Driving simulation training system for electric motorcycle racing

Abstract

The invention provides a driving simulation training system for electric motorcycle racing, and the system comprises a feature extraction module which is used for carrying out the feature extraction of standardized motion capture image data, and obtaining a joint motion matching point; the preprocessing module is used for preprocessing the joint action matching points to obtain preprocessed joint action matching points; the joint matching module is used for matching the preprocessed joint action matching points with joint points on a preset driver model to obtain a driver virtual character model; and the simulation training module is used for training in the constructed simulation racing track by using the driver virtual character model. By matching the joint action matching points with the joint points on the preset driver model, the driver virtual character model conforming to the user characteristics can be generated, so that the user can practice and improve the driving skill in a risk-free virtual environment, and accidents and injuries possibly occurring on a real racing track are avoided.

Description

Technical Field

[0001] The invention relates to the field of simulated driving, and in particular to a driving simulation training system for electric motorcycle racing. Background Art

[0002] With the aggravation of urban traffic congestion and the improvement of environmental awareness, electric motorcycles, as a green means of transportation, are gradually favored by consumers. At the same time, the design, performance and technology of electric motorcycles are also constantly developing. Electric motorcycle racing has become an emerging sporting event, attracting more and more racing enthusiasts and professional players.

[0003] However, driving an electric motorcycle is different from driving a traditional fuel motorcycle. Its power output characteristics, braking performance, and driving stability all present unique challenges. Since electric motorcycles usually have instantaneous acceleration and excellent low-speed torque, they place higher demands on the rider's reaction ability and driving skills in terms of high-speed driving and fine control. Traditional driving training methods mainly rely on field driving, which is limited by weather, venue, time and other factors, and it is difficult to provide sufficient and systematic training opportunities. Summary of the invention

[0004] In order to overcome the deficiencies of the prior art, an object of the present invention is to provide a driving simulation training system for electric motorcycle racing.

[0005] A driving simulation training system for electric motorcycle racing, comprising:

[0006] A motion capture module, used to capture user movements and generate motion capture image data;

[0007] A data standardization module, used for performing standardization processing on the motion capture image data to obtain standardized motion capture image data;

[0008] A feature extraction module, used to extract features from the standardized motion capture image data to obtain joint motion matching points;

[0009] A preprocessing module, used for preprocessing the joint action matching points to obtain preprocessed joint action matching points;

[0010] A joint matching module, used for matching the preprocessed joint action matching points with the joint points on the preset driver model to obtain a driver virtual character model;

[0011] The simulation training module is used to use the driver virtual character model to perform training in a constructed simulation track.

[0012] Preferably, the data standardization module includes:

[0013] A stray light component processing unit, used for processing the motion capture image data using a Gaussian function to obtain a stray light component;

[0014] An image enhancement processing unit, used for performing subtraction between the motion capture image data and the stray light component to obtain enhanced motion capture image data;

[0015] An image evaluation unit, configured to evaluate the enhanced motion capture image data to obtain an image quality evaluation value;

[0016] The image optimization unit is used to adjust the scale parameter until the image quality evaluation value is within the preset range when the image quality evaluation value is not within the preset range, and output the standardized motion capture image data.

[0017] Preferably, in the stray light component processing unit, the motion capture image data is processed using a Gaussian function to obtain the stray light component, including:

[0018] Using the formula:

[0019]

[0020] The motion capture image data is processed to obtain a stray light component; wherein K represents a normalization constant, σ represents a scale parameter, G(x, y) represents a Gaussian function, I(x, y) represents a pixel value of the motion capture image data at a position (x, y), and L(x, y) represents a stray light component.

[0021] Preferably, in the image enhancement processing unit, subtracting the motion capture image data from the stray light component to obtain enhanced motion capture image data comprises:

[0022] Using the formula:

[0023] logR(x,y)=logI(x,y)-logL(x,y)

[0024] The motion capture image data and the stray light component are subtracted to obtain enhanced motion capture image data; wherein R(x, y) represents the enhanced motion capture image data.

[0025] Preferably, in the image evaluation unit, evaluating the enhanced motion capture image data to obtain an image quality evaluation value includes:

[0026] Using the formula:

[0027]

[0028] The enhanced motion capture image data is evaluated to obtain an image quality evaluation value; wherein MSE represents a first image quality evaluation value, AG represents a second image quality evaluation value, SD represents a third image quality evaluation value, W represents a length of the motion capture image data, H represents a width of the motion capture image data, I(x, y) represents original motion capture image data, and R(x, y) represents enhanced motion capture image data. Represents the gradient of the image in the x-axis direction, represents the gradient of the image in the y-axis direction, avg represents the pixel mean of the enhanced motion capture image data, and gray(x,y) represents the pixel value on the enhanced motion capture image data.

[0029] Preferably, in the feature extraction module, feature extraction is performed on the standardized motion capture image data to obtain joint motion matching points, including:

[0030] Performing grayscale processing on the standardized motion capture image data to obtain grayscale motion capture image data;

[0031] Take a feature extraction window centered at any point on the grayscale motion capture image data;

[0032] Determine whether the pixel points on the feature extraction window meet the feature pixel conditions. If the conditions are met, the corresponding pixel points are used as joint action matching points.

[0033] Preferably, the grayscale formula is:

[0034] T(i,j)=0.299*R(i,j)+0.587*G(i,j)+0.114*B(i,j)

[0035] Wherein, T(i,j) represents the grayscale value of the grayscale motion capture image data at the position (i,j), R(i,j) represents the pixel value of the red channel of the standardized motion capture image data at the position (i,j), G(i,j) represents the pixel value of the green channel of the standardized motion capture image data at the position (i,j), and B(i,j) represents the pixel value of the blue channel of the standardized motion capture image data at the position (i,j).

[0036] Preferably, the characteristic pixel condition judgment formula is:

[0037] |Vf(x,y)-Vf(s,t)|<T

[0038] arctan(Gy / Gx)-arctan(Gt / Gs)<A

[0039] Among them, Vf(x,y) represents the gradient value of the grayscale motion capture image data at (x,y), Vf(s,t) represents the gradient value at (s,t) in the feature extraction window, arctan(Gy / Gx) represents the gradient direction of the grayscale motion capture image data at (x,y), arctan(Gt / Gs) represents the gradient direction at (s,t) in the feature extraction window, T represents the gradient threshold, and A represents the direction threshold.

[0040] Preferably, in the motion capture module, when capturing user actions, the capture time of each frame of the motion capture image is marked in chronological order to generate motion capture image data.

[0041] Preferably, in the preprocessing module, the joint motion matching points are smoothed to obtain preprocessed joint motion matching points.

[0042] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0043] The present invention relates to a driving simulation training system for electric motorcycle racing, comprising: a motion capture module for capturing user actions to generate motion capture image data; a data standardization module for standardizing the motion capture image data to obtain standardized motion capture image data; a feature extraction module for extracting features from the standardized motion capture image data to obtain joint action matching points; a preprocessing module for preprocessing the joint action matching points to obtain preprocessed joint action matching points; a joint matching module for matching the preprocessed joint action matching points with joint points on a preset driver model to obtain a driver virtual character model; and a simulation training module for using the driver virtual character model to perform training in a constructed simulated track. The present invention can generate a driver virtual character model that meets user characteristics by standardizing and feature extracting motion capture image data to generate corresponding joint action matching points, and matching the joint action matching points with joint points on a preset driver model, so that users can practice and improve driving skills in a risk-free virtual environment to avoid accidents and injuries that may occur on a real track. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1A schematic diagram of a driving simulation training system for electric motorcycle racing provided by an embodiment of the present invention;

[0046] Figure 2 A flow chart of a driving simulation training method for electric motorcycle racing provided by an embodiment of the present invention;

[0047] Figure 3 A schematic structural diagram of an electronic device for executing a driving simulation training method for electric motorcycle racing provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] Figure 1 The schematic diagram of a driving simulation training system for electric motorcycle racing is shown in FIG. Figure 1 As shown, the present invention provides a driving simulation training system for electric motorcycle racing, comprising:

[0051] A motion capture module, used to capture user movements and generate motion capture image data;

[0052] In the motion capture module, when capturing user actions, the capture time of each frame of the motion capture image is marked in chronological order to generate motion capture image data.

[0053] In actual applications, the motion capture device uses an inertial + optical hybrid device (6 optical devices, 1 set of inertial motion capture devices and a pair of gloves). The present invention uses optical and inertial hybrid sensors to collect motion capture image data, which is more accurate than traditional pure inertial or pure optical devices, and can achieve low-latency, anti-interference motion capture; when capturing real-life movements, first connect the receiver of the motion capture device to the computer, and then place the optical device 2 in front and behind the real person, 1 on the left and right, and 1.5 meters away from the real person; the inertial motion capture device is divided into a body-worn device and gloves, first wear the gloves, then wear the body-worn device; when the real-life model is dressed, turn on the body-worn device, gloves and the motion capture software on the computer, and start capturing the action after confirming that the computer and the motion capture device are normally connected.

[0054] A data standardization module, used for performing standardization processing on the motion capture image data to obtain standardized motion capture image data;

[0055] Furthermore, the data standardization module includes:

[0056] A stray light component processing unit, used for processing the motion capture image data using a Gaussian function to obtain a stray light component;

[0057] In the stray light component processing unit, the motion capture image data is processed using a Gaussian function to obtain a stray light component, including:

[0058] Using the formula:

[0059]

[0060] The motion capture image data is processed to obtain a stray light component; wherein K represents a normalization constant, σ represents a scale parameter, G(x, y) represents a Gaussian function, I(x, y) represents a pixel value of the motion capture image data at a position (x, y), and L(x, y) represents a stray light component.

[0061] An image enhancement processing unit, used for performing subtraction between the motion capture image data and stray light components to obtain enhanced motion capture image data;

[0062] In the image enhancement processing unit, the motion capture image data and the stray light component are subtracted to obtain enhanced motion capture image data, including:

[0063] Using the formula:

[0064] logR(x,y)=logI(x,y)-logL(x,y)

[0065] The motion capture image data and the stray light component are subtracted to obtain enhanced motion capture image data; wherein R(x, y) represents the enhanced motion capture image data.

[0066] The present invention has the following technical effects by eliminating stray light components in motion capture image data:

[0067] (1) Improve data quality: Stray light components can cause noise and distortion in the image, thus affecting the accuracy and reliability of motion capture data. By eliminating these stray lights, the quality of image data can be greatly improved, ensuring the accuracy of subsequent processing.

[0068] (2) Enhanced feature extraction effect: High-quality motion capture image data is more conducive to the work of the feature extraction module and can more clearly identify joint positions and motion features, thereby improving the accuracy and efficiency of feature extraction.

[0069] (3) Reduce the risk of mismatching: Stray light may cause mismatching of joint motion matching points, which is inconsistent with the standardized joint motion data. Eliminating these stray light components can reduce the risk of mismatching, thereby better achieving accurate generation of the user's virtual character model.

[0070] An image evaluation unit, configured to evaluate the enhanced motion capture image data to obtain an image quality evaluation value;

[0071] In the image evaluation unit, the enhanced motion capture image data is evaluated to obtain an image quality evaluation value, including:

[0072] Using the formula:

[0073]

[0074] The enhanced motion capture image data is evaluated to obtain an image quality evaluation value; wherein MSE represents a first image quality evaluation value, AG represents a second image quality evaluation value, SD represents a third image quality evaluation value, W represents a length of the motion capture image data, H represents a width of the motion capture image data, I(x, y) represents original motion capture image data, and R(x, y) represents enhanced motion capture image data. Represents the gradient of the image in the x-axis direction, represents the gradient of the image in the y-axis direction, avg represents the pixel mean of the enhanced motion capture image data, and gray(x,y) represents the pixel value on the enhanced motion capture image data.

[0075] The image optimization unit is used to adjust the scale parameter until the image quality evaluation value is within the preset range when the image quality evaluation value is not within the preset range, and output the standardized motion capture image data.

[0076] A feature extraction module, used to extract features from the standardized motion capture image data to obtain joint motion matching points;

[0077] In the feature extraction module, feature extraction is performed on the standardized motion capture image data to obtain joint motion matching points, including:

[0078] The standardized motion capture image data is grayed to obtain grayed motion capture image data; wherein the graying formula is:

[0079] T(i,j)=0.299*R(i,j)+0.587*G(i,j)+0.114*B(i,j)

[0080] Wherein, T(i,j) represents the grayscale value of the grayscale motion capture image data at the position (i,j), R(i,j) represents the pixel value of the red channel of the standardized motion capture image data at the position (i,j), G(i,j) represents the pixel value of the green channel of the standardized motion capture image data at the position (i,j), and B(i,j) represents the pixel value of the blue channel of the standardized motion capture image data at the position (i,j).

[0081] Take a feature extraction window centered at any point on the grayscale motion capture image data;

[0082] Determine whether the pixel points on the feature extraction window meet the feature pixel conditions. If the conditions are met, the corresponding pixel points are used as joint action matching points.

[0083] Furthermore, the characteristic pixel condition judgment formula is:

[0084] |Vf(x,y)-Vf(s,t)|<T

[0085] arctan(Gy / Gx)-arctan(Gt / Gs)<A

[0086] Among them, Vf(x,y) represents the gradient value of the grayscale motion capture image data at (x,y), Vf(s,t) represents the gradient value at (s,t) in the feature extraction window, arctan(Gy / Gx) represents the gradient direction of the grayscale motion capture image data at (x,y), arctan(Gt / Gs) represents the gradient direction at (s,t) in the feature extraction window, T represents the gradient threshold, and A represents the direction threshold.

[0087] During the user's movement, the gradient value and direction of the joints will change. By analyzing these changes, the user's operating dynamics can be tracked in real time to obtain a more accurate joint movement trajectory, which is helpful to establish an accurate driver virtual character model. For example, the gradient value and gradient direction can effectively reflect the strong edges and changing areas in the motion capture image. When the user performs a movement, the pixel intensity and gradient around his joints will also change, based on which the position of the joint points can be accurately located.

[0088] The preprocessing module is used to perform smooth preprocessing on the joint action matching points to obtain preprocessed joint action matching points.

[0089] When collecting data, due to the influence of factors such as the distance between the motion capture device and the human body, the environment or light, and the motion capture device itself, the key points of each joint may flicker and jump, resulting in poor continuity of the matching action. Therefore, the present invention can reduce the influence of these factors through smoothing methods to improve the continuity of the matching action.

[0090] Further, the joint motion matching points are smoothed and preprocessed to obtain preprocessed joint motion matching points, including:

[0091] Using the formula:

[0092]

[0093] Smoothing the matching points of each joint of the human body to obtain smoothed joint matching points; wherein, S t represents the estimated value of the matching point of the motion capture data in the tth frame, b t represents the feature value of the matching point of the motion capture data in the tth frame, y t represents the actual value of the matching point of the motion capture data in frame t, It represents the joint matching point after smoothing, α represents the smoothing parameter, which is generally a value between 0 and 1, β represents the correction parameter, and T represents the time parameter, which is generally slightly larger than t.

[0094] A joint matching module, used for matching the preprocessed joint action matching points with the joint points on the preset driver model to obtain a driver virtual character model;

[0095] In practical applications, the present invention can obtain the coordinates of the joint matching points through the GetJointKinectPosition() function under the Unity3D platform, and then correspond the coordinates to the corresponding joint points on the preset driver model one by one, so as to realize that the virtual character model follows the user's movement.

[0096] The simulation training module is used to use the driver virtual character model to perform training in a constructed simulation track.

[0097] Optionally, the simulated track constructed in this embodiment includes a driving model for electric motorcycle simulation. This embodiment takes into account that electric motorcycles are usually equipped with efficient motors and rechargeable batteries, have instant torque output, and provide sensitive and powerful acceleration performance. In the simulation training system, a corresponding driving model is designed according to the power characteristics of the electric motorcycle to help users better understand the acceleration and braking reactions of the electric motorcycle.

[0098] And unlike traditional motorcycles, electric motorcycles often implement regenerative braking, converting kinetic energy into electrical energy through the reverse work of the motor to improve energy efficiency. The driving model of this embodiment also includes a simulation of regenerative braking to help users adapt to the unique braking method of electric motorcycles and improve driving safety and efficiency. And many electric motorcycles provide a variety of driving modes (such as economic mode, sports mode, and rainy day mode, etc.) to adapt to different riding needs and environmental conditions. In the driving model of this embodiment, users can choose different driving modes for training, so as to improve driving skills in various situations.

[0099] Exemplarily, this embodiment first defines the dynamic model of regenerative braking, including the reverse working mechanism of the motor during braking, and the process of how kinetic energy is converted into electrical energy. In simulation training, when the user performs braking actions through a virtual character model, this embodiment will calculate the speed change of the electric motorcycle in real time, and dynamically adjust the effect of regenerative braking according to the user's braking force and timing. For example, if the user lightly steps on the brakes, the system will simulate a relatively gentle deceleration effect and display feedback on battery charging; if the user brakes suddenly, it will simulate a stronger deceleration effect, and at the same time prompt the user to pay attention to controlling the braking force to avoid loss of control. In this way, this embodiment allows users to repeatedly practice the operation of regenerative braking in a virtual environment, gradually adapt to the unique braking method of electric motorcycles, and thus improve their safety and driving skills in real riding.

[0100] Furthermore, the cruising range and charging time of an electric motorcycle are key factors affecting the driving experience. In the simulation training, this embodiment conducts cruising range evaluation and charging strategy learning to help users better plan their routes and avoid running out of power during driving.

[0101] Demonstratively, this embodiment will calculate the range in real time based on parameters such as the battery capacity, current power, driving speed, and road conditions of the electric motorcycle. In the simulation training, users can choose different driving routes, and this embodiment will provide the estimated power consumption and remaining battery life for each route based on real-time data analysis. In addition, this embodiment will also simulate different charging station locations and charging times, and users can learn how to choose the best charging strategy during training, such as choosing a suitable charging station for charging during driving to ensure sufficient power at the end of the ride. In this way, this embodiment allows users to not only experience different battery life scenarios in a virtual environment, but also master how to reasonably plan driving routes and charging strategies, thereby effectively avoiding low power during actual riding and improving riding safety and convenience.

[0102] Specifically, in a virtual environment, the virtual character will "drive" an electric motorcycle and perform operations such as acceleration, braking, and steering. At this time, the performance of the virtual character will be based on the user action data captured previously. During the training process, this embodiment monitors the relationship between the virtual character's actions and the electric motorcycle's behavior in real time, analyzes whether the user's actions are reasonable, and evaluates the driving behavior. For example, the user's braking timing, steering angle, and dynamic response of the electric motorcycle. According to the user's performance, this embodiment dynamically adjusts the training difficulty or environmental conditions. For example, after the user is familiar with basic driving, more complex traffic conditions, bad weather, etc. can be introduced to enhance the user's adaptability. Finally, this embodiment records every action and reaction of the user during the training process, uses the data analysis module to evaluate the user's progress, provides personalized improvement suggestions, and optimizes the subsequent training arrangements. And after each training, this embodiment also provides user feedback and suggestions, including improved driving skills, adaptive adjustments, etc., to help users better master the characteristics of electric motorcycles.

[0103] Through the above steps, the virtual character model of this embodiment and the driving model of the electric motorcycle can be combined to form a complete simulation training system. The user's driving experience can be truly reproduced in a virtual environment, allowing them to practice and improve their driving skills without risk, and adapt to the special characteristics of the electric motorcycle, thereby effectively improving the user's riding ability and safety awareness.

[0104] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0105] The present invention generates corresponding joint action matching points by standardizing and feature extracting motion capture image data, and matches the joint action matching points with the joint points on a preset driver model, thereby generating a driver virtual character model that meets the user's characteristics, thereby allowing the user to practice and improve driving skills in a risk-free virtual environment and avoid accidents and injuries that may occur on a real track.

[0106] See also Figure 2 The present invention also provides a driving simulation training method for electric motorcycle racing, comprising:

[0107] Capturing user movements to generate motion capture image data;

[0108] Performing standardization processing on the motion capture image data to obtain standardized motion capture image data;

[0109] Performing feature extraction on the standardized motion capture image data to obtain joint motion matching points;

[0110] Preprocessing the joint action matching points to obtain preprocessed joint action matching points;

[0111] Matching the preprocessed joint action matching points with the joint points on the preset driver model to obtain a driver virtual character model;

[0112] The driver virtual character model is used to perform training in a constructed simulated race track.

[0113] In addition, an embodiment of the present invention further provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned driving simulation training method embodiment for electric motorcycle racing is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0114] For details, see Figure 3 As shown, an embodiment of the present invention further provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0115] In an embodiment of the present invention, the electronic device also includes: a computer program stored in the memory 1150 and executable on the processor 1120, which implements the various processes of the above-mentioned driving simulation training method embodiment for electric motorcycle racing when executed by the processor 1120.

[0116] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0117] In an embodiment of the present invention, the bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, and bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150.

[0118] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an Accelerate Graphical Port (AGP), a processor, or a local bus using any of a variety of bus architectures. By way of example and not limitation, such architectures include: Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA), Peripheral Component Interconnect (PCI) bus.

[0119] The processor 1120 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processors include: a general-purpose processor, a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a complex programmable logic device (CPLD), a programmable logic array (PLA), a microcontroller unit (MCU) or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0120] Processor 1120 can be a microprocessor or any conventional processor. The method steps disclosed in conjunction with the embodiments of the present invention can be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in a readable storage medium known in the art, such as a random access memory (RAM), a flash memory (Flash Memory), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a register, etc. The readable storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware.

[0121] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, the embodiments of the present invention will not be further described.

[0122] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer system, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, and a stylus.

[0123] It should be understood that in an embodiment of the present invention, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above-mentioned network may be an adhoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless local area network (WLAN), a wide area network (WAN), a wireless wide area network (WWAN), a metropolitan area network (MAN), the Internet, a public switched telephone network (PSTN), a plain old telephone service network (POTS), a cellular telephone network, a wireless network, a wireless fidelity (Wi-Fi) network, and a combination of two or more of the above-mentioned networks. For example, the cellular telephone network and the wireless network can be a Global Mobile Communications (GSM) system, a Code Division Multiple Access (CDMA) system, a Worldwide Interoperability for Microwave Access (WiMAX) system, a General Packet Radio Service (GPRS) system, a Wideband Code Division Multiple Access (WCDMA) system, a Long Term Evolution (LTE) system, a LTE Frequency Division Duplex (FDD) system, a LTE Time Division Duplex (TDD) system, an Advanced Long Term Evolution (LTE-A) system, a Universal Mobile Telecommunications (UMTS) system, an Enhanced Mobile Broadband (Enhance Mobile Broadband, eMBB) system, a Massive Machine Type of Communication (massive Machine Type of Communication, mMTC) system, an Ultra Reliable Low Latency Communications (Ultra Reliable Low Latency Communications, uRLLC) system, etc.

[0124] It should be understood that the memory 1150 in the embodiment of the present invention may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0125] Volatile memory includes: Random Access Memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as: Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Synchronous Link Dynamic Random Access Memory (SLDRAM) and Direct Rambus RAM (DRRAM). The memory 1150 of the electronic device described in the embodiment of the present invention includes but is not limited to the above and any other suitable types of memory.

[0126] In the embodiment of the present invention, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0127] Specifically, the operating system 1151 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various applications, such as a media player (MediaPlayer) and a browser (Browser), which are used to implement various application services. The program for implementing the method of the embodiment of the present invention may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer system executable instructions that perform specific tasks or implement specific abstract data types.

[0128] In addition, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned driving simulation training method embodiment for electric motorcycle racing are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0129] Computer readable storage media include: permanent and non-permanent, removable and non-removable media, which are tangible devices that can retain and store instructions for use by instruction execution devices. Computer readable storage media include: electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, and any suitable combination of the above. Computer readable storage media include: phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassette storage, magnetic tape disk storage or other magnetic storage devices, memory sticks, mechanical encoding devices (such as punched cards or raised structures in grooves with instructions recorded thereon) or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined in the embodiments of the present invention, computer-readable storage media do not include temporary signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (such as light pulses passing through fiber optic cables), or electrical signals transmitted through wires.

[0130] In the several embodiments provided in the present application, it should be understood that the disclosed devices, electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or it can be an electrical, mechanical or other form of connection.

[0131] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one location or distributed over multiple network units. Some or all of the units may be selected according to actual needs to solve the problem to be solved by the embodiments of the present invention.

[0132] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention is essentially or part of the contribution to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (including: a personal computer, a server, a data center or other network device) to perform all or part of the steps of the method described in each embodiment of the present invention. The above-mentioned storage medium includes various media that can store program codes as listed above.

[0134] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0135] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, 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 the present invention.

Claims

1. A driving simulation training system for electric motorcycle racing, characterized in that: include: A motion capture module, used to capture user movements and generate motion capture image data; A data standardization module, used for performing standardization processing on the motion capture image data to obtain standardized motion capture image data; A feature extraction module, used to extract features from the standardized motion capture image data to obtain joint motion matching points; A preprocessing module, used for preprocessing the joint action matching points to obtain preprocessed joint action matching points; A joint matching module, used for matching the preprocessed joint action matching points with the joint points on the preset driver model to obtain a driver virtual character model; The simulation training module is used to use the driver virtual character model to perform training in a constructed simulation track.

2. The driving simulation training system for electric motorcycle racing according to claim 1, characterized in that: The data standardization module comprises: A stray light component processing unit, used for processing the motion capture image data using a Gaussian function to obtain a stray light component; An image enhancement processing unit, used for performing subtraction between the motion capture image data and stray light components to obtain enhanced motion capture image data; An image evaluation unit, configured to evaluate the enhanced motion capture image data to obtain an image quality evaluation value; The image optimization unit is used to adjust the scale parameter until the image quality evaluation value is within the preset range when the image quality evaluation value is not within the preset range, and output the standardized motion capture image data.

3. The driving simulation training system for electric motorcycle racing according to claim 2, characterized in that: In the stray light component processing unit, the motion capture image data is processed using a Gaussian function to obtain a stray light component, including: Using the formula: The motion capture image data is processed to obtain a stray light component; wherein K represents a normalization constant, σ represents a scale parameter, G(x, y) represents a Gaussian function, I(x, y) represents a pixel value of the motion capture image data at a position (x, y), and L(x, y) represents a stray light component.

4. The driving simulation training system for electric motorcycle racing according to claim 3, characterized in that: In the image enhancement processing unit, the motion capture image data and the stray light component are subtracted to obtain enhanced motion capture image data, including: Using the formula: logR(x,y)=logI(x,y)-logL(x,y) The motion capture image data and the stray light component are subtracted to obtain enhanced motion capture image data; wherein R(x, y) represents the enhanced motion capture image data.

5. The driving simulation training system for electric motorcycle racing according to claim 4, characterized in that: In the image evaluation unit, the enhanced motion capture image data is evaluated to obtain an image quality evaluation value, including: Using the formula: The enhanced motion capture image data is evaluated to obtain an image quality evaluation value; wherein MSE represents a first image quality evaluation value, AG represents a second image quality evaluation value, SD represents a third image quality evaluation value, W represents a length of the motion capture image data, H represents a width of the motion capture image data, I(x, y) represents original motion capture image data, and R(x, y) represents enhanced motion capture image data. Represents the gradient of the image in the x-axis direction, represents the gradient of the image in the y-axis direction, avg represents the pixel mean of the enhanced motion capture image data, and gray(x,y) represents the pixel value on the enhanced motion capture image data.

6. The driving simulation training system for electric motorcycle racing according to claim 5, characterized in that: In the feature extraction module, feature extraction is performed on the standardized motion capture image data to obtain joint motion matching points, including: Performing grayscale processing on the standardized motion capture image data to obtain grayscale motion capture image data; Take a feature extraction window centered at any point on the grayscale motion capture image data; Determine whether the pixel points on the feature extraction window meet the feature pixel conditions. If the conditions are met, the corresponding pixel points are used as joint action matching points.

7. The driving simulation training system for electric motorcycle racing according to claim 6, characterized in that: The grayscale formula is: T(i,j)=0.299*R(i,j)+0.587*G(i,j)+0.114*B(i,j) Wherein, T(i,j) represents the grayscale value of the grayscale motion capture image data at the position (i,j), R(i,j) represents the pixel value of the red channel of the standardized motion capture image data at the position (i,j), G(i,j) represents the pixel value of the green channel of the standardized motion capture image data at the position (i,j), and B(i,j) represents the pixel value of the blue channel of the standardized motion capture image data at the position (i,j).

8. The driving simulation training system for electric motorcycle racing according to claim 7, characterized in that: The characteristic pixel condition judgment formula is: |Vf(x,y)-Vf(s,t)|<T arctan(Gy / Gx)-arctan(Gt / Gs)<A Among them, Vf(x,y) represents the gradient value of the grayscale motion capture image data at (x,y), Vf(s,t) represents the gradient value at (s,t) in the feature extraction window, arctan(Gy / Gx) represents the gradient direction of the grayscale motion capture image data at (x,y), arctan(Gt / Gs) represents the gradient direction at (s,t) in the feature extraction window, T represents the gradient threshold, and A represents the direction threshold.

9. The driving simulation training system for electric motorcycle racing according to claim 1, characterized in that: In the motion capture module, when capturing user actions, the capture time of each frame of the motion capture image is marked in chronological order to generate motion capture image data.

10. The driving simulation training system for electric motorcycle racing according to claim 1, characterized in that: In the preprocessing module, the joint action matching points are smoothed to obtain preprocessed joint action matching points.

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