Lane keeping control method and system based on road cross slope estimation and feedforward compensation

Abstract

The application provides a lane keeping control method and system based on road surface transverse slope estimation and feedforward compensation, and relates to the technical field of automobile auxiliary driving, and the method comprises the following steps: acquiring vehicle state information; adopting a method of low-pass filtering and state observer fusion to estimate a road surface transverse slope angle in real time; calculating a feedforward compensation steering angle based on the estimated transverse slope angle, wherein a feedforward gain is adjusted online and adaptively according to a vehicle speed and a load; calculating a feedback control steering angle by adopting an LQR algorithm based on a transverse deviation and a yaw angle deviation; dynamically weighting and fusing the two by a feedforward-feedback coupling coordinator to generate a target front wheel steering angle and control vehicle steering. The application can effectively eliminate the steady-state transverse deviation caused by the road surface transverse slope, and significantly improve the lane keeping precision, system robustness and driving comfort.

Description

Technical Field

[0001] This invention relates to the field of automotive driver assistance technology, and in particular to a lane keeping control method and system based on road cross slope estimation and feedforward compensation. Background Technology

[0002] Lane Keeping Assist (LKA) is a core function of modern intelligent driving systems. It uses cameras to identify lane markings and a controller to drive the steering system, automatically keeping the vehicle centered in the lane, effectively reducing driver workload and improving driving safety. Existing LKA controllers mostly employ feedback control strategies, such as PID control, linear quadratic regulator (LQR) control, and model predictive control (MPC). These controllers detect the lateral deviation of the vehicle from the lane center (… ) and yaw angle deviation ( Using state variables such as , calculate the required steering angle to eliminate the current deviation.

[0003] However, such feedback control methods have inherent limitations when dealing with persistent, constant disturbances. For example, when driving on a road surface with a lateral slope (commonly known as a "shoulder," where one side is higher than the other), the vehicle's weight generates a continuous lateral force pointing towards the bottom of the slope, which forces the vehicle off-center from the lane. Traditional feedback controllers (such as LQR) aim to eliminate instantaneous deviations and cannot generate a steady-state control quantity to counteract such constant disturbances. This causes the vehicle to reach equilibrium at a position off-center from the lane, resulting in a systematic steady-state error that severely affects lane centering accuracy and driving experience. Summary of the Invention

[0004] To address the technical problem of steady-state lateral deviation in lane keeping systems caused by road cross slope, as described in the background, this invention proposes a lane keeping control method and system based on road cross slope estimation and feedforward compensation. This invention integrates low-pass filtering and a state observer to estimate the road cross slope angle in real time, calculates the feedforward compensation amount based on this, and then combines it with a feedforward-feedback dynamic coupling mechanism to actively generate the steady-state steering angle required to offset disturbances, thereby fundamentally eliminating the system steady-state error caused by constant lateral disturbances.

[0005] To achieve the above objectives, a first aspect of the present invention provides a lane keeping control method based on road cross slope estimation and feedforward compensation, comprising: Obtain vehicle status information, including lateral deviation. Yaw angle deviation lateral acceleration yaw rate and vehicle speed ; Based on the vehicle state information, a method combining low-pass filtering and a state observer is used to estimate the current lateral slope angle of the road surface in real time. ; Based on the estimated transverse slope angle Calculate the feedforward compensated steering angle based on preset vehicle dynamics parameters. ; Based on the lateral deviation and yaw angle deviation The LQR algorithm for a linear quadratic regulator is used to calculate the feedback control steering angle. ; Based on the feedforward compensation steering angle and the feedback control steering angle Perform dynamic weighted fusion to generate the target front wheel steering angle. ; Output the target front wheel steering angle To control the vehicle's steering and achieve lane keeping.

[0006] Furthermore, the method of estimating the lateral slope angle using a fusion of low-pass filtering and a state observer is described. The steps specifically include: Acquire the vehicle body roll angle signal measured by the inertial measurement unit (IMU); The vehicle roll angle signal is subjected to low-pass filtering, and the cutoff frequency of the low-pass filter is set to 0.2Hz to 0.5Hz to extract the static road cross slope component. Based on the vehicle dynamics model, a state observer is constructed to estimate the system state, including the road cross slope angle, in real time. The low-pass filtered signal is fused with the signal estimated by the state observer to obtain the road surface lateral slope angle. .

[0007] Furthermore, the calculation of the feedforward compensation steering angle In the steps, feedforward gain is used. According to vehicle speed and load estimates Perform online adaptive adjustment; the adjustment formula is as follows: ; in, To calibrate the reference gain, For vehicle speed adjustment function, This is the load adjustment function.

[0008] Furthermore, the vehicle speed adjustment function For a piecewise linear function, its calculation formula is as follows: when hour, ; when hour, ; when hour, ; in, , , , This is the calibration constant.

[0009] Furthermore, the load adjustment function Calculated using the following formula: ; in, For real-time estimation of vehicle sprung mass, For calibrating reference quality.

[0010] Furthermore, the feedforward compensation steering angle With feedback control steering angle The steps for dynamic weighted fusion specifically include: Get the current lateral deviation ; The lateral deviation The absolute value and a preset lateral deviation threshold Compare; Dynamically adjust the feedforward weights based on the comparison results. and feedback weights ,in ; Based on the adjusted weights, according to the formula Calculate the target front wheel steering angle ; Among them, when At the same time, increase the feedback weight. ;when Furthermore, at higher vehicle speeds, the feedforward weight is increased. .

[0011] A second aspect of the present invention provides a lane keeping control system based on road cross slope estimation and feedforward compensation, comprising: The perception module, including a camera, an inertial measurement unit (IMU), and a vehicle speed sensor, is used to collect vehicle status information; The decision control module, electrically connected to the sensing module, includes: The road cross slope estimation unit is configured to estimate the road cross slope angle in real time using a method that combines low-pass filtering with a state observer. ; The feedforward computing unit is configured to be based on Calculate the feedforward compensation steering angle It also features online adaptive adjustment of feedforward gain based on vehicle speed and load. Functions; The feedback control unit is configured to calculate the feedback control steering angle using the LQR algorithm. ; The feedforward-feedback coupled coordinator is configured to... and Perform dynamic weighted fusion to generate the target front wheel steering angle. ; The actuator module, electrically connected to the decision control module, includes an electric power steering system (EPS) for receiving and executing the target front wheel steering angle. .

[0012] A third aspect of the present invention provides an electronic device including a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps in the lane keeping control method based on road cross slope estimation and feedforward compensation as described in the first aspect of the present invention.

[0013] A fourth aspect of the present invention provides a computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in the first aspect of the present invention.

[0014] A fifth aspect of the present invention provides a computer program product comprising software code, wherein the program in the software code performs the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in the first aspect of the present invention.

[0015] Compared with existing technologies, the lane keeping control method and system based on road cross slope estimation and feedforward compensation provided by this invention have the following advantages: (1) Effectively eliminate steady-state error. By actively offsetting the constant lateral disturbance caused by the cross slope of the road surface through feedforward compensation, the problem of systematic yaw of vehicles on cross slope roads is fundamentally solved, and the accuracy of lane centering is significantly improved. Actual test data show that on cross slope roads of 3%-5%, the steady-state lateral deviation is reduced by more than 83% (see Table 1).

[0016] (2) Improve system robustness by combining feedback control with feedforward control. Feedback control focuses on suppressing dynamic and random disturbances, while feedforward control handles known steady-state disturbances and avoids control conflicts through dynamic coupling mechanism, making the system more adaptable to complex road conditions.

[0017] (3) Improve driving comfort. Since feedforward compensation provides most of the steady-state "support", the feedback controller does not need to perform large-scale and frequent correction actions, making the steering wheel adjustment smoother. The measured steering wheel angle fluctuation amplitude (standard deviation) is reduced by about 40%-45% (see Table 1), the intervention frequency is reduced by 50%, and the driving comfort is improved.

[0018] (4) Achieve intelligent coupling between feedforward and feedback, dynamically allocate control weights according to different working conditions, realize collaborative optimization between feedforward and feedback, and improve the overall coordination of the system.

[0019] (5) Provide verifiable performance improvement data. In actual cross slope road tests, the lateral deviation was reduced by more than 80% and the steering wheel intervention frequency was reduced by 50%. Specific data are shown in Table 1. Attached Figure Description

[0020] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0021] Figure 1 This is a flowchart of the lane keeping control method based on road cross slope estimation and feedforward compensation provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of road cross slope fusion estimation provided in Embodiment 1 of the present invention; Figure 3 This is a logic block diagram of the feedforward-feedback coupling coordinator provided in Embodiment 1 of the present invention; Figure 4 This is an architecture diagram of the lane keeping control system based on road cross slope estimation and feedforward compensation provided in Embodiment 2 of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0025] All data acquisition in this embodiment is carried out in accordance with laws and regulations and with user consent, and the data is used legally.

[0026] Example 1 This embodiment provides a lane keeping control method based on road cross slope estimation and feedforward compensation, including: Obtain vehicle status information, including lateral deviation. Yaw angle deviation lateral acceleration yaw rate and vehicle speed ; Based on the vehicle state information, a method combining low-pass filtering and a state observer is used to estimate the current lateral slope angle of the road surface in real time. ; Based on the estimated transverse slope angle Calculate the feedforward compensated steering angle based on preset vehicle dynamics parameters. ; Based on the lateral deviation and yaw angle deviation The LQR algorithm for a linear quadratic regulator is used to calculate the feedback control steering angle. ; Based on the feedforward compensation steering angle and the feedback control steering angle Perform dynamic weighted fusion to generate the target front wheel steering angle. ; Output the target front wheel steering angle To control the vehicle's steering and achieve lane keeping.

[0027] The method provided by this invention has a feedforward stage specifically designed to compensate for the predictable constant disturbance of road cross slope, while the feedback stage handles model errors and random disturbances. The two stages work together to solve the technical problem that single feedback control cannot eliminate steady-state deviations.

[0028] Specifically, the method of estimating the lateral slope angle by fusing low-pass filtering with a state observer is described. The steps specifically include: Acquire the vehicle body roll angle signal measured by the inertial measurement unit (IMU); The vehicle roll angle signal is subjected to low-pass filtering, and the cutoff frequency of the low-pass filter is set to 0.2Hz to 0.5Hz to extract the static road cross slope component. Based on the vehicle dynamics model, a state observer is constructed to estimate the system state, including the road cross slope angle, in real time. The low-pass filtered signal is fused with the signal estimated by the state observer to obtain the road surface lateral slope angle. .

[0029] Low-pass filtering (e.g., a 0.3Hz cutoff frequency) can effectively filter out high-frequency noise caused by vehicle roll motion and extract the static or gradually changing true road slope components. Meanwhile, a state observer (e.g., an extended Kalman filter), based on a vehicle dynamics model, can dynamically estimate the system state, including the cross slope angle, from signals such as lateral acceleration. The fusion of these two methods utilizes both the predictive power of the model and the direct measurements from sensors, improving the accuracy and robustness of the estimation—a prerequisite for effective feedforward compensation.

[0030] Specifically, the calculation of the feedforward compensation steering angle In the steps, feedforward gain is used. According to vehicle speed and load estimates Perform online adaptive adjustment; the adjustment formula is as follows: ; in, To calibrate the reference gain, For vehicle speed adjustment function, This is the load adjustment function.

[0031] Specifically, the vehicle speed adjustment function For a piecewise linear function, its calculation formula is as follows: when hour, ; when hour, ; when hour, ; in, , , , This is the calibration constant.

[0032] Vehicle speed adjustment function The piecewise linear form is adopted because the effect of lateral force offset caused by the same cross slope angle on steering demand is non-linear at different vehicle speeds. This design makes the compensation amount more consistent with actual dynamic characteristics.

[0033] Specifically, the load adjustment function Calculated using the following formula: ; in, For real-time estimation of vehicle sprung mass, For calibrating reference quality.

[0034] Vehicle load directly affects its mass and lateral stiffness, which in turn affects the steering angle required to resist lateral forces on the cross slope. This linear adjustment ensures the accuracy of compensation under different loads.

[0035] Specifically, the feedforward compensation steering angle With feedback control steering angle The steps for dynamic weighted fusion specifically include: Get the current lateral deviation ; The lateral deviation The absolute value and a preset lateral deviation threshold Compare; Dynamically adjust the feedforward weights based on the comparison results. and feedback weights ,in ; Based on the adjusted weights, according to the formula Calculate the target front wheel steering angle ; Among them, when At the same time, increase the feedback weight. ;when Furthermore, at higher vehicle speeds, the feedforward weight is increased. .

[0036] When lateral deviation A large value indicates that the vehicle has significantly deviated from its lane; in this case, the feedback weight should be increased. It can utilize the rapid convergence characteristic of LQR control for emergency correction, ensuring safety; when deviation... When the value is small and the vehicle speed is high, it indicates that the vehicle is in steady-state cruising, and the feedforward weight should be increased in this case. This approach fully leverages the strong anti-interference capabilities of feedforward control, smoothing control actions and enhancing comfort. This dynamic strategy achieves intelligent synergy and complementary advantages between feedforward and feedback.

[0037] In one specific embodiment, such as Figure 1 The specific steps of the control method provided by the present invention include: Step S100: Status Information Acquisition. Real-time acquisition of vehicle status information, including but not limited to: obtaining the lateral deviation between the vehicle and lane lines via an onboard camera (…). ) and yaw angle deviation ( ); The vehicle's lateral acceleration is obtained through the onboard inertial measurement unit (IMU). ) and yaw rate ( Vehicle speed is obtained via the vehicle's CAN bus. ).

[0038] Step S200: Road lateral slope estimation. Based on the vehicle state information, a method combining low-pass filtering and extended state observer is used to estimate the current road lateral slope angle in real time. Specifically, the vehicle roll angle signal measured by the IMU is used to extract the static road cross slope component through low-pass filtering (cutoff frequency set to 0.2Hz~0.5Hz); at the same time, an extended state observer (such as an extended Kalman filter) is designed to estimate the system state, including the road cross slope angle, in real time based on the vehicle dynamics model, and the two are fused to obtain a more accurate and robust estimate of the road cross slope angle.

[0039] Step S300: Calculation of feedforward compensation. Based on the estimated road lateral slope angle ( ) and vehicle dynamics parameters, to calculate the feedforward compensated steering angle ( The vehicle dynamics parameters include vehicle mass (). ), front axle lateral stiffness ( ), rear axle lateral stiffness ( ), distance from center of gravity to front axle ( ), distance from center of mass to rear axle ( ), wheelbase ( ) and gravitational acceleration ( The feedforward compensation steering angle ( The formula for calculating ) is: ,in This is the cross slope compensation gain coefficient.

[0040] Furthermore, to improve the adaptability of feedforward compensation, the feedforward gain in this invention... It can adaptively adjust online based on vehicle speed and load. Specifically, it uses the following formula: .in, This is the reference gain determined through calibration tests. Vehicle speed regulation function. Typically, it is designed as a function that monotonically decreases with increasing vehicle speed, for example, it can be implemented using piecewise linear interpolation (such as...). The specific parameters were obtained through calibration of the steady-state steering requirements of a real vehicle at different speeds. Load adjustment function Based on real-time estimated vehicle sprung mass Adjustments can be made, for example, by adopting a linear proportional relationship. ( (For calibrating the reference mass). This adaptive mechanism ensures the accuracy and robustness of the feedforward compensation under different vehicle speeds and load conditions.

[0041] Step S400: Feedback control quantity calculation. Using the lateral deviation ( ) and yaw angle deviation ( Using as the primary state variable, a linear quadratic regulator (LQR) algorithm is employed to calculate the feedback control steering angle. The LQR control law is as follows: ,in, The system state vector typically contains ; This is the optimal state feedback gain matrix obtained by solving the Riccati equation.

[0042] Step S500: Final control input synthesis and output. Through a feedforward-feedback coupling coordinator, the feedforward compensated steering angle ( ) and feedback control steering angle ( Dynamic weighted fusion is performed to generate the final target front wheel steering angle. ): ,in and These are dynamically adjusted weighting coefficients, and .

[0043] The coupling coordinator is based on the lateral deviation Size and speed The weights of feedforward and feedback are dynamically adjusted. The specific rule is: when... ( When the preset lateral deviation threshold is reached (e.g., 0.1m), the feedback weight is increased. Quickly correct deviations; when Furthermore, at higher vehicle speeds, the feedforward weight is increased. This enhances the steady-state disturbance rejection capability. The weight adjustment strategy can be implemented using fuzzy logic or a rule table.

[0044] Finally, the front wheel angle of the target ( The signal is sent to the electric power steering (EPS) system for execution, thereby achieving precise lane centering.

[0045] Specifically, the implementation process of the present invention is as follows: While the vehicle is in motion, the sensing modules (cameras, IMU, etc.) continuously collect data. The cameras calculate the lateral deviation. and yaw angle The IMU provides lateral acceleration. and yaw rate This data is then input into the decision control module.

[0046] In the decision control module, the road cross slope estimation unit performs low-pass filtering (cutoff frequency set to 0.3Hz) on the IMU's roll angle signal to remove high-frequency components caused by vehicle body sway. Simultaneously, the extended state observer performs state estimation based on the vehicle dynamics model (using lateral velocity, yaw rate, and road cross slope angle as states), and fuses the filtered signal with the observer's estimated value to obtain the road cross slope angle. θ (See) Figure 2 ).

[0047] The feedforward calculation unit calculates the vehicle parameters pre-stored in the controller. ) and real-time vehicle speed The load estimate is obtained by looking up a table or calculating the adaptive gain online. Specifically, the controller stores the calibrated reference gain. Vehicle speed adjustment function Interpolation tables or parameters, and load adjustment functions The computational logic or mapping table. For example, the vehicle speed adjustment function can be implemented using the following formula: when hour, ; when hour, ; when hour, ; in , , , This is a calibration constant. The load adjustment function can be obtained by real-time estimation of the sprung mass. With calibrated quality The ratio is obtained as follows: .final, And calculate the feedforward compensation angle. .

[0048] Meanwhile, the feedback control unit (LQR controller) based on the state vector (in and (This can be obtained through differential or observer methods, or through state feedback laws.) Calculate the feedback control angle .

[0049] The feedforward-feedback coupled coordinator is based on the real-time lateral deviation. and vehicle speed Dynamically adjust feedforward weights according to preset rules and feedback weights (See) Figure 3 Then calculate the final control quantity.

[0050] The control synthesis unit will The data is sent to the EPS for execution via the CAN bus.

[0051] Table 1. Performance comparison between the method of this invention and the conventional LQR method in cross slope pavement testing.

[0052] Example 2 like Figure 4 As shown, this embodiment provides a lane keeping control system based on road cross slope estimation and feedforward compensation, including: The perception module, including a camera, an inertial measurement unit (IMU), and a vehicle speed sensor, is used to collect vehicle status information; The decision control module, electrically connected to the sensing module, includes: The road cross slope estimation unit is configured to estimate the road cross slope angle in real time using a method that combines low-pass filtering with a state observer. ; The feedforward computing unit is configured to be based on Calculate the feedforward compensation steering angle It also features online adaptive adjustment of feedforward gain based on vehicle speed and load. Functions; The feedback control unit is configured to calculate the feedback control steering angle using the LQR algorithm. ; The feedforward-feedback coupled coordinator is configured to... and Perform dynamic weighted fusion to generate the target front wheel steering angle. ; The actuator module, electrically connected to the decision control module, includes an electric power steering system (EPS) for receiving and executing the target front wheel steering angle. .

[0053] Example 3 Embodiment 3 of the present invention provides an electronic device.

[0054] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the lane keeping control method based on road cross slope estimation and feedforward compensation as described in Embodiment 1 of the present invention.

[0055] The detailed steps are the same as those of the lane keeping control method based on road cross slope estimation and feedforward compensation provided in Example 1, and will not be repeated here.

[0056] Example 4 Embodiment 4 of the present invention provides a computer-readable storage medium.

[0057] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the lane keeping control method based on road cross slope estimation and feedforward compensation as described in Embodiment 1 of the present invention.

[0058] The detailed steps are the same as those of the lane keeping control method based on road cross slope estimation and feedforward compensation provided in Example 1, and will not be repeated here.

[0059] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0060] A computer program product includes software code, wherein the program in the software code performs the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in Embodiment 1 of the present invention.

[0061] The detailed steps are the same as those of the lane keeping control method based on road cross slope estimation and feedforward compensation provided in Example 1, and will not be repeated here.

[0062] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0067] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0068] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A lane keeping control method based on road cross slope estimation and feedforward compensation, characterized in that, The method includes: Obtain vehicle status information, including lateral deviation. Yaw angle deviation lateral acceleration yaw rate and vehicle speed ; Based on the vehicle state information, a method combining low-pass filtering and a state observer is used to estimate the current lateral slope angle of the road surface in real time. ; Based on the estimated transverse slope angle Calculate the feedforward compensated steering angle based on preset vehicle dynamics parameters. The feedforward compensation steering angle The calculation formula is: ,in The gain coefficient is the slope compensation factor; feedforward gain is used. According to vehicle speed and load estimates Perform online adaptive adjustment; the adjustment formula is as follows: ； in, To calibrate the reference gain, For vehicle speed adjustment function, The load adjustment function; the vehicle speed adjustment function For a piecewise linear function, its calculation formula is as follows: when hour, ; when hour, ; when hour, ; in, , , , These are calibration constants; The load adjustment function Calculated using the following formula: ； in, For real-time estimation of vehicle sprung mass, To calibrate the reference quality; Based on the lateral deviation and yaw angle deviation The LQR algorithm for a linear quadratic regulator is used to calculate the feedback control steering angle. ; Based on the feedforward compensation steering angle and the feedback control steering angle Perform dynamic weighted fusion to generate the target front wheel steering angle. ; Output the target front wheel steering angle To control the vehicle's steering and achieve lane keeping.

2. The method as described in claim 1, characterized in that, The method of estimating the lateral slope angle by fusing low-pass filtering and state observer is described. The steps specifically include: Acquire the vehicle body roll angle signal measured by the inertial measurement unit (IMU); The vehicle roll angle signal is subjected to low-pass filtering, and the cutoff frequency of the low-pass filter is set to 0.2Hz to 0.5Hz to extract the static road cross slope component. Based on the vehicle dynamics model, a state observer is constructed to estimate the system state, including the road cross slope angle, in real time. The low-pass filtered signal is fused with the signal estimated by the state observer to obtain the road surface lateral slope angle. .

3. The method as described in claim 1, characterized in that, Feedforward compensation steering angle With feedback control steering angle The steps for dynamic weighted fusion specifically include: Get the current lateral deviation ; The lateral deviation The absolute value and a preset lateral deviation threshold Compare; Dynamically adjust the feedforward weights based on the comparison results. and feedback weights ,in ; Based on the adjusted weights, according to the formula Calculate the target front wheel steering angle ; Among them, when At the same time, increase the feedback weight. ;when Furthermore, at higher vehicle speeds, the feedforward weight is increased. .

4. A lane keeping control system based on road cross slope estimation and feedforward compensation, characterized in that, include: The perception module, including a camera, an inertial measurement unit (IMU), and a vehicle speed sensor, is used to collect vehicle status information; The decision control module, electrically connected to the sensing module, includes: The road cross slope estimation unit is configured to estimate the road cross slope angle in real time using a method that combines low-pass filtering with a state observer. ; The feedforward computing unit is configured to be based on Calculate the feedforward compensation steering angle It also features online adaptive adjustment of feedforward gain based on vehicle speed and load. The function of the feedforward compensation steering angle; The calculation formula is: ,in The gain coefficient is the slope compensation factor; feedforward gain is used. According to vehicle speed and load estimates Perform online adaptive adjustment; the adjustment formula is as follows: ； in, To calibrate the reference gain, For vehicle speed adjustment function, The load adjustment function; the vehicle speed adjustment function For a piecewise linear function, its calculation formula is as follows: when hour, ; when hour, ; when hour, ; in, , , , These are calibration constants; The load adjustment function Calculated using the following formula: ； in, For real-time estimation of vehicle sprung mass, To calibrate the reference quality; The feedback control unit is configured to calculate the feedback control steering angle using the LQR algorithm. ; The feedforward-feedback coupled coordinator is configured to... and Perform dynamic weighted fusion to generate the target front wheel steering angle. ; The actuator module, electrically connected to the decision control module, includes an electric power steering system (EPS) for receiving and executing the target front wheel steering angle. .

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in any one of claims 1 to 3.

7. A computer program product, comprising software code, characterized in that, The program in the software code performs the steps of the lane keeping control method based on road cross slope estimation and feedforward compensation as described in any one of claims 1 to 3.

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