Vehicle and steering assist control method thereof

Abstract

The application is suitable for the field of vehicle control technology, and particularly relates to a vehicle and a steering assist control method thereof. The method obtains hand force torque data of a driver steering the vehicle, detects the hand force torque data for an anomaly, obtains a data detection result, when the data detection result is abnormal, determines a target hand force torque according to an abnormal type in the data detection result, determines a steering assist torque according to a safety curve when the vehicle enters a safety state and the target hand force torque, controls the vehicle steering according to the steering assist torque, and realizes that the vehicle enters the safety state and can provide certain steering assist when the torque sensor is abnormal, thereby avoiding the situation that the vehicle is difficult to control due to the failure of the torque sensor, and helping to improve the safety of the vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle and its steering assist control method. Background Technology

[0002] Currently, to provide drivers with greater control over the steering wheel, many vehicles are equipped with electric power steering (ESP) systems to assist in steering. Since ESP systems need to sense the torque input by the driver, they rely on torque signals detected by a torque sensor. However, when the torque sensor fails, the safety mechanism of the ESP system is to enter a safe state within a set time after the failure is detected. This safe state involves directly cutting off power steering to prevent unexpected steering assistance. However, after power steering is cut off, the driver still needs to exert considerable force to turn the steering wheel, making it difficult to control the vehicle safely. Therefore, how to change the safe state to provide partial power steering assistance when the torque sensor fails, thus preventing the difficulty in vehicle control caused by sensor failure, is a pressing issue that needs to be addressed. Summary of the Invention

[0003] In view of this, embodiments of this application provide a vehicle and a steering assist control method thereof to solve the problem of how to control the steering system to enter a safe state according to the degree of failure when the torque sensor fails, so as to provide partial assistance to the steering system.

[0004] In a first aspect, embodiments of this application provide a steering assist control method, the steering assist control method comprising:

[0005] Acquire data on the driver's hand force and torque when steering the vehicle;

[0006] Anomaly detection is performed on the hand force torque data to obtain the data detection results;

[0007] When the data detection results are abnormal, the target hand force torque is determined according to the type of abnormality in the data detection results;

[0008] Based on the safety curve of the vehicle entering a safe state and the target hand torque, the steering assist torque is determined, and steering assistance is provided to the vehicle according to the steering assist torque.

[0009] Secondly, embodiments of this application provide a vehicle that includes the steering assist control method as described in the first aspect.

[0010] The beneficial effects of this application embodiment compared with the prior art are as follows: This application obtains the hand torque data of the driver's steering, performs anomaly detection on the hand torque data, obtains data detection results, and when the data detection results show anomalies, determines the target hand torque based on the anomaly type in the data detection results. Based on the safety curve of the vehicle entering a safe state and the target hand torque, the steering assist torque is determined. Based on the steering assist torque, the vehicle steering is controlled, so that when the torque sensor is abnormal, the vehicle enters a safe state and can provide a certain steering assist, thereby avoiding the situation where the vehicle is difficult to control due to the failure of the torque sensor, and helping to improve the safety of the vehicle. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a schematic flowchart of a steering assist control method provided in Embodiment 1 of this application;

[0013] Figure 2 This is a schematic flowchart of a steering assist control method provided in Embodiment 2 of this application;

[0014] Figure 3 This is a schematic flowchart of a steering assist control method provided in Embodiment 3 of this application;

[0015] Figure 4 This is a schematic flowchart of a steering assist control method provided in Embodiment 4 of this application;

[0016] Figure 5 This is a schematic flowchart of a steering assist control method provided in Embodiment 5 of this application;

[0017] Figure 6 This is a flowchart illustrating a steering assist control method provided in Embodiment Six of this application;

[0018] Figure 7 This is a flowchart illustrating a steering assist control method provided in Embodiment 7 of this application;

[0019] Figure 8 This is a schematic diagram of the structure of an electric power steering system provided in Embodiment 8 of this application;

[0020] In the diagram, 1 is the electric power steering system, 2 is the torque sensor, 3 is the motor, 4 is the braking system, 11 is the torque calculation and diagnostic module, 12 is the normal feel module, 13 is the arbitration module, 14 is the safety monitoring status judgment module, 15 is the torque self-learning module, and 16 is the safety feel module. Detailed Implementation

[0021] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0022] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0023] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0025] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0026] To illustrate the technical solution of this application, specific embodiments are described below.

[0027] See Figure 1This is a flowchart illustrating a power steering control method provided in Embodiment 1 of this application. This power steering control method can operate within the control device of a vehicle's power steering system. The power steering system provides assistance to the vehicle's steering system, and may specifically include electric power steering, mechanical power steering, or other assistance methods. A power steering system employing electric power steering is called an Electric Power Steering System (EPS).

[0028] like Figure 1 As shown, the power steering control method may include the following steps:

[0029] Step S101: Obtain the hand force torque data of the driver controlling the vehicle steering.

[0030] In this embodiment of the application, the power steering system that provides steering assistance to the vehicle is designed with corresponding torque sensors and other measuring devices to measure the force applied by the driver when the driver controls the steering wheel to steer the vehicle. The corresponding measurement results are expressed as torque data, and the result measured by the corresponding torque sensor each time is the hand force torque data.

[0031] The hand force torque data may include at least one torque sensing data. If the hand force torque data consists of multiple torque sensing data, then the multiple torque sensing data may be two independent data collected by the same torque sensor at the same time and transmitted to the power steering system through different transmission paths.

[0032] In addition, the torque sensing data in this hand force torque data can be either a torque value or a torque signal value. If the hand force torque data is a torque signal value, then signal processing is required according to the sensor specification table to obtain the corresponding torque value.

[0033] Step S102: Perform anomaly detection on the manual force and torque data to obtain the data detection results.

[0034] In this embodiment, the hand force torque data only enters the power steering system through the measurement, transmission, and reception processes of the torque sensor. Therefore, abnormalities such as measurement anomalies, transmission anomalies, reception anomalies, etc., may occur during the above process. Correspondingly, appropriate detection methods are employed to detect each anomaly in the above process, thereby obtaining the corresponding data detection results.

[0035] For example, when performing anomaly detection on transmission, the corresponding communication protocol can be used for diagnosis to determine whether the requirements of the communication protocol are met. If the requirements of the communication protocol are not met, the transmission will be detected as an anomaly. Similarly, when performing anomaly detection on measured data, the data can be compared with upper and lower limit thresholds for diagnosis. When the measured data exceeds the range included by the upper and lower limit thresholds, it can be detected as a measurement anomaly.

[0036] In addition, the data detection results can also include anomaly types, which represent different abnormal situations. For example, transmission anomalies and measurement anomalies are two different types of anomalies. Transmission anomalies represent abnormal situations such as transmission paths or lines, that is, reflecting a line fault. Measurement anomalies represent abnormal situations of torque sensor measurement results, that is, reflecting a torque sensor fault.

[0037] Step S103: When there is an anomaly in the data detection result, determine the target hand force torque according to the anomaly type in the data detection result.

[0038] In this embodiment of the application, the data detection result includes the result of at least one anomaly detection. If there is an anomaly detection result, it is determined that there is an anomaly in the data detection result. If there are multiple anomaly detection results and all results are not anomaly, it is determined that there is no anomaly in the data detection result.

[0039] As can be seen from the analysis of the data detection results in step S102 above, the data detection results can reflect the type of abnormality. In this embodiment, different types of abnormality can be analyzed to obtain the corresponding steering assist torque.

[0040] For example, if there is an anomaly in the data detection results, the corresponding anomaly type is measurement anomaly, that is, a torque sensor malfunction. In this case, since accurate hand force torque data cannot be obtained, it is impossible to use the hand force torque data to obtain the steering assist torque. Instead, the vehicle's operating data can be used to predict a driver's hand force. In this case, analyzing the operating data can obtain parameters such as the vehicle's attitude. Combined with the corresponding prediction model, a driver's hand force can be obtained. This driver's hand force can then be used as the target hand force torque to calculate the steering assist torque.

[0041] Of course, in determining the driver's hand force based on the anomaly types in the data detection results, some preset parameters and models can be configured as needed to output the corresponding hand force torque. These parameters and models can be obtained through training, experimentation, or experience to ensure that a relatively accurate hand force torque can be output for different anomaly types.

[0042] Step S104: Determine the steering assist torque based on the safety curve and target hand torque when the vehicle enters a safe state, and provide steering assistance to the vehicle based on the steering assist torque.

[0043] In this embodiment, when the hand torque data collected by the torque sensor is abnormal, the vehicle enters the corresponding safe state, and the steering assist torque is obtained according to the safety curve and the target hand torque in the safe state, so that the vehicle has steering assist in the safe state.

[0044] Steering assist torque can be a parameter that enables the power steering system to work, enabling the power steering system to generate a corresponding assist torque, which acts on the steering system to provide steering assistance to the vehicle.

[0045] The above steps can analyze the anomaly and provide corresponding solutions to prevent the sudden disconnection of power steering caused by the anomaly, thus ensuring vehicle safety to a certain extent.

[0046] This application embodiment acquires the driver's hand torque data for steering the vehicle, performs anomaly detection on the hand torque data, obtains data detection results, and determines the target hand torque based on the anomaly type in the data detection results when an anomaly is found. Based on the safety curve of the vehicle entering a safe state and the target hand torque, the steering assist torque is determined. Based on the steering assist torque, the vehicle steering is controlled, so that when the torque sensor is abnormal, the vehicle enters a safe state and can provide a certain amount of steering assistance, thereby avoiding the situation where the vehicle is difficult to control due to the failure of the torque sensor and helping to improve vehicle safety.

[0047] See Figure 2 This is a flowchart illustrating a steering assist control method according to Embodiment 2 of this application. The hand force torque data includes two torque sensing data points. These two torque sensing data points can be two data points measured by a single sensor, or they can be data points measured separately by two sensors; no limitation is made here.

[0048] like Figure 2 As shown, in step S103 above, determining the steering assist torque based on the anomaly type in the data detection results can specifically include the following steps:

[0049] Step S201: If the abnormality type in the data detection result is that one torque sensing data is normal and another torque sensing data is abnormal, then the target hand torque is determined based on the normal torque sensing data.

[0050] In this embodiment of the application, anomaly types are defined to characterize the abnormal situation of two torque sensing data. There are three different anomaly types: only one torque sensing data is abnormal, both torque sensing data are normal, and both torque sensing data are abnormal. For the anomaly type corresponding to only one torque sensing data being abnormal, the other torque sensing data is normal. Therefore, the normal torque sensing data can be used to determine the target hand torque.

[0051] The torque value corresponding to the normal torque sensing data is the hand torque provided by the driver. The steering demand can be determined by this hand torque, and then the steering assist torque can be determined. For example, the steering assist torque is 90% of the steering demand, and the remaining 10% is provided by the driver.

[0052] Step S202: If the anomaly type in the data detection result is that both torque sensing data are abnormal, then the target hand torque is determined based on the current vehicle driving data.

[0053] In cases where both torque sensing data are abnormal, the torque sensing data cannot be used, meaning the hand torque provided by the driver cannot be sensed. Therefore, the target hand torque is obtained using the current vehicle driving data.

[0054] Current vehicle driving data refers to the vehicle's driving data at the current moment, including vehicle speed, yaw angle, longitudinal acceleration, lateral acceleration, steering wheel angle, and steering wheel angular velocity. This driving data can be provided by the vehicle's braking system. The vehicle's braking system, power steering system, and other systems can communicate via the Controller Area Network (CAN) bus; therefore, the power steering system can directly obtain driving data from the CAN bus.

[0055] In this embodiment of the application, the power steering system is pre-configured with a mapping relationship between vehicle driving data and target hand torque. Thus, after obtaining the current vehicle driving data, the target hand torque required at the current time can be determined, thereby realizing power steering in the event of abnormal torque perception.

[0056] See Figure 3 This is a flowchart illustrating a steering assist control method provided in Embodiment 3 of this application. Figure 3 As shown, in step S202 above, the target hand torque is determined based on the current vehicle driving data, which may specifically include the following steps:

[0057] Step S301: Check whether the current vehicle driving data is valid data.

[0058] The system checks the validity of current vehicle driving data. Only valid data can be used to determine the target hand torque; invalid data will lead to inaccurate determination of the target hand torque, thus affecting driving safety. When the braking system sends driving data, it attaches a validity tag to the data, allowing other devices to determine the validity of the driving data.

[0059] Step S302: If the current vehicle driving data is detected as valid data, the torque self-learning module is used to calculate the torque of the current vehicle driving data to obtain the target hand torque.

[0060] In this embodiment of the application, a torque self-learning model is provided. Using the torque self-learning model, the current vehicle driving data can be calculated when the current vehicle driving data is valid, thereby predicting the target hand force torque, which is equivalent to the torque measured by the torque sensor.

[0061] This torque self-learning model is used to analyze the driver's driving habits. Under normal conditions, the torque sensor, power steering system, steering system, and braking system of the vehicle are all functioning properly. It records the hand torque data and driving data during the vehicle's steering process and learns the mapping relationship between the hand torque data and driving data through a corresponding self-learning method. Thus, it can obtain the corresponding hand torque data based on a driving data.

[0062] This torque self-learning model can be reset after each vehicle start-up, and then it begins to learn the mapping relationship between the hand torque data and form data after this start-up. That is, the torque self-learning model is relearned every time the vehicle is started, so as to accurately map the relationship when the vehicle is driven by different drivers or under different driving conditions.

[0063] Step S303: If the current vehicle driving data is detected to be invalid, then the target hand torque is determined to be zero.

[0064] If the current vehicle driving data is invalid, it indicates that the driving data provided by the braking system is also unsafe. For vehicle safety, this current vehicle driving data cannot be used to determine the target hand torque. In this case, it is impossible to control the vehicle's steering assist using any form of data. Therefore, the target hand torque is set to zero, i.e., the power steering system's assistance to the steering system is cut off.

[0065] See Figure 4 This is a flowchart illustrating a steering assist control method provided in Embodiment 4 of this application. Figure 4 As shown, in step S104 above, the steering assist torque is determined based on the safety curve and target hand torque when the vehicle enters a safe state. This may further include the following steps:

[0066] Step S401: Obtain the safety curve of the vehicle entering a safe state.

[0067] Specifically, regarding abnormal situations, there is a first correspondence between steering assist torque and manual torque; under normal circumstances, there is a second correspondence between steering assist torque and the manual torque data sensed by the torque sensor. The first and second correspondences may be the same or different. In this embodiment, for abnormal situations, a new safety curve (i.e., the first correspondence) is recalibrated as the curve for the vehicle to enter a safe state, used to obtain the corresponding steering assist torque based on the manual torque and the safety curve.

[0068] Among them, the safety curve is a curve calibrated based on abnormal conditions. The safety curve is used to characterize the mapping relationship between hand torque, vehicle speed and steering assist.

[0069] Step S402: Obtain the steering assist torque based on the target hand force torque, the safety curve, and the current vehicle speed in the current vehicle driving data.

[0070] In the embodiments of this application, when the hand force torque is the same, the output steering assist torque may also be different depending on the vehicle speed. This, combined with the safety curve of vehicle speed, can more accurately determine the steering assist torque and ensure driving safety.

[0071] The current vehicle speed can be obtained from the current vehicle driving data mentioned above. Of course, if the current vehicle speed is not found in the current vehicle driving data, the vehicle speed data can be obtained from the corresponding sensor or CAN bus.

[0072] In one embodiment, determining the target hand torque based on normal torque sensing data includes:

[0073] Use normal torque sensing data as the target hand torque.

[0074] In cases where only one torque sensing data point is abnormal while the other is normal, the normal torque sensing data can be used to obtain the target hand torque. However, since the data is still abnormal, the aforementioned safety curve is needed to obtain the steering assist torque. This allows for a more accurate determination of the steering assist torque and ensures driving safety.

[0075] See Figure 5 This is a flowchart illustrating a steering assist control method provided in Embodiment 5 of this application. Based on Embodiment 2 above, where the hand force torque data includes two torque sensing data points, as shown... Figure 5 As shown, in step S102 above, anomaly detection is performed on the hand force and torque data to obtain the data detection results. Specifically, this may also include the following steps:

[0076] Step S501: Perform single-path verification on the two torque sensing data in the manual torque data to obtain the single-path verification result of each torque sensing data.

[0077] Step S502: Perform a synchronization check on the difference between the two torque sensing data to obtain the synchronization check result.

[0078] In this embodiment, a single-path verification method is used to verify each torque sensing data separately to obtain the corresponding verification result. A synchronous verification method is used to verify the difference between two torque sensing data to obtain the corresponding verification result.

[0079] Single-path verification is used to characterize whether each data point can pass the verification, for example, by using communication protocols or upper and lower thresholds. Synchronization verification judges the difference between two torque sensing data points. If the difference is large, exceeding a threshold, it indicates inconsistency and erroneous data. If the difference is small, not exceeding a threshold, it indicates known data and the sensing data may be valid.

[0080] Step S503: If at least one of the single-channel verification results and synchronous verification results is abnormal, then it is determined that the data detection result is abnormal.

[0081] Step S504: If all single-channel verification results and synchronous verification results are normal, then the data detection results are determined to be normal.

[0082] The data detection result is considered to be without abnormality only if all single-path verification results and synchronous verification results are without abnormality. In the case where there is no abnormality, the steering assist torque can be determined by referring to the method in Example 7 below. In other cases, the data detection result is abnormal, and the steering assist torque can be determined by referring to the methods in Examples 1 to 4 above.

[0083] This application embodiment verifies the torque sensing data of two devices to provide a more reasonable verification result. Based on this verification result, corresponding processing measures are then used to deal with different abnormal situations to ensure vehicle driving safety.

[0084] See Figure 6 This is a flowchart illustrating a steering assist control method provided in Embodiment Six of this application. Figure 6 As shown, after step S503 above, the following steps may be included:

[0085] Step S601: If the synchronous verification result is successful, and one of the two single-channel verification results is successful while the other is unsuccessful, then the abnormality type in the data detection result is determined to be that one torque sensing data is normal and the other torque sensing data is abnormal.

[0086] Step S602: If the synchronous verification result is a verification failure, or both single-path verification results are verification failures, then the abnormality type in the data detection result is determined to be that both torque sensing data are abnormal.

[0087] In this embodiment, the judgment of the anomaly type is adapted to the anomaly type classification method in Embodiment 2 above. When the synchronous verification result is a failure, or both single-path verification results are failures, normal torque sensing data cannot be determined. Therefore, processing the data using the method where both torque sensing data are abnormal can yield a relatively accurate steering assist torque. Similarly, if the difference between the two torque sensing data is small, and one of them is normal, the processing method corresponding to the normal torque sensing data can be used to obtain a relatively accurate steering assist torque.

[0088] See Figure 7 This is a flowchart illustrating a steering assist control method provided in Embodiment 7 of this application. Figure 7 As shown, in step S102 above, after performing anomaly detection on the hand force and torque data and obtaining the data detection result, the following steps are also included:

[0089] Step S701: If there are no abnormalities in the data detection results, determine the steering assist torque based on the hand torque data.

[0090] There were no abnormalities, and the hand force torque data was normal. Therefore, this hand force torque data can be used to determine the steering assist torque.

[0091] In one embodiment, the power steering system is equipped with a normal assist curve, which is a curve calibrated under normal torque perception conditions. The normal assist curve represents the mapping relationship between hand torque, vehicle speed, and steering assist torque. Based on this, the steering assist torque can be obtained from the hand torque data, thereby controlling the power steering system to provide assistance.

[0092] Regarding the case where the aforementioned hand force torque data includes two torque sensing data points, if both torque sensing data points are normal, it can be determined that the data detection result is normal, as in Example 5 above, where all single-path verification results and synchronous verification results show no abnormalities. In this case, the average value of the two torque sensing data points can be used as the hand force torque. Based on this hand force torque, the normal power assist curve, and the current vehicle speed, the steering assist torque can be obtained.

[0093] See Figure 8 This is a structural schematic diagram of an electric power steering system provided in Embodiment 8 of this application. Figure 8 The modules are obtained by modularizing all the functions in Embodiments 1 to 7. Taking EPS1 as an example, EPS1 drives a motor 3. EPS1 is equipped with a torque calculation and diagnosis module 11, a normal feel module 12, an arbitration module 13, a safety monitoring status judgment module 14, a torque self-learning module 15, and a safety feel module 16. In addition, EPS1 is connected to a torque sensor 2 to obtain two torque sensing data, T1 and T2. EPS1 is connected to a braking system 4 to obtain vehicle driving data.

[0094] The torque calculation and diagnostic module 11 receives T1 and T2, processes the signals according to the sensor specifications, first obtaining two torque values, Torque1 and Torque2, and then averaging the two torque values ​​to output Torque. On the other hand, it performs signal diagnostics, including but not limited to communication protocol diagnostics, upper and lower limit diagnostics, and two-channel synchronization checks. The single-channel check results are err_flagT1 and err_flagT2, respectively, and the synchronization check diagnostic result is err_flagT. The signal content of err_flagT is 0 for normal signal and 1 for abnormal signal.

[0095] Normal feel module 12 receives the vehicle speed and its validity signals from Torque and braking system 4, and requests assistance AssistTorReq according to the normal feel function output request torque. Normal feel function module generally includes multiple modules and can be configured with the above-mentioned assistance curves, such as basic assistance, return assist, damping assist, end protection, etc. The required signals are not limited to Torque and vehicle speed and their validity signals.

[0096] Arbitration module 13 receives the Status signal from safety monitoring status judgment module 14. When the Status signal value is 0, it means that all torque sensing data is normal, and arbitration module 13 outputs Torque_Arb to accept the request for assistance AssistTorReq from normal feel module 12 and ignores the request for safety assistance from safety feel module 16. When the Status signal value is 1, it means that the torque sensing is abnormal, and arbitration module 13 outputs Torque_Arb to use the request for safety assistance SafetyTorReq from safety feel module 16. When the Status signal value is 2, it means that both torque sensing data and driving data are abnormal and the vehicle status cannot be determined, and arbitration module 13 directly cuts off the assistance.

[0097] The safety monitoring status judgment module 14 receives Torque, Torque1, and Torque2, as well as err_flagT, err_flagT1, and err_flagT2 from the torque calculation and diagnosis module 11. It judges the three err_flag values ​​and obtains the values ​​of Status1, Status2, and Status. Status1 = 0 indicates no abnormality in the torque signal; Status1 = 1 indicates an abnormality in the T1 torque signal; Status1 = 2 indicates an abnormality in the T2 torque signal; Status1 = 3 indicates an abnormality in both torque signals. Status2 indicates the validity of the driving data; Status2 is 0 when all driving data is valid, and 1 when at least one data point is invalid. Status is obtained from Status1 and Status2. See Tables 1 and 2 below:

[0098] Table 1

[0099]

[0100] Table 2

[0101] Serial Number Status1 Status2 Status 1 0 0 0 2 1 0 1 3 2 0 1 4 3 0 1 5 0 1 0 6 1 1 1 7 2 1 1 8 3 1 2

[0102] The torque self-learning module 15 carries the aforementioned torque self-learning model, and its triggering conditions and output logic are as follows:

[0103] (1) When Status1 = 0 and Status2 = 0, it indicates that the torque signal and driving data are normal. At this time, the self-learning module is activated to record the value of Torque, and the hand torque output by the torque self-learning module is Torque.

[0104] (2) When Status1 = 0 and Status2 = 1, the driving data is abnormal, the self-learning module cannot be activated, and the hand torque = Torque;

[0105] (3) When Status1 = 1 and Status2 = 0, T1 fails and the output hand force torque = Torque2;

[0106] (4) When Status1 = 2 and Status2 = 0, T2 fails and the output hand torque = Torque1;

[0107] (5) When Status1 = 1 and Status2 = 1, T1 fails and the output hand force torque = Torque2;

[0108] (6) When Status1 = 2 and Status2 = 1, T2 fails and the output hand torque = Torque1;

[0109] (7) When Status1 = 3 and Status2 = 0, both torque sensors fail and the output hand force torque is equal to the calibrated torque output by the torque self-learning model.

[0110] (8) When Status1 = 3 and Status2 = 1, both torque sensors fail, the hand torque output value cannot be predicted based on driving data, and the power steering output must be cut off directly.

[0111] The safety feel module 16 internally calibrates a safety curve, which is calibrated independently of the feel module curve and is only related to vehicle speed and calibrated torque.

[0112] This application also provides a vehicle that includes the steering assist control method described in the above embodiments. Specifically, the device that carries the steering assist control method can be the controller of the power steering system in the vehicle.

[0113] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0114] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software 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 beyond the scope of this application.

[0115] In the embodiments provided in this application, it should be understood that the disclosed apparatus / control devices and methods can be implemented in other ways. For example, the apparatus / control device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0117] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A steering assist control method, characterized in that, The steering assist control method includes: Acquire data on the driver's hand force and torque when steering the vehicle; Anomaly detection is performed on the hand force torque data to obtain the data detection results; When the data detection results are abnormal, the target hand force torque is determined according to the type of abnormality in the data detection results; Based on the safety curve of the vehicle entering a safe state and the target hand torque, the steering assist torque is determined, and steering assist is provided to the vehicle according to the steering assist torque; The hand force torque data includes two torque sensing data points. The anomaly detection of the hand force torque data, yielding data detection results, includes: Perform single-channel verification on the two torque sensing data in the hand force torque data to obtain the single-channel verification result of each torque sensing data. The difference between the two torque sensing data is verified for synchronization, and the synchronization verification result is obtained. If at least one of the single-path verification results and the synchronous verification result is abnormal, then the data detection result is determined to be abnormal. If all single-path verification results and the synchronous verification results are normal, then the data detection results are determined to be normal.

2. The steering assist control method according to claim 1, characterized in that, After performing anomaly detection on the hand force torque data and obtaining the data detection result, the method further includes: If the data detection results show no abnormalities, the steering assist torque is determined based on the hand force torque data.

3. The steering assist control method according to claim 1, characterized in that, The step of determining the target hand force torque based on the anomaly type in the data detection results includes: If the anomaly type in the data detection result is that one torque sensing data is normal and the other torque sensing data is abnormal, then the target hand force torque is determined based on the normal torque sensing data. If the anomaly type in the data detection result is that both torque sensing data are abnormal, then the target hand torque is determined based on the current vehicle driving data.

4. The steering assist control method according to claim 3, characterized in that, Determining the target hand torque based on current vehicle driving data includes: Check if the current vehicle driving data is valid; If the current vehicle driving data is detected as valid data, then the torque self-learning model is used to calculate the torque of the current vehicle driving data to obtain the target hand force torque; If the current vehicle driving data is detected to be invalid, then the target hand force torque is determined to be zero.

5. The steering assist control method according to claim 4, characterized in that, The step of determining the steering assist torque based on the safety curve of the vehicle entering a safe state and the target hand torque includes: Obtain the safety curve of the vehicle entering a safe state, wherein the safety curve is a curve calibrated based on abnormal conditions, and the safety curve is used to characterize the mapping relationship between hand torque, vehicle speed and steering assist. The steering assist torque is obtained based on the target hand force torque, the safety curve, and the current vehicle speed in the current vehicle driving data.

6. The steering assist control method according to claim 5, characterized in that, The step of determining the target hand torque based on the normal torque sensing data includes: The normal torque sensing data is used as the target hand torque.

7. The steering assist control method according to claim 1, characterized in that, After determining that the data detection result is abnormal if at least one of the single-channel verification results and the synchronous verification results is abnormal, the method further includes: If the synchronous verification result is successful, and one of the two single-channel verification results is successful while the other is unsuccessful, then the anomaly type in the data detection result is determined to be that one torque sensing data is normal and the other torque sensing data is abnormal.

8. The steering assist control method according to claim 1, characterized in that, After determining that the data detection result is abnormal if at least one of the single-channel verification results and the synchronous verification results is abnormal, the method further includes: If the synchronous verification result is a verification failure, or if both single-channel verification results are verification failures, then the anomaly type in the data detection result is determined to be that both torque sensing data are abnormal.

9. A vehicle, characterized in that, The vehicle includes an electric power steering system for implementing the steering assist control method as described in any one of claims 1 to 8.

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