Vehicle-mounted VR helmet system

Abstract

The invention provides a vehicle-mounted VR helmet system, and the system comprises an imaging module which is configured on a helmet of a user, and is used for generating a virtual image which comprises the information of a target vehicle and the information of an environment to which the target vehicle belongs; the vehicle body posture acquisition module is used for acquiring posture information of a vehicle body of the target vehicle and updating the virtual image according to the posture information of the vehicle body; according to the vehicle-mounted VR helmet system provided by the invention, the delay and deviation between the helmet posture and the vehicle body posture can be effectively reduced even under the condition of complex movement of the vehicle, the high-precision synchronization of the helmet posture and the vehicle body posture is realized, and the vehicle-mounted VR helmet system has the advantages of being high in practicability and high in practicability. And tight matching between the virtual image and the head movement of the user is ensured.

Description

Technical Field

[0001] The present invention relates to the field of virtual reality technology, and in particular to a vehicle-mounted VR helmet system. Background Art

[0002] With the rapid development of virtual reality (VR) technology, its application potential in the fields of in-vehicle entertainment and assisted driving has attracted much attention. The in-vehicle VR helmet system is designed to use 3D VR helmets to generate virtual images to provide immersive experience for drivers and passengers, such as virtual driving scene simulation, in-vehicle entertainment interaction, and enhanced display of assisted driving information, which greatly enriches the time in the car and improves the driving experience.

[0003] However, in complex dynamic driving scenarios, the existing in-vehicle VR helmet system is often delayed and deviated from the synchronization of the helmet posture and the body posture due to interference from factors such as vehicle bumps, steering acceleration, sensor noise, and multi-source data fusion algorithm defects. When turning at high speed, the body roll and yaw cause the body coordinate system to change suddenly. The helmet posture acquisition is affected by delays and noise and cannot respond immediately and accurately, resulting in a mismatch between the virtual image and the real scene, causing dizziness and discomfort, reducing the immersion and interactive experience quality, especially in driving simulation applications, interfering with the driver's perception and judgment of vehicle control, adding potential safety concerns. Summary of the invention

[0004] The present invention provides a vehicle-mounted VR helmet system, which is used to solve the defect of mismatch between vehicle motion and helmet sensor motion in the prior art.

[0005] In one aspect, the present invention provides a vehicle-mounted VR helmet system, comprising:

[0006] An imaging module is configured on the helmet of the user, and is used to generate a virtual image including information of the target vehicle and information of the surrounding environment;

[0007] A vehicle body posture acquisition module, used to acquire the posture information of the vehicle body of the target vehicle and update the virtual image according to the posture information of the vehicle body;

[0008] A helmet posture collection module, used to collect the posture information of the helmet of the target vehicle user;

[0009] The controller module determines the actual posture of the helmet in the body coordinate system according to the collected body posture information and the helmet posture information, and ensures that the virtual image is highly matched with the user's actual head movement.

[0010] Optionally, the vehicle-mounted VR helmet system also includes a data communication module, which is used to enable communication between the vehicle body posture acquisition module and the helmet posture acquisition module and the controller module.

[0011] Optionally, the vehicle body posture acquisition module includes:

[0012] At least one first sensor, used to detect at least one first posture parameter of the vehicle body posture, wherein the first posture parameter includes but is not limited to vehicle acceleration information, vehicle rotation angle information, vehicle magnetic field information and vehicle posture angle information;

[0013] The helmet posture acquisition module includes:

[0014] At least one second sensor is used to detect at least one second posture parameter in the helmet posture, wherein the second posture parameter includes but is not limited to helmet acceleration information, helmet rotation angle information and helmet posture angle information.

[0015] Optionally, the controller module includes:

[0016] A communication connection module, used to establish a connection with the first sensor and the second sensor through a data communication module to obtain the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the user of the target vehicle

[0017] a delay compensation module, configured to calibrate a difference deltaT between the first sensor and the second sensor according to a communication delay between the first sensor and the second sensor;

[0018] The actual posture calculation module is used to calculate the actual posture of the target vehicle based on the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the target vehicle user. The actual posture of the helmet in the vehicle body coordinate system is calculated using the difference deltaT between the first sensor and the second sensor.

[0019] Optionally, the body coordinates (Xt, Yt, Zt) of the target vehicle are obtained and expressed by the following formula:

[0020] f(sensor01, sensor02,..., sensorα)=(Xt, Yt, Zt)

[0021] Wherein, (sensor01, sensor02, ..., sensorα) is the body posture data detected by the first sensor, (Xt, Yt, Zt) is the direction vector of the X, Y, and Z axes of the body posture acquisition module in the world coordinate system at time t, indicating the direction of the vehicle in the world coordinate system;

[0022] Get the helmet coordinates of the target vehicle user It is expressed by the following formula:

[0023]

[0024] In the formula, is the helmet posture data detected by the second sensor, is the direction vector of the X, Y, and Z axes of the helmet posture acquisition module in the world coordinate system at time t, indicating the direction of the helmet in the world coordinate system.

[0025] Optionally, the actual posture calculation module calculates the actual posture of the helmet in the vehicle body coordinate system including:

[0026] Integrate the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the target vehicle user and the difference deltaT between the first sensor and the second sensor, to obtain a basic information set of the actual posture of the helmet;

[0027] Acquire the relative motion relationship between the helmet and the vehicle body based on the basic information set;

[0028] The actual posture of the helmet in the vehicle body coordinate system is determined according to the relative motion relationship between the helmet and the vehicle body, and the virtual image is updated according to the actual posture of the helmet in the vehicle body coordinate system.

[0029] Optionally, also include:

[0030] An auxiliary module, used to collect auxiliary information, which can reflect the operating status of the vehicle and environmental changes from multiple dimensions;

[0031] The auxiliary information includes but is not limited to:

[0032] Vehicle speed information, steering angle information, acceleration and deceleration information, real-time suspension stiffness information, engine speed information, tire speed information, gear information and steering wheel angle information.

[0033] On the other hand, the present invention also provides a method for determining a helmet posture, which is applied to the above-mentioned vehicle-mounted VR helmet system, and the method comprises:

[0034] Obtaining a first posture parameter and a second posture parameter;

[0035] Using a posture calculation algorithm, the first posture parameter and the second posture parameter are deeply processed and analyzed to calculate the actual posture of the helmet in the vehicle body coordinate system;

[0036] Among them, the first posture parameter is the comprehensive body posture information collected by the body posture collection module, and the second posture parameter is the precise helmet posture information collected by the helmet posture collection module.

[0037] On the other hand, the present invention further provides a controller, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described methods when executing the program.

[0038] On the other hand, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which implements any of the above-described methods when executed by a processor.

[0039] The present invention provides a vehicle-mounted VR helmet system, which can accurately calculate the actual posture of the helmet in the vehicle body coordinate system through a vehicle body posture acquisition module, a helmet posture acquisition module and a carefully designed helmet actual posture calculation module, combined with sensor calibration and fusion algorithms. Even in the case of complex vehicle movement, it can effectively reduce the delay and deviation between the helmet posture and the vehicle body posture, achieve high-precision synchronization between the two, and ensure that the virtual image closely matches the user's head movement. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a module schematic diagram of a vehicle-mounted VR helmet system provided by an embodiment of the present invention;

[0042] Figure 2 Schematic diagram of a vehicle body posture acquisition module of a vehicle-mounted VR helmet system provided in an embodiment of the present invention;

[0043] Figure 3 2 is a schematic diagram of a helmet posture acquisition module of a vehicle-mounted VR helmet system provided by an embodiment of the present invention;

[0044] Figure 4 is a schematic diagram of a controller module of a vehicle-mounted VR helmet system provided by an embodiment of the present invention;

[0045] Figure 5 Schematic diagram of an auxiliary module of a vehicle-mounted VR helmet system provided by an embodiment of the present invention;

[0046] Figure 6 It is a schematic diagram of the structure of a controller provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0048] Embodiment 1

[0049] Combine the following Figure 1-Figure 4 The vehicle-mounted VR helmet system of the present invention is described.

[0050] Figure 1 It is a module schematic diagram of the vehicle-mounted VR helmet system provided by an embodiment of the present invention.

[0051] like Figure 1 As shown, the vehicle-mounted VR helmet system provided by the embodiment of the present invention includes:

[0052] An imaging module is configured on the helmet of the user, and is used to generate a virtual image including information of the target vehicle and information of the surrounding environment;

[0053] A vehicle body posture acquisition module is used to acquire the body posture information of the target vehicle and update the virtual image according to the body posture information;

[0054] A helmet posture collection module is used to collect the posture information of the helmet of the target vehicle user;

[0055] A data communication module, used to enable the vehicle body posture acquisition module and the helmet posture acquisition module to communicate with the controller module;

[0056] An auxiliary module, including at least one third sensor, for collecting auxiliary information, the auxiliary information being able to reflect the operating status of the vehicle and environmental changes from multiple dimensions;

[0057] The auxiliary information includes at least one of the following:

[0058] Vehicle speed information, steering angle information, acceleration and deceleration information, real-time suspension stiffness information, engine speed information, tire speed information, gear information and steering wheel angle information

[0059] The controller module determines the actual posture of the helmet in the body coordinate system based on the collected body posture information and the helmet posture information, ensuring that the virtual image is highly matched with the user's actual head movement.

[0060] Specifically, Figure 2 As shown, the vehicle body posture acquisition module includes:

[0061] At least one first sensor, used to detect at least one first posture parameter of the vehicle body posture, the first posture parameter including vehicle acceleration information, vehicle rotation angle information, vehicle magnetic field information and vehicle posture angle information;

[0062] For example, the first sensor may be a gyroscope, an accelerometer, and a magnetic sensor, which is used to collect vehicle acceleration information, vehicle rotation angle information, vehicle magnetic field information, and vehicle attitude angle information.

[0063] In this embodiment, the body posture acquisition module is installed on the vehicle body, and preferably installed near the front of the vehicle to ensure that in most cases (under normal vehicle conditions, the body movement posture can be transmitted to the controller module earlier than the helmet posture collected by the helmet posture acquisition module).

[0064] Specifically, Figure 3 As shown, the helmet posture acquisition module includes:

[0065] At least one second sensor is used to detect at least one second posture parameter in the helmet posture, and the second posture parameter includes helmet acceleration information, helmet rotation angle information and helmet posture angle information.

[0066] For example, the second sensor may be a gyroscope, an accelerometer, and a magnetic sensor, which is used to collect helmet acceleration information, helmet rotation angle information, helmet magnetic field information, and helmet posture angle information.

[0067] In this embodiment, the helmet posture acquisition module is installed on the user's helmet.

[0068] Specifically, Figure 4 As shown, the controller module includes a communication connection module, a delay compensation module and an actual posture calculation module.

[0069] More specifically, the communication connection module is used to establish a connection with the first sensor and the second sensor through the data communication module to obtain the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the user of the target vehicle

[0070] The delay compensation module is used to calibrate the difference deltaT between the first sensor and the second sensor according to the communication delay between the first sensor and the second sensor.

[0071] The body coordinates (Xt, Yt, Zt) of the target vehicle are obtained and expressed by the following formula:

[0072] f(sensor01, sensor02,..., sensorα)=(Xt, Yt, Zt)

[0073] Wherein, (sensor01, sensor02, ..., sensorα) is the body posture data detected by the first sensor, (Xt, Yt, Zt) is the direction vector of the X, Y, and Z axes of the body posture acquisition module in the world coordinate system at time t, indicating the direction of the vehicle in the world coordinate system.

[0074] Get the helmet coordinates of the target vehicle user It is expressed by the following formula:

[0075]

[0076] In the formula, is the helmet posture data detected by the second sensor, is the direction vector of the X, Y, and Z axes of the helmet posture acquisition module in the world coordinate system at time t, indicating the direction of the helmet in the world coordinate system.

[0077] For example, the delay between the first sensor and the second sensor is deltaT, and the body posture acquisition module uses an accelerometer, a gravimeter, and a magnetometer. The value of t at any time is: Sensor01, Sensor02, Sensor03. The algorithm for fusion using these sensors is expressed as:

[0078] f(sensor01,sensor02,sensor03)=(Xt,Yt,Zt)

[0079] (Xt, Yt, Zt) is the direction vector of the x, y, and z axes of the vehicle posture acquisition module in the world coordinate system at time t, indicating the direction of the vehicle in the world coordinate system.

[0080] Similarly, we can obtain the direction vector of the helmet in the world coordinate system at any time t

[0081] Based on the above scheme, the actual posture calculation module is used to calculate the actual posture of the target vehicle according to the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the target vehicle user. The actual posture of the helmet in the vehicle body coordinate system is calculated using the difference deltaT between the first sensor and the second sensor.

[0082] More specifically, firstly, the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the user of the target vehicle are integrated. The basic information set of the actual posture of the helmet is obtained by calculating the difference deltaT between the first sensor and the second sensor.

[0083] In this process, at time t0, since the helmet has a delay of deltaT compared to the vehicle, when calculating the direction of the helmet relative to the vehicle, the coordinate direction of the vehicle at t0-deltaT is used, and the value can be expressed as:

[0084] (X t0 ′-X t0-deltaT ,Y t0 ′-Y t0-deltaT , Z t0 ′-Z t0-deltaT ).

[0085] Using the above formula for position calculation inside a VR helmet can reduce the impact of moving vehicles on the helmet user.

[0086] Moreover, in the actual application process, based on the consistency between the longitudinal vehicle body bump and the user's perception in the helmet, the posture data of the helmet posture acquisition module can be directly used in the Z axis, (X t0 ′-X t0-deltaT ,Y t0 ′-Y t0-deltaT , Z t0 ′) as the direction vector inside the helmet.

[0087] Based on the above solution, in the actual application process, the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the user of the target vehicle can be directly obtained. The attitude of the helmet relative to the vehicle is calculated by the difference deltaT between the first sensor and the second sensor, which is expressed by the following formula:

[0088] f(sensor1, sensor02, sensor03, sensor01′, sensor02′, sensor03′)

[0089] =(Xt, Yt, Zt)

[0090] Then, after the direction of the helmet's world coordinate system is obtained, the final posture calculation process is carried out. The actual posture of the helmet in the body coordinate system is directly obtained through the sensor data on the body posture acquisition module and the helmet posture acquisition module through the fusion algorithm.

[0091] On the basis of the above scheme, in order to further improve the accuracy of the actual posture data of the helmet in the vehicle body coordinate system, the present invention also adopts an auxiliary module to predict the posture data of the vehicle body, for example, using wheel speed sensors, angle sensors, accelerometers, suspension sensors, crankshaft position sensors and gear sensors to obtain vehicle driving data.

[0092] The prediction process consists of the following steps:

[0093] Constructing a vehicle dynamics model: Using Newtonian mechanics and vehicle kinematics principles, construct a vehicle dynamics model. This model uses vehicle speed information, steering angle information, acceleration and deceleration information, tire speed information, gear information, and steering wheel angle information as input parameters to describe the vehicle's motion state and posture change rules during driving. For example, based on the vehicle's mass, moment of inertia, tire radius, wheelbase and other parameters, as well as the transmission ratio between the steering wheel angle and the wheel angle, a vehicle steering model is established to calculate the vehicle's yaw rate and center of mass sideslip angle during steering; the wheel slip rate is calculated through the relationship between vehicle speed and tire speed, and then the vehicle's driving stability and posture change trend are analyzed.

[0094] Build suspension model: Combined with real-time suspension stiffness information, the influence of suspension system on vehicle body posture is incorporated into the model. Changes in suspension stiffness will affect the vehicle body pitch, roll, and bounce during driving, so it is necessary to build a suspension model to describe the mechanical properties of suspension springs and shock absorbers, as well as their interaction with the vehicle body and wheels. At the same time, considering the elasticity and friction characteristics of the tire, according to factors such as tire speed, vehicle speed, and ground adhesion, calculate the force and torque of the tire on the vehicle body, further improve the vehicle dynamics model, and improve the accuracy of vehicle body posture prediction.

[0095] In addition to the above, it also includes:

[0096] The established vehicle dynamics model and suspension model are validated and evaluated using independent test datasets.

[0097] This includes inputting the sensor input data in the test data into the model to obtain the predicted body posture data, and comparing and analyzing it with the real body posture data in the test data. Indicators such as root mean square error (RMSE), mean absolute error (MAE) or correlation coefficient (R) are used to measure the accuracy and reliability of the model prediction.

[0098] Based on the same general inventive concept, the present invention also protects a method for determining a helmet posture, which is applied to the above-mentioned vehicle-mounted VR helmet system. The method for determining a helmet posture provided by the present invention is described below. The method for determining a helmet posture described below and the above-described vehicle-mounted VR helmet system can be referenced to each other.

[0099] Methods include:

[0100] 110. Obtain a first posture parameter and a second posture parameter;

[0101] 120. Using a posture calculation algorithm, deeply process and analyze the first posture parameter and the second posture parameter to calculate the actual posture of the helmet in the vehicle body coordinate system;

[0102] Among them, the first posture parameter is the comprehensive body posture information collected by the body posture collection module, and the second posture parameter is the precise helmet posture information collected by the helmet posture collection module.

[0103] In this embodiment, the vehicle-mounted VR helmet system is not limited to VR use, but can also be applied to AR, XR and MR.

[0104] Embodiment 2

[0105] On the basis of the scheme of embodiment 1, the present invention also provides another idea. Different from embodiment 1, when determining the actual posture of the helmet in the vehicle body coordinate system, the fusion algorithm is not used for calculation. Instead, a deep learning modeling method is adopted to collect and calibrate the data of the vehicle body posture acquisition module and the helmet posture acquisition module. Through training with a large amount of data, a high-precision prediction model is obtained to directly infer the relative posture of the helmet to the vehicle body through the model through the data of the vehicle body posture acquisition module, the helmet posture acquisition module and the controller module, thereby replacing the above-mentioned fusion algorithm.

[0106] The following steps are involved:

[0107] 210. Data collection and preprocessing;

[0108] Collect multi-source data: Use the vehicle body posture acquisition module (including accelerometer, gravimeter, magnetometer, etc.) and the helmet posture acquisition module (such as helmet accelerometer, gyroscope, etc.) to collect data simultaneously. Collect data under various vehicle driving conditions, including different vehicle speeds (such as low-speed driving, high-speed cruising, acceleration, deceleration), different steering angles (such as small-angle steering, large-angle turning, continuous curves), different road conditions (such as flat roads, bumpy roads, climbing, downhill), etc., to ensure that the collected data can fully reflect the posture changes of the vehicle and helmet under various actual working conditions.

[0109] In addition, in order to further improve accuracy, other relevant data in the controller module, such as the accelerator pedal and brake pedal pedal depth data, wheel speed sensor data, active suspension force feedback data, road pothole information from the road preview camera (if any), etc., are also collected to enrich the data source and provide more comprehensive information for subsequent model training.

[0110] For example, suppose the data collected by the vehicle body posture acquisition module at time t is:

[0111] Bt=(a t1 、a t2 、a t3 , g t1 , g t2 , gt3 , m t1 、m t2 、m t3 , v t ,θ t ,ω t …)

[0112] where a ti represents the measured value of the accelerometer in direction i (i = 1, 2, 3, corresponding to the three axes of the vehicle body coordinate system) at time t, g ti Represents the measurement value of the gravimeter in the i direction, m ti represents the measurement value of the magnetic induction meter in the i direction, v t represents vehicle speed information (which can be obtained from wheel speed sensors, etc.), θ t Represents the steering angle information, ω t Indicates steering wheel angle information, etc. (other relevant information that can be collected is similar).

[0113] The data collected by the helmet posture acquisition module at time t is:

[0114]

[0115] in represents the measurement value of the helmet accelerometer in direction i at time t, Represents the measurement value of the helmet gyroscope in direction i at time t.

[0116] Collect data at N moments to form a data set D = {(B 1 , H 1 ), (B 2 , H 2 ), (B 3 , H 3 ),…,(B N , H N ).

[0117] 220. Model training:

[0118] According to the data characteristics and prediction task requirements, a convolutional neural network (CNN) is selected to automatically extract features from the data through the convolution layer.

[0119] The preprocessed data set is divided into a training set, a validation set, and a test set according to a certain ratio, for example, the division ratio is: 70%:15%:15%.

[0120] The convolutional neural network learning model is trained using the training set. During the training process, a loss function, such as a mean square error (MSE) loss function, is defined to measure the difference between the relative posture of the helmet predicted by the model and the actual measured true posture.

[0121] The selected convolutional neural network deep learning model is trained using the training set. During the training process, a suitable loss function, such as the mean square error (MSE) loss function, is defined to measure the difference between the relative posture of the helmet predicted by the model and the actual measured true posture.

[0122] 230. Practical Application and Prediction:

[0123] When the vehicle-mounted VR helmet system is running, the real-time collected data from the vehicle body posture acquisition module and the helmet posture acquisition module are input into the trained convolutional neural network deep learning model. The convolutional neural network deep learning model is used to predict the actual posture of the helmet in the vehicle body coordinate system, ensuring that the virtual image is highly matched with the user's actual head movement.

[0124] Embodiment 3

[0125] On the basis of the first and second embodiments, the present invention further provides a method, wherein the VR helmet system includes:

[0126] A fusion calculation module, used for fusion calculation of the actual posture of the helmet in the vehicle body coordinate system according to the method steps of the first embodiment;

[0127] A neural network prediction module, used to predict the actual posture of the helmet in the vehicle body coordinate system according to the method steps of the second embodiment;

[0128] The virtual image optimization module is used to optimize the virtual image including the information of the target vehicle and the environment information according to the fusion calculation module and the neural network prediction module, so as to further improve the user experience.

[0129] Figure 6 It is a schematic diagram of the structure of a controller provided by an embodiment of the present invention.

[0130] like Figure 6 As shown, the controller may include: a processor 610, a communications interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communications interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call the logic instructions in the memory 630.

[0131] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0132] In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented to perform the above-described method when executed by a processor.

[0133] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0134] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle-mounted VR helmet system, characterized in that: include: An imaging module, configured on the helmet of the user, the imaging module is used to generate a virtual image; A vehicle body posture acquisition module, used to acquire the posture information of the vehicle body of the target vehicle, and update the virtual image according to the posture information of the vehicle body; A helmet posture collection module is used to collect the posture information of the helmet of the target vehicle user; The controller module determines the actual posture of the helmet in the body coordinate system according to the collected body posture information and the helmet posture information, and ensures that the virtual image is highly matched with the user's actual head movement.

2. The vehicle-mounted VR helmet system according to claim 1, characterized in that: The vehicle-mounted VR helmet system also includes a data communication module, which is used to enable the vehicle body posture acquisition module and the helmet posture acquisition module to communicate with the controller module.

3. The vehicle-mounted VR helmet system according to claim 1, characterized in that: The vehicle body posture acquisition module comprises: At least one first sensor, used to detect at least one first posture parameter of the vehicle body posture, wherein the first posture parameter includes vehicle acceleration information, vehicle rotation angle information, vehicle magnetic field information and vehicle posture angle information; The helmet posture acquisition module includes: At least one second sensor is used to detect at least one second posture parameter in the helmet posture, wherein the second posture parameter includes helmet acceleration information, helmet rotation angle information and helmet posture angle information.

4. The vehicle-mounted VR helmet system according to any one of claim 3, characterized in that: The controller module comprises: A communication connection module is used to establish a connection with the first sensor and the second sensor through a data communication module to obtain the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the user of the target vehicle a delay compensation module, configured to calibrate a difference deltaT between the first sensor and the second sensor according to a communication delay between the first sensor and the second sensor; The actual posture calculation module is used to calculate the actual posture of the target vehicle based on the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the target vehicle user. The actual posture of the helmet in the vehicle body coordinate system is calculated using the difference deltaT between the first sensor and the second sensor.

5. The vehicle-mounted VR helmet system according to claim 4, characterized in that: The body coordinates (Xt, Yt, Zt) of the target vehicle are obtained and expressed by the following formula: f(sensor01,sensor02,...,sensorα)=(Xt,Yt,Zt) Where, (sensor01, sensor02, ..., sensorα) is the body posture data detected by the first sensor, (Xt, Yt, Zt) is the direction vector of the X, Y, and Z axes of the body posture acquisition module in the world coordinate system at time t, indicating the direction of the vehicle in the world coordinate system; Get the helmet coordinates of the target vehicle user It is expressed by the following formula: Where, (sensor0′1, sensor0′2, ..., sensorβ) is the helmet posture data detected by the second sensor, is the direction vector of the X, Y, and Z axes of the helmet posture acquisition module in the world coordinate system at time t, indicating the direction of the helmet in the world coordinate system.

6. The vehicle-mounted VR helmet system according to claim 5, characterized in that: The actual posture calculation module calculates the actual posture of the helmet in the vehicle body coordinate system, including: Integrate the body coordinates (Xt, Yt, Zt) of the target vehicle and the helmet coordinates of the target vehicle user and the difference deltaT between the first sensor and the second sensor, to obtain a basic information set of the actual posture of the helmet; Acquire the relative motion relationship between the helmet and the vehicle body based on the basic information set; The actual posture of the helmet in the vehicle body coordinate system is determined according to the relative motion relationship between the helmet and the vehicle body, and the virtual image is updated according to the actual posture of the helmet in the vehicle body coordinate system.

7. The vehicle-mounted VR helmet system according to claim 1, characterized in that: Also includes: An auxiliary module, comprising at least one third sensor, for collecting auxiliary information, wherein the auxiliary information can reflect the operating status of the vehicle and environmental changes from multiple dimensions; The auxiliary information includes at least one of the following: Vehicle speed information, steering angle information, acceleration and deceleration information, real-time suspension stiffness information, engine speed information, tire speed information, gear information and steering wheel angle information.

8. A method for applying the vehicle-mounted VR helmet system according to any one of claims 1 to 7, characterized in that: The method comprises: Obtaining a first posture parameter and a second posture parameter; Using a posture calculation algorithm, the first posture parameter and the second posture parameter are deeply processed and analyzed to calculate the actual posture of the helmet in the vehicle body coordinate system; Among them, the first posture parameter is the comprehensive body posture information collected by the body posture collection module, and the second posture parameter is the precise helmet posture information collected by the helmet posture collection module.

9. A controller comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to claim 8 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to claim 8 is implemented.

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