Vehicle horizontal control method and system

Abstract

The invention relates to a vehicle suspension system control technology, and discloses a vehicle horizontal control method and system, and the method comprises the steps that an image collection unit collects an image of a front road in the vehicle driving process; the vehicle-mounted processing unit performs lane feature extraction on the acquired image to obtain an observed value of the lane feature; the estimated pitching degree theta'p of the vehicle, the estimated roll degree theta'r of the vehicle and the estimated height h 'of the vehicle are obtained; according to the estimated pitching degree theta'p of the vehicle, the estimated roll degree theta'r of the vehicle and the estimated height h 'of the vehicle, a control signal is obtained; the suspension system unit adjusts an air spring suspension system of the vehicle for the control signal. The present invention provides a cost-effective, accurate and efficient solution for maintaining an optimal vehicle level by reducing dependence on dedicated sensors and using advanced mathematical techniques for estimation, thereby improving passenger comfort and vehicle performance.

Description

Technical Field

[0001] The present invention relates to the control of vehicle suspension systems, and particularly to a vehicle level control method and system. Background Art

[0002] Commercial vehicles (such as buses and trailers) typically employ air spring suspension systems to enhance ride comfort and protect road infrastructure. These systems traditionally rely on dedicated height sensors connected between the chassis and the suspension or axles to adjust the vehicle's height, pitch, and roll. While equipping each air spring with a sensor can achieve optimal performance, this approach increases the complexity and cost of the system.

[0003] Manufacturers often seek to minimize the number of sensors by implementing estimation algorithms in the control system. However, due to inaccurate measurements, these methods may degrade performance. Advanced Driver Assistance Systems (ADAS) typically include calibrated front-facing cameras capable of detecting lane markings and other road features. Utilizing these existing cameras for vehicle level control presents an opportunity to reduce costs and potentially improve system performance.

[0004] As in the prior art 1: CN202210344500.1. Summary of the Invention

[0005] In view of the problems in the prior art that the adjustment of the suspension system is complex and costly, the present invention provides a vehicle level control method and system.

[0006] To solve the above technical problems, the present invention is solved by the following technical solutions:

[0007] A vehicle level control method includes an image acquisition unit, an on-vehicle processing unit, and a suspension system unit; the method includes:

[0008] The image acquisition unit acquires an image of the road ahead during vehicle travel;

[0009] Extraction of lane features, the on-vehicle processing unit extracts lane features from the acquired image to obtain an observed value of the lane features;

[0010] Based on the extracted lane features, the pitch angle θ p of the vehicle, the roll angle θ r of the vehicle, and the height h of the vehicle are estimated to obtain the estimated pitch angle θ' p of the vehicle, the roll angle θ' r of the vehicle, and the height h';

[0011] Based on the estimated pitch angle θ' p of the vehicle, the roll angle θ' rand the height h' of the vehicle to obtain a control signal;

[0012] The suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

[0013] Preferably, the estimation of the pitch angle θ of the vehicle p , the roll angle θ of the vehicle r and the height h of the vehicle includes:

[0014] Comparing the expected value of the lane feature with the position of the observed value of the lane feature through a mathematical model;

[0015] An error function that minimizes the difference between the expected value of the lane feature and the observed value of the lane feature.

[0016] Preferably, comparing the expected value of the lane feature with the position of the observed value of the lane feature through a mathematical model includes:

[0017] Conversion of the coordinate system;

[0018] The pitch angle θ of the vehicle p , the roll angle θ of the vehicle r and the construction of a mathematical model for the height h of the vehicle; based on the converted coordinate system, the pitch angle θ of the vehicle p , the roll angle θ of the vehicle r and the construction of a mathematical model for the height h of the vehicle;

[0019] Obtaining the position of the expected value of the lane feature and the observed value of the lane feature, including the image point (u exp,i , v exp,i ) of the expected value and the lane line point (u obs,i , v obs,i ) of the observed value; where u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, and v exp,i is the expected image point corresponding to the ordinate.

[0020] Preferably,

[0021] Using computer vision technology to extract the observed value of the lane feature from the camera image, the observed value of the lane feature includes the lane line point (u obs,i , v obs,i ) of the observed value; using the projection equation to calculate the expected value of the lane feature, the expected value of the lane feature includes the image point (u exp,i , v exp,i ) of the expected value;

[0022] For the horizontal axis coordinate point u, the vertical axis coordinate point v is obtained as follows:

[0023]

[0024] where f x is the focal length of the horizontal axis; f y is the focal length of the vertical axis; C x is the principal point coordinate of the horizontal axis, and C y is the principal point coordinate of the vertical axis.

[0025] Preferably, the coordinate system conversion includes:

[0026] Conversion from the world coordinate system to the vehicle coordinate system:

[0027]

[0028] where X w , Y w , Z w are the coordinates corresponding to the world coordinate system; X v , Y v , Z v are the coordinates corresponding to the vehicle; R vehicle [[ID=4o]]is the attitude transformation amount of the vehicle, and t vehicle is the translation amount of the vehicle;

[0029] Conversion from the vehicle coordinate system to the camera coordinate system:

[0030]

[0031] where X c , Y c , Z c are the coordinates corresponding to the camera; R camera is the attitude transformation amount of the camera, and t camera is the translation amount of the camera;

[0032] Conversion from the camera coordinate system to the image coordinate system:

[0033]

[0034] where U and V are the coordinates corresponding to the image, U is the horizontal axis, and V is the vertical axis.

[0035] Preferably, the construction of the mathematical model of the pitch angle θ p of the vehicle, the roll angle θ r of the vehicle, and the height h of the vehicle includes:

[0036] The model of the pitch angle θ p of the vehicle is:

[0037]

[0038] Among them, R pitch is the pitch parameter of the vehicle;

[0039] The roll degree θ of the vehicle r The model is:

[0040]

[0041] Among them, R roll is the roll parameter of the vehicle;

[0042] Combined rotation, R = R roll ·R pitch ; Among them, R is the offset of the rotation;

[0043] The model of the height translation h is:

[0044]

[0045] Among them, t is the height translation amount.

[0046] Preferably, the error function that minimizes the difference between the expected value of the lane feature and the observed value of the lane feature includes:

[0047] The error function E(θ p , θ r , h) is obtained by the lane line points (u obs,i , v obs,i ) of the observed value and the image points (u exp,i , v exp,i ) of the expected value to obtain the error function E(θ p , θ r , h);

[0048]

[0049] Among them, θ p is the pitch degree of the vehicle, θ r is the roll degree of the vehicle, h is the height of the vehicle; u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, v exp,i is the expected image point corresponding to the ordinate;

[0050] Find the pitch degree θ’ p of the vehicle corresponding to the minimized E(θ r , h), the roll degree θ’ p of the vehicle, θ’ rand the height h' of the vehicle, and the pitch angle θ' of the corresponding vehicle is obtained by minimizing an error function based on the difference between the expected and observed positions p and the roll angle θ' of the vehicle r and the height h' of the vehicle; then:

[0051]

[0052] wherein, θ' p is the pitch angle of the optimized vehicle, θ' r is the roll angle of the optimized vehicle, and h' is the height of the optimized vehicle.

[0053] To solve the above technical problems, the present invention also provides a vehicle leveling control system, which includes an image acquisition unit, an in-vehicle processing unit, and a suspension system unit; the method includes:

[0054] The image acquisition unit is used to acquire an image of the road ahead during the vehicle driving;

[0055] The lane feature extraction unit, the in-vehicle processing unit extracts lane features from the acquired image to obtain the observed values of the lane features;

[0056] The estimation unit estimates the pitch angle θ p and the roll angle θ r of the vehicle and the height h of the vehicle, and obtains the estimated pitch angle θ' p and the roll angle θ' r of the vehicle and the height h' of the vehicle;

[0057] The control signal acquisition unit obtains a control signal according to the estimated pitch angle θ' p and the roll angle θ' r of the vehicle and the height h' of the vehicle;

[0058] The adjustment unit, the suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

[0059] Due to the adoption of the above technical solutions, the present invention has remarkable technical effects:

[0060] The present invention estimates the pitch, roll and height adjustment of the vehicle by analyzing the difference between the expected and observed lane line positions in the camera image, so as to control the leveling of the vehicle. This system utilizes the ability of the calibrated camera to reduce the need for additional physical sensors.

[0061] The present invention provides real-time estimation and adjustment of the air spring to maintain the optimal vehicle level, enhancing ride comfort and safety.

[0062] The present invention reduces system complexity and cost by minimizing the number of physical sensors required for the suspension control system. Description of the Drawings

[0063] Figure 1 is a flowchart of the present invention.

[0064] Figure 2 is a flowchart for estimating the pitch, roll, and height of the vehicle of the present invention;

[0065] Figure 3 is a flowchart for coordinate system conversion of the present invention. Detailed Description of the Invention

[0066] The present invention will be further described in detail below in conjunction with the drawings and embodiments.

[0067] Embodiment 1

[0068] A vehicle horizontal control method, Figure 1 which includes an image acquisition unit, an in-vehicle processing unit, and a suspension system unit; the method includes:

[0069] The image acquisition unit acquires an image of the road ahead during vehicle driving;

[0070] Extraction of lane features, the in-vehicle processing unit extracts lane features from the acquired image to obtain observed values of the lane features;

[0071] Estimation of the pitch θ p of the vehicle, the roll θ r of the vehicle, and the height h of the vehicle to obtain the estimated pitch θ' p of the vehicle, the roll θ' r of the vehicle, and the height h';

[0072] Based on the estimated pitch θ' p of the vehicle, the roll θ' r of the vehicle, and the height h' of the vehicle, a control signal is obtained;

[0073] The suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

[0074] The estimation of the pitch θ p of the vehicle, the roll θ r of the vehicle, and the height h of the vehicle includes:

[0075] Comparing the expected value of the lane feature with the position of the observed value of the lane feature through a mathematical model;

[0076] An error function that minimizes the difference between the expected value of a lane feature and the observed value of the lane feature.

[0077] The positions for comparing the expected value of a lane feature with the observed value of the lane feature through a mathematical model include:

[0078] Figure 2 In it, the transformation of the coordinate system;

[0079] The pitch angle θ of the vehicle p and the roll angle θ of the vehicle r and the construction of a mathematical model for the height h of the vehicle; based on the transformed coordinate system, the pitch angle θ of the vehicle p and the roll angle θ of the vehicle r and the construction of a mathematical model for the height h of the vehicle;

[0080] Obtaining the positions of the expected value and the observed value of the lane feature, including the image point (u exp,i , v exp,i ) of the expected value and the lane line point (u obs,i , v obs,i ) of the observed value; where, u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, and v exp,i is the expected image point corresponding to the ordinate.

[0081] Using computer vision technology to extract the observed value of the lane feature from the camera image, the observed value of the lane feature includes the lane line point (u obs,i , v obs,i ) of the observed value; using the projection equation to calculate the expected value of the lane feature, the expected value of the lane feature includes the image point (u exp,i , v exp,i ) of the expected value;

[0082] The acquisition method for the abscissa point u and the ordinate point v of the vertical axis is;

[0083]

[0084] where, f x is the focal length of the horizontal axis; f y is the focal length of the vertical axis; C x is the principal point coordinate of the horizontal axis, and C y is the principal point coordinate of the vertical axis.

[0085] Figure 3 The transformation of the coordinate system includes:

[0086] Converting the world coordinate system to the vehicle coordinate system:

[0087]

[0088] Among them, X w , Y w , Z w are the coordinates corresponding to the world coordinate system; X v , Y v , Z v are the coordinates corresponding to the vehicle; R veh icle is the attitude transformation amount of the vehicle, and t veh icle is the translation amount of the vehicle;

[0089] Vehicle coordinate system conversion to camera coordinate system:

[0090]

[0091] Among them, X c , Y c , Z c are the coordinates corresponding to the camera; R camera is the attitude transformation amount of the camera, and t camera is the translation amount of the camera;

[0092] Camera coordinate system conversion to image coordinate system:

[0093]

[0094] Among them, U and V are the coordinates corresponding to the image, U is the horizontal axis, and V is the vertical axis.

[0095] The pitch angle θ of the vehicle p , the roll angle θ of the vehicle r and the construction of the mathematical model of the vehicle height h include;

[0096] The model of the pitch angle θ of the vehicle p is:

[0097]

[0098] Among them, R pitch is the pitch parameter of the vehicle;

[0099] The roll angle θ of the vehicle r model is:

[0100]

[0101] Among them, R roll is the roll parameter of the vehicle;

[0102] Combined rotation, R = R roll ·R pitch ; Among them, R is the offset of the rotation;

[0103] The model with a vertical translation h is:

[0104]

[0105] where t is the vertical translation amount.

[0106] The error function that minimizes the difference between the expected value and the observed value of the lane feature includes:

[0107] The error function E(θ p , θ r , h) is obtained by using the lane line points (u obs,i , v obs,i ) of the observed value and the image points (u exp,i , v exp,i ) of the expected value to obtain the error function E(θ p , θ r , h);

[0108]

[0109] where θ p is the pitch angle of the vehicle, θ r is the roll angle of the vehicle, h is the height of the vehicle; u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, v exp,i is the expected image point corresponding to the ordinate;

[0110] Find the pitch angle θ’ p of the vehicle, the roll angle θ’ r of the vehicle, and the height h’ of the vehicle corresponding to the minimized E(θ p , θ r , h). The corresponding pitch angle θ’ p of the vehicle, the roll angle θ’ r of the vehicle, and the height h’ of the vehicle are obtained by minimizing the error function based on the difference between the expected and observed positions; then:

[0111]

[0112] where θ’ p is the pitch angle of the optimized vehicle, θ’ r is the roll angle of the optimized vehicle, and h’ is the height of the optimized vehicle.

[0113] Example 2

[0114] Based on Embodiment 1, this embodiment provides a vehicle level control system, which includes an image acquisition unit, an in-vehicle processing unit, and a suspension system unit; the method includes:

[0115] The image acquisition unit is used to acquire images of the road ahead during vehicle driving;

[0116] The lane feature extraction unit, the in-vehicle processing unit extracts lane features from the acquired images to obtain the observed values of lane features;

[0117] The estimation unit, based on the extracted lane features, estimates the pitch angle θ of the vehicle p , the roll angle θ of the vehicle r and the height h of the vehicle, and obtains the estimated pitch angle θ' of the vehicle p , the roll angle θ' of the vehicle r and the height h';

[0118] The control signal acquisition unit, based on the estimated pitch angle θ' of the vehicle p , the roll angle θ' of the vehicle r and the height h' of the vehicle, thereby obtains the control signal;

[0119] The adjustment unit, the suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

[0120] Embodiment 3

[0121] Based on the above embodiment, the image acquisition unit in this embodiment is a front view ADAS camera: installed on the vehicle, calibrated, with known internal and external parameters, and capable of capturing images of the road ahead. The expected values of lane features are calculated using the projection equation, and the expected values of lane features include the image points (u exp,i , v exp,i ) of the expected values;

[0122] Assume that the lane lines are straight, parallel to the X w axis, the lateral direction is known, Y w = ±L / 2;

[0123] Converted to the camera coordinate system P w = (X w , Y w , 0);

[0124] For rotation, P v = R·P W ; where P v is the rotated camera coordinate system; the rotated coordinate system is translated to obtain the translated coordinate system P c , P c = P v+t; where t is the translation distance; through the internal parameter matrix K of the camera;

[0125] where,

[0126]

[0127] where, f x is the focal length on the x-axis, f y is the focal length on the y-axis, f x , f y is in pixels (c x , c y ) is the principal point coordinate.

Claims

1. A vehicle horizontal control method, comprising an image acquisition unit, an in-vehicle processing unit, and a suspension system unit; The method includes: An image acquisition unit acquires an image of the road ahead during vehicle driving; Extraction of lane features. The in-vehicle processing unit extracts lane features from the acquired image to obtain observed values of the lane features; Estimate the pitch angle θ of the vehicle based on the extracted lane features p , the roll angle θ r of the vehicle, and the height h of the vehicle, and obtain the estimated pitch angle θ’ p , the roll angle θ’ r of the vehicle, and the height h’ of the vehicle; Based on the estimated pitch angle θ’ of the vehicle p and roll angle θ’ of the vehicle r and height h’ of the vehicle, a control signal is obtained; The suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

2. The vehicle level control method according to claim 1, characterized in that, Pitch θ of the vehicle p 、Roll θ of the vehicle r and the estimation of the height h of the vehicle include: Compare the expected value of the lane feature with the position of the observed value of the lane feature through a mathematical model; An error function that minimizes the difference between the expected value of the lane feature and the observed value of the lane feature.

3. A vehicle level control method according to claim 1, wherein, Comparing the expected value of the lane feature with the position of the observed value of the lane feature through a mathematical model includes: Coordinate system conversion; Pitch angle θ of the vehicle p 、Roll angle θ of the vehicle r and construction of the mathematical model of the height h of the vehicle; Construction of the mathematical model of the pitch angle θ p 、Roll angle θ of the vehicle r and height h of the vehicle; Obtaining the positions of the expected value and the observed value of the lane feature, including the image point (u exp,i , v exp,i ) of the expected value and the lane line point (u obs,i , v obs,i ) of the observed value; where u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, and v exp,i is the expected image point corresponding to the ordinate.

4. A vehicle horizontal control method according to claim 1, characterized in that, Using computer vision technology to extract the observed values of lane features from camera images, the observed values of lane features include the lane line points (u obs,i , v obs,i ) of the observed values; using the projection equation to calculate the expected values of lane features, the expected values of lane features include the image points (u exp,i , v exp,i ) of the expected values; For the acquisition method of the horizontal axis coordinate point u and the vertical axis coordinate point v; Among them, f x is the focal length of the horizontal axis; f y is the focal length of the vertical axis; C x is the principal point coordinate of the horizontal axis, and C y is the principal point coordinate of the vertical axis.

5. A vehicle level control method according to claim 1, characterized in that, The coordinate system conversion includes: Conversion from the world coordinate system to the vehicle coordinate system: Among them, X w , Y w , Z w are the coordinates corresponding to the world coordinate system; X v , Y v , Z v are the coordinates corresponding to the vehicle; R vehicle is the attitude transformation amount of the vehicle, and t vehicle is the translation amount of the vehicle; Conversion from the vehicle coordinate system to the camera coordinate system: Among them, X c , Y c , Z c are the coordinates corresponding to the camera; R camera is the attitude transformation amount of the camera, and t camera is the translation amount of the camera; Conversion from the camera coordinate system to the image coordinate system: Wherein, U and V are the coordinates corresponding to the image, U is the horizontal axis, and V is the vertical axis.

6. A vehicle horizontal control method according to claim 1, characterized in that, Pitch angle θ of the vehicle p 、Roll angle θ of the vehicle r and the construction of the mathematical model of the height h of the vehicle includes; Pitch angle θ of the vehicle p The model is as follows: wherein, R pitch the pitch parameter of the vehicle; Roll angle θ of the vehicle r The model is as follows: wherein, R roll is the roll parameter of the vehicle; Combined rotation, R = R roll ·R pitch ; where R is the offset of the rotation; The model of the height translation h is: Wherein, t is the height translation amount.

7. A vehicle level control method according to claim 1, characterized in that The error function that minimizes the difference between the expected value of the lane feature and the observed value of the lane feature includes: The acquisition of the error function E(θ p , θ r , h) is obtained by using the lane line points (u obs,i , v obs,i ) of the observed values and the image points (u exp,i , v exp,i ) of the expected values to obtain the error function E(θ p , θ r , h); Among them, θ p is the pitch of the vehicle, θ r is the roll of the vehicle, h is the height of the vehicle; u obs,i is the lane line point corresponding to the abscissa, v obs,i is the lane line point corresponding to the ordinate, u exp,i is the expected image point corresponding to the abscissa, v exp,i is the expected image point corresponding to the ordinate; Find the minimized E(θ p , θ r , h) corresponding to the pitch θ' of the vehicle p , the roll θ' of the vehicle r and the height h' of the vehicle. The corresponding pitch θ' of the vehicle is obtained by minimizing the error function based on the difference between the expected and observed positions p , the roll θ' of the vehicle r and the height h' of the vehicle; then: where θ’ p is the pitch angle of the optimized vehicle, θ’ r is the roll angle of the optimized vehicle, and h’ is the height of the optimized vehicle.

8. A vehicle horizontal control system, characterized in that, Including an image acquisition unit, an in-vehicle processing unit, and a suspension system unit; The method includes: The image acquisition unit is used to acquire an image of the road ahead during vehicle driving; A lane feature extraction unit. The in-vehicle processing unit extracts lane features from the acquired image to obtain observed values of the lane features; An estimation unit estimates the pitch angle θ of the vehicle, the roll angle θ p of the vehicle, and the height h of the vehicle, and obtains the estimated pitch angle θ' r of the vehicle, the roll angle θ' p of the vehicle, and the height h' of the vehicle; r ​ A control signal acquisition unit, based on the estimated pitch angle θ’ of the vehicle p , the roll angle θ’ of the vehicle r and the height h’ of the vehicle, to obtain a control signal; An adjustment unit. The suspension system unit adjusts the air spring suspension system of the vehicle according to the control signal.

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