Method, device, controller, vehicle and program product for detecting fault

Abstract

The invention relates to a method, an apparatus, a controller, a vehicle and a program product for detecting a fault. The method includes acquiring a first yaw velocity of the vehicle, wherein the first yaw velocity is determined based on at least one of a plurality of perceived signals from an inertial measurement unit (IMU) of the vehicle. The method also includes acquiring a second yaw velocity of the vehicle, wherein the second yaw velocity is determined based on a steering angle signal from a steering angle sensor of the vehicle. Further, the method includes detecting a fault condition of the IMU based on the first yaw velocity and the second yaw velocity in a case where it is determined that a single perceived signal error does not occur among a plurality of perceived signals from a plurality of sensors of the vehicle, where the plurality of sensors includes at least a steering angle sensor. In this way, the fault state of the IMU of the vehicle can be detected more accurately on the basis of the sensing signals of the multiple sensors.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more specifically, to a method, apparatus, controller, vehicle, and program product for detecting faults. Background Technology

[0002] With technological advancements and improved living standards, vehicles are increasingly equipped with driver assistance features, such as stability control, steering assist, and autonomous driving. The realization of these features relies on accurate perception of the vehicle's state. This perception is typically achieved through various types of sensors deployed within the vehicle. For example, an inertial measurement unit (IMU) can measure the vehicle's yaw rate and lateral acceleration, wheel speed sensors can measure wheel speed, and steering angle sensors can measure the vehicle's steering angle.

[0003] In some cases, sensors may malfunction, causing them to inaccurately perceive the vehicle's status or resulting in abnormal sensor signals. This can prevent the functions relying on these sensors from operating properly; for example, the vehicle's autonomous driving function might execute incorrect autonomous driving strategies, impacting driving safety. To ensure driving safety, it is necessary to detect sensor malfunctions in the vehicle and adjust the execution methods of relevant vehicle functions based on the detection results. Summary of the Invention

[0004] Embodiments of this disclosure provide a method, apparatus, controller, vehicle, and program product for detecting faults.

[0005] In a first aspect of this disclosure, a method for detecting a fault is provided. The method includes acquiring a first yaw rate of a vehicle, wherein the first yaw rate is determined based on at least one of multiple sensing signals from an IMU (Integrated Device Unit) of the vehicle. The method further includes acquiring a second yaw rate of the vehicle, wherein the second yaw rate is determined based on a steering angle signal from a steering angle sensor of the vehicle. Furthermore, the method includes detecting a fault state of the IMU based on the first yaw rate and the second yaw rate, provided that no single sensing signal error is found among the multiple sensing signals from multiple sensors of the vehicle, wherein the multiple sensors include at least a steering angle sensor.

[0006] In a second aspect of this disclosure, an apparatus is provided. The apparatus includes a first acquisition unit configured to acquire a first yaw rate of a vehicle, wherein the first yaw rate is determined based on at least one of a plurality of sensing signals from an IMU (Integrated Device Unit) of the vehicle. The apparatus also includes a second acquisition unit configured to acquire a second yaw rate of the vehicle, wherein the second yaw rate is determined based on a steering angle signal from a steering angle sensor of the vehicle. Furthermore, the apparatus includes a fault detection unit configured to detect a fault state of the IMU based on the first yaw rate and the second yaw rate, provided that no single sensing signal error has occurred among the plurality of sensing signals from a plurality of sensors of the vehicle, wherein the plurality of sensors includes at least a steering angle sensor.

[0007] In a third aspect of this disclosure, a controller is provided. The controller includes one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement the method provided according to a first aspect of this disclosure.

[0008] In a fourth aspect of this disclosure, a vehicle is provided. The vehicle includes a controller provided according to a third aspect of this disclosure.

[0009] In a fifth aspect of this disclosure, a machine-readable storage medium is provided. The machine-readable storage medium stores machine-executable instructions, which are executed by a processor to implement the method provided according to a first aspect of this disclosure.

[0010] In a sixth aspect of this disclosure, a machine program product is provided, comprising machine executable instructions that are executed by a processor to implement the method provided according to a first aspect of this disclosure.

[0011] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0013] Figure 1 A schematic diagram of an example vehicle in which several embodiments of the present disclosure may be implemented is shown;

[0014] Figure 2A flowchart of a method for detecting faults according to some embodiments of the present disclosure is shown;

[0015] Figure 3 A schematic diagram of a scenario for detecting a fault is shown according to some embodiments of the present disclosure;

[0016] Figure 4 A schematic diagram showing the curves of yaw rate versus time according to some embodiments of the present disclosure is shown;

[0017] Figure 5 A schematic diagram of yet another scenario for detecting a fault, according to some embodiments of the present disclosure, is shown;

[0018] Figure 6 A schematic diagram of yet another scenario for detecting a fault, according to some embodiments of the present disclosure, is shown;

[0019] Figure 7 A schematic flowchart of a method for detecting faults according to some embodiments of the present disclosure is shown;

[0020] Figure 8 A block diagram of an apparatus for detecting faults according to some embodiments of the present disclosure is shown; and

[0021] Figure 9 A block diagram of a device that can implement several embodiments of the present disclosure is shown. Detailed Implementation

[0022] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0023] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0024] The term "vehicle" in this application is used in a broad sense and can refer to means of transportation (such as cars, trucks, motorcycles, airplanes, trains, ships, etc.), industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), agricultural equipment (such as lawnmowers, harvesters, etc.), amusement equipment, toy vehicles, etc. The embodiments of this application do not specifically limit the type of vehicle.

[0025] As mentioned earlier, to ensure the normal operation of various vehicle functions and guarantee driving safety, it is necessary to detect sensor malfunctions in the vehicle. In related technologies, multiple sensing signals from different sensors can be combined to detect whether a particular sensing signal is malfunctioning, thereby determining whether the corresponding sensor is faulty. The inventors of this application have discovered that this method requires acquiring multiple sensing signals from sensors and can only identify malfunctioning sensing signals when only one signal is malfunctioning. It cannot identify multiple malfunctioning sensing signals when two or more signals are malfunctioning simultaneously; that is, it can only identify single-point failures.

[0026] If multiple sensing signals from the same sensor malfunction simultaneously, the sensor's fault will go undetected. For example, a vehicle's IMU can simultaneously measure the vehicle's yaw rate and lateral acceleration. If the IMU malfunctions, both the yaw rate and lateral acceleration readings from it will be incorrect, making the IMU fault undetectable.

[0027] Therefore, embodiments of this disclosure propose a scheme for fault detection. In embodiments of this disclosure, a first yaw rate and a second yaw rate of the vehicle can be acquired, wherein the first yaw rate is determined based on at least one sensing signal from a plurality of sensing signals from the vehicle's IMU, and the second yaw rate is determined based on a steering angle signal from the vehicle's steering angle sensor. If no single sensing signal error is found among the plurality of sensing signals from the vehicle's multiple sensors, a fault state of the IMU can be detected based on the first yaw rate and the second yaw rate.

[0028] In this way, even when it has been determined that no single sensing signal error has occurred, the fault status of the IMU can be further detected. Even when multiple sensing signals from the IMU show errors, it is still possible to detect whether the IMU is faulty. This improves the detection rate and accuracy of vehicle sensor fault detection, enabling the detection of faults in more types of sensors within the vehicle, thereby enhancing vehicle driving safety and functional reliability.

[0029] Figure 1A schematic diagram of an example vehicle 100 in which various embodiments of the present disclosure may be implemented is shown. (See diagram for reference.) Figure 1 As shown, vehicle 100 may include multiple sensors, such as IMU 101, steering angle sensor 102, and wheel speed sensors 103-1 to 103-4. IMU 101 can be deployed under the chassis of vehicle 100 and can be used to measure and report the lateral acceleration, longitudinal acceleration, and yaw rate of vehicle 100. Steering angle sensor 102 can be deployed at the bottom of the steering column of vehicle 100 and can be used to measure the rotation angle and direction of the steering wheel of vehicle 100. Wheel speed sensors 103-1 to 103-4 can be deployed at each wheel of vehicle 100 and can be used to measure wheel speed.

[0030] Vehicle 100 may include sensors that measure only one parameter and report only one type of sensing signal. For example, steering angle sensor 102 may only measure the steering angle of the steering wheel of vehicle 100 and may only report a steering angle signal indicating that steering angle. As another example, wheel speed sensor 103-1 may only measure the wheel speed of one wheel of vehicle 100 and may only report a sensing signal indicating that wheel speed. Vehicle 100 may also include sensors that can measure multiple parameters and report multiple types of sensing signals. For example, IMU 101 may simultaneously measure the yaw rate and lateral acceleration of vehicle 100 and report sensing signals indicating both yaw rate and lateral acceleration.

[0031] The vehicle 100 may also include multiple electronic control units (ECUs) (not shown in the figure). These ECUs can use data measured by one or more sensors in the vehicle 100 to determine the vehicle status and the driver's intentions, and control the vehicle through actuators. The ECUs in the vehicle 100 may include, but are not limited to, motor controllers, body control modules, vehicle controllers, vehicle stability control modules, and braking system controllers.

[0032] In some embodiments, one or more ECUs in vehicle 100 can acquire sensing signals from sensors and detect whether the sensors are malfunctioning. For example, vehicle 100 may include a braking system controller that can acquire the lateral acceleration and yaw rate of vehicle 100 from IMU 101, the steering angle signal of vehicle 100 from steering angle sensor 102, and the wheel speeds of vehicle 100 from wheel speed sensors 103-1 to 103-4. Based on this, the braking system controller can detect whether IMU 101, steering angle sensor 102, and wheel speed sensors 103-1 to 103-4 are malfunctioning.

[0033] In the embodiments of this disclosure, the ECU in the vehicle can interact with other devices through wired or wireless communication methods (including but not limited to controller area network (CAN) bus, local interconnect network (LIN) bus, media oriented systems transport (MOST) bus, vehicle Ethernet, Wi-Fi, Bluetooth, etc.) to exchange data and information, such as acquiring sensing signals from sensors configured in the vehicle.

[0034] It should be understood that the above content is combined with Figure 1 The description of the vehicle 100 to which the solutions provided in the embodiments of this disclosure can be applied is merely illustrative and should not be construed as limiting the solutions provided in this disclosure. For example, in some embodiments, the vehicle 100 may include more or fewer sensors; in some embodiments, the sensors included in the vehicle 100 may have other types of functions; and in some embodiments, the sensors included in the vehicle 100 may be configured in other suitable locations.

[0035] Figure 2 A flowchart of a method 200 for detecting a fault according to some embodiments of the present disclosure is shown. Method 200 can be performed by a device for detecting a fault, which can be deployed in or independently of a vehicle, and can be implemented by software and / or hardware. Exemplarily, the device can be one or more ECUs in the aforementioned vehicle 100, such as a brake system controller. For ease of explanation, method 200 will now be illustrated using a detection device as the execution entity. Reference Figure 2 Method 200 may include boxes 202 to 206.

[0036] In block 202, the detection device acquires a first yaw rate of the vehicle, which is determined based on at least one of a variety of sensing signals from the vehicle's IMU. The IMU can be used to measure various parameters of the vehicle, including yaw rate, lateral acceleration, and longitudinal acceleration; that is, the sensing signals from the IMU can include sensing signals indicating yaw rate, lateral acceleration, and longitudinal acceleration.

[0037] Yaw rate refers to the angular velocity of a vehicle rotating about a z-axis perpendicular to the ground. Yaw rate can be directly measured by an IMU (Insulated Module Unit) or calculated from other parameters measured by the IMU or other sensors on the vehicle (e.g., wheel speed, lateral acceleration, steering angle, etc.). A first yaw rate is determined based on the sensing signals from the IMU; that is, the first yaw rate can be the yaw rate directly measured by the IMU, or it can be determined based on the lateral and longitudinal acceleration measured by the IMU. In some embodiments, the first yaw rate can be determined based on multiple parameters measured by the IMU.

[0038] In some embodiments, the first yaw rate may be determined by other devices based on the sensing signal from the IMU, and the detection device may acquire the first yaw rate from the other devices. In some embodiments, the detection device may directly acquire the sensing signal from the IMU and determine the first yaw rate based on it.

[0039] In block 204, the detection device acquires a second yaw rate of the vehicle, which is determined based on a steering angle signal from the vehicle's steering angle sensor. The steering angle signal indicates the steering angle of the vehicle's steering wheel; the steering angle sensor is a sensor used solely to measure the steering angle of the vehicle's steering wheel, and its sensing signal includes only the steering angle signal. In some embodiments, the second yaw rate may be determined by other devices based on the steering angle signal from the steering angle sensor, and the detection device may acquire the second yaw rate from these other devices. In some embodiments, the detection device may directly acquire the steering angle signal from the steering angle sensor and determine the second yaw rate based on it. It should be understood that the second yaw rate acquired in block 204 and the first yaw rate acquired in block 202 are the yaw rates of the same vehicle, but the methods for determining the second yaw rate are different from those for determining the first yaw rate.

[0040] In block 206, if it is determined that no single sensing signal error has occurred among multiple sensing signals from multiple sensors of the vehicle, the detection device detects the fault state of the IMU based on a first yaw rate and a second yaw rate, where the multiple sensors include a steering angle sensor. As previously mentioned, in some cases, the scheme for detecting whether a sensing signal is erroneous can only identify the erroneous sensing signal if one sensing signal is erroneous, i.e., it can only identify a single sensing signal error. If it is determined that no single sensing signal error has occurred among multiple sensing signals, there are two possibilities: either all sensing signals are erroneous or two or more sensing signals are erroneous.

[0041] For ease of explanation, vehicle sensors are divided into two categories: the first category consists of sensors that can detect only one parameter and report one sensing signal, and the second category consists of sensors that can detect multiple parameters and report multiple sensing signals. The steering angle sensor belongs to the first category, and the IMU belongs to the second category. If all multiple sensing signals from multiple sensors are error-free, or if two or more sensing signals are error-free, it indicates that either all multiple sensors are functioning normally, or one of the second-category sensors is faulty, or two or more sensors are faulty. For a vehicle, assuming functional safety requirements are met, the probability of two or more sensors malfunctioning simultaneously is extremely low. Therefore, it can be considered that if no single sensing signal error is found among the multiple sensing signals from multiple sensors, including the steering angle sensor, it can be determined that all first-category sensors are functioning normally; in other words, the steering angle sensor is not faulty.

[0042] Based on this, since the second yaw rate is determined based on the steering angle signal from the steering angle sensor, it can be considered a correct yaw rate. Using this as a reference, the accuracy of the first yaw rate can be determined, thereby determining the IMU's fault state and enabling the detection of IMU fault conditions.

[0043] It should be noted that, although in Figure 2 The diagram shows box 202 preceding box 204, but this is not intended to restrict the order of operations performed at boxes 202 and 204. Instead, the operations performed at boxes 202 and 204 can be performed in interchangeable orders or simultaneously. In this way, even if multiple sensing signals from the vehicle's IMU simultaneously malfunction, the IMU's fault state can be detected. This allows for the detection of faults that would otherwise be undetectable, improving the coverage of fault detection for the vehicle's sensors. Furthermore, for vehicles, to ensure that the IMU does not experience undetectable two-point faults (i.e., two sensing signals from the IMU malfunction simultaneously), the IMU needs an extremely high functional safety level, requiring IMUs deployed in vehicles to have extremely high stability and a rigorous development process. The solution provided in this disclosure allows for the detection of two-point faults in the IMU, thus eliminating the requirement for an extremely high functional safety level for the IMU. This allows vehicles to accommodate a wider variety of IMU types.

[0044] To more clearly illustrate the solution provided in this disclosure, the following is combined with... Figures 3 to 6 Some embodiments of this disclosure will be further described. Figure 3 A schematic diagram of scenario 300 for fault detection is shown in some embodiments of this disclosure. (See reference...) Figure 3Scenario 300 may include a detection device 301, an IMU 302, a steering angle sensor 303, and a detection device 304. The IMU 302 and the steering angle sensor 303 may be sensors deployed in the same vehicle. For example, the IMU 302 may correspond to the IMU 101 in the aforementioned vehicle 100, and can be used to detect the lateral acceleration, longitudinal acceleration, and yaw rate of the vehicle 100. The steering angle sensor 303 may correspond to the steering angle sensor in the aforementioned vehicle 100, and can be used to detect the steering angle of the steering wheel of the vehicle 100. The detection device 304 can be used to detect whether a single sensing signal is erroneous among multiple sensing signals from multiple sensors in the vehicle. For example, the detection device 304 may determine whether there is an erroneous sensing signal among multiple sensing signals from the IMU 101, the steering angle sensor 102, and the wheel speed sensors 103-1 to 103-4 in the vehicle 100. The detection device 304 may be one or more ECUs in the vehicle.

[0045] The detection device 301 can be used to perform the aforementioned method 200 to detect the fault state of the IMU 302. Exemplarily, the detection device 301 can acquire multiple sensing signals from the IMU 302 (e.g., sensing signals indicating the yaw rate of the vehicle and sensing signals indicating the lateral acceleration of the vehicle), and determine a first yaw rate based on at least one of the multiple sensing signals from the IMU 302.

[0046] In some embodiments, the detection device 301 can use the yaw rate measured by the IMU 302 as the first yaw rate. In some embodiments, the detection device 301 can determine the first yaw rate of the vehicle based on the lateral acceleration of the vehicle measured by the IMU 302, for example, by the following formula:

[0047] ω r = A y / v (1)

[0048] Where, ω r A represents the yaw rate of the vehicle. yLet v represent the lateral acceleration of the vehicle and v represent the vehicle's speed. In embodiments of this disclosure, the detection device can acquire the vehicle's speed v in a variety of predefined ways. For example, the vehicle's speed v can be acquired by the detection device 301 from the vehicle's speed sensor, from the vehicle's anti-lock braking system, from the vehicle's driver assistance system, or determined based on the vehicle's wheel speed, and so on, without listing all of them here. It should be understood that this disclosure does not limit the specific method of determining the vehicle's speed v.

[0049] The detection device 301 can acquire a steering angle signal from the steering angle sensor 303 and determine the vehicle's second yaw rate based on the steering angle signal. Exemplarily, in some embodiments, the second yaw rate can be determined by the following formula:

[0050] ω r = (v×δ) / L (2)

[0051] Where, ω r Let v represent the vehicle's yaw rate, δ represent the vehicle's speed, δ represent the vehicle's steering angle, and L represent the vehicle's wheelbase. The wheelbase L can be predefined.

[0052] Since the IMU 302 and the steering angle sensor 303 are deployed in the same vehicle, the first yaw rate determined based on the sensing signal of the IMU 302 and the second yaw rate determined based on the steering angle signal of the steering angle sensor 303 are the yaw rates of the same vehicle. Therefore, if the steering angle sensor 303 is not faulty and the second yaw rate is the correct value, the first yaw rate can be determined based on the second yaw rate, and thus the fault of the IMU 302 can be determined.

[0053] For example, Figure 4 The diagram shows curves illustrating the variation of a first yaw rate and a second yaw rate over time according to some embodiments of the present disclosure. Figure 4 The diagram includes curves 410 and 420. Curve 410 can be a curve showing the first yaw rate changing over time, and curve 420 can be a curve showing the second yaw rate changing over time. Before time t1, neither IMU 302 nor steering angle sensor 303 malfunctions, and curves 410 and 420 coincide. After time t1, IMU 302 malfunctions, and the sensing signal from IMU 302 becomes erroneous. The first yaw rate determined based on this signal becomes incorrect, causing curves 410 and 420 to no longer coincide.

[0054] The detection device 301 can acquire the detection results of the detection device 304, and if the detection results indicate that no single sensing signal error occurs among multiple sensing signals, including the steering angle signal of the steering angle sensor 303, the fault state of the IMU 302 is determined based on the first yaw rate and the second yaw rate. If the detection results of the detection device 304 indicate that no single signal error occurs, it can be considered that the steering angle sensor 303 is not faulty. Therefore, the correctness of the first yaw rate can be determined, and thus the fault state of the IMU 302 can be determined.

[0055] In some embodiments, the detection device 304 sends an indication message to the detection device 301 indicating that a single sensing signal error has occurred among the multiple sensing signals of the multiple sensors only if it determines that a single sensing signal error has occurred among the multiple sensing signals. In this case, the detection device 301 can determine the duration for which it has not received the indication message from the detection device 304. If the duration exceeds a predetermined duration, the detection device 301 can determine that no single sensing signal error has occurred among the multiple sensing signals of the multiple sensors, and determine the fault state of the IMU 302 based on the first yaw rate and the second yaw rate.

[0056] In some embodiments, the detection device 301 may determine whether the first yaw rate is correct based on whether the first yaw rate is equal to the second yaw rate. In some embodiments, the detection device 301 may determine whether the first yaw rate is correct based on the magnitude of the difference between the first yaw rate and the second yaw rate. In some embodiments, the detection device 301 may generate indication information 305 indicating the fault status of the IMU 302.

[0057] In some embodiments, the detection device 301 can determine the yaw rate difference between a first yaw rate and a second yaw rate, and determine the fault state of the IMU 302 based on the yaw rate difference and the vehicle's travel speed. For example, the detection device 301 can determine a yaw rate threshold (referred to as a first threshold for ease of distinction) based on the vehicle's travel speed. The detection device 301 can determine whether the yaw rate difference is greater than or equal to the first threshold. If the yaw rate difference is greater than or equal to the first threshold, the detection device 301 can determine that the IMU 302 has malfunctioned; if the yaw rate difference is less than the first threshold, the detection device 301 can determine that the IMU 302 has not malfunctioned.

[0058] In some embodiments, the detection device 301 can determine a first threshold based on the vehicle's speed and a predefined algorithm model. In some embodiments, the detection device 301 may have a predefined first correspondence between the vehicle's speed and the yaw rate threshold in its local memory, and the detection device 301 can determine the first threshold based on this first correspondence. For example, the first correspondence between the speed and the yaw rate threshold may be shown in Table 1:

[0059] Table 1: First Correspondence Between Driving Speed ​​and Yaw Rate Threshold

[0060] driving speed 10 30 50 70 90 120 threshold 0.62 0.34 0.23 0.17 0.14 0.13

[0061] It should be understood that the correspondence between driving speed and yaw rate threshold described in Table 1 is merely an example of embodiments of this disclosure and should not be construed as a limitation on the solutions provided by this disclosure. For example, the first correspondence between driving speed and yaw rate threshold may differ for different vehicles. In some embodiments, the comparison between the yaw rate difference and driving speed described above can be converted into each other. For example, the detection device 301 can determine the vehicle's driving speed threshold based on the yaw rate difference and compare the current driving speed of the vehicle with the driving speed threshold to determine whether the IMU 302 has malfunctioned.

[0062] In some embodiments, the detection device 301 can determine the fault state of the IMU 302 if it is determined that the vehicle is in a stable state. That is, before determining the fault state of the IMU 302, the detection device 301 can first determine whether the vehicle is in a stable state. In some embodiments, the detection device 301 can determine whether the vehicle is in a stable state based on the vehicle's current driving speed and second yaw rate.

[0063] For example, the detection device 301 can determine a yaw rate threshold (referred to as a second threshold for ease of distinction) based on the vehicle's travel speed. The detection device 301 can compare the second yaw rate with the second threshold, and determine that the vehicle is in a stable state if the second yaw rate is less than or equal to the second threshold.

[0064] In some embodiments, the detection device 301 can determine a second threshold based on the vehicle's speed and a predefined algorithm model. In some embodiments, the higher the speed, the smaller the determined second threshold. In some embodiments, the detection device 301 may have a predefined second correspondence between the vehicle's speed and the yaw rate threshold in its local memory, and the detection device 301 can determine the second threshold based on this second correspondence. For example, the second correspondence between the speed and the yaw rate threshold may be shown in Table 2:

[0065] Table 2: Second Correspondence Between Driving Speed ​​and Yaw Rate Threshold

[0066] driving speed 10 30 50 70 90 120 threshold 0.45 0.24 0.15 0.1 0.08 0.07

[0067] It should be understood that the correspondence between driving speed and yaw rate threshold described in Table 2 is merely an example of embodiments of this disclosure and should not be construed as a limitation on the solutions provided by this disclosure. For example, the second correspondence between driving speed and yaw rate threshold may differ for different vehicles. In some embodiments, the comparison between the second yaw rate and driving speed described above can be converted between each other. For example, the detection device 301 may determine the vehicle's driving speed threshold based on the second yaw rate and compare the current driving speed of the vehicle with the driving speed threshold to determine whether the vehicle is in a stable state.

[0068] When the vehicle is in a stable state, the sensors in the vehicle can measure the vehicle's parameters more accurately, and the yaw rate calculated based on the sensor signals will be more accurate. This, in turn, allows for more accurate detection of IMU malfunctions.

[0069] It should be understood that the above description of scenario 300 is merely an example of embodiments of this disclosure and should not be construed as limiting the solutions provided by the embodiments of this disclosure. For example, in some embodiments, the detection device 304 can acquire sensing signals from the IMU 302 and determine a first yaw rate, and can acquire steering angle signals from the steering angle sensor 303 and determine a second yaw rate. The detection device 301 can acquire the first yaw rate and the second yaw rate from the detection device 304. In some embodiments, the detection device 301 can acquire the first yaw rate and the second yaw rate from other devices. In some embodiments, the first yaw rate and the second yaw rate can be determined by other predefined physical models.

[0070] In some embodiments, detection device 301 and detection device 304 may be the same device. In some embodiments, detection device 301 may perform the functions of detection device 304. That is, detection device 301 may acquire multiple sensing signals from multiple sensors of the vehicle and detect whether a single sensing signal error occurs among these multiple sensing signals.

[0071] For example, Figure 5A schematic diagram of a scenario 500 for fault detection according to some embodiments of this disclosure is shown. Scenario 500 may include a detection device 510, an IMU 520, a wheel speed sensor 530, and a steering angle sensor 540. The detection device 510 may correspond to the aforementioned detection device 301, and may be a controller or ECU configured in the vehicle. The IMU 520, wheel speed sensor 530, and steering angle sensor 540 may be sensors configured in the same vehicle; for example, the IMU 520 may correspond to IMU 101 in vehicle 100, the wheel speed sensor 530 may correspond to wheel speed sensors 103-1, 103-2, 103-3, and 103-4 in vehicle 100, and the steering angle sensor 540 may correspond to steering angle sensor 102 in vehicle 100.

[0072] The detection device 510 can acquire various sensing signals from multiple sensors of the vehicle. For example, the detection device 510 can acquire the yaw rate measurement value 521 and lateral acceleration 522 of the vehicle from the IMU 520, the detection device 510 can acquire the wheel speed 531 of multiple wheels of the vehicle from the wheel speed sensor 530, and the detection device 510 can acquire the steering angle signal 541 from the steering angle sensor 540.

[0073] The detection device 510 can determine the yaw rate of the vehicle based on multiple sensing signals. For example, the detection device 510 can use the yaw rate measurement value 521 obtained by the IMU 520 as the first yaw rate. The detection device 510 may also include a first calculation module 511, a second calculation module 512, and a third calculation module 513. The first calculation module 511 can determine the third yaw rate 523 based on the lateral acceleration 522, for example, by using the aforementioned formula (1). The second calculation module 512 can determine the fourth yaw rate 532 based on the wheel speed 531, for example, by using the following formula:

[0074] ω r = (|ω1-ω2|×R) / A (3)

[0075] Where ω r Let |ω1-ω2| represent the yaw rate of the vehicle, |ω1-ω2| represent the absolute value of the difference between the wheel speeds of the two rear wheels of the vehicle, R represent the radius of the wheel, and A represent the distance between the two rear wheels, where R and A can be predefined. The third detection module 513 can determine the second yaw rate 542 based on the steering angle signal 541, for example, by using the aforementioned formula (2).

[0076] The detection device 510 may include a first detection module 514, which can determine whether any of the four sensing signals—yaw rate measurement value 521, lateral acceleration 522, wheel speed 531, and steering angle signal 541—is erroneous based on the yaw rate measurement value 521, the third yaw rate 523, the fourth yaw rate 532, and the second yaw rate 542.

[0077] For example, the first detection module 514 can determine whether there is an outlier among the yaw rate measurements 521, 523, 532, and 542. If an outlier exists, the first detection module 514 can determine that one of the sensing signals among the yaw rate measurements 521, 523, 532, and 542 is erroneous. Otherwise, it can be determined that no single sensing signal is erroneous.

[0078] In some embodiments, the first detection module 514 can determine the average value of the yaw rate measurements 521, 523, 532, and 542, and determine whether there exists a parameter among the yaw rate measurements 521, 523, 532, and 542 whose difference from the average value is significantly greater than the differences between the other three parameters and the average value. If so, the first detection module 514 can determine it as a sensing signal indicating an error. In some embodiments, the first detection module 514 can report indication information indicating a single sensing signal error among multiple sensing signals. Otherwise, the first detection module 514 can determine that no single sensing signal error has occurred.

[0079] The detection device 510 may include a second detection module 515. If the first detection module 514 determines that no single sensing signal error has occurred, the second detection module 515 may detect the fault state of the IMU 520 based on the yaw rate measurement value 521 and the second yaw rate 542. In some embodiments, the second detection module 515 may generate a detection result 550 indicating the fault state of the IMU 520. The method by which the second detection module 515 detects the fault state of the IMU 520 can be referred to the foregoing description of scenario 300, and will not be repeated here.

[0080] In some embodiments, the detection device 510 can send the detection result 550 to other devices in the vehicle, such as the VCU or CAN bus. In some embodiments, the detection device 510 can be a controller in the vehicle that needs to utilize the sensing signals of the IMU 520 to perform specific driver assistance functions. After determining the detection result 550, the detection device 510 can adjust the corresponding driver assistance function based on the detection result 550. For example, if the detection result 550 indicates that the IMU 520 has malfunctioned, the driver assistance function that needs to utilize the sensing signals of the IMU 520 can be turned off.

[0081] It should be understood that Figure 5 The scenario 500 shown is merely an example of some embodiments of this disclosure and should not be construed as limiting the embodiments of this disclosure. For example, in some embodiments, the detection device 510 may include more or fewer modules. In some embodiments, the detection device 510 may compensate or correct the sensing signals from the IMU 520, wheel speed sensor 530, and / or steering angle sensor 540 using a predefined compensation algorithm. It should also be understood that the formulas in the embodiments of this disclosure (e.g., the aforementioned formulas (1), (2), and (3)) are merely illustrative and should not be construed as limiting the embodiments of this disclosure; the formulas may be reasonably modified or substituted.

[0082] Figure 6 A schematic diagram of scenario 600 for fault detection in some embodiments of this disclosure is shown. Scenario 600 includes a detection device 610 and a detection device 620. Detection device 610 can detect whether a single sensing signal error occurs among multiple sensing signals from the vehicle's IMU 601, wheel speed sensor 602, and steering angle sensor 603. Detection device 620 can execute the aforementioned method 200 to further detect the fault state of IMU 601 based on the detection results of detection device 610. Detection device 610 may correspond, for example, to detection device 304 in scenario 300, and detection device 620 may correspond, for example, to detection device 301 in scenario 300.

[0083] The detection device 610 can acquire the vehicle's yaw rate measurement 611 and lateral acceleration 612 from the IMU 601, acquire the wheel speeds 613 of multiple wheels of the vehicle from the wheel speed sensor 502, and acquire the vehicle's steering angle signal 614 from the steering angle sensor 603. The detection device 610 can determine a first yaw rate 621 based on the yaw rate measurement 611, a third yaw rate 622 based on the lateral acceleration 612, a fourth yaw rate 623 based on the wheel speeds 613, and a second yaw rate 624 based on the steering angle signal 614.

[0084] The detection device 610 can determine whether any of the four sensing signals—yaw rate measurement 611, lateral acceleration 612, wheel speed 613, and steering angle signal 614—is erroneous based on the first yaw rate 621, the third yaw rate 622, the fourth yaw rate 623, and the second yaw rate 624. If an error is detected in any of the sensing signals, the detection device 610 can generate an error indication message.

[0085] If the detection device 610 does not generate an indication of an error, it indicates that no single signal among the four sensing signals—yaw rate measurement 611, lateral acceleration 612, wheel speed 613, and steering angle signal 614—is erroneous. In other words, all four sensing signals are correct, or two or more signals are erroneous. For vehicles that meet functional safety requirements, more than two sensors will not err simultaneously. Therefore, if the detection device 610 does not generate an indication of an error, it indicates that wheel speed 613 and steering angle signal 614 are correct, while yaw rate measurement 611 and lateral acceleration 612, both from IMU 601, may be erroneous simultaneously. In this case, the detection device 620 can further detect whether IMU 601 is erroneous.

[0086] The detection device 620 can acquire a first yaw rate 621 and a second yaw rate 624 from the detection device 610. Since the steering angle signal 614 is a correct sensing signal, the second yaw rate 624 determined based on the steering angle signal 614 is a correct yaw rate. By comparing the first yaw rate 621 and the second yaw rate 624, it can be determined whether the first yaw rate is correct, and thus whether the yaw rate measurement value 611 of the IMU 601 is correct. If the yaw rate measurement value 611 is incorrect, it indicates that the IMU 601 is faulty; if the yaw rate measurement value 611 is correct, it indicates that the IMU 601 is not faulty. In this way, the detection device 620 can detect the fault state of the IMU 601.

[0087] Figure 7 A schematic flowchart of a method 700 for detecting faults according to some embodiments of this disclosure is shown. Method 700 can be executed by a detection device, such as the aforementioned detection device 301, detection device 510, or detection device 620. The method 700 will now be illustrated schematically using a detection device as the executing entity. (See reference...) Figure 7 Method 700 may include boxes 702 to 722.

[0088] In block 702, the detection device acquires multiple sensing signals from multiple sensors of the vehicle, including an IMU, a steering angle sensor, and wheel speed sensors. The sensing signals may include yaw rate measurements and lateral acceleration measured by the IMU, steering angle measurements by the steering angle sensor, and wheel speed measurements by the wheel speed sensors. In block 704, the detection device uses the yaw rate measurements as a first yaw rate, determines a second yaw rate based on the steering angle, a third yaw rate based on the lateral acceleration, and a fourth yaw rate based on the wheel speeds.

[0089] In block 706, the detection device determines whether a single sensing signal error has occurred based on the first yaw rate, the second yaw rate, the third yaw rate, and the fourth yaw rate. If yes, proceed to block 708; otherwise, proceed to block 710. In block 708, the detection device reports an error message indicating that an erroneous sensing signal has occurred. In block 710, the detection device determines the vehicle's speed. In block 712, the detection device determines a first threshold and a second threshold based on the speed. For example, the first threshold is determined based on the aforementioned first correspondence, and the second threshold is determined based on the second correspondence.

[0090] In block 714, the detection device determines whether the second yaw rate is less than or equal to a second threshold. If yes, proceed to block 716; otherwise, return to block 702. In block 716, the detection device determines the yaw rate difference between the first and second yaw rates. In block 718, the detection device determines whether the yaw rate difference is greater than or equal to a first threshold. If yes, proceed to block 720; otherwise, proceed to block 722. In block 720, the detection device determines that the IMU is faulty and reports an erroneous indication indicating an IMU fault. In block 722, the detection device determines that the IMU, steering angle sensor, and wheel speed sensor are not faulty and reports an indication that the sensors are not faulty.

[0091] This method allows for a more accurate determination of whether a vehicle's IMU (Integrated Device Unit) is malfunctioning, based on two separate tests. It ensures that no potential IMU faults are overlooked during the detection of vehicle sensor malfunctions, leading to more accurate sensor fault detection results. In the event of an IMU malfunction, it can be detected promptly, potentially preventing accidents caused by sensor failure and improving vehicle functional stability and driving safety.

[0092] Figure 8 A block diagram of an apparatus 800 for detecting faults according to some embodiments of the present disclosure is shown. Apparatus 800 may, for example, correspond to the aforementioned detection apparatus 301, detection apparatus 510, or detection apparatus 620. Figure 8As shown, the device 800 includes a first acquisition unit 802 configured to acquire a first yaw rate of the vehicle, wherein the first yaw rate is determined based on at least one of a plurality of sensing signals from an inertial measurement unit (IMU) of the vehicle. The device 800 also includes a second acquisition unit 804 configured to acquire a second yaw rate of the vehicle, wherein the second yaw rate is determined based on a steering angle signal from a steering angle sensor of the vehicle. Furthermore, the device 800 includes a fault detection unit 806 configured to detect a fault state of the IMU based on the first yaw rate and the second yaw rate, provided that no single sensing signal error has occurred among the plurality of sensing signals from a plurality of sensors of the vehicle, wherein the plurality of sensors includes a steering angle sensor.

[0093] Figure 9 A schematic block diagram of an example device 900 that can be used to implement embodiments of the present disclosure is shown. Figure 3 The detection device 301 in the middle Figure 5 The detection device 510 and / or Figure 6 The detection device 620 in the middle can be implemented using equipment 900. For example... Figure 9 As shown, device 900 includes a processor 901, which can perform various appropriate actions and processes according to program instructions stored in read-only memory (ROM) 902 or loaded into random access memory (RAM) 903. RAM 903 may also store various programs and data required for the operation of device 900. The processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Input / output (I / O) interface 905 is also connected to bus 904.

[0094] The various processes and procedures described above, such as methods 200 and 700, can be executed by processor 901. For example, in some embodiments, methods 200 and 700 can be implemented as computer software programs tangibly contained in a machine-readable medium. In some embodiments, part or all of the computer program can be loaded and / or installed on device 900 via ROM 902. When the computer program is loaded into RAM 903 and executed by processor 901, one or more actions of methods 200 and 700 described above can be performed.

[0095] This disclosure can be a method, apparatus, system, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of this disclosure.

[0096] A computer-readable storage medium can be a tangible device capable of holding and storing instructions for use by an instruction execution device. A computer-readable storage medium can be, for example—but not limited to—an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), and any suitable combination thereof. The computer-readable storage medium as used herein is not to be construed as a transient signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0097] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0098] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0099] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0100] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0101] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0103] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method (200) for detecting a fault, comprising: (202) Obtain a first yaw rate of the vehicle, wherein the first yaw rate is determined based on at least one of a plurality of sensing signals from the vehicle’s inertial measurement unit; (204) Obtain the second yaw rate of the vehicle, wherein the second yaw rate is determined based on the steering angle signal from the steering angle sensor of the vehicle; as well as If no single sensing signal error is found among the multiple sensing signals from the multiple sensors of the vehicle, the fault state of the inertial measurement unit is detected (206) based on the first yaw rate and the second yaw rate, wherein the multiple sensors include at least the steering angle sensor.

2. The method (200) according to claim 1, wherein the at least one sensing signal includes a yaw rate measurement, the method further comprising: The lateral acceleration of the vehicle is obtained from the inertial measurement unit; Based on the lateral acceleration, the third yaw rate of the vehicle is determined; The wheel speed of the vehicle is obtained from the vehicle's wheel speed sensor; Based on the wheel speed, determine the fourth yaw rate of the vehicle; as well as Based on the first yaw rate, the second yaw rate, the third yaw rate, and the fourth yaw rate, it is determined that no single sensing signal error occurred among the various sensing signals from the multiple sensors.

3. The method (200) according to claim 2, wherein determining that no single sensing signal error occurs among the multiple sensing signals from the plurality of sensors comprises: It was determined that no single outlier value appeared among the first yaw rate, the second yaw rate, the third yaw rate, and the fourth yaw rate.

4. The method (200) according to claim 1, further comprising: In response to the determination that the duration for which an indication message indicating a single sensing signal error has occurred among the multiple sensing signals of the multiple sensors exceeds a predetermined duration, it is determined that no single sensing signal error has occurred among the multiple sensing signals of the multiple sensors.

5. The method (200) according to any one of claims 1 to 4, wherein detecting (206) the fault state of the inertial measurement unit comprises: Obtain the vehicle's speed; as well as The fault state of the inertial measurement unit is determined based on the difference in yaw rate between the first yaw rate and the second yaw rate and the travel speed.

6. The method (200) according to claim 5, wherein determining the fault state of the inertial measurement unit comprises: The first threshold is determined based on the first correspondence between the driving speed and the predefined driving speed and yaw rate threshold. as well as In response to the yaw rate difference being greater than or equal to the first threshold, it is determined that the inertial measurement unit has malfunctioned.

7. The method (200) according to claim 6, further comprising: Disabling driving functions associated with the sensing signals of the inertial measurement unit.

8. The method (200) according to any one of claims 1 to 4, further comprising, before detecting the fault state of the inertial measurement unit: Obtain the vehicle's speed; as well as Based on the driving speed and the second yaw rate, it is determined that the vehicle is in a stable state.

9. The method (200) of claim 8, wherein determining that the vehicle is in a stable state comprises: The second threshold is determined based on the second correspondence between the driving speed and the predefined driving speed and yaw rate threshold; as well as In response to the second yaw rate being less than or equal to the second threshold, it is determined that the vehicle is in a stable state.

10. A device (800) for detecting faults, comprising: The first acquisition unit (802) is configured to acquire a first yaw rate of the vehicle, wherein the first yaw rate is determined based on at least one of a plurality of sensing signals from the vehicle’s inertial measurement unit. The second acquisition unit (804) is configured to acquire a second yaw rate of the vehicle, wherein the second yaw rate is determined based on a steering angle signal from a steering angle sensor of the vehicle. as well as The fault detection unit (806) is configured to detect a fault state of the inertial measurement unit based on the first yaw rate and the second yaw rate, provided that no single sensing signal error has occurred among multiple sensing signals from multiple sensors of the vehicle, wherein the multiple sensors include at least the steering angle sensor.

11. A controller, comprising: At least one processor; as well as A memory coupled to the at least one processor and having instructions stored thereon, which, when executed by the at least one processor, cause the controller to perform the method according to any one of claims 1 to 9.

12. A vehicle comprising the controller according to claim 11.

13. A machine program product comprising machine executable instructions, wherein the machine executable instructions are executed by a processor to implement the method according to any one of claims 1 to 9.

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