System and method for detecting a handle on or off-handle

Abstract

The present disclosure provides systems and methods for detecting an in or out-of-hand state of a hand. A system for detecting an in or out-of-hand state of a hand comprises: a matrix processor for generating matrix factors; the pre-estimator is used for receiving the torsion bar lower angle of the vehicle steering wheel as input to obtain a torsion bar torque pre-estimated value of the vehicle steering wheel, and controlling the torsion bar torque pre-estimated value to follow the torsion bar torque reference value based on the matrix factor to obtain a driver hand torque pre-estimated value; the judging device is used for determining whether a vehicle steering wheel is in a hand-in state or a hand-out state based on the driver hand torque estimated value, and the estimating device is established based on the driver hand torque estimated value, the system inertia torque estimated value, the system damping torque estimated value and the torsion bar torque estimated value. Other embodiments are also described and claimed.

Description

Technical Field

[0001] The present application generally relates to the automotive field, and more particularly, to a system and method for detecting a hands-on or hands-off state. Background Art

[0002] In vehicles equipped with intelligent driving assistance systems / autonomous driving systems, it is necessary to detect whether the driver's hands are on or off the steering wheel to ensure driving safety. For example, in order to prevent the lateral control system from being misused, a hands-off detection function needs to be implemented; in order to prevent the autonomous driving system from being unable to handle and ensure that the driver is available, a hands-on detection function needs to be implemented.

[0003] Patent application DE102007039332 provides a method that includes detecting the steering movement of a steering handle using a torque sensor and an angle sensor, and modeling the free steering movement of the handle under observation of nonlinear friction effects based on experimental measurement data. The driver's steering torque is determined as a condition using a condition observer and evaluated using the observer. The contact condition between the driver's hand and the steering handle is detected by a hand-off detector using the evaluated steering torque.

[0004] The method provided in patent application US20200232859A1 models at least a portion of a steering system of a motor vehicle by means of a mathematical model, estimates a counter-torque, and uses the estimated torque sum and the counter-torque to determine whether the driver's hands are on the steering wheel.

[0005] Patent application US20200140007A1 models at least a portion of a steering system of a motor vehicle with the aid of a mathematical model. The rotation angle of the lower end and / or upper end of a torsion bar of the steering system is determined. The torque acting on the torsion bar is determined by a measuring device, and the total torque acting on the steering wheel and the rotational angular acceleration of the steering wheel are estimated by a Kalman filter. Summary of the invention

[0006] According to one aspect of the present application, a system for detecting a hands-on or hands-off state is disclosed, comprising: a matrix processor for generating matrix factors; an estimator for receiving a torsion bar lower angle of a vehicle steering wheel as input to obtain an estimated torsion bar torque of the vehicle steering wheel, and controlling the estimated torsion bar torque to follow a torsion bar torque reference value based on the matrix factors to obtain an estimated driver hand torque; and a judger for determining whether the vehicle steering wheel is in a hands-on state or a hands-off state based on the estimated driver hand torque, wherein the predictor is established based on the estimated driver hand torque, a system inertia torque, a system damping torque, and the estimated torsion bar torque.

[0007] According to one aspect of the present application, a method for detecting a hands-on or hands-off state is disclosed, comprising: generating matrix factors; inputting a torsion bar lower angle of a vehicle steering wheel into an estimation model for detecting a hands-on or hands-off state to obtain an estimated value of the torsion bar torque of the vehicle steering wheel; controlling the estimated value of the torsion bar torque to follow a torsion bar torque reference value based on the matrix factors to obtain an estimated value of a driver's hand torque; and determining whether the vehicle steering wheel is in a hands-on state or a hands-off state based on the estimated value of the driver's hand torque, wherein the estimation model is established based on the estimated value of the driver's hand torque, an estimated value of the system inertia torque, an estimated value of the system damping torque, and the estimated value of the torsion bar torque. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The present application may be better understood according to the description of the embodiments of the present application in conjunction with the following drawings, wherein:

[0009] Figure 1 A schematic diagram showing parameters of a theoretical system physical balance equation according to an embodiment of the present application.

[0010] Figure 2 A schematic diagram of a closed-loop control architecture according to an embodiment of the present application is shown.

[0011] Figure 3 A schematic diagram of an input module according to an embodiment of the present application is shown.

[0012] Figure 4 A schematic diagram of an input module according to an embodiment of the present application is shown.

[0013] Figure 5 A schematic diagram of a matrix processing module according to an embodiment of the present application is shown.

[0014] Figure 6 A schematic diagram of an estimation system module according to an embodiment of the present application is shown.

[0015] Figure 7 A schematic diagram of theoretical system submodules according to an embodiment of the present application is shown.

[0016] Figure 8 A schematic diagram of a state judgment module according to an embodiment of the present application is shown.

[0017] Fig. 9 A simulation schematic diagram of a closed-loop control architecture according to an embodiment of the present application is shown.

[0018] Fig.10 A schematic diagram of a system for detecting a hand-on or hand-off state according to an embodiment of the present application is shown.

[0019] Fig.11A flow chart of a method for detecting a hands-on or hands-off state according to an embodiment of the present application is shown.

[0020] Fig.12 A schematic diagram of the structure of an information processing device is shown. DETAILED DESCRIPTION

[0021] The features and exemplary embodiments of various aspects of the application will be described in detail below. The following description covers many specific details, so as to provide a comprehensive understanding of the application. However, it is apparent to those skilled in the art that the application can be implemented without the need for some of these specific details. The following description of the embodiments is only for providing a clearer understanding of the application by illustrating the example of the application. The application is in no way limited to any specific configuration proposed below, but covers any modification, replacement and improvement of related elements or parts without departing from the spirit of the application.

[0022] The traditional hands-on / hands-off detection function for steering systems is mainly based on the torsion bar torque signal. This method only considers static scenarios and cannot be applied to dynamic scenarios. On the other hand, the physical model of the system involved in the traditional hands-on / hands-off detection function uses an open-loop algorithm, which may cause detection failure due to the failure of the system to converge.

[0023] The present application recognizes the limitations of only based on the torsion bar torque signal and the open loop algorithm. Therefore, the present application not only considers the torsion bar torque, but also considers the inertia torque and the damping torque when establishing the system physical model, and also considers the closed loop algorithm.

[0024] In some embodiments, the following theoretical system physical balance equation is established:

[0025]

[0026] Figure 1 A schematic diagram showing the parameters of the theoretical system physical balance equation according to an embodiment of the present application is shown. Figure 1 As shown, T d is the driver's hand torque, T J is the system inertia torque, T D is the system damping torque, T tb is the torsion bar torque, J sw is the system moment of inertia, θ sw is the steering wheel angle, is the steering wheel angular velocity, is the steering wheel angular acceleration, B l is the system damping coefficient, k tb is the torsion bar stiffness, θ tbl is the lower angle of the torsion bar.

[0027] In the above embodiment, the system friction torque T is not considered. f , because it has no direct correlation with the state variable to be set. However, in some other embodiments, the influence of the system friction matrix on the driver's hand torque can be considered. This application does not limit this.

[0028] Based on the theoretical system physical equilibrium equation established above, the following theoretical system three-dimensional state space equation is established according to modern control theory.

[0029]

[0030] The state space equation can be understood as: without driver hand torque T d When inputting, the torsion bar lower angle θ tbl As input, based on the physical model (state variables can be observed in the system), the output torsion bar torque T tb The derivation process.

[0031] Considering the absence of driver's hand torque T d When the derivative, i.e. the torque change rate, is 0, the driver's hand torque T d As one of the state variables.

[0032] According to the rank criterion, if the rank of the matrix O is equal to the size of the state vector, then the system is observable and controllable. In other words, the system is convergent. For the system state space equation of the above theory, the rank of its matrix O is as follows.

[0033]

[0034] It can be seen that the rank of the matrix O of the system state space equation of the above theory is equal to the size of the state vector, which is 3. Therefore, the system physical model established in this application is observable and controllable.

[0035] The following will describe how to establish a closed-loop prediction system based on the above system physical model. In some embodiments, the following closed-loop prediction system can be established based on the above system physical model.

[0036]

[0037] In this closed-loop prediction system, the torsion bar lower angle is also used as input, and the torsion bar torque reference value y is also introduced. tb In some embodiments, the torsion bar torque reference value is, for example, the torsion bar torque measured in real time via a sensor. For example, different vehicles have different dynamic and static friction coefficients. When constructing a theoretical system, the system friction torque T is ignored. fWhen the torsion bar torque measured by the sensor in real time is affected, the torsion bar torque can be used as the estimated system following target. In some embodiments, the torsion bar torque reference value is a theoretical value of the torsion bar torque obtained based on a theoretical system model. This application does not limit this.

[0038] The closed-loop control using the prediction system model uses the actual output of the prediction system model (using the state quantity of the theoretical system mentioned above) to follow the output of the theoretical system model. For example, as described in the above equation, the closed-loop prediction system introduces more than the theoretical system model. That is to say, the torsion bar torque estimation is made by using the matrix L (or adjustment matrix). Following torsion bar torque reference value y tb Thus, the estimated system performance including the actual input (where the hand may be in the state, that is, the driver actually inputs torque) minus the theoretical system performance without the driver's hand torque input can be obtained.

[0039] In order to make The value of is the smallest, and the real part of the characteristic root of the closed-loop prediction system [A-LC] is negative (for example, the characteristic root value in the commercial vehicle EHPS system can be -5 to -20), that is, the pole is negative (-p), to ensure that the system remains convergent. The setting of the pole directly determines how fast the prediction system follows the theoretical system, and will also indirectly affect the responsiveness of hands-on and hands-off detection. Considering the different choices of system poles for different models, a clear mathematical relationship can be established between the L matrix and the system.

[0040] In some embodiments, the relationship between the L matrix and the poles can be established based on taking the characteristic root function Del, as shown in the following equation.

[0041]

[0042] Therefore, the L matrix can be adjusted by modifying the corresponding poles for different vehicle models, so as to obtain an adapted estimation system model.

[0043] In some embodiments, after obtaining the estimated value of the driver's hand torque based on the estimation system model, in order to ensure the accuracy of state detection, a torque determination threshold and a de-shaking period may be set to perform de-shaking processing on the estimated value of the driver's hand torque. For example, corresponding torque determination thresholds and / or de-shaking periods may be set for the hands-on state and the hands-off state, respectively.

[0044] In some embodiments, the hands-on / hands-off state judgment obtained based on the above estimation system can be used as one of the input items for the vehicle controller to comprehensively judge the hands-on / hands-off detection. The vehicle controller can comprehensively judge the hands-on state and the hands-off state by combining multiple sensors, for example, combining multiple aspects such as the driver monitoring system, the capacitive steering wheel, and the steering system sensor.

[0045] Figure 2 Schematic diagram of a closed-loop control architecture according to an embodiment of the present application is shown. Figure 2 As shown, the closed-loop control architecture 200 includes an input module 210 , a matrix processing module 220 , an estimation system module 230 , and a state determination module 240 .

[0046] Figure 3 FIG. 1 shows a schematic diagram of an input module according to an embodiment of the present application. Figure 3 As shown, the parameter torsion bar lower angle θ tbl , Torsion bar torque reference value y tb , torsion bar stiffness k tb 、System moment of inertia J sw 、System damping coefficient B l , extreme point (-p), driver's hand torque threshold T tsh , the hand is in the de-shaking period T Act , Hands-off de-shaking period T Deact Can be entered into the input module.

[0047] In some embodiments, the torsion bar lower angle θ tbl The units are converted in the input module via the degree to radian (D2R) submodule. In some embodiments, the torsion bar stiffness k tb The units are converted in the input module via the corresponding unit conversion submodule. In some embodiments, other input parameters may also be converted in the input module via the corresponding unit conversion submodule. This application does not limit this.

[0048] In some embodiments, one of the plurality of parameters input into the input module is a calibration quantity.

[0049] Figure 4 FIG. 1 shows a schematic diagram of an input module according to an embodiment of the present application. Figure 4 As shown, the torsion bar lower angle θ tbl and the torsion bar stiffness k tb After being processed by the corresponding unit conversion submodule, the corresponding units are changed. Parameter torsion bar stiffness k tb 、System moment of inertia J sw 、System damping coefficient B l , extreme point (-p), driver's hand torque threshold T tsh , the hand is in the de-shaking period TAct , Hands-off de-shaking period T Deact For calibration quantity.

[0050] Figure 5 FIG. 2 shows a schematic diagram of a matrix processing module according to an embodiment of the present application. Figure 5 As shown, the parameter torsion bar stiffness k from the input module tb 、System moment of inertia J sw 、System damping coefficient B l , the pole (-p) can be used as the input of the matrix processing module, and the matrix factor is obtained after being processed by the above equation (4), for example, l 1 , l 2 , l 3 .

[0051] In some embodiments, the estimated system module may include a theoretical system submodule.

[0052] Figure 6 FIG. 2 shows a schematic diagram of an estimation system module according to an embodiment of the present application. Figure 6 As shown, the parameter of the torsion bar lower angle θ from the input module tbl , torsion bar stiffness k tb 、System moment of inertia J sw 、System damping coefficient B l , Torsion bar torque reference value y tb , and the L matrix factor l from the matrix processing module 1 , l 2 , l 3 , can be used as the input of the estimation system module, and the steering wheel angular velocity estimation value is obtained through the processing of the above equation (3) Steering wheel angle estimate Torsion bar torque estimate And the estimated driver's hand torque Steering wheel angular velocity output by the theoretical system submodule of the estimated system module Steering wheel angle θ sw Can also be used to estimate the steering wheel angular velocity Steering wheel angle estimate Torsion bar torque estimate And the estimated driver's hand torque Estimates.

[0053] Figure 7 Schematic diagram of a theoretical system submodule according to an embodiment of the present application is shown. Figure 7 As shown, the parameter of the torsion bar lower angle θ from the input module tbl , torsion bar stiffness k tb 、System moment of inertia J sw、System damping coefficient B l It can be used as the input of the theoretical system submodule, and the steering wheel angular velocity is obtained by processing the above equation (2) Steering wheel angle θ sw , and the torsion bar torque T tb .

[0054] Figure 8 FIG. 2 shows a schematic diagram of a state judgment module according to an embodiment of the present application. Figure 8 As shown, the driver's hand torque estimate from the estimation system module can be used In some embodiments, the state judgment module may further include a state processing submodule. The state processing submodule may be based on the driver's hand torque threshold T from the input module. tsh , the hand is in the de-shaking period T Act , Hands-off de-shaking period T Deact Estimation of driver's hand torque The state judgment module can be based on the estimated value of the driver's hand torque after the de-shaking process. To get the hands-on / hands-off status flag.

[0055] Fig. 9 FIG. 2 shows a simulation schematic diagram of a closed-loop control architecture according to an embodiment of the present application. Fig. 9 As shown, given the torsion bar lower angle θ tbl and torsion bar torque T tb Based on the closed-loop control architecture of the embodiment of the present application, the driver's hand torque estimation is obtained. Based on the estimated driver hand torque To determine the hands-on / hands-off state flag. It can simulate the torsion bar lower angle θ under assisted driving or autonomous driving (for example, in angle control mode). tbl Due to frequent commutation, under the action of inertia torque and damping torque, even if there is no driver torque input, the torsion bar torque T tb (For example, the torsion bar torque measured by the sensor) still fluctuates, as shown at 910. When the hand torque input by the driver is small, the difference between the torque fluctuation caused by inertia and damping and the driver's hand torque measured by the sensor is large, and the driver's hand torque estimation value obtained by the estimation system model provided by the embodiment of the present application can reflect the actual situation, as shown at 920 and 930. Based on the driver's hand torque estimation value obtained by the embodiment of the present application, the hand-on state and the hand-off state can be judged more accurately, as shown at 940.

[0056] In the embodiment of the present application, the key factors supporting the detection of hands on / off based on the steering system, such as inertia torque, damping torque, etc., are obtained through the analysis of the physical system model, and the theoretical system state space based on the model is established. In addition, based on the closed-loop control strategy, the mathematical relationship between the pole setting and the L matrix is ​​derived, and the torsion bar lower angle of the existing sensor of the steering system and the measured torsion bar torque are used to establish a model of the prediction system following the theoretical system to predict the driver's hand torque. As a result, factors such as inertia and damping in the theoretical system model and the prediction system model are considered, which can weaken the influence of inertia torque and damping torque on the driver's hand torque under dynamic use cases, thereby improving the robustness of the system. In addition, a closed-loop control strategy is established, which uses existing sensors to obtain actual input, and identifies and estimates the input of the driver's hand torque by separating the contribution of the theoretical model state quantity, strengthens the characteristics of hands on / off, and improves accuracy while taking into account economy. In addition, the closed-loop control strategy can control the convergence speed of the prediction model while ensuring the convergence of the system by controlling the setting of poles, thresholds, and de-jittering, so it has better accuracy and responsiveness.

[0057] Fig.10 FIG. 1 is a schematic diagram of a system 1000 for detecting a hand-on or hand-off state according to an embodiment of the present application. Fig.10 As shown, the system 1000 includes a matrix processor 1010, an estimator 1020, and a judge 1030. The matrix processor 1010 can be used to generate matrix factors. The estimator 1020 can be used to receive the torsion bar lower angle of the vehicle steering wheel as an input to obtain the torsion bar torque estimate of the vehicle steering wheel, and control the torsion bar torque estimate to follow the torsion bar torque reference value based on the matrix factor to obtain the driver's hand torque estimate. The judge 1030 can be used to determine whether the vehicle steering wheel is in a hand-on state or a hand-off state based on the driver's hand torque estimate. The estimator 1010 is established based on the driver's hand torque estimate, the system inertia torque estimate, the system damping torque estimate, and the torsion bar torque estimate.

[0058] In some embodiments, the torsion bar torque reference value includes the torsion bar torque measured in real time. In some embodiments, the torsion bar torque reference value includes the torsion bar torque theoretical value, and the torsion bar torque theoretical value is obtained based on a theoretical model for detecting a hand-on or hand-off state with the torsion bar lower angle as input.

[0059] In some embodiments, the predictor is built based on a theoretical model for detecting hands-on or hands-off states.

[0060] In some embodiments, the theoretical model is established based on the theoretical value of the driver's hand torque, the theoretical value of the system inertia torque, the theoretical value of the system damping torque, and the theoretical value of the torsion bar torque.

[0061] In some embodiments, the matrix factors are associated with system poles.

[0062] In some embodiments, the determiner is further configured to: perform a de-shaking process on the driver's hand torque estimation value based on the torque determination threshold and the de-shaking period.

[0063] In some embodiments, the system 1000 is applicable to an electric hydraulic power steering system (EHPS), a hydraulic power steering system (HPS), or an electric power steering system (EPS).

[0064] Can be combined Figures 1 to 9 The system 1000 can be understood by referring to the related description of and other descriptions of the present application. The present application is not limited in this respect.

[0065] Fig.11 FIG. 1 is a flow chart of a method 1100 for detecting a hand-on or hand-off state according to an embodiment of the present application. Fig.11 As shown, the method 1100 includes operations 1110 to 1140.

[0066] At operation 1110, matrix factors are generated.

[0067] At operation 1120 , the torsion bar down angle of the vehicle steering wheel is input into an estimation model for detecting a hands-on or hands-off state to obtain an estimation value of the torsion bar torque of the vehicle steering wheel.

[0068] At operation 1130 , the torsion bar torque estimate is controlled to follow the torsion bar torque reference value based on the matrix factor to obtain the driver hand torque estimate.

[0069] At operation 1140 , it is determined whether the vehicle steering wheel is in a hands-on state or a hands-off state based on the driver hand torque estimate.

[0070] The estimation model is established based on the driver hand torque estimation value, the system inertia torque estimation value, the system damping torque estimation value, and the torsion bar torque estimation value.

[0071] Can be combined Figures 1 to 10 The method 1100 can be understood by referring to the related description of and other descriptions of the present application. The present application is not limited in this respect.

[0072] Fig.12 1 shows a schematic diagram of the structure of the information processing device 1200. The system 1000, the matrix processor 1010, the estimator 1020, and the judgement device 1030 in the embodiment of the present application can be implemented by the information processing device 1200. Fig.12As shown, device 1200 may include one or more of the following components: a processor 1220 , a memory 1230 , a power component 1240 , an input / output (I / O) interface 1260 , and a communication interface 1280 , which components may be communicatively connected via a bus 1210 , for example.

[0073] The processor 1220 controls the operation of the device 1200 as a whole, such as operations associated with data communication and computing processing. The processor 1220 may include one or more processing cores and may execute instructions to implement all or part of the steps of the method described in the present application. The processor 1220 may include various devices with processing functions, including but not limited to general-purpose processors, special-purpose processors, microprocessors, microcontrollers, graphics processors (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), etc. The processor 1220 may include a cache 1225 or may communicate with the cache 1225 to increase the access speed of data.

[0074] The memory 1230 is configured to store various types of instructions and / or data to support the operation of the device 1200. Examples of data include instructions, data, etc. for any application or method operating on the device 1200. The memory 1230 can be implemented by any type of volatile or non-volatile storage device or a combination thereof. The memory 1230 can include semiconductor memory, such as random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc. The memory 1230 can also include, for example, any memory using paper media, magnetic media and / or optical media, such as paper tape, hard disk, magnetic tape, floppy disk, magneto-optical disk (MO), CD, DVD, Blue-ray, etc.

[0075] Power supply assembly 1240 provides power to the various components of device 1200. Power supply assembly 1240 may include an internal battery and / or an external power interface, and may include a power management system and other components associated with generating, managing, and distributing power for device 1200.

[0076] The I / O interface 1260 provides an interface that enables a user to interact with the device 1200. The I / O interface 1260 may include, for example, interfaces based on technologies such as PS / 2, RS-232, USB, FireWire, Lightening, VGA, HDMI, DisplayPort, etc., so that a user can interact with the device 1200 through peripheral devices such as a keyboard, a mouse, a touch pad, a touch screen, a joystick, a button, a microphone, a speaker, a display, a camera, a projection port, etc.

[0077] The communication interface 1280 is configured to enable the device 1200 to communicate with other devices in a wired or wireless manner. The device 1200 can access a wireless network based on one or more communication standards, such as Wi-Fi, 2G, 3G, and 4G communication networks, through the communication interface 1280. In an exemplary embodiment, the communication interface 1280 can also receive a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. The exemplary communication interface 1280 may include an interface based on communication methods such as near field communication (NFC) technology, radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, and Bluetooth (BT) technology.

[0078] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0079] In the above text, "one embodiment", "another embodiment", "another embodiment", and "another embodiment" are mentioned. However, it should be understood that the features mentioned in each embodiment are not necessarily applicable to only that embodiment, but may be used in other embodiments. Features in one embodiment may be applied to another embodiment, or may be included in another embodiment.

[0080] It should be understood that the numerical labels of the devices, circuits, modules, and operations mentioned above are for the convenience of description and reference, and there is no order of precedence.

[0081] The present application has been described above with reference to the specific embodiments of the present application, but those skilled in the art should understand that the above embodiments are exemplary and non-restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Those skilled in the art should be able to understand and implement other variations of the disclosed embodiments based on a study of the drawings, the specification and the claims. Any figure marks in the claims should not be construed as limiting the scope of protection. The functions of multiple parts appearing in the claims can be implemented by a single hardware or software module. The appearance of certain technical features in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A system for detecting a hand-on or hand-off state, include: A matrix processor for generating matrix factors; an estimator, configured to receive a torsion bar lower angle of a vehicle steering wheel as an input to obtain an estimated value of a torsion bar torque of the vehicle steering wheel, and to control the estimated value of the torsion bar torque to follow a reference value of the torsion bar torque based on the matrix factor to obtain an estimated value of a driver's hand torque; as well as a judgement device, configured to determine whether the vehicle steering wheel is in a hands-on state or a hands-off state based on the driver's hand torque estimate, The estimator is established based on the driver's hand torque estimation value, the system inertia torque estimation value, the system damping torque estimation value, and the torsion bar torque estimation value.

2. The system according to claim 1, in, The torsion bar torque reference value is the torsion bar torque measured in real time.

3. The system according to claim 1, in, The torsion bar torque reference value is a torsion bar torque theoretical value, and the torsion bar torque theoretical value is obtained based on a theoretical model for detecting a hands-on or hands-off state with the torsion bar lower angle as an input.

4. The system according to claim 1, in, The estimator is built based on a theoretical model for detecting the hands-on or hands-off state.

5. The system according to claim 3 or 4, in, The theoretical model is established based on the theoretical value of the driver's hand torque, the theoretical value of the system inertia torque, the theoretical value of the system damping torque, and the theoretical value of the torsion bar torque.

6. The system of claim 1, in, The matrix factors are associated with the system poles.

7. A method for detecting a hand-on or hand-off state, include: Generate matrix factors; Inputting the torsion bar lower angle of the vehicle steering wheel into an estimation model for detecting a hands-on or hands-off state to obtain an estimated value of the torsion bar torque of the vehicle steering wheel; Controlling the torsion bar torque estimation value to follow the torsion bar torque reference value based on the matrix factor to obtain the driver's hand torque estimation value; as well as Determining whether the vehicle steering wheel is in a hands-on state or a hands-off state based on the driver's hand torque estimate, The estimation model is established based on the driver's hand torque estimation value, the system inertia torque estimation value, the system damping torque estimation value, and the torsion bar torque estimation value.

8. The method according to claim 7, in, The torsion bar torque reference value is the torsion bar torque measured in real time.

9. The method according to claim 7, in, The torsion bar torque reference value is a torsion bar torque theoretical value, which is obtained based on a theoretical model for detecting a hands-on or hands-off state by taking the torsion bar lower angle as an input.

10. The method according to claim 7, in, The estimation model is established based on a theoretical model for detecting a hands-on or hands-off state.

11. The method according to claim 9 or 10, in, The theoretical model is established based on the theoretical value of the driver's hand torque, the theoretical value of the system inertia torque, the theoretical value of the system damping torque, and the theoretical value of the torsion bar torque.

12. The method according to claim 7, in, The matrix factors are associated with the system poles.

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