A zero self-learning method and control device for vehicle steering system software

Abstract

The application provides a zero self-learning method and control equipment of vehicle steering system software, and relates to the technical field of vehicle control. The method comprises the following steps: obtaining running data of a target vehicle in a preset time period before the current time; determining whether the target vehicle meets a preset self-learning enabling condition at the current time; only when the current time meets the preset self-learning enabling condition, segmenting a vehicle speed signal, determining at least one target vehicle speed segment, and determining a corresponding steering angle signal of each target vehicle speed segment; combining a historical steering wheel zero position angle of each target vehicle speed segment to determine a target steering wheel zero position angle of each target vehicle speed segment; independently analyzing vehicle running data by segmenting vehicle speed; and automatically updating the steering wheel zero position angle when a specific working condition condition is met, thereby improving the accuracy of the target steering wheel zero position angle, adapting to steering characteristic differences under different driving speeds, effectively eliminating zero position deviation caused by long-term use, and improving the control precision and stability of the steering system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle control, in particular to a zero self-learning method of a vehicle steering system software and a control device. BACKGROUND

[0002] During long-term use of the vehicle, the software zero of the steering gear and the mechanical zero are prone to deviation due to various actual working condition factors such as dynamic change of tire pressure, load increase and decrease, and uneven load distribution, so that the two cannot be kept coincident. Specifically, when the vehicle is stationary, the front wheels are in a right posture, and the steering wheel needs to be turned to a certain angle to the right to match the mechanical zero. However, when the vehicle is in a full load driving state, the vehicle needs to keep straight driving, and the steering wheel needs to be turned to a certain angle to the left to offset the steering deviation caused by the load change. This zero deviation will directly affect the accuracy of the vehicle lateral control and become a key problem restricting the lateral control performance of the automatic driving and advanced auxiliary driving system.

[0003] In the prior art, the zero self-learning algorithm is mainly used for steering wheel zero calibration. The core logic is to collect sensor data such as steering angle, vehicle speed, lateral acceleration, etc. during vehicle driving, and automatically correct the software zero based on the preset threshold or statistical model. However, this kind of algorithm has key defects in commercial scenarios: a complete self-learning condition judgment mechanism is not constructed, and the zero learning process is started only when the basic driving conditions are met, and there is a lack of deep verification of the stability of the steering state. Specifically, when the vehicle is in a straight driving state and the vehicle speed is stable, the steering wheel may be slightly fluctuated in a small range due to factors such as road bumps and steering system mechanical clearance. At this time, the steering state does not meet the conditions required for stable calibration, but the existing algorithm does not identify and filter this unstable state, and directly starts learning and misjudges the fluctuation signal as an effective steering deviation, and then continuously adjusts the zero learning value, resulting in repeated oscillation of the learning value and significant decrease in prediction accuracy. SUMMARY

[0004] The present application aims to solve the above problems in the prior art, and provides a zero self-learning method of a vehicle steering system software and a control device, so as to obtain running data of a target vehicle in a preset time period before the current time, determine whether the target vehicle meets a preset self-learning enabling condition at the current time, only when the current time meets the preset self-learning enabling condition, segment the vehicle speed signal, determine at least one target vehicle speed segment, and determine the corresponding steering angle signal of each target vehicle speed segment, combine the historical steering wheel zero angle of each target vehicle speed segment, and determine the target steering wheel zero angle of each target vehicle speed segment.

[0005] To achieve the above purpose, the technical solutions adopted by the embodiments of the present application are as follows:

[0006] In a first aspect, the embodiments of the present application provide a zero self-learning method of a vehicle steering system software, the method comprising:

[0007] obtaining running data of a target vehicle in a preset time period before a current time, wherein the running data comprises a vehicle speed signal and a corresponding steering wheel angle signal;

[0008] obtaining a historical steering wheel zero angle for a plurality of preset vehicle speed segments of the steering system software of the target vehicle;

[0009] determining whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the running data;

[0010] if the target vehicle satisfies the preset self-learning enabling condition at the current time, segmenting the vehicle speed signal according to a vehicle speed range of the plurality of preset vehicle speed segments to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal;

[0011] determining a steering angle signal corresponding to the at least one target vehicle speed segment from the steering wheel angle signal;

[0012] determining a target steering wheel zero angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero angle of each target vehicle speed segment;

[0013] updating the historical steering wheel zero angle of each target vehicle speed segment to the target steering wheel zero angle.

[0014] In an optional implementation, the running data further comprises a plurality of detection signals corresponding to the vehicle speed signal, lane line data of a current driving lane identified by a camera perception system, driving control data, and lateral function state information;

[0015] The determining whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the running data comprises:

[0016] performing vehicle state detection on the target vehicle according to the plurality of detection signals and the driving control data;

[0017] if the vehicle state detection result indicates that the target vehicle has no communication fault and is in a steady-state driving state, performing driving scene detection on the target vehicle according to the lane line data;

[0018] if the driving scene detection result indicates that the target vehicle is in a straight-line driving scene, performing driving behavior detection on the target vehicle according to the driving control data;

[0019] If the driving behavior detection result indicates that the target vehicle is not in the driver override state, performing lane parallel detection on the target vehicle according to the lane line data;

[0020] If the lane parallel detection result indicates that the target vehicle is in the lane parallel driving state, performing lateral function state detection according to the lateral function state information;

[0021] If the lateral function state detection result indicates that the lateral function detection passes, determining that the preset self-learning enabling condition is met at the current moment.

[0022] In an optional implementation, the plurality of detection signals further include longitudinal acceleration signals, lateral acceleration signals, and yaw rate signals collected by an inertial navigation sensor;

[0023] The vehicle state detection on the target vehicle according to the plurality of detection signals and the driving operation data includes:

[0024] The communication state detection on the target vehicle according to the plurality of detection signals and the driving operation data includes:

[0025] If the target vehicle does not have a communication fault, determining whether the target vehicle is in a steady-state driving state according to a longitudinal acceleration value of the longitudinal acceleration signal, a lateral acceleration value of the lateral acceleration signal, and a yaw rate value of the yaw rate signal.

[0026] In an optional implementation, the lane line data includes polynomial coefficients of a current driving road of the target vehicle and a confidence degree, and the driving scene detection on the target vehicle according to the lane line data includes:

[0027] Determining that the current driving road is a straight-line driving road according to the polynomial coefficients of the current driving road;

[0028] If the current driving road is a straight-line driving road, determining whether the current driving road meets a preset lane line clear condition according to the confidence degree of the current driving road;

[0029] If the current driving road meets the preset lane line clear condition, determining that a current driving scene of the target vehicle is a lane line clear straight-line driving scene.

[0030] In an optional implementation, the driving operation data includes hand torque, steering wheel rotation speed, and steering wheel rotation angle, and the driving behavior detection on the target vehicle according to the driving operation data includes:

[0031] determining whether the target vehicle is in a driver control state according to the hand torque and the steering wheel rotation speed;

[0032] if the target vehicle is not in the driver control state, determining that the target vehicle is not in the driver override state;

[0033] if the target vehicle is in the driver control state, determining whether the target vehicle enters a turning mode according to the hand torque and the steering wheel rotation angle;

[0034] if the target vehicle switches to a steering wheel return state after entering the turning mode for a duration less than or equal to a preset time length, determining that the target vehicle is in the driver override state;

[0035] if the target vehicle switches to the steering wheel return state for a duration greater than the preset time length, determining that the target vehicle is not in the driver override state.

[0036] In an optional embodiment, the lane parallel detection of the target vehicle according to the lane line data comprises:

[0037] determining a heading angle of the target vehicle relative to the lane line and a preview deviation according to the lane line data;

[0038] determining whether the target vehicle is parallel to the road centerline according to the heading angle of the target vehicle relative to the lane line and the preview deviation;

[0039] if the target vehicle is parallel to the road centerline, determining that the target vehicle is in a lane parallel driving state.

[0040] In an optional embodiment, the lateral function state detection according to the lateral function state information comprises:

[0041] if the lateral function state information indicates that the lateral function of the target vehicle is not activated, determining that the lateral function detection of the target vehicle is passed;

[0042] if the lateral function state information indicates that the lateral function of the target vehicle is activated and the activation time is greater than a preset time length, and the corresponding performance of the steering function actuator corresponding to the lateral function meets a preset performance constraint, determining that the lateral function detection of the target vehicle is passed.

[0043] In an optional embodiment, the determination of the target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero position angle of each target vehicle speed segment comprises:

[0044] sampling the steering wheel angle signal corresponding to each target vehicle speed section in a pre-design calculation period to obtain a plurality of sampled steering wheel angle values in a current calculation period;

[0045] calculating an average steering wheel angle value as a calculation steering wheel angle value of the current calculation period according to the plurality of sampled steering wheel angle values;

[0046] determining a weighting coefficient according to the calculation steering wheel angle value, a preset learning angle threshold and a current zero drift level;

[0047] determining an adjustment coefficient according to the actual steering wheel zero drift value of each target vehicle speed section, a control input zero drift value of the lateral function in the target vehicle and the calculation steering angle value;

[0048] determining a target weight coefficient according to the weighting coefficient and the adjustment coefficient;

[0049] performing a weighting operation on the historical steering wheel zero position angle and the calculation steering angle value according to the target weight coefficient to obtain a target steering wheel zero position angle of each target vehicle speed section.

[0050] In an optional implementation, before the determining of the weighting coefficient according to the calculation steering wheel angle value, the preset learning angle threshold and the current zero drift level, the method further includes:

[0051] if the lateral function of the target vehicle is in an activated state, determining the current zero drift level according to the historical steering wheel zero position angle;

[0052] if the lateral function of the target vehicle is in an inactivated state, determining a preset minimum zero drift level as the current zero drift level.

[0053] In a second aspect, the embodiments of the present application further provide a zero position self-learning device of a vehicle steering system software, and the device includes:

[0054] an acquisition module configured to acquire running data of a target vehicle in a preset time period before a current time, wherein the running data includes a vehicle speed signal and a corresponding steering wheel angle signal;

[0055] the acquisition module is further configured to acquire historical steering wheel zero position angles of a plurality of preset vehicle speed sections of a steering system software of the target vehicle stored in advance;

[0056] a judgment module configured to judge whether the target vehicle meets a preset self-learning enabling condition at the current time according to the running data;

[0057] a segmentation module, configured to segment the vehicle speed signal according to the vehicle speed ranges of the plurality of preset vehicle speed segments to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal, if the current time meets the preset self-learning enabling condition;

[0058] a determination module, configured to determine a steering angle signal corresponding to the at least one target vehicle speed segment from the steering wheel angle signal;

[0059] The determination module is further configured to determine a target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and a historical steering wheel zero position angle of each target vehicle speed segment.

[0060] an updating module, configured to update the historical steering wheel zero position angle of each target vehicle speed segment to the target steering wheel zero position angle.

[0061] In a third aspect, an embodiment of the present application further provides a control device, including a processor, a storage medium and a bus, the storage medium stores program instructions executable by the processor, when the control device is running, the processor and the storage medium communicate through the bus, and the processor executes the program instructions to perform the steps of the zero position self-learning method of the vehicle steering system software according to any one of the first aspect.

[0062] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, the storage medium stores a computer program, when the computer program is run by a processor, the steps of the zero position self-learning method of the vehicle steering system software according to any one of the first aspect are executed.

[0063] The present application has the following beneficial effects:

[0064] The embodiments of the present application provide a zero position self-learning method of vehicle steering system software and a control device, the method includes: obtaining running data of a target vehicle in a preset time period before a current time, wherein the running data includes a vehicle speed signal and a corresponding steering wheel angle signal; obtaining historical steering wheel zero position angles of a plurality of preset vehicle speed segments of steering system software of the target vehicle stored in advance; determining whether the target vehicle meets a preset self-learning enabling condition at the current time according to the running data; if the current time meets the preset self-learning enabling condition, segmenting the vehicle speed signal according to vehicle speed ranges of the plurality of preset vehicle speed segments to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal; determining a steering angle signal corresponding to the at least one target vehicle speed segment from the steering wheel angle signal; determining a target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and a historical steering wheel zero position angle of each target vehicle speed segment; and updating the historical steering wheel zero position angle of each target vehicle speed segment to the target steering wheel zero position angle.

[0065] The method of the present application determines whether the target vehicle meets the preset self-learning enabling condition at the current time by obtaining the running data of the target vehicle in a preset time period before the current time. Only when the current time meets the preset self-learning enabling condition, the vehicle speed signal is segmented, at least one target vehicle speed segment is determined, and the corresponding steering angle signal of each target vehicle speed segment is determined. The target steering wheel zero position angle of each target vehicle speed segment is determined by combining the historical steering wheel zero position angle of each target vehicle speed segment. The vehicle running data is analyzed independently by dividing the vehicle speed segments, and the steering wheel zero position angle is automatically updated when the specific working condition condition is met. This not only improves the accuracy of the target steering wheel zero position angle, but also adapts to the difference in steering characteristics at different driving speeds, effectively eliminates the zero position deviation caused by long-term use, improves the control accuracy and stability of the steering system, reduces the need for manual calibration, and enhances the safety and driving comfort of vehicle steering. BRIEF DESCRIPTION OF DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0067] Figure 1 One of the flowcharts of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0068] Figure 2 The second flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0069] Figure 3 The third flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0070] Figure 4 The fourth flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0071] Figure 5 The fifth flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0072] Figure 6 The sixth flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiments of the present application;

[0073] Figure 7Fig. 7 is a flowchart of a seventh embodiment of a zero self-learning method of a vehicle steering system software according to the present application;

[0074] Figure 8 Fig. 8 is a flowchart of an eighth embodiment of a zero self-learning method of a vehicle steering system software according to the present application;

[0075] Figure 9 Fig. 9 is a functional module diagram of a zero self-learning device of a vehicle steering system software according to the present application;

[0076] Figure 10 Fig. 10 is a schematic diagram of a control device according to the present application. DETAILED DESCRIPTION

[0077] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings for the embodiments of the present application to make a clear and complete description of the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application.

[0078] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art without creative work based on the embodiments in the present application are within the scope of protection of the present application.

[0079] In the description of the present application, it should be noted that if the terms "upper", "lower", etc. indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, or the orientation or position relationship when the product of the present application is used, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0080] In addition, the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0081] It should be noted that the features in the embodiments of the present application can be combined with each other without conflict.

[0082] In order to obtain an accurate steering wheel zero position angle, the embodiments of the present application provide a zero position self-learning method of a vehicle steering system software. The running data of a target vehicle in a preset time period before the current time is obtained, and it is determined whether the target vehicle at the current time meets a preset self-learning enabling condition. Only when the current time meets the preset self-learning enabling condition, the vehicle speed signal is segmented, at least one target vehicle speed segment is determined, and the corresponding steering angle signal of each target vehicle speed segment is determined. The target steering wheel zero position angle of each target vehicle speed segment is determined by combining the historical steering wheel zero position angle of each target vehicle speed segment. The vehicle running data is independently analyzed by dividing the vehicle speed segments, the steering wheel zero position angle is automatically updated when a specific working condition condition is met, the steering characteristics difference under different driving speeds can be adapted, the zero position deviation caused by long-term use is effectively eliminated, the control accuracy and stability of the steering system are improved, the need for manual calibration is reduced, and the safety and driving comfort of vehicle steering are enhanced.

[0083] The zero position self-learning method of the vehicle steering system software provided by the embodiments of the present application is explained and described in detail below by specific examples in combination with the accompanying drawings, Figure 1 One of the flowcharts of the zero position self-learning method of the vehicle steering system software provided by the embodiments of the present application is shown in FIG. 1. As shown in FIG. 1, the method comprises the following steps. Figure 1

[0084] S101, obtaining the running data of the target vehicle in a preset time period before the current time.

[0085] The running data comprises a vehicle speed signal and a corresponding steering wheel steering angle signal.

[0086] In this embodiment, after the target vehicle is powered on, the running data of the target vehicle is collected, and the running data of the preset time period is collected. The running data of the preset time period is used to determine whether the target vehicle at the current time meets the preset self-learning enabling condition.

[0087] The running data of the preset time period is a plurality of frames of running data in the preset time period, and each frame of running data comprises a vehicle speed signal and a corresponding steering wheel steering angle signal.

[0088] S102, obtaining the historical steering wheel zero position angle of a plurality of preset vehicle speed segments of the steering system software of the target vehicle.

[0089] ​Specifically, the steering system software in the target vehicle pre-stores historical steering wheel zero position angles of multiple preset vehicle speed sections. It can be understood that the historical steering wheel zero position angles of multiple preset vehicle speed sections are the steering wheel zero position angles learned by the target vehicle historically, or the steering wheel zero position angles stored after the target vehicle is factory calibrated, and different preset vehicle speed sections correspond to one historical steering zero position angle.

[0090] S103, determining whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the running data.

[0091] S104, if the preset self-learning enabling condition is satisfied at the current time, segmenting the vehicle speed signal according to the vehicle speed ranges of multiple preset vehicle speed sections to obtain at least one target vehicle speed section corresponding to the vehicle speed signal.

[0092] Specifically, in order to ensure the accuracy of zero position learning and avoid self-learning under complex working conditions, it is determined whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the collected running data.

[0093] When it is determined that the target vehicle satisfies the preset self-learning enabling condition at the current time, the vehicle speed signal is segmented according to the vehicle speed ranges of multiple preset vehicle speed sections to obtain at least one target vehicle speed section corresponding to the vehicle speed signal.

[0094] For example, the vehicle speed ranges of multiple preset vehicle speed sections can include a first vehicle speed section, a second vehicle speed section, a third vehicle speed section, and a fourth vehicle speed section, wherein the vehicle speed range of the first vehicle speed section is greater than 10 km / h and less than or equal to 26 km / h, the vehicle speed range of the second vehicle speed section is greater than 26 km / h and less than or equal to 52 km / h, the vehicle speed range of the third vehicle speed section is greater than 52 km / h and less than or equal to 78 km / h, and the vehicle speed range of the fourth vehicle speed section is greater than 78 km / h. Then, the vehicle speed signal is segmented to obtain at least one target vehicle speed section corresponding to the vehicle speed signal, for example, the vehicle speed signal is 50 km / h, and it is determined that the target vehicle speed section corresponding to the vehicle speed signal is the second vehicle speed section.

[0095] Since the steering resistance is large when the vehicle is at low speed, the self-learning value is inaccurate, and therefore self-learning is not performed at low speed, for example, when the vehicle speed signal is less than 10 km / h, the vehicle speed signal is not processed.

[0096] S105, determining at least one target vehicle speed section corresponding to the steering angle signal from the steering wheel angle signal.

[0097] Specifically, after determining at least one target vehicle speed section, the steering angle signal corresponding to each vehicle speed signal is determined as the steering angle signal corresponding to at least one target vehicle speed section according to the vehicle speed signal in at least one target vehicle speed section.

[0098] S106, determine a target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero position angle of each target vehicle speed segment.

[0099] S107, update the historical steering wheel zero position angle of each target vehicle speed segment to the target steering wheel zero position angle.

[0100] Specifically, the steering wheel zero position angle of each target vehicle speed segment is self-learned according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero position angle of each target vehicle speed segment, to obtain the target steering wheel zero position angle of each target vehicle speed segment, and finally the historical steering wheel zero position angle of each target vehicle speed segment is updated to the target steering wheel zero position angle. The updated target steering wheel zero position angle will serve as a reference for subsequent vehicle steering control, such as the calibration of the assist curve of the electronic power steering system, the correction of the steering instruction of the automatic driving system, etc., to realize dynamic optimization of the zero position parameter.

[0101] In summary, the embodiment of the present application provides a zero self-learning method of vehicle steering system software, which comprises: obtaining running data of a target vehicle in a preset time period before the current time, wherein the running data comprises: a vehicle speed signal and a corresponding steering wheel steering angle signal; obtaining historical steering wheel zero position angles of a plurality of preset vehicle speed segments of the steering system software of the target vehicle stored in advance; determining whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the running data; if the current time satisfies the preset self-learning enabling condition, segmenting the vehicle speed signal according to the vehicle speed range of the plurality of preset vehicle speed segments to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal; determining a steering angle signal corresponding to at least one target vehicle speed segment from the steering wheel steering angle signal; determining a target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero position angle of each target vehicle speed segment; and updating the historical steering wheel zero position angle of each target vehicle speed segment to the target steering wheel zero position angle.

[0102] The method of this application obtains the operating data of the target vehicle for a preset time period before the current time, determines whether the target vehicle meets the preset self-learning enable conditions at the current time, and only if the preset self-learning enable conditions are met at the current time, segments the vehicle speed signal to determine at least one target vehicle speed segment, and determines the steering angle signal corresponding to each target vehicle speed segment. Combining the historical steering wheel zero angle of each target vehicle speed segment, the target steering wheel zero angle of each target vehicle speed segment is determined. By independently analyzing the vehicle operating data by vehicle speed segment, the steering wheel zero angle is automatically updated when specific operating conditions are met. This not only improves the accuracy of the target steering wheel zero angle, but also adapts to the differences in steering characteristics at different driving speeds, effectively eliminates zero deviation caused by long-term use, improves the control accuracy and stability of the steering system, reduces the need for manual calibration, and enhances the safety and driving comfort of vehicle steering.

[0103] This application also provides another possible implementation of a zero-position self-learning method for vehicle steering system software. The operating data also includes: multiple detection signals corresponding to the vehicle speed signal, lane line data of the current driving vehicle identified by the camera perception system, driving control data, and lateral function status information. Figure 2 This is a second flowchart illustrating a zero-position self-learning method for vehicle steering system software provided in an embodiment of this application. Figure 2 As shown, based on the operational data, it is determined whether the target vehicle meets the preset self-learning enable conditions at the current moment, including:

[0104] S201. Based on multiple detection signals and driving control data, perform vehicle status detection on the target vehicle.

[0105] S202. If the vehicle status detection result indicates that the target vehicle has no communication failure and is in a steady-state driving state, then the driving scenario detection of the target vehicle is performed based on the lane line data.

[0106] S203. If the driving scenario detection result indicates that the target vehicle is in a straight driving scenario, then the driving behavior of the target vehicle shall be detected based on the driving control data.

[0107] S204. If the driving behavior detection result indicates that the target vehicle is not in a state of driver over-control, then lane parallel detection is performed on the target vehicle based on the lane line data.

[0108] S205. If the lane parallel detection result indicates that the target vehicle is in a lane parallel driving state, then perform lateral function status detection based on the lateral function status information.

[0109] S206. If the horizontal function status detection result indicates that the horizontal function detection has passed, then it is determined that the preset self-learning enable condition is met at the current moment.

[0110] In the embodiment, a vehicle speed signal is acquired from a chassis system or a vehicle controller, and a plurality of detection signals corresponding to the vehicle speed signal are acquired, a state of the target vehicle is detected according to the plurality of detection signals and driving manipulation data, it is judged whether the target vehicle has a communication fault, and if it is judged that the target vehicle has a communication fault, the steering wheel zero position self-learning is stopped.

[0111] When it is judged that the target vehicle does not have a communication fault, it is further judged whether the target vehicle is in a steady state driving condition according to the plurality of detection data, a vehicle state detection result is obtained, only when the vehicle state detection result indicates that the target vehicle has no communication fault at the current time and is in a steady state driving condition, the target vehicle is subjected to driving scene detection according to lane line data, and a driving scene detection result is obtained. If the vehicle state detection result indicates that the target vehicle has a communication fault at the current time or is not in a steady state driving condition, the steering wheel zero position self-learning is stopped.

[0112] The target vehicle is subjected to driving scene detection, which is used to judge whether the target vehicle is in a straight line driving scene at the current time, and a driving scene detection result is obtained. If the driving scene detection result indicates that the target vehicle is in a straight line driving scene at the current time, the target vehicle is subjected to driving behavior detection according to driving manipulation data. If the driving scene detection result indicates that the target vehicle is not in a straight line driving scene at the current time, the steering wheel zero position self-learning is stopped.

[0113] The target vehicle is subjected to driving behavior detection, which is used to judge whether the target vehicle is in a driver override state at the current time, and a driving behavior detection result is obtained. If the driving behavior detection result indicates that the target vehicle is not in a driver override state at the current time, the target vehicle is subjected to lane parallel detection according to lane line data. If the driving behavior detection result indicates that the target vehicle is in a driver override state at the current time, the steering wheel zero position self-learning is stopped.

[0114] The target vehicle is subjected to lane parallel detection, which is used to judge whether the target vehicle is in a lane parallel driving state at the current time, and a lane parallel detection result is obtained. If the lane parallel detection result indicates that the target vehicle is in a lane parallel driving state, lateral function state detection is performed according to lateral function state information. If the lane parallel detection result indicates that the target vehicle is not in a lane parallel driving state, the steering wheel zero position self-learning is stopped.

[0115] The lateral function state information is subjected to lateral function state detection, which is used to judge whether the lateral function of the target vehicle at the current time is detected to pass, and a lateral function state detection result is obtained. If the lateral function state detection result indicates that the lateral function is detected to pass, it is determined that the target vehicle at the current time satisfies a preset self-learning enabling condition, and the steering wheel zero position self-learning can be started. If the lateral function state detection result indicates that the lateral function is not detected to pass, the steering wheel zero position self-learning is stopped.

[0116] In the method provided by the embodiment of the application, a multi-dimensional and progressive self-learning enabling condition judgment logic is constructed by introducing multiple detection signals, lane line data, driving control data and lateral function state information. First, the system is ensured to be fault-free and stable in driving through vehicle state detection, then the straight driving scene is confirmed relying on the lane line data, the driving control data is combined to exclude the driver's override interference, the driving trajectory is ensured to be compliant through lane parallel detection, and finally the system adaptability is verified through lateral function state detection. This process greatly improves the accuracy and reliability of the self-learning trigger condition, effectively avoids false learning under complex scenes, fault states or human intervention, ensures that zero position self-learning is only performed when the vehicle is in a stable and real straight driving working condition, thereby further improving the accuracy of the zero position angle of the steering system and enhancing the safety and stability of vehicle steering control.

[0117] The embodiment of the application also provides another possible implementation manner of the zero position self-learning method of the vehicle steering system software, and the multiple detection signals further include longitudinal acceleration signals, lateral acceleration signals and yaw rate signals collected by an inertial navigation sensor. Figure 3 As shown in FIG. 3, according to the multiple detection signals and the driving control data, vehicle state detection is performed on the target vehicle, including: Figure 3

[0118] S301, according to the multiple detection signals and the driving control data, communication state detection is performed on the target vehicle.

[0119] S302, if there is no communication fault of the target vehicle, according to the longitudinal acceleration value of the longitudinal acceleration signal, the lateral acceleration value of the lateral acceleration signal and the yaw rate value of the yaw rate signal, it is determined whether the target vehicle is in a steady state driving state.

[0120] In the embodiment, the inertial navigation sensor is an inertial measurement unit (IMU), and the longitudinal acceleration signal, the lateral acceleration signal and the yaw rate signal are obtained from the inertial navigation sensor. The longitudinal acceleration signal, the lateral acceleration signal, the yaw rate signal and the vehicle speed signal are detected in a communication state.

[0121] Specifically, each longitudinal acceleration signal, lateral acceleration signal, yaw rate signal and vehicle speed signal includes a specific numerical value and whether it is in an invalid state, and whether each longitudinal acceleration signal, lateral acceleration signal, yaw rate signal and vehicle speed signal is in an invalid state and whether the duration is greater than 200 ms is detected.

[0122] ​Optionally, the driving manipulation data includes: hand torque, steering wheel rotation speed and steering wheel rotation angle. Wherein, the driving manipulation data is obtained from a steering angle sensor (SAS), and each hand torque, steering wheel rotation speed and steering wheel rotation angle includes a specific value, and whether it is an invalid state, then it is detected whether each hand torque, steering wheel rotation speed and steering wheel rotation angle is an invalid state, and the duration is greater than 200ms, if each hand torque, steering wheel rotation speed and steering wheel rotation angle is not an invalid state, or the invalid state duration is less than or equal to 200ms, and each longitudinal acceleration signal, lateral acceleration signal, yaw rate signal and vehicle speed signal is not an invalid state, or the invalid state duration is less than or equal to 200ms, then it is preliminarily determined that the target vehicle does not exist communication failure.

[0123] In addition, it is necessary to confirm whether each hand torque, steering wheel rotation speed and steering wheel rotation angle, each longitudinal acceleration signal, lateral acceleration signal, yaw rate signal and vehicle speed signal occur packet loss, if none of them occur packet loss, then it is determined that the target vehicle does not exist communication failure.

[0124] Specifically, if the target vehicle does not exist communication failure, then the longitudinal acceleration value and the preset longitudinal acceleration threshold value, the lateral acceleration value and the preset lateral acceleration threshold value, the yaw rate value and the preset yaw rate threshold value are compared respectively to determine whether the target vehicle is in a steady state driving condition.

[0125] Wherein, the preset longitudinal acceleration threshold value can be set to 2m / s 2 , the preset lateral acceleration threshold value can be set to 1m / s 2 , and the preset yaw rate threshold value can be set to 0.02rad / s, then if the longitudinal acceleration value is less than or equal to 2m / s 2 , the lateral acceleration value is less than or equal to 1m / s 2 , and the lateral acceleration value is less than or equal to 0.02rad / s, then it is determined that the target vehicle is in a steady state driving condition.

[0126] In the method provided by the embodiment of the application, the vehicle communication state is first detected to ensure reliable data transmission, and then the longitudinal acceleration, lateral acceleration and yaw rate and other key parameters are combined to judge whether the vehicle is in a steady state driving condition, thereby constructing a layered and progressive vehicle state detection logic. Both invalid data interference caused by communication failure can be excluded, and whether the vehicle is in a stable driving condition such as no sudden acceleration, sudden deceleration, sharp turning and other dynamic changes can be accurately identified, thereby providing a reliable basic state guarantee for subsequent self-learning, avoiding starting self-learning when the vehicle is in a non-steady state or data is abnormal, and further improving the accuracy and effectiveness of zero position self-learning.

[0127] The embodiment of the present application further provides another possible implementation of the zero self-learning method of the vehicle steering system software, and the lane line data includes polynomial coefficients of a current driving road of the target vehicle and a confidence level. Figure 4 As shown in a fourth flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiment of the present application, Figure 4 the target vehicle is detected according to the lane line data, including:

[0128] S401, according to the polynomial coefficients of the current driving road, it is determined that the current driving road of the target vehicle is a straight driving road.

[0129] S402, if the current driving road is a straight driving road, according to the confidence level of the current driving road, it is determined whether the current driving road meets a preset lane line clear condition.

[0130] S403, if the current driving road meets the preset lane line clear condition, it is determined that the current driving scene of the target vehicle is a lane line clear straight driving scene.

[0131] In the embodiment, the lane line data is provided by a camera perception system, the road radius of the current driving road of the target vehicle is determined according to the polynomial coefficients of the current driving road, the road radius of the current driving road of the target vehicle is compared with a preset road radius threshold, if the preset road radius threshold is set to 10000m, the road radius is greater than or equal to 10000m, and the duration is greater than or equal to 200ms, it is determined that the current driving road of the target vehicle is a straight driving road.

[0132] The confidence level of the current driving road includes a left lane line confidence level and a right lane line confidence level, if the current driving road is a straight driving road, the left lane line confidence level is compared with a preset left lane line confidence threshold, and the right lane line confidence level is compared with a preset right lane line confidence threshold. If the preset left lane line confidence threshold is 30%, and the preset right lane line confidence threshold is also 30%, when the left lane line confidence level is greater than or equal to 30%, and the duration is greater than or equal to 200ms, and the right lane line confidence level is greater than or equal to 30%, and the duration is greater than or equal to 200ms, it is determined that the current driving road meets the preset lane line clear condition, and it is determined that the current driving scene of the target vehicle is a lane line clear straight driving scene.

[0133] The method provided in the embodiments of the present application determines whether the road is a straight line through lane line polynomial coefficients, and further verifies the clarity thereof in combination with lane line confidence, thereby forming progressive scene detection logic from road type to lane line quality. Not only is it ensured that the self-learning scene is a straight road, which meets the basic requirement of zero-position learning on driving trajectory, but also the lane line fuzzy and unreliable recognition conditions are excluded through confidence screening, thereby providing an accurate road reference benchmark for subsequent zero-position angle calculation, effectively avoiding learning deviation caused by curved roads or unclear lane lines, and further improving the scene adaptability and result accuracy of self-learning.

[0134] The embodiments of the present application also provide another possible implementation manner of the zero-position self-learning method of vehicle steering system software, Figure 5 As shown in FIG. 5, according to the driving control data, the driving behavior of the target vehicle is detected, including: Figure 5

[0135] S501, determining whether the target vehicle is in the driver control state according to the hand torque and the steering wheel rotation speed.

[0136] S502, if the target vehicle is not in the driver control state, determining that the target vehicle is not in the driver override state.

[0137] In the embodiments, if the hand torque is less than or equal to a preset first hand torque threshold, and the duration is greater than or equal to 200 ms, the steering wheel rotation speed is less than or equal to a preset steering wheel rotation speed threshold, and the duration is greater than or equal to 200 ms, it is determined that the target vehicle is not in the driver control state, and it is determined that the target vehicle is not in the driver override state. The preset first hand torque threshold and the preset steering wheel rotation speed threshold are related to the weight of the target vehicle and increase with the increase of the weight of the target vehicle.

[0138] S503, if the target vehicle is in the driver control state, determining whether the target vehicle enters the turning mode according to the hand torque and the steering wheel rotation angle.

[0139] S504, if the duration of the target vehicle switching to the steering wheel return center state after entering the turning mode is less than or equal to a preset time length, it is determined that the target vehicle is in the driver override state.

[0140] S505, if the duration of the target vehicle switching to the steering wheel return center state is greater than the preset time length, it is determined that the target vehicle is not in the driver override state.

[0141] ​Specifically, if the target vehicle is under the driver's control, it means that the hand torque is greater than the preset first hand torque threshold, or the steering wheel speed is greater than the preset steering wheel speed threshold. Based on the hand torque and steering wheel rotation angle, it is determined whether the target vehicle has entered the turning mode.

[0142] If the hand torque is greater than or equal to a preset second hand torque threshold, and the steering wheel rotation angle is greater than or equal to a first steering wheel rotation angle threshold, then the target vehicle is determined to have entered turning mode. Turning mode includes U-turn mode or large-angle turn mode. If the hand torque is less than or equal to a preset third hand torque threshold, and the steering wheel rotation angle is less than or equal to a second steering wheel rotation angle threshold, then the target vehicle is determined to have switched to the steering wheel centering state after entering turning mode. If the duration of the centering state is less than or equal to a preset duration of 3 seconds, then the target vehicle is determined to still be under the driver's control.

[0143] If the duration of the return-to-center state is greater than the preset duration of 3 seconds, it is determined that the target vehicle is not in a state of driver over-control.

[0144] Specifically, the preset second hand torque threshold is greater than the preset third hand torque threshold, and the preset third hand torque threshold is greater than the preset first hand torque threshold. The preset second hand torque threshold can be set to 1.8 Nm, the preset third hand torque threshold can be set to 0.5 Nm, and the preset first hand torque threshold can be set to 0.1 Nm. The second steering wheel rotation angle threshold is less than the first steering wheel rotation angle threshold.

[0145] The method provided in this application constructs a multi-parameter linked driving behavior detection logic using driving control data such as hand torque, steering wheel speed, and steering angle. First, it determines whether driver control is present based on hand torque and speed. Then, for cases where control is present, it analyzes whether a turning mode has been entered and the duration of the subsequent centering state, combining hand torque and steering angle, thereby accurately distinguishing the driver's over-control state. This effectively eliminates interference from the driver's active steering (such as turning or correcting direction) on zero-position self-learning, allowing self-learning only when the driver has no control or the steering wheel has stably returned to center for a sufficient duration after control. This ensures the learning process is not affected by temporary human intervention, further improving the stability and accuracy of zero-position self-learning.

[0146] This application also provides another possible implementation of a zero-position self-learning method for vehicle steering system software. Figure 6 This is the sixth flowchart illustrating a zero-position self-learning method for vehicle steering system software provided in this application embodiment. Figure 6 As shown, based on lane line data, lane parallelism detection is performed on the target vehicle, including:

[0147] S601. Based on the lane line data, determine the heading angle of the target vehicle relative to the lane line and the aiming deviation.

[0148] S602, determine whether the target vehicle is parallel to the road centerline according to the heading angle of the target vehicle relative to the lane line and the preview deviation.

[0149] S603, if the target vehicle is parallel to the road centerline, determine that the target vehicle is in a lane parallel driving state.

[0150] In this embodiment, according to the data of the lane line, the heading angle of the target vehicle relative to the lane line and the preview deviation are determined, if the heading angle is less than or equal to a preset heading angle threshold, and the duration is greater than or equal to 200 ms, and the preview deviation is less than a preset preview deviation, it is determined that the target vehicle is parallel to the road centerline, and it is determined that the target vehicle is in a lane parallel driving state. Wherein, the preset heading angle threshold can be set to 0.01 rad, and the preset preview deviation can be set to 0.09 m.

[0151] The calculation formula of the preview deviation is as follows:

[0152]

[0153] wherein, (i=1, 2, 3) is the cubic polynomial coefficient of the lane line, is the preview distance, is the whole vehicle yaw rate, is the preview time.

[0154] In the method provided by the embodiment of the application, the heading angle and the preview deviation of the vehicle relative to the lane line are calculated through the lane line data, so as to determine whether the vehicle is parallel to the road centerline, and a lane parallel detection logic based on quantitative parameters is constructed. The vehicle can be accurately identified as whether it is stably driving along the straight line of the lane, rather than deviating from the lane or obliquely driving, so as to ensure that the zero self-learning is performed in the ideal working condition that the vehicle trajectory is consistent with the lane line, avoid the deviation of the zero angle calculation caused by the deviation of the vehicle or the inclination of the trajectory, and further improve the accuracy of the self-learning scene and the reliability of the result.

[0155] The embodiment of the application also provides another possible implementation manner of the zero self-learning method of the vehicle steering system software, Figure 7 As shown in FIG. 7, the flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiment of the application, according to the lateral function state information, the lateral function state detection is performed, including: Figure 7

[0156] S701, if the lateral function state information indicates that the lateral function of the target vehicle is not activated, it is determined that the lateral function detection of the target vehicle is passed.

[0157] ​S702, if the lateral function state information indicates that the lateral function of the target vehicle has been activated and the activation time is greater than the preset time length, and the corresponding performance of the steering function actuator corresponding to the lateral function meets the preset performance constraint, it is determined that the lateral function detection of the target vehicle is passed.

[0158] In the embodiment, the lateral function state detection is performed according to the lateral function state information, to determine whether the lateral function of the target vehicle is activated. If the lateral function state information indicates that the lateral function of the target vehicle is not activated, it is determined that the lateral function detection of the target vehicle is passed, and the steering wheel angle self-learning can be performed.

[0159] If the lateral function state information indicates that the lateral function of the target vehicle has been activated, and the activation time is less than or equal to the preset time length 3s, it is determined that the lateral function detection of the target vehicle is not passed, and the steering wheel angle self-learning is not performed.

[0160] If the lateral function state information indicates that the lateral function of the target vehicle has been activated, and the activation time is greater than the preset time length 3s, it is determined whether the corresponding performance of the steering function actuator corresponding to the lateral function meets the preset performance constraint. Specifically, the difference between the requested steering angle and the actual steering angle of the steering function actuator is calculated, and it is determined whether the difference is less than the preset response threshold. If it is determined that the difference is less than the preset response threshold, it is determined that the corresponding performance of the steering function actuator meets the preset performance constraint, and it is determined that the lateral function detection of the target vehicle is passed, and the steering wheel angle self-learning can be performed.

[0161] If it is determined that the difference is greater than or equal to the preset response threshold, it is determined that the corresponding performance of the steering function actuator does not meet the preset performance constraint, and it is determined that the lateral function detection of the target vehicle is not passed, and the steering wheel angle self-learning is not performed.

[0162] In the method provided by the embodiment of the application, the lateral function state detection logic is constructed by distinguishing whether the lateral function is activated and the state after activation. For the case that the lateral function is not activated, it is directly confirmed that the detection is passed. For the case that the lateral function is activated, the activation time and the actuator performance constraint are verified to ensure that the lateral function is in a stable and reliable working state. The false judgment when the lateral function is not activated is avoided, and the interference when the function is just activated or the actuator performance is not up to standard is excluded, so that the zero self-learning is performed under the condition that the lateral function is adapted and stable, and the environmental adaptability and result accuracy of the self-learning are further improved.

[0163] The embodiment of the application also provides another possible implementation manner of the zero self-learning method of the vehicle steering system software, Figure 8 FIG. 8 is a flowchart of the zero self-learning method of the vehicle steering system software provided by the embodiment of the application, Figure 8As shown, the target steering wheel zero position angle of each target vehicle speed section is determined according to the corresponding steering angle signal and the historical steering wheel zero position angle of each target vehicle speed section, and the method comprises the following steps:

[0164] S801, sampling the steering angle signal corresponding to each target vehicle speed section to obtain a plurality of sampled steering wheel angle values in the current calculation period.

[0165] S802, calculating the average steering wheel angle value as the calculation steering wheel angle value of the current calculation period according to the plurality of sampled steering wheel angle values.

[0166] In this embodiment, each target vehicle speed section has a corresponding current zero drift level, and the pre-design calculation period of each target vehicle speed section is determined according to the current zero drift level. For example, if the current zero drift level of a target vehicle speed section is low, the target vehicle speed section has 20 sampling points as a pre-design calculation period; if the current zero drift level of a target vehicle speed section is medium, the target vehicle speed section has 40 sampling points as a pre-design calculation period; and if the current zero drift level of a target vehicle speed section is high, the target vehicle speed section has 60 sampling points as a pre-design calculation period. Then, the steering angle signal corresponding to each target vehicle speed section is sampled according to the pre-design calculation period to obtain a plurality of sampled steering wheel angle values in the current calculation period.

[0167] After the plurality of sampled steering wheel angle values of the current calculation period are sampled, the average steering wheel angle value is calculated as the calculation steering wheel angle value of the current calculation period.

[0168] It should be noted that the plurality of sampled steering wheel angle values of the current calculation period can be obtained by continuous sampling or non-continuous sampling, and the calculation steering wheel angle value is outputted only after a sufficient number of steering wheel angle values are sampled.

[0169] S803, determining a weighting coefficient according to the calculation steering wheel angle value, the preset learning steering angle threshold and the current zero drift level.

[0170] S804, determining an adjustment coefficient according to the actual steering wheel zero drift value of each target vehicle speed section, the control input zero drift value of the target vehicle lateral function and the calculation steering angle value.

[0171] S805, determining a target weight coefficient according to the weighting coefficient and the adjustment coefficient.

[0172] S806, performing weighted operation on the historical steering wheel zero position angle and the calculation steering angle value according to the target weight coefficient to obtain the target steering wheel zero position angle of each target vehicle speed section.

[0173] Specifically, the historical steering wheel zero position angle and the calculated steering wheel turning angle value of the current calculation period are weighted according to the target weight coefficient to obtain the steering wheel zero position angle of the current calculation period, and the calculation formula is represented as:

[0174]

[0175] wherein, represents the steering wheel zero position angle of the current calculation period, represents the steering wheel zero position angle of the previous calculation period, represents the serial number of the target vehicle speed section, i represents the serial number of the calculation period, represents the weighting coefficient, represents the adjustment coefficient, represents the calculated steering wheel turning angle value of the current calculation period, and it can be understood that if the current calculation period is the first calculation period after the target vehicle is powered on, the steering wheel zero position angle of the previous calculation period is the historical steering wheel zero position angle, until the steering wheel zero position angle of the last calculation period is obtained and used as the target steering wheel zero position angle of the target vehicle speed section, so as to obtain the target steering wheel zero position angle of each target vehicle speed section.

[0176] It should be noted that before the historical steering wheel zero position angle and the calculated steering wheel turning angle value are weighted according to the target weight coefficient, it is necessary to determine whether the historical steering wheel zero position angle exceeds the preset maximum steering wheel zero position angle. If it is determined that the historical steering wheel zero position angle exceeds the preset maximum steering wheel zero position angle, the historical steering wheel zero position angle is discarded and 0 is used for weighting operation; if it is determined that the historical steering wheel zero position angle does not exceed the preset maximum steering wheel zero position angle, the historical steering wheel zero position angle is used for weighting operation.

[0177] According to the calculated steering wheel turning angle value, the preset learning turning angle threshold and the current zero drift level, the weighting coefficient is determined. Specifically, if the current zero drift level of a target vehicle speed section is high, it is determined whether the calculated steering wheel turning angle value is less than the preset learning turning angle threshold. If the calculated steering wheel turning angle value is less than the preset learning turning angle threshold, the weighting coefficient at this time is determined to be 0.05. If the current zero drift level of a target vehicle speed section is medium or low, the weighting coefficient at this time is determined to be 1.

[0178] According to the actual steering wheel zero drift value, the control input zero drift value of the lateral function of the target vehicle, and the calculated steering angle value, the adjustment coefficient is determined from a preset adjustment coefficient table, wherein the preset adjustment coefficient table is composed of the difference between the actual steering wheel zero drift value, the control input zero drift value of the lateral function, and the calculated steering angle value. According to the actual steering wheel zero drift value, the control input zero drift value of the lateral function of the target vehicle, and the calculated steering angle value, the adjustment coefficient can be determined. The target weight coefficient is the product of the weighting coefficient and the adjustment coefficient.

[0179] It should be noted that when the target vehicle is first learned after being delivered, or when the controller memory unit is cleared and the steering wheel zero position self-learning is performed again, if the learning value in the current target speed segment is zero, it indicates that the target speed segment has not collected enough sample steering wheel angle values for one calculation period, and the calculated steering wheel angle value is not output, and the target steering wheel zero position angle of the target speed segment is not obtained. Then, the zero position deviation of the adjacent two speed segments is predicted, and when the target steering wheel zero position angle of the adjacent two speed segments is not zero, the value that is not zero is used, for example: if the target steering wheel zero position angle of the target speed segment is represented as , and the target steering wheel zero position angle of the previous speed segment is represented as , and the target steering wheel zero position angle of the next speed segment is represented as , then the target steering wheel zero position angle of the target speed segment is the target steering wheel zero position angle of the next speed segment, represented as: .

[0180] When the target steering wheel zero position angle of the adjacent two speed segments is not zero, the average of the target steering wheel zero position angles of the adjacent two speed segments is used as the target steering wheel zero position angle of the target speed segment, for example: if the target steering wheel zero position angle of the target speed segment is represented as , and the target steering wheel zero position angle of the previous speed segment is represented as , and the target steering wheel zero position angle of the next speed segment is represented as , then the target steering wheel zero position angle of the target speed segment is the average of the target steering wheel zero position angles of the adjacent two speed segments, represented as: .

[0181] If the current target speed segment is the minimum speed segment or the maximum speed segment, the learning value of the adjacent speed segment that is not zero is used, for example:

[0182] If the target steering wheel zero position angle of the target speed segment, i.e. the first speed segment, is represented as , and the target steering wheel zero position angle of the second speed segment is represented as , the target steering wheel zero position angle of the target vehicle speed section, i.e., the first vehicle speed section, is the target steering wheel zero position angle of the second vehicle speed section, denoted as .

[0183] If the target steering wheel zero position angle of the target vehicle speed section, i.e., the maximum vehicle speed section, is denoted as , and the target steering wheel zero position angle of the previous vehicle speed section is denoted as , then the target steering wheel zero position angle of the target vehicle speed section, i.e., the maximum vehicle speed section, is the target steering wheel zero position angle of the previous vehicle speed section, denoted as , where j is the maximum value. Since floating-point numbers or fixed-point numbers are used in the controller for calculation, 0.001 or other smaller values are used to determine whether a variable is zero.

[0184] If the target steering wheel zero position angle of only one vehicle speed section of the multiple vehicle speed sections is not 0, then the target steering wheel zero position angles of the remaining vehicle speed sections are adjusted to be the target steering wheel zero position angle of the vehicle speed section.

[0185] It should be noted that the initial value of the controller is set to 0 when the target vehicle is offline, i.e., when it is shipped. Then, before each power-off of the target vehicle, the learning value, i.e., the target steering wheel zero position angle of each target vehicle speed section, is stored. If the target vehicle is powered off within 30 seconds, the target steering wheel zero position angle of each target vehicle speed section is not updated, to prevent the recording of a new target steering wheel zero position angle that has not reached stability or the abnormal clearing of the last learned target steering wheel zero position angle.

[0186] In the method provided by the embodiments of the present application, the average value of the angle signals of the target vehicle speed section is taken as the calculation basis, the weighting coefficient is determined in combination with the calculated angle value, the preset threshold value, and the zero drift level, the adjustment coefficient is determined according to the actual zero drift value and the control input zero drift value, and finally the target zero position angle is determined by performing weighted operation on the historical zero position angle and the calculated angle value through the target weight coefficient. The stability of the current sampling data, the influence of the zero drift factor, and the reference significance of the historical data are comprehensively considered, the dynamic and accurate correction of the zero position angle is realized, the deviation caused by single data fluctuation is avoided, the zero reference is gradually optimized, and the calculation accuracy and reliability of the steering wheel zero position angle under different vehicle speed sections are effectively improved.

[0187] The embodiments of the present application also provide another possible implementation manner of the zero self-learning method of the vehicle steering system software, and before determining the weighting coefficient according to the calculated steering wheel angle value, the preset learning angle threshold value, and the current zero drift level, the method further includes:

[0188] If the lateral function of the target vehicle is in an activated state, the current zero drift level is determined according to the historical steering wheel zero position angle.

[0189] If the lateral function of the target vehicle is in the inactive state, the preset minimum zero drift level is determined as the current zero drift level.

[0190] In the embodiment, the historical steering wheel zero position angle indicates the historical steering wheel zero position angle of each vehicle speed stage stored in the steering system software before the target vehicle is powered on, or the newly learned steering wheel zero position angle of each vehicle speed stage after the target vehicle is powered on. If the lateral function of the target vehicle is in the active state, the historical steering wheel zero position angle, the first zero position angle threshold and the second zero position angle threshold are compared. If it is determined that the historical steering wheel zero position angle is less than the first zero position angle threshold, the current zero drift level of the current vehicle speed stage is determined as the low level. If it is determined that the historical steering wheel zero position angle is greater than or equal to the first zero position angle threshold and less than the second zero position angle threshold, the current zero drift level of the current vehicle speed stage is determined as the medium level. If it is determined that the historical steering wheel zero position angle is greater than or equal to the second zero position angle threshold, the current zero drift level of the current vehicle speed stage is determined as the high level. And the zero drift level in the current power-on cycle can only be unidirectionally raised, and the zero drift level is not powered off. The zero drift level is rejudged each time the vehicle is powered on.

[0191] If the lateral function of the target vehicle is in the inactive state, the preset minimum zero drift level is determined as the current zero drift level.

[0192] In the method provided by the embodiment of the present application, the current zero drift level is dynamically determined by combining the activation state of the vehicle lateral function. When the lateral function is activated, the zero drift level is accurately matched according to the historical steering wheel zero position angle. When the lateral function is not activated, the preset minimum zero drift level is directly used. The zero drift level determination logic is constructed to adapt to the state of the lateral function. The pertinence and accuracy of the zero drift level when the lateral function is activated are ensured, which provides a reference basis for the subsequent weighting coefficient calculation that fits the actual working conditions. When the lateral function is not activated, the determination process is simplified and the basic reliability is ensured, which avoids the deviation of the weighting coefficient caused by the misjudgment of the zero drift level, and further improves the accuracy and adaptability of the calculation of the target steering wheel zero position angle.

[0193] The zero self-learning device and the control device of the vehicle steering system software provided by any of the above embodiments of the present application are further explained as follows. The specific implementation process and the technical effects are the same as those of the corresponding method embodiments. For brief description, the parts not mentioned in the embodiment can be referred to the corresponding contents in the method embodiments.

[0194] Figure 9 A functional module schematic diagram of a zero self-learning device of a vehicle steering system software provided by the embodiment of the present application is shown in FIG. 1. Figure 9 As shown in FIG. 1, the zero self-learning device 100 of the vehicle steering system software includes:

[0195] The acquisition module 110 is configured to acquire running data of the target vehicle in a preset time period before the current time, wherein the running data comprises a vehicle speed signal and a corresponding steering wheel angle signal.

[0196] The acquisition module 110 is further configured to acquire historical steering wheel zero position angles of the target vehicle corresponding to a plurality of preset vehicle speed segments of the steering system software of the target vehicle.

[0197] The determination module 120 is configured to determine whether the target vehicle satisfies a preset self-learning enabling condition at the current time according to the running data.

[0198] The segmentation module 130 is configured to, if the target vehicle satisfies the preset self-learning enabling condition at the current time, segment the vehicle speed signal according to vehicle speed ranges of the plurality of preset vehicle speed segments, to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal.

[0199] The determination module 140 is configured to determine a steering angle signal corresponding to the at least one target vehicle speed segment from the steering wheel angle signal.

[0200] The determination module 140 is further configured to determine a target steering wheel zero position angle of each target vehicle speed segment according to the steering angle signal corresponding to each target vehicle speed segment and the historical steering wheel zero position angle of each target vehicle speed segment.

[0201] The update module 150 is configured to update the historical steering wheel zero position angle of each target vehicle speed segment to the target steering wheel zero position angle.

[0202] Optionally, the running data further comprises a plurality of detection signals corresponding to the vehicle speed signal, lane line data of a current lane recognized by a camera perception system, driving control data, and lateral function state information; the determination module 120 is further configured to perform vehicle state detection on the target vehicle according to the plurality of detection signals and the driving control data; if a vehicle state detection result indicates that the target vehicle has no communication fault and is in a steady-state driving state, perform driving scene detection on the target vehicle according to the lane line data; if a driving scene detection result indicates that the target vehicle is in a straight-line driving scene, perform driving behavior detection on the target vehicle according to the driving control data; if a driving behavior detection result indicates that the target vehicle is not in a driver override state, perform lane parallel detection on the target vehicle according to the lane line data; if a lane parallel detection result indicates that the target vehicle is in a lane parallel driving state, perform lateral function state detection according to the lateral function state information; if a lateral function state detection result indicates that the lateral function detection passes, it is determined that the target vehicle satisfies the preset self-learning enabling condition at the current time.

[0203] Optionally, the multiple detection signals also include: longitudinal acceleration signal, lateral acceleration signal and yaw rate signal collected by the inertial navigation sensor; the judgment module 120 is also used to detect the communication status of the target vehicle based on the multiple detection signals and driving control data; if the target vehicle does not have a communication fault, it determines whether the target vehicle is in a steady-state driving state based on the longitudinal acceleration value of the longitudinal acceleration signal, the lateral acceleration value of the lateral acceleration signal and the yaw rate value of the yaw rate signal.

[0204] Optionally, the lane line data includes: the polynomial coefficients of the current driving road of the target vehicle, and the confidence level; the judgment module 120 is further used to determine that the current driving road is a straight driving road based on the polynomial coefficients of the current driving road; if the current driving road is a straight driving road, then based on the confidence level of the current driving road, determine whether the current driving road meets the preset lane line clarity condition; if the current driving road meets the preset lane line clarity condition, then determine that the current driving scenario of the target vehicle is a straight driving scenario with clear lane lines.

[0205] Optionally, the driving control data includes: hand torque, steering wheel speed, and steering wheel angle; the judgment module 120 is also used to determine whether the target vehicle is under driver control based on the hand torque and steering wheel speed; if the target vehicle is not under driver control, it is determined that the target vehicle is not under driver over-control; if the target vehicle is under driver control, it is determined whether the target vehicle has entered turning mode based on the hand torque and steering wheel angle; if the duration of the target vehicle switching to the steering wheel centering state after entering turning mode is less than or equal to a preset duration, it is determined that the target vehicle is under driver over-control; if the duration of the target vehicle switching to the steering wheel centering state is greater than the preset duration, it is determined that the target vehicle is not under driver over-control.

[0206] Optionally, the judgment module 120 is also used to determine the heading angle and aiming deviation of the target vehicle relative to the lane line based on the lane line data; determine whether the target vehicle is parallel to the center line of the road based on the heading angle and aiming deviation of the target vehicle relative to the lane line; if the target vehicle is parallel to the center line of the road, then it is determined that the target vehicle is in a parallel driving state.

[0207] Optionally, the judgment module 120 is further configured to determine that the lateral function detection of the target vehicle has passed if the lateral function status information indicates that the lateral function of the target vehicle is not activated; and to determine that the lateral function detection of the target vehicle has passed if the lateral function status information indicates that the lateral function of the target vehicle has been activated and the activation time is greater than a preset duration, and the corresponding performance of the steering function actuator corresponding to the lateral function meets the preset performance constraints.

[0208] Optionally, the determining module 140 is further configured to sample the steering wheel rotation angle signal corresponding to each target vehicle speed segment in a preset calculation period to obtain a plurality of sampled steering wheel rotation angle values in the current calculation period; calculate an average steering wheel rotation angle value as a calculation steering wheel rotation angle value of the current calculation period according to the plurality of sampled steering wheel rotation angle values; determine a weighting coefficient according to the calculation steering wheel rotation angle value, a preset learning rotation angle threshold value and the current zero drift level; determine an adjustment coefficient according to the actual steering wheel zero drift value of each target vehicle speed segment, the control input zero drift value of the lateral function of the target vehicle and the calculation steering wheel rotation angle value; determine a target weight coefficient according to the weighting coefficient and the adjustment coefficient; and perform a weighted operation on the historical steering wheel zero position angle and the calculation steering wheel rotation angle value to obtain a target steering wheel zero position angle of each target vehicle speed segment according to the target weight coefficient.

[0209] Optionally, the determining module 140 is further configured to determine the current zero drift level according to the historical steering wheel zero position angle if the lateral function of the target vehicle is in an activated state, and determine a preset minimum zero drift level as the current zero drift level if the lateral function of the target vehicle is in an inactivated state.

[0210] The apparatus is used for executing the method provided by the foregoing embodiments, and has similar implementation principles and technical effects, which will not be described here.

[0211] The above modules can be one or more integrated circuits configured to implement the above method, for example, one or more application specific integrated circuits (ASICs), or one or more microprocessors, or one or more field programmable gate arrays (FPGAs), etc. For another example, when the above module is implemented in the form of a processing element scheduling code, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can invoke program code. For another example, the modules can be integrated together to be implemented in the form of a system on a chip (SOC).

[0212] Figure 10 A schematic diagram of a control device provided by an embodiment of the present application is shown in FIG. 2. The control device can be used for zero position self-learning of vehicle steering system software. As shown in FIG. 2, the control device includes a processor 210, a storage medium 220 and a bus 230. Figure 10

[0213] ​The storage medium 220 stores machine readable instructions executable by the processor 210. When the control device is running, the processor 210 communicates with the storage medium 220 through the bus 230. The processor 210 executes the machine readable instructions to perform the steps of the method embodiments described above. The specific implementation and technical effects are similar, and will not be repeated here.

[0214] Optionally, the present application also provides a storage medium 220, which stores a computer program. When the computer program is run by the processor, it performs the steps of the method embodiments described above. The specific implementation and technical effects are similar, and will not be repeated here.

[0215] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of the units is merely a logical function division. There can be another division manner for the actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0216] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0217] In addition, each functional unit in the various embodiments of the present application can be integrated into a processing unit, or each unit can be a physically independent unit, or two or more units can be integrated into a unit. The integrated unit can be implemented in the form of hardware, or in the form of hardware plus software function units.

[0218] The integrated unit in the form of software function unit can be stored in a computer readable storage medium. The software function unit is stored in a storage medium, and includes a plurality of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute part of steps of the method according to various embodiments of the present application. The storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various storage media capable of storing program codes.

[0219] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A zero-position self-learning method for vehicle steering system software, characterized in that, The method includes: Obtain the target vehicle's operating data for a preset time period prior to the current moment, wherein the operating data includes: vehicle speed signal and corresponding steering wheel angle signal; Obtain the historical steering wheel zero angle for multiple preset vehicle speed ranges that are stored in advance for the steering system software in the target vehicle; Based on the operational data, it is determined whether the target vehicle meets the preset self-learning enable conditions at the current moment; If the current time satisfies the preset self-learning enable condition, the vehicle speed signal is segmented according to the vehicle speed range of the multiple preset vehicle speed segments to obtain at least one target vehicle speed segment corresponding to the vehicle speed signal. Determine the angle signal corresponding to the at least one target vehicle speed segment from the steering wheel angle signal; The target steering wheel zero angle for each target speed segment is determined based on the steering angle signal corresponding to each target speed segment and the historical steering wheel zero angle for each target speed segment. Update the historical steering wheel zero angle for each target vehicle speed segment to the target steering wheel zero angle; The operational data also includes: multiple detection signals corresponding to the vehicle speed signal, lane line data of the current driving vehicle identified by the camera perception system, driving control data, and lateral function status information; The step of determining whether the target vehicle meets the preset self-learning enable conditions at the current moment based on the operational data includes: Based on the multiple detection signals and the driving control data, the vehicle status of the target vehicle is detected; If the vehicle status detection result indicates that the target vehicle has no communication failure and is in a steady-state driving state, then the driving scenario detection is performed on the target vehicle based on the lane line data. If the driving scenario detection result indicates that the target vehicle is in a straight driving scenario, then driving behavior detection is performed on the target vehicle based on the driving control data; If the driving behavior detection result indicates that the target vehicle is not in a state of driver over-control, then lane parallelism detection is performed on the target vehicle based on the lane line data; If the lane parallel detection result indicates that the target vehicle is in a lane parallel driving state, then lateral function state detection is performed based on the lateral function state information. If the horizontal function status detection result indicates that the horizontal function detection has passed, then it is determined that the current moment meets the preset self-learning enable condition. The driving control data includes: hand torque, steering wheel speed, and steering wheel angle; the driving behavior detection of the target vehicle based on the driving control data includes: Based on the hand torque and the steering wheel speed, determine whether the target vehicle is under driver control. If the target vehicle is not under driver control, then it is determined that the target vehicle is not under driver over-control. If the target vehicle is under driver control, then the driver determines whether the target vehicle has entered a turning mode based on the hand torque and the steering wheel rotation angle. If the duration for which the target vehicle switches to the steering wheel centering state after entering the turning mode is less than or equal to a preset duration, then the target vehicle is determined to be in a state of driver over-control. If the duration of the target vehicle switching to the steering wheel centering state is greater than the preset duration, then it is determined that the target vehicle is not in a state of driver over-control.

2. The method according to claim 1, characterized in that, The multiple detection signals also include: longitudinal acceleration signal, lateral acceleration signal and yaw rate signal collected by the inertial navigation sensor; The step of detecting the vehicle status of the target vehicle based on the multiple detection signals and the driving control data includes: Based on the multiple detection signals and the driving control data, the communication status of the target vehicle is detected; If the target vehicle does not have a communication fault, then the target vehicle is determined to be in a steady-state driving state based on the longitudinal acceleration value of the longitudinal acceleration signal, the lateral acceleration value of the lateral acceleration signal, and the yaw rate value of the yaw rate signal.

3. The method according to claim 1, characterized in that, The lane line data includes: the polynomial coefficients of the target vehicle's current driving road, and the confidence level; the driving scene detection of the target vehicle based on the lane line data includes: Based on the polynomial coefficients of the current driving road, the current driving road is determined to be a straight driving road; If the current driving road is a straight driving road, then based on the confidence level of the current driving road, determine whether the current driving road meets the preset lane line clear condition; If the current driving road meets the preset lane line clear condition, then the current driving scenario of the target vehicle is determined to be a straight driving scenario with clear lane lines.

4. The method according to claim 1, characterized in that, The step of performing lane parallelism detection on the target vehicle based on the lane line data includes: Based on the lane line data, determine the heading angle of the target vehicle relative to the lane line and the aiming deviation; Based on the heading angle and aiming deviation of the target vehicle relative to the lane line, determine whether the target vehicle is parallel to the center line of the road; If the target vehicle is parallel to the center line of the road, then the target vehicle is determined to be in a parallel driving state in the lane.

5. The method according to claim 1, characterized in that, The step of detecting the horizontal functional status based on the horizontal functional status information includes: If the lateral function status information indicates that the lateral function of the target vehicle is not activated, then the lateral function detection of the target vehicle is determined to be successful. If the lateral function status information indicates that the lateral function of the target vehicle has been activated and the activation time is greater than a preset duration, and the corresponding performance of the steering function actuator corresponding to the lateral function meets the preset performance constraints, then the lateral function detection of the target vehicle is determined to be successful.

6. The method according to claim 1, characterized in that, The step of determining the target steering wheel zero angle for each target speed segment based on the steering angle signal corresponding to each target speed segment and the historical steering wheel zero angle for each target speed segment includes: Within a preset calculation period, the steering angle signal corresponding to each target vehicle speed segment is sampled to obtain multiple sampled steering wheel angle values ​​within the current calculation period; Based on the multiple sampled steering wheel angle values, the average steering wheel angle value is calculated as the calculated steering wheel angle value for the current calculation cycle; The weighting coefficients are determined based on the calculated steering wheel angle value, the preset learning angle threshold, and the current zero-drift level. Based on the actual steering wheel zero-drift value for each target speed segment, the control input zero-drift value of the lateral function in the target vehicle, and the calculated steering angle value, an adjustment coefficient is determined. The target weight coefficient is determined based on the weighting coefficient and the adjustment coefficient. Based on the target weighting coefficient, the historical steering wheel zero angle and the calculated steering angle value are weighted and calculated to obtain the target steering wheel zero angle for each target speed segment.

7. The method according to claim 6, characterized in that, Before determining the weighting coefficient based on the calculated steering wheel angle value, the preset learning angle threshold, and the current zero-drift level, the method further includes: If the lateral function of the target vehicle is active, the current zero-drift level is determined based on the historical steering wheel zero angle. If the lateral function of the target vehicle is not activated, the preset minimum zero-drift level is determined as the current zero-drift level.

8. A control device, characterized in that, include: The system includes a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the control device is running, the processor communicates with the storage medium via the bus. The processor executes the program instructions to perform the steps of the zero-position self-learning method for vehicle steering system software as described in any one of claims 1 to 7.

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