Method for locating an off-vehicle vibration source, vehicle, storage medium and program product

Abstract

The application discloses a vehicle external vibration source positioning method, a vehicle, a storage medium and a program product, and belongs to the technical field of automatic driving perception. In view of the problems of low positioning accuracy of an external vibration source, easy interference of vehicle self-vibration and road medium in a vehicle mobile scene, a distributed vibration sensing array is arranged at a wheel end of a vehicle, vibration signals transmitted by a road are collected, after effective signals outside the vehicle are separated through pretreatment, a time difference of arrival between sensors is calculated, a hyperbolic positioning equation set is constructed in combination with a sensor calibration coordinate and a current road vibration transmission speed, and a planar coordinate, a direction angle and a straight line distance of the external vibration source are solved. The application can stably work in a main perception sensor failure working condition such as a preceding vehicle shielding and rainy and foggy weather, has high positioning accuracy and strong anti-interference capability, and is suitable for various automatic driving vehicles.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to methods for locating external vibration sources, vehicles, storage media, and computer program products. Background Technology

[0002] Autonomous driving systems rely on lidar, millimeter-wave radar, and cameras as primary perception sensors. However, they are prone to perception failure under conditions such as obstruction by the vehicle in front, rain, snow, fog, backlight, lens dirt, or sensor malfunction, which can affect driving safety.

[0003] To address perception blind spots, existing solutions primarily rely on millimeter-wave radar, ultrasonic radar, or surround-view cameras. However, these sensors still face performance limitations in scenarios such as close-range detection, inclement weather, and nighttime conditions. While roadside sensing devices can assist in filling blind spots, they depend on infrastructure deployment, resulting in limited coverage and high costs.

[0004] Existing applications of vibration sensors in vehicles are limited to monitoring the vehicle's own condition, such as tire pressure monitoring, wheel bearing fault diagnosis, and road surface type identification, without involving the perception and localization of external targets.

[0005] In the field of vibration positioning technology, existing solutions are mostly applied to fixed roadside scenarios, using fixedly arranged sensor arrays to detect and locate passing vehicles, which is not suitable for vehicle-mounted mobile scenarios.

[0006] Therefore, the reliability of existing autonomous driving perception systems under extreme conditions is still insufficient and urgently needs to be improved.

[0007] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0008] The main purpose of this application is to provide a method for locating external vibration sources, a vehicle, a storage medium, and a computer program product, aiming to solve the technical problems of low accuracy in locating the distance and direction of external vibration sources in vehicle-mounted mobile scenarios, which are easily affected by the vehicle's own vibration and changes in the road surface medium.

[0009] To achieve the above objectives, this application proposes a method for locating external vibration sources, the method comprising: Step S10: Deploy multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, and calibrate each vibration sensing unit to determine the coordinates of each vibration sensing unit in the vehicle coordinate system. Step S20: Collect vibration signals transmitted from the road surface through the distributed vibration sensing array, and preprocess the collected vibration signals to separate the effective vibration signals from the external vibration sources. Step S30: Calculate the arrival time difference of the effective vibration signal between any two vibration sensing units using a time delay estimation algorithm to obtain multiple sets of time difference datasets; Step S40: Based on the coordinates of the vibration sensing unit, the time difference dataset, and the vibration transmission wave velocity of the road surface, establish and solve the hyperbolic positioning equation system to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculate the direction angle and distance of the external vibration source relative to the vehicle.

[0010] In one embodiment, the calibration in step S10 includes: Establish a vehicle coordinate system and mark the spatial coordinates of each vibration sensing unit in the vehicle coordinate system; The sampling signals of each vibration sensing unit are synchronized in time so that each vibration sensing unit has a unified time reference.

[0011] In one embodiment, the preprocessing in step S20 includes: The original vibration signal is collected as input, filtered to remove high-frequency interference noise and retain the effective frequency band, and the basic signal is output. Using the aforementioned basic signal as input, and combining it with the vehicle's own vibration reference signal obtained from the vehicle bus, an adaptive filtering algorithm is used to separate the vehicle's own vibration noise and output a mixed signal. Using the mixed signal as input, and based on the cross-correlation coefficient between the signals of each vibration sensing unit, the residual vibration component of the unit itself is filtered out, and an effective vibration signal containing external vibration sources is output.

[0012] In one embodiment, the vibration transmission wave velocity of the road surface in step S40 is determined by the following method: Identify the current road surface type; Obtain the wave velocity value corresponding to the current road surface type from the pre-stored correspondence between road surface types and vibration transmission wave velocities.

[0013] In one embodiment, the hyperbolic positioning equation set is established in step S40 as follows: For any two vibration sensing units, the difference in distance from the external vibration source to the two vibration sensing units is set to be equal to the vibration transmission wave speed multiplied by the time difference of the signal reaching the two vibration sensing units. Based on the aforementioned settings, multiple sets of distance difference equations are formed, constituting a hyperbolic positioning equation set.

[0014] In one embodiment, the method further includes: The calculated direction angle and distance are smoothed and abnormal jump values ​​are filtered out to output the location results of the external vibration source.

[0015] In one embodiment, the method further includes: Under environmental interference conditions, environmental information is obtained from the vehicle bus; Based on the environmental information, the acquisition or processing parameters of the vibration signal are adjusted to compensate for the attenuation or interference of the vibration signal by environmental factors.

[0016] Furthermore, to achieve the above objectives, this application also proposes an external vibration source positioning device, which includes: The first module is used to deploy multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, and to calibrate each vibration sensing unit to determine the coordinates of each vibration sensing unit in the vehicle coordinate system. The second module is used to collect vibration signals transmitted from the road surface through the distributed vibration sensing array, and to preprocess the collected vibration signals to separate the effective vibration signals from the external vibration sources. The third module is used to calculate the arrival time difference of the effective vibration signal between any two vibration sensing units using a time delay estimation algorithm, and obtain multiple sets of time difference datasets. The fourth module is used to establish and solve a hyperbolic positioning equation set based on the coordinates of the vibration sensing unit, the time difference dataset, and the vibration transmission wave velocity of the road surface, to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculate the direction angle and distance of the external vibration source relative to the vehicle.

[0017] In addition, to achieve the above objectives, this application also proposes a vehicle comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the external vibration source localization method as described above.

[0018] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the vehicle external vibration source localization method described above.

[0019] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the vehicle external vibration source localization method described above.

[0020] One or more technical solutions proposed in this application have at least the following technical effects: By deploying a distributed vibration sensing array on the vehicle and utilizing vibration signals transmitted from the road surface, the distance and direction angle of external vibration sources (such as pedestrians, non-motorized vehicles, and other vehicles) were calculated in real time for the first time, filling the gap in existing vehicle-mounted vibration sensing systems that only monitor their own status.

[0021] It acquires vibration signals by using solid-borne sound transmission on the road surface, which is independent of light, line of sight and weather conditions. It can work stably under conditions where the main perception sensor fails, such as when the vehicle in front blocks the light, in rainy or foggy weather, at night, or when the lens is dirty, thus providing a reliable perception supplement for autonomous driving.

[0022] By performing preprocessing steps such as filtering, adaptive noise separation, and cross-correlation filtering on the acquired signals, weak external vibration source signals can be extracted from strong background noise, significantly reducing the impact of vehicle motion (such as engine, suspension, and tire rolling) on ​​positioning accuracy.

[0023] By introducing the vibration transmission wave velocity parameter of the road surface, the wave velocity value can be dynamically adjusted according to the current road surface type (asphalt, cement, gravel, etc.), avoiding time difference conversion errors caused by changes in the road surface medium, and improving the positioning robustness in vehicle-mounted mobile scenarios.

[0024] The establishment and solution of the hyperbolic positioning equations can simultaneously output the planar coordinates, straight-line distance, and orientation angle of the external vibration source in the vehicle coordinate system. The positioning results can be directly used for decision-making and control of the autonomous driving domain controller.

[0025] This can be achieved using low-cost MEMS vibration sensors. The sensors are installed in the unsprung area at the wheel end, requiring minimal modification to the vehicle's original architecture and making them easy to deploy in mass-produced models. Attached Figure Description

[0026] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a flowchart illustrating the first embodiment of the method for locating external vibration sources in this application. Figure 2 This is a schematic diagram of the vehicle coordinate system in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the principle of the hyperbolic positioning equation system in the embodiments of this application; Figure 4 This is a schematic diagram of the module structure of the vehicle external vibration source positioning device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the vehicle external vibration source positioning method in the embodiments of this application.

[0029] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0031] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0032] Autonomous driving systems typically rely on LiDAR, millimeter-wave radar, and vision cameras as primary perception sensors to perceive the vehicle's surrounding environment in real time. However, existing primary perception sensors have significant limitations in actual driving, and are susceptible to various operating conditions that can lead to perception failure. For example, obstruction by a vehicle ahead can prevent the primary perception sensor from detecting pedestrians, non-motorized vehicles, and other targets within the obstructed area; adverse weather conditions such as rain, snow, and dense fog can severely reduce the detection range and recognition accuracy of LiDAR and cameras; lens smudges, backlighting, and strong light can prevent cameras from properly recognizing targets; furthermore, malfunctions of the primary perception sensor itself can also cause perception interruptions. These perception failure scenarios can easily lead to collisions involving autonomous vehicles, seriously affecting the safety and reliability of autonomous driving, and are core pain points that the current autonomous driving industry urgently needs to address.

[0033] To address blind spots caused by obstructed vision from vehicles ahead, current blind spot mitigation solutions primarily rely on sensors such as millimeter-wave radar, ultrasonic radar, and surround-view cameras. For example, blind spot coverage is achieved by deploying short-range, wide-angle millimeter-wave radar arrays or fan-shaped focused ultrasonic sensor arrays on the sides of the vehicle, combined with ultra-wide-angle camera modules. However, these solutions still have significant shortcomings: millimeter-wave radar has detection blind spots at close range (e.g., within 0.5m) and insufficient short-range clutter suppression; cameras have a higher risk of failure under conditions such as nighttime, backlighting, rain, and snow; and single-sensor alarm strategies are difficult to adapt to complex scenarios. Furthermore, traditional ultrasonic radar blind spot monitoring systems suffer from rigid detection threshold adjustment mechanisms, insufficient distance measurement error compensation, and a single-dimensional risk analysis model, making them ill-suited to the safety requirements of complex road conditions and dynamic scenarios. In recent years, some solutions have explored using roadside sensing devices to assist vehicle-mounted sensing. For example, sensing devices installed on road poles can be used to create "roadside eyes" to help fill blind spots. However, this solution relies on the deployment of roadside infrastructure and has problems such as high construction costs, limited coverage, and inconsistent vehicle-road cooperative standards, making it difficult to achieve large-scale application in the short term.

[0034] Current applications of vibration sensors in vehicles are primarily focused on the wheel wells, used to monitor vehicle conditions such as tire pressure, wheel bearing malfunctions, road surface type identification, and suspension operation, thereby ensuring vehicle safety. For example, existing technologies use vibration sensors installed on the wheels or suspension to detect tire vibrations during driving, and then use frequency analysis to infer road conditions and tire status. Other technologies utilize MEMS sensors embedded in the inner wall of the tires to achieve vehicle control based on tactile perception of road surface conditions. However, these applications are limited to monitoring the vehicle's own condition and do not address the localization and identification of external targets or blind spot detection in autonomous driving.

[0035] In the field of vibration localization technology, existing technologies are mostly applied to fixed roadside scenarios. For example, existing technologies use linear vibration sensor arrays deployed along the road to detect and estimate the speed of moving vehicles by leveraging the similarity of the vehicle target's envelope on the sensor array and the correlation between its speed and direction. Other technologies use fiber optic sensing devices laid on both sides of the road to collect vibration data for highway vehicle localization and tracking. Furthermore, some solutions utilize grating array sensors to acquire vehicle vibration signals, enabling all-weather monitoring, localization, and speed measurement of vehicles. However, all of these technologies employ fixed vibration sensor arrays, meaning the sensor positions remain unchanged after deployment, making them unsuitable for mobile vehicle scenarios. Moreover, existing vibration signal processing technologies cannot address core challenges in mobile vehicle scenarios, such as the vehicle's own vibration noise intensity being far greater than the vibration signal from external targets, and the road surface medium changing in real time with the driving process. Therefore, they are difficult to directly apply to blind spot detection in autonomous driving.

[0036] In summary, existing blind spot compensation technologies for autonomous driving still lack sufficient reliability in extreme conditions, while the application scenarios of onboard vibration sensors are limited to monitoring the vehicle's own condition, and roadside vibration localization technology cannot be adapted to onboard mobile scenarios. Therefore, there is an urgent need for a technical solution that can accurately locate external vibration sources in onboard mobile scenarios to compensate for the perception blind spots of existing autonomous driving main perception sensors and improve the safety and reliability of autonomous driving under harsh conditions.

[0037] The main solution to this problem, according to the embodiments of this application, is: In this embodiment, for ease of description, the following description uses the vehicle domain controller as the execution subject.

[0038] This application provides a solution that enables a distributed vibration sensing array deployed at the wheel ends to collect road vibration signals in real time. After preprocessing, the effective vibration signals outside the vehicle are separated, and the arrival time difference between each sensor is obtained through a time delay estimation algorithm. By combining the road vibration transmission wave velocity and the sensor calibration coordinates, a hyperbolic positioning equation system is established, thereby calculating the direction angle and distance of the external vibration source relative to the vehicle, achieving accurate positioning of the external vibration source, and providing reliable perception blind spot filling for autonomous driving.

[0039] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a vehicle autonomous driving domain controller. The following description uses a vehicle autonomous driving domain controller as an example to illustrate this embodiment and the subsequent embodiments.

[0040] Based on this, embodiments of this application provide a method for locating external vibration sources, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for locating external vibration sources in this application.

[0041] In this embodiment, the method for locating the external vibration source includes steps S10 to S40: Step S10: Deploy multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, and calibrate each vibration sensing unit to determine the coordinates of each vibration sensing unit in the vehicle coordinate system. This embodiment provides a method for locating external vibration sources. This method can be applied to autonomous vehicles and can provide a blind spot detection function when the main sensing sensors (such as lidar, cameras, millimeter-wave radar) are obstructed or fail due to environmental interference.

[0042] One MEMS vibration acceleration sensor is deployed at each of the four wheel ends in the unsprung mass area (e.g., steering knuckle, wheel hub, or lower control arm). The four sensors form a rectangular distributed vibration sensing array. The sensors are fixed by polyurethane vibration isolation pads with a Shore hardness of 40-50 and a thickness of 2-5mm to isolate high-frequency vibration interference from the vehicle body and ensure accurate acquisition of road vibration signals.

[0043] The vibration sensing unit can be deployed not only at the steering knuckle at the wheel end, but also at other non-sprung mass areas such as the wheel hub and lower control arm, as long as it can effectively collect vibration signals transmitted from the road surface. Besides using MEMS vibration accelerometers, vibration signal acquisition can also employ non-contact road acquisition methods such as radar waves or lasers. When using non-contact methods, the acquisition algorithm needs to be optimized accordingly to avoid the influence of road surface roughness and vehicle suspension motion on the recognition results.

[0044] In one feasible implementation, the calibration in step S10 includes: Establish a vehicle coordinate system and mark the spatial coordinates of each vibration sensing unit in the vehicle coordinate system; The sampling signals of each vibration sensing unit are synchronized in time so that each vibration sensing unit has a unified time reference.

[0045] The calibration process includes two aspects: spatial calibration and time synchronization. During spatial calibration, a vehicle coordinate system is first established. In this embodiment, a fixed vehicle coordinate system is established with the midpoint of the rear axle as the origin O, the vehicle's forward direction as the positive Y-axis, the left lateral direction as the positive X-axis, and the vertical upward direction as the positive Z-axis. During vehicle manufacturing or maintenance, the precise coordinates of four sensors in this coordinate system are calibrated using laser ranging, resulting in the following coordinates: left front wheel FL (x1,y1,z1), right front wheel FR (x2,y2,z2), left rear wheel RL (x3,y3,z3), and right rear wheel RR (x4,y4,z4). The coordinate accuracy error is controlled within 0.01m.

[0046] During time synchronization, the sampling signals of the four sensors are synchronized with nanosecond-level timestamps through the hardware synchronization clock of the vehicle's autonomous driving domain controller. The sampling frequency is set to 10kHz~20kHz to ensure that the time resolution of subsequent delay extraction meets the positioning accuracy requirements.

[0047] Step S20: Collect vibration signals transmitted from the road surface through a distributed vibration sensing array, and preprocess the collected vibration signals to separate the effective vibration signals from the external vibration sources. Four vibration sensors collect vibration signals transmitted from the road surface in real time. The vibration signals include vibration signals generated by external vibration sources (such as pedestrians, bicycles, electric two-wheelers, passenger cars, etc.) as well as vibration noise signals from the vehicle itself (such as motor / engine vibration, suspension vibration, tire rolling vibration, braking vibration, etc.).

[0048] Preprocessing employs a three-stage cascaded processing approach: The first stage: Taking the acquired raw vibration signal as input, a hardware anti-aliasing filter removes high-frequency interference noise above 10kHz, and a digital bandpass filter retains the effective frequency band of 10Hz~2kHz, outputting the basic signal. This frequency band covers the vibration characteristic frequency band of various external targets, while filtering out the vehicle's own ultra-low frequency rigid body vibration and high-frequency resonance noise.

[0049] The second stage: taking the basic signal as the main input, it obtains a reference signal set of the vehicle's own vibration (including vehicle speed, motor / engine speed, brake line pressure, suspension travel, steering angle, etc.) from the vehicle's CAN bus, and uses the least mean square (LMS) adaptive filtering algorithm to fit and cancel the self-vibration noise component in the main input signal that is related to the reference signal, and outputs a mixed signal.

[0050] The third stage: Using the mixed signal as input, calculate the cross-correlation coefficient between the four mixed signals. Utilizing the spatial propagation difference between the vehicle's own vibration and external vibration sources—the time difference between the vehicle's own vibration reaching the four sensors is close to zero, resulting in a high cross-correlation coefficient (typically ≥90%); while there is a significant time difference between the arrival of external vibration sources at different sensors, resulting in a low cross-correlation coefficient (typically <85%)—filter out the residual components of the vehicle's own vibration with a cross-correlation coefficient ≥85%, and output only the effective vibration signal containing external vibration sources.

[0051] Step S30: Calculate the arrival time difference of the effective vibration signal between any two vibration sensing units using a time delay estimation algorithm to obtain multiple sets of time difference datasets; For the four valid vibration signals obtained in step S20, the generalized cross-correlation (GCC) method is used to calculate the time difference of arrival (TDOA) between each pair of sensors. Specifically, for any two valid vibration signals s from sensors i and j... i (t), s j (t), calculate the cross-correlation function R between the two. ij (τ), the offset τ corresponding to the peak value of the cross-correlation function ij This refers to the time difference in the time domain between the vibration source signal and the two sensors. By iterating through the four sensors, six independent time difference datasets are obtained {τ12,τ13,τ14,τ23,τ24,τ34}, with the time difference extraction accuracy controlled within 1ms.

[0052] In addition to the generalized cross-correlation method, the time delay estimation algorithm in step S30 can also be the adaptive eigenvalue decomposition method, the phase transformation weighting method, etc. Those skilled in the art can choose according to actual computing resources and accuracy requirements.

[0053] Step S40: Based on the vibration sensing unit coordinates, time difference dataset, and vibration transmission wave velocity of the road surface, establish and solve the hyperbolic positioning equation system to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculate the direction angle and distance of the external vibration source relative to the vehicle.

[0054] First, the vibration transmission velocity of the current road surface is determined. In this embodiment, a road surface type identification module (which can be based on a visual sensor or a pre-calibrated vibration feature library) identifies the current road surface type in real time, including asphalt pavement, cement pavement, and gravel pavement. Then, a pre-stored correspondence between road surface type and vibration transmission velocity is retrieved to obtain the corresponding velocity value. For example, the velocity of asphalt pavement is 300~800 m / s, cement pavement is 800~1500 m / s, and gravel pavement is 200~500 m / s.

[0055] Then, a hyperbolic positioning equation system is established. For any two vibration sensing units i and j, the difference in distance from the external vibration source to the two vibration sensing units is set to be equal to the vibration transmission wave speed multiplied by the time difference of the signal reaching the two vibration sensing units, that is: √[(xx i )²+(yy i )²] - √[(xx j )²+(yy j )²] = v·τ ij Where (x,y) are the coordinates of the external vibration source in the horizontal plane (XY plane) of the vehicle coordinate system, and (x i ,y i ) and (x j ,y j ) represent the planar coordinates of the i-th and j-th vibration sensors, respectively, v is the current road vibration transmission wave velocity, and τ is the velocity of the vibration. ij This is the arrival time difference obtained in step S30. Based on the above formula, multiple sets of distance difference equations can be formed for multiple pairs of sensors, constituting a hyperbolic positioning equation set.

[0056] Finally, the Chan algorithm was used to solve the above overdetermined hyperbolic positioning equations to obtain the planar coordinates (x, y) of the external vibration source, with the solution error controlled within 0.1m. Based on the planar coordinates, the straight-line distance d = √(x² + y²) of the external vibration source relative to the origin O was calculated, as well as the direction angle θ = arctan(x / y) relative to the vehicle's forward direction (positive Y-axis), thus completing the calculation of distance and direction.

[0057] In step S40, in addition to the Chan algorithm, other classic positioning algorithms such as Taylor series expansion and least squares method can also be used to solve the hyperbolic positioning equation system, which can also obtain positioning results that meet the accuracy requirements.

[0058] Reference Figure 2 , Figure 2 This is a schematic diagram of the vehicle coordinate system in an embodiment of this application. Figure 2 As shown, a fixed coordinate system for the vehicle body is established with the midpoint of the rear axle as the origin O, the forward direction of the vehicle as the positive Y-axis, the left lateral direction of the vehicle as the positive X-axis, and the vertical upward direction of the ground as the positive Z-axis. Four vibration sensing units are deployed in the unsprung mass regions of the left front wheel (FL), right front wheel (FR), left rear wheel (RL), and right rear wheel (RR), respectively, with coordinates (x1, y1), (x2, y2), (x3, y3), and (x4, y4) in the XY plane. This coordinate system provides a unified spatial reference for subsequent vibration sensing unit calibration, the establishment of the hyperbolic positioning equations, and the calculation of external vibration source coordinates.

[0059] Reference Figure 3 , Figure 3 This is a schematic diagram illustrating the principle of the hyperbolic positioning equation system in an embodiment of this application. For example... Figure 3 As shown, the coordinates of the external vibration source P in the XY plane are (x, y). Taking the left front wheel FL and the right front wheel FR as examples, the distance difference between the vibration source P and FL and FR is constant, i.e., |PFL| - |PFR| = v·τ12. This distance difference corresponds to the branches of a hyperbola with FL and FR as foci on the plane. Similarly, multiple hyperbolas can be constructed using different sensor pairs (such as FL and RL, FR and RR, etc.), and the intersection of each hyperbola is the precise location of the external vibration source P. Based on Figure 3 Based on the geometric relationships shown, the following system of hyperbolic positioning equations is established: √[(xx i )²+(yy i )²] - √[(xx j )²+(yy j )²] = v·τ ij ; The values ​​(i,j) can be taken as multiple pairs of combinations such as (1,2), (1,3), (1,4), (2,3), (2,4), and (3,4). By solving this system of equations, the planar coordinates (x,y) of the external vibration source can be obtained, and then its direction angle and straight-line distance relative to the vehicle can be calculated.

[0060] This embodiment achieves precise distance and direction positioning of external vibration sources in a vehicle-mounted mobile scenario through the above steps. This method is independent of light and line-of-sight, and can operate stably under conditions where the main sensing sensor fails, such as obstruction by a vehicle ahead, rain / fog, or nighttime. It effectively suppresses vehicle vibration interference through three-level preprocessing; overcomes the influence of medium changes through dynamic adaptation of road surface wave velocity; and requires only a low-cost MEMS vibration sensor, making it easy to mass-produce and deploy.

[0061] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, the preprocessing of step S20 specifically includes: The original vibration signal is collected as input, filtered to remove high-frequency interference noise and retain the effective frequency band, and the basic signal is output. Using the basic signal as input, combined with the self-vibration reference signal obtained from the vehicle bus, an adaptive filtering algorithm is used to separate the vehicle's own vibration noise and output a mixed signal; Using a mixed signal as input, and based on the cross-correlation coefficient between the signals of each vibration sensing unit, the residual vibration component of the unit itself is filtered out, and the effective vibration signal containing external vibration sources is output.

[0062] First-stage processing: Filtering. Using the acquired raw vibration signal as input, the signal undergoes hardware anti-aliasing filtering and digital bandpass filtering to remove high-frequency interference noise above 10kHz, retaining the effective frequency band from 10Hz to 2kHz, and outputting the basic signal. This stage primarily addresses high-frequency noise interference and spectral aliasing issues.

[0063] The second stage of processing is adaptive noise separation. Using the base signal as input, it acquires vehicle vibration reference signals such as vehicle speed, motor speed, brake pressure, and suspension travel from the vehicle's CAN bus. A least mean square (LMS) adaptive filtering algorithm is employed, using the reference signal as filter input and the vehicle's own vibration component from the base signal as the desired output. The filter coefficients are iteratively updated to fit the vehicle's own vibration component from the base signal, which is then subtracted from the base signal to output a mixed signal. This stage primarily addresses the suppression of strong background noise from the vehicle's own vibration.

[0064] The third stage of processing: cross-correlation purification. Using the mixed signal as input, the cross-correlation coefficients between the four mixed signals are calculated. As mentioned earlier, the vehicle's own vibration residual components typically have a cross-correlation coefficient ≥90% due to the extremely small time difference between their arrival at the four sensors; while external vibration sources typically have a cross-correlation coefficient <85% due to the significant time difference between their arrival. Based on this, a correlation threshold (e.g., 85%) is set, and signal components with cross-correlation coefficients exceeding the threshold are considered as residual vehicle vibrations and filtered out, resulting in an output containing only valid vibration signals from external vibration sources. This stage of processing further eliminates any residual vehicle vibration noise that may remain after adaptive filtering.

[0065] Through the above three-level cascaded processing, this embodiment can effectively extract weak external vibration source signals from strong background noise, significantly improving the accuracy and robustness of subsequent positioning.

[0066] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to the first embodiment can be referred to the above description, and will not be repeated hereafter. Based on this, the road vibration transmission wave velocity in step S40 is determined in the following way: Identify the current road surface type; Obtain the wave velocity value corresponding to the current road surface type from the pre-stored correspondence between road surface types and vibration transmission wave velocities.

[0067] First, the current road surface type is identified through a road surface type recognition module. This module can be implemented in several ways: for example, by acquiring images of the road surface ahead using a visual camera and identifying the road surface type (asphalt, cement, gravel, etc.) through a deep learning model; or by identifying the road surface type through vibration spectrum feature analysis based on tire rolling vibration signals acquired by wheel-end vibration sensors (for example, the vibration energy of asphalt pavement is concentrated in the low-frequency range, while cement pavement has obvious high-frequency components).

[0068] Then, the wave velocity value corresponding to the current road surface type is obtained from the pre-stored correspondence between road surface type and vibration transmission wave velocity. This correspondence can be obtained through pre-calibration experiments: conduct real vehicle tests on a known road surface type, measure the time difference of the signal reaching different wheels using a vibration source at a known distance (such as tapping the road surface), deduce the wave velocity value, and form a lookup table or fitting formula stored in the domain controller.

[0069] When the road surface changes during vehicle operation (e.g., from asphalt to concrete), this embodiment can update the wave velocity value in real time, thereby avoiding positioning errors caused by a fixed wave velocity and improving positioning adaptability in vehicle-mounted mobile scenarios.

[0070] Based on the first embodiment of this application, in the fourth embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description, and will not be repeated hereafter. In addition, the method further includes: The calculated direction angle and distance are smoothed and abnormal jump values ​​are filtered out to output the location results of the external vibration source.

[0071] In one implementation, the calculated distance d and orientation angle θ are smoothed inter-frame using a Kalman filter algorithm. The state vector of the Kalman filter is set as [d,θ,d',θ']. T , where d' and θ' are the rates of change of distance and orientation angle (i.e., radial velocity and angular velocity), respectively. The observation vectors are d and θ obtained directly from step S40. Through iterative calculation of the state prediction equation and the observation update equation, the smoothed distance and orientation angle are output.

[0072] In addition to smoothing, this embodiment also sets up an abnormal jump filtering mechanism: when the distance deviation of two consecutive frame positioning results exceeds 0.5m, or the direction angle deviation exceeds 15°, the result of that frame is marked as an abnormal jump value, the smooth value of the previous frame is used to replace the output of the current frame, and the confidence level corresponding to the process noise covariance matrix of Kalman filtering is reduced to avoid the influence of abnormal values ​​on the subsequent filtering state.

[0073] Through the above smoothing and filtering processes, this embodiment can output continuous and stable external vibration source positioning results, avoiding output jitter caused by single positioning errors or instantaneous signal interference.

[0074] Based on the first embodiment of this application, in the fifth embodiment of this application, the content that is the same as or similar to that in the first embodiment can be referred to the above description, and will not be repeated hereafter. In addition, the method further includes: Under environmental interference conditions, environmental information is obtained from the vehicle bus; The parameters for acquiring or processing vibration signals are adjusted based on environmental information to compensate for the attenuation or interference of environmental factors on the vibration signals.

[0075] In environments with interference such as strong winds or rain, this embodiment obtains environmental information from the vehicle's CAN bus, including signals such as rainfall intensity, external wind speed, road surface adhesion coefficient, and visual road recognition results. These signals are all outputs from sensors standard in mass-produced vehicles, requiring no additional hardware.

[0076] Based on the acquired environmental information, the parameters for acquiring or processing vibration signals are adjusted to compensate for the attenuation or interference of environmental factors on the vibration signals. Specific adjustment methods include, but are not limited to: Adjustments for rainy weather: When the rainfall level reaches level 3 or above, the impact of rainwater on the road surface will generate additional random noise, and the hydroplaning of tires will reduce the transmission efficiency of external vibration signals. Therefore, the vibration peak recognition threshold is lowered by 20% to 30% to compensate for signal attenuation. At the same time, the common random noise generated by rainwater impacting the road surface is eliminated by using correlation analysis of the four-wheel sensor signals (this noise is weakly correlated or has no correlation characteristics on the four wheels).

[0077] Adjustments for windy weather: When the external wind speed exceeds a certain threshold (e.g., 10m / s), wind noise may couple to the vehicle body and the road surface. In this case, the learning rate of the adaptive filter can be increased to enable it to track changes in environmental noise more quickly, and the filter order in the preprocessing stage can be appropriately increased to enhance the noise reduction effect.

[0078] Comprehensive calibration: Combining the road surface adhesion coefficient and visual road recognition results (such as water accumulation area recognition), the influence of the current environment on the vibration signal is comprehensively judged, and the filtering parameters and recognition threshold are dynamically adjusted.

[0079] Taking a real-world scenario as an example: a vehicle is traveling at 20 km / h on a rainy urban road. Due to rain and billboard obstruction, the visual and radar sensors cannot detect an electric two-wheeler in the blind spot to the right rear. This embodiment acquires environmental data with a rainfall level of 3 and a road surface adhesion coefficient of 0.5 via the vehicle's CAN bus. It automatically lowers the vibration peak recognition threshold of the electric two-wheeler by 20% to compensate for signal attenuation caused by hydroplaning of the tires in the rain. Simultaneously, based on the signal differences of the four-wheel sensors, it eliminates common random noise generated by rain impacting the road surface, separates and extracts the vibration characteristics of the electric two-wheeler at the right rear wheel, and finally outputs the positioning result to the autonomous driving domain controller to assist in lane change warning.

[0080] Through the above-mentioned environmental adaptive adjustment, this embodiment can still maintain reliable positioning performance under complex weather conditions.

[0081] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for locating external vibration sources in this application. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0082] This application also provides an external vibration source positioning device; please refer to [reference needed]. Figure 4 The external vibration source positioning device includes: The first module 10 is used to deploy multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, and to calibrate each vibration sensing unit to determine the coordinates of each vibration sensing unit in the vehicle coordinate system. The second module 20 is used to collect vibration signals transmitted from the road surface through the distributed vibration sensing array, and to preprocess the collected vibration signals to separate the effective vibration signals from the external vibration sources. The third module 30 is used to calculate the arrival time difference of the effective vibration signal between any two vibration sensing units using a time delay estimation algorithm, and obtain multiple sets of time difference datasets; The fourth module 40 is used to establish and solve a hyperbolic positioning equation set based on the coordinates of the vibration sensing unit, the time difference dataset, and the vibration transmission wave velocity of the road surface, to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculate the direction angle and distance of the external vibration source relative to the vehicle.

[0083] In one embodiment, the first module 10 is further configured to: Establish a vehicle coordinate system and mark the spatial coordinates of each vibration sensing unit in the vehicle coordinate system; The sampling signals of each vibration sensing unit are synchronized in time so that each vibration sensing unit has a unified time reference.

[0084] In one embodiment, the second module 20 is further configured to: The original vibration signal is collected as input, filtered to remove high-frequency interference noise and retain the effective frequency band, and the basic signal is output. Using the aforementioned basic signal as input, and combining it with the vehicle's own vibration reference signal obtained from the vehicle bus, an adaptive filtering algorithm is used to separate the vehicle's own vibration noise and output a mixed signal. Using the mixed signal as input, and based on the cross-correlation coefficient between the signals of each vibration sensing unit, the residual vibration component of the unit itself is filtered out, and an effective vibration signal containing external vibration sources is output.

[0085] In one embodiment, the fourth module 40 is further configured to: Identify the current road surface type; Obtain the wave velocity value corresponding to the current road surface type from the pre-stored correspondence between road surface types and vibration transmission wave velocities.

[0086] In one embodiment, the fourth module 40 is further configured to: For any two vibration sensing units, the difference in distance from the external vibration source to the two vibration sensing units is set to be equal to the vibration transmission wave speed multiplied by the time difference of the signal reaching the two vibration sensing units. Based on the aforementioned settings, multiple sets of distance difference equations are formed, constituting a hyperbolic positioning equation set.

[0087] In one embodiment, the external vibration source positioning device further includes a fifth module 50, used for: The calculated direction angle and distance are smoothed and abnormal jump values ​​are filtered out to output the location results of the external vibration source.

[0088] In one embodiment, the external vibration source locating device further includes a sixth module 60, used for: Under environmental interference conditions, environmental information is obtained from the vehicle bus; Based on the environmental information, the acquisition or processing parameters of the vibration signal are adjusted to compensate for the attenuation or interference of the vibration signal by environmental factors.

[0089] The vehicle external vibration source positioning device provided in this application, employing the vehicle external vibration source positioning method in the above embodiments, can solve the technical problem of low distance and direction positioning accuracy of external vibration sources in vehicle-mounted mobile scenarios, which is easily affected by vehicle vibration and changes in road surface medium. Compared with the prior art, the beneficial effects of the vehicle external vibration source positioning device provided in this application are the same as those of the vehicle external vibration source positioning method provided in the above embodiments, and other technical features in the vehicle external vibration source positioning device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0090] This application provides a vehicle, the vehicle including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the external vibration source localization method in Embodiment 1 above.

[0091] The following is for reference. Figure 5The diagram illustrates a structural schematic of a vehicle suitable for implementing embodiments of this application. The vehicle in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The vehicle shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0092] like Figure 5 As shown, the vehicle may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for vehicle operation. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the vehicle to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows vehicles with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0093] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0094] The vehicle provided in this application, employing the external vibration source localization method described in the above embodiments, can solve the technical problem of low distance and direction positioning accuracy of external vibration sources in vehicle-mounted mobile scenarios, which is easily affected by vehicle vibration and changes in road surface medium. Compared with the prior art, the beneficial effects of the vehicle provided in this application are the same as those of the external vibration source localization method provided in the above embodiments, and other technical features of the vehicle are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0095] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0097] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the vehicle external vibration source localization method in the above embodiments.

[0098] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having 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 thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0099] The aforementioned computer-readable storage medium may be included in the vehicle or may exist independently and not installed in the vehicle.

[0100] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by a vehicle, the vehicle: deploys multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, calibrates each vibration sensing unit, and determines the coordinates of each vibration sensing unit in the vehicle coordinate system; collects vibration signals transmitted from the road surface through the distributed vibration sensing array, preprocesses the collected vibration signals, and separates the effective vibration signals of external vibration sources; calculates the arrival time difference of the effective vibration signals between any two vibration sensing units using a time delay estimation algorithm, obtaining multiple sets of time difference datasets; and establishes and solves a hyperbolic positioning equation system based on the coordinates of the vibration sensing units, the time difference datasets, and the vibration transmission wave velocity of the road surface to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculates the direction angle and distance of the external vibration source relative to the vehicle.

[0101] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

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

[0103] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0104] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for locating external vibration sources. This solves the technical problem of low distance and direction positioning accuracy of external vibration sources in vehicle-mounted mobile scenarios, which is easily affected by vehicle vibration and changes in road surface conditions. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the external vibration source location method provided in the above embodiments, and will not be repeated here.

[0105] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method for locating external vibration sources.

[0106] The computer program product provided in this application can solve the technical problem of low accuracy in locating the distance and direction of external vibration sources in vehicle-mounted mobile scenarios, which is easily affected by vehicle vibration and changes in road surface medium. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the vehicle external vibration source location method provided in the above embodiments, and will not be repeated here.

[0107] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for locating an external vibration source, characterized in that, The method for locating the external vibration source includes: Step S10: Deploy multiple vibration sensing units on the vehicle to form a distributed vibration sensing array, and calibrate each vibration sensing unit to determine the coordinates of each vibration sensing unit in the vehicle coordinate system. Step S20: Collect vibration signals transmitted from the road surface through the distributed vibration sensing array, and preprocess the collected vibration signals to separate the effective vibration signals from the external vibration sources. Step S30: Calculate the arrival time difference of the effective vibration signal between any two vibration sensing units using a time delay estimation algorithm to obtain multiple sets of time difference datasets; Step S40: Based on the coordinates of the vibration sensing unit, the time difference dataset, and the vibration transmission wave velocity of the road surface, establish and solve the hyperbolic positioning equation system to obtain the planar coordinates of the external vibration source in the vehicle coordinate system, and then calculate the direction angle and distance of the external vibration source relative to the vehicle.

2. The method for locating an external vibration source according to claim 1, characterized in that, The calibration in step S10 includes: Establish a vehicle coordinate system and mark the spatial coordinates of each vibration sensing unit in the vehicle coordinate system; The sampling signals of each vibration sensing unit are synchronized in time so that each vibration sensing unit has a unified time reference.

3. The method for locating an external vibration source according to claim 1, characterized in that, The preprocessing described in step S20 includes: The original vibration signal is collected as input, filtered to remove high-frequency interference noise and retain the effective frequency band, and the basic signal is output. Using the aforementioned basic signal as input, and combining it with the vehicle's own vibration reference signal obtained from the vehicle bus, an adaptive filtering algorithm is used to separate the vehicle's own vibration noise and output a mixed signal. Using the mixed signal as input, and based on the cross-correlation coefficient between the signals of each vibration sensing unit, the residual vibration component of the unit itself is filtered out, and an effective vibration signal containing external vibration sources is output.

4. The method for locating an external vibration source according to claim 1, characterized in that, The vibration transmission wave velocity of the road surface in step S40 is determined by the following method: Identify the current road surface type; Obtain the wave velocity value corresponding to the current road surface type from the pre-stored correspondence between road surface types and vibration transmission wave velocities.

5. The method for locating an external vibration source according to claim 1, characterized in that, The method for establishing the hyperbolic positioning equation system in step S40 is as follows: For any two vibration sensing units, the difference in distance from the external vibration source to the two vibration sensing units is set to be equal to the vibration transmission wave speed multiplied by the time difference of the signal reaching the two vibration sensing units. Based on the aforementioned settings, multiple sets of distance difference equations are formed, constituting a hyperbolic positioning equation set.

6. The method for locating an external vibration source according to claim 1, characterized in that, The method further includes: The calculated direction angle and distance are smoothed and abnormal jump values ​​are filtered out to output the location results of the external vibration source.

7. The method for locating an external vibration source according to claim 1, characterized in that, The method further includes: Under environmental interference conditions, environmental information is obtained from the vehicle bus; Based on the environmental information, the acquisition or processing parameters of the vibration signal are adjusted to compensate for the attenuation or interference of the vibration signal by environmental factors.

8. A vehicle, characterized in that, The vehicle includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for locating external vibration sources as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for locating external vibration sources as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method for locating external vibration sources as described in any one of claims 1 to 7.

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