HUD virtual image jitter prevention method and device, in-vehicle infotainment system and readable storage medium

Abstract

The embodiment of the invention relates to an HUD virtual image jitter prevention method and device, a vehicle machine system and a readable storage medium, through the HUD virtual image jitter prevention method, the stability of an AR HUD imaging effect is ensured, the attention of a driver is prevented from being distracted due to picture jitter in the driving process, driving distraction is reduced, the driving safety is improved, and the driving experience is improved. The frequency that a driver needs to continuously adjust the sight line and the focus is reduced through stable image display, so that the driving fatigue is reduced, and the safety of long-distance driving is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of automotive electronics technology, and specifically to a HUD anti-virtual image jitter method, device, vehicle system and readable storage medium. Background Art

[0002] With the rapid development of intelligent driving and in-vehicle infotainment systems, drivers have higher requirements for the acquisition and display of vehicle information. As a technology that combines virtual information with the real environment, AR HUD provides drivers with a more intuitive and convenient way to display driving assistance information.

[0003] However, in practical applications, AR HUD faces many challenges, one of which is the jitter problem. However, the causes of jitter also involve many aspects, including jitter caused by vehicle suspension and body structure, jitter caused by HUD optical structure, jitter caused by fluctuations in perceived signals, and jitter caused by bumps during vehicle driving and uneven road conditions, which can cause jitter or offset in the displayed virtual information, thus affecting the driver's visual experience and driving safety. Summary of the invention

[0004] In view of the above problems, an example of the present invention provides a HUD anti-virtual image jitter method, device, vehicle system and readable storage medium, which are used to solve the problem in the prior art that the displayed virtual information appears jitter or offset, thereby affecting the driver's visual experience and driving safety.

[0005] According to one aspect of an example of the present invention, a method for preventing HUD virtual image jitter is provided, wherein the method comprises: Obtain eye pixel coordinates, target 3D coordinates, vehicle body transformation angle and world coordinate origin in real time; The eye position pixel coordinates are converted into virtual image display coordinates by a mapping transformation function, and a homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates is calculated; Constructing a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculating a correction change amount according to the position change image; The homography change matrix is ​​corrected by the correction change amount to obtain the correction change coordinates, and the virtual image display coordinates are adjusted by the correction change coordinates to obtain and display the target virtual image.

[0006] In some optional embodiments, the real-time acquisition of eye pixel coordinates, target three-dimensional coordinates, vehicle body transformation angle and world coordinate origin specifically includes: The projected virtual image is collected by an eye position camera, and key points in the projected virtual image are extracted as eye position pixel coordinates; Capturing an external image through an external camera, and extracting a target three-dimensional coordinate of a target object in the external image; The body transformation angle and world coordinate origin are collected through the inertial measurement unit.

[0007] In some optional embodiments, converting the eye position pixel coordinates into virtual image display coordinates by mapping a transformation function specifically includes: The mapping relationship between the eye pixel coordinates and the virtual image display coordinates is obtained through the mapping transformation function in the thin spline interpolation algorithm, and the virtual image display coordinates are obtained by inputting the eye pixel coordinates into the mapping transformation function.

[0008] In some optional embodiments, the vector parameters of the mapping transformation function are obtained by the following steps: Fix the eye position camera and the calibration plate, turn on the HUD, and use the eye position camera to capture the virtual image of the HUD projected onto the calibration plate; Extracting the first key point of the original projection image and the second key point of the projected virtual image respectively; The first key point and the second key point are input into a mapping transformation function, and a vector parameter is obtained by solving a linear equation. The vector parameter is a mapping relationship between eye position pixel coordinates and virtual image display coordinates.

[0009] In some optional embodiments, calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates specifically includes: Through the matrix , calculate the homography change matrix from the target 3D coordinates to the virtual image display coordinates, where M is the intrinsic parameter matrix of the camera, R is the rotation matrix, and T is the translation matrix.

[0010] In some optional embodiments, constructing a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculating the correction change amount according to the position change image specifically includes: The vertical projection coordinates from the world coordinate origin to the projection plane where the target three-dimensional coordinates are located are used as the origin projection coordinates; Constructing a position change image according to the target three-dimensional coordinates, the world coordinate origin, and the origin projection coordinates; The correction change amount is calculated based on the tangent relationship between the vehicle body change angle and the correction change amount.

[0011] In some optional embodiments, the homography change matrix is ​​corrected by the correction change amount to obtain the correction change coordinates, and the virtual image display coordinates are adjusted by the correction change coordinates to obtain and display the target virtual image, specifically including: The correction change coordinates are obtained by calculating the product of the correction change amount and the homography change matrix; Obtaining the target virtual image coordinates by summing the virtual image display coordinates and the corrected change coordinates; The target virtual image is adjusted according to the target virtual image coordinates.

[0012] According to another aspect of an embodiment of the present invention, a HUD anti-virtual image jitter device is provided, the device comprising: Information acquisition module, real-time acquisition of eye pixel coordinates, target 3D coordinates, vehicle body transformation angle and world coordinate origin; A virtual image calculation module, used for converting the eye position pixel coordinates into virtual image display coordinates through a mapping transformation function, and calculating a homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates; A change amount calculation module is used to construct a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculate the correction change amount according to the position change image; The adjustment module is used to correct the homography change matrix by the correction change amount to obtain the correction change coordinates, and adjust the virtual image display coordinates by the correction change coordinates to obtain and display the target virtual image.

[0013] According to another aspect of an embodiment of the present invention, a vehicle system is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the HUD anti-virtual image jitter method as described above.

[0014] According to another aspect of an embodiment of the present invention, a readable storage medium is provided, wherein the storage medium stores at least one executable instruction. When the executable instruction is executed on the above-mentioned vehicle system, the vehicle system executes the above-mentioned HUD anti-virtual image jitter method.

[0015] The present invention provides a HUD virtual image jitter prevention method, device, vehicle system and readable storage medium, which have the following beneficial effects: 1. The HUD anti-virtual image jitter method of the present invention ensures the stability of AR HUD imaging effect, avoids distraction of the driver due to image jitter during driving, reduces driving distraction, and thus improves driving safety. The stable image display reduces the frequency of the driver's need to constantly adjust his line of sight and focus, thereby reducing driving fatigue and improving the safety of long-distance driving.

[0016] 2. The HUD anti-virtual image jitter method of the present invention makes the AR HUD picture smoother and more natural, thereby improving the driving comfort and pleasure. The anti-shake algorithm helps to achieve a more realistic virtual-real fusion effect, allowing the virtual information displayed by the AR HUD to be more naturally integrated into the driving scene, providing the driver with more intuitive and richer driving information. The anti-shake algorithm can be adaptively adjusted according to different vehicle models, different driving environments and the personalized needs of the driver, ensuring that a stable and clear picture display can be provided in various situations.

[0017] The above description is only an overview of the technical solution of the example of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the example of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings: Figure 1 The present invention provides a schematic flow chart of a method for preventing HUD virtual image jitter according to Embodiment 1; Figure 2 The present invention provides a schematic diagram of a process for obtaining vector parameters of a mapping transformation function according to Embodiment 1; Figure 3 The present invention provides a schematic flow chart of the calculation of the correction variation according to Embodiment 1; Figure 4 The present invention provides a schematic diagram of the structure of the correction change amount calculation of embodiment 1; Figure 5 The schematic diagram of the structure of the HUD anti-virtual image jitter device according to Example 2 provided by the present invention is shown.

[0019] Figure 6 A schematic diagram of the structure of a vehicle system according to Embodiment 3 of the present invention is shown. DETAILED DESCRIPTION

[0020] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0021] Embodiment 1: Figure 1 The first embodiment of a HUD anti-virtual image jitter method of the present invention is shown. The method is applied to a vehicle system with a HUD module. The vehicle system displays driving data, road condition data, etc. through the HUD module. The HUD anti-virtual image jitter method of the present invention is used to timely adjust the image displayed by the HUD module when the vehicle jitters, so as to avoid jitter or deviation of the image caused by the jitter. Specifically, the method includes: 110, real-time acquisition of eye pixel coordinates, target three-dimensional coordinates, body transformation angle and world coordinate origin; in step 110, eye pixel coordinates, target three-dimensional coordinates, body transformation angle and world coordinate origin can be obtained by the information acquisition module in the vehicle system. The information acquisition module may include an eye camera, an off-vehicle camera, a laser radar and an inertial measurement unit. In a specific example, the projected virtual image is collected by the eye camera, and the key points in the projected virtual image are extracted as the eye pixel coordinates; the external image is collected by the off-vehicle camera, and the target three-dimensional coordinates of the target object in the external image are extracted; the body transformation angle and the world coordinate origin are collected by the inertial measurement unit.

[0022] 120, converting the eye pixel coordinates into virtual image display coordinates through a mapping transformation function, and calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates; In step 120, converting the eye pixel coordinates into the virtual image display coordinates through a mapping transformation function, specifically includes: obtaining the mapping relationship between the eye pixel coordinates and the virtual image display coordinates through the mapping transformation function in the thin spline interpolation algorithm, and obtaining the virtual image display coordinates by inputting the eye pixel coordinates into the mapping transformation function. Calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates specifically includes: using the matrix , calculate the homography change matrix from the target 3D coordinates to the virtual image display coordinates, where M is the intrinsic parameter matrix of the camera, R is the rotation matrix, and T is the translation matrix.

[0023] 130, construct a position change image of the current vehicle according to the body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculate the correction change amount according to the position change image; in step 130, obtain the correction change amount, and the specific steps may include: using the vertical projection coordinates from the world coordinate origin to the projection plane where the target three-dimensional coordinates are located as the origin projection coordinates; construct a position change image according to the target three-dimensional coordinates, the world coordinate origin, and the origin projection coordinates; calculate the correction change amount according to the tangent relationship between the body change angle and the correction change amount.

[0024] 140, correcting the homography change matrix by the correction change amount to obtain the correction change coordinates, and adjusting the virtual image display coordinates by the correction change coordinates to obtain and display the target virtual image. In step 140, correcting the homography change matrix by the correction change amount to obtain the correction change coordinates, and adjusting the virtual image display coordinates by the correction change coordinates to obtain and display the target virtual image, specifically includes: obtaining the correction change coordinates by calculating the product of the correction change amount and the homography change matrix; obtaining the target virtual image coordinates by the sum of the virtual image display coordinates and the correction change coordinates; and adjusting the target virtual image according to the target virtual image coordinates.

[0025] The HUD anti-virtual image jitter method of the present invention ensures the stability of the AR HUD imaging effect, avoids the driver's attention being distracted by the picture jitter during driving, reduces driving distraction, and thus improves driving safety. The stable picture display reduces the frequency with which the driver needs to constantly adjust his sight and focus, thereby reducing driving fatigue and improving the safety of long-distance driving. The HUD anti-virtual image jitter method of the present invention makes the picture of the AR HUD more smooth and natural, improves the driving comfort and pleasure, and the anti-shake algorithm helps to achieve a more realistic virtual-real fusion effect, allowing the virtual information displayed by the AR HUD to be more naturally integrated into the driving scene, providing the driver with more intuitive and richer driving information. The anti-shake algorithm can be adaptively adjusted according to different vehicle models, different driving environments and the personalized needs of the driver to ensure that a stable and clear picture display can be provided in various situations.

[0026] In some optional embodiments, the eye position pixel coordinates, target three-dimensional coordinates, body transformation angle and world coordinate origin are obtained in real time, specifically including: collecting the projected virtual image through the eye position camera, and extracting the key points in the projected virtual image as the eye position pixel coordinates; collecting the external image through the external camera, and extracting the target three-dimensional coordinates of the target object in the external image; collecting the body transformation angle and the world coordinate origin through the inertial measurement unit. In this embodiment, it is obtained through the information acquisition module in the vehicle system. The information acquisition module may include an eye position camera, an external camera, a laser radar and an inertial measurement unit. In a specific example, the projected virtual image is collected through the eye position camera, and the key points in the projected virtual image are extracted as the eye position pixel coordinates; the external image is collected through the external camera, and the target three-dimensional coordinates of the target object in the external image are extracted; the body transformation angle and the world coordinate origin are collected through the inertial measurement unit. The eye position pixel coordinates can be obtained by collecting the virtual image projection image; the target three-dimensional coordinates can be obtained by collecting the road line image in front of the current vehicle, such as the target object or the lane line. The world coordinate origin can be the center of the rear axle of the vehicle.

[0027] In some optional embodiments, the eye pixel coordinates are converted into virtual image display coordinates by a mapping transformation function, specifically including: obtaining a mapping relationship between the eye pixel coordinates and the virtual image display coordinates by a mapping transformation function in a thin spline interpolation algorithm, and obtaining the virtual image display coordinates by inputting the eye pixel coordinates into the mapping transformation function. In this embodiment, due to the influence of assembly errors and the windshield, the projected virtual image has a certain degree of distortion, and the corresponding key point thin spline interpolation is used to remove the distortion. The mapping transformation function is specifically: ,in , , , ; Parameters w and a are vector parameters of the mapping transformation function. By inputting the eye position pixel coordinates into the mapping transformation function, the virtual image display coordinates can be obtained. Specifically, the eye position pixel coordinates , virtual image display coordinates , .

[0028] The parameters w and a of the above mapping transformation function are shown in Figure 2 , obtained by the following steps: 210, fix the eye position camera and the calibration plate, turn on the HUD, and use the eye position camera to shoot the projected virtual image projected by the HUD onto the calibration plate; 220, respectively extract the first key point of the original projection image and the second key point of the projected virtual image; 230, input the first key point and the second key point into the mapping transformation function, and obtain the vector parameter by solving the linear equation. The vector parameter is the mapping relationship between the eye position pixel coordinates and the virtual image display coordinates. In this embodiment, the eye position camera and the calibration plate are fixed, the HUD is turned on, and the projected virtual image projected by the HUD onto the calibration plate is shot with the camera, and the key point information on the original projection image and the shot projected virtual image is extracted. is the first key point of the original projection image, is the second key point of the projected virtual image. Input the coordinates of the first and second key points into the mapping transformation function, and solve the linear equation to obtain the vector w, a. and Extracting the first key point from the original projection image and extracting the second key point from the projection virtual image includes but is not limited to any one of SIFT, HOG, SURF, ORB, LBP, HAAR, edge detection, feature extraction, feature matching and machine learning methods.

[0029] In some optional embodiments, calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates specifically includes: using the matrix , calculate the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates, where M is the intrinsic parameter matrix of the camera, R is the rotation matrix, and T is the translation matrix. In this embodiment, the target three-dimensional coordinates are obtained. , Virtual image display coordinates After that, according to the external parameters R and T of the eye position camera calibrated from the actual physical world coordinates, the formula , calculate the homography change matrix, where M is the intrinsic parameter matrix of the camera, S is the scaling factor, R is the rotation matrix, and T is the translation matrix.

[0030] In some optional embodiments, a position change image of the current vehicle is constructed according to the body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and the correction change amount is calculated according to the position change image, specifically including: 310, using the vertical projection coordinates from the world coordinate origin to the projection plane where the target three-dimensional coordinates are located as the origin projection coordinates; 320, constructing a position change image according to the target three-dimensional coordinates, the world coordinate origin, and the origin projection coordinates; 330, calculating the correction change amount according to the tangent relationship between the body change angle and the correction change amount.

[0031] In a specific example, the angle of the vehicle body changes due to the pitching of the vehicle The resulting virtual image position Where P(X,Y,Z) is the target three-dimensional coordinate. Figure 4 , the world coordinate origin is the center of the vehicle's rear axle, P (X, Y, Z) is the target three-dimensional coordinate, It is the three-dimensional coordinate projection coordinate of the actual object coordinate on the virtual image when the pitch angle of the vehicle changes by θ1-θ. Point A is the vertical projection of the world coordinate origin O to the virtual image section.

[0032] , , , Through the above formula, we can get Correction change: .

[0033] In some optional embodiments, the homography change matrix is ​​corrected by the correction change amount to obtain the correction change coordinates, and the virtual image display coordinates are adjusted by the correction change coordinates to obtain and display the target virtual image, specifically including: obtaining the correction change coordinates by calculating the product of the correction change amount and the homography change matrix; obtaining the target virtual image coordinates by the sum of the virtual image display coordinates and the correction change coordinates; and adjusting the target virtual image according to the target virtual image coordinates. In this embodiment, the homography change matrix and the actual physical three-dimensional coordinate change amount obtained in step 4 are , calculate the rendering correction change coordinates ,in: Then, the target virtual image coordinates are obtained by summing the corrected change coordinates with the virtual image display coordinates. And by adjusting the target virtual image coordinates, each point of the entire target virtual image is calculated by summing the corrected change coordinates.

[0034] Embodiment 2: Figure 2 The first embodiment of a HUD anti-virtual image jitter device 500 of the present invention is shown, and the device can be used to execute the HUD anti-virtual image jitter method in embodiment 1. Specifically, the device includes: The information acquisition module 510 is used to execute step 110 in Example 1; specifically, the eye position pixel coordinates, target three-dimensional coordinates, body transformation angle and world coordinate origin can be obtained through the information acquisition module in the vehicle system. The information acquisition module may include an eye position camera, an off-vehicle camera, a laser radar and an inertial measurement unit. In a specific example, the projected virtual image is collected by the eye position camera, and the key points in the projected virtual image are extracted as the eye position pixel coordinates; the external image is collected by the off-vehicle camera, and the target three-dimensional coordinates of the target object in the external image are extracted; the body transformation angle and the world coordinate origin are collected by the inertial measurement unit.

[0035] The virtual image calculation module 520 is used to execute step 120 in embodiment 1; specifically includes: converting the eye pixel coordinates into the virtual image display coordinates through the mapping transformation function, specifically including: obtaining the mapping relationship between the eye pixel coordinates and the virtual image display coordinates through the mapping transformation function in the thin spline interpolation algorithm, and obtaining the virtual image display coordinates by inputting the eye pixel coordinates into the mapping transformation function. Calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates specifically includes: using the matrix , calculate the homography change matrix from the target 3D coordinates to the virtual image display coordinates, where M is the intrinsic parameter matrix of the camera, R is the rotation matrix, and T is the translation matrix.

[0036] The change calculation module 530 is used to execute step 130 in Example 1; specifically includes: obtaining the correction change, and the specific steps may include: using the vertical projection coordinates from the world coordinate origin to the projection plane where the target three-dimensional coordinates are located as the origin projection coordinates; constructing a position change image according to the target three-dimensional coordinates, the world coordinate origin, and the origin projection coordinates; calculating the correction change according to the tangent relationship between the vehicle body change angle and the correction change.

[0037] The adjustment module 540 is used to execute step 140 in Example 1; specifically used for: specifically including: correcting the homography change matrix by correcting the change amount to obtain the corrected change coordinates, and adjusting the virtual image display coordinates by correcting the change coordinates to obtain and display the target virtual image, specifically including: obtaining the corrected change coordinates by calculating the product of the corrected change amount and the homography change matrix; obtaining the target virtual image coordinates by the sum of the virtual image display coordinates and the corrected change coordinates; adjusting the target virtual image according to the target virtual image coordinates.

[0038] This embodiment uses a HUD anti-virtual image jitter device to implement a HUD anti-virtual image jitter method, which significantly improves driving safety and driving experience by improving imaging stability and reducing driving fatigue.

[0039] Embodiment 3: Figure 6 The schematic diagram of the structure of the embodiment of the vehicle system of the present invention is shown. The specific embodiment of the present invention does not limit the specific implementation of the vehicle system.

[0040] like Figure 6 As shown, the vehicle system may include: a processor, a communication interface, a memory, and a communication bus.

[0041] The processor 610, the communication interface 640, and the memory 620 communicate with each other via the communication bus 630. The communication interface is used to communicate with other devices such as a client or other server network elements. The processor is used to execute the program 650, which can specifically execute the relevant steps in the above-mentioned embodiment of the vehicle-mounted key storage method.

[0042] Specifically, the program may include program code including computer executable instructions.

[0043] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the vehicle system may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0044] Memory is used to store programs. The memory may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk storage.

[0045] The program can be called by the processor to enable the vehicle system to execute Figure 1 Step 110 to step 140.

[0046] This embodiment also includes an information acquisition unit, an electronic control unit and a human-computer interaction unit. The information acquisition unit acquires multi-sensor data of the perception module to acquire more accurate vehicle and environmental information. The IMU is used to acquire the bumpy condition of the vehicle. The electronic control module fuses the acquired data through multi-sensor fusion technology to obtain a corrected change in the virtual image position based on the bumpy vehicle body. The human-computer interaction unit compensates for the relative position of virtual information rendered by OPENGL (including but not limited to using rendering engines such as KANZI, Unity, and COCO) in the image, reduces or eliminates the jitter or offset of the virtual image icon during the AR HUD display process, and provides the driver with a more stable and smooth display of driving assistance information so that the driver can take corresponding vehicle control measures to improve driving safety and user experience.

[0047] Embodiment 4: According to another aspect of an embodiment of the present invention, a readable storage medium is provided, wherein at least one executable instruction is stored in the readable storage medium, and when the executable instruction is run on the vehicle system of embodiment 3, the vehicle system executes the HUD anti-virtual image jitter method as described in embodiment 1. In this embodiment, by reading the readable storage medium located in the vehicle system or the background server, the HUD anti-virtual image jitter method can be obtained and executed; thereby ensuring the stability of the AR HUD imaging effect, avoiding the driver's attention being distracted by the image jitter during driving, reducing driving distraction, thereby improving driving safety, and the stable image display reduces the frequency of the driver's need to constantly adjust the line of sight and focus, thereby reducing driving fatigue and improving the safety of long-distance driving. The HUD anti-virtual image jitter method of the present invention makes the AR HUD image smoother and more natural, improves the driving comfort and pleasure, and the anti-shake algorithm helps to achieve a more realistic virtual-real fusion effect, allowing the virtual information displayed by the AR HUD to be more naturally integrated into the driving scene, providing the driver with more intuitive and richer driving information, and the anti-shake algorithm can be adaptively adjusted according to different vehicle models, different driving environments and the personalized needs of the driver to ensure that a stable and clear image display can be provided in various situations.

[0048] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system or other device. In addition, the embodiments of the present invention are not directed to any particular programming language.

[0049] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. Similarly, in order to simplify the present invention and help understand one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Wherein, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself is a separate embodiment of the present invention.

[0050] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive.

[0051] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A HUD anti-virtual image jitter method, characterized in that: The method comprises: Obtain eye pixel coordinates, target 3D coordinates, vehicle body transformation angle and world coordinate origin in real time; The eye position pixel coordinates are converted into virtual image display coordinates by a mapping transformation function, and a homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates is calculated; Constructing a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculating a correction change amount according to the position change image; The homography change matrix is ​​corrected by the correction change amount to obtain the correction change coordinates, and the virtual image display coordinates are adjusted by the correction change coordinates to obtain and display the target virtual image.

2. The HUD anti-virtual image jitter method according to claim 1, characterized in that: The real-time acquisition of eye pixel coordinates, target three-dimensional coordinates, vehicle body transformation angle and world coordinate origin specifically includes: The projected virtual image is collected by an eye position camera, and key points in the projected virtual image are extracted as eye position pixel coordinates; Capturing an external image through an external camera, and extracting a target three-dimensional coordinate of a target object in the external image; The body transformation angle and world coordinate origin are collected through the inertial measurement unit.

3. The HUD anti-virtual image jitter method according to claim 2, characterized in that: The step of converting the eye position pixel coordinates into virtual image display coordinates by mapping a transformation function specifically includes: The mapping relationship between the eye pixel coordinates and the virtual image display coordinates is obtained through the mapping transformation function in the thin spline interpolation algorithm, and the virtual image display coordinates are obtained by inputting the eye pixel coordinates into the mapping transformation function.

4. The HUD anti-virtual image jitter method according to claim 3, characterized in that: The vector parameters of the mapping transformation function are obtained by the following steps: Fix the eye position camera and the calibration plate, turn on the HUD, and use the eye position camera to capture the virtual image of the HUD projected onto the calibration plate; Extracting the first key point of the original projection image and the second key point of the projected virtual image respectively; The first key point and the second key point are input into a mapping transformation function, and a vector parameter is obtained by solving a linear equation. The vector parameter is a mapping relationship between eye position pixel coordinates and virtual image display coordinates.

5. The HUD anti-virtual image jitter method according to claim 3, characterized in that: Calculating the homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates specifically includes: Through the matrix , calculate the homography change matrix from the target 3D coordinates to the virtual image display coordinates, where M is the intrinsic parameter matrix of the camera, R is the rotation matrix, and T is the translation matrix.

6. The HUD anti-virtual image jitter method according to claim 5, characterized in that: The step of constructing a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculating the correction change amount according to the position change image, specifically includes: The vertical projection coordinates from the world coordinate origin to the projection plane where the target three-dimensional coordinates are located are used as the origin projection coordinates; Constructing a position change image according to the target three-dimensional coordinates, the world coordinate origin, and the origin projection coordinates; The correction change amount is calculated based on the tangent relationship between the vehicle body change angle and the correction change amount.

7. The HUD anti-virtual image jitter method according to claim 6, characterized in that: Correcting the homography change matrix by the correction change amount to obtain the correction change coordinates, and adjusting the virtual image display coordinates by the correction change coordinates to obtain and display the target virtual image, specifically including: The correction change coordinates are obtained by calculating the product of the correction change amount and the homography change matrix; Obtaining the target virtual image coordinates by summing the virtual image display coordinates and the corrected change coordinates; The target virtual image is adjusted according to the target virtual image coordinates.

8. A HUD anti-virtual image jitter device, characterized in that: The device comprises: Information acquisition module, real-time acquisition of eye pixel coordinates, target 3D coordinates, vehicle body transformation angle and world coordinate origin; A virtual image calculation module, used for converting the eye position pixel coordinates into virtual image display coordinates through a mapping transformation function, and calculating a homography change matrix from the target three-dimensional coordinates to the virtual image display coordinates; A change amount calculation module is used to construct a position change image of the current vehicle according to the vehicle body transformation angle, the target three-dimensional coordinates and the world coordinate origin, and calculate the correction change amount according to the position change image; The adjustment module is used to correct the homography change matrix by the correction change amount to obtain the correction change coordinates, and adjust the virtual image display coordinates by the correction change coordinates to obtain and display the target virtual image.

9. A vehicle computer system, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform the operation of the HUD anti-virtual image jitter method as described in any one of claims 1-7.

10. A readable storage medium, characterized in that: The storage medium stores at least one executable instruction. When the executable instruction is executed on the vehicle system according to claim 9, the vehicle system executes the HUD anti-virtual image jitter method according to any one of claims 1 to 7.

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