allowed, the child lock system is adjusted to the driving not allowed state, and the motor controller controls the motor to cut off the power to ensure the vehicle stops safely. In the driving allowed state, if the adult confirms that driving is allowed, the child lock system is adjusted to the driving allowed state, and the motor controller controls the motor to be powered on, but the motor power is limited to no more than 1.5kw.

[0157] a. Motor power limit: limit the motor power within 1.5kw to ensure safety and controllability during driving. P_motor<=1500W, P_motor is the motor power.

[0158] b. Speed ​​and acceleration settings: The motor speed is adjusted and a lower acceleration is set through the controller to ensure that the driving force is suitable for the teenager's driving skills.

[0159] c. Maximum driving speed: Set the maximum driving speed to 15km / h, keep it within the safe range, and avoid accidents caused by excessive speed. v_max = 20km / h, v_max is the maximum allowed driving speed.

[0160] 3) Youth mode (12-18 years old).

[0161] A. Startup control:

[0162] Normal start conditions: Allows young drivers to start the vehicle autonomously without special safety start controls.

[0163] B. Motor parameter control:

[0164] a. Motor power setting: The motor power is controlled at 2.5kw, providing sufficient power response while ensuring driving comfort and safety. P_motor <= 2500W.

[0165] b. Acceleration regulation: Allows moderate acceleration to support the comfortable and safe driving needs of young drivers.

[0166] c. Maximum driving speed setting: Set the maximum driving speed at a moderate level to 20 km / h, allowing young drivers to move faster while maintaining safety. v_max = 20 km / h.

[0167] 4) Adult mode (18-60 years old).

[0168] A. Startup control:

[0169] Normal start conditions: An adult driver can start the vehicle autonomously without additional confirmation or safety controls.

[0170] B. Motor parameter control:

[0171] a. Motor power adjustment: The motor power is controlled between 3.5-4kw, providing enough power to support higher speed and dynamic driving requirements. P_motor<=3500W or P_motor<=4000W.

[0172] b. Acceleration setting: Allows higher acceleration to ensure the vehicle has the ability to accelerate quickly and drive dynamically.

[0173] c. Maximum driving speed setting: Set the maximum driving speed to 35km / h to give full play to the vehicle's speed performance and meet the driving needs of adult drivers. v_max = 35km / h.

[0174] 5) Elderly Mode (60+ years old)

[0175] A. Startup control:

[0176] Normal starting conditions: Elderly drivers are allowed to start the vehicle independently without any special starting restrictions.

[0177] B. Motor parameter control:

[0178] a. Motor power adjustment: The motor power is controlled at 2kw, reducing the power output to ensure driving stability and safety. P_motor <= 2000W.

[0179] b. Acceleration limit: Limit the acceleration of the motor to avoid overly sensitive responses and reduce the risk of accidents during driving.

[0180] c. Maximum driving speed reduction: Reduce the maximum driving speed to 15km / h, limit it to a safe range, and improve the sense of control and safety during driving. v_max = 15km / h.

[0181] Step 5: Control signal transmission and processing.

[0182] Step 5.1, ECU processing of age recognition system:

[0183] The ECU receives the data after the camera image processing, including the recognition result of the main driver's age group.

[0184] Step 5.2, VCU communicates with the motor controller:

[0185] The recognition result of the main driver's age group is transmitted to the VCU.

[0186] The VCU adjusts the vehicle's driving mode and parameter settings based on the data received.

[0187] The child lock system of VCU receives driving mode status information through the CAN bus communication protocol. When the driving mode is updated, the system automatically detects and updates the child lock system status in real time. VCU is connected to an external electronic screen and two buttons. When the child lock system is turned on, the screen lights up to display the child lock system status. The child lock system status of VCU is divided into: not allowed to drive, allowed to drive, and safety protection.

[0188] In summary, in the all-terrain vehicle provided above in this application, a camera is installed at the front of the vehicle to shoot the driving area. Real-time image frames of the driver and passengers are obtained through the camera, and saved to the cloud server to establish a data set. The YOLOv5 model is used for training on the cloud server to achieve face target detection and age estimation. The trained model is deployed to the ECU, and the camera identifies the number of passengers through the age recognition model, and identifies the main driver in the front position through the binocular algorithm. The age of the main driver is identified by the age recognition model, which is divided into children (0-8 years old), teenagers (8-12 years old), youth (12-18 years old), adults (18-60 years old) and the elderly (60+ years old). Different age groups match different driving modes, and the child lock system is automatically adjusted to determine whether the vehicle is allowed to start, motor power, motor speed, acceleration, maximum allowable driving speed and other driving data.

[0189] Based on the above description, compared with the prior art, the present application has the following advantages:

[0190] 1. Intelligent safety control: This application introduces the YOLOv5 model for face target detection and age recognition to achieve dynamic recognition and position confirmation of the main driver. Compared with the static human-computer interaction interface of the traditional system, this method can more efficiently identify the driver's status and perform safety control through real-time monitoring and data processing.

[0191] 2. Dynamic adjustment of motor parameters: Intelligently adjust motor power, acceleration and maximum driving speed according to the age of the main driver to balance driving responsiveness and safety. This dynamic adjustment can better adapt to the actual needs of the driver and the driving environment, and has significant advantages over traditional fixed parameter settings.

[0192] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store multi-age mode intelligent safety control data of all-terrain vehicles. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-age mode intelligent safety control method for an all-terrain vehicle is implemented.

[0193] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0194] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0195] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0196] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0197] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0198] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0199] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0200] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An all-terrain vehicle multi-age mode intelligent safety control method, characterized in that: The multi-age mode intelligent safety control method for an all-terrain vehicle comprises: Acquire driver area image; Build a model for age recognition based on the YOLOv5s network model; Inputting the driver area image into the headcount age recognition model to obtain a headcount age recognition result; the headcount age recognition result includes the face detection frame coordinates and the age corresponding to the face detection frame; Determine a main driver's face detection frame based on the age recognition result of the number of people, and use the age corresponding to the main driver's face detection frame as the driver's age; Determine a driving mode based on the driver's age; the driving modes include: child mode, teenager mode, youth mode, adult mode and elderly mode; A control command for the all-terrain vehicle is generated based on the driving mode, and the control command is used to complete intelligent safety control of the all-terrain vehicle.

2. The multi-age mode intelligent safety control method for an all-terrain vehicle according to claim 1, characterized in that: Based on the YOLOv5s network model, a model for age recognition of people is constructed, including: Acquire a sample data set; the sample data set includes a training set and a test set; the training set and the test set each include a plurality of training sample pairs; each training sample pair includes a driver area historical image and annotated data corresponding to the driver area image; the annotated data includes: a face detection frame coordinate and an age corresponding to the face detection frame; Based on the YOLOv5s network model, use the Model class to create an initial model; The initial model is iteratively trained using a sample data set until a set condition is met, and the trained initial model is used as a model for identifying the age of a person.

3. The multi-age mode intelligent safety control method for an all-terrain vehicle according to claim 1, characterized in that: Determining the main driver's face detection frame based on the age recognition result of the number of people specifically includes: Determining the number of face detection frames in the age recognition result of the number of people; When the number of face detection frames is 1, this face detection frame is used as the main driver face detection frame; When the number of face detection frames is greater than 1, obtain the binocular point cloud distance collection of all pixels in each face detection frame based on the face detection frame coordinates; The average distance of the binocular point cloud distance collection is determined, and the face detection frame with the smallest average distance is determined as the main driver's face detection frame.

4. An all-terrain vehicle, characterized in that: The all-terrain vehicle comprises: An image acquisition device, used for acquiring an image of the driver area; The vehicle terminal is connected to the image acquisition device and is implanted with a headcount age recognition model, and is used to obtain a headcount age recognition result based on the driver area image using the headcount age recognition model; the headcount age recognition result includes the coordinates of a face detection frame and the age corresponding to the face detection frame; A vehicle controller connected to the vehicle-mounted terminal, used to determine a driving mode based on the age recognition result of the number of people, and used to generate a control instruction of the all-terrain vehicle based on the driving mode; the driving modes include: child mode, juvenile mode, youth mode, adult mode and elderly mode; A motor controller, connected to the vehicle controller, for generating a motor parameter control instruction based on the control instruction; The motor is connected to the motor controller and is used to execute the motor parameter control instruction.

5. The all-terrain vehicle according to claim 4, characterized in that: The vehicle controller includes: a child lock system; The states of the child lock system include: a driving-not-allowed state, a driving-allowed state, and a safety protection state.

6. The all-terrain vehicle according to claim 5, characterized in that: When the driving mode is the child mode, the child lock system is turned on, and the state of the child lock system is that driving is not allowed; The motor parameter control instruction generated by the motor controller is a motor power-off instruction.

7. The all-terrain vehicle according to claim 5, characterized in that: The child lock system includes an electronic screen; When the driving mode is the juvenile mode, the child lock system is turned on, and the state of the child lock system is a safety protection state; In the safety protection state, the electronic screen displays the words "adult confirmation is required" and generates a human-computer interaction interface including a confirmation button and a negative button accordingly; When the vehicle controller receives feedback information from the confirmation button, the state of the child lock system is changed to a driving permission state; When the vehicle controller receives the feedback information of the negative button, the state of the child lock system is changed to a driving permission state.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-age mode intelligent safety control method for an all-terrain vehicle according to any one of claims 1 to 3.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the multi-age mode intelligent safety control method for an all-terrain vehicle described in any one of claims 1 to 3 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the multi-age mode intelligent safety control method for an all-terrain vehicle described in any one of claims 1 to 3 is implemented.

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