Vehicle noise reduction method based on driving prediction and electronic equipment

Abstract

The invention provides a vehicle noise reduction method based on driving prediction and electronic equipment, and the method comprises the steps: determining the type of a current driving road surface of a vehicle in a vehicle driving process, and obtaining the current speed of the vehicle and the operation state of each noise element; and furthermore, by determining a predicted road surface noise feature, a predicted wind noise feature and a predicted mechanical noise feature and finally applying an interference wave for weakening noise in the vehicle based on the predicted road surface noise feature, the predicted wind noise feature and the predicted mechanical noise feature, noise prediction and noise reduction processing in the vehicle driving process are realized. According to the method, the problem that noise cannot be effectively interfered in real time due to high noise wave analysis complexity can be solved, interference waves capable of effectively interfering the noise are applied in real time, noise perception of a user is reduced in time, road noise, wind noise and mechanical noise generated in the vehicle driving process can be reduced, and the user experience is improved. And the noise perception of the user is further reduced.

Description

Technical Field

[0001] The present application relates to the field of automobile technology, and in particular to a vehicle noise reduction method and electronic equipment based on driving prediction. Background Art

[0002] The noise problem of vehicles during driving has always affected driving safety. Driving noise enters the ears of passengers through the vibration energy transmitted through the air. Therefore, in terms of noise optimization path, weakening or offsetting the sound field energy around the eardrum is one of the best solutions to reduce noise perception.

[0003] The current active noise reduction solution is to achieve noise reduction by analyzing sound waves and emitting coherent sound waves after the noise is sensed. However, this method has a certain lag effect, and if the noise wave is a sound wave synthesized from several sources, the analysis complexity will increase, the characteristic frequency of the sound wave will vary greatly, the analysis will be difficult, and the analysis time will be long. It is difficult to effectively interfere with the noise in real time, and even in some frequency bands, due to the time lag of the interference wave, it will aggravate the noise perception of the occupants. Summary of the invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present application aims to provide a vehicle noise reduction method and electronic device based on driving prediction to solve the problem that the prior art cannot effectively interfere with noise in real time.

[0005] The embodiment of the present application provides a vehicle noise reduction method based on driving prediction, the method comprising: During vehicle driving, determining the type of road surface the vehicle is currently traveling on, and obtaining the current speed of the vehicle and the operating status of each noise element in the vehicle; Determining a predicted road noise characteristic of the vehicle based on the current driving road surface type and the current vehicle speed; determining a predicted wind noise characteristic of the vehicle based on the current vehicle speed; determining a predicted mechanical noise signature of the vehicle based on an operating state of each of the noise elements; An interference wave for reducing noise is applied inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics.

[0006] Optionally, determining the predicted road noise characteristics of the vehicle based on the current driving road type and the current vehicle speed includes: In a pre-built road noise database, querying a road noise feature corresponding to the current driving road type and the current vehicle speed as a predicted road noise feature of the vehicle; The road noise database is used to describe the corresponding relationship between each vehicle speed, each driving road surface type and each road noise feature.

[0007] Optionally, determining the predicted wind noise characteristics of the vehicle based on the current vehicle speed includes: In a pre-built wind noise database, querying a wind noise feature corresponding to the current vehicle speed as the predicted wind noise feature of the vehicle, wherein the wind noise database is used to describe the corresponding relationship between each vehicle speed and each wind noise feature; After determining the predicted wind noise characteristic of the vehicle based on the current vehicle speed, the method further includes: The predicted wind noise characteristics are corrected based on the current ambient wind speed and the current ambient wind direction of the vehicle.

[0008] Optionally, the process of constructing the wind noise database includes: Testing the wind noise of the vehicle at each preset test speed to obtain the wind noise characteristics of each preset test speed; Determining the wind noise characteristics of a derived vehicle speed between the two adjacent preset test vehicle speeds based on various wind noise characteristics generated when the vehicle travels at different accelerations between two adjacent preset test vehicle speeds; A wind noise database is constructed based on the wind noise characteristics of each preset test vehicle speed and the wind noise characteristics of each derived vehicle speed.

[0009] Optionally, based on each wind noise characteristic generated when the vehicle travels at different accelerations between two adjacent preset test speeds, determining the wind noise characteristic of a derived vehicle speed between two adjacent preset test speeds includes: Based on various wind noise features generated when the vehicle travels at different accelerations between two adjacent preset test speeds, determining a noise feature intersection and a noise feature union between the various wind noise features, wherein the noise feature intersection describes the overlapping frequency bands and corresponding amplitudes between the various wind noise features, and the noise feature union describes the frequency bands and corresponding amplitudes after the wind noise features are merged; Based on the intersection of the noise features and the union of the noise features, a wind noise feature of a derived vehicle speed between two adjacent preset test vehicle speeds is determined.

[0010] Optionally, before applying an interference wave for eliminating noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, the method further includes: Determining whether the current driving road type is a preset road type, and if so, controlling the vehicle to drive and re-determining the predicted road noise characteristics of the vehicle; and / or, Determine whether there is a single peak noise in the predicted mechanical noise characteristics, or whether there is mixed noise between different noise elements. If so, control the operating power of at least one of the noise elements to change the operating state of the noise element and redetermine the predicted mechanical noise characteristics of the vehicle.

[0011] Optionally, applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, comprises: In a pre-constructed interference wave mapping feature library, querying interference wave features corresponding to the predicted road noise features, the predicted wind noise features, and the predicted mechanical noise features, wherein the interference wave mapping feature library is used to describe the corresponding relationship between each road noise feature, each wind noise feature, each mechanical noise feature and each interference wave feature, and the interference wave feature includes an interference wave amplitude and an interference wave frequency; An interference wave corresponding to the queried interference wave feature is generated, and the interference wave is applied inside the vehicle to weaken the predicted road noise feature, the predicted wind noise feature, and the predicted mechanical noise feature.

[0012] Optionally, after applying the interference wave inside the vehicle, the method further includes: Collecting the interference measurement noise of the vehicle based on a preset microphone device; Based on the post-interference measurement noise, the interference wave amplitude and / or the interference wave frequency of the interference wave in the interference wave mapping feature library is adjusted.

[0013] Optionally, before applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, the method further includes: Collecting the pre-interference measurement noise of the vehicle based on a preset microphone device; Based on the pre-interference measured noise, at least one of the predicted road noise feature, the predicted wind noise feature and the predicted mechanical noise feature is adjusted, and the adjusted noise feature is synchronously updated to the corresponding database.

[0014] The present application also provides an electronic device, the electronic device comprising: Processor and memory; The processor is used to execute the steps of the vehicle noise reduction method based on driving prediction provided in any embodiment of the present application by calling the program or instruction stored in the memory.

[0015] An embodiment of the present application also provides a computer-readable storage medium, which stores a program or instruction, and the program or instruction enables a computer to execute the steps of the vehicle noise reduction method based on driving prediction provided in any embodiment of the present application.

[0016] In summary, the present application proposes a vehicle noise reduction method based on driving prediction. During the vehicle driving process, the method determines the current driving road type of the vehicle, and obtains the current vehicle speed and the operating status of each noise element, and then determines the predicted road noise characteristics through the current driving road type and the current vehicle speed, determines the predicted wind noise characteristics through the current vehicle speed, and determines the predicted mechanical noise characteristics through the operating status of each noise element. Finally, based on the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, an interference wave for reducing noise is applied inside the vehicle, thereby realizing noise prediction and noise reduction processing during vehicle driving. The method predicts the corresponding noise through relevant information of the vehicle, and adopts an active noise reduction strategy, which can avoid parsing the noise wave, solves the problem that the noise cannot be effectively interfered with in real time due to the high complexity of noise wave parsing, and applies interference waves that can effectively interfere with noise in real time, so as to timely reduce the user's noise perception. In addition, the method can reduce the road noise, wind noise and mechanical noise generated during vehicle driving by predicting and reducing the road noise, wind noise and mechanical noise, and further reduce the user's noise perception. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 is a flow chart of a vehicle noise reduction method based on driving prediction provided in an embodiment of the present application; Figure 2 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] As mentioned in the background technology, in response to the problems in the prior art, this application proposes a vehicle noise reduction method based on driving prediction. Figure 1 is a flow chart of a vehicle noise reduction method based on driving prediction provided by an embodiment of the present application. Figure 1 , the vehicle noise reduction method based on driving prediction specifically includes: S110: During vehicle driving, determine the current road type of the vehicle, and obtain the current speed of the vehicle and the operating status of each noise element in the vehicle.

[0022] In the embodiment of the present application, during the driving process of the vehicle, the type of the road surface on which the vehicle is currently traveling, that is, the current road surface type, can be detected in real time. The current road surface type includes but is not limited to gravel road surface (semi-rigid road surface), asphalt road surface (flexible road surface), cement road surface (rigid road surface), flat road surface, concave and convex road surface, dry road surface, wet and slippery road surface, icy and snowy road surface, etc.

[0023] Specifically, the type of the road surface on which the vehicle is currently traveling can be determined by visual recognition. For example, a radar detection unit facing the road surface can be installed on the vehicle, point cloud data of the road surface can be acquired through the radar detection unit, and the type of the road surface on which the vehicle is currently traveling can be identified based on the point cloud data; or a camera facing the road surface can be installed on the vehicle, image data of the road surface can be acquired through the camera, and the type of the road surface on which the vehicle is currently traveling can be identified based on the image data.

[0024] In addition to determining the current driving road surface type by visual recognition, the current driving road surface type can also be obtained by other methods. For example, the vehicle's navigation data can be obtained, and the vehicle's current driving road surface type can be determined based on the navigation data; or the vehicle's positioning information can be obtained and uploaded to the cloud, so that the cloud can query the corresponding road surface type based on the positioning information, and then the road surface type fed back by the cloud is used as the current driving road surface type.

[0025] In an embodiment of the present application, in order to predict road noise, the current road surface type can be determined and the current vehicle speed can be obtained; in order to predict wind noise, the current vehicle speed can be obtained; in order to predict mechanical noise, the operating status of each noise element in the vehicle can be obtained.

[0026] The noise element may be a device that generates noise when running in the vehicle. For example, the noise element includes but is not limited to an engine, a motor, an air conditioning compressor, a water pump, a fan, etc. The running state of the noise element may describe whether the noise element is running and the speed at which it is running.

[0027] Exemplarily, a request may be sent to a control unit associated with each noise element, so that the control unit associated with each noise element feeds back the operating status of the noise element.

[0028] S120: Determine predicted road noise characteristics of the vehicle based on the current driving road type and the current vehicle speed.

[0029] In the embodiment of the present application, considering that road noise is the noise generated by tire rotation and road surface, and road noise generated under different road surface types and different vehicle speeds is different, in order to accurately predict road noise, the predicted road noise characteristics can be determined based on the current road surface type and current vehicle speed. The predicted road noise characteristics may include predicted road noise amplitude and predicted road noise frequency.

[0030] In a specific implementation, determining the predicted road noise characteristics of the vehicle based on the current driving road type and the current vehicle speed includes: In a pre-constructed road noise database, the road noise characteristics corresponding to the current driving road surface type and the current vehicle speed are queried as the predicted road noise characteristics of the vehicle; wherein the road noise database is used to describe the corresponding relationship between each vehicle speed, each driving road surface type and each road noise characteristic.

[0031] Among them, the road noise characteristics of the vehicle at different vehicle speeds and different road surface types can be collected in advance, and then a road noise database describing the correspondence between each vehicle speed, each road surface type and each road noise characteristic can be constructed; the road noise database can be an offline sound wave database.

[0032] Specifically, the corresponding road noise characteristics can be queried in a pre-built road noise database according to the current driving road type and current vehicle speed to obtain the predicted road noise characteristics. It can be understood that if the current vehicle speed is 0, that is, the vehicle is stationary, the queried road noise characteristics can be 0.

[0033] Considering that the number of vehicle speeds and road surface types pre-collected in the road noise database is limited, it may be impossible to directly query the road noise characteristics corresponding to the current road surface type and the current vehicle speed from the road noise database. In this case, the road noise characteristics corresponding to other road surface types similar to the current road surface type can be queried from the road noise database, or the road noise characteristics corresponding to other vehicle speeds similar to the current vehicle speed can be queried, and then the predicted road noise characteristics can be determined by interpolation based on the queried values.

[0034] Through the above implementation, road noise prediction based on the road noise database can be achieved with high prediction efficiency, and the real-time performance of noise interference can be guaranteed.

[0035] S130: Determine predicted wind noise characteristics of the vehicle based on the current vehicle speed.

[0036] In the embodiment of the present application, considering that wind noise is the noise generated by the movement of the vehicle and the air, the wind noise generated at different vehicle speeds is different. Therefore, in order to accurately predict the wind noise, the predicted wind noise characteristics of the vehicle can be determined based on the current vehicle speed. The predicted wind noise characteristics can include the predicted wind noise amplitude and the predicted wind noise frequency.

[0037] In a specific implementation, determining the predicted wind noise characteristics of the vehicle based on the current vehicle speed includes: In a pre-built wind noise database, a wind noise feature corresponding to the current vehicle speed is searched as a predicted wind noise feature of the vehicle, wherein the wind noise database is used to describe the corresponding relationship between each vehicle speed and each wind noise feature; After determining the predicted wind noise characteristics of the vehicle based on the current vehicle speed, the method further includes: correcting the predicted wind noise characteristics based on the current ambient wind speed and current ambient wind direction of the vehicle.

[0038] Among them, the wind noise characteristics of the vehicle at different vehicle speeds can be collected in advance, and then a wind noise database describing the corresponding relationship between each vehicle speed and each wind noise characteristic is constructed.

[0039] Specifically, the corresponding wind noise feature can be queried in a pre-built wind noise database according to the current vehicle speed to obtain the predicted wind noise feature. It is understandable that if the current vehicle speed is 0, that is, the vehicle is stationary, the queried wind noise feature may be 0.

[0040] It should be noted that, considering that different vehicle models may generate different wind noises at the same vehicle speed, corresponding wind noise databases can be constructed in advance for different vehicle models. Before querying the corresponding wind noise characteristics based on the current vehicle speed, the wind noise database corresponding to the vehicle model can be screened out from all wind noise databases, and then the wind noise characteristics can be queried and predicted.

[0041] In addition, considering that wind noise is affected by the ambient wind speed and direction in addition to the vehicle speed, after obtaining the predicted wind noise characteristics corresponding to the current vehicle speed based on the wind noise database query, the predicted wind noise characteristics can be further corrected in combination with the current ambient wind speed and the current ambient wind direction. The current ambient wind speed and the current ambient wind direction can be detected by sensors installed on the vehicle or obtained through the cloud.

[0042] For example, according to a pre-established wind noise nonlinear fitting model, the current ambient wind speed, the current ambient wind direction, and the predicted wind noise characteristics determined based on the current vehicle speed can be input into the wind noise nonlinear fitting model to obtain the corrected predicted wind noise characteristics. Alternatively, according to a pre-established wind noise correction neural network model, the current ambient wind speed, the current ambient wind direction, and the predicted wind noise characteristics determined based on the current vehicle speed can be input into the wind noise correction neural network model to obtain the corrected predicted wind noise characteristics.

[0043] Through the above implementation, wind noise prediction based on vehicle speed can be achieved, and after predicting wind noise based on vehicle speed, it can be further corrected according to ambient wind speed and ambient wind direction to improve the accuracy of predicted wind noise, thereby ensuring the accuracy of subsequent noise reduction processing.

[0044] It should be noted that, with respect to the wind noise database, in the process of pre-building the wind noise database, due to the large speed range, there are many vehicle speed values, and it is difficult to cover all vehicle speeds in the test. Therefore, in the embodiment of the present application, a limited number of vehicle speeds can be tested first to obtain the corresponding wind noise characteristics, and then the wind noise characteristics of the derived vehicle speed can be determined. This can improve the construction efficiency of the wind noise database while ensuring that the wind noise database can fully cover all vehicle speeds, thereby ensuring the accuracy of subsequent wind noise predictions.

[0045] Optionally, the process of building a wind noise database includes the following steps: Step 11, testing the wind noise of the vehicle at each preset test speed to obtain the wind noise characteristics of each preset test speed; Step 12: determining the wind noise characteristics of the derived vehicle speed between the two adjacent preset test speeds based on the wind noise characteristics generated when the vehicle travels at different accelerations between the two adjacent preset test speeds; Step 13: construct a wind noise database according to the wind noise characteristics of each preset test vehicle speed and the wind noise characteristics of each derived vehicle speed.

[0046] In step 11, the wind noise characteristics of the vehicle at different preset test speeds can be obtained by simulation test or real vehicle test. The preset test speed can be a preset test point speed. As shown in Table 1, the wind noise characteristics of a limited number of preset test speeds are shown.

[0047] Table 1 Wind noise characteristics at a limited number of preset test speeds

[0048] Further, in step 12, for a derived vehicle speed between two adjacent preset test speeds, such as a derived vehicle speed of 11 km / h between 10 km / h and 12 km / h, the wind noise characteristics generated by the vehicle traveling at different accelerations between the two adjacent preset test speeds can be tested, that is, the wind noise characteristics generated in the process of testing the vehicle changing from one preset test speed to another preset test speed at different accelerations; for example, the wind noise characteristic_1, wind noise characteristic_2, ..., wind noise characteristic_n of the test vehicle changing from 41 km / h to 42 km / h at acceleration_1, acceleration_2, ..., acceleration_n respectively; and then the wind noise characteristics of the derived vehicle speed are determined through the wind noise characteristics tested at each acceleration.

[0049] It should be noted that the purpose of testing to obtain the wind noise characteristics generated by the vehicle traveling at different accelerations between two adjacent preset test speeds is: taking into account that there are certain differences in the force with which different drivers trigger the pedal, in order to ensure the accuracy of the wind noise characteristics of the derived speed, the wind noise characteristics of the vehicle in the process of changing from one preset test speed to another preset test speed at different accelerations can be used to simulate different drivers changing the speed with different pedal triggering forces or triggering depths, thereby achieving accurate determination of the wind noise characteristics of the derived speed.

[0050] With respect to the above step 12, optionally, based on various wind noise characteristics generated when the vehicle travels at different accelerations between two adjacent preset test speeds, determining the wind noise characteristics of a derived vehicle speed between two adjacent preset test speeds includes the following steps: Step 121: based on wind noise features generated when the vehicle travels at different accelerations between two adjacent preset test speeds, determine a noise feature intersection and a noise feature union between the wind noise features, wherein the noise feature intersection describes the overlapping frequency bands and corresponding amplitudes between the wind noise features, and the noise feature union describes the frequency bands and corresponding amplitudes after the wind noise features are merged; Step 122: Determine the wind noise characteristics of the derived vehicle speed between two adjacent preset test vehicle speeds based on the intersection of the noise characteristics and the union of the noise characteristics.

[0051] Among them, in step 121, the overlapping frequency bands and corresponding amplitudes between the wind noise characteristics can be determined according to the wind noise characteristics generated by the vehicle traveling at different accelerations between two adjacent preset test speeds to obtain the intersection of the noise characteristics, and the frequency bands and corresponding amplitudes after the wind noise characteristics are merged can be determined to obtain the union of the noise characteristics.

[0052] Furthermore, in step 122, the wind noise characteristics of the derived vehicle speed between the two adjacent preset test speeds may be calculated by using the noise characteristic intersection and the noise characteristic union of each wind noise characteristic generated when the vehicle changes between the two adjacent preset test speeds.

[0053] Exemplarily, considering that the frequency difference between wind noise features under different accelerations is small, the wind noise frequency in the wind noise feature derived from the vehicle speed may be the frequency average of the wind noise features under different accelerations.

[0054] In addition, the wind noise amplitude in the wind noise feature of the derived vehicle speed may be: derived vehicle speed wind noise AMP = noise feature intersection_AMP + maximum empirical amplitude corresponding to the frequency of the symmetric difference set / 3. Among them, the noise feature intersection_AMP refers to the amplitude in the intersection of the noise features, the symmetric difference set refers to the noise feature union minus the noise feature intersection, and the maximum empirical amplitude corresponding to the frequency of the symmetric difference set may be the maximum value of the frequency in the symmetric difference set in the wind noise feature under each acceleration.

[0055] Through the above steps 121 and 122, the wind noise characteristics of the derived vehicle speed between any two adjacent preset test speeds can be determined. By taking the intersection and union to calculate the wind noise characteristics of the derived vehicle speed, the accuracy of the wind noise characteristics of the derived vehicle speed can be guaranteed.

[0056] Furthermore, in step 13, a wind noise database may be constructed according to the wind noise characteristics of each preset test vehicle speed and the wind noise characteristics of each derived vehicle speed.

[0057] Through the above steps 11 to 13, a limited number of vehicle speeds are tested first to obtain the corresponding wind noise characteristics, and then the wind noise characteristics of the derived vehicle speeds are determined. This can improve the efficiency of building the wind noise database while ensuring that the wind noise database can fully cover all vehicle speeds, thereby ensuring the accuracy of subsequent wind noise prediction.

[0058] S140 . Determine a predicted mechanical noise characteristic of the vehicle based on the operating status of each noise element.

[0059] In the embodiment of the present application, considering that mechanical noise is the noise generated by the operation of noise elements in the vehicle, there are differences in the mechanical noise generated under different operating conditions. Therefore, in order to achieve accurate prediction of wind noise, the predicted mechanical noise characteristics of the vehicle can be determined based on the operating state of each noise element.

[0060] The predicted mechanical noise characteristics may include predicted mechanical noise amplitude and predicted mechanical noise frequency; the predicted mechanical noise characteristics may be composed of noise characteristics corresponding to each noise element and mixed noise characteristics between multiple noise elements.

[0061] Specifically, a mechanical noise database describing the correspondence between each operating state of each noise element and each mechanical noise feature can be pre-built, so that the corresponding mechanical noise feature can be queried in the mechanical noise database according to the operating state of each noise element at the current moment, thereby obtaining the predicted mechanical noise feature. It can be understood that if the speed of a noise element is 0, that is, it is not working, the mechanical noise feature obtained by querying the noise element can be 0.

[0062] S150: applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics.

[0063] Specifically, after obtaining the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, further, interference waves for reducing the three types of noise can be generated and applied according to the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics.

[0064] In a specific implementation, applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics includes: In a pre-constructed interference wave mapping feature library, interference wave features corresponding to predicted road noise features, predicted wind noise features, and predicted mechanical noise features are queried, wherein the interference wave mapping feature library is used to describe the correspondence between each road noise feature, each wind noise feature, each mechanical noise feature and each interference wave feature, and the interference wave feature includes an interference wave amplitude and an interference wave frequency; an interference wave corresponding to the queried interference wave feature is generated, and the interference wave is applied inside the vehicle to weaken the predicted road noise feature, the predicted wind noise feature, and the predicted mechanical noise feature.

[0065] Among them, the interference wave characteristics that weaken each noise characteristic can be determined in advance by simulating each road noise characteristic, each wind noise characteristic, and each mechanical noise characteristic, thereby constructing an interference wave mapping feature library that describes the correspondence between each road noise characteristic, each wind noise characteristic, each mechanical noise characteristic and each interference wave characteristic.

[0066] It should be noted that, considering that if all noise is completely offset for the user, it may affect the user's driving experience, causing the user to lose the ability to perceive the external driving environment, psychological panic or driving safety hazards. Therefore, in the embodiment of the present application, in the process of constructing the interference wave mapping feature library, for the determination of each interference wave feature, it is necessary to determine the characteristics of each interference wave with the goal of the frequency spectrum characteristics of the noise after the interference wave offsets the noise falling into the comfort range (such as 250~4000Hz) to avoid excessive interference.

[0067] Specifically, the corresponding interference wave features, that is, the interference wave amplitude and the interference wave frequency, can be obtained by querying in the interference wave mapping feature library according to the predicted road noise features, the predicted wind noise features, and the predicted mechanical noise features.

[0068] Furthermore, based on the interference wave amplitude and interference wave frequency obtained through the query, an interference wave for reducing the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics may be generated and applied.

[0069] In the embodiment of the present application, considering that there may be errors in the interference wave features in the interference wave mapping feature library, in order to further ensure the reliability of noise reduction, after applying the interference wave to interfere with the noise, the interference wave features in the interference wave mapping feature library can be continuously corrected according to the interference results, so that the noise reduction is more accurate and the adaptive adjustment of the interference wave mapping feature library is achieved.

[0070] Optionally, after applying the interference wave inside the vehicle, the method further includes: The post-interference measurement noise of the vehicle is collected based on a preset microphone device; based on the post-interference measurement noise, the interference wave amplitude and / or the interference wave frequency of the interference wave in the interference wave mapping feature library is adjusted.

[0071] Specifically, after applying the interference wave, the noise after the vehicle interference can be collected through the microphone device set in the vehicle to obtain the vehicle's post-interference measurement noise. Further, it can be determined whether the interference wave measurement noise is within the comfort range. If not, the interference wave amplitude and / or interference wave frequency of the interference wave in the interference wave mapping feature library can be corrected. In the process of correction, if the interference wave feature is inconsistent with the noise amplitude locally, the tip local spectrum feature of the interference wave can be processed using the empirical amplitude maximum value of the corresponding spectrum.

[0072] Exemplarily, if the interference wave measurement noise is outside the comfort range, it can be judged based on the interference wave measurement noise whether the applied interference wave is excessive or insufficient interference. If it is excessive interference, the interference wave amplitude and / or interference wave frequency of the interference wave in the interference wave mapping feature library can be reduced. If it is insufficient interference, the interference wave amplitude and / or interference wave frequency of the interference wave in the interference wave mapping feature library can be increased.

[0073] Through the above optional implementation manner, the interference wave features in the interference wave mapping feature library can be continuously optimized, and adaptive adjustment of the interference wave mapping feature library can be achieved, which can further improve the accuracy of noise interference.

[0074] In addition to adaptively optimizing the interference wave mapping feature library, in the embodiments of the present application, it is taken into account that there may be errors in the predicted noise, for example, there is no noise feature directly corresponding to the current vehicle speed or the current driving road type in the pre-constructed data (i.e., the road noise database, the mechanical noise database, and the wind noise database), which may lead to deviations between the noise features predicted by interpolation and other methods and the actual noise features. Therefore, in order to solve this technical problem, the noise features in the database can also be updated before the interference noise, so as to achieve the purpose of adaptively learning the database and further improve the accuracy of subsequent noise reduction processing.

[0075] In one example, before applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, the method further includes: Based on a preset microphone device, the pre-interference measurement noise of the vehicle is collected; based on the pre-interference measurement noise, at least one of the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics is adjusted, and the adjusted noise characteristics are synchronously updated to the corresponding database.

[0076] Specifically, after the road noise, wind noise and mechanical noise are predicted and before the interference wave is applied, the noise of the vehicle before the interference wave is applied can be collected by a microphone inside the vehicle, that is, the pre-interference measurement noise is obtained.

[0077] Furthermore, the fused noise can be determined based on the road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, and then based on the gap between the measured noise before interference and the fused noise, it is determined whether it is necessary to update at least one of the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics. For example, if the gap between the measured noise before interference and the fused noise is greater than a preset threshold, at least one of the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics is updated to adjust the prediction results based on the observation results, so as to achieve the purpose of optimizing the prediction results.

[0078] At the same time, the adjusted noise characteristics can also be synchronously updated to the corresponding database. For example, if the predicted road noise characteristics are adjusted, the adjusted predicted road noise characteristics are updated to the road noise database; if the predicted wind noise characteristics are adjusted, the adjusted predicted wind noise characteristics are updated to the wind noise database; if the predicted mechanical noise characteristics are adjusted, the adjusted predicted mechanical noise characteristics are updated to the mechanical noise database.

[0079] Exemplarily, the adjusted noise characteristics are synchronously updated to the corresponding database, which can be: determining whether the current vehicle speed, the current road surface type, and the operating status of each noise element exist in the corresponding database; if so, using the adjusted noise characteristics to replace the noise characteristics queried in the corresponding database; if not, adding the adjusted noise characteristics to the corresponding database.

[0080] Through the above examples, the prediction results can be adjusted based on the observation results before the interference noise, and the noise features in the database can be updated to achieve the purpose of adaptive learning of the database, solve the problem of noise reduction errors caused by the absence of some noise features in the database, and further improve the noise reduction accuracy.

[0081] In the embodiment of the present application, in addition to applying interference waves inside the vehicle to reduce road noise, wind noise and mechanical noise, noise can also be reduced by controlling the working point of the noise element or driving control.

[0082] In a specific implementation, before applying an interference wave for eliminating noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, the method further includes: Determine whether the current driving road type is a preset road type, and if so, control the vehicle driving and re-determine the predicted road noise characteristics of the vehicle; And / or, determine whether there is a single peak noise in the predicted mechanical noise characteristics, or whether there is mixed noise between different noise elements. If so, control the operating power of at least one noise element to change the operating state of the noise element and redetermine the predicted mechanical noise characteristics of the vehicle.

[0083] Specifically, in order to avoid the single peak noise from being excited, thereby causing the interference wave to have high amplitude characteristics, it is also possible to first determine whether the current driving road surface type is a preset road surface type; wherein the preset road surface type can be a pre-set road surface type with a larger road noise, such as a bumpy ground.

[0084] If the current driving surface type is the preset road type, the vehicle can be driven at this time, including but not limited to reducing the vehicle speed, increasing the air shock height of the CDC (Continuous Damping Control, automatic adjustment and uninterrupted shock absorption control system), and raising the vehicle suspension.

[0085] By reducing the vehicle's speed, the impact sound between the wheels and the pit can be actively reduced, thereby reducing road noise. By increasing the CDC's air shock absorber height, a larger shock absorption space can be reserved for the body and wheels, thereby reducing road noise. By raising the vehicle suspension, the contact between the chassis and the road can be reduced, thereby reducing road noise. It should be noted that after the vehicle is driven and controlled, the road noise will change, and the predicted road noise characteristics of the vehicle need to be re-determined.

[0086] In addition to driving control of the vehicle, in order to avoid the excitation of single peak noise and to avoid the superposition of mixed noise between multiple noise elements to trigger a larger noise peak, it is also possible to determine whether there is single peak noise (i.e. the noise amplitude of a certain noise element reaches the maximum) or mixed noise between different noise elements (i.e. the noise between at least two noise elements affects each other to produce mixed noise) in the predicted mechanical noise characteristics. For example, the air conditioning compressor and the engine may produce mixed noise at single noise frequencies within a certain frequency range.

[0087] If there is single peak noise or mixed noise in the predicted mechanical noise characteristics, the purpose of reducing mechanical noise can be achieved by controlling the working power of the noise element, such as reducing the engine speed or motor speed, etc. Of course, after the working power of the noise element changes, the operating state of the noise element will also change, and the mechanical noise will change. At this time, the predicted mechanical noise characteristics of the vehicle can be re-determined.

[0088] Through the above implementation, the purpose of noise reduction can be achieved by controlling the driving of the vehicle or controlling the noise elements before applying the interference wave, avoiding the high amplitude characteristics of the interference wave, thereby ensuring the accuracy of noise reduction and ensuring the sound comfort of the user.

[0089] The vehicle noise reduction method based on driving prediction provided in the embodiment of the present application determines the current road surface type of the vehicle during vehicle driving, and obtains the current vehicle speed and the operating status of each noise element, and then determines the predicted road noise characteristics through the current road surface type and the current vehicle speed, determines the predicted wind noise characteristics through the current vehicle speed, and determines the predicted mechanical noise characteristics through the operating status of each noise element. Finally, based on the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics, an interference wave for reducing noise is applied inside the vehicle, thereby realizing noise prediction and noise reduction processing during vehicle driving. The method predicts the corresponding noise through relevant information of the vehicle, and adopts an active noise reduction strategy, which can avoid parsing the noise wave, solves the problem that the noise wave parsing cannot be effectively interfered with in real time due to the high complexity of the noise wave, and applies the interference wave that can effectively interfere with the noise in real time, so as to timely reduce the user's noise perception.

[0090] Based on the same purpose, the embodiment of the present application also provides a vehicle noise reduction system based on driving prediction, which is composed of a radar detection unit, a mechanical noise prediction unit, a wind noise prediction unit, a road noise prediction unit and a central processing unit, wherein: A radar detection unit is used to determine the type of road surface the vehicle is currently traveling on during the driving process of the vehicle; a mechanical noise prediction unit, for determining a predicted mechanical noise characteristic of the vehicle based on the operating state of each noise element; a wind noise prediction unit, for determining a predicted wind noise characteristic of the vehicle based on a current vehicle speed; A road noise prediction unit, used to determine predicted road noise characteristics of the vehicle based on the current driving road type and the current vehicle speed; The central processing unit is used to apply interference waves for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics.

[0091] In some implementations, the road noise prediction unit is specifically configured to: In a pre-constructed road noise database, a road noise feature corresponding to the current driving road surface type and the current vehicle speed is queried as a predicted road noise feature of the vehicle; wherein the road noise database is used to describe the correspondence between each vehicle speed, each driving road surface type and each road noise feature.

[0092] In some implementations, the wind noise prediction unit is specifically configured to: In a pre-constructed wind noise database, a wind noise feature corresponding to the current vehicle speed is queried as the predicted wind noise feature of the vehicle, wherein the wind noise database is used to describe the correspondence between each vehicle speed and each wind noise feature; and after determining the predicted wind noise feature of the vehicle based on the current vehicle speed, the predicted wind noise feature is corrected based on the current ambient wind speed and the current ambient wind direction of the vehicle.

[0093] In some embodiments, the wind noise prediction unit is also used to construct a wind noise database, and the construction process of the wind noise database includes: testing the wind noise of the vehicle at each preset test speed to obtain the wind noise characteristics of each preset test speed; based on the wind noise characteristics generated by the vehicle traveling at different accelerations between two adjacent preset test speeds, determining the wind noise characteristics of a derived speed between two adjacent preset test speeds; and constructing a wind noise database based on the wind noise characteristics of each preset test speed and the wind noise characteristics of each derived speed.

[0094] In some implementations, the wind noise prediction unit is further configured to: Based on various wind noise characteristics generated when the vehicle travels at different accelerations between two adjacent preset test speeds, the noise characteristic intersection and the noise characteristic union between the wind noise characteristics are determined, wherein the noise characteristic intersection describes the overlapping frequency bands and corresponding amplitudes between the wind noise characteristics, and the noise characteristic union describes the frequency bands and corresponding amplitudes after the wind noise characteristics are merged; based on the noise characteristic intersection and the noise characteristic union, the wind noise characteristics of the derived vehicle speed between the two adjacent preset test speeds are determined.

[0095] In some embodiments, the central processor is further configured to: Determine whether the current driving road type is a preset road type, and if so, control the driving of the vehicle and redetermine the predicted road noise characteristics of the vehicle; and / or determine whether there is a single peak noise in the predicted mechanical noise characteristics, or there is mixed noise between different noise elements, and if so, control the operating power of at least one of the noise elements to change the operating state of the noise element, and redetermine the predicted mechanical noise characteristics of the vehicle.

[0096] In some embodiments, the central processor is further configured to: In a pre-constructed interference wave mapping feature library, the interference wave feature corresponding to the predicted road noise feature, the predicted wind noise feature and the predicted mechanical noise feature is queried, wherein the interference wave mapping feature library is used to describe the correspondence between each road noise feature, each wind noise feature, each mechanical noise feature and each interference wave feature, and the interference wave feature includes an interference wave amplitude and an interference wave frequency; an interference wave corresponding to the queried interference wave feature is generated, and the interference wave is applied inside the vehicle to weaken the predicted road noise feature, the predicted wind noise feature and the predicted mechanical noise feature.

[0097] In some embodiments, the vehicle noise reduction system based on driving prediction may further include a microphone device, the microphone device is used to collect the post-interference measurement noise and the pre-interference measurement noise of the vehicle; a central processor, further used to: The post-interference measurement noise of the vehicle is collected based on a preset microphone device; and based on the post-interference measurement noise, the interference wave amplitude and / or the interference wave frequency of the interference wave in the interference wave mapping feature library is adjusted.

[0098] In some embodiments, the central processor is further configured to: Based on a preset microphone device, the pre-interference measurement noise of the vehicle is collected; based on the pre-interference measurement noise, at least one of the predicted road noise characteristics, the predicted wind noise characteristics and the predicted mechanical noise characteristics is adjusted, and the adjusted noise characteristics are synchronously updated to the corresponding database.

[0099] The vehicle noise reduction system based on driving prediction provided in the embodiment of the present application can execute the steps in the vehicle noise reduction method based on driving prediction provided in the method embodiment of the present application, and the execution steps and beneficial effects are not repeated here.

[0100] Figure 2 Schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 2 As shown, the electronic device 400 includes one or more processors 401 and a memory 402 .

[0101] The processor 401 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0102] The memory 402 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 401 may run the program instructions to implement the vehicle noise reduction method based on driving prediction and / or other desired functions of any embodiment of the present application described above. Various contents such as initial external parameters, thresholds, etc. may also be stored in the computer-readable storage medium.

[0103] In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 403 may include, for example, a keyboard, a mouse, etc. The output device 404 may output various information to the outside, including early warning prompt information, braking force, etc. The output device 404 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0104] Of course, to simplify, Figure 2 Only some of the components related to the present application in the electronic device 400 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 400 may also include any other appropriate components.

[0105] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the vehicle noise reduction method based on driving prediction provided in any embodiment of the present application.

[0106] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0107] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the vehicle noise reduction method based on driving prediction provided in any embodiment of the present application.

[0108] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0109] It should be noted that the terms used in this application are only for describing specific embodiments, rather than limiting the scope of this application. As shown in the specification and claims of this application, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0110] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0111] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and its core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of this application, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the concept and technical solution of the invention to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A vehicle noise reduction method based on driving prediction, characterized in that: include: During vehicle driving, determining the type of road surface the vehicle is currently traveling on, and obtaining the current speed of the vehicle and the operating status of each noise element in the vehicle; Determining a predicted road noise characteristic of the vehicle based on the current driving road surface type and the current vehicle speed; determining a predicted wind noise characteristic of the vehicle based on the current vehicle speed; determining a predicted mechanical noise signature of the vehicle based on an operating state of each of the noise elements; An interference wave for reducing noise is applied inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics.

2. The method according to claim 1, characterized in that Determining the predicted road noise characteristics of the vehicle based on the current driving road type and the current vehicle speed includes: In a pre-built road noise database, querying a road noise feature corresponding to the current driving road type and the current vehicle speed as a predicted road noise feature of the vehicle; The road noise database is used to describe the corresponding relationship between each vehicle speed, each driving road surface type and each road noise feature.

3. The method according to claim 1, characterized in that: Determining a predicted wind noise characteristic of the vehicle based on the current vehicle speed includes: In a pre-built wind noise database, querying a wind noise feature corresponding to the current vehicle speed as the predicted wind noise feature of the vehicle, wherein the wind noise database is used to describe the corresponding relationship between each vehicle speed and each wind noise feature; After determining the predicted wind noise characteristic of the vehicle based on the current vehicle speed, the method further includes: The predicted wind noise characteristics are corrected based on the current ambient wind speed and the current ambient wind direction of the vehicle.

4. The method according to claim 3, characterized in that The construction process of the wind noise database includes: Testing the wind noise of the vehicle at each preset test speed to obtain the wind noise characteristics of each preset test speed; Determining the wind noise characteristics of a derived vehicle speed between the two adjacent preset test vehicle speeds based on various wind noise characteristics generated when the vehicle travels at different accelerations between two adjacent preset test vehicle speeds; A wind noise database is constructed based on the wind noise characteristics of each preset test vehicle speed and the wind noise characteristics of each derived vehicle speed.

5. The method according to claim 4, characterized in that Based on various wind noise characteristics generated when the vehicle travels at different accelerations between two adjacent preset test speeds, a wind noise characteristic of a derived vehicle speed between the two adjacent preset test speeds is determined, including: Based on various wind noise features generated when the vehicle travels at different accelerations between two adjacent preset test speeds, determining a noise feature intersection and a noise feature union between the various wind noise features, wherein the noise feature intersection describes the overlapping frequency bands and corresponding amplitudes between the various wind noise features, and the noise feature union describes the frequency bands and corresponding amplitudes after the wind noise features are merged; Based on the intersection of the noise features and the union of the noise features, a wind noise feature of a derived vehicle speed between two adjacent preset test vehicle speeds is determined.

6. The method according to claim 1, characterized in that Before applying an interference wave for eliminating noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, the method further includes: Determining whether the current driving road type is a preset road type, and if so, controlling the vehicle to drive and re-determining the predicted road noise characteristics of the vehicle; and / or, Determine whether there is a single peak noise in the predicted mechanical noise characteristics, or whether there is mixed noise between different noise elements. If so, control the operating power of at least one of the noise elements to change the operating state of the noise element and redetermine the predicted mechanical noise characteristics of the vehicle.

7. The method according to claim 1, characterized in that Applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, includes: In a pre-constructed interference wave mapping feature library, querying interference wave features corresponding to the predicted road noise features, the predicted wind noise features, and the predicted mechanical noise features, wherein the interference wave mapping feature library is used to describe the corresponding relationship between each road noise feature, each wind noise feature, each mechanical noise feature and each interference wave feature, and the interference wave feature includes an interference wave amplitude and an interference wave frequency; An interference wave corresponding to the queried interference wave feature is generated, and the interference wave is applied inside the vehicle to weaken the predicted road noise feature, the predicted wind noise feature, and the predicted mechanical noise feature.

8. The method according to claim 7, characterized in that After the interference wave is applied inside the vehicle, the method further includes: Collecting the interference-measured noise of the vehicle based on a preset microphone device; Based on the post-interference measurement noise, the interference wave amplitude and / or the interference wave frequency of the interference wave in the interference wave mapping feature library is adjusted.

9. The method according to claim 1, characterized in that: Before applying an interference wave for reducing noise inside the vehicle according to the predicted road noise characteristics, the predicted wind noise characteristics, and the predicted mechanical noise characteristics, the method further includes: Collecting the pre-interference measurement noise of the vehicle based on a preset microphone device; Based on the pre-interference measured noise, at least one of the predicted road noise feature, the predicted wind noise feature and the predicted mechanical noise feature is adjusted, and the adjusted noise feature is synchronously updated to the corresponding database.

10. An electronic device, characterized in that: The electronic device comprises: Processor and memory; The processor is used to execute the steps of the vehicle noise reduction method based on driving prediction as described in any one of claims 1 to 9 by calling the program or instruction stored in the memory.

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