Around view online calibration method and device for truck and truck

Abstract

The invention belongs to the field of vehicle calibration, and particularly discloses an all-round view online calibration method and device for a truck and the truck, and the method comprises the steps: calculating the vertical offset, the vertical pitch angle, the horizontal offset angle and the horizontal offset of each camera according to vehicle information; the angle change of the vehicle in the horizontal direction is calculated; constructing a vertical angle correction matrix, a horizontal angle correction matrix, a vertical direction offset correction matrix and a horizontal direction offset correction matrix through the parameters; the vertical angle correction matrix, the horizontal angle correction matrix and the current rotation matrix are multiplied to obtain a correction rotation matrix, the vertical direction offset correction matrix, the horizontal direction offset correction matrix and the current translation matrix are multiplied to obtain a correction translation matrix, and the correction rotation matrix and the correction translation matrix form new look-around external calibration parameters. When the state of the vehicle is changed, the calibration parameters are automatically recalculated according to the vehicle information, and the looking-around precision of the vehicle under different conditions is improved.

Description

Technical Field

[0001] The invention belongs to the field of vehicle calibration, and in particular relates to a surround view online calibration method and device for a truck, and a truck. Background Art

[0002] 360-degree surround view is an emerging technology. Through surround view cameras arranged around the vehicle body, images around the vehicle body are displayed on the central control screen. The driver can understand the situation around the vehicle body to ensure driving safety. Due to the different shapes of different vehicle models and the different installation positions of 360-degree surround view cameras, the 360-degree surround view needs to be calibrated before use to ensure the accuracy of image display. Compared with passenger cars, trucks have different uses, which makes more factors affect the accuracy of 360-degree surround view calibration. For example, the driver will adjust the height of the truck under different loads, which will affect the surround view accuracy. When the vehicle turns or the road conditions are poor during driving, the changes in the vehicle's yaw angle and tire side slip angle will also affect the surround view accuracy. Therefore, it is necessary to develop a surround view online calibration system and method for trucks to improve the surround view accuracy of trucks.

[0003] At present, the main methods of surround view calibration include manual calibration and system automatic calibration. Manual calibration is to manually select specific calibration points at a specific calibration site for manual calibration. This calibration method is easily affected by human subjectivity and has poor accuracy. System automatic calibration is to automatically select specific points for calibration at a specific calibration site. This method is more objective and has higher accuracy than manual calibration. However, for trucks, under different loads and driving conditions, the parameters of the truck offline calibration will be inaccurate, thus affecting the surround view accuracy. Only offline automatic calibration can be achieved, and online automatic calibration cannot be achieved. It cannot respond to real-time situations quickly, affecting the surround view accuracy. Summary of the invention

[0004] To solve the above problems, the present invention provides a method and device for online surround view calibration of a truck, and a truck. When the vehicle state changes, the calibration parameters are automatically recalculated according to the vehicle information to improve the surround view accuracy of the vehicle under different conditions.

[0005] In a first aspect, the technical solution of the present invention provides a surround view online calibration method for a truck, wherein the vehicle comprises three axles, a front axle, a first rear axle, and a second rear axle, and a 360 surround view camera is respectively arranged on the left, right, front, and rear of the vehicle. When the vehicle is offline for calibration, initial surround view external calibration parameters are stored, and the initial surround view external calibration parameters include an initial rotation matrix and an initial translation matrix for transforming a camera coordinate system into a world coordinate system. The method comprises the following steps: Get the height of the airbags on each axle of the vehicle, calculate the vertical offset of the vehicle according to the change of the height of the airbags on each axle of the vehicle, and calculate the vertical offset of each camera according to the vertical offset of the vehicle; Obtaining a first rear axle weight and a second rear axle weight of the vehicle, calculating a pitch angle of the vehicle according to the first rear axle weight and the second rear axle weight of the vehicle, and determining a vertical pitch angle of each camera according to the pitch angle of the vehicle; Obtain the lateral acceleration of the vehicle, calculate the side slip angles of each tire according to the lateral acceleration of the vehicle, and calculate the horizontal deviation angles of each camera according to the side slip angles of each tire; Calculate the left-right offset of each tire according to the side slip angle of each tire, and calculate the horizontal offset of each camera according to the left-right offset of each tire; Obtain the vehicle's yaw angle and yaw angle, and calculate the angle change in the vehicle's horizontal direction according to the vehicle's yaw angle and yaw angle; Multiply the vertical pitch angle and horizontal offset angle of each camera by the angle change in the horizontal direction of the vehicle, the vertical offset, and the horizontal offset to construct a vertical angle correction matrix, a horizontal angle correction matrix, a vertical offset correction matrix, and a horizontal offset correction matrix respectively; The vertical angle correction matrix, the horizontal angle correction matrix, and the current rotation matrix are multiplied to obtain the corrected rotation matrix. The vertical offset correction matrix, the horizontal offset correction matrix, and the current translation matrix are multiplied to obtain the corrected translation matrix. The corrected rotation matrix and the corrected translation matrix constitute the new surround external calibration parameters.

[0006] In an optional implementation, the height of the airbags at each axle of the vehicle is obtained, the vertical offset of the vehicle is calculated according to the change of the height of the airbags at each axle of the vehicle, and the vertical offset of each camera is calculated according to the vertical offset of the vehicle, specifically including: Get the airbag height of each axis in the current sampling cycle and the previous sampling cycle; The height change of each axis airbag is calculated by the following formula: △H1=H1'-H1 △H2=H2'-H2 △H3=H3'-H3 Among them, H1, H2, H3 are the airbag heights of the front axle, the first rear axle, and the second rear axle in the previous sampling period, respectively; H1', ​​H2', H3' are the airbag heights of the front axle, the first rear axle, and the second rear axle in the current sampling period, respectively; △H1, △H2, △H3 are the airbag height changes of the front axle, the first rear axle, and the second rear axle, respectively; Calculate the average value of △H1, △H2, and △H3 as the vehicle's up and down offset; The vertical offset of the vehicle's front camera is the vehicle's up and down offset; The vertical offset of the left and right cameras of the vehicle = the vertical offset of the vehicle + the front overhang length × tan (the vertical pitch angle of the camera); The vertical offset of the vehicle's rear camera = the vehicle's up and down offset + the vehicle's front length × tan (the camera's vertical pitch angle).

[0007] In an optional implementation, obtaining a first rear axle weight and a second rear axle weight of the vehicle, calculating a pitch angle of the vehicle according to the first rear axle weight and the second rear axle weight of the vehicle, and determining a vertical pitch angle of each camera according to the pitch angle of the vehicle specifically includes: Obtain the first rear axle weights M2 and m2 in the current sampling period and the previous sampling period, and calculate the first rear axle weight difference △m2=M2-m2; Obtain the second rear axle weights M3 and m3 in the current sampling cycle and the previous sampling cycle, and calculate the second rear axle weight difference △m3=M3-m3; Calculate the front axle weight difference △m1, including: (1) When the vehicle driving mode is 6×2 rear lift, ; (2) When the vehicle driving mode is 6×2 rear lift front lift, ; (3) When the vehicle driving mode is 6×2 medium lift, ; (4) When the vehicle driving mode is 6×2 mid-lift front lift, ; Among them, L is the distance from the center of mass of the vehicle to the middle of the two rear axles, L1 is the main vehicle wheelbase, and L2 is the distance between the first rear axle and the second rear axle; The vehicle pitch angle is calculated by the following formula , ; Determine the vertical pitch angle of each camera as the vehicle pitch angle .

[0008] In an optional implementation, obtaining the lateral acceleration of the vehicle, calculating the side slip angles of each tire according to the lateral acceleration of the vehicle, and calculating the horizontal deviation angles of each camera according to the side slip angles of each tire, specifically includes: Calculate the side slip angle of each tire using the following formula , ; in, , ; In the formula, For vehicle load, is the vehicle lateral acceleration, is the tire pressure adjustment factor, is the nominal cornering stiffness, , , is the test calibration coefficient, is the number of tires; The horizontal deviation angle of the camera on the right side of the vehicle is the average of the side slip angles of each tire on the right side; The horizontal deviation angle of the camera on the left side of the vehicle is the average of the side slip angles of each tire on the left side; The horizontal offset angle of the front and rear cameras of the vehicle is the average of the sideslip angles of all tires.

[0009] In an optional implementation, the left-right offset of each tire is calculated according to the side slip angle of each tire, and the horizontal offset of each camera is calculated according to the left-right offset of each tire, which specifically includes: The left and right offset y of each tire is calculated by the following formula: y = v × t ×tan(δ); In the formula, v is the vehicle speed and t is the vehicle travel time; The horizontal offset of the camera on the right side of the vehicle is the average of the left and right offsets of each tire on the right side; The horizontal offset of the camera on the left side of the vehicle is the average of the left and right offsets of each tire on the left side; The horizontal offset of the front and rear cameras of the vehicle is the average of the left and right offsets of all tires.

[0010] In an optional embodiment, the angle change in the horizontal direction of the vehicle is calculated according to the yaw angle and the yaw angle of the vehicle, specifically including calculating the angle change in the horizontal direction of the vehicle by the following formula: :

[0011] in, is the vehicle yaw angle, is the vehicle yaw angle.

[0012] In an optional embodiment, the method further comprises the following steps: When the vehicle's vertical offset, vehicle pitch angle, each tire's side slip angle, and each tire's left and right offset are less than the corresponding preset thresholds, the corresponding changes will not be used as correction values ​​to participate in the correction of the surround view external calibration parameters; After obtaining the vertical angle correction matrix, horizontal angle correction matrix, vertical offset correction matrix, and horizontal offset correction matrix, if the time since the last calibration parameter update is greater than the update time threshold, and the number of updates within a certain period of time is less than the update number threshold, the surround view external calibration parameters are updated.

[0013] In an optional embodiment, the method further comprises the following steps: Configure the weighted value of each influencing factor, which includes the vertical pitch angle, horizontal offset angle, vertical offset, and horizontal offset; The recommended values ​​of each influencing factor are calculated by the following formula ,

[0014] in, is the weighted value of the influencing factors, is the influencing factor value for each statistic, The number of times counted; Manually recommend calibration based on the recommended values ​​of each influencing factor.

[0015] In a second aspect, the technical solution of the present invention provides a surround view online calibration device for trucks, including a main controller, which is electrically connected to an air suspension controller, an EBS / ABS controller, and a steering wheel controller, respectively, and obtains the airbag height of each axle of the vehicle from the air suspension controller, obtains the vehicle yaw angle and the vehicle lateral acceleration from the EBS / ABS controller, and obtains the vehicle yaw angle from the steering wheel controller. The main controller executes any of the above-mentioned surround view online calibration methods for trucks, and feeds back new surround view external calibration parameters to the surround view system.

[0016] In a third aspect, the technical solution of the present invention provides a truck equipped with the above-mentioned device.

[0017] The method and device for online surround view calibration for trucks provided by the present invention, as well as the truck, have the following beneficial effects compared to the prior art: obtaining parameters such as vehicle airbag height, axle weight, and lateral acceleration, calculating the changes in the vertical and horizontal directions of the camera according to the vehicle parameters, and then correcting the surround view external calibration parameters of the camera according to the changes in the two directions of the camera. When the vehicle load or driving state changes, the present invention automatically recalculates the calibration parameters through information such as the vehicle airbag height, vehicle yaw angle, vehicle axle load, and tire slip angle, thereby improving the surround view accuracy of the vehicle under different conditions. Furthermore, the collected automatic calibration parameters are calculated to obtain recommended points for use by the driver during manual calibration, thereby improving the accuracy of the manual calibration parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1It is a schematic diagram of the architecture of a surround view online calibration device for trucks provided in an embodiment of the present invention.

[0020] Figure 2 The present invention is a flowchart of an online surround view calibration method for trucks provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in this specific embodiment. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this patent application.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] Figure 1 It is a schematic diagram of the architecture of a surround view online calibration device for trucks provided in an embodiment of the present invention, including a main controller, which is electrically connected to an air suspension controller, an EBS / ABS controller, and a steering wheel controller respectively.

[0024] The air suspension controller is used to collect the airbag height of each axle of the vehicle and transmit it to the main controller, the EBS / ABS controller is used to collect the vehicle yaw angle and the vehicle lateral acceleration and transmit them to the main controller, and the steering wheel controller is used to collect the vehicle yaw angle and transmit it to the main controller. The main controller executes the truck surround view online calibration method based on the acquired information, obtains new surround view external calibration parameters and feeds back to the surround view system for recalibration of the calibration parameters. The specific steps of the truck surround view online calibration method will be described in detail in the subsequent embodiments. At the same time, the main controller receives the image around the vehicle body collected by the 360 ​​surround view camera, and provides calibration data for manual calibration and the first automatic calibration. The 360 ​​surround view camera consists of four cameras installed on the front, left, right and rear of the vehicle body. In addition, the truck vehicle of this embodiment is a three-axle vehicle including a front axle, a first rear axle and a second rear axle.

[0025] This embodiment performs a truck surround view online calibration method during vehicle driving, and realizes calibration parameters according to vehicle state changes. Manual calibration is performed before the vehicle is put into use and automatic calibration is performed when the vehicle is offline.

[0026] The first manual calibration default parameters. In order to ensure the accuracy of automatic calibration, manual calibration is required for the first time before the surround view system is installed to load the default parameters for the system (including the position / angle parameters of each camera, distortion correction coefficient, perspective transformation parameters, and stitching parameters). The default parameters are calibrated by relevant personnel at a specific site and are shared by vehicles of the same model.

[0027] The vehicle is automatically calibrated when it rolls off the assembly line. After the surround view system is installed and debugged and the rest of the adjustments are completed, it is necessary to go to a specific site (completely consistent with the manual calibration site) for automatic calibration to correct the default parameters. The correction process includes obtaining the automatic calibration parameter coordinates, comparing the automatic calibration parameter coordinates with the manual calibration parameter coordinates, obtaining the coordinate X-axis and Y-axis offset angles, correcting the manual calibration parameters by the offset angles, and obtaining new images according to the corrected parameters for stitching.

[0028] The manual calibration and automatic calibration mentioned above are performed when the vehicle just rolls off the production line. For a truck, the vehicle has no trailer or load at this time. After the vehicle is equipped with a trailer and loaded, the height of the vehicle and the axle loads of each axle change. At the same time, the vehicle posture will also change under different road conditions when the vehicle is driving. The above factors will affect the vehicle's yaw angle, pitch angle, up and down offset, left and right offset, and tire side slip angle. Changes in these factors will cause the position of the surround view camera to change relative to the installation calibration, thereby reducing the surround view accuracy or causing misalignment at the splicing. Therefore, this embodiment sends the relevant information collected by the controller to the main controller through the vehicle CAN bus. The main controller uses the information obtained to execute the truck surround view online calibration method to recalculate the calibration parameters.

[0029] Figure 2 FIG. 1 is a flow chart of an online surround view calibration method for a truck provided by an embodiment of the present invention. Figure 2 As shown, the method includes the following steps. According to different requirements, the order of the steps in the flow chart can be changed.

[0030] S1, obtaining the height of the airbags at each axis of the vehicle, calculating the vertical offset of the vehicle according to the change of the airbag height at each axis of the vehicle, and calculating the vertical offset of each camera according to the vertical offset of the vehicle.

[0031] S2, obtaining a first rear axle weight and a second rear axle weight of the vehicle, calculating a pitch angle of the vehicle according to the first rear axle weight and the second rear axle weight of the vehicle, and determining a vertical pitch angle of each camera according to the pitch angle of the vehicle.

[0032] S3, obtaining the lateral acceleration of the vehicle, calculating the slip angles of each tire according to the lateral acceleration of the vehicle, and calculating the horizontal deviation angles of each camera according to the slip angles of each tire.

[0033] S4, calculating the left-right offset of each tire according to the side slip angle of each tire, and calculating the horizontal offset of each camera according to the left-right offset of each tire.

[0034] S5, obtaining the vehicle yaw angle and the vehicle yaw angle, and calculating the angle change in the horizontal direction of the vehicle according to the vehicle yaw angle and the vehicle yaw angle.

[0035] S6, multiplying the vertical pitch angle and horizontal offset angle of each camera by the angle change in the horizontal direction of the vehicle, the vertical offset, and the horizontal offset to construct a vertical angle correction matrix, a horizontal angle correction matrix, a vertical offset correction matrix, and a horizontal offset correction matrix, respectively.

[0036] S7, multiply the vertical angle correction matrix, the horizontal angle correction matrix, and the current rotation matrix to obtain a corrected rotation matrix, multiply the vertical offset correction matrix, the horizontal offset correction matrix, and the current translation matrix to obtain a corrected translation matrix, and the corrected rotation matrix and the corrected translation matrix constitute new surround external calibration parameters.

[0037] When the vehicle is calibrated offline, the initial parameter information of the vehicle is stored, including the total height of the vehicle, the axle load of each axle, and the surround calibration parameters, wherein the surround calibration parameters include internal parameters and external parameters, the internal parameters refer to equipment parameters such as focal length, optical center, and lens distortion, and the external parameters are the rotation matrix and translation matrix of the camera coordinate system transformed to the world coordinate system. In this embodiment, the modification of the calibration parameters refers to the modification of the rotation matrix and the translation matrix. The above steps S1 and S2 realize the calculation of the vertical direction correction value of the camera, steps S3-S5 realize the calculation of the horizontal direction correction value of the camera and the angle change in the horizontal direction of the vehicle, step S6 realizes the construction of the correction matrix based on the vertical direction and horizontal direction correction values ​​of the camera and the angle change in the horizontal direction of the vehicle, and step S7 realizes the update of the surround external calibration parameters based on the correction matrix. The above steps are described in detail below.

[0038] (1) Camera vertical correction value When the vehicle is adding a trailer or loading, the air suspension controller sends the collected vehicle airbag height information and the vehicle's first rear axle weight and second rear axle weight information to the main controller through the CAN bus. The main controller calculates the first front axle weight after loading based on the first rear axle weight and the second rear axle weight, thereby obtaining the first front axle weight difference before and after loading, and then obtaining the weight difference of each axle before and after loading, and calculates the up and down offset of the vehicle based on the change in airbag height, and calculates the vehicle pitch angle based on the vehicle axle weight difference and the up and down offset.

[0039] Step 1: Calculate the vertical offset of the vehicle according to the change in the airbag height of each axis of the vehicle, and calculate the vertical offset of each camera according to the vertical offset of the vehicle.

[0040] Step 1.1, obtain the airbag height of each axis in the current sampling cycle and the previous sampling cycle.

[0041] Step 1.2, calculate the height change of each axis airbag by the following formula, △H1=H1'-H1 △H2=H2'-H2 △H3=H3'-H3 Among them, H1, H2, H3 are the airbag heights of the front axle, the first rear axle, and the second rear axle in the previous sampling period, respectively; H1', ​​H2', H3' are the airbag heights of the front axle, the first rear axle, and the second rear axle in the current sampling period, respectively; △H1, △H2, △H3 are the airbag height changes of the front axle, the first rear axle, and the second rear axle, respectively.

[0042] Step 1.3, calculate the average value of △H1, △H2, and △H3 as the up and down offset of the vehicle.

[0043] Step 1.4, the vertical offset of the vehicle's front camera is the vehicle's up and down offset; the vertical offset of the vehicle's left and right cameras = vehicle's up and down offset + front overhang length × tan (camera vertical pitch angle); the vertical offset of the vehicle's rear camera = vehicle's up and down offset + vehicle front length × tan (camera vertical pitch angle).

[0044] Specifically, the vertical offset of each axis is calculated by the airbag height of each axis collected on the CAN bus, wherein the first front axle height offset △H1, the first rear axle airbag height offset △H2 and the second rear axle airbag height offset △H3 are defined, the first front axle height H1 before loading, the first rear axle height H2 before loading, the second rear axle height H3 before loading, the first front axle height H1' after loading, the first rear axle height H2' after loading, the second rear axle height H3' after loading, and the height offset of each axis is calculated by the above formula. The vertical pitch angle of the camera is calculated by the following step 2.

[0045] Step 2: Obtain the first rear axle weight and the second rear axle weight of the vehicle, calculate the vehicle pitch angle according to the first rear axle weight and the second rear axle weight of the vehicle, and determine the vertical pitch angle of each camera according to the vehicle pitch angle.

[0046] Step 2.1, obtain the first rear axle weights M2 and m2 in the current sampling period and the previous sampling period, and calculate the first rear axle weight difference △m2=M2-m2.

[0047] Step 2.2, obtain the second rear axle weights M3 and m3 in the current sampling period and the previous sampling period, and calculate the second rear axle weight difference △m3=M3-m3.

[0048] Step 2.3, calculating the front axle weight difference △m1, including: 1) When the vehicle driving mode is 6×2 rear lift, ; 2) When the vehicle driving mode is 6×2 rear lift front lift, ; 3) When the vehicle driving mode is 6×2 medium lift and lift, ; 4) When the vehicle driving mode is 6×2 mid-lift front lift, ; Among them, L is the distance from the center of mass of the vehicle to the middle of the two rear axles, L1 is the main vehicle wheelbase, and L2 is the distance between the first rear axle and the second rear axle.

[0049] Step 2.4, calculate the vehicle pitch angle using the following formula , .

[0050] Step 2.5: Determine the vertical pitch angle of each camera as the vehicle pitch angle .

[0051] It should be noted that the calculation method of the front axle weight after loading is different under different driving modes, and the above specific calculation method is selected according to the driving mode of the vehicle. In an optional embodiment, the posture change in the vertical direction of the vehicle is obtained according to the above calculation and the vertical external calibration correction value is obtained. When the above changes are less than the preset threshold, the changes are not used as the correction value, otherwise the above values ​​are used as the vertical correction values ​​of the external calibration parameters.

[0052] (2) Camera horizontal correction value When a loaded vehicle is driving on the road, in addition to continuing to collect relevant information sent by the air suspension controller, the steering wheel controller sends the vehicle yaw angle to the main controller via the CAN bus, and the EBS / ABS controller sends the vehicle yaw angle and vehicle lateral acceleration to the main controller via the CAN bus. The vehicle yaw angle and yaw angle can be used to calculate the angle change in the horizontal direction of the vehicle. At the same time, the tire side slip angle is calculated through the vehicle lateral acceleration, tire pressure change and tire model, and the left and right offset of the vehicle is calculated based on the tire side slip angle.

[0053] Step 1: Calculate the side slip angles of each tire according to the lateral acceleration of the vehicle, and calculate the horizontal deviation angles of each camera according to the side slip angles of each tire.

[0054] Step 1.1, calculate the side slip angle of each tire using the following formula , ; in, , .

[0055] In the formula, For vehicle load, is the vehicle lateral acceleration, is the tire pressure adjustment factor, is the nominal cornering stiffness, , , is the test calibration coefficient, is the number of tires.

[0056] Step 1.2, the horizontal offset angle of the camera on the right side of the vehicle is the average of the side slip angles of all the tires on the right side; the horizontal offset angle of the camera on the left side of the vehicle is the average of the side slip angles of all the tires on the left side; the horizontal offset angle of the camera on the front and rear sides of the vehicle is the average of the side slip angles of all the tires.

[0057] The tire slip angle refers to the angle between the tire's moving direction and the tire's actual rolling direction. The calculation of the slip angle is usually based on the tire's mechanical model and is mainly related to the following factors: vehicle load, tire pressure, tire model, and tire lateral force. First, the vehicle load directly affects the tire's vertical load. , the higher the vertical load, the larger the tire's contact patch, resulting in a higher cornering stiffness Change, the vertical load calculation formula is: Secondly, the tire pressure P determines the rigidity of the tire. Too low pressure will reduce the cornering stiffness, resulting in a larger slip angle. The pressure adjustment coefficient Kp can be obtained through test calibration and used to adjust the slip angle under different pressures. At the same time, different tires have different cornering stiffness. .

[0058] The slip angle is related to the tire lateral force and satisfies the following relationship: ;in It is the product of vehicle load and vehicle lateral acceleration. The lateral acceleration can be obtained through the EBS / ABS sensor on the CAN bus. Therefore, the tire slip angle calculation formula is: Among them, the lateral stiffness The calculation formula is: . for The load influence function is usually fitted with a quadratic function: .

[0059] Step 2: Calculate the left-right offset of each tire according to the side slip angle of each tire, and calculate the horizontal offset of each camera according to the left-right offset of each tire.

[0060] Step 2.1, calculate the left and right offset y of each tire using the following formula: y = v × t ×tan(δ); Where v is the vehicle speed and t is the vehicle travel time.

[0061] The lateral offset is mainly affected by the following factors: Side slip angle: The lateral angle caused by tire sliding. Vehicle speed: The higher the driving speed, the greater the offset. Driving time: The longer the driving time, the more significant the lateral offset.

[0062] Step 2.2, the horizontal offset of the right camera of the vehicle is the average left-right offset of each tire on the right side; the horizontal offset of the left camera of the vehicle is the average left-right offset of each tire on the left side; the horizontal offset of the front and rear cameras of the vehicle is the average left-right offset of all tires.

[0063] Step 3: Calculate the angle change in the horizontal direction of the vehicle based on the vehicle yaw angle and the vehicle yaw angle.

[0064] Specifically, the angle change in the horizontal direction of the vehicle is calculated by the following formula: :

[0065] in, is the vehicle yaw angle, is the vehicle yaw angle.

[0066] In an optional embodiment, the horizontal external calibration correction value is obtained according to the above calculated vehicle posture change in the horizontal direction. When the above changes are less than a preset threshold, the changes are not used as correction values, otherwise the above values ​​are used as horizontal correction values ​​of external calibration parameters.

[0067] (3) Update of external calibration parameters Set the update count threshold and update time threshold within a period of time. After calculating the horizontal and vertical correction values ​​of the external calibration parameters, if the time since the last parameter update is greater than the update time threshold and the number of updates within a period of time is less than the set update count threshold within a period of time, the surround external calibration parameters are updated.

[0068] The updating method is to obtain the correction matrix of the horizontal correction value and the vertical correction value compared with the initial parameters, multiply the correction matrix with the initial rotation and translation matrix to obtain the correction matrix B', and then correct the camera coordinates according to the obtained correction matrix. First, the vertical pitch angle and horizontal offset angle of each camera are multiplied by the angle change in the horizontal direction of the vehicle, the vertical offset, and the horizontal offset to construct the vertical angle correction matrix, the horizontal angle correction matrix, the vertical offset correction matrix, and the horizontal offset correction matrix respectively. After that, the vertical angle correction matrix, the horizontal angle correction matrix, and the current rotation matrix are multiplied to obtain the correction rotation matrix, and the vertical offset correction matrix, the horizontal offset correction matrix, and the current translation matrix are multiplied to obtain the correction translation matrix. The correction rotation matrix and the correction translation matrix constitute the new surround view external calibration parameters.

[0069] Specifically, the look-around external calibration parameter B consists of a 4×4 rotation matrix R and a 4×1 translation matrix t: B=[R|t] The correction matrix is ​​composed of the pitch angle, vertical offset, horizontal offset angle (including horizontal angle change β and tire side slip angle) and horizontal offset of each camera in the vertical direction. The vertical angle correction matrix is: , the horizontal angle correction matrix is , the vertical offset correction matrix is , the horizontal offset correction matrix is , the above matrices are all 4×4 matrices. Multiply the above angle correction matrix with the rotation matrix R to obtain the correction matrix R', multiply the above translation correction matrix with the translation matrix t to obtain the correction matrix t', and obtain the new correction matrix B' based on R' and t', and then calculate the correction parameters of each camera based on B'.

[0070] (4) Manual calibration recommended values In an optional embodiment, the surround view external calibration parameters recalculated each time are classified and stored in the main controller according to the vehicle driving mode, vehicle load, vertical pitch angle, vertical offset, horizontal offset angle and horizontal offset, and the recommended value is calculated.

[0071] Step 1: configure the weighted value of each influencing factor, where the influencing factors include the vertical pitch angle, the horizontal offset angle, the vertical offset, and the horizontal offset.

[0072] Specifically, weighted values ​​are set for four main influencing factors, namely, the vertical pitch angle, the vertical offset, the horizontal offset angle, and the horizontal offset according to different driving modes and different loads.

[0073] Step 2: Calculate the recommended value of each influencing factor using the following formula: ,

[0074] in, is the weighted value of the influencing factors, is the influencing factor value for each statistic, The number of times counted.

[0075] Step 3: Perform manual recommendation calibration based on the recommended values ​​of each influencing factor.

[0076] When the driver needs to calibrate manually, the recommended calibration points are displayed on the surround view system based on the vehicle information to improve the accuracy of manual calibration.

[0077] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A surround view online calibration method for a truck, wherein the vehicle comprises three axles, a front axle, a first rear axle, and a second rear axle, and a 360 surround view camera is respectively arranged on the left, right, front, and rear of the vehicle. When the vehicle is offline for calibration, initial surround view external calibration parameters are stored, and the initial surround view external calibration parameters include an initial rotation matrix and an initial translation matrix for transforming a camera coordinate system into a world coordinate system, and the method is characterized in that: The method comprises the following steps: Get the height of the airbags on each axle of the vehicle, calculate the vertical offset of the vehicle according to the change of the height of the airbags on each axle of the vehicle, and calculate the vertical offset of each camera according to the vertical offset of the vehicle; Obtaining a first rear axle weight and a second rear axle weight of the vehicle, calculating a pitch angle of the vehicle according to the first rear axle weight and the second rear axle weight of the vehicle, and determining a vertical pitch angle of each camera according to the pitch angle of the vehicle; Obtain the lateral acceleration of the vehicle, calculate the side slip angles of each tire according to the lateral acceleration of the vehicle, and calculate the horizontal deviation angles of each camera according to the side slip angles of each tire; Calculate the left-right offset of each tire according to the side slip angle of each tire, and calculate the horizontal offset of each camera according to the left-right offset of each tire; Obtain the vehicle's yaw angle and yaw angle, and calculate the angle change in the vehicle's horizontal direction according to the vehicle's yaw angle and yaw angle; Multiply the vertical pitch angle and horizontal offset angle of each camera by the angle change in the horizontal direction of the vehicle, the vertical offset, and the horizontal offset to construct a vertical angle correction matrix, a horizontal angle correction matrix, a vertical offset correction matrix, and a horizontal offset correction matrix respectively; The vertical angle correction matrix, the horizontal angle correction matrix, and the current rotation matrix are multiplied to obtain the corrected rotation matrix. The vertical offset correction matrix, the horizontal offset correction matrix, and the current translation matrix are multiplied to obtain the corrected translation matrix. The corrected rotation matrix and the corrected translation matrix constitute the new surround external calibration parameters.

2. The truck surround view online calibration method according to claim 1, characterized in that: Get the height of the airbags on each axis of the vehicle, calculate the vertical offset of the vehicle according to the change of the height of the airbags on each axis of the vehicle, and calculate the vertical offset of each camera according to the vertical offset of the vehicle, including: Get the airbag height of each axis in the current sampling cycle and the previous sampling cycle; The height change of each axis airbag is calculated by the following formula: △H1=H1'-H1 △H2=H2'-H2 △H3=H3'-H3 Among them, H1, H2, H3 are the airbag heights of the front axle, the first rear axle, and the second rear axle in the previous sampling period, respectively; H1', ​​H2', H3' are the airbag heights of the front axle, the first rear axle, and the second rear axle in the current sampling period, respectively; △H1, △H2, △H3 are the airbag height changes of the front axle, the first rear axle, and the second rear axle, respectively; Calculate the average value of △H1, △H2, and △H3 as the vehicle's up and down offset; The vertical offset of the vehicle's front camera is the vehicle's up and down offset; The vertical offset of the left and right cameras of the vehicle = the vertical offset of the vehicle + the front overhang length × tan (the vertical pitch angle of the camera); The vertical offset of the vehicle's rear camera = the vehicle's up and down offset + the vehicle's front length × tan (the camera's vertical pitch angle).

3. The truck surround view online calibration method according to claim 2, characterized in that: Obtain the first rear axle weight and the second rear axle weight of the vehicle, calculate the vehicle pitch angle according to the first rear axle weight and the second rear axle weight of the vehicle, and determine the vertical pitch angle of each camera according to the vehicle pitch angle, specifically including: Obtain the first rear axle weights M2 and m2 in the current sampling period and the previous sampling period, and calculate the first rear axle weight difference △m2=M2-m2; Obtain the second rear axle weights M3 and m3 in the current sampling cycle and the previous sampling cycle, and calculate the second rear axle weight difference △m3=M3-m3; Calculate the front axle weight difference △m1, including: (1) When the vehicle driving mode is 6×2 rear lift, ; (2) When the vehicle driving mode is 6×2 rear lift front lift, ; (3) When the vehicle driving mode is 6×2 medium lift, ; (4) When the vehicle driving mode is 6×2 mid-lift front lift, ; Among them, L is the distance from the center of mass of the vehicle to the middle of the two rear axles, L1 is the main vehicle wheelbase, and L2 is the distance between the first rear axle and the second rear axle; The vehicle pitch angle is calculated by the following formula , ； Determine the vertical pitch angle of each camera as the vehicle pitch angle .

4. The truck surround view online calibration method according to claim 3, characterized in that: Obtain the lateral acceleration of the vehicle, calculate the side slip angles of each tire according to the lateral acceleration of the vehicle, and calculate the horizontal deviation angles of each camera according to the side slip angles of each tire, including: Calculate the side slip angle of each tire using the following formula , ； in, , ; In the formula, For vehicle load, is the vehicle lateral acceleration, is the tire pressure adjustment factor, is the nominal cornering stiffness, , , is the test calibration coefficient, is the number of tires; The horizontal deviation angle of the camera on the right side of the vehicle is the average of the side slip angles of each tire on the right side; The horizontal deviation angle of the camera on the left side of the vehicle is the average of the side slip angles of each tire on the left side; The horizontal offset angle of the front and rear cameras of the vehicle is the average of the sideslip angles of all tires.

5. The truck surround view online calibration method according to claim 4, characterized in that: The left and right offsets of each tire are calculated according to the side slip angles of each tire, and the horizontal offsets of each camera are calculated according to the left and right offsets of each tire, including: The left and right offset y of each tire is calculated by the following formula: y = v × t ×tan(δ); In the formula, v is the vehicle speed and t is the vehicle travel time; The horizontal offset of the camera on the right side of the vehicle is the average of the left and right offsets of each tire on the right side; The horizontal offset of the camera on the left side of the vehicle is the average of the left and right offsets of each tire on the left side; The horizontal offset of the front and rear cameras of the vehicle is the average of the left and right offsets of all tires.

6. The truck surround view online calibration method according to claim 5, characterized in that: The angle change in the horizontal direction of the vehicle is calculated based on the vehicle yaw angle and the vehicle yaw angle, specifically including calculating the angle change in the horizontal direction of the vehicle by the following formula : in, is the vehicle yaw angle, is the vehicle yaw angle.

7. The truck surround view online calibration method according to any one of claims 1 to 6, characterized in that: The method further comprises the following steps: When the vehicle's vertical offset, vehicle pitch angle, each tire's side slip angle, and each tire's left and right offset are less than the corresponding preset thresholds, the corresponding changes will not be used as correction values ​​to participate in the correction of the surround view external calibration parameters; After obtaining the vertical angle correction matrix, horizontal angle correction matrix, vertical offset correction matrix, and horizontal offset correction matrix, if the time since the last calibration parameter update is greater than the update time threshold, and the number of updates within a certain period of time is less than the update number threshold, the surround view external calibration parameters are updated.

8. The truck surround view online calibration method according to any one of claims 1 to 6, characterized in that: The method further comprises the following steps: Configure the weighted value of each influencing factor, which includes the vertical pitch angle, horizontal offset angle, vertical offset, and horizontal offset; The recommended values ​​of each influencing factor are calculated by the following formula , in, is the weighted value of the influencing factors, is the influencing factor value for each statistic, The number of times counted; Manually recommend calibration based on the recommended values ​​of each influencing factor.

9. A surround view online calibration device for trucks, characterized in that: The invention comprises a main controller, which is electrically connected to an air suspension controller, an EBS / ABS controller and a steering wheel controller respectively, and obtains the airbag height of each axle of the vehicle from the air suspension controller, obtains the vehicle yaw angle and the vehicle lateral acceleration from the EBS / ABS controller, and obtains the vehicle yaw angle from the steering wheel controller. The main controller executes the surround view online calibration method for trucks as described in any one of claims 1 to 8, and feeds back new surround view external calibration parameters to the surround view system.

10. A truck, characterized in that: The device according to claim 9 is provided.

Images