System and method for recognizing hand in / out-of-hand state

Abstract

A method of identifying a hand in / out-of-hand state, the method comprising: low-pass filtering a torsion bar torque signal of an electro-hydraulic power steering system of a vehicle to obtain a filtered torsion bar torque signal (S101); determining a low frequency factor based on the filtered torsion bar torque signal and a steering wheel angular velocity of the vehicle (S102); determining a high frequency factor based on the filtered torsion bar torque signal and a steering wheel angular velocity of the vehicle (S103); determining a feature factor based on the high frequency factor and the low frequency factor (S104); and identifying a hand in / out-of-hand state based on the feature factor (S105). The invention further discloses a system for recognizing the hand in / out-of-hand state.

Description

System and method for identifying hands-on / hands-off status Technical Field

[0001] The present application relates to the field of automobile technology, and more particularly, to a system and method for identifying hands-on / hands-off states. Background Art

[0002] Vehicles equipped with intelligent driver assistance systems (IDAS) or automated driving systems require hands-off detection to prevent misuse of the lateral control system. Hands-on detection is also required to prevent the automated driving system from being overwhelmed and to ensure driver availability. Therefore, hands-on / hands-off status recognition is crucial for IAS / AD systems.

[0003] Summary of the Invention

[0004] In one aspect, an embodiment of the present application provides a method for identifying a hands-on / hands-off state, comprising: low-pass filtering a torsion bar torque signal of an electric hydraulic power steering system of a vehicle to obtain a filtered torsion bar torque signal; determining a low-frequency factor based on the filtered torsion bar torque signal and a steering wheel angular velocity of the vehicle; determining a high-frequency factor based on the filtered torsion bar torque signal and a steering wheel angular velocity of the vehicle; determining a characteristic factor based on the high-frequency factor and the low-frequency factor; and identifying a hands-on / hands-off state based on the characteristic factor.

[0005] In one aspect, an embodiment of the present application provides a system for identifying hands-on / hands-off status, comprising: a torsion bar torque processing module configured to low-pass filter a torque signal of a torsion bar of a vehicle's electric hydraulic power steering system to obtain a filtered torque signal; a low-frequency processing module configured to determine a low-frequency factor based on the filtered torque signal and the angular velocity of the vehicle's steering wheel; a high-frequency processing module configured to determine a high-frequency factor based on the filtered torque signal and the angular velocity of the vehicle's steering wheel; a feature processing module configured to determine a feature factor based on the high-frequency factor and the low-frequency factor; and a state processing module configured to identify the hands-on / hands-off status based on the feature factor.

[0006] In one aspect, an embodiment of the present application provides a vehicle controller comprising: a processor; and a memory storing computer-readable instructions, wherein the processor is configured to execute the computer-readable instructions to implement a method for identifying hands-on / hands-off status according to an embodiment of the present application.

[0007] In one aspect, an embodiment of the present application provides a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, enable the one or more processors to implement a method for identifying hands-on / hands-off status according to an embodiment of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly describes the drawings involved in the embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without creative work. In the drawings:

[0009] FIG1 shows a flow chart of a method for identifying a hands-on / hands-off state according to an embodiment of the present application;

[0010] FIG2 is a schematic diagram showing a torsion bar torque processing according to an embodiment of the present application;

[0011] FIG3 shows a schematic diagram of low-frequency processing according to an embodiment of the present application;

[0012] FIG4 shows a schematic diagram of high-frequency processing according to an embodiment of the present application;

[0013] FIG5 shows a schematic diagram of feature processing according to an embodiment of the present application;

[0014] FIG6 shows a schematic diagram of state processing according to an embodiment of the present application;

[0015] FIG7 is a waveform diagram showing the implementation performance of the method for identifying hands-on / hands-off states according to an embodiment of the present application;

[0016] FIG8 is a schematic block diagram of a system for identifying hands-on / hands-off states according to an embodiment of the present application;

[0017] FIG9 shows a schematic block diagram of a torsion bar torque processing module according to an embodiment of the present application;

[0018] FIG10 shows a schematic block diagram of a high-frequency processing module according to an embodiment of the present application;

[0019] FIG11 shows a schematic block diagram of a characteristic factor processing module according to an embodiment of the present application;

[0020] FIG12 shows a schematic block diagram of a state processing module according to an embodiment of the present application; and

[0021] FIG13 shows a schematic block diagram of a vehicle controller according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, scheme, and advantages of the present application clearer, the details of the present application are further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. For those skilled in the art, the present application can be implemented without some of the details in these specific details. The following description of the embodiments is only for providing a better understanding of the present application by illustrating the examples of the present application.

[0023] It should be noted that, in this article, relational terms such as first, second, third, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. In addition, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, and the process, method, article or equipment including a series of elements includes not only these elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of additional elements in the process, method, article or equipment including the elements.

[0024] As mentioned above, the recognition of hands-on / hands-off states is very important for intelligent driving assistance systems / autonomous driving systems. Currently, some existing solutions use certain thresholds and durations to judge the torsion bar torque signal of the Electric Hydraulic Power Steering (EHPS) to identify the hands-on / hands-off states. However, existing technologies only consider relatively static scenarios. On the one hand, the torsion bar torque signal is not clearly distinguishable in the hands-on / hands-off states, which may lead to misidentification. On the other hand, it is challenging to set a threshold for the torsion bar torque signal to ensure recognition accuracy in different use cases.

[0025] In view of this, the present application provides a system and method for identifying hands-on / hands-off states, which can accurately and in real time identify hands-on / hands-off states.

[0026] Fig. 1 shows a flow chart of a method for identifying a hands-on / hands-off state according to an embodiment of the present application. As shown in Fig. 1 , the method 100 for identifying a hands-on / hands-off state according to an embodiment of the present application includes steps S101-S105.

[0027] At S101, a torsion bar torque signal of an electric hydraulic power steering system (EHPS) of a vehicle is low-pass filtered to obtain a filtered torsion bar torque signal. The torsion bar torque signal may be measured by a sensor provided for a torsion bar of the electric hydraulic power steering system. It should be understood that the torsion bar torque signal may be measured in real time by the sensor.

[0028] In some implementations, the low-pass filtering may include multiple stages of low-pass filtering in series. By using multiple stages of low-pass filtering in series, excessive hysteresis in the torsion bar torque processing stage can be avoided, thereby ensuring responsiveness in hands-on / hands-off state recognition.

[0029] Figure 2 shows a schematic diagram of torsion bar torque processing S101 according to an embodiment of the present application. As shown in Figure 2 , torsion bar torque processing S101 according to an embodiment of the present application may include three stages of low-pass filtering: low-pass filtering 1, low-pass filtering 2, and low-pass filtering 3. However, it should be understood that torsion bar torque processing S101 may include more or fewer stages of low-pass filtering. As shown in Figure 2 , the torsion bar torque signal measured by the sensor is subjected to three stages of low-pass filtering to obtain a filtered torsion bar torque signal.

[0030] At S102, a low-frequency factor is determined based on the filtered torsion bar torque signal and the steering wheel angular velocity of the vehicle. The steering wheel angular velocity of the vehicle can be measured by a sensor provided for the steering wheel of the vehicle. It should be understood that the steering wheel angular velocity can be measured in real time by the sensor.

[0031] In some implementations, determining the low-frequency factor may include: determining the low-frequency factor by looking up a table in a preset low-frequency map based on a filtered torsion bar torque signal and a steering wheel angular velocity of the vehicle, the preset low-frequency map including low-frequency factors corresponding to high-frequency and low-frequency regions of the torsion bar torque at different steering wheel angular velocities.

[0032] From a system perspective, the frequency of the torsion bar torque signal undergoes a clear change from hands-off to hands-on, rapidly transitioning from a high-frequency region to a low-frequency region. This step identifies the torsion bar torque points (torque values) at which the torsion bar enters the low-frequency region at different steering wheel angular velocities. Higher steering wheel angular velocities result in higher torque values ​​at the point of entry into the low-frequency region, effectively mitigating the effects of inertial torque and damping in different use cases.

[0033] FIG3 shows a schematic diagram of low-frequency processing S102 according to an embodiment of the present application. As shown in FIG3 , low-frequency processing S102 determines a low-frequency factor by looking up a table in a preset low-frequency graph based on the filtered torsion bar torque signal and the steering wheel angular velocity of the vehicle. As shown in FIG3 , the horizontal axis coordinate of the low-frequency graph is the filtered torsion bar torque (for example, in the range of 0 Nm to 10 Nm), and the vertical axis coordinate is the steering wheel angular velocity of the vehicle (for example, in the range of 0 degrees / second to 540 degrees / second). By looking up a table in the low-frequency graph, the low-frequency factor can be determined.

[0034] For the low-frequency graph, the range of the low-frequency factor in the low-frequency region determines the speed of the opponent's state detection. Therefore, the low-frequency factor in the low-frequency region is set to a large positive value (for example, 1 to 20). In the high-frequency region (for example, the torsion bar torque fluctuates between 0 and 0.1 Nm), the low-frequency factor is set to a small negative value (for example, -0.03 to -0.3). This weakens and retains the characteristics of the high-frequency region.

[0035] At S103 , a high frequency factor is determined based on the filtered torsion bar torque signal and the steering wheel angular velocity of the vehicle. It should be understood that the determination of the high frequency factor may be performed before, after, or simultaneously with the determination of the low frequency factor.

[0036] In some implementations, determining the high-frequency factor may include: high-pass filtering the filtered torsion bar torque signal to extract a high-frequency signal portion; low-pass filtering the extracted high-frequency signal portion; and determining the high-frequency factor by looking up a table in a preset high-frequency map based on the filtered high-frequency signal portion and the steering wheel angular velocity of the vehicle, the preset high-frequency map including high-frequency factors of high-frequency and low-frequency regions of torsion bar torque corresponding to different steering wheel angular velocities.

[0037] FIG4 shows a schematic diagram of high-frequency processing S103 according to an embodiment of the present application. As shown in FIG4 , after high-pass filtering and low-pass filtering are performed on the filtered torsion bar torque signal by high-frequency processing S103, a high-frequency factor is determined by looking up a table in a preset high-frequency graph based on the filtered high-frequency signal portion and the steering wheel angular velocity of the vehicle. As shown in FIG4 , the horizontal axis coordinate of the high-frequency graph is the filtered torsion bar torque signal (for example, in the range of 0 Nm to 10 Nm), and the vertical axis coordinate is the steering wheel angular velocity of the vehicle (for example, in the range of 0 degrees / second to 540 degrees / second). By looking up a table in the high-frequency graph, the high-frequency factor can be determined.

[0038] For the high-frequency graph, negative high-frequency factors are primarily set for the high-frequency factors in the high-frequency region (typically, there are no positive high-frequency factors in the high-frequency region). The range of high-frequency factors in the high-frequency region determines the speed of hands-off detection. Furthermore, this range can be smaller than the low-frequency factor's low-frequency region value, for example, it can be set to -1 to -10. This is because hands-off detection is more responsive than hands-on detection. In the low-frequency region, the high-frequency factor can be set, for example, to 1 to 10.

[0039] At S104 , a characteristic factor is determined based on the high-frequency factor and the low-frequency factor.

[0040] In some implementations, determining the characteristic factor includes: superimposing a high-frequency factor and a low-frequency factor to obtain a superimposed factor; performing gain processing on the superimposed factor; accumulating the gain-processed superimposed factor with the characteristic factor of the previous moment; and performing slope and maximum and minimum value limiting processing on the accumulated factor to obtain the current characteristic factor.

[0041] FIG5 shows a schematic diagram of feature processing S104 according to an embodiment of the present application. As shown in FIG5 , first, the high-frequency factor and the low-frequency factor are superimposed (as indicated by “+”). Next, the superimposed factor is subjected to gain processing (i.e., multiplied by the gain factor, as indicated by “×”). Then, the superimposed factor subjected to gain processing is added to the feature factor of the previous moment (after delay processing, as indicated by “Z^-1”) (as indicated by “+”), and the accumulated factor is subjected to slope and maximum and minimum value limiting processing as the current feature factor.

[0042] As shown in Figure 5, a cyclic accumulation is established. It is during this cyclic accumulation that the high-frequency and low-frequency factors control the speed of state recognition. In this process, characteristic factors of the high-frequency and low-frequency factors can be obtained under continuous operating conditions, enabling dynamic, real-time recognition.

[0043] At S105 , the hands-on / hands-off state is identified based on the characteristic factors.

[0044] In some implementations, identifying the hands-on / hands-off state includes: low-pass filtering the characteristic factor; adding a bias to the filtered characteristic factor to control it within a predetermined range; comparing the filtered characteristic factor with the added bias with a threshold; and determining the hands-on / hands-off state based on the result of the comparison.

[0045] Figure 6 shows a schematic diagram of state processing S105 according to an embodiment of the present application. As shown in Figure 6, the characteristic factor is low-pass filtered, a bias (as indicated by "+") is added (for example, to control the characteristic factor within a range of 0 to 1), and compared with a threshold to determine whether the hands-on / hands-off state is present.

[0046] In some implementations, if the biased filtered characteristic factor is less than a hands-off threshold, a hands-off state is determined; if the biased filtered characteristic factor is greater than or equal to the hands-off threshold, a hands-on state is determined. In some implementations, the hands-off state can be indicated by, for example, "0," and the hands-on state can be indicated by, for example, "1." However, it should be understood that the present application is not limited to this, and other flag values ​​can be used to distinguish.

[0047] Figure 7 shows a waveform diagram illustrating the implementation of the method for identifying hands-on / hands-off states according to an embodiment of the present application. Figure 7 shows a waveform diagram 700 generated by randomly simulating hands-on and hands-off motions after the EHPS steering system enters angle control mode.

[0048] The upper portion of waveform graph 700 is the test data for the torsion bar torque signal and steering wheel angular velocity, with the horizontal axis representing time and the vertical axis representing the magnitude of the torsion bar torque. The middle portion is a graphical representation of the characteristic factor, with the horizontal axis representing time and the vertical axis representing the magnitude of the characteristic factor. The lower portion is a graphical representation of the state flag, with the horizontal axis representing time and the vertical axis representing the value of the state flag. As can be seen from FIG7 , the method for identifying the hands-on / hands-off state according to an embodiment of the present application can accurately and in real time determine the hands-on / hands-off state based on the characteristic factors derived from the bar torque signal combined with the steering wheel angular velocity. For different vehicle models, the accuracy and responsiveness of hands-on / hands-off state recognition can be better balanced by adjusting calibration values ​​such as the gain strength, low-pass filtering, and threshold values ​​of the low-frequency and high-frequency graphs.

[0049] According to the method for identifying the hands-on / hands-off state according to the embodiment of the present application, the hands-on / hands-off state can be identified by utilizing the frequency change of the torsion bar torque signal. By filtering and extracting the high and low frequency characteristics of the torsion bar torque signal respectively, and setting the gain effect under different torsion bar torques and steering wheel speeds in the form of a graph, the discrimination degree of the torsion bar torque signal and the adaptability to multiple working conditions are obtained, making the state identification more accurate and reliable. Using a cyclic accumulation method, the hands-on / hands-off state recognition speed can be continuously controlled by adjusting the gain strength of the low-frequency graph and the high-frequency graph, thereby improving the robustness of the hands-on / hands-off state recognition.

[0050] FIG8 shows a schematic block diagram of a system for identifying a hands-on / hands-off state according to an embodiment of the present application. As shown in FIG8 , a system 800 for identifying a hands-on / hands-off state according to an embodiment of the present application includes:

[0051] a torsion bar torque processing module 801 configured to perform low-pass filtering on a torque signal of a torsion bar of an electric hydraulic power steering system of a vehicle to obtain a filtered torque signal;

[0052] a low frequency processing module 802 configured to determine a low frequency factor based on the filtered torque signal and an angular velocity of a steering wheel of the vehicle;

[0053] a high frequency processing module 803 configured to determine a high frequency factor based on the filtered torque signal and an angular velocity of a steering wheel of the vehicle;

[0054] A feature processing module 804 is configured to determine a feature factor based on the high-frequency factor and the low-frequency factor; and

[0055] The state processing module 805 is configured to identify the hands-on / hands-off state based on the characteristic factors.

[0056] Figure 9 shows a schematic block diagram of a torsion bar torque processing module according to an embodiment of the present application. As shown in Figure 9, in some implementations, the torsion bar torque processing module 801 according to an embodiment of the present application includes multiple stages of low-pass filtering units connected in series, namely, low-pass filtering unit 8011, low-pass filtering unit 8012, and low-pass filtering unit 8013. Low-pass filtering is performed step by step through these multiple stages of low-pass filtering units. It should be understood that the torsion bar torque processing module 801 may include more or fewer stages of low-pass filtering units.

[0057] In some implementations, the low-frequency processing module 802 is configured to determine a low-frequency factor by looking up a table in a preset low-frequency map based on the filtered torsion bar torque signal and the steering wheel angular velocity of the vehicle, wherein the preset low-frequency map includes low-frequency factors corresponding to high-frequency and low-frequency regions of the torsion bar torque at different steering wheel angular velocities.

[0058] FIG10 shows a schematic block diagram of a high-frequency processing module according to an embodiment of the present application. As shown in FIG10 , in some implementations, the high-frequency processing module 803 according to an embodiment of the present application includes:

[0059] a high-pass filtering unit 8031 ​​configured to perform high-pass filtering on the filtered torsion bar torque signal to extract a high-frequency signal portion;

[0060] A low-pass filtering unit 8032 is configured to perform low-pass filtering on the extracted high-frequency signal portion; and

[0061] The high-frequency factor determination unit 8033 is configured to determine the high-frequency factor by looking up a table in a preset high-frequency map based on the filtered high-frequency signal portion and the steering wheel angular velocity of the vehicle. The preset high-frequency map includes high-frequency factors in the high-frequency area and low-frequency area of ​​the torsion bar torque corresponding to different steering wheel angular velocities.

[0062] FIG11 shows a schematic block diagram of a feature factor processing module according to an embodiment of the present application. As shown in FIG11 , in some implementations, the feature factor processing module 804 according to an embodiment of the present application includes:

[0063] The superposition unit 8041 is configured to superimpose the high-frequency factor and the low-frequency factor to obtain a superimposed factor;

[0064] The gain unit 8042 is configured to perform gain processing on the superimposed factors;

[0065] An accumulation unit 8043 is configured to accumulate the superimposed factor after gain processing and the characteristic factor at the previous moment;

[0066] The limiting unit 8044 is configured to perform slope and maximum and minimum value limiting processing on the accumulated factor and use it as the current characteristic factor.

[0067] FIG12 shows a schematic block diagram of a state processing module according to an embodiment of the present application. As shown in FIG12 , in some implementations, the state processing module 805 according to an embodiment of the present application includes:

[0068] a low-pass filtering unit 8051 configured to perform low-pass filtering on the characteristic factors;

[0069] a bias unit 8052 configured to add a bias to the filtered characteristic factor to control it within a predetermined range; and

[0070] A comparison unit 8053 is configured to compare the filtered characteristic factor with the added bias with a threshold; and

[0071] The state determination unit 8054 is configured to determine the hand-on / hand-off state based on the comparison result of the comparison unit.

[0072] In some implementations, the state determination unit 8054 is configured to: determine the hands-off state when the filtered characteristic factor with the added bias is less than the hands-off threshold; and determine the hands-on state when the filtered characteristic factor with the added bias is greater than or equal to the hands-off threshold.

[0073] According to the system for identifying the hands-on / hands-off state according to the embodiment of the present application, the frequency changes of the torsion bar torque signal can be used to identify the hands-on / hands-off state. By filtering and extracting the high and low frequency characteristics of the torsion bar torque signal respectively, and setting the gain effect under different torsion bar torques and steering wheel speeds in the form of a graph, the discrimination of the torsion bar torque signal and the adaptability to multiple working conditions are obtained, making the state identification more accurate and reliable. Using a cyclic accumulation method, the gain strength of the low-frequency graph and the high-frequency graph can be adjusted to continuously control the speed of hands-on / hands-off state recognition, thereby improving the robustness of hands-on / hands-off state recognition.

[0074] An embodiment of the present application also provides a vehicle controller, comprising: a processor; a memory storing program instructions, wherein the processor is configured to execute the program instructions stored in the memory to perform a method for identifying hands-on / hands-off status according to an embodiment of the present application.

[0075] Figure 13 shows a schematic block diagram of a controller that can be used to implement a front-wheel independent steering control device according to an embodiment of the present application. As shown in Figure 13, the vehicle controller 1300 according to an embodiment of the present application may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage device 1303 into a random access memory (RAM) 1304. In RAM 1304, various programs and data required for the operation of controller 1300 are also stored. Processing device 1301, ROM 1302, and RAM 1304 are connected to each other via bus 1305. Interface 13013 is also connected to bus 1305. Through interface 13013, it can be connected to an external device. It should be understood that the number of interfaces is not limited to one, but can be more, to be connected to corresponding external devices respectively. It should be understood that the block diagram shown in FIG13 is merely an example to provide an understanding of the embodiments of the present application, and there may be more or fewer components, for example, the microcontroller further includes a master clock circuit and a slave clock circuit.

[0076] Although FIG13 shows a vehicle controller 1300 having various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively. Each block shown in FIG13 may represent one device or may represent multiple devices as needed.

[0077] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure provides a computer-readable storage medium that stores a computer program containing program code for executing the method shown in Figure 1. In such an embodiment, the computer program can be installed from storage device 1303, or installed from ROM 1302, or downloaded and installed from a network. When the computer program is executed by processing device 1301, the method shown in Figure 1 is implemented.

[0078] It should be noted that the computer-readable medium according to an embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. The computer-readable storage medium according to an embodiment of the present invention may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0079] Computer program code for carrying out operations according to embodiments of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages.

[0080] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions, and operations of the systems and methods according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, which contains one or more executable instructions for implementing the specified logical function.

[0081] Those skilled in the art will understand that the present application is not limited to the specific structures and steps described above and shown in the accompanying drawings. For the sake of simplicity, the description of known structures and methods is omitted herein. In the above embodiments, several specific steps are described and shown as examples. However, the method of the present application is not limited to the specific steps described and shown. Without departing from the scope of the present application, those skilled in the art can make various changes, modifications and additions to the embodiments of the present application, or change the order between the steps.

[0082] The above disclosures are only some specific implementation methods of the present application. Those skilled in the art will understand that the scope of protection of the present application is not limited thereto. Rather, various equivalent modifications or replacements can be conceived within the technical scope disclosed in the present application, and these equivalent modifications or replacements are all covered by the scope of protection of the present application.

Claims

1. A method for identifying the hand-on / off state, comprising: Performing low-pass filtering on the torsion bar torque signal of the electric hydraulic power steering system of the vehicle to obtain a filtered torsion bar torque signal; Determining a low-frequency factor based on the filtered torsion bar torque signal and the angular velocity of the vehicle's steering wheel; Determining a high-frequency factor based on the filtered torsion bar torque signal and the angular velocity of the vehicle's steering wheel; Determining a characteristic factor based on the high-frequency factor and the low-frequency factor; And Identifying the hand-on / off state based on the characteristic factor.

2. The method according to claim 1, wherein The low-pass filtering includes a multi-stage low-pass filter connected in series.

3. The method according to claim 1, wherein, Determining the low-frequency factor includes: Determining the low-frequency factor by looking up a table in a preset low-frequency map based on the filtered torsion bar torque signal and the angular velocity of the vehicle's steering wheel, where the preset low-frequency map includes low-frequency factors corresponding to different angular velocities of the steering wheel in the high-frequency and low-frequency regions of the torsion bar torque.

4. The method according to claim 1, wherein Determining the high-frequency factor includes: Performing high-pass filtering on the filtered torsion bar torque signal to extract the high-frequency signal part; Performing low-pass filtering on the extracted high-frequency signal part; and Determining the high-frequency factor by looking up a table in a preset high-frequency map based on the filtered high-frequency signal part and the angular velocity of the vehicle's steering wheel, where the preset high-frequency map includes high-frequency factors corresponding to different angular velocities of the steering wheel in the high-frequency and low-frequency regions of the torsion bar torque.

5. The method according to claim 1, wherein Determining the characteristic factor includes: Superposing the high-frequency factor and the low-frequency factor to obtain a superposed factor; Performing gain processing on the superposed factor; Accumulating the gain-processed superposed factor with the characteristic factor at the previous moment; Performing slope and maximum / minimum value limiting processing on the accumulated factor as the current characteristic factor.

6. The method according to claim 1, wherein Identifying the hand-on / off state includes: Performing low-pass filtering on the characteristic factor; Adding a bias to the filtered characteristic factor to control it within a predetermined range; Comparing the filtered characteristic factor with the bias added to a threshold value; and Determining the hand-on / off state based on the result of the comparison.

7. The system according to claim 6, wherein When the filtered characteristic factor with the bias added is less than the off-hand threshold, the off-hand state is determined; When the filtered characteristic factor with the bias added is greater than or equal to the off-hand threshold, the hand-on state is determined.

8. A system for identifying the hand-on / off state, comprising: A torsion bar torque processing module configured to perform low-pass filtering on the torque signal of the torsion bar of the electric hydraulic power steering system of the vehicle to obtain a filtered torque signal; A low-frequency processing module configured to determine a low-frequency factor based on the filtered torque signal and the angular velocity of the vehicle's steering wheel; A high-frequency processing module configured to determine a high-frequency factor based on the filtered torque signal and the angular velocity of the vehicle's steering wheel; A characteristic processing module configured to determine a characteristic factor based on the high-frequency factor and the low-frequency factor; And A state processing module configured to identify the hand-on / off state based on the characteristic factor.

9. The system according to claim 8, wherein The torsion bar torque processing module includes a multi-stage low-pass filtering unit connected in series, where the low-pass filtering is performed step by step through the multi-stage low-pass filtering unit.

10. The system according to claim 8, wherein The low-frequency processing module is configured to: Based on the filtered torsion bar torque signal and the angular velocity of the vehicle's steering wheel, determine a low-frequency factor by looking up a table in a preset low-frequency map, where the preset low-frequency map includes low-frequency factors corresponding to different steering wheel angular velocities in the high-frequency and low-frequency regions of the torsion bar torque.

11. The system according to claim 8, wherein, The high-frequency processing module includes: A high-pass filtering unit configured to perform high-pass filtering on the filtered torsion bar torque signal To extract the high-frequency signal portion; A low-pass filtering unit configured to perform low-pass filtering on the extracted high-frequency signal portion; and A high-frequency factor determination unit configured to determine a high-frequency factor by looking up a table in a preset high-frequency map based on the filtered high-frequency signal portion and the angular velocity of the vehicle's steering wheel, where the preset high-frequency map includes high-frequency factors corresponding to different steering wheel angular velocities in the high-frequency and low-frequency regions of the torsion bar torque.

12. The system according to claim 8, wherein, The feature processing module includes: An overlay unit configured to overlay the high-frequency factor and the low-frequency factor to obtain an overlaid factor; A gain unit configured to perform a gain process on the overlaid factor; An accumulation unit configured to accumulate the gain-processed overlaid factor with the feature factor at the previous moment; and A limiting unit configured to perform slope and maximum / minimum value limiting processes on the accumulated factor and use it as the current feature factor.

13. The system according to claim 8, wherein The state processing module includes: A low-pass filtering unit configured to perform low-pass filtering on the feature factor; A bias unit configured to add a bias to the filtered feature factor to control it within a predetermined range; A comparison unit configured to compare the filtered feature factor with the bias added to a threshold; and A state determination unit configured to determine the hand-on / off state based on the comparison result of the comparison unit.

14. The system according to claim 13, wherein, The hand-on / off state determination unit is configured to: Determine the off state when the filtered feature factor with the bias added is less than the off threshold; Determine the on state when the filtered feature factor with the bias added is greater than or equal to the off threshold.

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