Vehicle whistling sound adjusting method, system and device and readable storage medium
Through machine learning models, real-time environmental data is analyzed and vehicle horn sound wave parameters are dynamically adjusted, which solves the problem of vehicle horn sound interfering with residents' living and working environment, and realizes intelligent adjustment and effective control of horn sound.
Patent Information
- Application Number
- CN202411914530.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-24
- Publication Date
- 2025-04-11
AI Technical Summary
The sound of vehicle whistle sounds has significantly disturbed residents' quality of life and working environment, and the prior art has failed to effectively control the sound of whistle sounds.
The real-time environmental data is analyzed through a preset machine learning model, the real-time area where the target vehicle is located is determined, and the target sound parameters (such as loudness, tone, propagation direction) are dynamically adjusted based on this area to achieve intelligent adjustment of the vehicle's whistle sound.
Effective control of vehicle horn sound is achieved, reducing the interference of horn sound on residents' quality of life and working environment.
Smart Images
Figure CN119806465A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of noise control, and in particular to a method, system, device and readable storage medium for adjusting the sound of a vehicle horn. Background Art
[0002] As the number of vehicles increases year by year, traffic congestion and driving pressure gradually increase, resulting in increasingly serious noise pollution caused by vehicle horns. At present, relevant regulations only limit the time and frequency of horn honking, but do not make clear provisions for the specific control of horn sound.
[0003] The sound of vehicle horns has a high decibel level and strong penetrating power, especially in pedestrian, shop, residential, school and office areas in cities. The sound of horns often causes significant interference to residents and staff, seriously affecting the quality of life and working environment.
[0004] Therefore, how to effectively control the sound of whistles to improve the impact of whistle sounds on residents' quality of life and working environment is an issue that needs to be addressed urgently. Summary of the invention
[0005] The present application provides a vehicle horn sound adjustment method, system, device and readable storage medium, which can effectively control the horn sound to reduce the impact of the horn sound on residents' quality of life and working environment.
[0006] In a first aspect, an embodiment of the present application provides a method for adjusting the sound of a vehicle horn, the method comprising:
[0007] Analyze the real-time environmental data based on the preset machine learning model to obtain the real-time area where the target vehicle is located;
[0008] The target sound wave parameters are adjusted based on the real-time area to achieve the regulation of the vehicle horn sound.
[0009] In combination with the first aspect, in one implementation, analyzing the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located includes:
[0010] Based on the machine learning model, environmental features are extracted from the input real-time environmental data, and the real-time area where the target vehicle is located is determined based on the extracted target environmental features, wherein the target environmental features include road types, traffic signs, building density, pedestrian density, and weather conditions.
[0011] In combination with the first aspect, in one implementation, the target sound wave parameter includes at least one of a target sound wave loudness, a target sound wave pitch, and a target sound wave propagation direction.
[0012] In combination with the first aspect, in one implementation, the target sound wave parameter is a target sound wave loudness, and the adjusting the target sound wave parameter based on the real-time area includes:
[0013] If the real-time area is a noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset first loudness;
[0014] If the real-time area is a non-noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset second loudness, and the first loudness is lower than the second loudness.
[0015] In combination with the first aspect, in one implementation, the target sound wave parameter is a target sound wave pitch, and the adjusting the target sound wave parameter based on the real-time area includes:
[0016] Obtaining the number of pedestrians, pedestrian status, and target vehicle status in the real-time area;
[0017] If the number of pedestrians is less than a preset number threshold or the pedestrians are stationary, the target sound wave pitch is controlled and adjusted with a preset first pitch;
[0018] If the number of pedestrians is not less than a preset number threshold or the pedestrians are in a walking state or the target vehicle is in a lane changing state, the target sound wave tone is controlled and adjusted with a preset second tone;
[0019] If the pedestrian is in a fast-crossing state or a turning state or the target vehicle is in an overtaking state, the target sound wave tone is controlled and adjusted with a preset third tone, and the tones are arranged from small to large as follows: first tone, second tone, third tone.
[0020] In combination with the first aspect, in one implementation, the target sound wave parameter is a target sound wave propagation direction, and the adjusting the target sound wave parameter based on the real-time area includes:
[0021] Determine the target direction based on the target environment characteristics corresponding to the real-time area, wherein the target environment characteristics also include traffic flow, noise level, and road structure;
[0022] Using the target direction as the target sound wave propagation direction of the target vehicle;
[0023] Based on beamforming technology, the target vehicle's horn sound waves are directed toward the target direction.
[0024] In combination with the first aspect, in one implementation, the method further includes:
[0025] If the target vehicle is detected to be in a horn-prohibited area, the horn function of the target vehicle is controlled to be turned off;
[0026] If the target vehicle is detected to be in an emergency situation, the vehicle horn sound is controlled and adjusted at the maximum volume.
[0027] In a second aspect, an embodiment of the present application provides a vehicle horn sound adjustment system, the vehicle horn sound adjustment system comprising:
[0028] A first processing module, which is used to analyze the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located;
[0029] The second processing module is used to adjust the target sound wave parameters based on the real-time area to achieve the adjustment of the vehicle horn sound.
[0030] In a third aspect, an embodiment of the present application provides a vehicle horn sound adjustment device, which includes a processor, a memory, and a vehicle horn sound adjustment program stored in the memory and executable by the processor, wherein when the vehicle horn sound adjustment program is executed by the processor, the steps of the vehicle horn sound adjustment method as described in any of the preceding items are implemented.
[0031] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a vehicle horn sound adjustment program is stored. When the vehicle horn sound adjustment program is executed by a processor, the steps of the vehicle horn sound adjustment method as described in any of the preceding items are implemented.
[0032] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:
[0033] The real-time environment data is analyzed by a preset machine learning model to obtain the real-time area where the target vehicle is located; based on the real-time area, the target sound wave parameters are dynamically adjusted to achieve intelligent adjustment of the vehicle horn sound. This application combines real-time environment data with a machine learning model to intelligently adjust the sound wave parameters of the vehicle horn sound, thereby achieving effective control of the horn sound, thereby reducing the interference of the horn sound on the quality of life and working environment of residents. BRIEF DESCRIPTION OF THE DRAWINGS
[0034] Figure 1 This is a flow chart of an embodiment of a method for adjusting the sound of a vehicle horn according to the present application;
[0035] Figure 2 For this application Figure 1 Detailed flow chart of step S20;
[0036] Figure 3 This is a flow chart of the vehicle horn sound adjustment control under special circumstances in the embodiment of the present application;
[0037] Figure 4 This is a schematic diagram of the hardware structure of the vehicle horn sound adjustment device involved in the embodiment of the present application. DETAILED DESCRIPTION
[0038] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0039] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.
[0040] In a first aspect, an embodiment of the present application provides a method for adjusting the sound of a vehicle horn.
[0041] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of an embodiment of the vehicle horn sound adjustment method of the present application. Figure 1 As shown, the vehicle horn sound adjustment method includes:
[0042] Step S10: Analyze the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located.
[0043] For example, in the embodiment of the present application, the identification of the area where the vehicle is located will be realized through the trained machine learning model. The training process of the machine learning model will be described below:
[0044] Step M1: Data collection and preprocessing
[0045] Before training the machine learning model, the historical environmental data can be collected and processed through the vehicle's positioning system and environmental perception system; the positioning system uses GPS or Beidou satellite signals to obtain the target vehicle's precise location information such as longitude, latitude and altitude, and then combines this information with high-precision map data. It can not only identify the area where the vehicle is located (such as residential areas, schools, suburbs, highways, etc.), but also calculate and update the distance between the vehicle and the regional boundary in real time when the vehicle enters a special area (such as a residential area, school), ensuring the accuracy and timeliness of the positioning data. The environmental perception system collects real-time environmental data such as images, point cloud data (including distance information), and speed around the vehicle through the collaborative work of sensors such as cameras and radars. A series of operations such as data cleaning, formatting, synchronization, data fusion, feature extraction, normalization, enhancement and segmentation are performed on the environmental data obtained by the above positioning system and environmental perception system to achieve data preprocessing, thereby improving data quality; among them, data fusion algorithms such as Kalman filtering and particle filtering can be used to integrate heterogeneous data sources from different sensors (such as GPS, cameras, radars, etc.) to help the model make more accurate judgments in complex and dynamic traffic environments. It should be noted that the specific implementation methods and principles of data preprocessing and data fusion are common knowledge in this field, and for the sake of brevity of description, they will not be repeated here.
[0046] Step M2: Data annotation and feature extraction
[0047] Image recognition technology is used to finely annotate the collected image data to identify key elements such as pedestrians, other vehicles, traffic signs and obstacles. At the same time, the point cloud data is processed to extract key features such as the shape, size, and position of the object. Features that are crucial to driving decisions are extracted from image, point cloud, speed and other data, such as the object's speed, direction, road type, traffic flow, weather conditions, etc. The above features constitute the input data set for model training.
[0048] Step M3: Model training
[0049] The preprocessed and labeled environmental data is input into the machine learning model for training. The machine learning model continuously optimizes its perception and decision-making capabilities by repeatedly learning the features and patterns in different environments to achieve accurate identification of environmental areas. During the training process, the internal parameters of the machine learning model will be continuously adjusted to minimize the prediction error, thereby improving recognition accuracy and decision accuracy.
[0050] Specifically, by applying the trained machine learning model, real-time environmental data can be analyzed in real time, so as to accurately identify the area where the target vehicle is currently located based on the real-time environmental data; this process relies on the machine learning model's learning and understanding of various environmental characteristics, such as traffic flow, road type, weather conditions, etc., so that it can dynamically adapt to and process the input real-time environmental data in real time. The machine learning model can not only distinguish different geographical areas, but also quickly determine the vehicle's location based on the real-time changing environmental data, providing a basis for further decision-making.
[0051] Step S20: Adjust the target sound wave parameters based on the real-time area to adjust the vehicle horn sound.
[0052] By way of example, it should be understood that a car horn usually generates sound through electromagnetic vibration, and its volume is closely related to sound wave parameters (such as loudness, pitch, and propagation direction). Therefore, the sound of the vehicle horn can be adjusted by adjusting the sound wave parameters. In addition, the propagation of sound waves is affected by environmental factors, that is, different areas (such as highways, residential areas, school surroundings, etc.) have different environmental noise levels and tolerance to noise. Therefore, the sound wave parameters of the vehicle horn can be dynamically adjusted and optimized according to the real-time area where the target vehicle is located, so as to achieve the purpose of effectively warning surrounding pedestrians and vehicles without causing unnecessary noise interference.
[0053] The present application uses a preset machine learning model to analyze real-time environmental data to obtain the real-time area where the target vehicle is located; based on the real-time area, the target sound wave parameters are dynamically adjusted to achieve intelligent adjustment of the vehicle horn sound. The present application combines real-time environmental data with a machine learning model to intelligently adjust the sound wave parameters of the vehicle horn sound, thereby achieving effective control of the horn sound, thereby reducing the interference of the horn sound on the quality of life and working environment of residents.
[0054] Furthermore, in one embodiment, the analyzing of the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located includes:
[0055] Based on the machine learning model, environmental features are extracted from the input real-time environmental data, and the real-time area where the target vehicle is located is determined based on the extracted target environmental features, wherein the target environmental features include road types, traffic signs, building density, pedestrian density, and weather conditions.
[0056] Exemplarily, in an embodiment of the present application, after acquiring real-time environmental data around the vehicle, these real-time data are processed and analyzed through a machine learning model to extract key environmental features such as road type, traffic signs, building density, pedestrian density, weather conditions, etc.
[0057] It should be understood that the extracted environmental features can be trained and classified through machine learning models (such as support vector machines, random forests or deep neural network classification algorithms). It should be noted that if the support vector machine is selected as the classification algorithm, it will help to distinguish between high-noise and low-noise environments; if the random forest is selected as the classification algorithm, it can integrate multiple environmental features and adapt to different scenarios, thereby giving the most appropriate whistle strategy; if the deep neural network is selected as the classification algorithm, it can achieve multi-level learning and in-depth analysis of complex environmental data.
[0058] It can be understood that, assuming that a machine learning model has been trained with historical environmental feature data, the machine learning model can map different environmental features to specific area types; specifically, based on the trained machine learning model, environmental data can be collected in real time and input into the machine learning model. The machine learning model first extracts features from the real-time environmental data to identify and classify key environmental features in the surrounding environment, such as road types, traffic signs, building density, pedestrian density, weather conditions, etc.; then, based on these extracted environmental features, the specific area where the target vehicle is located is determined, such as cities, suburbs, schools, hospitals, commercial areas, highways or intersections, etc.; for example, by identifying road types and traffic signs, it can be determined whether the vehicle is in a specific area such as a school, hospital or commercial area; by identifying pedestrian density and building density, urban environments and suburban environments can be distinguished.
[0059] Furthermore, in one embodiment, the target sound wave parameter includes at least one of a target sound wave loudness, a target sound wave pitch, and a target sound wave propagation direction.
[0060] For example, in the embodiments of the present application, loudness is the subjective feeling of the human ear to the strength of sound, which is usually related to the amplitude of the sound wave. The larger the amplitude, the greater the loudness; the smaller the amplitude, the smaller the loudness; in addition, loudness is also related to the propagation distance of the sound. The farther from the sound source, the smaller the loudness of the sound; the unit of loudness is sone, and 1 sone is the loudness of a pure tone corresponding to a frequency of 1kHz and a sound pressure level of 40dB. Pitch refers to the height of the sound perceived by the human ear, which is mainly determined by the frequency of the sound. The higher the frequency, the higher the pitch; the lower the frequency, the lower the pitch. The hearing range of the human ear is approximately between 20Hz and 20kHz; the propagation direction describes the spatial directionality of the propagation of the sound wave and determines the propagation path of the sound from the source to the receiving point.
[0061] Specifically, target sound wave parameters such as target sound wave loudness, target sound wave pitch, and target sound wave propagation direction affect the overall characteristics of the sound wave and determine its performance and perception effect in a specific environment. Therefore, when adjusting the target sound wave parameters to adjust the vehicle horn sound, at least one of the above-mentioned loudness, pitch, and propagation direction should be adjusted.
[0062] Further, in one embodiment, referring to Figure 2 As shown, the target sound wave parameter is the target sound wave loudness, and the adjusting the target sound wave parameter based on the real-time area includes:
[0063] Step S201: If the real-time area is a noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset first loudness;
[0064] Step S202: If the real-time area is a non-noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset second loudness, and the first loudness is lower than the second loudness.
[0065] Exemplarily, in the embodiment of the present application, the specific values of the preset first loudness and the preset second loudness can be determined according to actual needs and are not limited here. It is only necessary to ensure that the first loudness is always lower than the second loudness, thereby ensuring that the sound wave output in the noise-sensitive area is not too strong, thereby achieving the effect of environmental protection and noise control.
[0066] Specifically, when the adjusted target sound wave parameter is the target sound wave loudness, if the real-time area is determined to be a noise-sensitive area (such as a residential area, school, hospital, etc.), the loudness of the target sound wave needs to be adjusted and controlled according to a preset first loudness, and the loudness value at this time is relatively low to avoid causing excessive noise interference to the surrounding environment; if the real-time area is a non-noise-sensitive area (such as suburbs, highways and other open areas), the loudness of the target sound wave can be adjusted according to a preset second loudness, and the loudness value at this time is relatively high, allowing a certain degree of noise to exist to meet the usage needs of the area.
[0067] Further, in one embodiment, the target sound wave parameter is a target sound wave pitch, and the adjusting the target sound wave parameter based on the real-time region includes:
[0068] Obtaining the number of pedestrians, pedestrian status, and target vehicle status in the real-time area;
[0069] If the number of pedestrians is less than a preset number threshold or the pedestrians are stationary, the target sound wave pitch is controlled and adjusted with a preset first pitch;
[0070] If the number of pedestrians is not less than a preset number threshold or the pedestrians are in a walking state or the target vehicle is in a lane changing state, the target sound wave tone is controlled and adjusted with a preset second tone;
[0071] If the pedestrian is in a fast-crossing state or a turning state or the target vehicle is in an overtaking state, the target sound wave tone is controlled and adjusted with a preset third tone, and the tones are arranged from small to large as follows: first tone, second tone, third tone.
[0072] Exemplarily, in the embodiment of the present application, the specific values of the preset first tone, the preset second tone, and the preset third tone can be determined according to actual needs and are not limited here. It is only necessary that the tones are sorted from small to large as follows: first tone, second tone, third tone; the specific value of the preset quantity threshold can be determined according to actual needs and is not limited here.
[0073] Specifically, in the real-time area, when the target sound wave parameter to be adjusted is the target sound wave tone, the sound wave tone is dynamically adjusted by obtaining the number of pedestrians, the pedestrian status and the target vehicle status; when the number of pedestrians is less than the preset number threshold or the pedestrians are stationary, the system will adjust the target sound wave tone to a lower first tone to avoid unnecessary interference to the environment; when the number of pedestrians is greater than or equal to the preset number threshold or the pedestrians are walking or the target vehicle is changing lanes, the system will adjust the target sound wave tone to a second tone to ensure that the sound wave tone can be transmitted more clearly and effectively in this more complex environment; if the pedestrians are in a fast crossing state, a turning state or the target vehicle is in an overtaking state, considering the above-mentioned highly dynamic environment, the target sound wave tone can be adjusted to a higher third tone. The entire process of adjusting the target sound wave tone is to respond to the specific behaviors of pedestrians and vehicles in the environment, and the order of tone changes is: the first tone is the lowest, the second tone is moderate, and the third tone is the highest.
[0074] Further, in one embodiment, the target sound wave parameter is a target sound wave propagation direction, and the adjusting the target sound wave parameter based on the real-time area includes:
[0075] Determine the target direction based on the target environment characteristics corresponding to the real-time area, wherein the target environment characteristics also include traffic flow, noise level, and road structure;
[0076] Using the target direction as the target sound wave propagation direction of the target vehicle;
[0077] Based on beamforming technology, the target vehicle's horn sound waves are directed toward the target direction.
[0078] For example, in the embodiment of the present application, traffic flow will affect the efficiency of sound wave propagation, the noise level determines the clarity and recognizability of the sound, and the road structure provides information about obstacles that may be encountered during sound wave propagation; the target direction refers to the direction in which the target vehicle needs to emit the horn sound waves, that is, the target vehicle adjusts the emission angle of its horn according to the environmental characteristics to ensure that the sound waves are propagated in a directionally directed manner to where they are most needed.
[0079] Specifically, the best angle for sending sound waves is selected by analyzing the road structure (such as curvature, slope, etc.). For example, if a vehicle is turning, the sound emission direction needs to be adjusted to ensure that the sound can penetrate the curve and be transmitted to other upcoming vehicles; in combination with traffic flow and noise level, the sound propagation direction is adjusted in areas with large traffic to ensure that the target sound can penetrate the background noise, that is, the current road conditions are analyzed through the above-mentioned environmental characteristics such as traffic flow, noise level and road structure, and the best target direction is determined to ensure that the horn sound waves of the target vehicle can be transmitted as efficiently and clearly as possible; after determining the target direction, it is used as the sound wave propagation direction of the target vehicle's horn, and precise directional transmission is achieved through beamforming technology; it should be noted that the implementation method and principle of beamforming technology are common knowledge in the field, and for the sake of brevity of description, they are not elaborated here.
[0080] It should be noted that the embodiments of the present application adjust the propagation direction of the sound waves to ensure that the sound is concentrated in the selected direction (i.e., the target direction), thereby effectively avoiding the waste of sound waves and unnecessary noise interference. For example, when the target vehicle is traveling on a densely trafficked urban road, the system can dynamically adjust the propagation path of the whistle sound waves according to the traffic flow to ensure that the sound is clearly transmitted to the pedestrians or vehicles in front, thereby improving driving safety and improving the comfort of the surrounding environment; and when the target vehicle approaches noise-sensitive areas such as schools, hospitals, and residential areas, the system will automatically adjust the propagation direction of the whistle according to the real-time traffic flow and the surrounding noise level, so that the sound waves are emitted in the opposite direction of the noise-sensitive area to avoid excessive noise interference.
[0081] Further, in one embodiment, referring to Figure 3 As shown, the method also includes:
[0082] Step P10: If it is detected that the target vehicle is in a horn-prohibited area, the horn function of the target vehicle is controlled to be turned off;
[0083] Step P20: If it is detected that the target vehicle is in an emergency situation, the vehicle horn sound is controlled and adjusted at the maximum loudness.
[0084] Exemplarily, in an embodiment of the present application, a positioning system and an environmental perception system are combined to compare the vehicle position with the map data of the prohibited honking area. If the target vehicle is detected to enter the prohibited honking area, the current environmental restrictions will be automatically identified and the vehicle's honking function will be immediately turned off to comply with relevant traffic regulations or noise control regulations to ensure that no unnecessary interference is caused to the surrounding environment; radar and emergency brake assist systems are combined to detect whether the target vehicle is in an emergency such as a collision. If the target vehicle is detected to be in an emergency, the system will immediately adjust the loudness of the horn to the maximum to ensure that the sound is loud enough to attract the attention of surrounding drivers and pedestrians, thereby improving safety. At this time, the vehicle's horn sound will be intelligently adjusted to maximize its penetration and effectiveness, ensuring that the emergency can be responded to and handled in a timely manner. It should be understood that the identification methods and principles of honking areas and emergency situations are common knowledge in the field, and for the sake of brevity of description, they will not be repeated here.
[0085] It should be noted that the specific implementation scheme of the embodiment of the present application may include the following aspects:
[0086] Hardware deployment: Multiple microphones and speakers are installed on the target vehicle to form an array to achieve directional emission of sound waves.
[0087] Software implementation: Develop a central control software that can receive sensor data in real time, perform environmental analysis, formulate horn strategies, and control hardware devices.
[0088] User Interface: Provide a user interface to display the current horn status, including the loudness, pitch and propagation direction of the sound wave, and allow the driver to manually override the automatic settings in special circumstances or adjust the horn parameters according to personal preferences.
[0089] Feedback mechanism: The system can collect driver feedback and environmental change data, and continuously optimize the horn strategy to ensure optimal road safety and environmental protection.
[0090] In a second aspect, the present application also provides a vehicle horn sound adjustment system. In one embodiment, the vehicle horn sound adjustment system includes:
[0091] A first processing module, which is used to analyze the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located;
[0092] The second processing module is used to adjust the target sound wave parameters based on the real-time area to achieve the adjustment of the vehicle horn sound.
[0093] Furthermore, in one embodiment, the first processing module is specifically configured to:
[0094] Based on the machine learning model, environmental features are extracted from the input real-time environmental data, and the real-time area where the target vehicle is located is determined based on the extracted target environmental features, wherein the target environmental features include road types, traffic signs, building density, pedestrian density, and weather conditions.
[0095] Furthermore, in one embodiment, the second processing module is specifically configured to:
[0096] The target sound wave parameters include at least one of target sound wave loudness, target sound wave pitch, and target sound wave propagation direction.
[0097] Furthermore, in one embodiment, the second processing module is further configured to:
[0098] If the real-time area is a noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset first loudness;
[0099] If the real-time area is a non-noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset second loudness, and the first loudness is lower than the second loudness.
[0100] Furthermore, in one embodiment, the second processing module is further configured to:
[0101] Obtaining the number of pedestrians, pedestrian status, and target vehicle status in the real-time area;
[0102] If the number of pedestrians is less than a preset number threshold or the pedestrians are stationary, the target sound wave pitch is controlled and adjusted with a preset first pitch;
[0103] If the number of pedestrians is not less than a preset number threshold or the pedestrians are in a walking state or the target vehicle is in a lane changing state, the target sound wave tone is controlled and adjusted with a preset second tone;
[0104] If the pedestrian is in a fast-crossing state or a turning state or the target vehicle is in an overtaking state, the target sound wave tone is controlled and adjusted with a preset third tone, and the tones are arranged from small to large as follows: first tone, second tone, third tone.
[0105] Furthermore, in one embodiment, the second processing module is further configured to:
[0106] Determine the target direction based on the target environment characteristics corresponding to the real-time area, wherein the target environment characteristics also include traffic flow, noise level, and road structure;
[0107] Using the target direction as the target sound wave propagation direction of the target vehicle;
[0108] Based on beamforming technology, the target vehicle's horn sound waves are directed toward the target direction.
[0109] Furthermore, in one embodiment, the second processing module is further configured to:
[0110] If the target vehicle is detected to be in a horn-prohibited area, the horn function of the target vehicle is controlled to be turned off;
[0111] If the target vehicle is detected to be in an emergency situation, the vehicle horn sound is controlled and adjusted at the maximum volume.
[0112] The present application uses a preset machine learning model to analyze real-time environmental data to obtain the real-time area where the target vehicle is located; based on the real-time area, the target sound wave parameters are dynamically adjusted to achieve intelligent adjustment of the vehicle horn sound. The present application combines real-time environmental data with a machine learning model to intelligently adjust the sound wave parameters of the vehicle horn sound, thereby achieving effective control of the horn sound, thereby reducing the interference of the horn sound on the quality of life and working environment of residents.
[0113] Among them, the functional implementation of each module in the above-mentioned vehicle horn sound adjustment system corresponds to the various steps in the above-mentioned vehicle horn sound adjustment method embodiment, and its functions and implementation processes will not be repeated here one by one.
[0114] In a third aspect, an embodiment of the present application provides a vehicle horn sound adjustment device, which may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.
[0115] Reference Figure 4 , Figure 4 Schematic diagram of the hardware structure of the vehicle horn sound adjustment device involved in the embodiment of the present application. In the embodiment of the present application, the vehicle horn sound adjustment device may include a processor, a memory, a communication interface and a communication bus.
[0116] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.
[0117] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the components inside the vehicle horn sound adjustment device, and an interface used to interconnect the vehicle horn sound adjustment device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.
[0118] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.
[0119] The processor may be a general-purpose processor, which may call the vehicle horn sound adjustment program stored in the memory and execute the vehicle horn sound adjustment method provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the vehicle horn sound adjustment program is called may refer to the various embodiments of the vehicle horn sound adjustment method of the present application, which will not be repeated here.
[0120] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.
[0121] In a fourth aspect, an embodiment of the present application also provides a readable storage medium.
[0122] A vehicle horn sound adjustment program is stored on the readable storage medium of the present application, wherein when the vehicle horn sound adjustment program is executed by the processor, the steps of the vehicle horn sound adjustment method as described above are implemented.
[0123] Among them, the method implemented when the vehicle horn sound adjustment program is executed can refer to the various embodiments of the vehicle horn sound adjustment method of the present application, and will not be repeated here.
[0124] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.
[0125] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.
[0126] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.
[0127] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0128] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.
[0129] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.
[0130] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.
Claims
1. A method for adjusting the sound of a vehicle horn, characterized in that: The vehicle whistle sound adjustment method comprises: Analyze the real-time environmental data based on the preset machine learning model to obtain the real-time area where the target vehicle is located; The target sound wave parameters are adjusted based on the real-time area to achieve the regulation of the vehicle horn sound.
2. The vehicle horn sound adjustment method according to claim 1, characterized in that: The real-time environment data is analyzed based on a preset machine learning model to obtain the real-time area where the target vehicle is located, including: Based on the machine learning model, environmental features are extracted from the input real-time environmental data, and the real-time area where the target vehicle is located is determined based on the extracted target environmental features, wherein the target environmental features include road types, traffic signs, building density, pedestrian density, and weather conditions.
3. The vehicle horn sound adjustment method according to claim 1, characterized in that: The target sound wave parameters include at least one of target sound wave loudness, target sound wave pitch, and target sound wave propagation direction.
4. The vehicle horn sound adjustment method according to claim 3, characterized in that: The target sound wave parameter is a target sound wave loudness, and the adjusting the target sound wave parameter based on the real-time area includes: If the real-time area is a noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset first loudness; If the real-time area is a non-noise-sensitive area, the target sound wave loudness is controlled and adjusted with a preset second loudness, and the first loudness is lower than the second loudness.
5. The vehicle horn sound adjustment method according to claim 3, characterized in that: The target sound wave parameter is a target sound wave pitch, and the adjusting the target sound wave parameter based on the real-time area includes: Obtaining the number of pedestrians, pedestrian status, and target vehicle status in the real-time area; If the number of pedestrians is less than a preset number threshold or the pedestrians are stationary, the target sound wave pitch is controlled and adjusted with a preset first pitch; If the number of pedestrians is not less than a preset number threshold or the pedestrians are in a walking state or the target vehicle is in a lane changing state, the target sound wave tone is controlled and adjusted with a preset second tone; If the pedestrian is in a fast-crossing state or a turning state or the target vehicle is in an overtaking state, the target sound wave tone is controlled and adjusted with a preset third tone, and the tones are arranged from small to large as follows: first tone, second tone, third tone.
6. The vehicle horn sound adjustment method according to claim 3, characterized in that: The target sound wave parameter is a target sound wave propagation direction, and the adjusting the target sound wave parameter based on the real-time area includes: Determine the target direction based on the target environment characteristics corresponding to the real-time area, wherein the target environment characteristics also include traffic flow, noise level, and road structure; Using the target direction as the target sound wave propagation direction of the target vehicle; Based on beamforming technology, the target vehicle's horn sound waves are directed toward the target direction.
7. The vehicle horn sound adjustment method according to claim 1, characterized in that: The method further comprises: If the target vehicle is detected to be in a horn-prohibited area, the horn function of the target vehicle is controlled to be turned off; If the target vehicle is detected to be in an emergency situation, the vehicle horn sound is controlled and adjusted at the maximum volume.
8. A vehicle horn sound adjustment system, characterized in that: The vehicle whistle sound adjustment system comprises: A first processing module, which is used to analyze the real-time environmental data based on a preset machine learning model to obtain the real-time area where the target vehicle is located; The second processing module is used to adjust the target sound wave parameters based on the real-time area to achieve the adjustment of the vehicle horn sound.
9. A vehicle horn sound adjustment device, characterized in that: The vehicle whistle sound adjustment device includes a processor, a memory, and a vehicle whistle sound adjustment program stored in the memory and executable by the processor, wherein when the vehicle whistle sound adjustment program is executed by the processor, the steps of the vehicle whistle sound adjustment method as described in any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a vehicle whistle sound adjustment program, wherein when the vehicle whistle sound adjustment program is executed by the processor, the steps of the vehicle whistle sound adjustment method according to any one of claims 1 to 7 are implemented.