Method and device for detecting sensor

Abstract

The invention discloses a method and a device for detecting a sensor. The method comprises the steps that first sensor data detected by a first sensor and second sensor data detected by a second sensor are obtained, and the first sensor and the second sensor are arranged at the two ends of a first steering shaft respectively; mapping the first sensor data into third sensor data, wherein the third sensor data is used for representing data collected by the second sensor at the position side of the first sensor; acquiring a data difference between the second sensor data and the third sensor data; under the condition that it is determined that the first steering shaft has a sensor fault according to the data difference, threshold comparison is conducted on the first sensor data and the second sensor data, and a comparison result is obtained; and determining a fault sensor from the first sensor and the second sensor according to a comparison result. According to the scheme provided by the embodiment of the invention, the accuracy of sensor fault detection can be improved.

Description

Technical Field

[0001] This application belongs to the field of vehicle control technology, and in particular relates to a method and apparatus for detecting sensors. Background Technology

[0002] To improve vehicle stability, sensors are typically deployed within vehicles. By analyzing the data collected by these sensors, vehicle malfunctions can be detected promptly, enhancing operational safety. However, if a sensor malfunctions and is not detected in time, it can affect vehicle control and even threaten life and property.

[0003] In related technologies, signal-based fault diagnosis or model-based fault diagnosis is usually used to diagnose sensor faults. However, this method has the problem of false alarms and missed alarms, and cannot accurately detect faulty sensors. Summary of the Invention

[0004] This application provides a method and apparatus for detecting sensors, which can improve the accuracy of sensor fault detection.

[0005] In a first aspect, embodiments of this application provide a method for detecting sensors, applied in a vehicle controller. The vehicle has multiple steering shafts, and sensors are disposed at both ends of each steering shaft. The method includes: acquiring first sensor data detected by a first sensor and second sensor data detected by a second sensor, wherein the first sensor and the second sensor are respectively disposed at both ends of a first steering shaft, and the first steering shaft is any one of the multiple steering shafts; mapping the first sensor data to third sensor data according to the mapping relationship between the sensor data at both ends of the steering shaft, wherein the third sensor data is used to characterize the data collected by the second sensor at the position side of the first sensor; acquiring the data difference between the second sensor data and the third sensor data; if it is determined that there is a sensor fault in the first steering shaft based on the data difference, performing threshold comparison on the first sensor data and the second sensor data respectively to obtain a comparison result; and determining the faulty sensor from the first sensor and the second sensor based on the comparison result.

[0006] Secondly, embodiments of this application provide a sensor detection device applied in a vehicle controller. The vehicle has multiple steering shafts, and sensors are disposed at both ends of each steering shaft. The device includes: a data acquisition module for acquiring first sensor data detected by a first sensor and second sensor data detected by a second sensor, wherein the first sensor and the second sensor are respectively disposed at both ends of a first steering shaft, and the first steering shaft is any one of the multiple steering shafts; a data mapping module for mapping the first sensor data to third sensor data according to the mapping relationship between the sensor data at both ends of the steering shaft, wherein the third sensor data is used to characterize the data collected by the second sensor at the position of the first sensor; a difference acquisition module for acquiring the data difference between the second sensor data and the third sensor data; a data comparison module for performing threshold comparison on the first sensor data and the second sensor data respectively, and obtaining a comparison result, if a sensor fault is determined to exist in the first steering shaft based on the data difference; and a fault determination module for determining the faulty sensor from the first sensor and the second sensor based on the comparison result.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement the method for detecting a sensor as described in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the method for detecting a sensor as described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the sensor detection method as described in the first aspect.

[0010] As described above, in this embodiment, for two sensors on the same steering shaft, the data from both ends of the steering shaft are mapped to one end through a mapping relationship between the data from the sensors at both ends of the steering shaft. Data comparison allows for the determination of whether a sensor on the steering shaft is faulty. Compared to existing technologies, this method eliminates the need to construct a vehicle model, requiring only simple data comparison, thus reducing the complexity of data processing and analysis, improving the accuracy of data analysis, and consequently enhancing the accuracy of sensor fault detection. Furthermore, in this embodiment, the steering shaft with the faulty sensor is first identified. Then, the sensor data from both ends of the faulty steering shaft are compared with their respective thresholds. This comprehensive comparative diagnosis of sensors at different locations allows for accurate localization of the faulty sensor, achieving precise sensor location.

[0011] As can be seen from the above, the solution provided in this application embodiment can improve the accuracy of sensor fault detection. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the structure of a vehicle controller provided in one embodiment of this application;

[0014] Figure 2 This is a schematic flowchart of a method for detecting a sensor provided in one embodiment of this application;

[0015] Figure 3 This is a schematic diagram of the fault detection logic flow corresponding to a fault detection unit provided in one embodiment of this application;

[0016] Figure 4 This is a schematic flowchart of the fault sensor location logic provided in one embodiment of this application;

[0017] Figure 5 This is a timing diagram of sensor fault detection when the fault detection triggering condition is met, according to an embodiment of this application.

[0018] Figure 6 This is a timing diagram of sensor fault detection when the fault detection triggering condition is not met, according to one embodiment of this application.

[0019] Figure 7 This is a schematic diagram of the structure of a detection sensor device provided in another embodiment of this application;

[0020] Figure 8 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0021] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0023] To facilitate understanding, before explaining the solution provided in this application, the background of the solution provided in this application will be explained first.

[0024] Multi-axle steering technology is widely used in heavy-duty freight vehicles and special-purpose vehicles. By enabling all wheels to steer, it allows for a smaller turning radius and better maneuverability, thereby improving the handling stability of heavy vehicles and reducing tire wear. Combined with intelligent technology, multi-axle steering autonomous vehicles can effectively reduce manpower input in the production process and lower production costs, and are widely used in various scenarios such as mines, ports, and factories. For multi-axle steering autonomous vehicles, wheel angle sensors are key sensors that provide real-time feedback on the current steering angle information of each wheel. If a malfunction occurs and there is no effective fault-tolerant mechanism, the autonomous vehicle will struggle to perform steering control and adjust its vehicle posture, potentially leading to serious consequences such as loss of control and even threatening the lives and property of the driver.

[0025] For multi-axle steering autonomous vehicles, it is impossible to simultaneously balance vehicle control costs and the low failure rate of wheel angle sensors. Therefore, related technologies need to effectively detect and safely handle wheel angle sensor failures so that when a wheel angle sensor fails, it can be detected in a timely manner, the failure level can be classified, an early warning can be issued, and corresponding measures can be taken to handle the situation, thereby ensuring the safety of multi-axle steering autonomous vehicles in the production process.

[0026] In related technologies, fault diagnosis of steering sensors in autonomous vehicles typically focuses on steering wheel angle sensors. This primarily includes signal-based fault diagnosis methods and model-based fault diagnosis methods. Signal-based methods involve transforming and processing the sensor data collected by the wheel angle sensors to extract their features, and then using feature analysis to diagnose the sensor fault. Model-based methods involve establishing an accurate vehicle kinematic or dynamic model offline, then calculating the residual between the actual wheel angle and the wheel angle output by the mathematical model online, and using this residual to determine if the sensor is faulty.

[0027] In related technologies, the aforementioned signal-based and model-based fault diagnosis methods are typically applied to sensor fault detection in vehicles with a single axle. Compared to front-wheel steering vehicles, multi-axle steering vehicles have a greater number of steering angle sensors and more complex steering patterns. Therefore, the steering angle sensor fault diagnosis methods in related technologies are difficult to directly apply to multi-axle steering vehicles. Specifically, in related technologies, signal-based fault diagnosis methods usually perform fault diagnosis based on the time-domain or frequency-domain characteristics of the steering angle signal of a single steering wheel, which is difficult to apply to some important fault conditions, such as gradually changing faults. In addition, related technologies usually only perform feature analysis on the data collected by a single sensor, without comprehensively considering the data collected by other wheel steering angle sensors or vehicle state information, making it difficult to guarantee the accuracy of sensor fault detection results. On the other hand, model-based fault diagnosis methods usually require the establishment of an accurate mathematical model of vehicle steering. Unlike ordinary front-wheel steering vehicles, multi-axle vehicles have complex and diverse steering patterns, making it difficult to apply a unified mathematical model to describe the steering angle of each wheel. Moreover, the fault detection methods for steering sensors in related technologies usually do not define the preconditions for fault detection and the fault confirmation method. After a sensor fault is detected, the vehicle is usually stopped directly. This method can cause false alarms from the sensor and cannot effectively utilize useful information from other steering wheels, resulting in low work efficiency.

[0028] Therefore, further research is still needed on fault detection of wheel angle sensors and the design of system-wide safety methods for multi-axle steering autonomous vehicles with multiple steering wheels.

[0029] To address the problems of the prior art, embodiments of this application provide a method and apparatus for detecting sensors. The sensor detection method provided in this application can be implemented by a vehicle controller.

[0030] In one embodiment, Figure 1 A schematic diagram of the vehicle controller structure is shown, such as... Figure 1 As shown, the vehicle controller mainly includes the following units: fault detection trigger unit 10, fault detection unit 20, fault confirmation unit 30, fault level determination unit 40, and mode control unit 50.

[0031] like Figure 1 As shown, the fault detection trigger unit 10 is used to obtain the vehicle power supply voltage, the bus status of the steering angle sensor bus, and the sensor power supply voltage from the vehicle's operating status and CAN (Controller Area Network) signals, to determine whether there is a fault in the vehicle power supply, the steering angle sensor bus, and the wheel hard wire acquisition. When it is detected that there are no faults in the vehicle power supply, the steering angle sensor bus, and the wheel hard wire acquisition, a sensor fault detection enable signal is generated. This enable signal is transmitted to the fault detection unit 20, so that the fault detection unit 20 detects whether there is a fault in the steering sensor on the steering shaft, and locates the faulty sensor when a faulty sensor is found on the steering shaft. To reduce the risk of false sensor fault detection due to noise in the sensor data, algorithm misjudgment, etc., after the fault detection unit 20 outputs the faulty sensor, the fault confirmation unit 30 counts the number of faults of the sensor within a preset time period. After the number of faults reaches a certain value, the sensor is confirmed as a faulty sensor. Furthermore, the fault level determination unit 40 determines the corresponding fault level of the vehicle based on data such as the number and distribution of fault sensors, and then the mode control unit 50 determines the vehicle's control mode based on the fault level to ensure the vehicle's safety during operation.

[0032] The following combination Figure 1 The method for detecting sensors provided in the embodiments of this application will be described.

[0033] It should be noted that the sensor can be a wheel angle sensor or a wheel speed sensor. In this embodiment, the wheel angle sensor is used as an example for explanation.

[0034] In addition, in this embodiment, the vehicle can be an autonomous vehicle such as a heavy-duty freight vehicle or a special vehicle. The vehicle has multiple steering axes, and sensors are provided at both ends of each steering axis. That is, the solution provided in this embodiment can realize the fault detection of sensors in multi-axle steering vehicles.

[0035] Figure 2 A flowchart illustrating a method for detecting a sensor according to an embodiment of this application is shown. Figure 2 As shown, the method includes the following steps S201 to S205:

[0036] Step S201: Obtain the first sensor data detected by the first sensor and the second sensor data detected by the second sensor.

[0037] In step S201, the first sensor and the second sensor are respectively disposed at both ends of the first steering shaft. The first steering shaft is any one of a plurality of steering shafts. The first sensor and the second sensor are sensors of the same type. In this embodiment of the application, the first sensor and the second sensor are both angle sensors.

[0038] In one example, sensors at both ends of each steering shaft of the vehicle are connected to the vehicle controller, so that the vehicle controller can obtain the sensor data collected by the sensors at both ends of each steering shaft. In the case of the sensor being a steering angle sensor, the sensor data may include, but is not limited to, the wheel steering angle.

[0039] Step S202: Based on the mapping relationship between the sensor data at both ends of the steering shaft, the first sensor data is mapped to the third sensor data.

[0040] In step S202, the third sensor data is used to characterize the data collected by the second sensor at the position of the first sensor.

[0041] In one example, the mapping relationship between the sensor data at both ends of the steering shaft can be represented by formula (1):

[0042]

[0043] In formula (1), δ i,left The data collected by the first sensor on the i-th steering axis is the first sensor data. This is data from the third sensor, specifically data from the first sensor δ. i,left Data obtained from the second sensor side through mapping; A 4,i A 3,i A 2,i A 1,i and A 0,iThese are the mapping function coefficients of the data detected by the sensors on both sides of the i-th steering shaft. In practical engineering, these coefficients can be obtained by measuring and calibrating the actual turning angles of the wheels at both ends of the steering shaft during steering.

[0044] Step S203: Obtain the data difference between the second sensor data and the third sensor data.

[0045] In step S203, the data difference between the second sensor data and the third sensor data can be expressed by formula (2):

[0046]

[0047] In formula (2), Raxis,i represents the data difference corresponding to the i-th steering axis, which is the difference between the right wheel steering angle sensor reading obtained through mapping and the actual right wheel steering angle sensor reading; δ i,right This is data from the second sensor.

[0048] Step S204: If it is determined that there is a sensor fault in the first steering shaft based on the data difference, threshold comparison is performed on the first sensor data and the second sensor data respectively to obtain the comparison result.

[0049] In step S204, the vehicle controller can determine whether there is a sensor fault in the first steering shaft based on the magnitude of the data difference.

[0050] In one embodiment, the vehicle controller can obtain the fault threshold corresponding to the first steering shaft, and determine that there is a sensor fault in the first steering shaft if the data difference is greater than the fault threshold.

[0051] As an example, the presence of a sensor malfunction in the first steering shaft can be determined using formula (3):

[0052]

[0053] In formula (3), Error axis,i This is a fault flag indicating whether a sensor malfunction exists on the i-th steering shaft, where Error axis,i =1 indicates that there is a sensor malfunction in the i-th steering axle, Error axis,i =0 indicates that there is no sensor fault on the i-th steering axle; δ th,i The fault threshold corresponding to the i-th steering shaft can be obtained by calibrating the false alarm rate and the false alarm rate in engineering practice. The fault threshold may be different for different steering shafts.

[0054] In formula (3), if the data difference is greater than or equal to the fault threshold, i.e., Error axis,iIf the value is 1, then a sensor fault exists in the first steering shaft; if the data difference is less than the fault threshold, i.e., Error... axis,i If the value is 0, it can be determined that there is no sensor fault in the first steering shaft.

[0055] After determining that a sensor fault exists in the first steering shaft, the specific faulty sensor is further inspected. In this embodiment, the vehicle controller can determine whether a sensor is faulty by comparing the sensor data corresponding to each sensor with the threshold value corresponding to that sensor, thereby locating the faulty sensor.

[0056] Step S205: Determine the faulty sensor from the first sensor and the second sensor based on the comparison results.

[0057] In step S205, if the sensor data of a certain sensor is outside the range of data collected during its normal operation, the sensor can be determined to be a faulty sensor. Furthermore, the vehicle controller can locate the faulty sensor based on the steering shaft position of the steering shaft where the faulty sensor is located and the position of the wheel on the steering shaft.

[0058] Based on the scheme defined in steps S201 to S205 above, it can be understood that in this embodiment, for two sensors on the same steering shaft, the data from the sensors at both ends of the steering shaft are mapped to one end through the mapping relationship between the data. Data comparison can then determine whether a sensor on the steering shaft is faulty. Compared with existing technologies, this eliminates the need to construct a vehicle model; only simple data comparison is required, reducing the complexity of data processing and analysis, improving the accuracy of data analysis, and consequently improving the accuracy of sensor fault detection. Furthermore, in this embodiment, the steering shaft with sensor faults is first identified, and then the sensor data at both ends of the steering shaft with sensor faults are compared with their respective thresholds. A comprehensive comparative diagnosis of sensors at different locations allows for accurate localization of the faulty sensor, thus achieving precise localization of the faulty sensor.

[0059] As can be seen from the above, the solution provided in this application embodiment can improve the accuracy of sensor fault detection.

[0060] The implementation process of the method provided in the embodiments of this application is described below.

[0061] In one embodiment, before performing fault detection on the sensor and acquiring sensor data, the vehicle controller first checks whether the triggering conditions for sensor fault detection are met.

[0062] Specifically, in response to a fault detection command, the vehicle controller collects steering shaft data corresponding to the first steering shaft; then, if the steering shaft data meets preset conditions, it generates fault detection information.

[0063] In the above embodiments, the fault detection command can be generated by the driver operating the vehicle's fault detection control, or it can be generated by the vehicle during self-check.

[0064] In the above embodiments, the steering shaft data includes: the vehicle's overall power supply voltage, the bus status of the sensor on the first steering shaft, and the power supply voltage of the sensor corresponding to the first steering shaft. The bus status of the sensor on the first steering shaft includes an on state and an off state. The fault detection information is used to instruct the vehicle controller to perform fault detection on the sensor on the first steering shaft, that is, the sensor on the vehicle's steering shaft is faulty and the faulty sensor needs to be located.

[0065] In the above embodiments, the preset conditions include at least the following conditions:

[0066] Condition 1: The vehicle's power supply voltage is within the first voltage range.

[0067] Condition 2: The bus containing the sensor on the first steering shaft is in the open state.

[0068] Condition 3: The sensor power supply voltage is within the second voltage range.

[0069] In condition one above, the first voltage range is the power supply voltage range that enables the vehicle to operate normally. The first voltage range can be... V vel The current vehicle power supply voltage, These are the lower and upper limits of the first voltage range, respectively.

[0070] In condition two above, the bus state of the bus where the sensor is located can be represented by formula (4):

[0071]

[0072] In formula (4), Flag busoff,i,j This is the status flag bit of the bus containing the j-th sensor on the i-th steering axis, where i = 1, 2, ..., n, j = left, right, j = left represents the status flag bit of the bus containing the first sensor, and j = right represents the status flag bit of the bus containing the second sensor; Flag busoff,i,j =1 indicates that the bus containing the j-th sensor of the i-th steering axle is in the on state; Flag busoff,i,j=0 indicates that the bus containing the j-th sensor of the i-th steering axis is in the off state, and the bus is in normal working state at this time.

[0073] In condition three above, the second voltage range is the power supply voltage range that enables the sensor to operate normally. By detecting whether the sensor's power supply voltage is within the second voltage range, it can be determined whether each wheel has a hard-wire acquisition fault. The second voltage range can be...

[0074] V ss,i,j This represents the power supply voltage of the j-th sensor on the i-th steering axis, where i = 1, 2, ..., n, j = left, right, j = left represents the power supply voltage of the first sensor, and j = right represents the power supply voltage of the second sensor; These are the lower and upper limits of the second voltage range, respectively.

[0075] Upon receiving a fault detection command, and after the steering shaft data corresponding to the first steering shaft meets the above three conditions, the vehicle controller can determine that the sensor in the vehicle's steering shaft is faulty. At this time, the vehicle controller needs to use the fault detection algorithm in the fault detection unit to locate the steering shaft with the faulty sensor, and then locate the faulty sensor in that steering shaft. The fault detection logic corresponding to the fault detection unit is as follows: Figure 3 As shown, after the fault detection trigger unit detects that the vehicle's steering axis data meets the above three conditions, it generates a fault detection flag bit Con. i The fault flag and the sensor data collected by the sensors at both ends of the first steering shaft are transmitted to the fault detection unit. If Con i =1 indicates that there is a sensor fault in the first steering shaft. At this time, the fault detection unit performs sensor fault detection based on the sensor data collected by the sensors at both ends of the first steering shaft and locates the faulty sensor.

[0076] like Figure 3 As shown, the fault detection unit maps the sensor data at both ends of the first steering shaft to one end. For example, in Figure 3In this process, based on the mapping relationship L1 between left-wheel sensor data and right-wheel sensor data, and the mapping relationship L2 between right-wheel sensor data and left-wheel sensor data, the mapping between sensor data on different sides of the same axle is established. This allows for the calculation of the data difference between the mapped sensor data at one end and the sensor data at the other end, i.e., logical judgment is performed to determine whether a sensor in the steering shaft is faulty. If the data difference is small, it can be determined that the sensor in the steering shaft is not faulty; if the data difference is large, it can be determined that the sensor in the steering shaft is faulty. In this case, a threshold comparison is used to locate the faulty sensor in the steering shaft.

[0077] In one embodiment, the vehicle controller acquires the data range of the target sensor under normal operating conditions. Then, it checks whether the target sensor's data is within the data range. If the target sensor's data is outside the data range, the target sensor is determined to be faulty. To improve the accuracy of faulty sensor location, the vehicle controller also counts the number of faults of the target sensor within a preset time period. Only when the number of faults exceeds a preset threshold is the target sensor determined to be faulty, thus reducing the risk of misjudgment of sensor faults caused by sensor reading noise.

[0078] It should be noted that in the above embodiments, the target sensor is either the first sensor or the second sensor.

[0079] In one example Figure 4 The fault sensor location logic diagram is shown, such as... Figure 4 As shown, in the Error flag bit axis,i When δ = 1, the fault detection unit receives sensor data δ from both ends of the steering shaft. i,left and δ i,right Threshold comparisons were performed separately to locate the faulty sensor, where Error i,j Error is the fault flag bit for sensor j on steering shaft i. i,j =1 indicates that sensor j on steering shaft i is a faulty sensor, Error i,j =0 indicates that sensor j of steering shaft i is a non-faulty sensor.

[0080] Specifically, the input to the fault detection unit is a flag indicating whether a fault exists in steering shaft i (Error). axis,i δ, the number of data from the first sensor i,left Second sensor data δ i,right The output is the error flag bit of the sensor at both ends of steering shaft i. i,j Where i = 1, 2, ..., n, j = left, right. The fault detection logic of the fault detection unit is as follows:

[0081] When Error axis,i When = 1, that is, when the sensor on steering shaft i is faulty, the faulty sensor can be determined by formula (5):

[0082]

[0083] In formula (5), i = 1, 2, ..., n, j = left, right, Error i,j For the fault flag bit of sensor j on steering shaft i; Let be the lower limit of the reading of sensor j on steering axis i. The upper limit of the reading of sensor j on steering shaft i is the value used in engineering practice. and It can be obtained through calibration, which combines the requirements of robustness and accuracy.

[0084] When Error axis,i When = 0, that is, when there is no fault in the sensor on steering shaft i, then Error i,j =0,i=1,2,…,n,j=left,right.

[0085] To reduce false detections of sensor malfunctions caused by sensor reading noise or algorithm misjudgment, after identifying a faulty sensor, the fault confirmation unit counts the number of malfunctions over a certain period of time. That is, the sensor is only confirmed to be faulty if the number of malfunctions reaches a certain number within that period of time, thereby reducing the risk of false alarms of sensor malfunctions.

[0086] In one example, the number of sensor failures can be determined using formula (6):

[0087]

[0088] In formula (6), C i,j,k Let C be the number of sensor failures at both ends of steering shaft i during the k-th sensor sampling period; when k = 0, C i,j,k =0; Error i,j,k This is the fault flag bit of sensor j for steering shaft i during the kth sampling period.

[0089] The faulty sensor can be determined using formula (7):

[0090]

[0091] In formula (7), For the sensor fault confirmation flags at both ends of steering shaft i, C th,i,j The threshold is the cumulative number of faults of the sensors at both ends of steering shaft i.

[0092] To improve vehicle operational stability, after detecting and locating a sensor malfunction, the vehicle controller can adjust the vehicle's operating status based on the level and / or type of the corresponding sensor malfunction.

[0093] In one embodiment, after identifying the faulty sensor from the first and second sensors based on the comparison results, the vehicle controller also acquires the distribution data of the faulty sensors in the vehicle, then determines the corresponding fault level and fault type based on the distribution data, and controls the vehicle's operating status based on the fault level and / or fault type.

[0094] In the above embodiments, the distribution data of the fault sensors includes, but is not limited to, the number of fault sensors and their positions on multiple steering axes. For example, in a vehicle with three steering axes, there are three fault sensors, each located on the left wheel of each steering axis. Figure 1 In this system, the fault level determination unit can determine the vehicle's fault level and fault type based on the distribution data of the fault sensors. The fault level can be divided into three levels: Level 1, Level 2, and Level 3, with Level 3 being higher than Level 2 and Level 2 being higher than Level 1. The fault type is the fault type corresponding to the fault level. The fault types corresponding to the three levels are respectively the first fault type representing a single-axle single-wheel sensor fault, the second fault type representing a multi-axle same-side wheel sensor fault, and the third fault type representing a coaxial dual-wheel sensor fault.

[0095] In one embodiment, when there is only one faulty sensor, the fault level determination unit determines the fault level as the first level and the fault type as the first fault type; when there are multiple faulty sensors and the multiple faulty sensors are located on different steering shafts, the fault level is determined as the second level and the fault type as the second fault type; when there are multiple sensors and at least one sensor at both ends of a steering shaft is faulty, the fault level is determined as the third level and the fault type as the third fault type.

[0096] It should be noted that vehicle R&D personnel can define vehicle fault levels and fault types based on vehicle performance and other factors, and are not limited to the three fault levels and three fault types mentioned above.

[0097] In one embodiment, after the fault level determination unit determines the fault level and fault type corresponding to the vehicle, the mode control unit can control the vehicle's operating state based on the fault level and / or fault type. Specifically, when the fault level is the first or second level, or the fault type is the first or second fault type, the mode control module maps the sensor data corresponding to the fault sensor to the opposite end of the target steering shaft where the fault sensor is located, according to the mapping relationship, to obtain the fourth sensor data. Then, based on the correlation between the sensor data and the vehicle's operating state, it determines the target operating state corresponding to the fourth sensor data and controls the vehicle to operate in the target operating state. When the fault level is the third level, or the fault type is the third fault type, the mode control module controls the vehicle to enter a parking state.

[0098] In one example, such as Figure 1 As shown, for different fault states, the fault level determination unit transmits the corresponding fault level and fault type of the vehicle to the mode control unit, thereby the mode control unit controls the operation of the vehicle according to the fault level and / or fault type. In this embodiment, the vehicle control modes include the expected operation mode, the fault-tolerant operation mode, and the safe parking mode.

[0099] For the expected operating mode, when no sensor malfunction occurs in a vehicle with multiple steering axes, the vehicle enters the expected operating mode. In this mode, the vehicle controller controls the vehicle's operation based on sensor data collected by the vehicle's steering angle sensors.

[0100] For the fault-tolerant operation mode, when some of the vehicle's sensors fail, and the faulty sensors are located on different sides of different steering shafts, that is, at most one sensor on the same steering shaft is faulty, the vehicle enters the fault-tolerant operation mode. In this mode, the actual sensor data obtained by the vehicle is shown in formulas (8) and (9):

[0101] When the right wheel sensor malfunctions and the left wheel sensor is not malfunctioning, the sensor data collected by the left wheel sensor is mapped to obtain the corresponding sensor data for the right wheel sensor:

[0102]

[0103] In formula (8), This is the correction value for the right wheel sensor data of steering axis i.

[0104] When the left wheel sensor malfunctions and the right wheel sensor is not malfunctioning, the sensor data collected by the right wheel sensor is mapped to obtain the corresponding sensor data from the left wheel sensor:

[0105]

[0106] In formula (9), This is the correction value for the left wheel sensor data of steering axis i.

[0107] It should be noted that, in practical applications, the maximum speed of the vehicle can also be limited to improve the stability of vehicle operation.

[0108] In the safe parking mode, when multiple sensors malfunction, and the malfunctioning sensors are located on the same steering axis, the vehicle controller is unable to perform fault-tolerant correction of the sensor data, and the vehicle enters the safe parking mode. In this mode, the autonomous driving system cannot control the vehicle until the sensor malfunctions are resolved, in order to ensure vehicle safety.

[0109] The following example illustrates the specific implementation of the method provided in this application, using sensor fault detection under given fault detection trigger conditions as an example. This example sets up two scenarios: fault detection trigger conditions are met and not met, to verify the performance of the method provided in this application. The following embodiment is a specific implementation method; the application of the fault detection and fault tolerance method described in this application is not limited to those described herein, and it can be applied to other scenarios after appropriate modifications.

[0110] When the fault detection trigger condition is met, the enable flag bit output by the fault detection trigger unit is set to 1, the fault detection algorithm of the fault detection unit is triggered, the fault detection algorithm is executed, and the output result is... Figure 5As shown. During the 0-3s period, the input signal remains 0. During this time, the flags output by the fault detection unit, fault confirmation unit, and fault level determination unit are all 0, indicating that no fault occurred during this period, consistent with the system's input signal. During the 3-7s period, the input signal remains 1. The flags output by the fault detection unit are 1, and the result from the fault confirmation unit is 0 during the 3-4s period. Although the flags output by the fault detection unit are 1 during this period, the result from both the fault confirmation unit and the fault level determination unit remains 0 because the number of fault occurrences has not reached the threshold set by the fault confirmation unit. At the 4th second, the cumulative number of faults output by the fault confirmation unit reaches the preset threshold. At this point, the flags output by the fault confirmation unit change from 0 to 1, and the result from the fault confirmation unit remains 1 for the subsequent 4-7s period. During this period, the fault level determination unit generates the corresponding result. During the 7-10s period, the input signal remains at 0, the flag bit output by the fault detection unit is 0, and the result of the fault confirmation unit is 1 during the 7-8s period. Since the cumulative fault count output by the fault detection unit is still greater than the threshold during this time, the flag bit output by the fault confirmation unit is 1. During this 7-8s period, the fault level determination unit generates the corresponding result. At the 8th second, the cumulative fault count is less than the threshold, and the result output by the fault confirmation unit changes from 1 to 0. The fault type is also adjusted accordingly, and after a delay, the output result is 0. This demonstrates that when the fault detection conditions are met, the subsequent fault detection algorithm, fault debouncing confirmation, and fault level generation can be adjusted accordingly to changes in the input signal, and it possesses fault debouncing functionality.

[0111] When the fault detection trigger condition is not met, the enable flag of the fault detection trigger condition output is 0, and the subsequent fault detection algorithm is not executed, and the output result is... Figure 6 As shown, the input signal remains 0 for 0-3 seconds, 1 for 3-7 seconds, and 0 for 7-10 seconds. Although the input signal flag changes, the outputs of the fault detection algorithm, fault debouncing confirmation, and fault code generation are all 0 for 0-10 seconds, indicating that subsequent units will not execute if the fault detection conditions are not met.

[0112] This concludes the introduction of the methods provided in the embodiments of this application.

[0113] As described above, the solution provided in this application realizes fault detection and safety handling of the wheel angle sensors of a multi-axle steering autonomous vehicle, ensuring vehicle safety under faults of varying severity. In this application, firstly, a mapping relationship is established using the numerical constraints of the left and right wheel angle sensors of each steering axle, avoiding the need to build a complex vehicle model and reducing the difficulty of fault detection; the comparison and judgment of readings from different wheel sensors is introduced, improving the accuracy of fault detection. A general framework for the failure detection and fault tolerance method of wheel angle sensors in a multi-axle steering autonomous vehicle is designed, defining the triggering conditions and fault confirmation methods for fault detection, and judging whether to perform fault detection and whether to confirm a reported fault, thus improving the accuracy of fault detection. A graded operating mode for faults of different severity is defined, including the expected operating mode, the fault-tolerant operating mode, and the safe stopping mode, improving the safety of the multi-axle steering vehicle during operation and making it easy to apply in practical engineering.

[0114] This application also provides a device for detecting sensors, applied in a vehicle controller. The vehicle has multiple steering shafts, and sensors are installed at both ends of each steering shaft, such as... Figure 7 As shown, the device 700 includes: a data acquisition module 701, a data mapping module 702, a difference acquisition module 703, a data comparison module 704, and a fault determination module 705.

[0115] The data acquisition module 701 is used to acquire first sensor data detected by the first sensor and second sensor data detected by the second sensor, wherein the first sensor and the second sensor are respectively disposed at both ends of the first steering shaft, and the first steering shaft is any one of a plurality of steering shafts;

[0116] The data mapping module 702 is used to map the first sensor data to the third sensor data according to the mapping relationship between the sensor data at both ends of the steering shaft, wherein the third sensor data is used to characterize the data collected by the second sensor at the position side of the first sensor.

[0117] The difference acquisition module 703 is used to acquire the data difference between the data from the second sensor and the data from the third sensor.

[0118] The data comparison module 704 is used to perform threshold comparison on the data of the first sensor and the data of the second sensor respectively, and obtain the comparison result, when it is determined that there is a sensor fault in the first steering shaft based on the data difference.

[0119] The fault determination module 705 is used to determine the faulty sensor from the first sensor and the second sensor based on the comparison results.

[0120] In one embodiment, the sensor detection device further includes a first data acquisition module and an information generation module. The first data acquisition module is configured to, in response to a fault detection command, acquire steering shaft data corresponding to the first steering shaft before acquiring first sensor data detected by the first sensor and second sensor data detected by the second sensor. The information generation module is configured to generate fault detection information when the steering shaft data meets preset conditions, wherein the fault detection information is used to instruct the vehicle controller to perform fault detection on the sensor on the first steering shaft.

[0121] In one embodiment, the steering shaft data includes: the vehicle's overall power supply voltage, the bus status of the sensors on the first steering shaft, and the power supply voltage of the sensors corresponding to the first steering shaft;

[0122] The preset conditions include:

[0123] The vehicle's power supply voltage is within the first voltage range, which is the power supply voltage range that enables the vehicle to operate normally.

[0124] The bus containing the sensor on the first steering shaft is in the on state;

[0125] The sensor's power supply voltage is within the second voltage range, which is the range of power supply voltages required for the sensor to operate normally.

[0126] In one embodiment, the sensor detection device further includes a threshold acquisition module and a first fault determination module. The threshold acquisition module is used to acquire a fault threshold corresponding to the first steering shaft after acquiring the data difference between the second sensor data and the third sensor data; the first fault determination module is used to determine that a sensor fault exists in the first steering shaft if the data difference is greater than the fault threshold.

[0127] In one embodiment, the fault determination module includes: a data range acquisition module, a second fault determination module, a data statistics module, and a third fault determination module. The data range acquisition module is used to acquire the data range of data collected by the target sensor under normal operating conditions, wherein the target sensor is a first sensor or a second sensor. The second fault determination module is used to determine that the target sensor is faulty when the sensor data corresponding to the target sensor is outside the data range. The data statistics module is used to count the number of faults of the target sensor within a preset time period. The third fault determination module is used to determine that the target sensor is a faulty sensor when the number of faults exceeds a preset threshold.

[0128] In one embodiment, the sensor detection device further includes: a second data acquisition module, a fault level determination module, and a vehicle control module. The second data acquisition module is used to acquire distribution data of the faulty sensors in the vehicle after identifying the faulty sensor from the first and second sensors based on comparison results. The distribution data includes the number of faulty sensors and their positions on multiple steering shafts. The fault level determination module is used to determine the corresponding fault level and fault type of the vehicle based on the distribution data. The vehicle control module is used to control the vehicle's operating state based on the fault level and / or fault type.

[0129] In one embodiment, the fault level determination module is specifically used to determine the fault level as a first level and the fault type as a first fault type when there is only one fault sensor; to determine the fault level as a second level and the fault type as a second fault type when there are multiple fault sensors located on different steering shafts; and to determine the fault level as a third level and the fault type as a third fault type when there are multiple sensors and at least one sensor at both ends of a steering shaft is faulty. The third level is higher than the second level, and the second level is higher than the first level.

[0130] In one embodiment, the vehicle control module is specifically used to, when the fault level is a first level or a second level, or the fault type is a first fault type or a second fault type, map the sensor data corresponding to the fault sensor to the opposite end of the target steering shaft where the fault sensor is located according to the mapping relationship to obtain the fourth sensor data; determine the target operating state corresponding to the fourth sensor data according to the correlation between the sensor data and the vehicle operating state; and control the vehicle to operate in the target operating state.

[0131] In one embodiment, the vehicle control module is specifically used to control the vehicle to enter a parking state when the fault level is level three or the fault type is type three.

[0132] The detection sensor device provided in this application embodiment can implement the various processes implemented in the foregoing method embodiments, and will not be described again here to avoid repetition.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0134] Figure 8 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0135] Electronic devices may include a processor 801 and a memory 802 storing computer program instructions.

[0136] Specifically, the processor 801 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0137] Memory 802 may include mass storage for data or instructions. For example, and not limitingly, memory 802 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 802 may include removable or non-removable (or fixed) media. Where appropriate, memory 802 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 802 is non-volatile solid-state memory.

[0138] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0139] The processor 801 implements any of the sensor detection methods described in the above embodiments by reading and executing computer program instructions stored in the memory 802.

[0140] In one example, the electronic device may also include a communication interface 803 and a bus 810. For example, Figure 8 As shown, the processor 801, memory 802, and communication interface 803 are connected through bus 810 and complete communication with each other.

[0141] The communication interface 803 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0142] Bus 810 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 810 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0143] Furthermore, in conjunction with the sensor detection methods described in the above embodiments, this application can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the sensor detection methods described in the above embodiments.

[0144] Furthermore, in conjunction with the sensor detection method in the above embodiments, this application embodiment can provide a computer program product for implementation. When the instructions in this computer program product are executed by the processor of an electronic device, the electronic device performs the sensor detection method as described in any of the above embodiments.

[0145] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0146] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0147] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0148] The foregoing flowcharts and / or block diagrams of methods and apparatus for detecting sensors according to embodiments of the present disclosure have described various aspects of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to create a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0149] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for detecting a sensor, characterized in that, In a vehicle controller used in a vehicle, the vehicle has multiple steering shafts, and sensors are installed at both ends of each steering shaft. The method includes: Acquire first sensor data detected by a first sensor and second sensor data detected by a second sensor, wherein the first sensor and the second sensor are respectively disposed at both ends of a first steering shaft, and the first steering shaft is any one of the plurality of steering shafts; Based on the mapping relationship between the sensor data at both ends of the steering shaft, the first sensor data is mapped to the third sensor data, wherein the third sensor data is used to characterize the data collected by the second sensor at the position of the first sensor; Acquire the data difference between the second sensor data and the third sensor data; If it is determined that the first steering shaft has a sensor fault based on the data difference, threshold comparison is performed on the first sensor data and the second sensor data respectively to obtain the comparison result; The faulty sensor is determined from the first sensor and the second sensor based on the comparison results.

2. The method according to claim 1, characterized in that, Before acquiring the first sensor data detected by the first sensor and the second sensor data detected by the second sensor, the method further includes: In response to a fault detection command, the steering shaft data corresponding to the first steering shaft is collected; When the steering shaft data meets preset conditions, fault detection information is generated, wherein the fault detection information is used to instruct the vehicle controller to perform fault detection on the sensor on the first steering shaft.

3. The method according to claim 2, characterized in that, The steering shaft data includes: the vehicle's overall power supply voltage, the bus status of the sensors on the first steering shaft, and the power supply voltage of the sensors corresponding to the first steering shaft. The preset conditions include: The vehicle power supply voltage is within a first voltage range, which is the power supply voltage range that enables the vehicle to operate normally. The bus containing the sensor on the first steering shaft is in the on state; The sensor's power supply voltage is within a second voltage range, which is the power supply voltage range that enables the sensor to operate normally.

4. The method according to claim 1, characterized in that, After acquiring the data difference between the second sensor data and the third sensor data, the method further includes: Obtain the fault threshold corresponding to the first steering shaft; If the data difference is greater than the fault threshold, it is determined that the first steering shaft has a sensor fault.

5. The method according to claim 1, characterized in that, The step of determining the faulty sensor from the first sensor and the second sensor based on the comparison result includes: Obtain the data range of the target sensor under normal operating conditions, wherein the target sensor is the first sensor or the second sensor; If the sensor data corresponding to the target sensor is outside the data range, it is determined that the target sensor is faulty. Count the number of failures of the target sensor within a preset time period; If the number of failures exceeds a preset threshold, the target sensor is identified as the faulty sensor.

6. The method according to any one of claims 1 to 5, characterized in that, After determining the faulty sensor from the first sensor and the second sensor based on the comparison result, the method further includes: Acquire distribution data of fault sensors in the vehicle, wherein the distribution data includes the number of fault sensors and the positions of the fault sensors on the plurality of steering shafts; The fault level and fault type of the vehicle are determined based on the distribution data; The operating status of the vehicle is controlled according to the fault level and / or fault type.

7. The method according to claim 6, characterized in that, Determining the fault level and fault type of the vehicle based on the distribution data includes: When the number of faulty sensors is one, the fault level is determined to be the first level, and the fault type is determined to be the first fault type; When there are multiple fault sensors, and the multiple fault sensors are located on different steering shafts, the fault level is determined to be the second level, and the fault type is the second fault type. When there are multiple sensors, and at least one sensor at both ends of the steering shaft is faulty, the fault level is determined to be the third level, and the fault type is the third fault type, wherein the third level is higher than the second level, and the second level is higher than the first level.

8. The method according to claim 7, characterized in that, The method of controlling the operating state of the vehicle according to the fault level and / or fault type includes: When the fault level is the first level or the second level, or when the fault type is the first fault type or the second fault type, the sensor data corresponding to the fault sensor is mapped to the opposite end of the target steering shaft where the fault sensor is located according to the mapping relationship to obtain the fourth sensor data. Based on the correlation between sensor data and vehicle operating status, the target operating status corresponding to the fourth sensor data is determined; Control the vehicle to operate in the target operating state.

9. The method according to claim 7, characterized in that, The method of controlling the operating state of the vehicle according to the fault level and / or fault type includes: If the fault level is the third level, or the fault type is the third fault type, control the vehicle to enter a parking state.

10. A device for detecting sensors, characterized in that, In a vehicle controller used in a vehicle, the vehicle has multiple steering shafts, and sensors are installed at both ends of each steering shaft. The device includes: The data acquisition module is used to acquire first sensor data detected by the first sensor and second sensor data detected by the second sensor, wherein the first sensor and the second sensor are respectively disposed at both ends of the first steering shaft, and the first steering shaft is any one of the plurality of steering shafts; The data mapping module is used to map the first sensor data to the third sensor data according to the mapping relationship between the sensor data at both ends of the steering shaft, wherein the third sensor data is used to characterize the data collected by the second sensor at the position side of the first sensor; The difference acquisition module is used to acquire the data difference between the second sensor data and the third sensor data; The data comparison module is used to perform threshold comparison on the first sensor data and the second sensor data respectively to obtain the comparison result when it is determined that there is a sensor fault in the first steering shaft based on the data difference. The fault determination module is used to determine the faulty sensor from the first sensor and the second sensor based on the comparison results.

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