Zero bias estimation method and device for steering system, and electronic equipment

Abstract

This application provides a method, apparatus, and electronic device for zero-bias estimation of a steering system, relating to the fields of smart mining, autonomous driving, and unmanned vehicles. The zero-bias estimation method for a steering system provided in this application includes: determining the steering geometry parameters and lateral dynamic parameters of the target vehicle during its driving process based on the vehicle's driving state data and structural feature data; determining the lateral position deviation of the target vehicle based on the steering geometry parameters and lateral dynamic parameters during its driving process; and determining the zero-bias estimate of the target vehicle based on the lateral position deviation, thereby achieving real-time estimation of the zero-bias of the vehicle's front wheel steering angle.

Description

Technical Field

[0001] This application relates to the fields of smart mining, autonomous driving, and unmanned vehicle technology, specifically to a method and device for zero bias estimation of a steering system, and electronic equipment. Background Technology

[0002] When a vehicle operates for extended periods or lacks regular maintenance, factors such as mechanical wear, sensor drift, or hydraulic system performance degradation can cause the steering wheel to exhibit zero-skewness. Zero-skewness results in a deviation between the actual steering angle and the control system's desired value. Without zero-skewness estimation and compensation, the vehicle's lateral control accuracy will be affected. In severe cases, it may cause the vehicle to deviate from its intended trajectory, compromising driving safety. Summary of the Invention

[0003] This application provides a method, apparatus, and electronic device for zero-bias estimation of a steering system, so as to achieve real-time estimation of zero-bias of the front wheel steering angle of a vehicle.

[0004] In a first aspect, embodiments of this application provide a method for zero-bias estimation of a steering system, comprising: determining the steering geometric parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural feature data of the target vehicle; determining the lateral position deviation of the target vehicle based on the steering geometric parameters and lateral dynamic parameters of the target vehicle during driving; and determining the zero-bias estimate of the target vehicle based on the lateral position deviation of the target vehicle.

[0005] In conjunction 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 target vehicle's driving state data and structural characteristic data, including: determining the target vehicle's turning radius during driving based on the target vehicle's driving state data and structural characteristic data; and determining the target vehicle's centrifugal effect coefficient during driving based on the target vehicle's driving state data and turning radius during driving.

[0006] In conjunction with the first aspect, in some possible implementations, the turning radius of the target vehicle during driving is determined based on the driving state 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 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, which is the ratio of the wheelbase of the target vehicle to the tangent of the front wheel angle of the target vehicle.

[0007] In conjunction with the first aspect, in some possible implementations, the centrifugal effect coefficient of the target vehicle during its driving process is determined based on the target vehicle's driving state data and its turning radius during driving. This includes: determining the centripetal acceleration of the target vehicle based on its speed and turning radius during driving; and calculating the centrifugal effect coefficient of the target vehicle based on its centripetal acceleration and gravitational acceleration.

[0008] In conjunction 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, which is the ratio of the centripetal acceleration to the gravitational acceleration of the target vehicle; and determining the centrifugal effect coefficient of the target vehicle based on the second ratio.

[0009] In conjunction 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. This includes: determining a geometric term for the lateral position deviation based on the turning radius and wheelbase of the target vehicle during driving; the geometric term is used to introduce a first lateral offset caused by geometric steering when the vehicle speed is less than a first speed threshold; determining a centrifugal correction term for the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during driving; the centrifugal correction term is used to introduce a second lateral offset caused by centrifugal force when the vehicle speed is greater than a second speed threshold, the second speed threshold being greater than or equal to the first speed threshold; and determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term.

[0010] In conjunction with the first aspect, in some possible implementations, the geometric term is determined based on the 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 a set constant.

[0011] In conjunction 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 conjunction with the first aspect, in some possible implementations, determining the zero-bias estimate of the target vehicle based on its lateral position deviation includes: using the lateral position deviation of the target vehicle as a predicted value of its lateral position deviation; calculating the measured value of the lateral position deviation of the target vehicle based on its actual position and target position; calculating the residual between the predicted and measured values ​​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 and measured values ​​of the lateral position deviation of the target vehicle.

[0013] In conjunction 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 and measured values ​​of the lateral position deviation of the target vehicle. This includes: dynamically adjusting the forgetting factor of the estimator based on the vehicle speed of the target vehicle; and inputting the residual into the estimator to obtain the zero-bias estimate of the target vehicle.

[0014] In conjunction 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 period is calculated, as well as the product of the difference and the Kalman gain. Based on the sum of the product of the difference and the Kalman gain and the zero-bias estimate of the previous period, the zero-bias estimate of the target vehicle in the current period is determined.

[0015] In conjunction 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, then a first value is determined as the forgetting factor; if the speed of the target vehicle is greater than or equal to a fourth speed threshold, then 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.

[0016] In conjunction with the first aspect, some possible implementation methods also include: determining feedback compensation based on the zero-bias estimate of the target vehicle; and performing lateral control compensation on the target vehicle based on the feedback compensation.

[0017] In conjunction with the first aspect, in some possible implementation methods, 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 conjunction with the first aspect, some possible implementation methods also include: determining feedforward compensation based on the curvature of the target vehicle's trajectory, vehicle speed, and wheelbase; and performing lateral control compensation on the target vehicle based on the feedforward compensation.

[0019] In conjunction with the first aspect, in some possible implementations, feedforward compensation is determined based on the curvature of the target vehicle's trajectory, vehicle speed, and wheelbase. This includes: calculating a first product, which is the product of the target vehicle's wheelbase and the curvature of its trajectory; calculating a second product, which is the product of the square of the target vehicle's speed and the curvature of its trajectory; and determining the feedforward compensation based on the sum of the first and second products.

[0020] Secondly, embodiments of this application also provide a zero-bias estimation device for a steering system, comprising: a first determining module, configured to determine the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the driving state data and structural feature data of the target vehicle; a second determining module, configured 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 determining module, configured to determine the zero-bias estimate of the target vehicle based on the lateral position deviation of the target vehicle.

[0021] Thirdly, embodiments of this application also provide an electronic device, including: 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 for the steering system mentioned in the first aspect of embodiments of this application; and a memory connected to the processor for storing executable instructions of the processor.

[0022] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program for executing the zero-bias estimation method for the steering system mentioned in the first aspect of embodiments of this application.

[0023] The technical solution of this application determines the lateral position deviation of the target vehicle based on its steering geometry parameters and lateral dynamic parameters during driving. Based on this lateral position deviation, a zero-bias estimate of the target vehicle is determined, enabling real-time estimation of the front wheel steering angle zero-bias. This effectively solves the problem of lateral control accuracy being affected by steering wheel zero-bias. Furthermore, the technical solution of this application automatically estimates the zero-bias of the target vehicle based on its driving state data and structural feature data, overcoming the problem that conventional zero-bias estimation methods require parameter calibration, incurring additional manpower and time costs.

[0024] Furthermore, the technical solution of this application embodiment 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 efficiency. Attached Figure Description

[0025] Figure 1 The diagram shown is a flowchart illustrating a zero-bias estimation method for a steering system provided in an exemplary embodiment of this application.

[0026] Figure 2 The diagram shown is a flowchart illustrating a parameter determination method provided in an exemplary embodiment of this application.

[0027] Figure 3The diagram shown is a flowchart illustrating a method for determining lateral position deviation provided in an exemplary embodiment of this application.

[0028] Figure 4 The diagram shown is a flowchart illustrating a method for determining a zero-biased estimate provided in an exemplary embodiment of this application.

[0029] Figure 5 The diagram shown is a flowchart illustrating another zero-bias estimation method for a steering system provided in an exemplary embodiment of this application.

[0030] Figure 6 The diagram shown is a flowchart illustrating another zero-bias estimation method for a steering system provided in an exemplary embodiment of this application.

[0031] Figure 7 The diagram shown is a flowchart illustrating another zero-bias estimation method for a steering system provided in an exemplary embodiment of this application.

[0032] Figure 8 The diagram shown is a structural schematic of a zero-bias estimation device for a steering system provided in an exemplary embodiment of this application.

[0033] Figure 9 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0034] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0035] Steering wheel zero-bias refers to a situation where the vehicle does not travel in a straight line when the steering wheel is at zero. Zero position is the ideal neutral position of the steering wheel, that is, the position the steering wheel should be in when the vehicle is traveling in a straight line. Steering wheel zero-bias is transmitted to the front wheels through the steering gear ratio, resulting in zero-bias in the front wheel steering angle. Zero-bias in the front wheel steering angle refers to the deviation between the actual steering angle of the front wheels and the ideal zero position (straight-line position). This zero-bias in the front wheel steering angle will 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-bias will cause deviation in control commands, and the assist curve of electric power steering may become inaccurate due to zero-bias.

[0036] Traditional zero-bias estimation methods typically rely on a fixed mapping between steering wheel angle and front wheel angle, calculating lateral position deviation through a lookup table. The core problems with this method are: High dependence on manual calibration: A static mapping table of steering wheel angle and front wheel angle needs to be established beforehand, and the calibration process must be conducted under specific conditions (such as a dedicated test track), consuming significant manpower and time. Parameter inaccuracies: After long-term vehicle use, mechanical wear of the steering system, changes in suspension geometry (such as tire wear and four-wheel alignment misalignment), or environmental factors (such as hardening of rubber bushings due to low temperatures) can all cause deviations between calibrated and actual parameters, thus introducing zero-bias estimation errors. Insufficient adaptability: Static parameter tables cannot dynamically respond to changes in vehicle conditions (such as load changes and differences in road surface adhesion coefficients), leading to decreased estimation accuracy in complex scenarios.

[0037] The technical solution of this application embodiment achieves zero-bias estimation by fusing vehicle driving state data and structural feature data to dynamically calculate steering geometry parameters and lateral dynamic parameters. Compared with traditional methods, the technical solution provided by this application embodiment can achieve the following effects: Calibration-free design: It does not rely on manually preset static parameter tables, but automatically fits parameters through real-time data-driven algorithms (such as least squares method and state observer), significantly reducing deployment costs. Dynamic adaptability: Based on actual vehicle operating data, parameters are updated online, 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 optimizes the accuracy of zero-bias estimation, which is especially suitable for high-precision control scenarios such as autonomous driving.

[0038] In practical applications, the working environment for unmanned mining trucks is typically harsh, leading to faster wear and tear on vehicle components and a greater likelihood of steering wheel deviation. The technical solution of this application enables unmanned mining trucks to accurately estimate the zero deviation value in real time and provide timely feedback and feedforward compensation, effectively preventing deviations from the predetermined trajectory due to zero deviation and ensuring efficient mining operations. Furthermore, in the overall layout of a smart mine, multiple unmanned mining trucks operate collaboratively. If one truck deviates from its trajectory due to zero deviation, it not only affects its own operational efficiency but may also threaten the safety of surrounding vehicles. The zero deviation estimation method and related technical solutions of this application ensure the stability and accuracy of each vehicle's driving, contributing to improved operational efficiency and safety of the entire smart mine system. From a broader perspective of autonomous driving, different types of unmanned vehicles, such as unmanned buses and logistics delivery vehicles, while operating in different environments than unmanned mining trucks, may also face the problem of steering wheel deviation. The technical solution of this application is universal and can be applied to different types of vehicles.

[0039] The technical solutions of this application embodiment can be applied to remote servers or vehicle-side controllers. Optionally, the vehicle-side controller can be a steering controller or a domain controller. For example, if applied to a vehicle's steering controller, the steering controller can use the technical solutions of this application embodiment to acquire real-time vehicle driving status data and structural characteristic data, thereby accurately calculating steering geometric parameters, lateral dynamic parameters, and lateral position deviation, and quickly obtaining a zero-bias estimate. Based on this, the steering controller can adjust steering commands in a timely manner according to the zero-bias estimate, achieving precise compensation or early warning for vehicle lateral control.

[0040] Figure 1 The diagram shown is a flowchart illustrating a zero-bias estimation method for a steering system provided in an exemplary embodiment of this application. See also... Figure 1 This application provides a method for zero bias estimation of a steering system, which includes the following steps.

[0041] S10, based on the target vehicle's driving status data and structural characteristic data, determine the target vehicle's steering geometry parameters and lateral dynamic parameters during driving.

[0042] The target vehicle is the vehicle to be subjected to zero-bias estimation. This application embodiment does not limit the type of the target vehicle. Optionally, the target vehicle can be a passenger car or a commercial vehicle, an autonomous vehicle or a manually driven vehicle; for example, the target vehicle is an autonomous mining truck. The target vehicle's driving state data is relevant data describing the target vehicle's state during driving, such as vehicle speed. The structural feature data is data related to the target vehicle's structure, such as wheelbase.

[0043] Steering geometry parameters are geometric characteristic parameters related to vehicle steering. For example, steering geometry parameters may include steering radius and / or steering diameter. Lateral dynamic parameters are parameters describing the lateral motion characteristics of the vehicle. For example, lateral dynamic parameters may include slip angle and slip stiffness, or centrifugal effect coefficient. The centrifugal effect coefficient can be determined based on the product of slip angle and slip stiffness. By analyzing the target vehicle's driving state data and structural characteristic data, these parameters can be accurately determined, providing a data basis for subsequent zero-bias estimation.

[0044] In some possible implementations, the steering geometry parameter is the turning radius of the target vehicle, and the lateral dynamics parameter is the centrifugal effect coefficient of the target vehicle during driving. Figure 2 The diagram shown is a flowchart illustrating a parameter determination method provided in an exemplary embodiment of this application. Figure 1 Based on publicly available information, see [link / reference] Figure 2 S10 may include the following steps.

[0045] S11, Based on the target vehicle's driving status data and structural characteristic data, determine the target vehicle's turning radius during driving.

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

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

[0048] The geometric steering model of a target vehicle refers to the geometric relationship model of the motion trajectory of each wheel during steering. This model describes the geometric characteristics of vehicle steering based on the basic structural parameters of the target vehicle and the angle changes during steering. The wheelbase of the target vehicle is the distance between the centers of the front and rear wheels.

[0049] In the geometric steering model provided in this application embodiment, 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 steering angle of the target vehicle. For example, the geometric steering model provided in this application embodiment can be:

[0050]

[0051] Where R is the turning radius of the target vehicle. It is the wheelbase of the target vehicle. It is the steering angle of the front wheels of the target vehicle.

[0052] S12, Based on the driving status data of the target vehicle and the turning radius of the target vehicle during driving, determine the centrifugal effect coefficient of the target vehicle during driving.

[0053] See Figure 2 In some possible implementations, S12 may include the following steps.

[0054] S121, Determine the centripetal acceleration of the target vehicle based on its speed and turning radius during operation.

[0055] The centripetal acceleration of a target vehicle refers to the acceleration directed towards the center of the circle when the target vehicle is making circular motion. Centripetal acceleration can be calculated from the vehicle's speed and turning radius. For example, centripetal acceleration can be determined based on the ratio of the square of the vehicle speed to the turning radius.

[0056] S122, Calculate the centrifugal effect coefficient of the target vehicle based on its centripetal acceleration and gravitational acceleration.

[0057] In some possible implementations, calculating the centrifugal effect coefficient of the target vehicle based on its centripetal acceleration and gravitational acceleration includes: calculating a second ratio, which is the ratio of the centripetal acceleration to the gravitational acceleration of the target vehicle; 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... The calculation formula is:

[0058]

[0059] in, R is the target vehicle's speed, and R is the target vehicle's turning radius during its journey. It is gravitational acceleration.

[0060] The method for determining steering geometry and lateral dynamics parameters provided in this application analyzes the driving state data and structural feature data of the target vehicle, and calculates the steering radius and centrifugal effect coefficient using a specific geometric steering model and related formulas, ensuring that the calculated steering radius and centrifugal effect coefficient are accurate and reliable. Furthermore, the steering geometry and lateral dynamics parameters determined in this way can reflect the steering characteristics and lateral motion characteristics of the target vehicle during actual driving.

[0061] S20, 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.

[0062] Lateral position deviation of a target vehicle refers to the deviation in the lateral direction between its actual driving trajectory and its ideal driving trajectory. Changes in steering geometry and lateral dynamics parameters alter the vehicle's trajectory, leading to lateral position deviation. Changes in the centrifugal effect coefficient also affect the vehicle's lateral stability during driving, similarly causing lateral position deviation.

[0063] Figure 3 The diagram shown is a flowchart illustrating a method for determining lateral position deviation provided in an exemplary embodiment of this application. Figure 1 Based on publicly available information, see [link / reference] Figure 3 In some possible implementations, S20 may include the following steps.

[0064] S21, Determine the geometric term of the lateral position deviation based on the turning radius and wheelbase of the target vehicle during its driving process.

[0065] The geometric term of lateral position deviation refers to the lateral position offset caused by the vehicle's steering geometry. This geometric term is used to introduce the first lateral offset caused by geometric steering when the target vehicle's speed is less than a first speed threshold. This application does not limit the range of the first speed threshold; it can be determined based on actual needs and vehicle dynamics characteristics. For example, the range of the first speed threshold can be from 0 km / h to 20 km / h, and in one possible implementation, the first speed threshold can be 10 km / h. During vehicle steering, the ratio of the steering radius to the wheelbase determines the curvature of the vehicle's trajectory, thus affecting the lateral position deviation. Specifically, if the steering 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, with a constant wheelbase, the smaller the steering radius, the sharper the vehicle's turn, 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 target vehicle's wheelbase to the steering radius. For example, the geometric term of the lateral position deviation has the following functional relationship: The parameters are defined as above.

[0066] S22, Determine the centrifugal correction term for the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during its driving process.

[0067] The centrifugal correction term refers to the lateral positional offset caused by the centrifugal force during vehicle movement. It is used to introduce a second lateral offset due to centrifugal force when the target vehicle's speed exceeds a second speed threshold, where the second speed threshold is greater than or equal to a first speed threshold. This application does not limit the range of the second speed threshold; it can be determined based on actual needs and vehicle dynamics characteristics. For example, the second speed threshold can be greater than or equal to 20 km / h. In one possible implementation, both the first and second speed thresholds are equal to 20 km / h. When a vehicle travels at a high speed and turns, centrifugal force causes the vehicle to tend to deviate outwards, resulting in a lateral positional offset. The larger the centrifugal effect coefficient, the greater the influence of the centrifugal force on the vehicle's trajectory, and consequently, the larger the centrifugal correction term for the lateral positional offset. 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 for the lateral positional offset can be... The parameters are defined as above.

[0068] S23, Determine the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term.

[0069] 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.

[0070] The lateral position deviation determination method provided in this application calculates the geometric and centrifugal correction terms of the lateral position deviation by considering the influence of steering geometry parameters 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 operation and can accurately reflect the deviation between the vehicle's actual driving trajectory and its ideal driving trajectory.

[0071] S30, determine the zero-bias estimate of the target vehicle based on the lateral position deviation of the target vehicle.

[0072] The zero-bias estimate of a target vehicle refers to the estimated value of the zero bias of the 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 calculated. In practice, specific mathematical models or mapping relationships can be established to link the lateral position deviation with the zero-bias estimate. For example, based on a mapping table of lateral position deviation and zero-bias estimate, the zero-bias estimate of the target vehicle can be obtained by looking up the table based on the lateral position deviation. However, this method may have certain limitations. Since the mapping table is usually established based on specific operating conditions and experimental data, the zero-bias estimate obtained by looking up the table may not be accurate enough when there are differences between the actual driving conditions and the experimental conditions. To improve the accuracy of the zero-bias estimate, more complex algorithms can be used, such as regression algorithms based on machine learning. 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 acquired lateral position deviation is input into the trained model, and the model can output a more accurate zero-bias estimate.

[0073] The zero-bias estimation method for steering systems provided in this application provides a basis for adjusting and optimizing the vehicle's steering system by determining the zero-bias estimate of the target vehicle. In practical applications, accurate zero-bias estimates help drivers promptly detect and correct steering system deviations, improving driving safety and comfort. For autonomous vehicles, zero-bias estimates are a key factor in ensuring the vehicle travels along a predetermined trajectory. Based on the zero-bias estimation method in this application, related applications can be further expanded. For example, combining zero-bias estimation technology with a vehicle fault diagnosis system can achieve rapid location and early warning of steering system faults. When the zero-bias estimate exceeds the normal range, the system can issue an alarm in a timely manner, prompting maintenance personnel to inspect and maintain the steering system, thus preventing safety accidents caused by steering system faults.

[0074] Figure 4 The diagram shown is a flowchart illustrating a method for determining a zero-bias estimate according to an exemplary embodiment of this application. Figure 1 Based on publicly available information, see [link / reference] Figure 4 In some possible implementations, S30 may include the following steps.

[0075] S31, the lateral position deviation of the target vehicle is used as the predicted value of the lateral position deviation of the target vehicle, and the measured value of the lateral position deviation of the target vehicle is calculated based on the actual position and the target position of the target vehicle.

[0076] The actual position of the target vehicle is its location during actual driving, which can be obtained through sensors such as the Global Positioning System (GPS) and Inertial Measurement Unit (IMU). The target position is the location the vehicle should reach according to its ideal driving trajectory, usually determined by the vehicle's driving plan or a preset route. By comparing and analyzing the actual position information of the target vehicle obtained through sensors and other means with the pre-set target position, the measured value of the lateral position deviation of the target vehicle can be accurately calculated.

[0077] The predicted value of the lateral position deviation is calculated based on the steering geometry parameters and lateral dynamic parameters according to the method described above.

[0078] S32, calculate the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle.

[0079] The residual is used to describe the error between the predicted and measured values. By calculating the difference between the predicted and measured values ​​of the lateral position deviation of the target vehicle, the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle is obtained.

[0080] S33. Determine the zero-bias estimate of the target vehicle based on the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle.

[0081] For example, the zero bias can be directly estimated from the mean of the residuals within a sliding window.

[0082] In some possible implementations, the zero-bias estimate of the target vehicle is determined based on the residual between the predicted and measured values ​​of the target vehicle's lateral position deviation. This includes: dynamically adjusting the forgetting factor of the estimator based on the target vehicle's speed; and inputting the residual into the estimator to obtain the zero-bias estimate of the target vehicle. For example, the estimator can 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 through Kalman filtering.

[0083] In some possible implementations, the estimator is a recursive least squares estimator. In this estimator, the difference between the residual and the zero-bias estimate of the previous period is calculated, along with the product of this difference and the Kalman gain. Based on the sum of the product of the difference and the Kalman gain and the zero-bias estimate of the previous period, the zero-bias estimate of the target vehicle in the current period is determined. For example, the formula for calculating the zero-bias estimate could be:

[0084]

[0085] in, It is the zero-biased estimate for the current period. It is the zero-biased estimate of the previous period. It is the covariance matrix of the previous period. It is the covariance matrix of the current period. It is the measured value of the lateral position deviation of the target vehicle. It is the predicted value of the lateral position deviation of the target vehicle. It is the forgetting factor, Represents Kalman gain.

[0086] In some possible implementations, the forgetting factor of the estimator is dynamically adjusted based on the target vehicle's speed. This includes: if the target vehicle's speed is less than a third speed threshold, a first value is determined as the forgetting factor; if the target vehicle's speed 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. This application does not limit the range of the third and fourth speed thresholds, or the range of the first and second values, and these ranges can be determined based on actual needs and application scenarios, as different needs and scenarios require different forgetting factors. For example, the range of the third speed threshold can be from 0 km / h to 20 km / h, and the range of the fourth speed threshold can be 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 at different stages. Optionally, to improve the accuracy of the zero-bias estimate and lateral position deviation, the high-speed and low-speed scenarios in different stages can be different, specifically by setting different vehicle speed thresholds. For example, the first value can range from 0.90 to 0.98, and the second value can range from 0.95 to 0.99. In one possible implementation, the first value can be 0.95, and the second value can be 0.99. This dynamic adjustment of the forgetting factor based on vehicle speed allows the recursive least squares estimator to better adapt to the actual driving conditions of the vehicle under different speed conditions, thus improving the accuracy of the zero-bias estimate.

[0087] During the zero-bias estimation process, the estimator outputs a zero-biased estimate based on the input residuals and the dynamic adjustment of the forgetting factor according to vehicle speed. The forgetting factor in the estimator controls the degree of forgetting of historical data. When the vehicle speed is low, the vehicle's driving state changes rapidly, requiring a smaller forgetting factor to make the estimator rely more on recent data, as recent data better reflects the vehicle's current true state, thus enabling faster tracking. When the vehicle speed is high, the vehicle's driving state is relatively stable, allowing the forgetting factor to be appropriately increased, enabling the estimator to consider more historical data and avoid estimation errors caused by data fluctuations.

[0088] The zero-bias estimation method provided in this application compares and analyzes the predicted and measured values ​​of the lateral position deviation, introducing residuals to quantify the prediction error. Based on this, an estimator is used, combined with dynamic adjustment of the forgetting factor based on vehicle speed, to accurately calculate the zero-bias estimate of the target vehicle. This method fully considers the diversity of vehicle driving states and the characteristics of data at different speeds, exhibiting higher adaptability and accuracy compared to traditional mapping table-based methods. Specifically, the recursive least squares estimator effectively integrates residuals and the previous period's zero-bias estimate by continuously updating the covariance matrix and incorporating Kalman gain, making the calculation of the zero-bias estimate more accurate. Simultaneously, the mechanism of dynamically adjusting the forgetting factor based on vehicle speed further optimizes the estimator's performance under different driving conditions.

[0089] Figure 5 The diagram shown is a flowchart illustrating another method for estimating the zero bias of a steering system provided in an exemplary embodiment of this application. To compensate for the zero bias, in... Figure 1 Based on publicly available information, see [link / reference] Figure 5 The embodiments of this application may also include the following steps.

[0090] S40 determines feedback compensation based on the zero-bias estimate of the target vehicle, and performs lateral control compensation on the target vehicle based on the feedback compensation.

[0091] In this embodiment, feedback compensation refers to the compensation amount determined based on the zero-bias estimate for adjusting the vehicle's driving state. Because the compensation amount generates the residual between the predicted and measured values, which depend on the lateral position deviation, this process forms a closed-loop adjustment, hence the term feedback compensation. This feedback compensation value reflects the degree of adjustment required by the steering system to bring the target vehicle's trajectory back to the ideal state.

[0092] In some possible implementations, determining feedback compensation based on the zero-bias estimate of the target vehicle includes: integrating the zero-bias estimate of the target vehicle, and determining the feedback compensation based on the sum of the integral result and the zero-bias estimate of the target vehicle. For example, the calculation formula for feedback compensation is as follows:

[0093]

[0094] in, It is feedback and compensation. It is a zero-biased estimate. and These represent different coefficients.

[0095] This application embodiment determines feedback compensation based on the zero-bias estimate of the target vehicle, and then performs lateral control compensation on the target vehicle based on the feedback compensation to achieve precise adjustment of the target vehicle's driving trajectory. Specifically, by performing integral calculations, the zero-bias estimate is accumulated over time and then added to the zero-bias estimate itself. This provides a more comprehensive reflection of the long-term and short-term impact of the zero-bias on the vehicle's driving trajectory, thereby determining a more accurate feedback compensation.

[0096] This feedback compensation mechanism is of great significance for autonomous vehicles. In autonomous driving scenarios, vehicles need to drive based on high-precision maps and preset routes. However, zero bias in the steering system can cause the vehicle to gradually deviate from the ideal trajectory. By calculating feedback compensation in real time and performing lateral control compensation, autonomous vehicles can correct their driving direction in a timely manner, always staying on the predetermined route, effectively avoiding driving deviations caused by the accumulation of zero bias, and improving the safety and reliability of autonomous driving.

[0097] For traditionally manually driven vehicles, this feedback compensation also provides strong support for the driver. When the vehicle's steering system experiences zero deviation, the driver may need to constantly adjust the steering wheel to maintain straight-line driving or turn along the intended trajectory, which increases driver fatigue and operational difficulty. However, a feedback compensation mechanism based on zero deviation estimation can automatically make fine adjustments to the vehicle's direction, reducing the driver's burden.

[0098] Figure 6 The diagram shown is a flowchart illustrating another method for estimating the zero bias of a steering system provided in an exemplary embodiment of this application. To compensate for the zero bias, in... Figure 1 Based on publicly available information, see [link / reference] Figure 6 The embodiments of this application may also include the following steps.

[0099] S50 determines feedforward compensation based on the curvature of the target vehicle's trajectory, vehicle speed, and wheelbase; and performs lateral control compensation for the target vehicle based on the feedforward compensation.

[0100] Feedforward compensation refers to the amount of compensation calculated and applied in advance based on the geometric characteristics and dynamic parameters of the vehicle's trajectory during driving. It aims to pre-correct lateral deviations caused by the inherent characteristics of the vehicle's steering system and driving conditions, thereby enabling the vehicle to travel more accurately along the expected trajectory. Feedforward compensation is mainly determined based on several key parameters: trajectory curvature, vehicle speed, and wheelbase. Trajectory curvature reflects the degree of curvature of the vehicle's driving path; a greater curvature indicates a sharper turn, requiring correspondingly greater feedforward compensation to adjust the steering system and ensure the vehicle travels smoothly along the curved path. Vehicle speed is also an important factor; higher speeds result in more significant dynamic effects such as centrifugal force during cornering, necessitating feedforward compensation to balance these effects and ensure driving stability. Wheelbase affects the vehicle's steering geometry; vehicles with different wheelbases require different steering angles and feedforward compensation amounts for the same turning radius.

[0101] In some possible implementations, feedforward compensation is determined based on the target vehicle's trajectory curvature, speed, and wheelbase. This includes: calculating a first product, which is the product of the target vehicle's wheelbase and trajectory curvature; calculating a second product, which is the product of the target vehicle's speed squared and trajectory curvature; and determining the feedforward compensation based on the sum of the first and second products. For example, the formula for calculating feedforward compensation is as follows:

[0102]

[0103] in, It is the curvature of the target vehicle's trajectory. It is the velocity gain coefficient.

[0104] The execution order of S50 in this embodiment is not limited. It can be executed before S10, after S30, or interspersed before any step in S10 to S30.

[0105] The technical solution of this application embodiment determines feedforward compensation based on the curvature of the target vehicle's trajectory, speed, and wheelbase. Based on this feedforward compensation, lateral control compensation is applied to the target vehicle, achieving multi-dimensional optimization of lateral control. The feedforward compensation mechanism can predict potential lateral deviations during vehicle operation. Compared to relying solely on feedback compensation, this proactive intervention allows for more timely adjustments to the vehicle's steering system, improving driving accuracy and stability. In practical applications, whether on complex urban roads or highways, vehicle trajectories and speeds constantly change. Feedforward compensation can rapidly adjust the compensation amount based on real-time trajectory curvature, speed, and the vehicle's wheelbase parameters, enabling the vehicle to better adapt to various driving conditions. For example, before entering a curve, the system calculates an appropriate feedforward compensation amount based on the detected curve curvature and current speed, actively adjusting the steering system to help the vehicle enter the curve more smoothly, reducing safety risks caused by understeer or oversteer.

[0106] Figure 7 The diagram shown is a flowchart illustrating another zero-bias estimation method for a steering system provided in an exemplary embodiment of this application. See also... Figure 7 Another method for zero bias estimation of a steering system provided in this application includes the following steps.

[0107] S60, Determine the feedback compensation for the target vehicle.

[0108] S70, Determine the feedforward compensation for the target vehicle.

[0109] S80. Based on the feedback compensation and feedforward compensation of the target vehicle, lateral control compensation is performed on the target vehicle.

[0110] The determination of feedback compensation and feedforward compensation is the same as described in the above embodiments, and will not be repeated in this embodiment. This embodiment does not limit the execution order of S60 and S70; optionally, S70 may be executed before S60.

[0111] For example, the formula for lateral control compensation of the target vehicle based on feedback compensation and feedforward compensation can be:

[0112] +

[0113] in, The front wheel steering angle of the target vehicle. For feedforward compensation, For feedback and compensation.

[0114] To ensure the stability of vehicle lateral control, the technical solution of this application combines feedforward compensation and feedback compensation. It calculates the feedforward amount for zero-bias compensation using path curvature and the feedback amount for zero-bias compensation using recursive least squares. This allows for a rapid and accurate representation of the vehicle's lateral zero-bias magnitude, and accordingly, compensation is made for the vehicle's steering wheel zero-bias, achieving precise tracking of the target vehicle's lateral control and comprehensive optimization of its lateral control. This combined approach fully leverages the advantages of feedforward compensation for early prediction and feedback compensation for real-time correction, enabling the target vehicle to maintain high driving accuracy and stability under various driving conditions.

[0115] Figure 8 The diagram shown is a structural schematic of a zero-bias estimation device for a steering system provided in an exemplary embodiment of this application. See also... Figure 8 The zero-bias estimation device 800 for a steering system provided in this application includes: a first determining 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 feature data of the target vehicle; a second determining 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 determining module 803, used to determine the zero-bias estimate of the target vehicle based on the lateral position deviation of the target vehicle.

[0116] The first determining module 801 is also used to: determine the turning radius of the target vehicle during driving based on the driving state data and structural feature data of the target vehicle; and determine the centrifugal effect coefficient of the target vehicle during driving based on the driving state data and the turning radius of the target vehicle during driving.

[0117] The first determining module 801 is further configured 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 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, the first ratio being the ratio of the wheelbase of the target vehicle to the tangent of the front wheel angle of the target vehicle.

[0118] The first determining module 801 is also used to: determine the centripetal acceleration of the target vehicle based on the vehicle speed and the turning radius of the target vehicle during driving; and calculate the centrifugal effect coefficient of the target vehicle based on the centripetal acceleration and gravitational acceleration of the target vehicle.

[0119] The first determining module 801 is also used to: calculate a second ratio, which is the ratio of the centripetal acceleration of the target vehicle to the gravitational acceleration; and determine the centrifugal effect coefficient of the target vehicle based on the second ratio.

[0120] The second determining 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 driving; the geometric term is 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 driving; the centrifugal correction term is used to introduce a second lateral offset caused by centrifugal force when the target vehicle's speed is greater than a second speed threshold, the second speed threshold being 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 its turning radius; the centrifugal correction term is determined based on the sum of the centrifugal effect coefficient and a set constant.

[0121] The second determining module 802 is also used to: calculate the product of the geometric term and the centrifugal correction term; and determine the lateral position deviation of the target vehicle based on the product of the geometric term and the centrifugal correction term.

[0122] The third determining 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 and the 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 based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle.

[0123] The third determining module 803 is also used to: dynamically adjust the forgetting factor of the estimator according to the speed of the target vehicle; input the residual into the estimator to obtain the 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 of the previous period is calculated, as well as the product of the difference and the Kalman gain; based on the sum of the product of the difference and the Kalman gain and the zero-bias estimate of the previous period, the zero-bias estimate of the target vehicle in the current period is determined.

[0124] The third determining module 803 is further configured to: if the speed of the target vehicle is less than the third speed threshold, determine the first value as the forgetting factor; if the speed of the target vehicle is greater than or equal to the fourth speed threshold, determine the 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.

[0125] The apparatus provided in this application embodiment further includes a first compensation module, used for: determining feedback compensation based on the zero bias estimate of the target vehicle; and performing lateral control compensation on the target vehicle based on the feedback compensation.

[0126] In some possible implementations, the first compensation module is also used to: integrate the zero-bias estimate of the target vehicle, and determine the feedback compensation based on the sum of the integration result and the zero-bias estimate of the target vehicle.

[0127] The device provided in this application embodiment further includes a second compensation module, used for: determining feedforward compensation based on the curvature of the target vehicle's trajectory, vehicle speed, and wheelbase; and performing lateral control compensation on the target vehicle based on the feedforward compensation.

[0128] In some possible implementations, the second compensation module is also used to: calculate a first product, which is the product of the target vehicle's wheelbase and the curvature of its driving trajectory; calculate a second product, which is the product of the square of the target vehicle's speed and the curvature of its driving trajectory; and determine feedforward compensation based on the sum of the first and second products.

[0129] The zero-bias estimation device for the steering system provided in this application embodiment, through close cooperation between the first determination module, the second determination module, and the third determination module, can comprehensively and accurately determine the zero-bias estimate of the target vehicle. The first determination module calculates steering geometric parameters and lateral dynamic parameters based on the vehicle's driving state data and structural characteristic data, laying the foundation for subsequent zero-bias estimation. For example, it determines the steering radius and centrifugal effect coefficient through complex formula calculations; these parameters reflect the geometric and dynamic characteristics of the vehicle during driving. The second determination module further determines the lateral position deviation based on the parameters obtained by the first determination module. This application embodiment considers the influence of geometric steering and centrifugal force on the vehicle's lateral offset under different vehicle speed conditions, calculating geometric terms and centrifugal correction terms respectively, thereby determining the lateral position deviation. This comprehensive consideration of different factors makes the determination of the lateral position deviation more consistent with actual driving conditions. The third determination module, based on the lateral position deviation, calculates the residual by comparing the predicted value with the measured value, and finally determines the zero-bias estimate. Moreover, it can dynamically adjust the forgetting factor according to the vehicle speed to adapt to the accuracy requirements of zero-bias estimation at different driving speeds.

[0130] Simultaneously, after the zero-bias estimate and feedforward compensation are determined, the first and second compensation modules promptly perform lateral control compensation on the vehicle. The collaborative work of these two compensation modules, combining the advantages of feedback compensation and feedforward compensation, improves the accuracy and stability of the vehicle's lateral control.

[0131] Figure 9 The diagram shown is a structural schematic of an electronic device provided in an exemplary embodiment of this 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 domain controller on the vehicle, or a remote server that is communicatively connected to the vehicle.

[0132] The data acquisition unit 101, connected to sensors on the vehicle, is used to acquire driving status data of the target vehicle. Optionally, it can also acquire structural feature data of the target vehicle. Driving status data includes information such as vehicle speed and front wheel steering 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 steering angle reflects the vehicle's steering operation. Structural feature data covers parameters such as the vehicle's wheelbase. As an inherent structural parameter of the vehicle, the wheelbase plays a crucial role in the vehicle's steering geometry.

[0133] The processor 102, connected to the data acquisition unit 101 and the memory 103, executes the instructions stored in the memory 103 to implement the zero-bias estimation method for the aforementioned steering system. The processor 102 acts as the "brain" of the electronic device, performing complex calculations based on the acquired data and following preset algorithms and logic. For example, it calculates steering geometry parameters and lateral dynamic parameters based on driving state data and structural feature data, thereby determining the lateral position deviation and the zero-bias estimate. Simultaneously, the processor 102, based on the calculation results, coordinates with the first and second compensation modules to achieve lateral control compensation for the vehicle, ensuring vehicle stability. The memory 103 stores the data and program code required for the processor 102 to execute instructions. It acts as the "memory warehouse" of the electronic device, providing necessary support for the operation of the processor 102. The stored data includes not only various initial parameters of the vehicle 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; while real-time storage of driving state data allows the processor 102 to easily access the data at any time for real-time analysis and control of the vehicle's state.

[0134] The driving state data and structural feature data acquired by the data acquisition unit 101 are continuously transmitted to the processor 102. The processor 102 executes the corresponding zero-bias estimation method based on this data, which involves a large number of complex mathematical operations and logical judgments. For example, when calculating the turning radius, it is necessary to make accurate calculations based on the geometric steering model and the relationship between the front wheel angle and the wheelbase; when determining the centrifugal effect coefficient, it is necessary to combine data from multiple aspects such as vehicle speed, centripetal acceleration, and gravitational acceleration.

[0135] Simultaneously, the electronic device 100 communicates with the vehicle. If the electronic device 100 is a remote server, it interacts with the vehicle via a communication link. On one hand, it receives driving status data and structural characteristic data from the vehicle; on the other hand, it sends calculated control commands, such as the specific values ​​of feedforward compensation and feedback compensation, to the vehicle to achieve precise adjustment of the vehicle's lateral control.

[0136] Furthermore, the performance and reliability of the electronic device 100 play a decisive role in the zero-bias estimation and lateral control compensation of the vehicle's steering system. The high-performance processor 102 can process large amounts of data quickly, ensuring the real-time nature and accuracy of the calculation results. Meanwhile, the stable and reliable memory 103 ensures the safe storage and rapid retrieval of data, avoiding control errors caused by data loss or retrieval mistakes. For example, during long-term vehicle operation, the memory 103 needs to operate continuously and stably, providing accurate data support to the processor 102 to ensure that the zero-bias estimation and lateral control compensation of the vehicle's steering system are always in optimal condition.

[0137] As vehicle technology continues to advance, the performance and functional requirements of electronic device 100 are also constantly increasing. In the future, electronic device 100 may integrate more advanced sensor data fusion technology, not only processing existing data such as vehicle speed, front wheel angle, and wheelbase, but also fusing data from sensors such as vehicle attitude sensors and road condition sensors. This will allow for more comprehensive and accurate zero-bias estimation and lateral control compensation of the vehicle steering system, further enhancing vehicle safety and comfort. Simultaneously, to adapt to the needs and application scenarios of different vehicles, electronic device 100 may possess greater scalability and flexibility, enabling personalized parameter settings and function customization based on vehicle type and configuration. For example, for vehicles with different wheelbases and power performance, electronic device 100 can automatically adjust relevant algorithms and parameters to achieve optimal control.

[0138] 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), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and processor 102 may execute the program instructions to implement the zero-bias estimation method and / or other desired functions of the steering system of the various embodiments of this application described above.

[0139] In some implementations, the processor 102 is connected to a display, which can show the vehicle's current zero-bias estimate, allowing the driver to intuitively understand the status of the vehicle's steering system and detect potential problems in advance. For example, when the zero-bias estimate exceeds the normal range, the driver can take timely measures to avoid safety accidents caused by steering system malfunctions. It can also display the predicted and measured values ​​of lateral position deviation, helping technicians analyze the lateral offset during vehicle operation and providing data support for optimizing vehicle steering performance. Simultaneously, it displays the specific values ​​of feedback compensation and feedforward compensation, enabling operators to clearly understand the degree of lateral control compensation.

[0140] In some embodiments, the electronic device may further include input devices and output devices, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0141] Input devices may include, for example, touchscreens, keyboards, and mice. Output devices may output filtered point cloud data to external systems. These output devices may include displays, vehicle-mounted terminals, communication networks, and connected remote output devices.

[0142] Of course, for the sake of simplicity, Figure 9 Only some of the components of the electronic device 100 relevant to this application are shown in this illustration, omitting components such as input devices, output devices, buses, input / output interfaces, etc. In addition, the electronic device 100 may include any other suitable components depending on the specific application.

[0143] Embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the zero-bias estimation method for the steering system according to various embodiments of this application described above.

[0144] Computer program products can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can 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.

[0145] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the zero-bias estimation method for the steering system according to various embodiments of this application described above.

[0146] Computer-readable storage media may take the form of any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may, for example, include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0147] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0148] The block diagrams of devices, apparatuses, devices, 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 those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0149] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0150] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this 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.

[0151] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary 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 method for estimating the zero bias of a steering system, characterized in that, include: Based on the target vehicle's driving status data and structural characteristic data, determine the target vehicle's steering geometry parameters and lateral dynamic parameters during driving. Based on the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving, determine the lateral position deviation of the target vehicle; The lateral position deviation of the target vehicle is used as the predicted value of the lateral position deviation of the target vehicle, and the measured value of the lateral position deviation of the target vehicle is calculated based on the actual position and the target position of the target vehicle. Calculate the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle; determine the zero-bias estimate of the target vehicle based on the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle. The step of determining the lateral position deviation of the target vehicle based on its steering geometry parameters and lateral dynamic parameters during driving includes: determining a geometric term of the lateral position deviation based on the turning radius and wheelbase of the target vehicle during driving; determining a centrifugal correction term of the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during driving; and determining the lateral position deviation of the target vehicle based on the geometric term and the centrifugal correction term.

2. The method according to claim 1, characterized in that, The step of determining the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving, based on the target vehicle's driving state data and structural feature data, includes: Based on the driving status data and structural feature data of the target vehicle, determine the turning radius 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, the centrifugal effect coefficient of the target vehicle during driving is determined.

3. The method according to claim 2, characterized in that, Determining the turning radius of the target vehicle during driving based on its driving status data and structural feature data includes: 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 steering 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, which is the ratio of the wheelbase of the target vehicle to the tangent of the front wheel steering angle of the target vehicle.

4. The method according to claim 2, characterized in that, The step of 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, includes: The centripetal acceleration of the target vehicle is determined based on the vehicle speed and the turning radius of the target vehicle during its journey. Calculate the centrifugal effect coefficient of the target vehicle based on its centripetal acceleration and gravitational acceleration. The step of calculating the centrifugal effect coefficient of the target vehicle based on its centripetal acceleration and gravitational acceleration includes: Calculate the second ratio, which is the ratio of the centripetal acceleration of the target vehicle to the gravitational acceleration; The centrifugal effect coefficient of the target vehicle is determined based on the second ratio.

5. The method according to claim 1, characterized in that, 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 centrifugation correction term is determined based on the sum of the centrifugation 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: Calculate the product of the geometric term and the centrifugal correction term; The lateral position deviation of the target vehicle is determined based on the product of the geometric term and the centrifugal correction term.

6. The method according to claim 1, characterized in that, The step of determining the zero-bias estimate of the target vehicle based on the residual between the predicted and measured values ​​of the lateral position deviation of the target vehicle includes: The forgetting factor of the estimator is dynamically adjusted based on the speed of the target vehicle; The residual is input into the estimator to obtain the zero-bias estimate of the target vehicle; The estimator is a recursive least squares estimator, in which the difference between the residual and the zero-bias estimate of the previous period is calculated, and the product of the difference and the Kalman gain is calculated. The zero-bias estimate of the target vehicle in the current period is determined by summing the product of the difference and the Kalman gain with the zero-bias estimate of the previous period. The forgetting factor of the estimator is dynamically adjusted based on the speed of the target vehicle, including: If the speed of the target vehicle is less than the third speed threshold, then the first value is determined as the forgetting factor; If the target vehicle's speed is greater than or equal to the fourth speed threshold, then the 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 includes: Based on the zero-bias estimate of the target vehicle, determine the feedback compensation; Based on the feedback compensation, lateral control compensation is performed on the target vehicle; The step of determining feedback compensation based on the zero-bias estimate of the target vehicle includes: The zero-bias estimate of the target vehicle is integrated, and the feedback compensation is determined based on the sum of the integration result and the zero-bias estimate of the target vehicle.

8. The method according to claim 1, characterized in that, The method further includes: The feedforward compensation is determined based on the curvature of the target vehicle's trajectory, its speed, and its wheelbase. Based on the feedforward compensation, lateral control compensation is performed on the target vehicle; The step of determining feedforward compensation based on the curvature of the target vehicle's trajectory, vehicle speed, and wheelbase includes: Calculate the first product, which is the product of the wheelbase of the target vehicle and the curvature of the driving trajectory; Calculate the second product, which is the product of the square of the target vehicle's speed and the curvature of the driving trajectory; The feedforward compensation is determined based on the sum of the first product and the second product.

9. A zero-bias estimation device for a steering system, characterized in that, include: The first determining module is used to determine the steering geometry parameters and lateral dynamic parameters of the target vehicle during driving based on the target vehicle's driving state data and structural feature data. The second determining module is 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. The third determining module is used to take 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 and the 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 based on the residual between the predicted value and the measured value of the lateral position deviation of the target vehicle. The second determining module is specifically used to: determine the geometric term of the lateral position deviation based on the turning radius of the target vehicle during driving and the wheelbase of the target vehicle; and determine the centrifugal correction term of the lateral position deviation based on the centrifugal effect coefficient of the target vehicle during driving. The lateral position deviation of the target vehicle is determined based on the geometric term and the centrifugal correction term.

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

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