Zero offset estimation method and device of steering system and electronic equipment

Abstract

The invention provides a zero offset estimation method and device of a steering system and electronic equipment, and relates to the technical field of smart mines, unmanned driving and unmanned vehicles. The zero offset estimation method of the steering system provided by the embodiment of the invention comprises the following steps: determining steering geometric parameters and transverse kinetic parameters of a target vehicle in a driving process according to driving state data and structural feature data of the target vehicle; according to the steering geometric parameters and the transverse kinetic parameters of the target vehicle in the driving process, determining the transverse position deviation of the target vehicle; and determining a zero offset estimation value of the target vehicle according to the transverse position deviation of the target vehicle so as to realize real-time estimation of the zero offset of the front wheel angle of the vehicle.

Description

Technical Field

[0001] The present application relates to the technical fields of smart mines, unmanned driving, and unmanned vehicles, and specifically to a zero-bias estimation method and device, and electronic equipment for a steering system. Background Art

[0002] Over extended periods of operation or lack of regular maintenance, the steering wheel may experience zero offset due to mechanical wear, sensor drift, or hydraulic system degradation. This offset can cause the vehicle's actual steering angle to deviate from the control system's expected value. Without zero offset estimation and compensation, this can affect the vehicle's lateral control accuracy. In severe cases, it can cause the vehicle to deviate from its intended trajectory, compromising driving safety. Summary of the Invention

[0003] The embodiments of the present application provide a method and device for estimating the zero bias of a steering system, and an electronic device to achieve real-time estimation of the zero bias of the front wheel steering angle of a vehicle.

[0004] In a first aspect, an embodiment of the present application provides a zero bias estimation method for a steering system, comprising: determining the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural characteristic data of the target vehicle; determining the lateral position deviation of the target vehicle based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving; and determining the zero bias estimation value of the target vehicle based on the lateral position deviation of the target vehicle.

[0005] In combination with the first aspect, in some possible implementations, the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving are determined based on the driving status data and structural characteristic data of the target vehicle, including: determining the turning radius of the target vehicle during driving based on the driving status data and structural characteristic data of the target vehicle; determining the centrifugal effect coefficient of the target vehicle during driving based on the driving status data of the target vehicle and the turning radius of the target vehicle during driving.

[0006] In combination with the first aspect, in some possible implementations, the turning radius of the target vehicle during driving is determined based on the driving status data and structural feature data of the target vehicle, including: based on the geometric steering model of the target vehicle, the turning radius of the target vehicle during driving is determined according to the front wheel turning angle and the wheelbase of the target vehicle; in the geometric steering model, the turning radius of the target vehicle is determined according to a first ratio, and the first ratio is the ratio of the wheelbase of the target vehicle to the tangent value of the front wheel turning angle of the target vehicle.

[0007] In combination with the first aspect, in some possible implementations, the centrifugal effect coefficient of the target vehicle during driving is determined based on the driving status data of the target vehicle and the turning radius of the target vehicle during driving, including: determining the centripetal acceleration of the target vehicle based on the speed of the target vehicle and the turning radius of the target vehicle during driving; calculating the centrifugal effect coefficient of the target vehicle based on the centripetal acceleration of the target vehicle and the acceleration of gravity.

[0008] In combination with the first aspect, in some possible implementations, the centrifugal effect coefficient of the target vehicle is calculated based on the centripetal acceleration and gravitational acceleration of the target vehicle, including: calculating a second ratio, where the second ratio is the ratio of the centripetal acceleration of the target vehicle to the gravitational acceleration; and determining the centrifugal effect coefficient of the target vehicle based on the second ratio.

[0009] In combination with the first aspect, in some possible implementations, the lateral position deviation of the target vehicle is determined based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving, including: determining a geometric term of the lateral position deviation based on the turning radius and the wheelbase of the target vehicle during driving, the geometric term being used to introduce a first lateral offset caused by geometric steering when the speed of the target vehicle is less than a first speed threshold; determining a centrifugal correction term of the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during driving, the centrifugal correction term being used to introduce a second lateral offset caused by centrifugal force when the speed of the target vehicle is greater than a second speed threshold, and the second speed threshold is greater than or equal to the first speed threshold; determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term.

[0010] In combination with the first aspect, in some possible implementations, the geometric term is determined based on a third ratio, which is the ratio of the square of the wheelbase of the target vehicle to the turning radius; the centrifugal correction term is determined based on the sum of the centrifugal effect coefficient and the set constant.

[0011] In combination with the first aspect, in some possible implementations, the lateral position deviation of the target vehicle is determined based on the geometric term and the centrifugal correction term, including: calculating the product of the geometric term and the centrifugal correction term; and determining the lateral position deviation of the target vehicle based on the product of the geometric term and the centrifugal correction term.

[0012] In combination with the first aspect, in some possible implementations, the zero bias estimate of the target vehicle is determined based on the lateral position deviation of the target vehicle, including: taking the lateral position deviation of the target vehicle as the predicted value of the lateral position deviation of the target vehicle, and calculating the measured value of the lateral position deviation of the target vehicle based on the actual position and target position of the target vehicle; calculating the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle; and determining the zero bias estimate of the target vehicle based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

[0013] In combination with the first aspect, in some possible implementations, the zero bias estimate of the target vehicle is determined based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle, including: dynamically adjusting the forgetting factor of the estimator according to the speed of the target vehicle; inputting the residual into the estimator to obtain the zero bias estimate of the target vehicle.

[0014] In combination with the first aspect, in some possible implementations, the estimator is a recursive least squares estimator. In the recursive least squares estimator, the difference between the residual and the zero bias estimate of the previous cycle, as well as the product of the difference and the Kalman gain are calculated; based on the product of the difference and the Kalman gain and the sum of the product and the zero bias estimate of the previous cycle, the zero bias estimate of the target vehicle in the current cycle is determined.

[0015] In combination with the first aspect, in some possible implementations, the forgetting factor of the estimator is dynamically adjusted according to the speed of the target vehicle, including: if the speed of the target vehicle is less than a third speed threshold, determining a first value as the forgetting factor; if the speed of the target vehicle is greater than or equal to a fourth speed threshold, determining a second value as the forgetting factor, wherein the third speed threshold is less than or equal to the fourth speed threshold, and the first value is less than the second value.

[0016] In combination with the first aspect, in some possible implementations, the method further includes: determining feedback compensation based on a zero bias estimate of the target vehicle; and performing lateral control compensation on the target vehicle based on the feedback compensation.

[0017] In combination with the first aspect, in some possible implementations, feedback compensation is determined based on the zero bias estimate of the target vehicle, including: integrating the zero bias estimate of the target vehicle, and determining the feedback compensation based on the sum of the integration result and the zero bias estimate of the target vehicle.

[0018] In combination with the first aspect, in some possible implementations, the method further includes: determining feedforward compensation based on the curvature of the driving trajectory, the vehicle speed and the wheelbase of the target vehicle; and performing lateral control compensation on the target vehicle based on the feedforward compensation.

[0019] In combination with the first aspect, in some possible implementations, feedforward compensation is determined based on the curvature of the target vehicle's driving trajectory, the vehicle speed and the wheelbase, including: calculating a first product, where the first product is the product of the wheelbase of the target vehicle and the curvature of the driving trajectory; calculating a second product, where the second product is the product of the square of the target vehicle's speed and the curvature of the driving trajectory; and determining the feedforward compensation based on the sum of the first product and the second product.

[0020] In a second aspect, an embodiment of the present application also provides a zero bias estimation device for a steering system, comprising: a first determination module for determining the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural characteristic data of the target vehicle; a second determination module for determining the lateral position deviation of the target vehicle based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving; and a third determination module for determining the zero bias estimation value of the target vehicle based on the lateral position deviation of the target vehicle.

[0021] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a data acquisition unit for acquiring driving status data of a target vehicle; a processor connected to the data acquisition unit for executing the zero bias estimation method of the steering system mentioned in the first aspect of the embodiment of the present application; and a memory connected to the processor for storing executable instructions of the processor.

[0022] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the storage medium stores a computer program, and the computer program is used to execute the zero bias estimation method of the steering system mentioned in the first aspect of the embodiment of the present application.

[0023] The technical solution of the embodiment of the present application determines the lateral position deviation of the target vehicle based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving, and determines the zero bias estimation value of the target vehicle based on the lateral position deviation of the target vehicle, thereby achieving real-time estimation of the zero bias of the vehicle's front wheel angle, effectively solving the problem of the vehicle's lateral control accuracy being affected by the zero bias of the steering wheel. At the same time, the technical solution of the embodiment of the present application automatically realizes the zero bias estimation of the target vehicle based on the driving state data and structural feature data of the target vehicle, solving the problem that conventional zero bias estimation methods require parameter calibration, which incurs additional manpower and time costs.

[0024] In addition, the technical solution of the embodiment of the present application further improves the accuracy of vehicle lateral control by performing feedback compensation and feedforward compensation on the zero bias estimate, reduces the risk of the vehicle deviating from the predetermined trajectory due to zero bias, and improves vehicle operation safety and operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Shown is a flow chart of a zero bias estimation method for a steering system provided by an exemplary embodiment of the present application.

[0026] Figure 2 Shown is a flow chart of a parameter determination method provided by an exemplary embodiment of the present application.

[0027] Figure 3Shown is a flow chart of a method for determining a lateral position deviation provided by an exemplary embodiment of the present application.

[0028] Figure 4 The figure shows a flow chart of a method for determining a zero bias estimate value provided by an exemplary embodiment of the present application.

[0029] Figure 5 Shown is a flow chart of another zero bias estimation method for a steering system provided by an exemplary embodiment of the present application.

[0030] Figure 6 Shown is a flow chart of another zero bias estimation method for a steering system provided by an exemplary embodiment of the present application.

[0031] Figure 7 Shown is a flow chart of another zero bias estimation method for a steering system provided by an exemplary embodiment of the present application.

[0032] Figure 8 Shown is a structural schematic diagram of a zero bias estimation device for a steering system provided by an exemplary embodiment of the present application.

[0033] Figure 9 Shown is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present disclosure without making any creative efforts shall fall within the scope of protection of the present disclosure.

[0035] Steering wheel offset refers to the situation where the vehicle is not traveling in a straight line when the steering wheel is in zero position. Zero position is the ideal neutral position of the steering wheel, which is the position the steering wheel should be in when the vehicle is traveling in a straight line. Steering wheel offset is transmitted to the front wheels through the steering gear ratio, resulting in zero front wheel steering angle offset. Front wheel steering angle offset refers to the deviation of the actual front wheel steering angle from the ideal zero position (straight-ahead position). Front wheel steering angle offset can cause steady-state lateral deviation of the vehicle. For example, in an autonomous driving system, the lateral controller calculates the desired path based on the front wheel steering angle. Zero offset can lead to deviations in control commands and may cause the power assist curve of the electric power steering to be inaccurate due to zero offset.

[0036] Traditional zero-bias estimation methods are usually based on a fixed mapping relationship between the steering wheel angle and the front wheel angle, and the lateral position deviation is calculated by a table lookup method. The core problem of this method is that it is highly dependent on manual calibration: a static mapping table of the steering wheel angle-front wheel angle needs to be established in advance, and the calibration process needs to be carried out under specific working conditions (such as a dedicated test site), which consumes a lot of manpower and time costs. Parameters are easy to lose accuracy: after long-term use of the vehicle, mechanical wear of the steering system, changes in suspension geometry (such as tire wear, four-wheel alignment offset) or environmental factors (such as hardening of rubber bushings due to low temperatures) may cause the calibration parameters to deviate from the actual parameters, thereby introducing zero-bias estimation errors. Insufficient adaptability: The static parameter table cannot dynamically respond to changes in vehicle status (such as load changes, differences in road adhesion coefficients), resulting in reduced estimation accuracy in complex scenarios.

[0037] The technical solution of the embodiment of the present application dynamically calculates the steering geometry parameters and lateral dynamic parameters by fusing the vehicle driving state data and structural feature data, thereby realizing zero bias estimation. Compared with the traditional method, the technical solution provided by the embodiment of the present application can achieve the following effects: Calibration-free design: No need to rely on manually preset static parameter tables, automatic parameter fitting through real-time data-driven algorithms (such as least squares method, state observer), significantly reducing deployment costs. Dynamic adaptability: Parameters are updated online based on the actual operating data of the vehicle, which can automatically compensate for system deviations caused by mechanical aging, environmental disturbances, etc., and improve the robustness of zero bias estimation. Multi-source data fusion: Combining steering geometry parameters and lateral dynamic parameters to optimize the accuracy of zero bias estimation, especially suitable for high-precision control scenarios such as autonomous driving.

[0038] In actual application scenarios, for unmanned mining vehicles, their working environment is usually relatively harsh, the wear rate of vehicle parts is relatively fast, and the steering wheel zero deviation is more likely to occur. By adopting the technical solution of the embodiment of the present application, the unmanned mining vehicle can accurately estimate the zero deviation value in real time, and perform feedback compensation and feedforward compensation in a timely manner, effectively avoiding the vehicle from deviating from the predetermined trajectory due to zero deviation, and ensuring the efficient operation of the mine. In addition, in the overall layout of the smart mine, multiple unmanned mining vehicles work together. If one of the vehicles deviates from the driving trajectory due to zero deviation, it will not only affect its own operating efficiency, but may also pose a threat to the driving safety of surrounding vehicles. The zero deviation estimation method and related technical solutions of the present application ensure the driving stability and accuracy of each vehicle, which helps to improve the operating efficiency and safety of the entire smart mining system. From the perspective of the broader unmanned driving field, different types of unmanned vehicles, such as unmanned buses, logistics distribution vehicles, etc., although the working scenarios are different from those of unmanned mining vehicles, may also face the problem of steering wheel zero deviation. The technical solution of the present application is universal and can be applied to different types of vehicles.

[0039] The technical solutions of the embodiments of the present application can be applied to a remote server or a vehicle-side controller. Optionally, the vehicle-side controller can be a steering controller or a domain controller. For example, if applied to a steering controller of a vehicle, the steering controller can obtain the vehicle's driving status data and structural characteristic data in real time through the technical solutions of the embodiments of the present application, and then accurately calculate the steering geometry parameters, lateral dynamic parameters and lateral position deviation, and quickly obtain the zero bias estimation value. Based on this, the steering controller can adjust the steering command in time according to the zero bias estimation value to achieve accurate compensation or early warning of the vehicle's lateral control.

[0040] Figure 1 FIG2 is a flow chart of a zero bias estimation method for a steering system provided by an exemplary embodiment of the present application. Figure 1 , an embodiment of the present application provides a zero bias estimation method for a steering system including the following steps.

[0041] S10 , determining steering geometry parameters and lateral dynamic parameters of the target vehicle during driving according to the driving state data and structural characteristic data of the target vehicle.

[0042] The target vehicle is the vehicle for which zero-bias estimation is to be performed. The present embodiment of the application does not limit the type of target vehicle. Alternatively, the target vehicle can be a passenger car or a commercial vehicle, an unmanned vehicle or a manually driven vehicle. Exemplarily, the target vehicle is an unmanned mining vehicle. The target vehicle's driving state data is data describing the target vehicle's state during driving, such as vehicle speed. Structural feature data is data related to the target vehicle's structure, such as wheelbase.

[0043] Steering geometry parameters are geometric characteristics related to vehicle steering. Examples include the steering radius and / or the steering diameter. Lateral dynamics parameters describe the vehicle's lateral motion characteristics. Examples include slip angle and cornering stiffness, or the centrifugal effect coefficient. The centrifugal effect coefficient can be determined by multiplying the slip angle and cornering stiffness. By analyzing the target vehicle's driving state data and structural characteristics, these parameters can be accurately determined, providing a data foundation for subsequent zero-bias estimation.

[0044] In some possible implementations, the steering geometry parameter is a turning radius of the target vehicle, and the lateral dynamics parameter is a centrifugal effect coefficient of the target vehicle during driving. Figure 2 The figure shows a flow chart of a parameter determination method provided by an exemplary embodiment of the present application. Figure 1 Based on the public content, see Figure 2 , S10 may include the following steps.

[0045] S11 , determining a turning radius of the target vehicle during driving according to the driving state data and structural characteristic data of the target vehicle.

[0046] See also Figure 2 In some possible implementations, S11 may include the following steps.

[0047] S111 , based on a geometric steering model of the target vehicle, according to a front wheel steering angle of the target vehicle and a wheelbase of the target vehicle, determining a turning radius of the target vehicle during driving.

[0048] The target vehicle's geometric steering model describes the geometric relationships between the trajectories of each wheel during steering. This model describes the vehicle's steering geometry based on its basic structural parameters and the angle changes during steering. The target vehicle's wheelbase is the distance between the center of its front and rear wheels.

[0049] In the geometric steering model provided in the embodiment of the present application, the turning radius of the target vehicle is determined according to a first ratio, which is the ratio of the wheelbase of the target vehicle to the tangent of the front wheel turning angle of the target vehicle. For example, the geometric steering model provided in the embodiment of the present application can be: Where R is the turning radius of the target vehicle, is the wheelbase of the target vehicle, is the front wheel turning angle of the target vehicle.

[0050] S12, determining a centrifugal effect coefficient of the target vehicle during the driving process according to the driving state data of the target vehicle and the turning radius of the target vehicle during the driving process.

[0051] See also Figure 2 In some possible implementations, S12 may include the following steps.

[0052] S121, determining the centripetal acceleration of the target vehicle according to the speed of the target vehicle and the turning radius of the target vehicle during driving.

[0053] The centripetal acceleration of a target vehicle refers to the acceleration toward the center of a circular motion. The centripetal acceleration can be calculated using the target vehicle's speed and turning radius. For example, the centripetal acceleration can be determined based on the ratio of the square of the speed to the turning radius.

[0054] S122, calculating the centrifugal effect coefficient of the target vehicle according to the centripetal acceleration and gravitational acceleration of the target vehicle.

[0055] In some possible implementations, the centrifugal effect coefficient of the target vehicle is calculated based on the centripetal acceleration and gravitational acceleration of the target vehicle, including: calculating a second ratio, the second ratio being the ratio of the centripetal acceleration of the target vehicle to the gravitational acceleration; and determining the centrifugal effect coefficient of the target vehicle based on the second ratio. For example, the centrifugal effect coefficient of the target vehicle is The calculation formula is: in, is the speed of the target vehicle, R is the turning radius of the target vehicle during driving, is the acceleration due to gravity.

[0056] The method for determining steering geometry and lateral dynamic parameters provided in the embodiments of the present application analyzes the target vehicle's driving state data and structural characteristics, and utilizes a specific geometric steering model and related formulas to calculate the steering radius and centrifugal effect coefficient. This ensures that the calculated steering radius and centrifugal effect coefficient are accurate and reliable. Furthermore, the steering geometry and lateral dynamic parameters determined in this manner can reflect the target vehicle's steering and lateral motion characteristics during actual driving.

[0057] S20 , determining a lateral position deviation of the target vehicle according to the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving.

[0058] The target vehicle's lateral position deviation refers to the lateral deviation between the target vehicle's actual trajectory and its ideal trajectory. Changes in steering geometry and lateral dynamics alter the vehicle's trajectory, leading to lateral position deviation. Changes in the centrifugal effect coefficient affect the vehicle's lateral stability during driving, similarly causing lateral position deviation.

[0059] Figure 3 FIG. 1 is a flow chart of a method for determining a lateral position deviation provided by an exemplary embodiment of the present application. Figure 1 Based on the public content, see Figure 3 In some possible implementations, S20 may include the following steps.

[0060] S21 : determining a geometric term of a lateral position deviation according to a turning radius of the target vehicle during driving and a wheelbase of the target vehicle.

[0061] The geometric term of the lateral position deviation refers to the lateral position offset caused by the geometric relationship of vehicle steering. The geometric term is used to introduce a first lateral offset caused by geometric steering when the speed of the target vehicle is less than a first speed threshold. The embodiment of the present application does not limit the value range of the first speed threshold, and it can be determined according to actual needs and vehicle dynamics. For example, the value range of the first speed threshold can be 0km / h to 20km / h. In a possible implementation, the first speed threshold can be 10km / h. During the vehicle steering process, the proportional relationship between the turning radius and the wheelbase determines the degree of curvature of the vehicle's driving trajectory, which in turn affects the lateral position deviation. Specifically, if the turning radius is fixed, the larger the wheelbase, the larger the geometric term of the lateral position deviation when the vehicle turns; conversely, the smaller the wheelbase, the smaller the geometric term. Similarly, when the wheelbase remains unchanged, the smaller the turning radius, the sharper the vehicle turns, and the larger the geometric term of the lateral position deviation. In some possible implementations, the geometric term is determined based on a third ratio, which is the ratio of the square of the wheelbase of the target vehicle to the turning radius. For example, the functional relationship of the geometric term of the lateral position deviation is , the definition of parameters is the same as above.

[0062] S22: Determine a centrifugal correction term for the lateral position deviation according to the centrifugal effect coefficient of the target vehicle during driving.

[0063] The centrifugal correction term refers to the lateral position offset caused by the centrifugal force when the vehicle is traveling. The centrifugal correction term is used to introduce a second lateral offset caused by centrifugal force when the speed of the target vehicle is greater than the second speed threshold, and the second speed threshold is greater than or equal to the first speed threshold. The embodiment of the present application does not limit the value range of the second speed threshold, and it can be determined according to actual needs and vehicle dynamics. For example, the value range of the second speed threshold can be greater than or equal to 20km / h. In one possible implementation, the first speed threshold and the second speed threshold are both equal to 20km / h. When a vehicle is traveling at a higher speed and turning, the centrifugal force will cause the vehicle to have a tendency to deviate outward, thereby generating a lateral position deviation. The larger the centrifugal effect coefficient, the greater the influence of the centrifugal force on the driving trajectory of the vehicle, and the larger the centrifugal correction term of the corresponding lateral position deviation. In some possible implementations, the centrifugal correction term is determined based on the sum of the centrifugal effect coefficient and a set constant. For example, the functional relationship of the centrifugal correction term of the lateral position deviation can be , the definition of parameters is the same as above.

[0064] S23, determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term.

[0065] In some possible implementations, determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term includes: calculating the product of the geometric term and the centrifugal correction term; and determining the lateral position deviation of the target vehicle based on the product of the geometric term and the centrifugal correction term.

[0066] The lateral position deviation determination method provided in the embodiments of this application calculates the geometric term and the centrifugal correction term of the lateral position deviation by separately considering the effects of steering geometry and lateral dynamic parameters on the lateral position deviation, thereby determining the lateral position deviation of the target vehicle. This method comprehensively considers multiple factors during vehicle driving and can accurately reflect the deviation of the vehicle's actual driving trajectory from its ideal driving trajectory.

[0067] S30 , determining a zero bias estimation value of the target vehicle according to the lateral position deviation of the target vehicle.

[0068] The target vehicle's zero-bias estimate is an estimate of the zero-bias of the target vehicle's steering system. This value reflects the degree of deviation between the vehicle's steering wheel zero position and the ideal neutral position. By accurately determining the lateral position deviation, the zero-bias estimate can be further inferred. In practice, a specific mathematical model or mapping relationship can be established to link the lateral position deviation with the zero-bias estimate. For example, a mapping table of lateral position deviation and zero-bias estimate can be used to obtain the target vehicle's zero-bias estimate by looking up the table based on the lateral position deviation. However, this method may have certain limitations. Since the mapping table is typically based on specific operating conditions and experimental data, the zero-bias estimate obtained by looking up the table may be inaccurate when the vehicle's actual driving conditions differ from the experimental conditions. To improve the accuracy of the zero-bias estimate, more complex algorithms, such as machine learning-based regression algorithms, can be used. By collecting a large amount of lateral position deviation data under different operating conditions and the corresponding accurate zero-bias values, a regression model can be trained. In practical applications, the real-time lateral position deviation is input into the trained model, which then outputs a more accurate zero-bias estimate.

[0069] The zero bias estimation method of the steering system provided in the embodiment of the present application provides a basis for the adjustment and optimization of the vehicle steering system by determining the zero bias estimation value of the target vehicle. In practical applications, accurate zero bias estimation values ​​help the driver to promptly detect and correct the deviation of the steering system, thereby improving driving safety and comfort. For autonomous driving vehicles, the zero bias estimation value is even more a key factor in ensuring that the vehicle travels along a predetermined trajectory. Based on the zero bias estimation method of the embodiment of the present application, relevant applications can be further expanded. For example, combining the zero bias estimation technology with the vehicle fault diagnosis system can achieve rapid positioning and early warning of steering system faults. When the zero bias estimation value exceeds the normal range, the system can promptly issue an alarm to prompt maintenance personnel to inspect and maintain the steering system to avoid safety accidents caused by steering system faults.

[0070] Figure 4 FIG. 1 is a flow chart of a method for determining a zero bias estimate value provided by an exemplary embodiment of the present application. Figure 1 Based on the public content, see Figure 4 In some possible implementations, S30 may include the following steps.

[0071] S31 , taking the lateral position deviation of the target vehicle as a predicted value of the lateral position deviation of the target vehicle, and calculating a measured value of the lateral position deviation of the target vehicle according to the actual position of the target vehicle and the target position.

[0072] The target vehicle's actual position is its actual location during driving, captured by sensors such as the Global Positioning System and Inertial Measurement Unit (IMU). The target position is the desired location for the vehicle according to its ideal driving trajectory, typically determined by the vehicle's planned or pre-set route. By comparing the target vehicle's actual position, captured by sensors and other means, with the pre-set target position, the measured lateral deviation of the target vehicle can be accurately calculated.

[0073] The predicted value of the lateral position deviation is a lateral position deviation value calculated based on the steering geometry parameters and the lateral dynamic parameters according to the method described above.

[0074] S32, calculating the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

[0075] The residual describes the error between the predicted and measured values. The residual is calculated by taking the difference between the predicted and measured values ​​of the target vehicle's lateral position deviation.

[0076] S33, determining a zero bias estimation value of the target vehicle based on a residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

[0077] For example, the zero bias can be directly estimated by the residual mean within the sliding window.

[0078] In some possible implementations, determining a target vehicle's zero bias estimate based on a residual between a predicted and measured lateral position deviation of the target vehicle includes dynamically adjusting an estimator's forgetting factor based on the target vehicle's speed; and inputting the residual into the estimator to obtain the target vehicle's zero bias estimate. Exemplarily, the estimator may be a Kalman filter-based estimator, in which the zero bias is extended as a state variable to the system state, and the vehicle state and the zero bias are jointly estimated using the Kalman filter.

[0079] In some possible implementations, the estimator is a recursive least squares estimator. In the recursive least squares estimator, the difference between the residual and the zero bias estimate of the previous cycle and the product of the difference and the Kalman gain are calculated; the zero bias estimate of the target vehicle in the current cycle is determined based on the sum of the product of the difference and the Kalman gain and the zero bias estimate of the previous cycle. For example, the calculation formula of the zero bias estimate can be: in, is the zero-bias estimate for the current period, is the zero-biased estimate of the previous period, is the covariance matrix of the previous period, is the covariance matrix of the current period, is the measured value of the lateral position deviation of the target vehicle, is the predicted value of the lateral position deviation of the target vehicle, is the forgetting factor, stands for Kalman gain.

[0080] In some possible implementations, the forgetting factor of the estimator is dynamically adjusted based on the target vehicle's speed, including: if the target vehicle's speed is less than a third speed threshold, determining a first value as the forgetting factor; if the target vehicle's speed is greater than or equal to a fourth speed threshold, determining a second value as the forgetting factor, wherein the third speed threshold is less than or equal to the fourth speed threshold, and the first value is less than the second value. This embodiment of the application does not limit the ranges of the third and fourth speed thresholds, as well as the first and second values. These ranges can be determined based on actual needs and application scenarios, as different needs and scenarios have different requirements for the forgetting factor. For example, the third speed threshold can range from 0 km / h to 20 km / h, and the fourth speed threshold can range from greater than or equal to 20 km / h. In one possible implementation, the first, second, third, and fourth speed thresholds are all equal to 20 km / h. This implementation achieves uniformity for high-speed and low-speed scenarios across different phases. Optionally, in order to improve the accuracy of the zero bias estimate and the lateral position deviation, the high-speed scenes and low-speed scenes in different stages may also be different, which can be achieved by setting different vehicle speed thresholds. For example, the first value may range from 0.90 to 0.98, and the second value may range from 0.95 to 0.99. In one possible implementation, the first value may be 0.95, and the second value may be 0.99. In this way, the forgetting factor is dynamically adjusted according to the vehicle speed, so that the recursive least squares estimator can better adapt to the actual driving conditions of the vehicle under different vehicle speed conditions, thereby improving the accuracy of the zero bias estimate.

[0081] During the zero-bias estimation process, the estimator outputs a zero-bias estimate based on the input residual and the dynamic adjustment of the forgetting factor to account for vehicle speed. The forgetting factor is used in the estimator to control the degree of forgetting of historical data. At low speeds, the vehicle's driving state changes rapidly, and the forgetting factor needs to be reduced, allowing the estimator to rely more on recent data. This data, in this case, better reflects the vehicle's current state, enabling rapid tracking. At higher speeds, the vehicle's driving state is relatively stable, and the forgetting factor can be appropriately increased to allow the estimator to comprehensively consider more historical data, thereby avoiding estimation errors caused by data fluctuations.

[0082] The method for determining the zero bias estimate provided in the embodiment of the present application compares and analyzes the predicted value of the lateral position deviation with the measured value, and introduces the residual to quantify the prediction error. On this basis, the accurate calculation of the zero bias estimate of the target vehicle is achieved by utilizing the estimator and combining the dynamic adjustment of the forgetting factor according to the vehicle speed. This method fully considers the diversity of the vehicle's driving state and the characteristics of the data at different speeds. Compared with the traditional mapping table-based method, it has higher adaptability and accuracy. Specifically, the recursive least squares estimator effectively integrates information such as the residual and the zero bias estimate of the previous cycle by continuously updating the covariance matrix and combining the Kalman gain, making the calculation of the zero bias estimate more accurate. At the same time, the mechanism of dynamically adjusting the forgetting factor according to the vehicle speed further optimizes the performance of the estimator under different driving conditions.

[0083] Figure 5 FIG. 1 is a flow chart of another method for estimating the zero bias of a steering system provided by an exemplary embodiment of the present application. Figure 1 Based on the public content, see Figure 5 , the embodiment of the present application may further include the following steps.

[0084] S40 , determining feedback compensation according to the zero bias estimation value of the target vehicle, and performing lateral control compensation on the target vehicle according to the feedback compensation.

[0085] In this embodiment, feedback compensation refers to the amount of compensation used to adjust the vehicle's driving state, determined based on the zero-bias estimate. Because the compensation depends on the residual between the predicted and measured lateral position deviations, this process forms a closed-loop regulation, hence the name feedback compensation. This feedback compensation value reflects the degree of adjustment required by the steering system to return the target vehicle's trajectory to the desired state.

[0086] In some possible implementations, determining feedback compensation based on the target vehicle's zero bias estimate includes integrating the target vehicle's zero bias estimate and determining the feedback compensation based on the sum of the integral result and the target vehicle's zero bias estimate. For example, the feedback compensation is calculated using the following formula: in, is the feedback compensation, is a zero-biased estimate, and Indicates different coefficients.

[0087] The present embodiment achieves precise adjustment of the target vehicle's trajectory by determining feedback compensation based on the target vehicle's bias estimate and then applying lateral control compensation to the target vehicle based on the feedback compensation. Specifically, by integrating the bias estimate over time and then adding it to the bias estimate itself, the bias estimate more comprehensively reflects both the long-term and short-term effects on the vehicle's trajectory, thereby determining more accurate feedback compensation.

[0088] This feedback compensation mechanism is crucial for autonomous vehicles. In autonomous driving scenarios, vehicles must navigate according to high-precision maps and pre-set routes. However, steering system offsets can cause the vehicle to gradually deviate from its ideal trajectory. By calculating feedback compensation in real time and performing lateral control compensation, autonomous vehicles can promptly correct their course and maintain their intended route, effectively avoiding deviations caused by accumulated offsets and improving the safety and reliability of autonomous driving.

[0089] This feedback compensation also provides strong support for drivers in traditional manually driven vehicles. When the vehicle's steering system exhibits zero bias, the driver may need to constantly adjust the steering wheel to maintain straight driving or turn on the intended trajectory, which increases driving fatigue and operational difficulty. However, the feedback compensation mechanism, based on the zero bias estimate, can automatically fine-tune the vehicle's direction, alleviating the driver's burden.

[0090] Figure 6 FIG. 1 is a flow chart of another method for estimating the zero bias of a steering system provided by an exemplary embodiment of the present application. Figure 1 Based on the public content, see Figure 6 , the embodiment of the present application may further include the following steps.

[0091] S50 , determining a feedforward compensation according to the curvature of the driving track, the speed, and the wheelbase of the target vehicle; and performing lateral control compensation on the target vehicle according to the feedforward compensation.

[0092] Feedforward compensation refers to pre-calculated and applied compensation based on the vehicle's trajectory geometry and dynamic parameters during driving. This compensation aims to preemptively correct for lateral deviations caused by the inherent characteristics of the vehicle's steering system and driving conditions, thereby ensuring the vehicle more accurately follows the intended trajectory. Feedforward compensation is primarily determined by key parameters: trajectory curvature, vehicle speed, and wheelbase. Path curvature reflects the curvature of the vehicle's path. Greater curvature indicates sharper turns, requiring greater feedforward compensation to adjust the steering system and ensure smooth traversal. Vehicle speed is also a significant factor. Higher speeds increase the magnitude of dynamic effects, such as centrifugal force, generated during cornering. Feedforward compensation is required to balance these effects and maintain driving stability. Wheelbase influences the vehicle's steering geometry. Vehicles with different wheelbases require different steering angles and feedforward compensation for the same turning radius.

[0093] In some possible implementations, feedforward compensation is determined based on the curvature, speed, and wheelbase of the target vehicle's driving trajectory, including: calculating a first product, the first product being the product of the target vehicle's wheelbase and the curvature of the driving trajectory; calculating a second product, the second product being the product of the square of the target vehicle's speed and the curvature of the driving trajectory; and determining the feedforward compensation based on the sum of the first product and the second product. Exemplarily, the feedforward compensation calculation formula is as follows: in, is the curvature of the target vehicle’s trajectory, is the speed gain coefficient.

[0094] The embodiment of the present application does not limit the execution order of S50. It can be executed before S10, or after S30, or before any step from S10 to S30.

[0095] The technical solution of the embodiment of the present application achieves multi-dimensional optimization of vehicle lateral control by determining feedforward compensation based on the curvature, speed, and wheelbase of the target vehicle's driving trajectory; and performing lateral control compensation on the target vehicle based on the feedforward compensation. The feedforward compensation mechanism can predict possible lateral deviations during vehicle driving. Compared with relying solely on feedback compensation, this early intervention method can adjust the vehicle steering system more promptly, improving the accuracy and stability of vehicle driving. In actual application scenarios, whether on complex urban roads or highways, the vehicle's driving trajectory and speed are constantly changing. Feedforward compensation can quickly adjust the compensation amount based on the real-time driving trajectory curvature, speed, and the vehicle's own wheelbase parameters, allowing the vehicle to better adapt to various driving conditions. For example, before the vehicle enters a curve, the system calculates the appropriate feedforward compensation amount in advance based on the detected curve curvature and current speed, and actively adjusts the steering system to help the vehicle enter the curve with a more stable posture, reducing safety risks caused by understeer or oversteer.

[0096] Figure 7 FIG2 is a flow chart of another method for estimating the zero bias of a steering system provided by an exemplary embodiment of the present application. Figure 7 Another zero bias estimation method for a steering system provided in an embodiment of the present application includes the following steps.

[0097] S60: Determine feedback compensation for the target vehicle.

[0098] S70: Determine the feedforward compensation of the target vehicle.

[0099] S80: Perform lateral control compensation on the target vehicle based on the feedback compensation and feedforward compensation of the target vehicle.

[0100] The determination of feedback compensation and feedforward compensation is the same as described in the above embodiment, which will not be described in detail in this embodiment. This embodiment does not limit the execution order of S60 and S70. Optionally, S70 can be executed before S60.

[0101] For example, based on the feedback compensation and feedforward compensation of the target vehicle, the formula for performing lateral control compensation on the target vehicle may be: + in, is the front wheel turning angle of the target vehicle, is the feedforward compensation, Compensation for feedback.

[0102] To ensure the stability of the vehicle's lateral control, the technical solution of the embodiment of the present application monitors the target vehicle in real time. By combining feedforward compensation with feedback compensation, the feedforward amount of zero-bias compensation is calculated using path curvature, and the feedback amount of zero-bias compensation is calculated using recursive least squares. This allows for a rapid and accurate expression of the vehicle's lateral zero-bias amplitude, and accordingly compensates for the vehicle's steering wheel zero-bias, to achieve precise tracking of the target vehicle's lateral control and achieve comprehensive optimization of the target vehicle's lateral control. This combination fully leverages the advantages of feedforward compensation's advance prediction and feedback compensation's real-time correction, enabling the target vehicle to maintain high driving accuracy and stability under various driving conditions.

[0103] Figure 8 FIG2 is a schematic diagram showing the structure of a zero bias estimation device for a steering system provided by an exemplary embodiment of the present application. Figure 8 A zero bias estimation device 800 for a steering system provided in an embodiment of the present application includes: a first determination module 801, used to determine the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural characteristic data of the target vehicle; a second determination module 802, used to determine the lateral position deviation of the target vehicle based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving; and a third determination module 803, used to determine the zero bias estimation value of the target vehicle based on the lateral position deviation of the target vehicle.

[0104] The first determination module 801 is further used to: determine the turning radius of the target vehicle during driving based on the driving state data and structural characteristic data of the target vehicle; and determine the centrifugal effect coefficient of the target vehicle during driving based on the driving state data of the target vehicle and the turning radius of the target vehicle during driving.

[0105] The first determination module 801 is also used to: determine the turning radius of the target vehicle during driving based on the geometric steering model of the target vehicle, according to the front wheel turning angle of the target vehicle and the wheelbase of the target vehicle; in the geometric steering model, the turning radius of the target vehicle is determined according to a first ratio, and the first ratio is the ratio of the wheelbase of the target vehicle to the tangent value of the front wheel turning angle of the target vehicle.

[0106] The first determination module 801 is further configured to: determine the centripetal acceleration of the target vehicle according to the speed of the target vehicle and the turning radius of the target vehicle during driving; and calculate the centrifugal effect coefficient of the target vehicle according to the centripetal acceleration and the acceleration of gravity of the target vehicle.

[0107] The first determination module 801 is further configured to: calculate a second ratio, where the second ratio is the ratio of the centripetal acceleration of the target vehicle to the acceleration due to gravity; and determine the centrifugal effect coefficient of the target vehicle according to the second ratio.

[0108] The second determination module 802 is further configured to: determine a geometric term for the lateral position deviation based on the turning radius and wheelbase of the target vehicle during travel, the geometric term being used to introduce a first lateral offset caused by geometric steering when the target vehicle's speed is less than a first speed threshold; determine a centrifugal correction term for the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during travel, the centrifugal correction term being used to introduce a second lateral offset caused by centrifugal force when the target vehicle's speed is greater than a second speed threshold, where the second speed threshold is greater than or equal to the first speed threshold; and determine the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term. The geometric term is determined based on a third ratio, which is the ratio of the square of the target vehicle's wheelbase to the turning radius; and the centrifugal correction term is determined based on the sum of the centrifugal effect coefficient and a set constant.

[0109] The second determination module 802 is further configured to: calculate the product of the geometric term and the centrifugal correction term; and determine the lateral position deviation of the target vehicle according to the product of the geometric term and the centrifugal correction term.

[0110] The third determination module 803 is also used to: use the lateral position deviation of the target vehicle as the predicted value of the lateral position deviation of the target vehicle, calculate the measured value of the lateral position deviation of the target vehicle based on the actual position of the target vehicle and the target position; calculate the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle; and determine the zero bias estimate of the target vehicle based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

[0111] The third determination module 803 is further configured to dynamically adjust the forgetting factor of the estimator based on the speed of the target vehicle; input the residual into the estimator to obtain a zero-bias estimate of the target vehicle. In some possible implementations, the estimator is a recursive least squares estimator. In the recursive least squares estimator, the difference between the residual and the zero-bias estimate in the previous cycle and the product of the difference and the Kalman gain are calculated; and the zero-bias estimate of the target vehicle in the current cycle is determined based on the sum of the product of the difference and the Kalman gain and the zero-bias estimate in the previous cycle.

[0112] The third determination module 803 is further used to: if the speed of the target vehicle is less than the third speed threshold, determine a first value as the forgetting factor; if the speed of the target vehicle is greater than or equal to the fourth speed threshold, determine a second value as the forgetting factor, wherein the third speed threshold is less than or equal to the fourth speed threshold, and the first value is less than the second value.

[0113] The apparatus provided in the embodiment of the present application further includes a first compensation module, which is configured to: determine feedback compensation based on a zero bias estimate of the target vehicle; and perform lateral control compensation on the target vehicle based on the feedback compensation.

[0114] In some possible implementations, the first compensation module is further configured to: integrate the zero bias estimate of the target vehicle, and determine feedback compensation according to a sum of the integration result and the zero bias estimate of the target vehicle.

[0115] The device provided in the embodiment of the present application also includes a second compensation module, which is used to: determine feedforward compensation based on the curvature of the driving trajectory, the speed and the wheelbase of the target vehicle; and perform lateral control compensation on the target vehicle based on the feedforward compensation.

[0116] In some possible implementations, the second compensation module is further used to: calculate a first product, where the first product is the product of the wheelbase of the target vehicle and the curvature of the driving trajectory; calculate a second product, where the second product is the product of the square of the speed of the target vehicle and the curvature of the driving trajectory; and determine feedforward compensation based on the sum of the first product and the second product.

[0117] The steering system zero-bias estimation device provided in embodiments of the present application, through the close collaboration of first, second, and third determination modules, can comprehensively and accurately determine the target vehicle's zero-bias estimate. The first determination module calculates steering geometry and lateral dynamic parameters based on the vehicle's driving state data and structural characteristics, laying the foundation for subsequent zero-bias estimation. For example, it uses complex formulas to determine the turning radius and centrifugal effect coefficient, which reflect the vehicle's geometric and dynamic characteristics during driving. The second determination module further determines the lateral position deviation based on the parameters derived by the first determination module. This embodiment of the present application considers the effects of geometric steering and centrifugal force on vehicle lateral offset under different vehicle speed conditions, calculating the geometric term and the centrifugal correction term separately to determine the lateral position deviation. This comprehensive consideration of various factors makes the determination of lateral position deviation more consistent with actual driving conditions. The third determination module, based on the lateral position deviation, compares the predicted value with the measured value, calculates the residual, and ultimately determines the zero-bias estimate. Furthermore, it dynamically adjusts the forgetting factor based on vehicle speed to meet the accuracy requirements of zero-bias estimation at different driving speeds.

[0118] Simultaneously, the first and second compensation modules implement timely lateral control compensation after determining the zero-bias estimate and feedforward compensation. The collaborative operation of these two compensation modules, combining the advantages of feedback and feedforward compensation, improves the accuracy and stability of the vehicle's lateral control.

[0119] Figure 9 The figure shows a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present application. Figure 9As shown, the electronic device 100 includes: a data acquisition unit 101, a processor 102 and a memory 103. The electronic device 100 can be a vehicle-side controller, such as a steering controller or a domain controller on a vehicle, or a remote server in communication with the vehicle.

[0120] The data acquisition unit 101 is connected to the vehicle-side sensors and is used to acquire the target vehicle's driving status data. Optionally, it can also acquire the target vehicle's structural characteristic data. Driving status data includes information such as vehicle speed and front wheel angle, which reflect the vehicle's current operating status in real time. For example, changes in vehicle speed directly affect the vehicle's dynamic characteristics, while the front wheel angle reflects the vehicle's steering operation. Structural characteristic data includes parameters such as the vehicle's wheelbase. As an inherent structural parameter of the vehicle, the wheelbase plays a key role in the vehicle's steering geometry.

[0121] Processor 102 is connected to data acquisition unit 101 and memory 103 and is configured to execute instructions stored in memory 103 to implement the aforementioned zero-bias estimation method for the steering system. Processor 102 acts as the "brain" of the electronic device, performing complex calculations based on various acquired data and pre-set algorithms and logic. For example, it calculates steering geometry and lateral dynamic parameters based on driving state data and structural feature data, thereby determining lateral position deviation and zero-bias estimates. Simultaneously, based on the calculation results, processor 102 collaborates with the first and second compensation modules to implement lateral control compensation for the vehicle, ensuring vehicle stability. Memory 103 stores the data and program code required by processor 102 to execute instructions. It acts as the "memory warehouse" of the electronic device, providing essential support for the operation of processor 102. The stored data includes not only various initial vehicle parameters but also continuously updated driving state data and intermediate calculation results during vehicle operation. Initial parameters are the basis for feedforward compensation and zero-bias estimation calculations. Real-time storage of driving state data facilitates the processor 102's access to these parameters at any time, enabling real-time analysis and control of the vehicle's status.

[0122] The data acquisition unit 101 continuously transmits driving status data and structural feature data to the processor 102. Based on this data, the processor 102 executes the corresponding zero-bias estimation method. This process involves a large number of complex mathematical operations and logical judgments. For example, when calculating the turning radius, it is necessary to accurately calculate it based on the geometric steering model and the relationship between the front wheel angle and wheelbase. When determining the centrifugal effect coefficient, it is necessary to combine multiple data such as vehicle speed, centripetal acceleration, and gravitational acceleration.

[0123] At the same time, the electronic device 100 communicates with the vehicle. If the electronic device 100 is a remote server, it exchanges data with the vehicle via a communication link. It receives driving status data and structural characteristic data from the vehicle and sends calculated control instructions, such as specific values ​​for feedforward and feedback compensation, to the vehicle to achieve precise adjustment of the vehicle's lateral control.

[0124] Furthermore, the performance and reliability of the electronic device 100 play a crucial role in the vehicle's steering system zero-bias estimation and lateral control compensation. The high-performance processor 102 can rapidly process large amounts of data, ensuring the real-time and accurate calculation results. The stable and reliable memory 103 ensures secure data storage and rapid access, preventing control errors caused by data loss or read errors. For example, during extended periods of vehicle travel, the memory 103 needs to operate continuously and stably, providing accurate data support to the processor 102 to ensure that the vehicle's steering system zero-bias estimation and lateral control compensation are always optimal.

[0125] With the continuous development of vehicle technology, the performance and functional requirements of the electronic device 100 are also constantly increasing. In the future, the electronic device 100 may integrate more advanced sensor data fusion technology, which can not only process existing data such as vehicle speed, front wheel angle, wheelbase, etc., but also integrate more data from body posture sensors, road condition sensors, etc., so as to more comprehensively and accurately estimate the zero bias and compensate for the lateral control of the vehicle steering system, further improving the safety and comfort of vehicle driving. At the same time, in order to adapt to the needs and application scenarios of different vehicles, the electronic device 100 may have stronger scalability and flexibility, and can be personalized according to the type and configuration of the vehicle. For example, for vehicles with different wheelbases and different power performance, the electronic device 100 can automatically adjust the relevant algorithms and parameters to achieve the best control effect.

[0126] The memory 103 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 102 may execute the program instructions to implement the zero-bias estimation method for the steering system according to various embodiments of the present application described above and / or other desired functions.

[0127] In certain implementations, processor 102 is connected to a display, which controls the display to display the vehicle's current zero-bias estimate, allowing the driver to intuitively understand the status of the vehicle's steering system and identify potential problems in advance. For example, when the zero-bias estimate exceeds the normal range, the driver can take timely measures to prevent safety accidents caused by steering system failures. The display can also display predicted and measured lateral position deviations, helping technicians analyze lateral excursions during vehicle operation and providing data support for optimizing vehicle steering performance. Furthermore, the display displays specific values ​​for feedback compensation and feedforward compensation, allowing the operator to clearly understand the intensity of the vehicle's lateral control compensation.

[0128] In some embodiments, the electronic device may further include: an input device and an output device, and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0129] The input device may include, for example, a touch screen, a keyboard, a mouse, etc. The output device may output filtered point cloud data to the outside, etc. The output device may include a display, a vehicle-mounted terminal, a communication network and its connected remote output device, etc.

[0130] Of course, to simplify, Figure 9 Only some of the components related to the present application in the electronic device 100 are shown, and components such as input devices, output devices, buses, input / output interfaces, etc. are omitted. In addition, the electronic device 100 may further include any other appropriate components according to specific application scenarios.

[0131] The embodiments of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the processor executes the steps of the zero bias estimation method of the steering system according to various embodiments of the present application described above in this specification.

[0132] The computer program product may be written in any combination of one or more programming languages ​​to implement the program code of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0133] In addition, an embodiment of the present application may also be a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processor executes the steps of the zero bias estimation method of the steering system according to various embodiments of the present application described above in this specification.

[0134] Computer-readable storage media can take the form of any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0135] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0136] The block diagrams of the devices, devices, equipment, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0137] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0138] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0139] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A zero bias estimation method for a steering system, characterized in that: include: determining steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural characteristic data of the target vehicle; determining a lateral position deviation of the target vehicle according to steering geometry parameters and lateral dynamic parameters of the target vehicle during driving; Taking the lateral position deviation of the target vehicle as a predicted value of the lateral position deviation of the target vehicle, and calculating a measured value of the lateral position deviation of the target vehicle according to the actual position and the target position of the target vehicle; Calculating the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle; and determining the zero bias estimate of the target vehicle based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

2. The method according to claim 1, characterized in that The determining, based on the driving state data and the structural characteristic data of the target vehicle, the steering geometry parameters and the lateral dynamic parameters of the target vehicle during driving, includes: Determining a turning radius of the target vehicle during driving according to the driving state data and structural characteristic data of the target vehicle; The centrifugal effect coefficient of the target vehicle during the driving process is determined according to the driving state data of the target vehicle and the turning radius of the target vehicle during the driving process.

3. The method according to claim 2, characterized in that Determining the turning radius of the target vehicle during driving according to the driving state data and the structural characteristic data of the target vehicle includes: Determining a turning radius of the target vehicle during driving based on a geometric steering model of the target vehicle and a front wheel turning angle of the target vehicle and a wheelbase of the target vehicle; In the geometric steering model, the turning radius of the target vehicle is determined according to a first ratio, where the first ratio is a ratio of a wheelbase of the target vehicle to a tangent value of a front wheel turning angle of the target vehicle.

4. The method according to claim 2, characterized in that Determining the centrifugal effect coefficient of the target vehicle during driving according to the driving state data of the target vehicle and the turning radius of the target vehicle during driving includes: Determining the centripetal acceleration of the target vehicle according to the speed of the target vehicle and the turning radius of the target vehicle during driving; Calculating the centrifugal effect coefficient of the target vehicle according to the centripetal acceleration and gravitational acceleration of the target vehicle; Calculating the centrifugal effect coefficient of the target vehicle according to the centripetal acceleration and gravitational acceleration of the target vehicle includes: calculating a second ratio, where the second ratio is a ratio of the centripetal acceleration of the target vehicle to the acceleration due to gravity; The centrifugal effect coefficient of the target vehicle is determined according to the second ratio.

5. The method according to claim 1, characterized in that Determining the lateral position deviation of the target vehicle according to the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving includes: determining, based on a turning radius of the target vehicle during travel and a wheelbase of the target vehicle, a geometric term of the lateral position deviation, the geometric term being used to introduce a first lateral offset caused by geometric steering when a speed of the target vehicle is less than a first speed threshold; determining, based on a centrifugal effect coefficient of the target vehicle during travel, a centrifugal correction term for the lateral position deviation, the centrifugal correction term being used to introduce a second lateral offset caused by centrifugal force when a speed of the target vehicle is greater than a second speed threshold, where the second speed threshold is greater than or equal to the first speed threshold; determining a lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term; The geometric term is determined according to a third ratio, wherein the third ratio is a ratio of the square of the wheelbase of the target vehicle to the turning radius; The centrifugal correction term is determined based on the sum of the centrifugal effect coefficient and a set constant; Determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term includes: calculating the product of the geometric term and the eccentricity correction term; A lateral position deviation of the target vehicle is determined according to a product of the geometric term and the centrifugal correction term.

6. The method according to claim 1, characterized in that Determining a zero bias estimate of the target vehicle based on a residual between a predicted value and a measured value of the lateral position deviation of the target vehicle comprises: Dynamically adjusting the forgetting factor of the estimator according to the speed of the target vehicle; Inputting the residual into the estimator to obtain a zero-bias estimate of the target vehicle; The estimator is a recursive least squares estimator, in which a difference between the residual and a zero-bias estimate of a previous cycle and a product of the difference and a Kalman gain are calculated; Determine a zero bias estimate of the target vehicle in a current cycle based on a sum of a product of the difference and a Kalman gain and a zero bias estimate of the previous cycle; The dynamically adjusting the forgetting factor of the estimator according to the speed of the target vehicle includes: If the speed of the target vehicle is less than a third speed threshold, determining a first value as the forgetting factor; If the speed of the target vehicle is greater than or equal to a fourth speed threshold, a second value is determined as the forgetting factor, wherein the third speed threshold is less than or equal to the fourth speed threshold, and the first value is less than the second value.

7. The method according to claim 1, characterized in that The method further comprises: determining feedback compensation according to the zero bias estimate of the target vehicle; performing lateral control compensation on the target vehicle according to the feedback compensation; The step of determining feedback compensation according to the zero bias estimation value of the target vehicle includes: The zero bias estimation value of the target vehicle is integrated, and the feedback compensation is determined according to a summation result of the integration result and the zero bias estimation value of the target vehicle.

8. The method according to claim 1, characterized in that The method further comprises: Determining feedforward compensation according to the curvature of the driving trajectory, the speed and the wheelbase of the target vehicle; performing lateral control compensation on the target vehicle according to the feedforward compensation; The determining of the feedforward compensation according to the curvature of the driving track, the speed and the wheelbase of the target vehicle comprises: Calculating a first product, where the first product is the product of the wheelbase of the target vehicle and the curvature of the driving trajectory; Calculating a second product, where the second product is the product of the square of the speed of the target vehicle and the curvature of the driving trajectory; The feedforward compensation is determined according to a summation result of the first product and the second product.

9. A zero bias estimation device for a steering system, characterized in that: include: a first determining module, configured to determine steering geometry parameters and lateral dynamic parameters of the target vehicle during driving according to driving state data and structural characteristic data of the target vehicle; a second determining module, configured to determine a lateral position deviation of the target vehicle according to steering geometry parameters and lateral dynamic parameters of the target vehicle during driving; The third determination module is used to use the lateral position deviation of the target vehicle as the predicted value of the lateral position deviation of the target vehicle, calculate the measured value of the lateral position deviation of the target vehicle according to the actual position and target position of the target vehicle; calculate the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle; and determine the zero bias estimate of the target vehicle according to the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

10. An electronic device, characterized in that: include: A data acquisition unit, configured to acquire driving status data of the target vehicle; a processor, connected to the data acquisition unit, configured to calculate a zero bias estimation value of the target vehicle based on the zero bias estimation method for the steering system according to any one of claims 1 to 8, and perform zero bias compensation on the target vehicle based on the zero bias estimation value; A memory is connected to the processor and is used to store executable instructions of the processor.

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