A processing method for online identification of steering wheel zero offset based on driving state

Abstract

An embodiment of the present invention relates to a processing method for online identification of the zero bias of the steering wheel based on the driving state. The method includes: at time t, obtaining the driving state of the vehicle; and obtaining three groups of angular displacement sampling data sequences from time t - k - 1 to time t according to a preset specified length k; confirming whether the execution condition for online identification of the zero bias of the steering wheel is satisfied based on the driving state; if it is confirmed that the condition is satisfied, determining the zero bias iteration function of the steering wheel angle; and selecting two corresponding sequences based on the driving state for estimating the steering wheel delay period; and substituting the two corresponding sequences and the steering wheel delay period into the zero bias iteration function of the steering wheel angle based on the driving state for iterative processing to obtain the zero bias amount of the steering wheel angle at time t; and performing a warning process when the zero bias amount of the steering wheel angle exceeds a threshold. Through the present invention, the zero bias of the vehicle steering wheel can be monitored in real time, improving the safety guarantee of vehicle driving.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to a processing method for online identification of steering wheel zero bias based on driving state. Background Art

[0002] In the autonomous driving state, the lateral control accuracy has high requirements for the calibration of the steering wheel zero position deviation (i.e., the steering wheel degree when the front wheel angle is 0 degrees). If the calibration is inaccurate, the vehicle will continuously deviate to one side. Currently, there is only one way to solve this type of problem, which is to send the vehicle to a professional 4S store for steering wheel zero bias identification, and perform feedforward compensation on the vehicle control module based on the identification result. This processing method is also called the offline identification method of steering wheel zero bias. Obviously, the offline identification method does not have real-time performance. Summary of the Invention

[0003] The purpose of the present invention is to provide a processing method, an electronic device, and a computer-readable storage medium for online identification of steering wheel zero bias based on driving state, aiming at the defects of the existing technology. During the vehicle driving process, the driving state of the vehicle is automatically identified, and corresponding online zero bias identification processing is performed based on the identification result of the driving state (autonomous driving state or manual driving state). Through the present invention, the steering wheel zero bias of the vehicle can be monitored in real time, thereby improving the sensitivity of the vehicle to the change of the steering wheel zero bias and enhancing the vehicle safety driving guarantee.

[0004] To achieve the above purpose, a first aspect of an embodiment of the present invention provides a processing method for online identification of steering wheel zero bias based on driving state, and the method includes:

[0005] At time t, obtain the driving state of the vehicle; and obtain three groups of corner sampling data sequences from time t - k - 1 to time t according to a preset specified length k; the driving state includes an autonomous driving state and a manual driving state; the three groups of corner sampling data sequences include the vehicle pose estimation corner sequence δ p , the steering wheel control corner sequence δ c and the steering wheel control feedback corner sequence δ s ;

[0006] Based on the driving state, confirm whether the execution condition for online identification of steering wheel zero bias has been met;

[0007] If it is confirmed that the execution condition for online identification of steering wheel zero bias has been met, then determine the steering wheel corner zero bias iteration function;

[0008] And based on the driving state, select two corresponding sequences from the three groups of corner sampling data sequences, and perform steering wheel delay period estimation processing to generate the corresponding steering wheel delay period;

[0009] And select two corresponding sequences from the three groups of corner sampling data sequences based on the driving state, and substitute the two corresponding sequences and the corresponding steering wheel delay period into the steering wheel corner zero bias iteration function for iterative processing to obtain the steering wheel corner zero bias at time t;

[0010] And perform warning processing when the steering wheel corner zero bias exceeds a preset zero bias threshold.

[0011] Preferably, the vehicle pose estimation corner sequence δ p is (δ p,1 … δ p,i … δ p,k ), the steering wheel control corner sequence δ c is (δ c,1 … δ c,i … δ c,k ), the steering wheel control feedback corner sequence δ s is (δ s,1 … δ s,i … δ s,k ), 1 ≤ i ≤ k.

[0012] Preferably, determining whether the execution condition of the steering wheel zero bias online identification based on the driving state is satisfied specifically includes:

[0013] When the driving state is an autonomous driving state, obtain the lateral planning error, heading planning error, front wheel control steering angle, front wheel feedback steering angle and real-time vehicle speed of the vehicle; and when the lateral planning error, the heading planning error, the front wheel control steering angle and the front wheel feedback steering angle are all less than their respective specified thresholds and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the steering wheel zero bias online identification is satisfied;

[0014] When the driving state is a manual driving state, obtain the front wheel feedback steering angle and the real-time vehicle speed of the vehicle; and when the front wheel feedback steering angle is less than the corresponding specified threshold and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the steering wheel zero bias online identification is satisfied.

[0015] Preferably, determining the steering wheel corner zero bias iteration function specifically includes:

[0016] Determine the steering wheel corner zero bias iteration function as

[0017]

[0018] Wherein, is the steering wheel corner zero bias iteration result corresponding to the sorting index i of the three groups of corner sampling data sequences, is the preset initial value of the zero offset of the steering wheel angle, α is the iterative weight parameter, and its value range is between 0 and 1, and z j-1 is the iterative variable factor.

[0019] Preferably, two corresponding sequences are selected from the three groups of corner sampling data sequences based on the driving state, and the steering wheel delay period is estimated and processed to generate the corresponding steering wheel delay period, which specifically includes:

[0020] When the driving state is the autonomous driving state, the vehicle pose estimation corner sequence δ p and the steering wheel control corner sequence δ c are selected as the two corresponding sequences; and according to the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) for autonomous driving state steering wheel delay period estimation processing to generate the corresponding steering wheel delay period;

[0021] When the driving state is the manual driving state, the vehicle pose estimation corner sequence δ p and the steering wheel control feedback corner sequence δ s are selected as the two corresponding sequences; and according to the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback corner sequence δ s (δ s,1 …δ s,i …δ s,k ) for manual driving state steering wheel delay period estimation processing to generate the corresponding steering wheel delay period.

[0022] Furthermore, the autonomous driving state steering wheel delay period is estimated and processed according to the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) to generate the corresponding steering wheel delay period, which specifically includes:

[0023] Set the steering wheel delay cycle variable as x, and use the vehicle pose to estimate the corner sequence δ p For the steering wheel control corner sequence δ c Determine the first objective function for the delay reference sequence of

[0024]

[0025] Solve for the value of the steering wheel delay cycle variable x that minimizes the first objective function, and use the solution result as the steering wheel delay cycle d1 corresponding to the autopilot state.

[0026]

[0027] Furthermore, the vehicle pose is used to estimate the corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback corner sequence δ s (δ s,1 …δ s,i …δ s,k ) are used to perform manual driving state steering wheel delay cycle estimation processing to generate the corresponding steering wheel delay cycle, specifically including:

[0028] Set the steering wheel delay cycle variable as x, and use the vehicle pose to estimate the corner sequence δ p For the steering wheel control feedback corner sequence δ s Determine the second objective function for the delay reference sequence of

[0029]

[0030] Solve for the value of the steering wheel delay cycle variable x that minimizes the second objective function, and use the solution result as the steering wheel delay cycle d2 corresponding to the manual driving state.

[0031]

[0032] Preferably, based on the driving state, two corresponding sequences are selected from the three groups of corner sampling data sequences, and the corresponding two sequences and the corresponding steering wheel delay cycle are substituted into the steering wheel corner zero bias iteration function for iterative processing to obtain the steering wheel corner zero bias at time t, specifically including:

[0033] When the driving state is the autopilot state, select the vehicle pose estimation corner sequence δ p and the steering wheel control corner sequence δ cAs two corresponding sequences; and determine the corresponding iterative variable factor expression according to the corresponding steering wheel delay period d1 as And based on the currently determined iterative variable factor expression, the vehicle pose estimated corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) are substituted into the steering wheel corner zero bias iterative function to perform an operation on the steering wheel corner zero bias at i = k to obtain And use the current iteration result As the steering wheel corner zero bias at time t;

[0034] When the driving state is the manual driving state, select the vehicle pose estimated corner sequence δ p And the steering wheel control feedback corner sequence δ s From the three groups of corner sampling data sequences as two corresponding sequences; and according to the corresponding iterative variable factor expression corresponding to the steering wheel delay period d2 as And based on the currently determined iterative variable factor expression, the vehicle pose estimated corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback corner sequence δ s (δ s,1 …δ s,i …δ s,k ) are substituted into the steering wheel corner zero bias iterative function to perform an operation on the steering wheel corner zero bias at i = k to obtain And use the current iteration result As the steering wheel corner zero bias at time t.

[0035] A second aspect of the embodiments of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

[0036] The processor is used to be coupled with the memory, read and execute the instructions in the memory to implement the method steps described in the first aspect above;

[0037] The transceiver is coupled with the processor, and the processor controls the transceiver to perform message sending and receiving.

[0038] In a third aspect of the embodiments of the present invention, there is provided a computer-readable storage medium storing computer instructions, which, when executed by a computer, cause the computer to execute the instructions of the method described in the first aspect above.

[0039] The embodiments of the present invention provide a processing method, an electronic device, and a computer-readable storage medium for online identification of the zero offset of a steering wheel based on the driving state. During the driving process of a vehicle, the driving state of the vehicle is automatically identified, and corresponding online zero-offset identification processing is performed based on the identification result of the driving state (autopilot state or manual driving state). Through the present invention, the zero offset of the vehicle steering wheel can be monitored in real time, thereby improving the sensitivity of the vehicle to changes in the zero offset of the steering wheel and enhancing the safety guarantee of vehicle driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of a processing method for online identification of the zero offset of a steering wheel based on the driving state provided in Embodiment 1 of the present invention;

[0041] Figure 2 It is a schematic structural diagram of an electronic device provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] Embodiment 1 of the present invention provides a processing method for online identification of the zero offset of a steering wheel based on the driving state, as Figure 1 shown in the schematic diagram of the processing method for online identification of the zero offset of a steering wheel based on the driving state provided in Embodiment 1 of the present invention. The method mainly includes the following steps:

[0044] Step 1, at time t, obtain the driving state of the vehicle; and obtain three groups of corner sampling data sequences from time t - k - 1 to time t according to a preset specified length k;

[0045] Among them, the driving state includes an autopilot state and a manual driving state; the three groups of corner sampling data sequences include a vehicle pose estimation corner sequence δ p , a steering wheel control corner sequence δ c , and a steering wheel control feedback corner sequence δ s ; the vehicle pose estimation corner sequence δ p is (δ p,1…δ p,i …δ p,k ),the steering wheel control angle sequence δ c is (δ c,1 …δ c,i …δ c,k ),the steering wheel control feedback angle sequence δ s is (δ s,1 …δ s,i …δ s,k ), 1 ≤ i ≤ k.

[0046] Here, the vehicle pose estimation angle sequence δ p in δ p,i is the angle information estimated in real time according to the real-time road curvature ρ obtained at the vehicle chassis module i and the front wheel steering angle δ p,i = arctan(road curvature ρ i * vehicle wheelbase L), and in the embodiments of the present invention, it is used as the actual observation information; the steering wheel control angle sequence δ c in δ c,i is the real-time control angle information sent to the steering wheel at the corresponding moment of i; the steering wheel control feedback angle sequence δ s in δ s,i is the feedback angle information received at the corresponding moment of i for the steering wheel control angle information sent at a previous moment, and theoretically, this feedback angle information should be approximate to the corresponding control angle information. It should be noted that for a vehicle in the autonomous driving state, the above three groups of angle sampling data sequences can be obtained, but for a vehicle in the manual driving state, only the vehicle pose estimation angle sequence δ p and the steering wheel control feedback angle sequence δ s can be guaranteed to be obtained.

[0047] Step 2, based on the driving state, confirm whether the execution condition of the online identification of the steering wheel zero bias has been met;

[0048] Specifically, it includes: Step 21, when the driving state is the autonomous driving state, obtain the lateral planning error, heading planning error, front wheel control steering angle, front wheel feedback steering angle, and real-time vehicle speed of the vehicle; and when the lateral planning error, heading planning error, front wheel control steering angle, and front wheel feedback steering angle are all less than their respective specified thresholds and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the online identification of the steering wheel zero bias has been met;

[0049] Here, the real-time lateral planning error refers to the lateral position error between the current position of the vehicle and the planned trajectory, and the heading planning error refers to the heading angle error between the current position of the vehicle and the planned trajectory. The lateral planning error and the heading planning error can be obtained from the vehicle planning module; the real-time front-wheel control steering angle and the front-wheel feedback steering angle can be obtained from the vehicle control module; the real-time vehicle speed can be obtained from the vehicle chassis module. When the lateral planning error, the heading planning error, the front-wheel control steering angle, and the front-wheel feedback steering angle are all less than their respective specified thresholds and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, it indicates that the current automatic driving condition of the vehicle is normal, the interference error is not obvious, and the vehicle is not in a parking, turning, or lane-changing state. At this time, online zero-offset identification of the steering wheel can be performed on the vehicle.

[0050] Step 22, when the driving state is the manual driving state, obtain the front-wheel feedback steering angle and the real-time vehicle speed of the vehicle; and when the front-wheel feedback steering angle is less than the corresponding specified threshold and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the online zero-offset identification of the steering wheel has been met.

[0051] Here, because it is in the manual driving state, there is no real-time lateral planning error, heading planning error, and front-wheel control steering angle information; when the front-wheel feedback steering angle is less than the corresponding specified threshold and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, it indicates that the vehicle is not in a parking, turning, or lane-changing state at present. At this time, online zero-offset identification of the steering wheel can be performed on the vehicle.

[0052] In addition, if it is confirmed through the above Step 21 or 22 that the execution condition of the online zero-offset identification of the steering wheel is not met, the implementation of the present invention will immediately stop executing the subsequent steps.

[0053] Step 3, if it is confirmed that the execution condition of the online zero-offset identification of the steering wheel has been met, then determine the steering wheel angle zero-offset iterative function; specifically:

[0054] Determine the steering wheel angle zero-offset iterative function as

[0055]

[0056] Where, is the iterative result of the steering wheel angle zero-offset at the moment corresponding to the sorting index i of the three groups of corner sampling data sequences, is the preset initial value of the steering wheel angle zero-offset, α is the iterative weight parameter, and its value range is between 0 and 1, z j-1 is the iterative variable factor.

[0057] Here, the derivation process of the steering wheel angle zero-offset iterative function is briefly introduced as follows:

[0058] Let Then:

[0059]

[0060]

[0061]

[0062] And so on, finally we can get

[0063] It can be easily seen from the above function that the closer to the current moment, the larger α·(1 - α) is and the smaller (1 - α) is. That is to say, as time goes by, the influence of the initial value i-j becomes weaker, while the influence of the iterative variable factor z i at the previous moment becomes stronger. It should be noted that when using the above steering wheel angle zero-offset iterative function for iteration, the initial several iterative variable factors z can be calculated based on the corresponding data before time t - k - 1. j-1 j-1

[0064] Step 4: Select two corresponding sequences from the three groups of corner sampling data sequences based on the driving state, and perform steering wheel delay period estimation processing to generate the corresponding steering wheel delay period;

[0065] Specifically, it includes: Step 41, when the driving state is the automatic driving state, select the vehicle pose estimation corner sequence δ p and the steering wheel control corner sequence δ c from the three groups of corner sampling data sequences as the two corresponding sequences; and according to the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) perform automatic driving state steering wheel delay period estimation processing to generate the corresponding steering wheel delay period;

[0066] Specifically, it includes: Step 411, select the vehicle pose estimation corner sequence δ p and the steering wheel control corner sequence δ c from the three groups of corner sampling data sequences as the two corresponding sequences;

[0067] Here, as known from the previous text, the vehicle pose estimation corner sequence δ p is the real-time vehicle steering angle observation information, and the steering wheel control corner sequence δ c ​​To issue the real-time steering wheel control angle information, these two sequences are selected as the two sequences corresponding to the autonomous driving state, in order to calculate the delay period based on the delay characteristics between the steering wheel control issued and the chassis feedback in subsequent calculations;

[0068] Step 412. Estimate the angle sequence δ according to the vehicle pose p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control angle sequence δ c (δ c,1 …δ c,i …δ c,k ) to perform the estimation process of the steering wheel delay period in the autonomous driving state and generate the corresponding steering wheel delay period;

[0069] Specifically, it includes: Step 4121. Set the steering wheel delay period variable as x, and use the angle sequence δ estimated from the vehicle pose p as the delay reference sequence of the steering wheel control angle sequence δ c to determine the first objective function as

[0070] Step 4122. Solve the value of the steering wheel delay period variable x that makes the first objective function reach the minimum value, and use the solution result as the steering wheel delay period d1 corresponding to the autonomous driving state,

[0071] Step 42. When the driving state is the manual driving state, select the angle sequence δ estimated from the vehicle pose from the three groups of angle sampling data sequences p and the steering wheel control feedback angle sequence δ s as the corresponding two sequences; and according to the angle sequence δ estimated from the vehicle pose p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback angle sequence δ s (δ s,1 …δ s,i …δ s,k ) to perform the estimation process of the steering wheel delay period in the manual driving state and generate the corresponding steering wheel delay period;

[0072] Specifically, it includes: Step 421. Select the angle sequence δ estimated from the vehicle pose and the steering wheel control feedback angle sequence δ p from the three groups of angle sampling data sequences as the corresponding two sequences; s

[0073] ​Here, as known from the foregoing, when the driving state is the manual driving state, since the vehicle's automatic driving control module does not participate in the driving task, only the vehicle pose estimation angle sequence δ can be guaranteed to be obtained. p and the steering wheel control feedback angle sequence δ s . Therefore, these two sequences are used as the two sequences corresponding to the manual driving state. Correspondingly, in subsequent calculations, the delay period is calculated based on the delay characteristics between the steering wheel control feedback and the chassis feedback.

[0074] Step 422, perform manual driving state steering wheel delay period estimation processing on the vehicle pose estimation angle sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback angle sequence δ s (δ s,1 …δ s,i …δ s,k ) to generate the corresponding steering wheel delay period.

[0075] Specifically, it includes: Step 4221, set the steering wheel delay period variable as x, and use the vehicle pose estimation angle sequence δ p as the delay reference sequence for the steering wheel control feedback angle sequence δ s to determine the second objective function as

[0076] Step 4222, solve the value of the steering wheel delay period variable x that makes the second objective function reach the minimum, and use the solution result as the steering wheel delay period d2 corresponding to the manual driving state.

[0077] Here, through the above steps 41 - 42, it is not difficult to see that neither the steering wheel delay periods d1 nor d2 will be fixed values. If the vehicle condition is stable, the delay is small or stable, the steering wheel delay period will not change significantly. Otherwise, it will fluctuate. It should be noted that if the steering wheel delay period of the vehicle is too large or too unstable, it will affect the accuracy of zero bias identification. Therefore, in Embodiment 1 of the present invention, in order to ensure the accuracy of zero bias identification, a zero bias identification protection mechanism based on the steering wheel delay period threshold is also given. Specifically: identify the obtained steering wheel delay periods (d1, d2); if the current steering wheel delay period exceeds the preset first - level threshold of the delay period, alarm for the excessive delay period and count the number of alarms for the excessive delay period within the recently specified time. If the counted number does not exceed the preset total threshold, do not interrupt the execution of the subsequent zero bias identification processing steps. If the counted number has exceeded the preset total threshold, interrupt the execution of the subsequent zero bias identification processing steps; if the current steering wheel delay period exceeds the preset second - level threshold of the delay period, stop the current zero bias identification processing and alarm for the excessive delay period; where the first - level threshold of the delay period < the second - level threshold of the delay period.

[0078] Step 5: Based on the driving state, select the corresponding two sequences from the three groups of corner sampling data sequences, and substitute the corresponding two sequences and the corresponding steering wheel delay period into the steering wheel corner zero bias iterative function for iterative processing to obtain the steering wheel corner zero bias at time t;

[0079] Specifically, it includes: Step 51, when the driving state is the autonomous driving state, select the vehicle pose estimation corner sequence δ p and the steering wheel control corner sequence δ c as the corresponding two sequences; and determine the corresponding iterative variable factor expression according to the corresponding steering wheel delay period d1 as Based on the currently determined iterative variable factor expression, substitute the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) into the steering wheel corner zero bias iterative function, and perform an operation on the steering wheel corner zero bias at i = k to obtain And use the current iterative result as the steering wheel corner zero bias at time t;

[0080] Step 52, when the driving state is the manual driving state, select the vehicle pose estimation corner sequence δ from the three groups of corner sampling data sequencesp and the steering wheel control feedback cornering sequence δ s as two corresponding sequences; and according to the corresponding iteration variable factor expression corresponding to the steering wheel delay period d2 is and based on the currently determined iteration variable factor expression, the vehicle pose estimation cornering sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback cornering sequence δ s (δ s,1 …δ s,i …δ s,k ) are substituted into the steering wheel corner zero bias iteration function, and the steering wheel corner zero bias at i = k is calculated to obtain and the current iteration result is used as the steering wheel corner zero bias at time t.

[0081] Here, it is not difficult to see from the iteration variable factor expression that the iteration variable factor in the steering wheel corner zero bias iteration function is actually a differential factor between input and feedback. In the autonomous driving state, the steering wheel control cornering is used as the input and the vehicle pose estimation cornering is used as the feedback to obtain the iteration variable factor, while in the manual driving state, the steering wheel control feedback cornering is used as the input and the vehicle pose estimation cornering is used as the feedback to obtain the iteration variable factor; the steering wheel corner zero bias iteration function is actually based on an initial value and continuously performs differential iteration to obtain the zero bias identification and estimation value at the current moment.

[0082] Step 6, perform a warning process when the steering wheel corner zero bias exceeds a preset zero bias threshold.

[0083] Here, through the threshold warning method in the current step, it is possible to perform real-time warning of excessive steering wheel zero bias during vehicle driving, achieving the purpose of real-time monitoring of the vehicle steering wheel zero bias, improving the sensitivity to the change of the steering wheel zero bias, and improving the vehicle safety driving guarantee.

[0084] It should be noted that when the current steering wheel corner zero bias does not exceed the preset zero bias threshold, the embodiment of the present invention will save it as the latest steering wheel corner zero bias; and when the vehicle control system outputs the current steering wheel control cornering, the latest steering wheel corner zero bias is used to correct the current steering wheel control cornering. In this way, the problem of vehicle deviation caused by the zero bias of the steering wheel can be avoided.

[0085] Figure 2Schematic diagram of the structure of an electronic device provided in the second embodiment of the present invention. The electronic device may be the aforementioned terminal device or server, or may be a terminal device or server connected to the aforementioned terminal device or server to implement the method embodiment of the present invention. As Figure 2 shown, the electronic device may include: a processor 301 (such as a CPU), a memory 302, and a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiver actions of the transceiver 303. Various instructions may be stored in the memory 302 for completing various processing functions and implementing the processing steps described in the foregoing method embodiments. Preferably, the electronic device related to the embodiment of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to implement communication connections between components. The aforementioned communication port 306 is used for the electronic device to connect and communicate with other peripherals.

[0086] In Figure 2 mentioned, the system bus 305 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus may be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface is used to implement communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries). The memory may include a Random Access Memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0087] The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Graphics Processing Unit (GPU), etc.; it may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0088] It should be noted that the embodiments of the present invention further provide a computer-readable storage medium, in which instructions are stored. When the instructions run on a computer, the computer is enabled to execute the methods and processes provided in the above embodiments.

[0089] The embodiments of the present invention further provide a chip for running instructions, and the chip is used to execute the processing steps described in the foregoing method embodiments.

[0090] The embodiments of the present invention provide a processing method, an electronic device, and a computer-readable storage medium for online identification of the zero offset of a steering wheel based on a driving state. During the driving process of a vehicle, the driving state of the vehicle is automatically recognized, and corresponding online zero-offset identification processing is performed based on the recognition result of the driving state (automatic driving state or manual driving state). Through the present invention, the zero offset of the vehicle steering wheel can be monitored in real time, thereby improving the sensitivity of the vehicle to the change of the zero offset of the steering wheel and improving the safety guarantee of vehicle driving.

[0091] Those skilled in the art should further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0092] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be implemented by hardware, software modules executed by a processor, or a combination of the two. The software modules can be placed in a random access memory (RAM), memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the technical field.

[0093] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A processing method for online identification of steering wheel zero bias based on driving state, characterized in that, The method includes: At time t, obtain the driving state of the vehicle; and obtain three groups of corner sampling data sequences from time t - k - 1 to time t according to a preset specified length k; the driving state includes an autonomous driving state and a manual driving state; the three groups of corner sampling data sequences include a vehicle pose estimation corner sequence δ p , a steering wheel control corner sequence δ c and a steering wheel control feedback corner sequence δ s ; Based on the driving state, confirm whether the execution condition of the online identification of the steering wheel zero bias has been met; If it is confirmed that the execution condition of the online identification of the steering wheel zero bias has been met, determine the steering wheel angle zero bias iteration function; And based on the driving state, select two corresponding sequences from the three groups of corner sampling data sequences, and perform steering wheel delay period estimation processing to generate the corresponding steering wheel delay period; And based on the driving state, select two corresponding sequences from the three groups of corner sampling data sequences, and substitute the two corresponding sequences and the corresponding steering wheel delay period into the steering wheel angle zero bias iteration function for iterative processing to obtain the steering wheel angle zero bias at time t; And perform warning processing when the steering wheel angle zero bias exceeds a preset zero bias threshold; Among them, the vehicle pose estimation corner sequence δ p is (δ p,1 … δ p,i … δ p,k ), the steering wheel control corner sequence δ c is (δ c,1 … δ c,i … δ c,k ), the steering wheel control feedback corner sequence δ s is (δ s,1 … δ s,i … δ s,k ), 1 ≤ i ≤ k; the δ p in the vehicle pose estimation corner sequence δ p,i is the front wheel steering angle, δ p,i = arctan(ρ i × L), ρ i is the real-time road curvature obtained from the vehicle chassis module, and L is the vehicle wheelbase; The determination of the steering wheel angle zero bias iteration function specifically includes: Determine the steering wheel angle zero bias iteration function as Among them, is the iteration result of the steering wheel angle zero bias corresponding to the sorting index i of the three groups of corner sampling data sequences, δ0 * is the preset initial value of the steering wheel angle zero bias, α is the iteration weight parameter, and its value range is between 0 and 1, z j-1 is the iteration variable factor; The step of, based on the driving state, selecting two corresponding sequences from the three groups of corner sampling data sequences, substituting the two corresponding sequences and the corresponding steering wheel delay period into the steering wheel angle zero bias iteration function for iterative processing to obtain the steering wheel angle zero bias at time t specifically includes: When the driving state is the autonomous driving state, select the vehicle pose estimation corner sequence δ from the three groups of corner sampling data sequences p and the steering wheel control corner sequence δ c as two corresponding sequences; and determine the corresponding iterative variable factor expression according to the corresponding steering wheel delay period d1 as And based on the currently determined iterative variable factor expression, substitute the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) into the steering wheel corner zero bias iterative function, and perform an operation on the steering wheel corner zero bias at i = k to obtain And use the current iteration result as the steering wheel corner zero bias at time t; When the driving state is the manual driving state, select the vehicle pose estimation corner sequence δ from the three groups of corner sampling data sequences p and the steering wheel control feedback corner sequence δ s as two corresponding sequences; and according to the corresponding iterative variable factor expression corresponding to the steering wheel delay period d2, it is And based on the currently determined iterative variable factor expression, substitute the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback corner sequence δ s (δ s,1 …δ s,i …δ s,k ) into the steering wheel corner zero bias iterative function, and perform an operation on the steering wheel corner zero bias at i = k to obtain And use the current iteration result as the steering wheel corner zero bias at time t.

2. The processing method for online identification of the zero offset of the steering wheel based on the driving state according to claim 1, characterized in that The step of, based on the driving state, confirming whether the execution condition of the online identification of the steering wheel zero bias has been met specifically includes: When the driving state is the autonomous driving state, obtain the vehicle's lateral planning error, heading planning error, front wheel control steering angle, front wheel feedback steering angle, and real-time vehicle speed; and when the lateral planning error, the heading planning error, the front wheel control steering angle, and the front wheel feedback steering angle are all less than their respective specified thresholds and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the online identification of the steering wheel zero bias has been met; When the driving state is the manual driving state, obtain the front wheel feedback steering angle and the real-time vehicle speed of the vehicle; and when the front wheel feedback steering angle is less than the corresponding specified threshold and the absolute value of the real-time vehicle speed is higher than the corresponding specified threshold, confirm that the execution condition of the online identification of the steering wheel zero bias has been met.

3. The processing method for online identification of the zero offset of the steering wheel based on the driving state according to claim 1, characterized in that, The step of, based on the driving state, selecting two corresponding sequences from the three groups of corner sampling data sequences, performing steering wheel delay period estimation processing to generate the corresponding steering wheel delay period specifically includes: When the driving state is the autonomous driving state, select the vehicle pose estimation corner sequence δ from the three groups of corner sampling data sequences p and the steering wheel control corner sequence δ c as two corresponding sequences; and according to the vehicle pose estimation corner sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control corner sequence δ c (δ c,1 …δ c,i …δ c,k ) perform autonomous driving state steering wheel delay period estimation processing to generate the corresponding steering wheel delay period; When the driving state is the manual driving state, select the vehicle pose estimation steering angle sequence δ from the three groups of corner sampling data sequences p and the steering wheel control feedback steering angle sequence δ s as two corresponding sequences; and according to the vehicle pose estimation steering angle sequence δ p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback steering angle sequence δ s (δ s,1 …δ s,i …δ s,k ) perform manual driving state steering wheel delay period estimation processing to generate the corresponding steering wheel delay period.

4. The processing method for online identification of the zero offset of the steering wheel based on the driving state according to claim 3, wherein The estimated steering angle sequence δ according to the vehicle pose p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control angle sequence δ c (δ c,1 …δ c,i …δ c,k ) are processed to estimate the steering wheel delay period in the autonomous driving state to generate the corresponding steering wheel delay period, specifically including: Let the steering wheel delay cycle variable be x, and use the vehicle pose to estimate the corner sequence δ p For the steering wheel control corner sequence δ c Determine the first objective function for the delay reference sequence of Solve for the value of the steering wheel delay period variable x that minimizes the first objective function, and use the solution result as the steering wheel delay period d1 corresponding to the autonomous driving state, 5. The processing method for online identification of the zero offset of the steering wheel based on the driving state according to claim 3, characterized in that, The estimated steering angle sequence δ according to the vehicle pose p (δ p,1 …δ p,i …δ p,k ) and the steering wheel control feedback angle sequence δ s (δ s,1 …δ s,i …δ s,k ) are processed to estimate the steering wheel delay period in the manual driving state to generate the corresponding steering wheel delay period, specifically including: Set the steering wheel delay cycle variable as x, and use the vehicle pose to estimate the corner sequence δ p For the steering wheel control feedback corner sequence δ s Determine the second objective function for the delay reference sequence of Solve for the value of the steering wheel delay period variable x that minimizes the second objective function, and use the solution result as the steering wheel delay period d2 corresponding to the manual driving state, 6. An electronic device, characterized in that, Includes: A memory, a processor, and a transceiver; The processor is used to be coupled with the memory, read and execute the instructions in the memory to implement the method according to any one of claims 1-5; The transceiver is coupled with the processor, and the processor controls the transceiver to perform message sending and receiving.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a computer, cause the computer to perform the method according to any one of claims 1-5.

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