WO2021012528A1 - Driving safety assistance method and apparatus, vehicle, and readable storage medium - Google Patents

Abstract

A driving safety assistance method, comprising: acquiring identity information of a current driver of a vehicle, and acquiring, according to the identity information of the current driver, a driving record corresponding to the current driver; calling a driving proficiency recognition model generated by means of pre-training, and recognizing, according to the driving record corresponding to the current driver, the driving proficiency of the current driver; and triggering, according to the driving proficiency of the current driver, a warning mechanism during the driving of the vehicle. In addition, further provided are an apparatus for implementing the driving safety assistance method, a vehicle, and a readable storage medium. This method can make it possible to trigger a warning mechanism according to the driving proficiency of a driver, thereby improving the driving safety.

Description

Driving safety assistance method, device, vehicle, and readable storage medium

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on July 25, 2019, the application number is 201910679052.9 and the invention title is "driving safety assistance method, device, vehicle, and readable storage medium", the entire content of which is approved The reference is incorporated in this application.

Technical field

This application relates to the technical field of vehicle management and control, and in particular to a driving safety assistance method, device, vehicle, and readable storage medium.

Background technique

Reversing radar, also called "parking assist device", is a safety assist device when a car is parking or reversing. It can notify the driver of obstacles around the driver by sound or a more intuitive display to improve driving safety.

However, the reversing radar uses the same warning mechanism for any driver driving any vehicle. Such warning methods are not suitable for all drivers of different driving proficiency levels. For example, the vehicle will issue a warning when the obstacle is less than a preset distance when reversing. After the warning is issued, the skilled driver can still control and adjust the operation of the vehicle. For drivers who are not proficient in their driving skills, they may not be able to control and adjust the vehicle due to the small distance from the obstacle.

Summary of the invention

In view of the above, it is necessary to provide a driving safety assistance method, device, vehicle, and readable storage medium to solve the technical problem of low driving safety.

The first aspect of the present application provides a driving safety assistance method, the method includes:

Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

Determine the warning distance value according to the driving proficiency of the current driver;

Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

A second aspect of the present application provides a vehicle, the vehicle includes a processor and a memory, the memory is configured to store at least one computer-readable instruction, and the processor is configured to execute the at least one computer-readable instruction to implement the following steps :

Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

Determine the warning distance value according to the driving proficiency of the current driver;

Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

A third aspect of the present application provides a non-volatile readable storage medium, the non-volatile readable storage medium stores at least one computer readable instruction, which is implemented when the at least one computer readable instruction is executed by a processor The following steps:

Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

Determine the warning distance value according to the driving proficiency of the current driver;

Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

A fourth aspect of the present application provides a driving safety auxiliary device, which includes:

The acquisition module is used to acquire the identity information of the current driver of the vehicle, and acquire the driving record corresponding to the current driver according to the identity information of the current driver;

The execution module is used to call the driving proficiency recognition model generated by pre-training, and recognize the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

The execution module is also used to trigger a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

Determine the warning distance value according to the driving proficiency of the current driver;

Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

The driving safety assistance method, device, vehicle, and readable storage medium described in the embodiments of this application obtain the identity information of the current driver of the vehicle and obtain the corresponding current driver according to the identity information of the current driver. Driving record; call the driving proficiency recognition model generated by pre-training, and identify the driving proficiency of the current driver according to the driving record corresponding to the current driver; and trigger according to the driving proficiency of the current driver The warning mechanism can be triggered according to the driver’s proficiency program to effectively improve driving safety.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only It is an embodiment of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative work.

Fig. 1 is a flowchart of a driving safety assistance method provided by a preferred embodiment of the present application.

Fig. 2 is a structural diagram of a driving safety auxiliary device provided by a preferred embodiment of the present application.

Fig. 3 is a schematic diagram of a vehicle provided by a preferred embodiment of the present application.

The following specific embodiments will further illustrate this application in conjunction with the above-mentioned drawings.

Detailed ways

In order to be able to understand the above objectives, features and advantages of the application more clearly, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are set forth in order to fully understand the present application. The described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terms used in the description of the application herein are only for the purpose of describing specific embodiments, and are not intended to limit the application.

Fig. 1 is a flowchart of a driving safety assistance method provided by a preferred embodiment of the present application.

In this embodiment, the driving safety assistance method can be applied to vehicles. For vehicles that require driving safety assistance, the functions for driving safety assistance provided by the method of this application can be directly integrated on the vehicle, or Software Development Kit (SDK) runs on the vehicle in the form of a software development kit.

As shown in Figure 1, the driving safety assistance method specifically includes the following steps. According to different requirements, the sequence of the steps in the flowchart can be changed, and some steps can be omitted.

Step S1: Obtain the identity information of the current driver of the vehicle, and obtain the driving record corresponding to the current driver according to the identity information of the current driver.

In this embodiment, the driving record includes, but is not limited to, the time when the current driver receives the driving license and the auto insurance claim record. In an embodiment, the auto insurance claim settlement record includes, but is not limited to, the number of accidents, the frequency of accidents, the degree of damage, and the amount of claims.

The vehicle described in the embodiment of the present application establishes a communication connection with the server through a network (such as WIFI, radio, etc.). The server stores driving records corresponding to each driver. The server may belong to different insurance companies.

In one embodiment, a user interface may be displayed on the display screen of the vehicle for the driver to input identity information. The identity information may be the driver's fingerprint, ID number, or other information that can verify the driver's identity.

Step S2: Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver.

Specifically, the driving record corresponding to the current driver is input into the driving proficiency recognition model generated by the pre-training to obtain the driving proficiency of the current driver.

In this embodiment, the driving proficiency can be divided into general proficiency, relatively proficient, and proficient.

In this embodiment, the driving record corresponding to the general proficiency level belongs to the first parameter range, the driving record corresponding to the relatively proficient driving level belongs to the second parameter range, and the driving proficiency level corresponds to the proficiency level. The driving record belongs to the third parameter range. The first parameter range, the second parameter range, and the third parameter range are different parameter ranges.

Preferably, the method for training the driving proficiency recognition model includes:

1) Obtain a preset number of driving records corresponding to the different driving proficiency levels, and mark the types of driving records corresponding to each driving proficiency level, so that the driving records corresponding to each driving proficiency level The category label is carried, and the preset number of driving records corresponding to different driving proficiency levels after the category labeling are used as training samples.

For example, 500 driving records corresponding to the general proficiency level are selected, and the 500 driving records are marked as "1", that is, "1" is used as a label. Similarly, 500 driving records corresponding to when the driving proficiency is relatively proficient are selected, and the 500 driving records are marked as "2", that is, "2" is used as a label. Select 500 driving records corresponding to the proficiency of driving proficiency, and mark the 500 driving records as "3", that is, use "3" as a label.

2) Randomly divide the training sample into a training set with a first preset ratio and a verification set with a second preset ratio, use the training set to train a deep neural network to obtain the driving proficiency recognition model, and use the verification The accuracy of the driving proficiency recognition model after the set verification training.

For example, you can first distribute the driving records corresponding to different driving proficiency levels to different folders according to the marked categories. For example, the driving record corresponding to the driving proficiency level is generally proficient is distributed to the first folder, the driving record corresponding to the driving proficiency level is relatively proficient is distributed to the second folder, and the driving The driving record corresponding to the proficiency level is distributed to the third folder. Then extract the driving records of the first preset ratio (for example, 70%) from different folders as the training set to train the deep neural network to obtain the driving proficiency recognition model, and take the remaining driving records from the different folders. The second preset ratio (for example, 30%) of driving records is used as a verification set, and the verification set is used to verify the accuracy of the driving proficiency recognition model obtained by training.

3) If the accuracy rate is greater than or equal to the preset accuracy rate, the training is ended.

If the accuracy rate is less than the preset accuracy rate, increase the number of training samples in the step 1), that is, obtain more training samples, and use the more training samples to perform a new operation according to the above step 2). The deep neural network is trained until the accuracy rate of the re-obtained driving proficiency recognition model is greater than or equal to the preset accuracy rate.

Step S3, triggering a warning mechanism according to the driving proficiency of the current driver during the running of the vehicle.

Preferably, the triggering of the warning mechanism according to the driving proficiency of the current driver includes steps (y1)-(y3):

In step (y1), the warning distance value is determined according to the driving proficiency of the current driver.

In an embodiment, the determining the alarm distance value according to the driving proficiency of the current driver includes: pre-establishing a correspondence between the driving proficiency and the preset distance value, wherein different driving proficiency corresponds to different A preset distance value; when the driving proficiency of the current driver is identified by the driving proficiency recognition model, the preset corresponding to the driving proficiency of the current driver is determined according to the pre-established correspondence The distance value, the determined preset distance value is used as the alarm distance value.

Taking the driving proficiency divided into general proficiency, relatively proficient, and proficient as an example, in this embodiment, when the driving proficiency is preset as general proficiency, it corresponds to the preset first distance value; When the proficiency is relatively proficient, it corresponds to the preset second distance value; and when the driving proficiency is preset to be proficient, it corresponds to the preset third distance value. Thus, when the driving proficiency of the current driver is recognized by the driving proficiency recognition model, the driving proficiency corresponding to the current driver's driving proficiency can be determined according to the pre-established correspondence relationship. Alarm distance value.

Preferably, the first distance value is greater than the second distance value and the third distance value.

Preferably, the first distance value is greater than the second distance value, and the second distance value is greater than the third distance value.

Step (y2), detecting the distance between the vehicle and the obstacle during the running of the vehicle.

Preferably, the distance between the vehicle and an obstacle can be detected when the vehicle is reversing.

Preferably, the distance between the vehicle and the obstacle can be detected when the vehicle is moving forward.

In an embodiment, the obstacle may refer to an object in a stationary state or a pedestrian or a vehicle in a dynamic state.

In an embodiment, the distance between the vehicle and an obstacle may refer to the distance between the vehicle and an obstacle located in front, rear, left, or right of the vehicle.

Specifically, a radar installed on the vehicle can be used to detect the distance value between the vehicle and the obstacle.

In step (y3), when the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

In one embodiment, the triggering warning mechanism may refer to controlling the buzzer of the vehicle to emit a warning sound effect, and/or displaying text information on the display screen of the vehicle to prompt the current driver.

In other embodiments, it is also possible to control the vehicle to decelerate when the detected distance value is less than the determined alarm distance value.

Preferably, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

Step S41: Detect the road conditions in front of the vehicle in real time, where the road conditions in front include, but are not limited to: the number of lanes, the degree of traffic congestion, whether a school road section, visibility, etc.

In one embodiment, the forward road condition may refer to the road condition on the road ahead that is a preset distance (for example, 1 km) from the vehicle.

Specifically, a preset map (such as Google Maps, Baidu Maps) can be invoked to obtain the number of lanes included in the road conditions ahead, the degree of traffic congestion, whether the road ahead includes school sections, etc., and invoke the preset weather forecast It is assumed that the software obtains the visibility index and so on.

Step S42: Determine whether to issue a prompt according to the front road condition and the driving proficiency of the current driver, and prompt the current driver to replan the travel route.

Because the same road conditions are different for drivers with different driving proficiency levels, the driving difficulty is also different, so you can set a rule to determine what kind of driving proficiency drivers need in which road conditions ahead. Issue a reminder to re-plan the route.

For example, for the school road section, because the driver may need to stop and wait for the students to pass by at any time, the driver's driving proficiency is relatively high. Therefore, it can be set in the rule: if the road ahead includes a school section, and the driving proficiency of the current driver is general proficiency, the prompt will be issued to remind the current driver whether to re-plan the route of travel Make a selection.

The foregoing is only an example, and should not be construed as a limitation on the technical solution for determining whether to issue a prompt according to the forward road conditions and the driving proficiency of the current driver.

In step S43, when the re-planning route is determined, the route is re-planned according to the driving proficiency of the current driver.

Still like the above example, assuming that the road ahead includes a school section, and the driving proficiency of the current driver is generally proficient, a new travel route that can avoid the school section can be replanned.

In summary, the driving safety assistance method described in the embodiments of the present application obtains the current driver's identity information of the vehicle, and obtains the driving record corresponding to the current driver according to the current driver's identity information; The driving proficiency recognition model generated by pre-training is used to identify the driving proficiency of the current driver according to the driving record corresponding to the current driver; and the warning mechanism is triggered according to the driving proficiency of the current driver. The driver’s driving proficiency program triggers the warning mechanism, effectively improving driving safety.

The above-mentioned Figure 1 describes in detail the driving safety assistance method of the present application, and in conjunction with Figures 2 to 3, the functional modules of the software device for implementing the driving safety assistance method and the hardware device architecture for implementing the driving safety assistance method are introduced. .

It should be understood that the described embodiments are for illustrative purposes only, and are not limited by this structure in the scope of the patent application.

Refer to FIG. 2, which is a structural diagram of the driving safety auxiliary device provided by the preferred embodiment of the present application.

In some embodiments, the driving safety assist device 30 runs in a vehicle. The vehicle is connected to external equipment through a network. The driving safety auxiliary device 30 may include multiple functional modules composed of program code segments. The program code of each program segment in the driving safety auxiliary device 30 may be stored in the memory of the vehicle and executed by the at least one processor to realize the driving safety auxiliary function (see FIG. 2 for details).

In this embodiment, the driving safety auxiliary device 30 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 301 and an execution module 302. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the function of each module will be described in detail in subsequent embodiments.

The obtaining module 301 obtains the identity information of the current driver of the vehicle, and obtains the driving record corresponding to the current driver according to the identity information of the current driver.

In this embodiment, the driving record includes, but is not limited to, the time when the current driver receives the driving license and the auto insurance claim record. In an embodiment, the auto insurance claim settlement record includes, but is not limited to, the number of accidents, the frequency of accidents, the degree of damage, and the amount of claims.

The vehicle described in the embodiment of the present application establishes a communication connection with the server through the network. The server stores driving records corresponding to each driver. The server may belong to different insurance companies.

In one embodiment, the vehicle can be connected to the server via any traditional wireless network communication via the network. The wireless network can be any type of traditional wireless communication, such as radio, wireless fidelity (Wireless Fidelity, WIFI), cellular, satellite, broadcast, etc. Wireless communication technologies may include, but are not limited to, Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (W-CDMA), CDMA2000, IMT Single Carrier (IMT Single Carrier), Enhanced Data Rates for GSM Evolution (EDGE), Long-Term Evolution Technology (Long-Term Evolution, LTE) , Advanced long-term evolution technology, Time-Division LTE (TD-LTE), High-Performance Radio Local Area Network (HiperLAN), High-Performance Radio Wide Area Network (HiperWAN) , Local Multipoint Distribution Service (LMDS), Worldwide Interoperability for Microwave Access (WiMAX), ZigBee Protocol (ZigBee), Bluetooth, Orthogonal Frequency Division Multiplexing (Flash Orthogonal) Frequency-Division Multiplexing, Flash-OFDM), High Capacity Spatial Division Multiple Access (HC-SDMA), Universal Mobile Telecommunications System (UMTS), Universal Mobile Telecommunications System, Time Division Dual Industrial (UMTS Time-Division Duplexing, UMTS-TDD), Evolved High Speed Packet Access (HSPA+), Time Division Synchronous Code Division Multiple Access (Time Division Multiple Access, TD-SCDMA), Evolution Data optimization (Evolution-Data Optimized, EV-DO), Digital Enhanced Cordless Telecommunications (DECT) and others.

In an embodiment, the acquisition module 301 may display a user interface on the display screen of the vehicle for the driver to input identity information. The identity information may be the driver's fingerprint, ID number, or other information that can verify the driver's identity.

The execution module 302 is configured to call a driving proficiency recognition model generated by pre-training, and identify the driving proficiency of the current driver according to the driving record corresponding to the current driver.

Specifically, the execution module 302 inputs the driving record corresponding to the current driver into the driving proficiency recognition model generated by the pre-training to obtain the driving proficiency of the current driver.

In this embodiment, the driving proficiency can be divided into general proficiency, relatively proficient, and proficient.

In this embodiment, the driving record corresponding to the general proficiency level belongs to the first parameter range, the driving record corresponding to the relatively proficient driving level belongs to the second parameter range, and the driving proficiency level corresponds to the proficiency level. The driving record belongs to the third parameter range. The first parameter range, the second parameter range, and the third parameter range are different parameter ranges.

Preferably, the execution module 302 is also used to train the driving proficiency recognition model.

Specifically, the execution module 302 obtains a preset number of driving records corresponding to the different driving proficiency levels, and marks the types of driving records corresponding to each driving proficiency level, so that the driving record is compatible with each driving proficiency level. The driving record corresponding to the degree carries a category label, and the preset number of driving records corresponding to different driving proficiency levels after the category labeling are used as training samples.

For example, 500 driving records corresponding to the general proficiency level are selected, and the 500 driving records are marked as "1", that is, "1" is used as a label. Similarly, 500 driving records corresponding to when the driving proficiency is relatively proficient are selected, and the 500 driving records are marked as "2", that is, "2" is used as a label. Select 500 driving records corresponding to the proficiency of driving proficiency, and mark the 500 driving records as "3", that is, use "3" as a label.

The execution module 302 randomly divides the training samples into a training set with a first preset ratio and a verification set with a second preset ratio, uses the training set to train a deep neural network to obtain the driving proficiency recognition model, and uses The verification set verifies the accuracy of the trained driving proficiency recognition model.

For example, you can first distribute the driving records corresponding to different driving proficiency levels to different folders according to the marked categories. For example, the driving record corresponding to the driving proficiency level is generally proficient is distributed to the first folder, the driving record corresponding to the driving proficiency level is relatively proficient is distributed to the second folder, and the driving The driving record corresponding to the proficiency level is distributed to the third folder. Then extract the driving records of the first preset ratio (for example, 70%) from different folders as the training set to train the deep neural network to obtain the driving proficiency recognition model, and take the remaining driving records from the different folders. The second preset ratio (for example, 30%) of driving records is used as a verification set, and the verification set is used to verify the accuracy of the driving proficiency recognition model obtained by training.

If the accuracy rate is greater than or equal to the preset accuracy rate, the execution module 302 ends the training.

If the accuracy rate is less than the preset accuracy rate, the execution module 302 increases the number of training samples to obtain more training samples, and uses the more training samples to retrain the deep neural network until it is obtained again The accuracy rate of the driving proficiency recognition model is greater than or equal to the preset accuracy rate.

The execution module 302 is also used to trigger a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle.

In a preferred embodiment, the execution module 302 triggers a warning mechanism according to the driving proficiency of the current driver including:

The execution module 302 determines the warning distance value according to the driving proficiency of the current driver.

In an embodiment, the determining the alarm distance value according to the driving proficiency of the current driver includes: pre-establishing a correspondence between the driving proficiency and the preset distance value, wherein different driving proficiency corresponds to different A preset distance value; when the driving proficiency of the current driver is identified by the driving proficiency recognition model, the preset corresponding to the driving proficiency of the current driver is determined according to the pre-established correspondence The distance value, the determined preset distance value is used as the alarm distance value.

Taking the driving proficiency divided into general proficiency, relatively proficient, and proficient as an example, in this embodiment, when the driving proficiency is preset as general proficiency, it corresponds to the preset first distance value; the driving is preset When the proficiency is relatively proficient, it corresponds to the preset second distance value; and when the driving proficiency is preset to be proficient, it corresponds to the preset third distance value. Thus, when the driving proficiency of the current driver is recognized by the driving proficiency recognition model, the driving proficiency corresponding to the current driver's driving proficiency can be determined according to the pre-established correspondence relationship. Alarm distance value.

Preferably, the first distance value is greater than the second distance value and the third distance value.

Preferably, the first distance value is greater than the second distance value, and the second distance value is greater than the third distance value.

The execution module 302 also detects the distance between the vehicle and the obstacle during the running of the vehicle.

Preferably, the distance between the vehicle and an obstacle can be detected when the vehicle is reversing.

Preferably, the distance between the vehicle and the obstacle can be detected when the vehicle is moving forward.

In an embodiment, the obstacle may refer to an object in a stationary state or a pedestrian or a vehicle in a dynamic state.

In an embodiment, the distance between the vehicle and an obstacle may refer to the distance between the vehicle and an obstacle located in front, rear, left, or right of the vehicle.

Specifically, a radar installed on the vehicle can be used to detect the distance value between the vehicle and the obstacle.

When the detected distance value is less than the determined alarm distance value, the execution module 302 triggers the alarm mechanism.

In one embodiment, the triggering warning mechanism may refer to controlling the buzzer of the vehicle to emit a warning sound effect, and/or displaying text information on the display screen of the vehicle to prompt the current driver.

In other embodiments, the execution module 302 may also control the vehicle to decelerate when the detected distance value is less than the determined alarm distance value.

In another preferred embodiment, the execution module 302 triggering a warning mechanism according to the current driver’s driving proficiency includes:

The execution module 302 detects the road conditions in front of the vehicle in real time, where the road conditions in front include, but are not limited to: the number of lanes, the degree of traffic congestion, whether a school road section is or not, and visibility.

In one embodiment, the forward road condition may refer to the road condition on the road ahead that is a preset distance (for example, 1 km) from the vehicle.

Specifically, a preset map (such as Google Maps, Baidu Maps) can be invoked to obtain the number of lanes included in the road conditions ahead, the degree of traffic congestion, whether the road ahead includes school sections, etc., and invoke the preset weather forecast It is assumed that the software obtains the visibility index and so on.

The execution module 302 determines whether to issue a prompt according to the front road condition and the driving proficiency of the current driver, and prompts the current driver to re-plan the travel route.

Because the same road conditions are different for drivers with different driving proficiency levels, the driving difficulty is also different, so you can set a rule to determine what kind of driving proficiency drivers need in which road conditions ahead. Issue a reminder to re-plan the route.

For example, for the school road section, because the driver may need to stop and wait for the students to pass by at any time, the driver's driving proficiency is relatively high. Therefore, it can be set in the rule: if the road ahead includes a school section, and the driving proficiency of the current driver is general proficiency, the prompt will be issued to remind the current driver whether to re-plan the route of travel Make a selection.

The foregoing is only an example, and should not be construed as a limitation on the technical solution for determining whether to issue a prompt according to the forward road conditions and the driving proficiency of the current driver.

The execution module 302 re-plans the route according to the driving proficiency of the current driver when determining to re-plan the travel route.

Still like the above example, assuming that the road ahead includes a school section, and the driving proficiency of the current driver is generally proficient, a new travel route that can avoid the school section can be replanned.

In summary, the driving safety assistance device described in the embodiment of the present application obtains the driving record corresponding to the current driver according to the current driver's identity information by acquiring the identity information of the current driver of the vehicle; The driving proficiency recognition model generated by pre-training is used to identify the driving proficiency of the current driver according to the driving record corresponding to the current driver; and the warning mechanism is triggered according to the driving proficiency of the current driver. The driver’s driving proficiency program triggers the warning mechanism, effectively improving driving safety.

Refer to FIG. 3, which is a schematic structural diagram of a vehicle provided by a preferred embodiment of this application. In a preferred embodiment of the present application, the vehicle 3 includes a memory 31, at least one processor 32, and at least one communication bus 33. Those skilled in the art should understand that the structure of the vehicle shown in FIG. 3 does not constitute a limitation of the embodiment of the present application. It may be a bus structure or a star structure. The vehicle 3 may also include more More or less other hardware or software, or different component arrangements.

In some embodiments, the vehicle 3 includes a terminal that can automatically perform numerical calculation and/or information processing according to pre-set or stored computer-readable instructions, and its hardware includes, but is not limited to, a microprocessor, a dedicated integrated circuit Circuits, programmable gate arrays, digital processors and embedded devices, etc.

It should be noted that the vehicle 3 is only an example, and other existing or future vehicles that can be adapted to this application should also be included in the protection scope of this application and included here by reference.

In some embodiments, the memory 31 is used to store program codes and various data, such as the driving safety auxiliary device 30 installed in the vehicle 3, and realize the high-speed and automatic completion of the program during the operation of the vehicle 3 Or data access. The memory 31 includes Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), and Erasable Programmable Read-Only Memory (EPROM) , One-time Programmable Read-Only Memory (OTPROM), Electronically-Erasable Programmable Read-Only Memory (EEPROM), CD-ROM (Compact Disc Read- Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other non-volatile readable storage medium that can be used to carry or store data.

In some embodiments, the at least one processor 32 may be composed of integrated circuits, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one Or a combination of multiple central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips. The at least one processor 32 is the control core (Control Unit) of the vehicle 3, which uses various interfaces and lines to connect various components of the entire vehicle 3, and by running or executing programs or modules stored in the memory 31, And call the data stored in the memory 31 to execute various functions of the vehicle 3 and process data, for example, to execute the function of driving safety assistance.

In some embodiments, the at least one communication bus 33 is configured to implement connection and communication between the memory 31 and the at least one processor 32 and the like.

Although not shown, the vehicle 3 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 32 through a power management device, so as to realize management through the power management device. Functions such as charging, discharging, and power management. The power supply may also include one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, power supply status indicators and other arbitrary components. The vehicle 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the described embodiments are for illustrative purposes only, and are not limited by this structure in the scope of the patent application.

The above-mentioned integrated unit implemented in the form of a software function module may be stored in a nonvolatile readable storage medium. The above-mentioned software function module includes a number of computer-readable instructions to enable a vehicle or a processor to execute part of the method described in each embodiment of the present application.

In a further embodiment, with reference to FIG. 2, the at least one processor 32 can execute the operating device of the vehicle 3 and various installed applications (such as the driving safety auxiliary device 30), program codes, etc., For example, the various modules mentioned above.

The memory 31 stores program codes, and the at least one processor 32 can call the program codes stored in the memory 31 to execute related functions. For example, the various modules described in FIG. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to realize the functions of the various modules to achieve the purpose of driving safety assistance.

In an embodiment of the present application, the memory 31 stores a plurality of computer-readable instructions, and the plurality of computer-readable instructions are executed by the at least one processor 32 to achieve the purpose of driving safety assistance. Specifically, for the specific implementation method of the above-mentioned computer-readable instructions by the at least one processor 32, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG. 1, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed device, vehicle, and method may be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any reference signs in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other elements or, and the singular does not exclude the plural. Multiple units or devices stated in the device claims can also be implemented by one unit or device through software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Modifications or equivalent replacements are made without departing from the spirit and scope of the technical solution of this application.

Claims (20)

1. A driving safety assistance method, characterized in that the method includes:

   Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

   Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

   Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

   Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

   Determine the warning distance value according to the driving proficiency of the current driver;

   Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

   When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.
2. The driving safety assistance method according to claim 1, wherein training the driving proficiency recognition model comprises:

   Acquire a preset number of driving records corresponding to different driving proficiency levels, and mark the types of driving records corresponding to each type of driving proficiency, so that the driving records corresponding to each type of driving proficiency carry category labels, Taking the preset number of driving records respectively corresponding to different driving proficiency levels marked with categories as training samples;

   The training samples are randomly divided into a training set with a first preset ratio and a verification set with a second preset ratio, a deep neural network is trained using the training set to obtain the driving proficiency recognition model, and the verification set is used to verify The accuracy of the driving proficiency recognition model; and

   If the accuracy rate is greater than or equal to the preset accuracy rate, the training ends; if the accuracy rate is less than the preset accuracy rate, increase the number of training samples and retrain the deep neural network until the re-obtained The accuracy rate of the driving proficiency recognition model is greater than or equal to the preset accuracy rate.
3. The driving safety assistance method according to claim 1, wherein the driving record includes the time when the current driver obtains the driver's license and the auto insurance claim record, wherein the auto insurance claim record includes the number of accidents, the frequency of accidents, The degree of damage and the amount of compensation.
4. The driving safety assistance method according to claim 1, wherein the determining the warning distance value according to the driving proficiency of the current driver comprises:

   The corresponding relationship between the driving proficiency and the preset distance value is established in advance, wherein different driving proficiency levels correspond to different preset distance values; and

   When the driving proficiency of the current driver is identified by the driving proficiency recognition model, the preset distance value corresponding to the driving proficiency of the current driver is determined according to the pre-established correspondence, and the The determined preset distance value is used as the alarm distance value.
5. The driving safety assistance method of claim 4, wherein the driving proficiency is divided into general proficiency, relatively proficiency, and proficiency, wherein the corresponding relationship between the driving proficiency and the preset distance value is established in advance Including: when the driving proficiency is preset to be general proficiency, corresponding to a preset first distance value; when the driving proficiency is preset to be relatively proficient, corresponding to a preset second distance value; and to preset the driving When the proficiency is proficient, it corresponds to the preset third distance value;

   Wherein, the first distance value is greater than the second distance value, and the second distance value is greater than the third distance value.
6. The driving safety assistance method according to claim 1, wherein the triggering a warning mechanism according to the current driver's driving proficiency further comprises:

   Real-time detection of road conditions in front of the vehicle;

   Determine whether to issue a prompt according to the road conditions ahead and the driving proficiency of the current driver to remind the current driver to re-plan the route of travel; and

   When determining the re-planning route, the route is re-planned according to the driving proficiency of the current driver.
7. The driving safety assistance method according to claim 6, wherein the front road conditions include: number of lanes, traffic congestion, school road section, visibility, wherein the front road conditions refer to a preset distance from the vehicle The road conditions of the road ahead.
8. A vehicle, characterized in that the vehicle includes a processor and a memory, the memory is configured to store at least one computer-readable instruction, and the processor is configured to execute the at least one computer-readable instruction to implement the following steps:

   Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

   Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

   Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

   Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

   Determine the warning distance value according to the driving proficiency of the current driver;

   Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

   When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.
9. The vehicle according to claim 8, wherein when the processor executes the at least one computer-readable instruction to implement the training of the driving proficiency recognition model, it specifically comprises:

   Acquire a preset number of driving records corresponding to different driving proficiency levels, and mark the types of driving records corresponding to each type of driving proficiency, so that the driving records corresponding to each type of driving proficiency carry category labels, Taking the preset number of driving records respectively corresponding to different driving proficiency levels marked with categories as training samples;

   The training samples are randomly divided into a training set with a first preset ratio and a verification set with a second preset ratio, a deep neural network is trained using the training set to obtain the driving proficiency recognition model, and the verification set is used to verify The accuracy of the driving proficiency recognition model; and

   If the accuracy rate is greater than or equal to the preset accuracy rate, the training ends; if the accuracy rate is less than the preset accuracy rate, increase the number of training samples and retrain the deep neural network until the re-obtained The accuracy rate of the driving proficiency recognition model is greater than or equal to the preset accuracy rate.
10. The vehicle according to claim 8, wherein the driving record includes the time when the current driver obtains the driver's license and the auto insurance claim record, wherein the auto insurance claim record includes the number of accidents, the frequency of accidents, and the degree of damage. , Claim amount.
11. The vehicle according to claim 8, wherein when the processor executes the at least one computer-readable instruction to determine the alarm distance value according to the driving proficiency of the current driver, it specifically includes:

    The corresponding relationship between the driving proficiency and the preset distance value is established in advance, wherein different driving proficiency levels correspond to different preset distance values; and

    When the driving proficiency of the current driver is identified by the driving proficiency recognition model, the preset distance value corresponding to the driving proficiency of the current driver is determined according to the pre-established correspondence, and the The determined preset distance value is used as the alarm distance value.
12. The vehicle according to claim 11, wherein the driving proficiency is divided into general proficiency, relatively proficient, and proficient, wherein the pre-established correspondence between the driving proficiency and the preset distance value includes: When the driving proficiency is generally proficient, it corresponds to the preset first distance value; when the driving proficiency is preset to be relatively proficient, it corresponds to the preset second distance value; and the driving proficiency is preset to When proficient, corresponds to the preset third distance value;

    Wherein, the first distance value is greater than the second distance value, and the second distance value is greater than the third distance value.
13. The vehicle of claim 8, wherein when the processor executes the at least one computer-readable instruction to implement the triggering of a warning mechanism according to the driving proficiency of the current driver, further comprising:

    Real-time detection of road conditions in front of the vehicle;

    Determine whether to issue a prompt according to the road conditions ahead and the driving proficiency of the current driver to remind the current driver to re-plan the route of travel; and

    When determining the re-planning route, the route is re-planned according to the driving proficiency of the current driver.
14. A non-volatile readable storage medium, wherein the non-volatile readable storage medium stores at least one computer readable instruction, and when the at least one computer readable instruction is executed by a processor, the following steps are implemented :

    Acquiring the identity information of the current driver of the vehicle, and acquiring the driving record corresponding to the current driver according to the identity information of the current driver;

    Invoking the driving proficiency recognition model generated by pre-training, and identifying the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

    Triggering a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

    Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

    Determine the warning distance value according to the driving proficiency of the current driver;

    Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

    When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.
15. The non-volatile readable storage medium according to claim 14, wherein when the at least one computer readable instruction is executed by the processor to implement the training of the driving proficiency recognition model, it specifically comprises:

    Acquire a preset number of driving records corresponding to different driving proficiency levels, and mark the types of driving records corresponding to each type of driving proficiency, so that the driving records corresponding to each type of driving proficiency carry category labels, Taking the preset number of driving records respectively corresponding to different driving proficiency levels marked with categories as training samples;

    The training samples are randomly divided into a training set with a first preset ratio and a verification set with a second preset ratio, a deep neural network is trained using the training set to obtain the driving proficiency recognition model, and the verification set is used to verify The accuracy of the driving proficiency recognition model; and

    If the accuracy rate is greater than or equal to the preset accuracy rate, the training ends; if the accuracy rate is less than the preset accuracy rate, increase the number of training samples and retrain the deep neural network until the re-obtained The accuracy rate of the driving proficiency recognition model is greater than or equal to the preset accuracy rate.
16. The non-volatile readable storage medium according to claim 14, wherein the driving record includes the time when the current driver obtains the driver's license and a record of auto insurance claims, wherein the record of auto insurance claims includes the number of times of accidents. , Frequency of risk, degree of damage, and amount of compensation.
17. The non-volatile readable storage medium of claim 14, wherein the at least one computer readable instruction is executed by the processor to implement the determination of the alarm according to the driving proficiency of the current driver The distance value includes:

    The corresponding relationship between the driving proficiency and the preset distance value is established in advance, wherein different driving proficiency levels correspond to different preset distance values; and

    When the driving proficiency of the current driver is identified by the driving proficiency recognition model, the preset distance value corresponding to the driving proficiency of the current driver is determined according to the pre-established correspondence, and the The determined preset distance value is used as the alarm distance value.
18. The non-volatile readable storage medium of claim 17, wherein the driving proficiency is divided into general proficiency, relatively proficiency, and proficiency, wherein the pre-established driving proficiency and a preset distance value The correspondence relationship between the two includes: when the driving proficiency is preset to be general proficiency, corresponding to a preset first distance value; when the driving proficiency is preset to be relatively proficient, corresponding to a preset second distance value; and When the driving proficiency is proficient, it corresponds to the preset third distance value;

    Wherein, the first distance value is greater than the second distance value, and the second distance value is greater than the third distance value.
19. The non-volatile readable storage medium of claim 14, wherein the at least one computer readable instruction is executed by a processor to realize the triggering of the warning mechanism according to the driving proficiency of the current driver ,Also includes:

    Real-time detection of road conditions in front of the vehicle;

    Determine whether to issue a prompt according to the road conditions ahead and the driving proficiency of the current driver to remind the current driver to re-plan the route of travel; and

    When determining the re-planning route, the route is re-planned according to the driving proficiency of the current driver.
20. A driving safety auxiliary device, characterized in that the device includes:

    The acquisition module is used to acquire the identity information of the current driver of the vehicle, and acquire the driving record corresponding to the current driver according to the identity information of the current driver;

    The execution module is used to call the driving proficiency recognition model generated by pre-training, and recognize the driving proficiency of the current driver according to the driving record corresponding to the current driver; and

    The execution module is also used to trigger a warning mechanism according to the driving proficiency of the current driver during the driving of the vehicle,

    Wherein, the triggering of a warning mechanism according to the driving proficiency of the current driver includes:

    Determine the warning distance value according to the driving proficiency of the current driver;

    Detecting the value of the distance between the vehicle and the obstacle during the running of the vehicle; and

    When the detected distance value is less than the determined alarm distance value, the warning mechanism is triggered.

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