Vehicle noise reduction method, device and vehicle

Abstract

The application relates to a vehicle noise reduction method and device and a vehicle, wherein the vehicle noise reduction method comprises the following steps: when it is monitored that a noise-polluting vehicle exists around the current vehicle, determining a predicted time length for the noise-polluting vehicle to drive to a specified position on the current vehicle; determining an advance time length for the current vehicle to trigger a noise reduction action, and determining a target time for triggering the noise reduction action based on the advance time length, the predicted time length and a current time, so that the current vehicle executes the noise reduction action based on a preset noise reduction strategy when the target time arrives, and noise signals generated by the noise-polluting vehicle are offset, thereby the vehicle can timely trigger active noise reduction before the noise arrives.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle noise reduction method, device, and vehicle. Background Technology

[0002] With the continuous development of road traffic networks, passenger vehicles frequently encounter various vehicles passing by on the side. Among them, large trucks and heavy trucks generate strong low-frequency, high-instantaneous-impact, directional road noise when passing by passenger vehicles. This type of noise is characterized by drastic amplitude changes, concentrated duration, and strong subjective discomfort, easily causing irritability and fatigue for drivers and passengers, and seriously reducing driving comfort.

[0003] To address these issues, existing vehicle noise reduction methods typically rely on acoustic signal thresholds to trigger noise reduction. However, this method suffers from response lag and cannot trigger active vehicle noise reduction in time before the noise arrives.

[0004] There is currently no effective solution to the problem that related technologies cannot trigger active noise cancellation in vehicles in time before noise arrives. Summary of the Invention

[0005] This embodiment provides a vehicle noise reduction method, device, and vehicle, which aims to trigger active noise reduction in the vehicle in a timely manner before noise arrives, avoiding noise reduction lag.

[0006] Firstly, this embodiment provides a vehicle noise reduction method, the vehicle noise reduction method comprising:

[0007] When a noise-polluting vehicle is detected around the current vehicle, the predicted time for the noise-polluting vehicle to travel to a designated position on the current vehicle is determined.

[0008] The advance time for the current vehicle to trigger noise reduction action is determined, and the target time for triggering noise reduction action is determined based on the advance time, the predicted time, and the current time, so that the current vehicle can perform noise reduction action based on a preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

[0009] Secondly, this embodiment provides a vehicle noise reduction device, the vehicle noise reduction device comprising:

[0010] The monitoring module is used to determine the predicted time it will take for the noise-polluting vehicle to travel to a designated position on the current vehicle when a noise-polluting vehicle is detected around the current vehicle.

[0011] The noise reduction module is used to determine the advance time for the current vehicle to trigger noise reduction action, and to determine the target time for triggering noise reduction action based on the advance time, the predicted time and the current time, so that the current vehicle can perform noise reduction action based on a preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

[0012] Thirdly, this embodiment provides a vehicle, including:

[0013] A control device, the control device including a memory, a processor, and a computer program;

[0014] The computer program is stored in the memory and configured to be executed by the processor to implement the vehicle noise reduction method described in the first aspect above.

[0015] Compared with related technologies, the vehicle noise reduction method, device and vehicle provided in this embodiment, when a noise-polluting vehicle is detected around the current vehicle, determines the predicted time when the noise-polluting vehicle travels to a designated position on the current vehicle; determines the advance time for the current vehicle to trigger noise reduction action; and determines the target time for triggering noise reduction action based on the advance time, the predicted time and the current time, so that the current vehicle can perform noise reduction action based on a preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise-polluting vehicle.

[0016] In this way, by monitoring the noise pollution vehicles around the vehicle in real time, predicting the estimated time for them to arrive at the designated location, and combining the advance time of the vehicle to trigger noise reduction action, the target time for noise reduction is accurately calibrated. Then, noise reduction action can be executed according to the preset noise reduction strategy at the target time, avoiding the response lag problem caused by sound propagation and recognition delay in the acoustic signal threshold triggering method, and triggering the vehicle's active noise reduction in time before the noise arrives.

[0017] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0019] Figure 1 This is a flowchart of Embodiment 1 of the vehicle noise reduction method provided in this application;

[0020] Figure 2 This is a flowchart of Embodiment 2 of the vehicle noise reduction method provided in this application;

[0021] Figure 3 This is a flowchart of Embodiment 3 of the vehicle noise reduction method provided in this application;

[0022] Figure 4 This is a flowchart of Embodiment 4 of the vehicle noise reduction method provided in this application;

[0023] Figure 5 This is a flowchart of Embodiment 5 of the vehicle noise reduction method provided in this application;

[0024] Figure 6 This is a flowchart of Embodiment Six of the vehicle noise reduction method provided in this application;

[0025] Figure 7 This is a flowchart of Embodiment Seven of the vehicle noise reduction method provided in this application;

[0026] Figure 8 This is a flowchart of Embodiment 8 of the vehicle noise reduction method provided in this application;

[0027] Figure 9 This is a structural block diagram of the vehicle noise reduction device provided in this application. Detailed Implementation

[0028] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0029] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0030] The following specific embodiments are given to illustrate the technical solution of this application in detail.

[0031] Figure 1 This is a flowchart of an embodiment of the vehicle noise reduction method provided in this application. Please refer to... Figure 1 The method provided in this embodiment may include the following steps:

[0032] Step S101: When noise pollution vehicles are detected around the current vehicle, determine the predicted time for the noise pollution vehicles to travel to the designated position on the current vehicle.

[0033] In practice, for example, the vehicle's onboard LiDAR scans the surrounding environment to generate 3D point cloud data. Then, 3D point cloud clusters representing vehicle targets are segmented from this data. The actual size parameters of each vehicle target are calculated and fitted. Subsequently, the actual size parameters of each vehicle target are matched with pre-stored size parameters of various noise-polluting vehicles. If the actual size parameters match the size parameters of the noise-polluting vehicles, it is determined that the noise-polluting vehicle exists in the vicinity of the current vehicle.

[0034] Among them, noise pollution vehicles refer to motor vehicles whose driving noise exceeds national limits and disturbs the surrounding living environment. In urban road scenarios, such vehicles are most typically modified vehicles that produce abnormal roaring noises, and also include old diesel trucks, vehicles with malfunctions and abnormal noises, and motorcycles that roar at night.

[0035] Furthermore, the actual position of the noise-polluting vehicle relative to the current vehicle is measured using an onboard LiDAR. Based on the current vehicle's position and the actual position of the noise-polluting vehicle, the longitudinal distance (longitudinal distance being the current vehicle's direction of travel) between the two vehicles is calculated. Simultaneously, the onboard LiDAR tracks the noise-polluting vehicle for multiple consecutive frames. The ratio of the longitudinal displacement difference between adjacent frames to the inter-frame time interval is determined as the relative speed between the noise-polluting and current vehicles. Subsequently, based on the longitudinal offset, longitudinal distance, and relative speed of the designated position on the current vehicle relative to the radar mounting position, the predicted time for the noise-polluting vehicle to travel to the designated position on the current vehicle is calculated. The specific calculation formula is as follows:

[0036]

[0037] In equation (1), T represents the predicted time for the noise-polluting vehicle to travel to the specified position on the current vehicle, for example, T is 3 seconds; d represents the longitudinal distance of the noise-polluting vehicle relative to the current vehicle. This indicates the longitudinal offset of a specified position on the vehicle relative to the radar mounting position; This indicates the relative speed between the noise-polluting vehicle and the currently moving vehicle. It should be understood that this is typically required... This indicates that a noisy vehicle is approaching the current vehicle. If the noise-polluting vehicle does not approach, the prediction duration calculation will not be triggered.

[0038] It should be noted that the designated location on the vehicle can be flexibly selected according to actual application needs, including but not limited to the vehicle's centerline (perpendicular to the vehicle's direction of travel), noise monitoring points near the driver or passengers, etc. For example, when the radar installation location is selected as the designated location, .

[0039] Step S102: Determine the advance time for the current vehicle to trigger noise reduction action, and determine the target time for triggering noise reduction action based on the advance time, the predicted time and the current time, so that the current vehicle can perform noise reduction action based on the preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

[0040] It should be noted that advance time refers to the amount of time that needs to be added before the predicted arrival time of the noise. The purpose of introducing advance time is to compensate for the time delay required from the generation of the anti-noise signal to the emission of sound waves from the speaker, and the propagation of the sound waves inside the vehicle. This ensures that the actual action time of the anti-noise signal is precisely synchronized with the arrival time of the target noise, avoiding missing the strongest transient phase of the noise, improving the noise reduction response speed and peak noise suppression rate, and thus achieving a better noise reduction effect.

[0041] In this step, the difference between the lead time and the prediction time for the current vehicle to trigger noise reduction is pre-determined. Then, based on this difference and the current time, the target time is determined. The specific formula for calculating the target time is as follows:

[0042]

[0043] In equation (2), This indicates the target time for noise reduction, i.e., the time when the preset noise reduction strategy is planned to be activated; Indicates the current time; T represents the prediction duration; This indicates the lead time before the vehicle triggers noise reduction. This lead time can be pre-calibrated according to actual application requirements, for example, .

[0044] When the vehicle's system clock reaches the target time, noise reduction is immediately executed based on a preset noise reduction strategy. This preset noise reduction strategy instructs the current vehicle to generate a target noise reduction signal that is opposite in phase and similar in amplitude to the noise signal generated by the noise-polluting vehicle, according to a preset noise reduction signal generation method. The target noise reduction signal is then output through the loudspeaker in the current vehicle, so that the target noise reduction signal and the noise signal cancel each other out, thereby effectively canceling the noise signal generated by the noise-polluting vehicle.

[0045] For example, in one embodiment, several types of common noise-polluting vehicles can be pre-selected as calibration targets. The current vehicle is placed under standard test conditions (e.g., noise-polluting vehicles are present around the current vehicle, and these vehicles are traveling in a straight line at a constant speed). When a noise-polluting vehicle reaches a designated position on the current vehicle, a noise signal is collected using the microphone on the current vehicle. Subsequently, the collected noise signal is analyzed using digital signal processing to extract its frequency domain characteristics, time domain characteristics, and phase characteristics. Based on the analysis results, the optimal anti-noise signal parameters that can achieve destructive interference with the noise signal are calculated. The calculated optimal anti-noise signal parameters are associated with and stored with the category label of the noise-polluting vehicle to form a parameter calibration library. In practical applications, when the target time arrives, the optimal anti-noise signal parameters corresponding to the currently identified noise-polluting vehicle are retrieved from the parameter calibration library. The onboard digital signal processor synthesizes the target anti-noise signal in real time based on the optimal anti-noise signal parameters and outputs the target anti-noise signal through the speakers in the current vehicle. In this way, a rapid response to road noise is achieved, effectively reducing the output delay of the anti-noise signal.

[0046] For example, a first microphone array can be deployed on the exterior of the vehicle (such as the outside of the doors or fenders). When the target time arrives, the first noise signal collected in real time by the first microphone array is acquired and input into an adaptive filter (such as a filter using the FxLMS algorithm). A target anti-noise signal with the opposite phase to the original noise is generated in real time and output through the speakers inside the vehicle. In this way, it is possible to dynamically adapt to noise changes in different target vehicles and driving conditions, improving the generalization ability and accuracy of noise reduction processing.

[0047] It should be noted that the above-described method for generating noise-resistant signals is merely an illustrative example, and those skilled in the art can flexibly select or combine them according to actual application scenarios and hardware configurations.

[0048] The vehicle noise reduction method provided in this embodiment, when detecting noise-polluting vehicles around the current vehicle, determines the predicted time it will take for the noise-polluting vehicle to reach a designated position on the current vehicle; determines the advance time for the current vehicle to trigger noise reduction action; and, based on the advance time, predicted time, and current time, determines the target time for triggering noise reduction action. This allows the current vehicle to execute noise reduction action according to a preset noise reduction strategy when the target time arrives, thereby canceling the noise signal generated by the noise-polluting vehicle. In this way, by monitoring noise-polluting vehicles around the vehicle in real time, predicting their arrival time at a designated position, and accurately determining the target time for noise reduction by combining the advance time for triggering noise reduction action, the method can execute noise reduction action according to a preset noise reduction strategy at the target time. This avoids the response lag problem caused by sound propagation and recognition delays in acoustic signal threshold triggering methods, and triggers active noise reduction in the vehicle before the noise arrives, effectively improving the timeliness and accuracy of noise reduction, and enhancing the driving experience and passenger comfort.

[0049] Figure 2 This is a flowchart of Embodiment 2 of the vehicle noise reduction method provided in this application. Please refer to... Figure 2 Based on the above embodiments, the advance timing for triggering noise reduction action of the current vehicle is determined, including:

[0050] Step S201: Obtain the current actual driving speed of the vehicle, and find the first target driving speed range corresponding to the actual driving speed from a plurality of preset first driving speed ranges.

[0051] In this embodiment, multiple consecutive and non-overlapping first driving speed ranges are pre-defined. For example, the preset multiple first driving speed ranges include v=0km / h, 0km / h<v≤30km / h, and v>30km / h. Specifically, when the vehicle's driving speed v=0km / h, it indicates that the vehicle is in a parked state; when the vehicle's driving speed 0km / h<v≤30km / h, it indicates that the vehicle is in a low-speed state, typically traveling on urban roads or congested sections; when the vehicle's driving speed v>30km / h, it indicates that the vehicle is in a medium-to-high-speed state, typically traveling on expressways or highways.

[0052] In practice, the actual driving speed of the current vehicle is obtained from the wheel speed sensors in the current vehicle. The actual driving speed is compared with a number of preset first driving speed ranges one by one to determine the first driving speed range into which the actual driving speed falls, and the first driving speed range into which the actual driving speed falls is determined as the first target driving speed range.

[0053] Based on the above example, if the actual driving speed of the vehicle is 25 km / h, then 0 km / h < v ≤ 30 km / h is determined as the first target driving speed range.

[0054] Step S202: Determine the advance time corresponding to the first target driving speed range based on the first target driving speed range and the preset advance time correspondence with the first driving speed range.

[0055] In this embodiment, the correspondence between the advance time and the first driving speed range is pre-defined. The advance time refers to the amount of time that noise reduction needs to be initiated in advance of the predicted noise arrival time.

[0056] It should be understood that different driving speeds correspond to different operating conditions, and the temporal characteristics of noise and the sensitivity of drivers and passengers to noise vary significantly under different operating conditions. When the vehicle is parked or operating at low speed, the noise generated by passing vehicles is characterized by its suddenness and significant transient impact, making drivers and passengers more sensitive to the noise. In this case, a longer lead time is required to ensure that the anti-noise signal can cover the peak area of ​​the noise and achieve precise noise suppression. When the vehicle is operating at medium to high speed, the noise generated by passing vehicles tends to be continuous and stable. In this case, a shorter lead time is required to avoid premature output of the anti-noise signal, which could cause additional acoustic distortion. Based on this, by configuring differentiated lead times, optimal synchronization between noise reduction actions and noise arrival can be achieved.

[0057] In practice, the advance time corresponding to the first target driving speed range is found from the preset correspondence between advance time and the first driving speed range.

[0058] Based on the above examples, for the first driving speed range v=0km / h, the corresponding advance warning time is 200ms; for the first driving speed range 0km / h<v≤30km / h, the corresponding advance warning time is 150ms; and for the first driving speed range v>30km / h, the corresponding advance warning time is 100ms. If the current vehicle's actual driving speed is 25km / h, then the advance warning time corresponding to the first target driving speed range is determined to be 150ms.

[0059] It should be noted that the above-described method of dividing the first driving speed range and the specific values ​​of the corresponding advance time are for illustrative purposes only. In practical applications, those skilled in the art can make flexible adjustments according to actual noise reduction needs.

[0060] Step S203: Determine the advance time corresponding to the first target driving speed range as the advance time for the current vehicle to trigger noise reduction action.

[0061] Understandably, by matching the advance time obtained based on the current vehicle speed with the current driving conditions, and using this advance time as the advance time for triggering noise reduction actions, the advance time can be adaptively adjusted with vehicle speed, thereby ensuring that the noise reduction activation timing under different driving conditions can be adapted to external noise.

[0062] The method provided in this embodiment achieves precise compensation for the noise reduction triggering time by configuring differentiated advance durations according to vehicle speed, thereby obtaining the best noise reduction effect.

[0063] Figure 3 This is a flowchart of Embodiment 3 of the vehicle noise reduction method provided in this application. Please refer to... Figure 3 Based on the above embodiments, noise reduction actions are performed based on a preset noise reduction strategy, including:

[0064] Step S301: According to a preset acquisition cycle, acquire the first noise signal collected by the first microphone array deployed outside the current vehicle and the second noise signal collected by the second microphone array deployed inside the current vehicle.

[0065] It should be noted that the first microphone array is deployed outside the vehicle (e.g., on the outside of the doors, fenders, etc.) to collect the original noise signal before it enters the vehicle, i.e., the first noise signal. The second microphone array is deployed inside the vehicle (e.g., in the headliner, driver's headrest, passenger's headrest, etc.) to collect the actual residual noise inside the vehicle, i.e., the second noise signal.

[0066] Specifically, starting from the target time, signal acquisition is performed periodically according to a preset acquisition cycle. The first acquisition occurs at the target time, acquiring the noise signals output by the first and second microphone arrays at that moment. Subsequently, at the arrival of each preset acquisition cycle, the first noise signal acquired by the first microphone array and the second noise signal acquired by the second microphone array are acquired. The preset acquisition cycle can be set according to actual noise reduction requirements to ensure real-time signal acquisition.

[0067] Step S302: Generate the first anti-noise signal corresponding to the first noise signal.

[0068] Specifically, the first noise signal is input into an adaptive filter (e.g., a filter using the FxLMS algorithm) for processing to generate a first anti-noise signal that is out of phase and has a similar amplitude to the first noise signal. This first anti-noise signal is designed to cancel out noise before it enters the vehicle.

[0069] Step S303: Determine the signal correction amount corresponding to the first anti-noise signal based on the second noise signal, and determine the second anti-noise signal based on the first anti-noise signal and the signal correction amount.

[0070] Specifically, the second noise signal is processed by an adaptive feedback controller (e.g., a PID controller or an adaptive filter) to generate a signal correction value. This signal correction value reflects the deviation between the actual noise reduction effect and the ideal effect of the first noise reduction signal inside the vehicle. Subsequently, the first noise reduction signal and the signal correction value are superimposed to obtain the second noise reduction signal.

[0071] Step S304: Perform a weighted operation on the first anti-noise signal and the second anti-noise signal to obtain the target anti-noise signal, and output the target anti-noise signal.

[0072] Specifically, the first anti-noise signal generated by the feedforward is weighted and the second anti-noise signal corrected by feedback to obtain the final target anti-noise signal, which is then output. The specific calculation formula for this target anti-noise signal is as follows:

[0073] (3)

[0074] In equation (3), Indicates the target's noise immunity signal; Indicates the first noise immunity signal; This represents the feedforward algorithm weights, i.e., the first weights corresponding to the first anti-noise signal; Indicates the second noise immunity signal; This represents the feedback algorithm weight, which is the second weight corresponding to the second noise-resistant signal.

[0075] It should be noted that the first and second weights mentioned above can be preset fixed weights (e.g., , It can also be dynamically adjusted according to the current vehicle type, vehicle acceleration and deceleration status, and different noise reduction processing stages after the target time, without specific limitations here.

[0076] The method provided in this embodiment utilizes an external first microphone array to collect the original noise signal and generate a corresponding first anti-noise signal for feedforward cancellation. Simultaneously, an internal second microphone array collects residual noise in real time and generates a corresponding signal correction value to dynamically compensate the feedforward signal, forming a closed-loop correction. Furthermore, the feedforward and feedback signals are fused and output through weighted operations, which can adaptively adjust the weight allocation according to different operating conditions. This significantly improves noise reduction accuracy and stability while ensuring fast response, making it suitable for complex and ever-changing driving scenarios.

[0077] Figure 4 The flowchart for Embodiment 4 of the vehicle noise reduction method provided in this application is shown below. Please refer to... Figure 4Based on the above embodiments, a weighted operation is performed on the first anti-noise signal and the second anti-noise signal to obtain the target anti-noise signal, including:

[0078] Step S401: Obtain the current actual driving speed of the vehicle, and find the second target driving speed range corresponding to the actual driving speed from a plurality of preset second driving speed ranges.

[0079] In this embodiment, multiple consecutive and non-overlapping second driving speed ranges are pre-defined. For example, the preset multiple second driving speed ranges include v=0km / h, 0km / h<v≤30km / h, and v>30km / h. Specifically, when the vehicle's driving speed v=0km / h, it indicates that the vehicle is in a parked state; when the vehicle's driving speed 0km / h<v≤30km / h, it indicates that the vehicle is in a low-speed state, typically traveling on urban roads or congested sections; when the vehicle's driving speed v>30km / h, it indicates that the vehicle is in a medium-to-high-speed state, typically traveling on expressways or highways.

[0080] In practice, the actual driving speed of the current vehicle is obtained from the wheel speed sensors in the current vehicle. The actual driving speed is compared with a number of preset second driving speed ranges one by one to determine the second driving speed range into which the actual driving speed falls. The second driving speed range into which the actual driving speed falls is determined as the second target driving speed range.

[0081] Based on the above example, if the actual driving speed of the vehicle is 25 km / h, then 0 km / h < v ≤ 30 km / h is determined as the second target driving speed range.

[0082] Step S402: Determine the weight coefficient corresponding to the second target driving speed range based on the second target driving speed range and the correspondence between the preset weight coefficient and the second driving speed range; wherein, the weight coefficient includes the first weight corresponding to the first anti-noise signal and the second weight corresponding to the second anti-noise signal.

[0083] In this embodiment, the correspondence between the weighting coefficients and the second driving speed range is pre-calibrated. The weighting coefficients include a first weight corresponding to the first anti-noise signal and a second weight corresponding to the second anti-noise signal.

[0084] In practice, the weight coefficient corresponding to the second target driving speed range is found from the preset correspondence between the weight coefficient and the second driving speed range.

[0085] Based on the above examples, for the second driving speed range v=0km / h, the corresponding first weight is 0.8 and the second weight is 0.2; for the second driving speed range 0km / h<v≤30km / h, the corresponding first weight is 0.7 and the second weight is 0.3; for the second driving speed range v>30km / h, the corresponding first weight is 0.4 and the second weight is 0.6. If the current actual driving speed of the vehicle is 25km / h, then the first weight corresponding to the second target driving speed range is determined to be 0.7 and the second weight is 0.3.

[0086] It should be noted that the above-described method for dividing the second driving speed range and the specific values ​​of the corresponding weighting coefficients are merely illustrative examples. In practical applications, those skilled in the art can make flexible adjustments according to actual noise reduction needs.

[0087] Step S403: Based on the weighting coefficients corresponding to the second target driving speed range, perform a weighted operation on the first anti-noise signal and the second anti-noise signal to obtain the target anti-noise signal.

[0088] In this step, the specific calculation formula for the target noise immunity signal can be referred to the above formula (3), and will not be repeated here.

[0089] The method provided in this embodiment achieves dynamic optimization of the feedforward-feedback fusion strategy by adaptively adjusting the first and second anti-noise signals according to the vehicle's driving speed. This can accurately adapt to the differences in noise characteristics under different vehicle speed conditions while ensuring the real-time noise reduction response, thereby effectively improving the comfort of passengers in the vehicle.

[0090] Figure 5 This is a flowchart of Embodiment 5 of the vehicle noise reduction method provided in this application. Please refer to... Figure 5 Based on the above embodiments, the vehicle noise reduction method further includes:

[0091] Step S501: Obtain the current road image captured by the vehicle-mounted camera in the current vehicle.

[0092] In this embodiment, the vehicle-mounted camera can be a monocular, binocular, or surround-view camera, and the vehicle-mounted camera can be installed in a position such as below the rearview mirror.

[0093] Step S502: Perform target recognition on the current road image to obtain the recognition result.

[0094] Specifically, a well-trained object detection algorithm (such as YOLO, Faster R-CNN, etc.) can be used to identify objects in the current road image and obtain the recognition results. These results can include information such as the object's category label and bounding box location.

[0095] It should be noted that, in order to obtain the above-mentioned fully trained target detection algorithm, road image samples containing various types of vehicles under different lighting, occlusion and angle conditions were collected in advance. The noise pollution vehicles in the road image samples were labeled, and the target detection algorithm was iteratively optimized based on the labeled road image samples so that the trained target detection algorithm can accurately identify noise pollution vehicles.

[0096] Step S503: When the recognition result indicates that there are noise-polluting vehicles in the current road image, determine that there are noise-polluting vehicles around the current vehicle.

[0097] Specifically, when the identification result contains a category label for a noise-polluting vehicle, it is determined that the noise-polluting vehicle exists around the current vehicle. As a further optional implementation, to improve detection reliability, it can be determined that the noise-polluting vehicle exists around the current vehicle if the same noise-polluting vehicle is identified in multiple consecutive frames of the current road image.

[0098] The method provided in this embodiment acquires road images using an onboard camera and employs a target detection algorithm for identification, enabling precise monitoring of vehicles causing noise pollution. Simultaneously, it provides accurate vehicle type classification information for subsequent noise reduction strategies, facilitating precise noise reduction.

[0099] Figure 6 This is a flowchart of Embodiment Six of the vehicle noise reduction method provided in this application. Please refer to... Figure 6 Based on the above embodiments, the predicted time for the noise-polluting vehicle to travel to the designated location on the current vehicle is determined, including:

[0100] Step S601: Determine the first distance between the designated location and the noise-polluting vehicle in the current driving direction of the vehicle.

[0101] For example, in one embodiment, target feature points on a noise-polluting vehicle in a current road image are identified; wherein the target feature points are used to characterize the location of the vehicle generating the maximum noise; a first coordinate of the target feature point is determined, and a second distance between the target feature point and the vehicle-mounted camera used to acquire the current road image in the driving direction is determined based on the first coordinate; a third distance between the pre-calibrated vehicle-mounted camera and the centerline of the vehicle body in the driving direction is obtained; and a first distance is determined based on the second distance and the third distance.

[0102] It should be noted that, in this embodiment, the designated position is the center line of the vehicle body perpendicular to the driving direction.

[0103] Specifically, feature points of noisy vehicles in the current road image are identified in advance to obtain target feature points on the noisy vehicles. These target feature points are used to characterize the location of the maximum noise generation on the noisy vehicle, such as the widest part of the vehicle body, the middle part of the vehicle body, and the engine compartment.

[0104] Furthermore, the first coordinates of the target feature point in the current road image are converted into the spatial position in the vehicle camera coordinate system to obtain the corresponding second coordinates. The second distance between the target feature point and the vehicle camera in the driving direction is then determined based on the second coordinates. For example, when the vehicle camera coordinate system has the direction of the vehicle centerline as the x-axis and the driving direction as the y-axis, the absolute value of the y-axis coordinate in the second coordinate system is determined as the second distance.

[0105] Then, the first distance is obtained by adding the pre-calibrated vehicle camera distance and the third and second distances along the vehicle's centerline in the driving direction.

[0106] Step S602: Determine the relative speed between the noise-polluting vehicle and the current vehicle.

[0107] Optionally, in one possible implementation, multiple preceding road images corresponding to the current road image are acquired, and the position coordinates of the noise-polluting vehicles in each preceding road image are determined; the position coordinates of the noise-polluting vehicles in the current road image are determined, and the actual moving distance of the noise-polluting vehicles is determined based on the position coordinates of the noise-polluting vehicles in each preceding road image and the position coordinates of the noise-polluting vehicles in the current road image; the relative speed is determined based on the actual moving distance and the acquisition time interval between the first frame image in the multiple preceding road images and the current road image.

[0108] In practice, the current road image is taken as the latest frame, and multiple preceding road images (e.g., 5 consecutive frames or 10 consecutive frames) are acquired. Target detection is performed on each preceding road image to determine the location coordinates of the noise-polluting vehicles in each preceding road image. At the same time, target detection is performed on the current road image to determine the location coordinates of the noise-polluting vehicles in the current road image.

[0109] Subsequently, the position coordinates of the noise-polluting vehicles in each preceding road image and the current road image are converted into their corresponding actual physical coordinates. Based on the actual physical coordinates of the noise-polluting vehicles in each preceding road image and the current road image, the actual travel distance of the noise-polluting vehicles is calculated, and the acquisition time interval between the first frame of each preceding road image and the current road image is recorded. Furthermore, the ratio of the actual travel distance to the acquisition time interval is calculated; this ratio represents the relative speed.

[0110] Step S603: Determine the prediction duration based on the first distance and relative velocity.

[0111] Specifically, the ratio of the first distance to the relative speed is calculated, and this ratio is the predicted duration.

[0112] The method provided in this embodiment determines the first distance between a specified position on the current vehicle and the noise-polluting vehicle by using the feature point with the strongest noise as the ranging reference. At the same time, it uses multiple frames of road images to calculate the relative speed between the noise-polluting vehicle and the current vehicle, thereby achieving accurate measurement of distance and vehicle speed and improving the accuracy of the prediction duration calculation.

[0113] Figure 7 This is a flowchart of Embodiment Seven of the vehicle noise reduction method provided in this application. Please refer to... Figure 7 Based on the above embodiments, after performing noise reduction actions based on a preset noise reduction strategy, the vehicle noise reduction method further includes:

[0114] Step S701: When the duration of the target noise reduction signal being less than a preset signal threshold is greater than a preset duration threshold, control the current vehicle to stop performing noise reduction actions.

[0115] Specifically, during the noise reduction process, the target noise reduction signal is continuously monitored. When the amplitude of the target noise reduction signal remains below a preset noise threshold and the duration exceeds a preset duration threshold (e.g., 2 seconds), it indicates that the noise from the noise-polluting vehicle has been significantly reduced or eliminated. At this point, the vehicle is controlled to stop performing the noise reduction action to avoid unnecessary power consumption.

[0116] Step S702, and / or, determine the real-time distance between the current vehicle and the noise-polluting vehicle.

[0117] Specifically, during the noise reduction process, the real-time distance between the current vehicle and the noise-polluting vehicle is continuously monitored. This real-time distance can be obtained through vehicle-mounted radar, visual ranging, or other methods.

[0118] Step S703: When the real-time distance is greater than a preset distance threshold, control the current vehicle to stop performing noise reduction actions.

[0119] In this step, the real-time distance is compared with a preset distance threshold. When the real-time distance is greater than the preset distance threshold (e.g., the preset distance threshold is 10 meters), it indicates that the noise-polluting vehicle has moved away from the current vehicle. At this point, the current vehicle is controlled to stop performing noise reduction actions to reduce system power consumption.

[0120] In addition, when the duration of the first noise signal collected by the first microphone array being less than a preset noise threshold is greater than a preset duration threshold, the current vehicle can be controlled to stop performing noise reduction actions.

[0121] The method provided in this embodiment automatically stops noise reduction processing when the target noise is significantly reduced or the noise-polluting vehicle leaves, and waits for the next noise reduction action to be triggered, thereby effectively avoiding the power consumption waste caused by ineffective noise reduction and improving the intelligence level of the system.

[0122] The present embodiment will now be described and illustrated through preferred embodiments.

[0123] Figure 8 The flowchart of Embodiment 8 of the vehicle noise reduction method provided in this application is as follows: Figure 8 As shown, the vehicle noise reduction method includes the following steps:

[0124] Step S801: Obtain the current road image captured by the vehicle-mounted camera in the current vehicle; perform target recognition on the current road image to obtain the recognition result; when the recognition result indicates that there is a noise-polluting vehicle in the current road image, determine that there is a noise-polluting vehicle around the current vehicle.

[0125] Step S802: Determine the first distance between the designated location and the noise-polluting vehicle in the current vehicle's direction of travel; determine the relative speed between the noise-polluting vehicle and the current vehicle; and determine the prediction duration based on the first distance and relative speed.

[0126] Step S803: Obtain the current actual driving speed of the vehicle, and search for the first target driving speed range corresponding to the actual driving speed from a plurality of preset first driving speed ranges.

[0127] Step S804: Based on the first target driving speed range and the correspondence between the preset advance time and the first driving speed range, determine the advance time corresponding to the first target driving speed range as the advance time for the current vehicle to trigger noise reduction action.

[0128] Step S805: Determine the difference between the predicted duration and the advance duration, and determine the target time based on the difference and the current time.

[0129] Step S806: When the target time arrives, acquire the first noise signal collected by the first microphone array deployed outside the current vehicle and the second noise signal collected by the second microphone array deployed inside the current vehicle according to the preset acquisition cycle.

[0130] Step S807: Generate a first anti-noise signal corresponding to the first noise signal, determine the signal correction amount corresponding to the first anti-noise signal based on the second noise signal, and determine the second anti-noise signal based on the first anti-noise signal and the signal correction amount.

[0131] In step S808, the first noise suppression signal and the second noise suppression signal are weighted and processed to obtain the target noise suppression signal, and the target noise suppression signal is output to cancel the noise signal generated by the noise pollution vehicle.

[0132] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0133] This embodiment also provides a vehicle noise reduction device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0134] Figure 9 This is a structural block diagram of the vehicle noise reduction device in this embodiment, as shown below. Figure 9 As shown, the device includes:

[0135] The monitoring module 10 is used to determine the predicted time for a noise-polluting vehicle to travel to a designated position on the current vehicle when a noise-polluting vehicle is detected around the current vehicle.

[0136] The noise reduction module 20 is used to determine the advance time for the current vehicle to trigger the noise reduction action, and to determine the target time for triggering the noise reduction action based on the advance time, the predicted time and the current time, so that the current vehicle can perform the noise reduction action based on the preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

[0137] Optionally, in one possible implementation, the noise reduction module 20 is specifically used for:

[0138] Obtain the current actual driving speed of the vehicle, and find the first target driving speed range corresponding to the actual driving speed from a number of preset first driving speed ranges;

[0139] Based on the first target driving speed range and the correspondence between the preset advance time and the first driving speed range, determine the advance time corresponding to the first target driving speed range;

[0140] The advance time corresponding to the first target driving speed range is determined as the advance time for the current vehicle to trigger noise reduction action.

[0141] Optionally, in one possible implementation, the noise reduction module 20 is specifically used for:

[0142] According to the preset acquisition cycle, the first noise signal collected by the first microphone array deployed outside the current vehicle and the second noise signal collected by the second microphone array deployed inside the current vehicle are acquired.

[0143] Generate a first anti-noise signal corresponding to the first noise signal;

[0144] Based on the second noise signal, determine the signal correction amount corresponding to the first anti-noise signal, and based on the first anti-noise signal and the signal correction amount, determine the second anti-noise signal;

[0145] The first and second anti-noise signals are weighted to obtain the target anti-noise signal, which is then output.

[0146] Optionally, in one possible implementation, the noise reduction module 20 is specifically used for:

[0147] Obtain the current actual driving speed of the vehicle, and find the second target driving speed range corresponding to the actual driving speed from a number of preset second driving speed ranges;

[0148] Based on the second target driving speed range and the correspondence between the preset weight coefficients and the second driving speed range, the weight coefficients corresponding to the second target driving speed range are determined; wherein, the weight coefficients include the first weight corresponding to the first anti-noise signal and the second weight corresponding to the second anti-noise signal;

[0149] Based on the weighting coefficients corresponding to the second target speed range, the first and second anti-noise signals are weighted to obtain the target anti-noise signal.

[0150] Optionally, in one possible implementation, the monitoring module 10 is specifically used for:

[0151] Acquire current road images captured by the vehicle's onboard camera;

[0152] Perform target recognition on the current road image to obtain the recognition results;

[0153] When the identification result indicates that there are noisy vehicles in the current road image, it is determined that there are noisy vehicles around the current vehicle.

[0154] Optionally, in one possible implementation, the monitoring module 10 is specifically used for:

[0155] Determine the first distance between the designated location and the noise-polluting vehicle in the current direction of travel of the vehicle;

[0156] Determine the relative speed between the noise-polluting vehicle and the current vehicle;

[0157] The prediction duration is determined based on the first distance and relative velocity.

[0158] Optionally, in one possible implementation, the monitoring module 10 is specifically used for:

[0159] Identify target feature points on vehicles causing noise pollution in the current road image; where the target feature points are used to characterize the location of the vehicle generating the maximum noise.

[0160] Determine the first coordinates of the target feature point, and based on the first coordinates, determine the second distance between the target feature point and the vehicle-mounted camera used to acquire the current road image in the driving direction;

[0161] Obtain the third distance between the pre-calibrated vehicle camera and the centerline of the vehicle body in the direction of travel;

[0162] The first distance is determined based on the second and third distances.

[0163] Optionally, in one possible implementation, the monitoring module 10 is specifically used for:

[0164] Obtain multiple preceding road images corresponding to the current road image, and determine the location coordinates of noise-polluting vehicles in each preceding road image;

[0165] Determine the location coordinates of the noise-polluting vehicles in the current road image, and based on the location coordinates of the noise-polluting vehicles in each previous road image and the location coordinates of the noise-polluting vehicles in the current road image, determine the actual distance traveled by the noise-polluting vehicles.

[0166] The relative speed is determined based on the actual distance traveled and the time interval between the acquisition of the first frame of multiple preceding road images and the current road image.

[0167] Optionally, in one possible implementation, in Figure 9 In addition to the above, the device also includes a control module for:

[0168] When the duration of the target noise reduction signal being less than the preset signal threshold exceeds the preset duration threshold, control the current vehicle to stop performing noise reduction actions;

[0169] And / or,

[0170] Determine the real-time distance between the current vehicle and the noise-polluting vehicle;

[0171] When the real-time distance exceeds a preset distance threshold, control the current vehicle to stop performing noise reduction actions.

[0172] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0173] This embodiment also provides a vehicle including a control device, which includes a memory, a processor, and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor from the steps of any of the above method embodiments.

[0174] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0175] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0176] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0177] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0178] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A vehicle noise reduction method, characterized in that, The vehicle noise reduction method includes: When a noise-polluting vehicle is detected around the current vehicle, the predicted time for the noise-polluting vehicle to travel to a designated position on the current vehicle is determined. The advance time for the current vehicle to trigger noise reduction action is determined, and the target time for triggering noise reduction action is determined based on the advance time, the predicted time, and the current time, so that the current vehicle can perform noise reduction action based on a preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

2. The vehicle noise reduction method according to claim 1, characterized in that, Determining the advance timing for triggering noise reduction action in the current vehicle includes: The actual driving speed of the current vehicle is obtained, and a first target driving speed range corresponding to the actual driving speed is found from a plurality of preset first driving speed ranges; Based on the first target driving speed range and the correspondence between the preset advance time and the first driving speed range, the advance time corresponding to the first target driving speed range is determined; The advance time corresponding to the first target driving speed range is determined as the advance time for the current vehicle to trigger noise reduction action.

3. The vehicle noise reduction method according to claim 1, characterized in that, The noise reduction action based on the preset noise reduction strategy includes: According to a preset acquisition cycle, the first noise signal collected by the first microphone array deployed outside the current vehicle and the second noise signal collected by the second microphone array deployed inside the current vehicle are acquired. Generate a first anti-noise signal corresponding to the first noise signal; Based on the second noise signal, determine the signal correction amount corresponding to the first noise immunity signal, and based on the first noise immunity signal and the signal correction amount, determine the second noise immunity signal; The first noise immunity signal and the second noise immunity signal are weighted and processed to obtain the target noise immunity signal, and the target noise immunity signal is output.

4. The vehicle noise reduction method according to claim 3, characterized in that, The step of weighting the first anti-noise signal and the second anti-noise signal to obtain the target anti-noise signal includes: The actual driving speed of the current vehicle is obtained, and the second target driving speed range corresponding to the actual driving speed is found from a plurality of preset second driving speed ranges; Based on the second target driving speed range and the preset correspondence between the weighting coefficient and the second driving speed range, the weighting coefficient corresponding to the second target driving speed range is determined; wherein, the weighting coefficient includes the first weight corresponding to the first anti-noise signal and the second weight corresponding to the second anti-noise signal; The first anti-noise signal and the second anti-noise signal are weighted according to the weighting coefficients corresponding to the second target driving speed range to obtain the target anti-noise signal.

5. The vehicle noise reduction method according to claim 1, characterized in that, The vehicle noise reduction method further includes: Acquire the current road image captured by the vehicle-mounted camera in the current vehicle; Target recognition is performed on the current road image to obtain the recognition result; When the identification result indicates that the noise-polluting vehicle exists in the current road image, it is determined that there is a noise-polluting vehicle around the current vehicle.

6. The vehicle noise reduction method according to claim 1 or claim 5, characterized in that, The method of determining the predicted time for the noise-polluting vehicle to travel to the designated position on the current vehicle includes: Determine the first distance between the designated location and the noise-polluting vehicle in the direction of travel of the current vehicle; Determine the relative speed between the noise-polluting vehicle and the current vehicle; The predicted duration is determined based on the first distance and the relative speed.

7. The vehicle noise reduction method according to claim 6, characterized in that, The designated location is the centerline of the vehicle body perpendicular to the direction of travel; determining the first distance between the designated location and the noise-polluting vehicle in the direction of travel of the current vehicle includes: Identify target feature points on noise-polluting vehicles in the current road image; wherein the target feature points are used to characterize the location of the noise-polluting vehicle generating the maximum noise; Determine the first coordinates of the target feature point, and based on the first coordinates, determine the second distance between the target feature point and the vehicle-mounted camera used to acquire the current road image in the driving direction; Obtain the third distance between the pre-calibrated vehicle-mounted camera and the centerline of the vehicle body in the driving direction; The first distance is determined based on the second distance and the third distance.

8. The vehicle noise reduction method according to claim 6, characterized in that, Determining the relative speed between the noise-polluting vehicle and the current vehicle includes: Obtain multiple preceding road images corresponding to the current road image, and determine the location coordinates of noise-polluting vehicles in each preceding road image; The location coordinates of the noise-polluting vehicles in the current road image are determined, and the actual distance traveled by the noise-polluting vehicles is determined based on the location coordinates of the noise-polluting vehicles in each of the preceding road images and the location coordinates of the noise-polluting vehicles in the current road image. The relative speed is determined based on the actual distance traveled and the time interval between the acquisition of the first frame image in the plurality of preceding road images and the current road image.

9. The vehicle noise reduction method according to claim 3 or claim 4, characterized in that, After performing noise reduction actions based on a preset noise reduction strategy, the vehicle noise reduction method further includes: When the duration of the target noise reduction signal being less than a preset signal threshold is greater than a preset duration threshold, the current vehicle is controlled to stop performing the noise reduction action; And / or, Determine the real-time distance between the current vehicle and the noise-polluting vehicle; When the real-time distance is greater than a preset distance threshold, the current vehicle is controlled to stop performing the noise reduction action.

10. A vehicle noise reduction device, characterized in that, The vehicle noise reduction device includes: The monitoring module is used to determine the predicted time it will take for the noise-polluting vehicle to travel to a designated position on the current vehicle when a noise-polluting vehicle is detected around the current vehicle. The noise reduction module is used to determine the advance time for the current vehicle to trigger noise reduction action, and to determine the target time for triggering noise reduction action based on the advance time, the predicted time and the current time, so that the current vehicle can perform noise reduction action based on a preset noise reduction strategy when the target time arrives, in order to cancel the noise signal generated by the noise pollution vehicle.

11. A vehicle, characterized in that, include: A control device, the control device including a memory, a processor, and a computer program; The computer program is stored in the memory and configured to be executed by the processor to implement the vehicle noise reduction method as described in any one of claims 1 to 9.

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