Methods, apparatuses, devices, and media for identifying a vehicle tire blowout

Abstract

The present disclosure relates to a method, device, equipment and medium for identifying a tire burst of a vehicle. The method comprises detecting, by one or more sensors in the vehicle, an abnormal indicator associated with the tire burst of the vehicle. The method further comprises, in response to detecting the abnormal indicator, obtaining a plurality of features associated with the tire burst from perception data of the one or more sensors. The method further comprises identifying the tire burst of the vehicle in response to a correlation between the plurality of features satisfying a first condition. Through the method of the embodiments of the present disclosure, the tire burst of the vehicle can be identified based on the correlation between the plurality of features, thereby improving the accuracy of identifying the tire burst of the vehicle.

Description

Technical Field

[0001] This disclosure relates to the field of vehicles, and more specifically, to methods, apparatus, devices, and media for identifying tire blowouts in vehicles. Background Technology

[0002] During vehicle operation, especially at high speeds, while cornering, changing lanes, or under conditions of asymmetrical road surface adhesion, a tire blowout causes abrupt changes in parameters such as longitudinal force, lateral force, and effective rolling radius. This significantly impacts the vehicle's dynamic characteristics, potentially leading to issues like vehicle swerving, yaw instability, abnormal vehicle posture, and deviation from the driving trajectory. Therefore, quickly and accurately identifying tire blowouts can improve vehicle stability and safety. Summary of the Invention

[0003] Embodiments of this disclosure provide a method, apparatus, device, and medium for identifying vehicle tire blowouts.

[0004] In a first aspect of this disclosure, a method for identifying a vehicle tire blowout is provided. The method includes detecting an abnormal indicator via one or more sensors in the vehicle, the abnormal indicator being associated with a tire blowout. The method further includes, in response to detecting the abnormal indicator, acquiring multiple features from perception data of the one or more sensors that are associated with the tire blowout. The method further includes identifying a vehicle tire blowout in response to a correlation between the multiple features satisfying a first condition.

[0005] In a second aspect of this disclosure, an apparatus for identifying a vehicle tire blowout is provided. The apparatus includes a detection module configured to detect an abnormal indicator associated with a tire blowout via one or more sensors in the vehicle. The apparatus also includes an acquisition module configured to acquire multiple features associated with the tire blowout from sensing data from the one or more sensors in response to the detection of the abnormal indicator. The apparatus further includes an identification module configured to identify a vehicle tire blowout in response to a first condition being met by a correlation between the multiple features.

[0006] 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.

[0007] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions, which are executed by a processor to implement the method provided according to a first aspect of this disclosure.

[0008] 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

[0009] 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:

[0010] Figure 1 A schematic diagram of an example environment in which several embodiments of the present disclosure may be implemented is shown;

[0011] Figure 2 A flowchart of a method for identifying a vehicle tire blowout according to some embodiments of the present disclosure is shown;

[0012] Figure 3 An example diagram of the architecture of a system for identifying vehicle tire blowouts according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A flowchart is shown for another method of identifying a vehicle tire blowout according to some embodiments of the present disclosure;

[0014] Figure 5 A block diagram of a device for identifying a vehicle tire blowout according to some embodiments of the present disclosure is shown; and

[0015] Figure 6 A schematic block diagram of a controller according to some embodiments of the present disclosure is shown. Detailed Implementation

[0016] 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.

[0017] 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.

[0018] As mentioned above, quickly and accurately identifying tire blowouts can improve vehicle driving stability and safety. Some existing solutions use tire pressure monitoring systems (TPMS) as the main trigger source, with wheel speed sensors and inertial measurement units (IMUs) only serving as auxiliary verification. These solutions are highly dependent on a single sensor. For example, when the TPMS malfunctions, the tire blowout identification function will fail. Other solutions only perform independent threshold comparisons of each sensor signal and then take the logical intersection. They compare abnormal tire pressure, abnormal wheel speed, and abnormal yaw with their corresponding thresholds, and then determine whether a tire blowout has occurred based on the logical AND relationship of multiple results. These solutions do not utilize the correlation between tire blowout events and multiple sensor signals. For example, a tire blowout usually causes a sudden drop in tire pressure, a change in the effective rolling state of the tire, abnormal wheel speed, and changes in vehicle yaw response. These changes are not independent of each other, but have a deterministic correlation.

[0019] Therefore, embodiments of this disclosure propose a scheme for identifying vehicle tire blowouts. In embodiments of this disclosure, the method includes detecting an abnormal indicator via one or more sensors in the vehicle, the abnormal indicator being associated with a tire blowout. The method further includes, in response to detecting the abnormal indicator, acquiring multiple features associated with the tire blowout from sensing data of the one or more sensors. The method further includes identifying a vehicle tire blowout in response to a first condition being met by a correlation between the multiple features.

[0020] By identifying tire blowouts based on the correlation between multiple features associated with blowouts in the perceived data, the accuracy and reliability of tire blowout identification can be improved, and false positives can be reduced.

[0021] Figure 1 A schematic diagram of an example environment 100 in which various embodiments of this disclosure may be implemented is shown. For example... Figure 1 As shown, environment 100 includes vehicle 102, which refers to any type of motorized or non-motorized vehicle capable of carrying people and / or goods and being mobile. Figure 1 As shown, vehicle 102 is illustrated as a car. It should be understood that although vehicle 102 is... Figure 1 The vehicle 102 is illustrated as a sedan, but this is merely exemplary and far from limited to this; examples may also include buses, trucks, motorcycles, and electric vehicles. In some embodiments of this disclosure, the vehicle 102 may include a controller 104, which may be an Electronic Control Unit (ECU), a domain controller, a central controller, or a control system comprising multiple control units.

[0022] like Figure 1 As shown, vehicle 102 may include one or more sensors 106, which may be distributed at different locations on vehicle 102 to collect perception data related to the road environment and vehicle motion state. For example, sensors may include at least one of the following: cameras, lidar, millimeter-wave radar, inertial measurement units, ultrasonic sensors, speed sensors, or any other sensors capable of acquiring vehicle perception data. Different types of sensors can collect different types of perception data; for example, cameras can collect image data, lidar can collect point cloud data, and inertial measurement units can collect vehicle attitude data.

[0023] like Figure 1 As shown, the controller 104 can detect anomaly indicators 108 based on the perception data of one or more sensors 106. These anomaly indicators 108 are associated with a tire blowout. In some embodiments of this disclosure, the anomaly indicator 108 may include at least one of abnormal tire pressure, abnormal wheel speed, and abnormal posture. For example, abnormal tire pressure may be a sudden drop, continuous decrease, or drop below a preset threshold in tire pressure; abnormal wheel speed may be a sudden change or significant deviation in the wheel speed of the target wheel relative to other wheels; and abnormal posture may be a sudden change in the vehicle's yaw rate, yaw acceleration, or lateral acceleration. After the sensor 106 detects the anomaly indicator 108, the controller 104 can obtain multiple features 112-1, 112-2, ..., 112-N associated with the tire blowout from the perception data 110 of one or more sensors 106. For example, these features may include abnormal tire pressure, abnormal wheel speed, wheel speed anomaly timing stability, blowout location, and yaw moment direction.

[0024] like Figure 1As shown, the controller 104 can determine whether the correlation 114 between multiple features 112-1, 112-2, ..., 112-N satisfies a first condition. If the first condition is satisfied, the identification result 116 is that a vehicle tire blowout has been identified. In some embodiments of this disclosure, the correlation 114 may include constraints satisfied between multiple features. For example, the correlation 114 may include at least two of the following: a first constraint between abnormal tire pressure and abnormal wheel speed; a second constraint between abnormal wheel speed and the temporal stability of abnormal wheel speed; and a third constraint between the blowout location and the direction of the yaw moment. In some embodiments of this disclosure, the controller 104 may verify multiple features 112-1, 112-2, ..., 112-N based on at least two of the first constraint, second constraint, or third constraint to obtain a verification result. The verification result is determined based on at least two of the first indicator, second indicator, or third indicator, wherein the first indicator is associated with the first constraint, the second indicator is associated with the second constraint, and the third indicator is associated with the third constraint. Then, the controller 104 may determine whether the verification result satisfies the second condition. If the second condition is satisfied, the identification result 116 is identified as a vehicle tire blowout. The second condition may be that the verification result is equal to a preset threshold.

[0025] In this way, after detecting abnormal indicators associated with tire blowouts, vehicle tire blowouts are identified based on the correlation between multiple features. This not only improves the accuracy and reliability of vehicle tire blowout identification, but also provides a reliable foundation for subsequent vehicle stability control.

[0026] The following will combine Figures 2 to 6 Detailed description of exemplary embodiments according to this disclosure. For ease of understanding, the specific data mentioned in the following description are exemplary and not intended to limit the scope of this disclosure. It is understood that the embodiments described below may also include additional actions not shown and / or actions shown may be omitted, and the scope of this disclosure is not limited in this respect.

[0027] Figure 2 A flowchart of a method 200 for identifying a vehicle tire blowout according to some embodiments of the present disclosure is shown. Method 200 can be performed by a device for identifying a vehicle tire blowout, which may be, for example, a standalone device or system. This device can be implemented in software and / or hardware. The method 200 will now be illustrated schematically using a device for identifying a vehicle tire blowout as an example. Method 200 includes blocks 202, 204, and 206.

[0028] like Figure 2 As shown in box 202, method 200 can detect abnormal indicators associated with a tire blowout using one or more sensors in the vehicle. For example, in... Figure 1In the environment 100 shown, one or more sensors 106 in the vehicle 102 can collect perception data related to the vehicle's operating state, and the controller 104 can detect anomaly indicators 108 based on the perception data. Sensors may include at least one of the following: cameras, lidar, millimeter-wave radar, inertial measurement units, ultrasonic sensors, speed sensors, or any other sensors capable of acquiring vehicle perception data. Different types of sensors can collect different types of perception data; for example, cameras can collect image data, lidar can collect point cloud data, and inertial measurement units can collect vehicle attitude data. Anomaly indicators 108 may include at least one of abnormal tire pressure, abnormal wheel speed, and abnormal attitude. For example, abnormal tire pressure may be a sudden drop, continuous decrease, or drop below a preset threshold in tire pressure; abnormal wheel speed may be a sudden change or significant deviation in the wheel speed of the target wheel relative to other wheels; and abnormal attitude may be a sudden change in the vehicle's yaw rate, yaw acceleration, or lateral acceleration.

[0029] In box 204, method 200 may, in response to detecting an abnormal indicator, acquire multiple features associated with a tire blowout from the sensing data of the one or more sensors. For example, in... Figure 1 In the environment 100 shown, after the controller 104 detects the abnormal indicator 108, it can obtain multiple features 112-1, 112-2, ..., 112-N from the sensing data 110. For example, the multiple features may include abnormal tire pressure, abnormal wheel speed, abnormal wheel speed timing stability, tire blowout location, and yaw moment direction, etc.

[0030] In box 206, method 200 can identify a vehicle tire blowout in response to the association between the plurality of features satisfying a first condition. For example, in... Figure 1 In the environment 100 shown, the controller 104 can determine whether a vehicle has experienced a tire blowout based on the correlation 114 between multiple features 112-1, 112-2, ..., 112-N. If the correlation 114 satisfies a first condition, the identification result 116 is that the vehicle has experienced a tire blowout. In some embodiments of this disclosure, the correlation 114 may include constraints satisfied between multiple features. For example, the correlation 114 may include at least two of the following: a first constraint between abnormal tire pressure and abnormal wheel speed; a second constraint between abnormal wheel speed and the temporal stability of abnormal wheel speed; and a third constraint between the location of the blowout and the direction of the yaw moment.

[0031] In this way, after detecting abnormal indicators associated with tire blowout, method 200 identifies vehicle tire blowout based on the correlation between multiple features, thereby not only improving the accuracy and reliability of vehicle tire blowout identification, but also providing a reliable foundation for subsequent vehicle stability control.

[0032] Figure 3 An example diagram illustrating the architecture of a system 300 for identifying vehicle tire blowouts according to some embodiments of this disclosure is shown. Figure 3 As shown, the system 300 may include a perception module 302, a left area control unit 304, a cockpit control unit 306, and an actuator 308. The perception module 302 can be used to acquire perception data related to tire blowout, the left area control unit 304 can be used to preprocess the perception data, verify the consistency of physical constraints, and make tire blowout identification decisions, the cockpit control unit 306 can be used to provide human-machine interaction prompts, and the actuator 308 can be used to execute vehicle control operations corresponding to the tire blowout identification results.

[0033] like Figure 3 As shown, the sensing module 302 may include a tire pressure sensor 310, a wheel speed sensor 312, and an inertial measurement unit 314. The tire pressure sensor 310 can collect the tire pressure or temperature of each wheel of the vehicle; the wheel speed sensor 312 can collect the wheel speed or wheel speed difference of each wheel; and the inertial measurement unit 314 can collect attitude-related data of the vehicle, such as yaw rate, lateral acceleration, and longitudinal acceleration. The various sensors in the sensing module 302 can communicate with the left area control unit 304 via the vehicle bus. The left area control unit 304 can receive the sensing data collected by the various sensors and identify tire blowouts based on the received sensing data.

[0034] like Figure 3 As shown, the left region control unit 304 may include a signal filtering module 316, a physical constraint signal consistency verification module 318, and a three-level redundancy decision module 320. The signal filtering module 316 can be used to preprocess the raw sensing data output by the sensing module 302; for example, it can filter the raw sensing data. The physical constraint signal consistency verification module 318 can be used to verify the correlation between multiple features to determine whether the multiple features satisfy the physical constraint relationships associated with tire blowout. For example, the physical constraint signal consistency verification module 318 can verify at least two of the following constraints: a first constraint between abnormal tire pressure and abnormal wheel speed, a second constraint between abnormal wheel speed and the temporal stability of abnormal wheel speed, and a third constraint between the blowout location and the direction of the yaw moment.

[0035] In some embodiments of this disclosure, the first constraint can be used to verify whether the blown tire identified by the tire pressure sensor has indeed experienced a significant sudden change in rotational speed. If the tire pressure sensor detects a sudden drop in tire pressure, the wheel speed sensor of that wheel should show a significant difference in rotational speed compared to the other three wheels. This is because the forced dynamic effect caused by the sudden increase in rolling resistance at the moment of the blowout causes a sharp decrease in the angular velocity of the blown tire. Regardless of how the subsequent slippage evolves or how the radius changes, there must be a detectable difference in rotational speed between the blown tire and the normal tire. The first constraint only verifies whether a sudden change in rotational speed exists, not the magnitude of the change. It does not depend on tire models, does not predict theoretical values, and is not affected by the bleed window or the amount of slippage.

[0036] In some embodiments of this disclosure, in response to detecting an abnormal tire pressure at a target wheel, a first index is determined based on the difference between the wheel speed of the target wheel and a reference wheel speed. For example, when a tire pressure sensor detects a sudden drop in tire pressure at a certain wheel, and the tire pressure meets the following criteria... ,in, P 1 This indicates the current tire pressure of the wheel. P 0 This indicates the reference tire pressure for that wheel, Δ P threshold This indicates the calibrable tire pressure drop threshold. After identifying a candidate tire blowout wheel, the current angular velocity of the wheel speed sensor corresponding to that candidate tire blowout wheel can be obtained.

[0037] After identifying the candidate tire blowout wheel, the current angular velocity of the corresponding wheel speed sensor can be obtained. ω burst It also acquires the angular velocities of the wheel speed sensors corresponding to the other three wheels to calculate the average angular velocities of the other three wheels. ω ref Then, the angular velocity of the candidate tire blowout can be used as a basis. ω burst With the average angular velocity ω ref The difference between the values ​​is used to verify the consistency between abnormal tire pressure and abnormal wheel speed.

[0038] For example, if the difference between the angular velocity of the candidate tire blowout and the average angular velocity satisfies in, If a calibrable threshold for detecting sudden changes in tire speed is set, then it can be determined that the candidate tire that experienced a blowout also exhibits a significant anomaly in the wheel speed dimension. At this point, the tire pressure monitoring results and wheel speed monitoring results are consistent at the level of the fact that "the candidate tire that experienced a blowout exhibits an anomaly." Therefore, it can be determined that the tire pressure anomaly and the wheel speed anomaly satisfy the first constraint, and the first indicator... score 1The value is set to 1.0. If the difference between the angular velocity of the candidate tire blowout and the average angular velocity satisfies... If the candidate tire blowout wheel shows abnormalities in tire pressure, it can be considered that although it shows no significant abnormalities in wheel speed, there is an inconsistency between the tire pressure monitoring results and the wheel speed monitoring results. Therefore, it can be determined that the first constraint is not satisfied between the abnormal tire pressure and abnormal wheel speed, and the first index is... score 1 It is set to 0.0.

[0039] In some embodiments of this disclosure, the second constraint assesses the persistence of the abnormal rotational speed based on the time series of the wheel speed sensor itself. When a tire blowout actually occurs, the braking deceleration caused by the tire collapse does not immediately recover, and the abnormal wheel speed of the blown wheel will persist for a period of time. In contrast, the sudden drop in rotational speed caused by road impact or electromagnetic interference is transient, manifested as a short-term change in wheel speed followed by a rapid recovery. When the vehicle is in normal operating condition, the first constraint has been cross-source verified through the tire pressure sensor. The second indicator associated with the second constraint is calculated in parallel as an auxiliary diagnostic quantity. In the degraded state of tire pressure sensor failure, since cross-verification cannot rely on tire pressure information, the second constraint can serve as the core basis in physical constraint verification to determine whether the abnormal wheel speed is a persistent abnormality or a transient interference.

[0040] In some embodiments of this disclosure, wheel speed relationship data is determined based on the wheel speed of the target wheel and a reference wheel speed. Then, a second index is determined based on the variation characteristics of the wheel speed relationship data over multiple consecutive sampling periods. For example, the ratio of the angular velocity of the candidate abnormal wheel to the reference angular velocity can be defined as... ,in, This represents the angular velocity of the candidate abnormal wheel at time t. This represents the reference angular velocity. In some embodiments, the reference angular velocity... The average of the remaining three wheel angular velocities can be taken. Then, a sliding window containing K consecutive sampling periods is used to measure the ratio. Statistical analysis is performed, where K is a calibrable parameter; for example, K can be 8. Then, the ratio within the sliding window is calculated. Standard deviation and based on the standard deviation Characterize the temporal stability of wheel speed anomalies.

[0041] When a vehicle experiences a real tire blowout, the candidate abnormal wheel's angular velocity relative to the reference angular velocity decreases rapidly due to the change in its rolling state after the blowout. It rapidly decreases to less than 1 and remains relatively stable for a period of time thereafter. Correspondingly, within the sliding window, The fluctuation is small, and the standard deviation is small. It is also relatively small. Conversely, when wheel speed anomalies are caused by road impacts, electromagnetic interference, or other instantaneous disturbances, the angular velocity of the candidate abnormal wheel may change abruptly in a short time, but the anomaly usually recovers within a short period of time. It will drop briefly and then rise rapidly, thus causing the sliding window to... Increased volatility corresponds to a higher standard deviation. It is also relatively large.

[0042] In some embodiments of this disclosure, the standard deviation can be used as a reference. Determine a second metric associated with the temporal stability of wheel speed anomalies. For example, the second metric can be determined as follows: ,in, This represents the maximum permissible standard deviation, which can be a pre-calibrated parameter, for example, 0.10. Therefore, when... When it is smaller, the result is score 2 A larger value indicates that the abnormal wheel speed of the candidate abnormal wheel has higher persistence and stability, thus more likely corresponding to a real tire blowout; when When it is large, the result is score 2 A smaller value indicates that the abnormal wheel speed is more likely caused by a transient disturbance.

[0043] In some embodiments of this disclosure, during the first K-1 sampling periods after the system enters a degraded state, the controller may temporarily not calculate the second index because the sliding window has not yet been filled. score 2 Furthermore, during this stage, physical consistency verification is not based on the wheel speed anomaly timing stability constraint. Only after the sliding window reaches the preset length will physical consistency verification be performed based on the standard deviation. The second indicator is calculated and used in the subsequent tire blowout identification process.

[0044] In some embodiments of this disclosure, when verifying the association relationship through a third constraint, a third indicator associated with the third constraint can be determined. score 3 In some embodiments of this disclosure, a first attitude direction is determined based on the position of the target wheel; then, a second attitude direction is determined based on the sensing data from the attitude sensor; and finally, a third index is determined based on the relationship between the first and second attitude directions. For example, the rolling resistance of a blown-out tire increases sharply due to tread collapse, generating an additional longitudinal force. This will generate an additional yaw moment with a defined direction: The sign of the yaw rate is uniquely determined by the location of the tire blowout. A blowout on the left side produces yaw moments in opposite directions to a blowout on the right side, and this directional relationship can be obtained through offline analysis and calculation of vehicle parameters (wheelbase, track width, center of gravity position) or through actual vehicle calibration. IMU measured yaw acceleration. The measured yaw direction should be consistent with the theoretical yaw moment. If the measured yaw direction is opposite to the predicted direction of the blowout location, then a blowout is ruled out. In some embodiments of this disclosure,

[0045] In some embodiments of this disclosure, the theoretical yaw moment can be determined based on the location of the tire blowout. The direction is determined, and the actual yaw moment of the vehicle is determined based on the sensing data from the attitude sensors. The direction of the theoretical yaw moment or the corresponding yaw response direction. When the theoretical yaw moment direction is consistent with the actual yaw moment direction, and the vehicle's yaw rate... Not less than the preset yaw rate threshold At that time, the third indicator can be determined. score 3 =1.0, the vehicle's yaw rate Less than the preset yaw rate threshold At that time, the third indicator can be determined. score 3 =0.5, where the theoretical yaw moment direction is inconsistent with the actual yaw moment direction, i.e., their directions are opposite. score 3 =0.0,

[0046] In some embodiments of this disclosure, verification results C phy =min( score 1 , score 2 , score 3 ), C phy =1 indicates that all sensor signals are physically self-consistent. C phy A value approaching 0 indicates an irreconcilable physical contradiction between signals. Since the failure of any constraint signifies a break in the physical causal chain, the overall confidence level should reflect the weakest link. Therefore, by taking the minimum value, if any constraint fails, then... C phy ≤0 can effectively filter out some features that are accidentally triggered due to sensor coupling interference.

[0047] In some embodiments of this disclosure, the three-level redundancy decision module 320 can be used to determine the tire blowout state of a vehicle based on abnormal indicators, multiple features, and the correlation between the features. For example, the three-level redundancy decision module 320 may include a three-level progressive decision-making process for potential tire blowout, suspected tire blowout, and confirmed tire blowout. The first and second level decisions are unified in both normal and degraded states, used to initiate monitoring and pre-preparation. The third level trigger is unified across all modes by... C phy The availability of constraints is determined by the controller's state. In the normal state, the relationships between multiple features can be verified based on the first, second, and third constraints. C phy =min( score 1 , score 2 , score 3 The threshold can be set to greater than or equal to 0.7, and the confirmation time can be set to greater than or equal to 150ms.

[0048] In some embodiments of this disclosure, in the first-level decision stage, if any one of the multiple sensors detects a corresponding abnormal indicator, the third-level redundancy decision module 320 can determine that the vehicle is in a first warning state. In the second-level decision stage, if at least two of the multiple sensors detect the corresponding abnormal indicator, the third-level redundancy decision module 320 can determine that the vehicle is in a second warning state. In the third-level decision stage, if the vehicle is in a second warning state and the correlation between the multiple features meets a first condition, the third-level redundancy decision module 320 can identify a tire blowout.

[0049] In some embodiments of this disclosure, system 300 can also support degraded identification in sensor failure scenarios. For example, if any one of the multiple sensors (which can also be referred to as the first sensor) fails, the left region control unit 304 can still identify a tire blowout based on the first condition being met by the correlation between multiple features associated with a tire blowout in the sensing data of at least one of the second or third sensors. For example, in the case of a tire pressure sensor 310 failure, the left region control unit 304 can verify multiple features of the sensing data collected by the wheel speed sensor 312 and the inertial measurement unit 314 based on a second constraint on wheel speed anomaly and the temporal stability of wheel speed anomaly, and a third constraint on the blowout location and the direction of yaw moment, to obtain a verification result. In this case, C phy =min( score 2 , score 3The threshold can be set to greater than or equal to 0.6, and the confirmation time can be set to greater than or equal to 200ms. In the event of a failure in the inertial measurement unit 314, the left region control unit 304 can verify tire pressure-related characteristics and wheel speed-related characteristics based on the first and second constraints. C phy =min( score 1 , score 2 The threshold can be set to greater than or equal to 0.65, and the confirmation time can be set to greater than or equal to 200ms. In the event of a failure of the wheel speed sensor 312 on a non-exploded tire, the left area control unit 304 can verify multiple features based on the first constraint, the second constraint, and the third constraint to obtain the verification result. In this case, C phy =min( score 1 , score 2 , score 3 The threshold can be set to 0.66 or higher, and the confirmation time can be set to 200ms or higher. This allows for the maintenance of the tire blowout detection function even during downgrades. It should be understood that the threshold and confirmation time can be flexibly set and are not limited to the values ​​described above.

[0050] like Figure 3 As shown, the cockpit control unit 306 may include a human-machine interface module 322. The human-machine interface module 322 can receive the tire blowout identification result output by the left area control unit 304 and provide corresponding prompts to the driver. For example, when the vehicle is in a second warning state, the human-machine interface module 322 can output a warning prompt such as "Abnormal tire pressure, please pay attention." When a tire blowout is confirmed, the human-machine interface module 322 can output higher priority alarm information to remind the driver to take timely countermeasures. In some embodiments of this disclosure, the human-machine interface module 322 can output prompts to the driver through the instrument panel, sound prompts, display interface, or other human-machine interaction methods.

[0051] like Figure 3As shown, actuator 308 may include steering adjustment 324, brake adjustment 326, suspension adjustment 328, and power adjustment 330. Actuator 308 can receive control commands output from the left area control unit 304 and execute corresponding vehicle control operations. For example, steering adjustment 324 can be used to control the vehicle's steering actuator to suppress yaw caused by a tire blowout; brake adjustment 326 can be used to apply braking force to each wheel to achieve vehicle deceleration or stability control; suspension adjustment 328 can be used to adjust the vehicle's suspension state to improve vehicle posture after a tire blowout; and power adjustment 330 can be used to adjust the vehicle's driving force or torque output to reduce the impact of a tire blowout on vehicle stability.

[0052] In this way, the system 300 can verify and classify the correlation between multiple features of the sensing data from multiple sensors through the signal consistency verification module 318 of physical constraints and the three-level redundancy decision module 320, thereby improving the accuracy of vehicle tire blowout recognition and thus improving the stability of vehicle control.

[0053] Figure 4 A flowchart of a method 400 for identifying vehicle tire blowout stability according to some embodiments of the present disclosure is shown. Figure 4 As shown in block 402, method 400 can synchronously acquire data from multiple sensors. For example, method 400 can receive sensing data related to a tire blowout from multiple sensors in the vehicle. For example, the controller can receive tire pressure data output from the tire pressure sensor, wheel speed data output from the wheel speed sensor, and attitude data such as yaw rate, lateral acceleration, and longitudinal acceleration output from the inertial measurement unit.

[0054] In block 404, method 400 can perform signal filtering preprocessing on the acquired multi-sensor data. For example, the controller can smooth the tire pressure signal, de-glitch and anti-vibration process the wheel speed signal, and filter signals such as yaw rate, lateral acceleration, and longitudinal acceleration output by the inertial measurement unit.

[0055] In box 406, method 400 can determine whether the sensor has malfunctioned. If it is determined that no sensor malfunction has occurred, box 408 is executed; if it is determined that a sensor malfunction has occurred, box 410 is executed.

[0056] In block 408, method 400 can perform independent triggering of the three-level redundant decision feature layer. In some embodiments of this disclosure, method 400 can detect abnormal indicators related to vehicle tire blowout based on multi-sensor perception data, and obtain multiple features associated with the blowout based on the abnormal indicators. For example, the tire pressure sensor can detect a sudden drop in tire pressure or an abnormal tire pressure change rate, the wheel speed sensor can detect a sudden change in wheel speed difference or an abnormal wheel speed, and the inertial measurement unit can detect an abnormal yaw rate or a sudden change in attitude.

[0057] In box 412, method 400 can determine whether a single feature meets the corresponding judgment threshold. A single feature can include a sudden drop in tire pressure, a sudden change in wheel speed difference, or a sudden change in yaw rate. If it does not meet the threshold, proceed to box 424. In box 424, the normal state excludes tire blowout, monitoring continues, and then returns to box 402. If the threshold is met, proceed to box 414. In box 414, method 400 can determine a level one trigger, i.e., determine that the vehicle is in a potential tire blowout state, and switch to continuous monitoring. Next, proceed to box 416. In box 416, method 400 can determine whether two features meet the corresponding judgment threshold. For example, method 400 can determine whether at least two of the multiple sensors have detected the corresponding abnormal indicators, or whether at least two features related to tire blowout simultaneously meet the corresponding threshold. If the judgment result is "no," return to box 414, continue to maintain the potential tire blowout state and continue monitoring. If the judgment result is "yes," proceed to box 418.

[0058] In box 418, method 400 can determine the secondary trigger, determine that the vehicle is in a suspected tire blowout state, the steering system and braking system enter the pre-preparation state, and send a prompt to the cabin domain control unit, for example, outputting warning information such as "abnormal tire pressure, please pay attention" to the driver through the instrument panel, sound prompt or display interface.

[0059] In box 420, method 400 can determine whether the three features are triggered simultaneously. For example, whether multiple features related to tire blowout meet the corresponding thresholds. If the determination result is "no", it returns to box 418, continues to maintain the suspected tire blowout state and continuously verify, and if the determination result is "yes", method 400 can execute box 422.

[0060] In box 422, method 400 can determine a three-level trigger, namely, confirming a tire blowout, outputting a tire blowout confirmation signal including a position code, and broadcasting the tire blowout confirmation signal through the bus to trigger stability control, thereby improving the vehicle's driving stability and safety after a tire blowout.

[0061] In block 410, method 400 can activate a corresponding degradation mode when a sensor malfunction is detected, tightening the threshold, forcing physical constraint verification, and extending the confirmation period. In block 426, method 400 can forcibly calculate corresponding degradation constraints. For example, in the case of a tire pressure sensor malfunction, method 400 can verify multiple features of the sensing data collected by the wheel speed sensor and the inertial measurement unit based on a second constraint regarding wheel speed anomaly and the temporal stability of wheel speed anomaly, and a third constraint regarding the location of the blowout and the direction of the yaw moment, to obtain verification results. In the case of an inertial measurement unit malfunction, method 400 can verify tire pressure-related features and wheel speed-related features based on a first and second constraint. In the case of a wheel speed sensor malfunction, method 400 can verify multiple features based on the first, second, and third constraints to obtain the verification results. In this way, the degradation maintenance of the blowout detection function can be achieved.

[0062] In box 428, method 400 can make a degraded-state redundancy decision based on available features and degrade constraints. In box 430, method 400 can determine whether the available features meet the degrade threshold. If the determination result is "no", then execute box 436, the degraded state excludes tire blowout, and monitoring continues. If the determination result is "yes", then execute box 432. In box 432, method 400 can determine whether the comprehensive score is not less than the degrade threshold and continues to correspond to the confirmation period. If the determination result is "no", then execute box 434. In box 434, method 400 can confirm tire blowout in the degraded state, output a tire blowout confirmation signal including location encoding, and broadcast the tire blowout confirmation signal through the bus to trigger stability control, thereby improving the vehicle's driving stability and safety after a tire blowout. If the determination result is "yes", then execute box 436. In box 436, the degraded state excludes tire blowout, and monitoring continues.

[0063] In this way, method 400 can verify and classify the correlation between multiple features of the sensing data from multiple sensors through signal consistency verification under physical constraints and three-level redundancy decision-making, thereby improving the accuracy of vehicle tire blowout recognition and thus improving the stability of vehicle control.

[0064] Figure 5 A block diagram of a device 500 for identifying a vehicle tire blowout according to some embodiments of the present disclosure is shown. Figure 5As shown, the device 500 includes a detection module 502 configured to detect an abnormal indicator associated with a tire blowout via one or more sensors in the vehicle. The device also includes an acquisition module 504 configured to acquire multiple features associated with the tire blowout from the perception data of one or more sensors in response to the detection of the abnormal indicator. The device further includes an identification module 506 configured to identify a tire blowout in response to a first condition being met by the correlation between the multiple features.

[0065] In some embodiments, the correlation includes at least two of the following: a first constraint between abnormal tire pressure and abnormal wheel speed, a second constraint between abnormal wheel speed and the temporal stability of abnormal wheel speed, or a third constraint between the location of the tire blowout and the direction of the yaw moment.

[0066] In some embodiments, the identification module 506 includes a verification module configured to verify multiple features based on at least two of a first constraint, a second constraint, or a third constraint to obtain a verification result. The verification result is determined based on at least two of a first indicator, a second indicator, or a third indicator, wherein the first indicator is associated with the first constraint, the second indicator is associated with the second constraint, and the third indicator is associated with the third constraint. The identification module 506 also includes a verification result usage module configured to identify a vehicle tire blowout in response to the verification result satisfying a second condition.

[0067] In some embodiments, the device 500 further includes a first index determination module configured to determine a first index based on the difference between the wheel speed of the target wheel and a reference wheel speed in response to detecting an abnormal tire pressure of the target wheel.

[0068] In some embodiments, the device 500 further includes a wheel speed relationship determination module, configured to determine wheel speed relationship data based on the wheel speed of the target wheel and a reference wheel speed. The device 500 also includes a second index determination module, configured to determine a second index based on the variation characteristics of the wheel speed relationship data over multiple consecutive sampling periods.

[0069] In some embodiments, the device 500 further includes a first attitude direction determination module, configured to determine a first attitude direction based on the position of the target wheel. The device 500 also includes a second attitude direction determination module, configured to determine a second attitude direction based on sensing data from an attitude sensor. The device 500 further includes a third index determination module, configured to determine a third index based on the relationship between the first attitude direction and the second attitude direction.

[0070] In some embodiments, the device 500 further includes a first warning state determination module, configured to determine that the vehicle is in a first warning state in response to a first sensor among a plurality of sensors detecting a corresponding abnormal indicator. The device 500 further includes a second warning state determination module, configured to determine that the vehicle is in a second warning state in response to at least two sensors among a plurality of sensors respectively detecting corresponding abnormal indicators.

[0071] In some embodiments, the identification module 506 further includes a second warning state usage module, configured to identify a tire blowout in response to the vehicle being in a second warning state and the correlation between multiple features satisfying a first condition.

[0072] In some embodiments, the identification module 506 further includes a residual feature utilization module configured to identify a vehicle tire blowout in response to a failure of a first sensor among a plurality of sensors and a first condition being met in the perception data of at least one of a second or third sensor.

[0073] In some embodiments, the verification module includes a first verification module configured to verify multiple features based on a second constraint and a third constraint in response to a tire pressure sensor malfunction, in order to obtain verification results.

[0074] In some embodiments, the verification module includes a second verification module configured to verify multiple features based on a first constraint and a second constraint in response to a failure of the attitude sensor, in order to obtain a verification result.

[0075] In some embodiments, the verification module includes a third verification module configured to verify multiple features based on a first constraint, a second constraint, and a third constraint in response to a wheel speed sensor malfunction, in order to obtain verification results.

[0076] It is understood that by utilizing the apparatus 500 of this disclosure, at least one of the many advantages achievable by the methods or processes described above can be realized. For example, the apparatus 500 can verify and classify the correlation between multiple features of sensing data from multiple sensors through signal consistency verification under physical constraints and three-level redundancy decision-making, thereby improving the accuracy of vehicle tire blowout detection and thus improving the stability of vehicle control.

[0077] Figure 6 A block diagram of a controller 600 that can implement various embodiments of the present disclosure is shown. References Figure 6As shown, the controller 600 includes a processor 601, which can perform various appropriate actions and processes based on computer program instructions loaded into random access memory (RAM) 603 according to computer program instructions stored in read-only memory (ROM) 602. The RAM 603 may also store various programs and data required for the operation of the controller 600. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0078] Processor 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 601 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program tangibly contained in a machine-readable medium. In some embodiments, part or all of the computer program may be loaded and / or mounted to controller 600 via ROM 602. When the computer program is loaded into RAM 603 and executed by processor 601, one or more steps of method 200 described above may be performed. Alternatively, in other embodiments, processor 601 may be configured to perform method 200 by any other suitable means (e.g., by means of firmware).

[0079] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0080] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0081] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0082] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for identifying a vehicle tire blowout, comprising: Abnormal indicators are detected by multiple sensors in the vehicle, and these abnormal indicators are associated with tire blowouts. In response to the detection of the abnormal indicator, multiple features associated with tire blowout are acquired from the sensing data of the multiple sensors; as well as In response to the fact that the association between the plurality of features satisfies a first condition, the vehicle tire blowout is identified, wherein the association includes constraints satisfied between the plurality of features. The aforementioned association includes at least two of the following: The first constraint between abnormal tire pressure and abnormal wheel speed; The second constraint on wheel speed anomalies and their temporal stability; or The third constraint between the location of the tire blowout and the direction of the yaw moment. Wherein, in response to the correlation between the plurality of features satisfying a first condition, identifying the vehicle tire blowout includes: The plurality of features are verified based on at least two of the first constraint, the second constraint, or the third constraint to obtain a verification result, wherein the verification result is determined based on at least two of the first indicator, the second indicator, or the third indicator, wherein the first indicator is associated with the first constraint, the second indicator is associated with the second constraint, and the third indicator is associated with the third constraint; and In response to the verification result satisfying the second condition, the vehicle is identified as having a tire blowout.

2. The method according to claim 1, further comprising: In response to the detection of abnormal tire pressure at the target wheel, the first index is determined based on the difference between the wheel speed of the target wheel and a reference wheel speed.

3. The method according to claim 1, further comprising: The wheel speed relationship data is determined based on the wheel speed of the target wheel and the reference wheel speed; as well as The second index is determined based on the variation characteristics of the wheel speed relationship data over multiple consecutive sampling periods.

4. The method according to claim 1, further comprising: Determine the first attitude direction based on the position of the target wheel; The second attitude direction is determined based on the sensing data from the attitude sensor; as well as The third index is determined based on the relationship between the first attitude direction and the second attitude direction.

5. The method according to claim 1, further comprising: In response to the first sensor among the plurality of sensors detecting the corresponding abnormal indicator, it is determined that the vehicle is in a first warning state; as well as In response to at least two of the plurality of sensors detecting the corresponding abnormal indicators, the vehicle is determined to be in a second warning state.

6. The method according to claim 5, wherein identifying the vehicle tire blowout in response to the association between the plurality of features satisfying a first condition comprises: In response to the vehicle being in a second warning state and the correlation between the plurality of features satisfying a first condition, the vehicle is identified as having a tire blowout.

7. The method according to claim 1, wherein identifying the vehicle tire blowout in response to the association between the plurality of features satisfying a first condition comprises: In response to a failure of the first of the plurality of sensors, and the correlation between the plurality of features associated with a tire blowout in the perception data of the second and third sensors satisfying the first condition, the vehicle tire blowout is identified.

8. The method according to claim 7, wherein verifying the plurality of features based on at least two of the first constraint, the second constraint, or the third constraint to obtain a verification result includes: In response to a tire pressure sensor malfunction, the plurality of features are verified based on the second constraint and the third constraint to obtain the verification results.

9. The method according to claim 7, wherein verifying the plurality of features based on at least two of the first constraint, the second constraint, or the third constraint to obtain a verification result includes: In response to a failure of the attitude sensor, the plurality of features are verified based on the first constraint and the second constraint to obtain the verification result.

10. The method according to claim 7, wherein verifying the plurality of features based on at least two of the first constraint, the second constraint, or the third constraint to obtain a verification result includes: In response to a wheel speed sensor malfunction, the plurality of features are verified based on the first constraint, the second constraint, and the third constraint to obtain the verification result.

11. A device for detecting tire blowouts in vehicles, comprising: The detection module is configured to detect abnormal indicators through multiple sensors in the vehicle, the abnormal indicators being associated with a tire blowout. The acquisition module is configured to acquire multiple features associated with a tire blowout from the sensing data of the multiple sensors in response to the detection of the abnormal indicator; as well as The identification module is configured to identify a vehicle tire blowout in response to a first condition being met by a correlation among the plurality of features, wherein the correlation includes constraints satisfied among the plurality of features. The aforementioned association includes at least two of the following: The first constraint between abnormal tire pressure and abnormal wheel speed; The second constraint on wheel speed anomalies and their temporal stability; or The third constraint between the location of the tire blowout and the direction of the yaw moment. The identification module includes a verification module, which is configured as follows: The plurality of features are verified based on at least two of the first constraint, the second constraint, or the third constraint to obtain a verification result, wherein the verification result is determined based on at least two of the first indicator, the second indicator, or the third indicator, wherein the first indicator is associated with the first constraint, the second indicator is associated with the second constraint, and the third indicator is associated with the third constraint; and In response to the verification result satisfying the second condition, the vehicle is identified as having a tire blowout.

12. 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 10.

13. A computer-readable storage medium having stored thereon computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, cause the method according to any one of claims 1 to 10 to be performed.

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