CN114162042A - Self-adaptive vehicle horn developed based on BP neural network - Google Patents
Self-adaptive vehicle horn developed based on BP neural network Download PDFInfo
- Publication number
- CN114162042A CN114162042A CN202111673726.8A CN202111673726A CN114162042A CN 114162042 A CN114162042 A CN 114162042A CN 202111673726 A CN202111673726 A CN 202111673726A CN 114162042 A CN114162042 A CN 114162042A
- Authority
- CN
- China
- Prior art keywords
- neural network
- module
- vehicle
- nodes
- horn
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q5/00—Arrangement or adaptation of acoustic signal devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
- B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
- B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
- B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Mechanical Engineering (AREA)
- Acoustics & Sound (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to the technical field of vehicle-mounted devices, in particular to a self-adaptive vehicle horn developed based on a BP neural network, which comprises an environmental information acquisition module, an integrated processing information module and an execution device, wherein the environmental information acquisition module comprises a noise acquisition module, a GPS positioning module and a road sign module, the integrated processing information module adopts a vehicle control unit VCU, the execution device is an existing vehicle horn control device, the vehicle control unit VCU comprises the BP neural network module, and when a vehicle runs on a noisy road, the situation of whistle is occasionally required, the invention firstly carries out information acquisition, preliminarily confirms the position of the vehicle through the GPS positioning module, further confirms the position of the vehicle in combination with road sign information acquired by a front camera of the vehicle, carries out current environmental noise level evaluation through the noise acquisition module, transmits the information to the VCU through a CAN bus after the environmental evaluation is finished, and the loudspeaker sound range and the allowed sound volume are output after calculation through a BP neural network.
Description
Technical Field
The invention relates to the technical field of vehicle-mounted devices, in particular to a self-adaptive vehicle horn developed based on a BP neural network.
Background
The horn is an audible signal device of an automobile. During the driving process of the automobile, a driver sends out necessary sound signals according to needs and regulations to warn pedestrians and attract the attention of other vehicles, so that the traffic safety is ensured, and meanwhile, the driver is also used for urging and transmitting signals.
Today, automobile horns have become a diverse mood that can be expressed by horns whether you respect or anger. You can express thank you by whistling when a car gives you a good way. Of course, if there is a car that blocks the direction you are heading, you can also alert the other party by whistling. However, the existing automobile horn is often loud, and the whistle can cause great influence on other people in quiet environments such as residential areas, late nights, hospitals and the like.
With the acceleration of life rhythm and the power promotion of loudspeaker, the noise influence that causes increases gradually, and it is not obvious to rely on moral restraint driver to restrict the effect of whistling. This situation is mainly caused by: the rhythm of life is accelerated, and the emotion of the driver is abreacted to the requirement of the current environment; the driver can not pay attention to various tinnitus forbidding requirements of the current environment; if the environment is noisy, too little horn volume does not play a desirable role.
Chinese patent No. CN201110092184.5 discloses an automatic volume adjusting system for car horn and a method and device thereof, which have the following disadvantages: the sound volume of the automobile whistle cannot be automatically controlled in a specific area and time period, and meanwhile, the horn decibel cannot be regulated and controlled after actively intervening in a system when danger is met.
Disclosure of Invention
In order to solve the technical problems, the invention discloses an adaptive vehicle horn developed based on a BP neural network, which can adaptively adjust the volume and the tone according to the position of a region and the noise degree of the surrounding environment.
The invention adopts the following specific technical scheme:
the utility model provides an adaptive vehicle horn based on development of BP neural network, includes environmental information collection module, integrated processing information module and executing device, and environmental information collection module includes noise collection module, GPS orientation module, road sign module, and integrated processing information module adopts vehicle control unit VCU, and executing device is current vehicle horn controlling means, and vehicle control unit VCU contains BP neural network module.
The invention further improves the BP neural network module, wherein the BP neural network module comprises a neural network input layer, a neural network output layer, the number of layers of the neural network and the number of nodes of the hidden layer, the input parameters of the neural network input layer are environmental noise, a distance from a sound limiting area and an accessory crowd vexation index, the neural network output layer outputs the proper loudspeaker volume and the selection of high-low loudspeakers which can generate an actual prompt effect and cannot be polluted by noise in the current environment, the number of layers of the neural network and the number of nodes of the hidden layer are preliminarily set through an empirical formula and then are continuously corrected to obtain the optimal number of layers and the optimal number of nodes.
The invention further improves that the optimal selection formula of the number of layers of the neural network and the number of nodes of the hidden layer is as follows:
k is the number of samples;
n1-the optimal number of hidden layers;
n is the number of input nodes;
m is the number of output nodes;
a is a constant between 1 and 10.
The specific working process of the invention is as follows: when a vehicle runs on a noisy road, the situation that whistle is needed occasionally is met, firstly, information acquisition is carried out, the position of the vehicle is preliminarily confirmed through a GPS positioning module, the requirement of sound prohibition of the position where the vehicle is located is further confirmed through combination of road sign information acquired by a front camera of the vehicle, the noise acquisition module carries out current environmental noise level evaluation, after the environmental evaluation is completed, the information is transmitted to a VCU through a CAN bus, and a horn range and allowed volume are output after calculation through a BP neural network.
The invention has the beneficial effects that: the method and the device can confirm whether the vehicle is in the whistling volume limiting area to adaptively reduce the volume of the horn according to the position of the vehicle on a map and the road identification, can detect the noise degree of the environment, judge whether the volume of the horn can achieve the effect, and adaptively increase the volume of the horn to meet the warning requirement (not exceeding the national standard). The invention is provided with the high pitch loudspeaker and the low pitch loudspeaker at the same time, can activate the proper loudspeaker according to the actual requirement, and can reasonably adjust the volume of the loudspeaker through the device in the complex road environment. The invention can effectively solve the problem that the vehicle horn uses high-pitch whistle in improper occasions and time, and a vehicle owner can use the horn with ease.
Drawings
Fig. 1 is a schematic structural view of the present invention.
Fig. 2 is a schematic diagram of a BP neural network module.
FIG. 3 is a schematic diagram of a trained BP neural network module according to the present invention.
Detailed Description
For the purpose of enhancing the understanding of the present invention, the present invention will be described in further detail with reference to the accompanying drawings and examples, which are provided for the purpose of illustration only and are not intended to limit the scope of the present invention.
Example (b): as shown in fig. 1, an adaptive vehicle horn developed based on a BP neural network includes an environmental information collection module, an integrated processing information module and an execution device, wherein the environmental information collection module includes a noise collection module, a GPS positioning module and a road sign module, the integrated processing information module adopts a vehicle control unit VCU, the execution device is an existing vehicle horn control device, and the vehicle control unit VCU includes a BP neural network module.
The vehicle control unit VCU serving as the comprehensive processing information module is responsible for processing all information and making a decision according to a judgment criterion (including a trained BP neural network module for information processing), the environment monitoring module collects environment information and transmits the environment information to the VCU for processing through an electric signal, and the VCU activates the loudspeaker volume adjusting module through the electric signal after making the decision to change the volume or the type of the loudspeaker.
The BP neural network module comprises a neural network input layer, a neural network output layer, the number of layers of the neural network and the number of nodes of the hidden layer, input parameters of the neural network input layer are environmental noise, a distance from a sound limiting area and an accessory crowd vexation index, the neural network output layer outputs a proper loudspeaker volume and a selection of high-low loudspeakers which can generate an actual prompt effect and cannot be polluted by noise under the current environment, the number of layers of the neural network and the number of nodes of the hidden layer are preliminarily set through an empirical formula and then are continuously corrected to obtain the optimal number of layers and the optimal number of nodes.
As shown in fig. 2, in this embodiment, each row in the GBH _ data matrix for classifying input data represents a group of data, and different parts in each group of data are divided into three types: firstly, inputting ambient noise (preliminarily set as a 1 st column in a matrix); secondly, inputting distance from the sound limiting zone (preliminarily setting the distance to 3 rd column to 8 th column in a matrix); third, the crowd annoyance score (initially set as column 2 in the matrix) is shown in fig. 3.
(1) Input layer of neural network: the input parameters are ambient noise, distance from the sound limiting area, and the annoyance index of the accessory population.
(2) Output layer of neural network: output quantity is proper horn volume which can not only produce actual prompt effect but also can not be polluted by noise under the current environment; output is the choice of the high-low tone horn.
(3) The number of layers of the neural network and the number of hidden layer nodes:
the selection of the number of layers and the number of nodes of the neural network has no strict theoretical guidance, and can only be preliminarily set through an empirical formula and then continuously corrected to obtain the optimal number of layers and the optimal number of nodes. The more the number of layers of the neural network is, the more the number of nodes of the hidden layer is, the more the neural network is complex, and the more powerful the performance of the neural network is easily understood. Generally speaking, the more complex we have a requirement for a neural network, the larger the size of the neural network required. When we use a BP neural network to approximate a non-linear mapping, if the mapping itself is a complex mapping, such as recognizing a human face, and the node selection is too few, then under-fitting occurs, and even if more data is used to train the model, it cannot achieve the desired effect.
The trade-off between the number of layers and the number of nodes is a problem that needs sufficient experience, theoretically, if a sufficient number of nodes can exist, the BP neural network can complete all the problems only by one hidden layer, but actually, the input and the output of the system are limited, so the balance between the number of layers and the number of nodes needs to be carefully considered.
The optimal number of hidden layer cells is selected by the following formula:
k is the number of samples;
n1-most preferablyThe number of hidden layers is optimized;
n is the number of input nodes;
m is the number of output nodes;
a is a constant between 1 and 10.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. The self-adaptive vehicle horn based on the BP neural network development is characterized by comprising an environment information acquisition module, an integrated processing information module and an execution device, wherein the environment information acquisition module comprises a noise acquisition module, a GPS positioning module and a road sign module, the integrated processing information module adopts a Vehicle Control Unit (VCU), the execution device is an existing vehicle horn control device, and the VCU contains the BP neural network module.
2. The adaptive vehicle horn developed based on the BP neural network according to claim 1, wherein the BP neural network module includes a neural network input layer, a neural network output layer, and the number of layers and the number of hidden layer nodes of the neural network, the input parameters of the neural network input layer are environmental noise, a distance from a sound-limited area, and an accessory crowd annoyance index, the neural network output layer outputs a selection of a proper horn volume and a high-low tone horn which produce an actual prompt effect and do not cause noise pollution in a current environment, and the number of layers and the number of hidden layer nodes of the neural network are preliminarily set by an empirical formula and then continuously corrected to obtain the optimal number of layers and the number of nodes.
3. The adaptive vehicle horn developed based on the BP neural network of claim 2, wherein the optimal selection formula of the number of layers of the neural network and the number of nodes of the hidden layer is as follows:
k is the number of samples;
n1-the optimal number of hidden layers;
n is the number of input nodes;
m is the number of output nodes;
a-constant between 1-10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111673726.8A CN114162042A (en) | 2021-12-31 | 2021-12-31 | Self-adaptive vehicle horn developed based on BP neural network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111673726.8A CN114162042A (en) | 2021-12-31 | 2021-12-31 | Self-adaptive vehicle horn developed based on BP neural network |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114162042A true CN114162042A (en) | 2022-03-11 |
Family
ID=80488930
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111673726.8A Pending CN114162042A (en) | 2021-12-31 | 2021-12-31 | Self-adaptive vehicle horn developed based on BP neural network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114162042A (en) |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011235765A (en) * | 2010-05-11 | 2011-11-24 | Mazda Motor Corp | Sound producing apparatus for vehicle |
| CN102717747A (en) * | 2012-05-25 | 2012-10-10 | 苏州市职业大学 | Working method for intelligent acoustic and optical warning system for automotive horn |
| US20170094434A1 (en) * | 2015-09-28 | 2017-03-30 | International Business Machines Corporation | Electronic media volume control |
| CN106882182A (en) * | 2017-02-24 | 2017-06-23 | 江苏理工学院 | A kind of automobile intelligent obstacle avoidance system |
| CN106933146A (en) * | 2017-03-14 | 2017-07-07 | 吉林大学 | Electrocar pedestrian's caution sound method for designing, caution sound control system and method |
| CN107111955A (en) * | 2015-01-09 | 2017-08-29 | 三菱电机株式会社 | Operating control device |
| KR101821923B1 (en) * | 2017-02-20 | 2018-01-24 | 인하대학교 산학협력단 | Method for evaluating sound quality of electric vehicle warning sound considering masking effect and apparatus thereof |
| DE102018200878B3 (en) * | 2018-01-19 | 2019-02-21 | Zf Friedrichshafen Ag | Detection of dangerous sounds |
| US10242665B1 (en) * | 2017-12-29 | 2019-03-26 | Apex Artificial Intelligence Industries, Inc. | Controller systems and methods of limiting the operation of neural networks to be within one or more conditions |
| CN109591693A (en) * | 2018-10-31 | 2019-04-09 | 清华大学苏州汽车研究院（相城） | A kind of electric vehicle motion sound quality active sonification system |
| CN109878412A (en) * | 2017-12-06 | 2019-06-14 | 江苏省丹阳中等专业学校 | A kind of vehicle whistle volume self-adaptation control method based on urban area identification |
| CN110654308A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle and whistle control method and device of vehicle |
| US10547941B1 (en) * | 2019-01-16 | 2020-01-28 | Ford Global Technologies, Llc | Vehicle acoustic transducer operation |
| CN111071148A (en) * | 2018-10-19 | 2020-04-28 | 宝马股份公司 | Method, device and system for adaptively controlling volume of vehicle horn |
| US20200156538A1 (en) * | 2018-11-16 | 2020-05-21 | Zoox, Inc. | Dynamic sound emission for vehicles |
| EP3667659A1 (en) * | 2018-12-13 | 2020-06-17 | Hyundai Motor Company | System and method for controlling engine tone by artificial intelligence based on sound index of vehicle |
| CN111902864A (en) * | 2018-06-04 | 2020-11-06 | 奥迪股份公司 | Method for operating a sound output device of a motor vehicle, speech analysis and control device, motor vehicle and server device outside the motor vehicle |
| CN111976592A (en) * | 2020-07-02 | 2020-11-24 | 汉腾汽车有限公司 | Automatic whistle control system for automatic driving vehicle |
| CN111976593A (en) * | 2020-08-21 | 2020-11-24 | 大众问问(北京)信息科技有限公司 | Voice prompt method, device, equipment and storage medium for vehicle external object |
| CN113525264A (en) * | 2020-04-20 | 2021-10-22 | 上汽通用汽车有限公司 | In-vehicle atmosphere control system and method |
| US20210362649A1 (en) * | 2020-05-21 | 2021-11-25 | Toyota Motor Engineering & Manufacturing North America, Inc. | Adaptive vehicle horn |
-
2021
- 2021-12-31 CN CN202111673726.8A patent/CN114162042A/en active Pending
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011235765A (en) * | 2010-05-11 | 2011-11-24 | Mazda Motor Corp | Sound producing apparatus for vehicle |
| CN102717747A (en) * | 2012-05-25 | 2012-10-10 | 苏州市职业大学 | Working method for intelligent acoustic and optical warning system for automotive horn |
| CN107111955A (en) * | 2015-01-09 | 2017-08-29 | 三菱电机株式会社 | Operating control device |
| US20170094434A1 (en) * | 2015-09-28 | 2017-03-30 | International Business Machines Corporation | Electronic media volume control |
| KR101821923B1 (en) * | 2017-02-20 | 2018-01-24 | 인하대학교 산학협력단 | Method for evaluating sound quality of electric vehicle warning sound considering masking effect and apparatus thereof |
| CN106882182A (en) * | 2017-02-24 | 2017-06-23 | 江苏理工学院 | A kind of automobile intelligent obstacle avoidance system |
| CN106933146A (en) * | 2017-03-14 | 2017-07-07 | 吉林大学 | Electrocar pedestrian's caution sound method for designing, caution sound control system and method |
| CN109878412A (en) * | 2017-12-06 | 2019-06-14 | 江苏省丹阳中等专业学校 | A kind of vehicle whistle volume self-adaptation control method based on urban area identification |
| US10242665B1 (en) * | 2017-12-29 | 2019-03-26 | Apex Artificial Intelligence Industries, Inc. | Controller systems and methods of limiting the operation of neural networks to be within one or more conditions |
| DE102018200878B3 (en) * | 2018-01-19 | 2019-02-21 | Zf Friedrichshafen Ag | Detection of dangerous sounds |
| CN111902864A (en) * | 2018-06-04 | 2020-11-06 | 奥迪股份公司 | Method for operating a sound output device of a motor vehicle, speech analysis and control device, motor vehicle and server device outside the motor vehicle |
| CN110654308A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle and whistle control method and device of vehicle |
| CN111071148A (en) * | 2018-10-19 | 2020-04-28 | 宝马股份公司 | Method, device and system for adaptively controlling volume of vehicle horn |
| CN109591693A (en) * | 2018-10-31 | 2019-04-09 | 清华大学苏州汽车研究院（相城） | A kind of electric vehicle motion sound quality active sonification system |
| US20200156538A1 (en) * | 2018-11-16 | 2020-05-21 | Zoox, Inc. | Dynamic sound emission for vehicles |
| EP3667659A1 (en) * | 2018-12-13 | 2020-06-17 | Hyundai Motor Company | System and method for controlling engine tone by artificial intelligence based on sound index of vehicle |
| CN111322163A (en) * | 2018-12-13 | 2020-06-23 | 现代自动车株式会社 | System and method for controlling engine tone through artificial intelligence based on vehicle sound index |
| CN111447534A (en) * | 2019-01-16 | 2020-07-24 | 福特全球技术公司 | Vehicle acoustic transducer operation |
| US10547941B1 (en) * | 2019-01-16 | 2020-01-28 | Ford Global Technologies, Llc | Vehicle acoustic transducer operation |
| CN113525264A (en) * | 2020-04-20 | 2021-10-22 | 上汽通用汽车有限公司 | In-vehicle atmosphere control system and method |
| US20210362649A1 (en) * | 2020-05-21 | 2021-11-25 | Toyota Motor Engineering & Manufacturing North America, Inc. | Adaptive vehicle horn |
| CN111976592A (en) * | 2020-07-02 | 2020-11-24 | 汉腾汽车有限公司 | Automatic whistle control system for automatic driving vehicle |
| CN111976593A (en) * | 2020-08-21 | 2020-11-24 | 大众问问(北京)信息科技有限公司 | Voice prompt method, device, equipment and storage medium for vehicle external object |
Non-Patent Citations (1)
| Title |
|---|
| 王嵘冰等: "BP神经网络隐含层节点数确定方法研究", 《计算机技术与发展》, vol. 28, no. 04, pages 31 - 35 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11231905B2 (en) | Vehicle with external audio speaker and microphone | |
| JP3913771B2 (en) | Voice identification device, voice identification method, and program | |
| US10137902B2 (en) | Adaptive interactive voice system | |
| US7697698B2 (en) | Sound-based vehicle safety system | |
| CN103183032B (en) | Limiting vehicle speed safety assisting system and the safe householder method of limiting vehicle speed | |
| CN107018467A (en) | The system and method that external voice for vehicle is synthesized | |
| CN107509138A (en) | Pedestrian's warning system | |
| CN108831460A (en) | A kind of interactive voice control system and method based on fatigue monitoring | |
| DE112017007284B4 (en) | Message controller and method for controlling message | |
| CN107089186A (en) | Warning for vehicle method and apparatus | |
| CN106379229B (en) | A kind of electric car pedestrian warning system and method | |
| CN109740433A (en) | A kind of vehicle preventing collision method and car-mounted terminal | |
| DE102018202394A1 (en) | Method for operating a sound output device of a motor vehicle, control device, and motor vehicle | |
| JP2016207001A (en) | Driving support device | |
| CN116729403A (en) | Emotion recognition reminding and control system and method in automobile driving process | |
| US11021147B2 (en) | Vehicles and methods for determining objects of driver focus | |
| CN108621933A (en) | A kind of vehicle-mounted control method and system based on active safety technologies | |
| CN113002421A (en) | Vehicle exterior safety prompting method and device | |
| CN114162042A (en) | Self-adaptive vehicle horn developed based on BP neural network | |
| CN107031494A (en) | Vehicle front lighting lamp control method based on voice recognition | |
| CN112706691B (en) | Vehicle reminding method and device | |
| CN111660927A (en) | Whistling method and system | |
| CN115042710A (en) | Control method and device for automobile horn and automobile | |
| CN114763097A (en) | Intelligent warning control method and system for vehicle exterior whistling | |
| CN111806432A (en) | Vehicle avoiding method and device, vehicle and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |