CN118220130A

CN118220130A - Reminding processing method, device, equipment and medium

Info

Publication number: CN118220130A
Application number: CN202211650570.6A
Authority: CN
Inventors: 蒋腾飞
Original assignee: Beijing Co Wheels Technology Co Ltd
Current assignee: Beijing Co Wheels Technology Co Ltd
Priority date: 2022-12-21
Filing date: 2022-12-21
Publication date: 2024-06-21

Abstract

The embodiment of the disclosure relates to a reminding processing method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring current sound information identified by a preset sound sensor; identifying a first sound wave parameter and a second sound wave parameter of an obstacle around the vehicle according to the current sound information; determining the object attribute and the object relative motion type according to the first acoustic wave parameter; determining object motion speed information according to the first sound wave parameter and the second sound wave parameter; when the relative motion type of the object is a preset relative approaching motion type, determining the current driving risk level according to the object attribute and the object motion speed information, and carrying out driving safety reminding processing according to the current driving risk level. In the embodiment of the disclosure, the current driving risk level of the surrounding obstacles of the vehicle to the driving can be determined based on the sound sensor, the determination mode of the driving risk level is expanded, the comprehensiveness of the safety level reminding is improved, and the driving safety is improved.

Description

Reminding processing method, device, equipment and medium

Technical Field

The disclosure relates to the technical field of vehicle control, and in particular relates to a reminding processing method, a device, equipment and a medium.

Background

With the popularization of vehicles, people have a higher and higher interest in safe driving of vehicles. Therefore, driving safety is improved as a main driving requirement.

In the related art, in order to ensure driving safety, a speed reminding mode is generally adopted, for example, when the vehicle speed is higher, a user is reminded to perform a speed reduction process and the like. However, the safety reminding mode is only based on the detection of the vehicle speed, the safety reminding is not comprehensive, and the driving safety is low.

Disclosure of Invention

In order to solve the technical problems or at least partially solve the technical problems, the disclosure provides a reminding processing method, a device, equipment and a medium, and the current driving risk level of surrounding obstacles of a vehicle to driving can be determined based on a sound sensor, so that the determination mode of the driving risk level is expanded, the comprehensiveness of safety level reminding is improved, and the driving safety is improved.

The embodiment of the disclosure provides a reminding processing method, which comprises the following steps: acquiring current sound information identified by a preset sound sensor; identifying a first sound wave parameter and a second sound wave parameter of obstacles around the vehicle according to the current sound information; determining object properties and object relative motion types according to the first acoustic parameters; determining object movement speed information according to the first sound wave parameter and the second sound wave parameter; when the relative motion type of the object is a preset relative approaching motion type, determining a current driving risk level according to the object attribute and the object motion speed information, and carrying out driving safety reminding processing according to the current driving risk level.

The embodiment of the disclosure also provides a reminding processing device, which comprises: the acquisition module is used for acquiring current sound information identified by a preset sound sensor; the identifying module is used for identifying first sound wave parameters and second sound wave parameters of obstacles around the vehicle according to the current sound information; the first determining module is used for determining the object attribute and the object relative motion type according to the first acoustic wave parameters; the second determining module is used for determining object movement speed information according to the first sound wave parameter and the second sound wave parameter; the third determining module is used for determining the current driving risk level according to the object attribute and the object movement speed information when the object relative movement type is a preset relative approaching movement type; and the reminding processing module is used for carrying out driving safety reminding processing according to the current driving risk level.

The embodiment of the disclosure also provides an electronic device, which comprises: a processor; a memory for storing the processor-executable instructions; the processor is configured to read the executable instruction from the memory and execute the instruction to implement the alert processing method provided by the embodiment of the present disclosure.

The embodiment of the present disclosure also provides a computer-readable storage medium storing a computer program for executing the alert processing method as provided by the embodiment of the present disclosure.

Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages: according to the reminding processing scheme provided by the embodiment of the disclosure, the current sound information identified by the preset sound sensor is obtained, the first sound wave parameter and the second sound wave parameter of the obstacle around the vehicle are identified according to the current sound information, further, the object attribute and the object relative motion type are determined according to the first sound wave parameter, the object motion speed information is determined according to the first sound wave parameter and the second sound wave parameter, when the object relative motion type is the preset relatively close motion type, the current driving risk level is determined according to the object attribute and the object motion speed information, and the driving safety reminding processing is carried out according to the current driving risk level. In the embodiment of the disclosure, the current driving risk level of the surrounding obstacles of the vehicle to the driving can be determined based on the sound sensor, the determination mode of the driving risk level is expanded, the comprehensiveness of the safety level reminding is improved, and the driving safety is improved.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

Fig. 1 is a flow chart of a method for processing a reminder according to an embodiment of the disclosure;

Fig. 2 is a schematic diagram of a reminder processing scenario provided in an embodiment of the present disclosure;

fig. 3 is a schematic diagram of another alert processing scenario provided in an embodiment of the present disclosure;

fig. 4 is a flowchart of another alert processing method according to an embodiment of the present disclosure;

Fig. 5 is a schematic structural diagram of a reminding processing device according to an embodiment of the disclosure;

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

In order to solve the above-mentioned problems, the embodiments of the present disclosure provide a method for processing a reminder, and the method is described below with reference to specific embodiments.

Fig. 1 is a flow chart of a method for processing a reminder, which may be performed by a device for processing a reminder, where the device may be implemented in software and/or hardware, and may be generally integrated in an electronic device. As shown in fig. 1, the method includes:

Step 101, acquiring current sound information identified by a preset sound sensor.

The preset installation positions and the preset installation number of the sound sensors can be set according to scene requirements, and in some possible examples, as shown in fig. 2, the sound sensors can be installed at four corners of the vehicle so as to realize comprehensive detection of obstacles around the vehicle.

It is easily understood that from the perspective of the vehicle and the person, the person can determine what type of vehicle is a car, a heavy truck, etc. by hearing the whistling sound of the vehicle behind, the engine sound, or even empirically by the friction sound of the tires of the vehicle behind and the ground. Based on the frequency of the sound, the direction and speed of the current vehicle can be determined. Based on this, an evasive action can be made in time. Based on the principle, on the vehicle, the Sound Sensor can timely collect the Sound information of the surrounding of the vehicle, and no matter the vehicle is in a stationary state or a driving state, the Sound Sensor can timely act after collecting the information in real time. At rest, for pedestrians or motor vehicles which are suddenly inserted in front or behind, the sound sensor can timely collect sound, and the driver can be found and reminded early.

In addition, because the camera and the millimeter wave radar have the risk of failure, possible risk factors of the surrounding environment of the vehicle cannot be effectively or timely detected in extreme weather, such as rainy days or foggy weather; under specific scenes, for example, pedestrians cross suddenly in the driving direction, when the pedestrians cross, the cameras or millimeter wave radars are blocked by environmental objects, the pedestrians cannot be found out in time, and then the vehicles and drivers cannot be informed, so that safety accidents are caused. Under the condition that the millimeter wave radar or the camera fails, the driving risk level detected by the preset sound sensor in the embodiment can be used as a backup scheme, so that driving safety is ensured.

Thus, in one embodiment of the present disclosure, current sound information identified by a preset sound sensor is acquired in order to determine a driving risk level or the like further based on the current sound information.

Step 102, identifying a first acoustic parameter and a second acoustic parameter according to the current acoustic information.

As mentioned above, the current sound information may reflect much information of obstacles around the vehicle, and thus, in one embodiment of the present disclosure, a first sound wave parameter and a second sound wave parameter reflecting a motion situation of the object may be extracted according to the current sound information, wherein the first sound wave parameter may be any one of a sound wave frequency, a sound wave amplitude, a sound wave tone, etc., and the second sound wave parameter may be a sound wave wavelength, etc.

And step 103, determining the object attribute and the object relative motion type according to the first acoustic wave parameters.

It is readily understood that the object properties are used to indicate the object type of obstacle around the vehicle, etc., for example, the object properties may include pedestrians, trucks, cars, motorcycles, etc., the object movement speed is used to indicate specific speed information of the object movement, and the object relative operation type is used to indicate whether the corresponding vehicle is relatively close or relatively far away. In an actual application scenario, if the object is moving away from the vehicle, a potential safety hazard is not generally brought to driving safety of the vehicle, so that in order to timely remind driving safety, a relative movement type of the object needs to be determined, wherein the relative movement type of the object comprises a relative moving away type, a relative approaching movement type and the like.

In one embodiment of the present disclosure, a first deep learning model may be trained in advance from a large amount of sample data, and in turn, a first acoustic parameter is input into the first deep learning model to obtain the object relative motion type and the object property.

In one embodiment of the present disclosure, the type of relative motion of the object and the object properties may be determined from the first acoustic parameters based on the doppler principle.

In this embodiment, the first acoustic parameter includes a current acoustic frequency, and a historical acoustic frequency corresponding to the current acoustic frequency is obtained, where the historical acoustic frequency may be an acoustic frequency obtained by sampling at a previous sampling time, or a plurality of acoustic frequencies obtained by sampling at a previous sampling time, and so on. As shown in fig. 3, since the closer the object is to the vehicle, the higher the sound frequency, whereas the farther the object is from the vehicle, the lower the sound frequency, so in this embodiment, the type of relative motion of the object is determined according to the current sound frequency and the historical sound frequency, for example, when the sound frequency gradually increases from the historical sound frequency to the current sound frequency, the type of relative motion of the object is the type of relative motion close to the vehicle, whereas when the sound frequency gradually decreases from the historical sound frequency to the current sound frequency, the type of relative motion of the object is the type of relative motion far from the vehicle.

It should be understood that the sound wave frequencies corresponding to different object attributes are different, for example, the sound wave frequency corresponding to a human body and the sound wave frequency corresponding to a motorcycle are different, so in this embodiment, the object attribute may be determined according to the current sound frequency information, for example, the preset database is queried according to the current sound wave frequency to determine the object attribute, in some possible embodiments, in order to avoid misjudgment, the object attribute may be further determined together with the sound wave frequency obtained by sampling at a plurality of consecutive sampling time points corresponding to the current sound wave frequency, and the object attribute with the highest proportion among the determined object attributes is the object attribute corresponding to the current sound wave frequency.

Step 104, determining object movement speed information according to the first sound wave parameter and the second sound wave parameter.

In one embodiment of the present disclosure, a second deep learning model may be trained in advance according to a large amount of sample data, and further, the first acoustic wave parameter and the second acoustic wave parameter are input into the second deep learning model to acquire the object relative movement velocity information.

In one embodiment of the present disclosure, object movement velocity information of obstacles around the vehicle is determined based on current sound information based on the doppler principle.

In this embodiment, when the first acoustic parameter and the second acoustic parameter include the current acoustic frequency and the current acoustic wavelength respectively, the current acoustic frequency and the current acoustic wavelength are further calculated according to a preset algorithm to obtain the object movement speed information, where the preset algorithm may be implemented by a doppler algorithm in the prior art, and the details are not described herein.

And 105, when the relative motion type of the object is a preset relative approaching motion type, determining the current driving risk level according to the object attribute and the object motion speed information, and carrying out driving safety reminding processing according to the current driving risk level.

In one embodiment of the disclosure, when the relative movement type of the object is a preset relatively far away movement type, determining that the current driving risk level is a preset risk level, wherein the preset risk level is smaller than a first preset level threshold, the first preset level threshold is a relatively lower level, and generally when the current driving risk level is the preset risk level, it means that the current driving is safer and has relatively no driving risk.

In an embodiment of the present disclosure, when the relative motion type of the object is a preset relative approaching motion type, the current driving risk level is further determined according to the object motion speed information and the object attribute in the embodiment, that is, in the embodiment, the current driving risk level is determined by combining the object motion speed information and the object attribute, wherein the current driving risk level indicates that the potential safety hazard to the vehicle is more likely.

In some possible embodiments, considering that even if the same object movement speed information has different degrees of influence of different object attributes on the driving safety of the vehicle, for example, the same object movement speed information truck and the motorcycle have different degrees of influence on the driving safety of the vehicle, in this embodiment, a preset corresponding relationship is constructed for each object attribute in advance, where the preset corresponding relationship includes each driving risk level and the corresponding object movement speed information range of the corresponding object movement speed information, and therefore, in this embodiment, the preset corresponding relationship of the object attribute information is determined, and the current driving risk level corresponding to the object movement speed information is determined according to the preset corresponding relationship.

In some possible embodiments, preset weight values are set for object attribute information, object motion speed information and object relative motion type, product values of the object attribute information, the object motion speed information and the object relative motion type and corresponding preset weight values are calculated respectively, product values of the object attribute information, the object motion speed information and the object relative motion type are summed, and a preset corresponding relation is queried according to a summation result to obtain a current driving risk level, wherein the preset corresponding relation in the embodiment comprises a sum range of product values corresponding to different driving risk levels.

Further, after determining the current driving risk level, performing driving safety processing according to the current driving risk level, where it needs to be described that, in different application scenarios, the manner of performing driving safety reminding processing according to the current driving risk level is different, and the following is exemplified:

in some possible examples, the driving safety reminding information can be played in a voice playing mode, wherein the driving safety reminding information comprises object attributes, object movement speed information, object relative movement types and current driving risk levels.

In some possible embodiments, the reminding device and the reminding parameter information corresponding to the current driving risk level are determined, wherein, for example, a preset database can be queried to obtain the reminding device and the reminding parameter information corresponding to the current driving risk level, the reminding device comprises a loudspeaker, a sound and the like, the reminding parameter information comprises at least one of the reminding frequency, the reminding volume and the reminding duration of the reminding device, and the like, and the reminding device is further controlled to carry out safe reminding according to the reminding parameter information, so that safe driving reminding of a driver can be realized, pedestrians and vehicles around the vehicle can be warned, and driving safety is further ensured. Of course, in this embodiment, if the current driving risk level is low, the driving safety reminding process may not be performed.

Of course, in the actual implementation process, the preset sound sensors may be disposed in a plurality of directions of the vehicle, for example, in four corners of the vehicle, so, in order to further improve the accuracy of the safety reminding, in this embodiment, after the current driving risk level corresponding to each preset sound sensor is obtained, in order to avoid erroneous judgment, for example, the preset sensor a is higher in current driving risk level, but because it is driven on a crowded road section, the vehicle in the a neighboring lane is closer to the current vehicle and causes the a current driving risk level to be higher, therefore, in this embodiment, when there is a target sound sensor with the current driving risk level higher than a third preset level threshold (generally, a higher safety level threshold), the lane type around the target sound sensor is also obtained, the driving risk level compensation value is determined according to the lane type, the difference between the current driving risk level and the driving risk level compensation value is calculated, and the current driving risk level is updated according to the difference result.

In summary, according to the reminding processing method of the embodiment of the disclosure, the first sound wave parameter and the second sound wave parameter of the obstacle around the vehicle are identified according to the current sound information, then the object attribute and the object relative motion type are determined according to the first sound wave parameter, the object motion speed information is determined according to the first sound wave parameter and the second sound wave parameter, when the object relative motion type is the preset relative approaching motion type, the current driving risk level is determined according to the object attribute and the object motion speed information, and the driving safety reminding processing is performed according to the current driving risk level. In the embodiment of the disclosure, the current driving risk level of the surrounding obstacles of the vehicle to the driving can be determined based on the sound sensor, the determination mode of the driving risk level is expanded, the comprehensiveness of the safety level reminding is improved, and the driving safety is improved.

Based on the above embodiment, in order to further improve the accuracy of the determination of the current driving risk level, when the camera and the vehicle-mounted radar are included in the vehicle, the current driving risk level is further determined in combination with the camera and the vehicle-mounted radar.

In one embodiment of the present disclosure, as shown in fig. 4, before the driving safety reminding process according to the current driving risk level, the method further includes:

Step 401, acquiring image information of obstacles around the vehicle, which is shot by a camera, under the condition that the current driving risk level is smaller than a second preset level threshold.

In this embodiment, in order to further avoid misjudgment and ensure driving safety only when the current driving risk level detected by the preset sound sensor is smaller than the second preset level threshold, image information of surrounding obstacles of the vehicle, which is captured by the camera, is also acquired, where the image information is generally the image information of the preset sound sensor in the corresponding direction of the current driving risk level is smaller than the second preset level threshold.

Step 402, determining a reference object attribute according to the image information.

After the image information is acquired, the reference object attribute is determined directly according to the image recognition technology.

Step 403, obtaining the movement speed information of the reference object detected by the vehicle-mounted radar.

In this embodiment, the reference object movement speed information detected by the vehicle-mounted radar is obtained, and similarly, the reference object movement speed information detected by the vehicle-mounted radar is movement speed information of a corresponding obstacle in a direction corresponding to a preset sound sensor with a current driving risk level smaller than a second preset level threshold.

Step 404, determining a reference driving risk level according to the reference object attribute and the reference object movement speed information.

After the reference object attribute information and the reference object movement speed information are determined, in the present embodiment, the reference driving risk level is determined according to the reference object attribute and the reference object movement speed information, that is, in the present embodiment, the reference risk level is determined in combination with the camera and the vehicle-mounted radar at the same time.

And step 405, updating the current driving risk level according to the reference driving risk level to obtain an updated current driving risk level.

In one embodiment of the present disclosure, updating the current driving risk level according to the reference driving risk level to obtain an updated current driving risk level, where, in order to ensure driving safety when the reference driving risk level is higher than the current driving risk level, the reference driving risk level is taken as the updated current driving risk level, and in the case that the reference driving risk level is less than or equal to the risk level and higher than the current driving risk level, the current driving risk level is still taken as the final current driving risk level.

In summary, the reminding processing method of the embodiment of the disclosure combines the vehicle-mounted camera and the vehicle-mounted radar to assist in determining the current driving risk level, improves the accuracy of determining the current driving risk level, and further ensures driving safety.

In order to achieve the above embodiment, the present disclosure further provides a reminder processing device.

Fig. 5 is a schematic structural diagram of a device for reminding processing according to an embodiment of the present disclosure, where the device may be implemented by software and/or hardware, and may be generally integrated in an electronic device to perform reminding processing. As shown in fig. 5, the apparatus includes: an acquisition module 501, an identification module 520, a first determination module 530, a second determination module 540, a third determination module 550, and a reminder processing module 560, wherein,

An obtaining module 510, configured to obtain current sound information identified by a preset sound sensor;

An identifying module 520, configured to identify a first sound wave parameter and a second sound wave parameter of an obstacle around the vehicle according to the current sound information;

A first determining module 530, configured to determine an object attribute and a type of relative motion of the object according to the first acoustic parameter;

a second determining module 540, configured to determine object motion velocity information according to the first acoustic parameter and the second acoustic parameter;

A third determining module 550, configured to determine a current driving risk level according to the object attribute and the object movement speed information when the object relative movement type is a preset relative approaching movement type;

and the reminding processing module 560 is used for carrying out driving safety reminding processing according to the current driving risk level.

In some alternative embodiments, when the first acoustic parameter includes a current acoustic frequency and the second acoustic parameter includes a current acoustic wavelength, the first determining module 530 is specifically configured to:

acquiring a historical sound wave frequency corresponding to the current sound wave frequency;

determining the relative motion type of the object according to the current sound wave frequency and the historical sound wave frequency;

The current sound wave frequency is input into a preset object attribute identification model to determine the object attribute, and the second determining module 540 is configured to calculate the current sound wave frequency and the current sound wave wavelength according to a preset algorithm to obtain the object movement speed information.

In some alternative embodiments, the third determining module 550 is specifically configured to:

Determining a preset corresponding relation of the object attribute information;

And determining the current driving risk level corresponding to the object movement speed information according to the preset corresponding relation.

In some alternative embodiments, the third determining module 550 is further configured to:

And when the relative movement type of the object is a preset relative far-away movement type, determining that the current driving risk level is a preset risk level, wherein the preset risk level is smaller than a first preset level threshold.

In some alternative embodiments, further comprising: an updating module for:

Acquiring image information of obstacles around the vehicle shot by a camera under the condition that the current driving risk level is smaller than a second preset level threshold;

determining a reference object attribute according to the image information;

acquiring the motion speed information of a reference object detected by a vehicle-mounted radar;

determining a reference driving risk level according to the reference object attribute and the reference object movement speed information;

And updating the current driving risk level according to the reference driving risk level to acquire the updated current driving risk level.

In some alternative embodiments, further comprising: an updating module for:

and taking the reference driving risk level as the updated current driving risk level under the condition that the reference driving risk level is higher than the current driving risk level.

In some alternative embodiments, the alert processing module 630 is specifically configured to:

determining reminding equipment and reminding parameter information corresponding to the current driving risk level;

And controlling the reminding equipment to carry out safety reminding processing according to the reminding parameter information.

The reminding processing device provided by the embodiment of the disclosure can execute the reminding processing method provided by any embodiment of the disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

To implement the above embodiments, the present disclosure also proposes a computer program product comprising a computer program/instruction which, when executed by a processor, implements the alert processing method in the above embodiments.

Referring now in particular to fig. 6, a schematic diagram of an electronic device 600 suitable for use in implementing embodiments of the present disclosure is shown. The electronic device 600 in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), car terminals (e.g., car navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 6, the electronic device 600 may include a processor (e.g., a central processing unit, a graphics processor, etc.) 601 that may perform various suitable actions and processes according to programs stored in a Read Only Memory (ROM) 602 or programs loaded from a memory 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic apparatus 600 are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

In general, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; memory 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 shows an electronic device 600 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication means 609, or from memory 608, or from ROM 602. The above-described functions defined in the alert processing method of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: and identifying a first sound wave parameter and a second sound wave parameter of obstacles around the vehicle according to the current sound information, further determining an object attribute and an object relative motion type according to the first sound wave parameter, determining object motion speed information according to the first sound wave parameter and the second sound wave parameter, determining a current driving risk level according to the object attribute and the object motion speed information when the object relative motion type is a preset relative approaching motion type, and carrying out driving safety reminding processing according to the current driving risk level. In the embodiment of the disclosure, the current driving risk level of the surrounding obstacles of the vehicle to the driving can be determined based on the sound sensor, the determination mode of the driving risk level is expanded, the comprehensiveness of the safety level reminding is improved, and the driving safety is improved.

The electronic device may write computer program code for performing the operations of the present disclosure in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. The reminding processing method is characterized by comprising the following steps of:

acquiring current sound information identified by a preset sound sensor;

Identifying a first sound wave parameter and a second sound wave parameter of obstacles around the vehicle according to the current sound information;

Determining object properties and object relative motion types according to the first acoustic parameters;

Determining object movement speed information according to the first sound wave parameter and the second sound wave parameter;

When the relative motion type of the object is a preset relative approaching motion type, determining a current driving risk level according to the object attribute and the object motion speed information, and carrying out driving safety reminding processing according to the current driving risk level.

2. The method of claim 1, wherein, when the first acoustic parameter comprises a current acoustic frequency and the second acoustic parameter comprises a current acoustic wavelength,

The determining the object attribute and the object relative motion type according to the first acoustic wave parameter comprises the following steps:

Inputting the current sound wave frequency into a preset object attribute identification model to determine the object attribute;

The determining object motion speed information according to the first acoustic parameter and the second acoustic parameter comprises:

And calculating the current sound wave frequency and the current sound wave wavelength according to a preset algorithm to acquire the object movement speed information.

3. The method of claim 1, wherein said determining a current driving risk level based on said object attribute, object movement speed information, comprises:

4. The method as recited in claim 1, further comprising:

5. The method of claim 1, further comprising, prior to said running safety reminder processing according to said current driving risk level:

determining a reference object attribute according to the image information;

6. The method of claim 5, wherein updating the current driving risk level according to the reference driving risk level to obtain an updated current driving risk level comprises:

7. The method according to any one of claims 1-6, wherein the driving safety reminding process according to the current driving risk level comprises:

8. A reminder processing apparatus, comprising:

The acquisition module is used for acquiring current sound information identified by a preset sound sensor;

The identifying module is used for identifying first sound wave parameters and second sound wave parameters of obstacles around the vehicle according to the current sound information;

the first determining module is used for determining the object attribute and the object relative motion type according to the first acoustic wave parameters;

the second determining module is used for determining object movement speed information according to the first sound wave parameter and the second sound wave parameter;

The third determining module is used for determining the current driving risk level according to the object attribute and the object movement speed information when the object relative movement type is a preset relative approaching movement type;

And the reminding processing module is used for carrying out driving safety reminding processing according to the current driving risk level.

9. An electronic device, the electronic device comprising:

A processor;

A memory for storing the processor-executable instructions;

the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the alert processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the alert processing method according to any one of the preceding claims 1-7.