CN110525451B - Driving safety assisting method and device, vehicle and readable storage medium - Google Patents
Driving safety assisting method and device, vehicle and readable storage medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/06—Automatic manoeuvring for parking
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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Abstract
The invention provides a driving safety auxiliary method, which comprises the steps of obtaining the identity information of the current driver of a vehicle, and obtaining the driving record corresponding to the current driver according to the identity information of the current driver; calling a driving proficiency recognition model generated by pre-training, and recognizing 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 in the driving process of the vehicle. The invention also provides a device, a vehicle and a readable storage medium for realizing the driving safety auxiliary method. The invention can solve the technical problem of low driving safety in the driving process of the vehicle.
Description
Technical Field
The invention relates to the technical field of safety control, in particular to a driving safety assisting method, a driving safety assisting device, a vehicle and a readable storage medium.
Background
A parking radar, also called a parking assist device, is a safety assist device for parking or backing a car, and can inform a driver of the situation of surrounding obstacles by sound or more intuitive display, thereby improving the driving safety.
However, the reverse radar is the same warning mechanism used by any driver for driving any vehicle. Such a warning manner is not applicable to drivers with different driving proficiency levels. For example, when the distance to the obstacle is less than the preset distance in the process of backing a car, the vehicle can give out a warning. After the warning is sent out, the driver with skilled driving skills can still well control and adjust the vehicle operation. For a driver with less skilled driving skills, the driver is likely to be unable to adjust the control to the vehicle due to the small distance to the obstacle.
Disclosure of Invention
In view of the foregoing, 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.
A first aspect of the present invention provides a driving safety assistance method, including:
acquiring identity information of a current driver of a vehicle, and acquiring a driving record corresponding to the current driver according to the identity information of the current driver;
calling a driving proficiency recognition model generated by pre-training, and recognizing 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 in the driving process of the vehicle,
wherein the triggering of the warning mechanism according to the driving proficiency of the current driver comprises:
determining a warning distance value according to the driving proficiency of the current driver;
detecting a distance value between the vehicle and an obstacle in the driving process of the vehicle; and
and when the detected distance value is smaller than the determined alarm distance value, triggering an alarm mechanism.
Preferably, the method of training the driving proficiency recognition model includes:
acquiring a preset number of driving records respectively corresponding to different driving proficiency degrees, labeling the type of the driving record corresponding to each driving proficiency degree, enabling the driving record corresponding to each driving proficiency degree to carry a type label, and taking the preset number of driving records respectively corresponding to different driving proficiency degrees after type labeling as training samples;
randomly dividing the training sample into a training set with a first preset proportion and a verification set with a second preset proportion, training the deep neural network by using the training set to obtain the driving proficiency recognition model, and verifying the accuracy of the driving proficiency recognition model by using the verification set; and
if the accuracy is greater than or equal to the preset accuracy, ending the training; and if the accuracy is smaller than the preset accuracy, increasing the number of samples of training samples to retrain the deep neural network until the accuracy of the driving proficiency recognition model obtained again is larger than or equal to the preset accuracy.
Preferably, the driving record comprises the time of taking the driving license by the current driver and a vehicle insurance claim record, wherein the vehicle insurance claim record comprises the number of insurance times, the insurance frequency, the damage degree and the claim amount.
Preferably, the determining of the warning distance value according to the driving proficiency of the current driver includes:
the method comprises the steps of establishing a corresponding relation between driving proficiency and preset distance values in advance, wherein different driving proficiency correspond to different preset distance values; and
and when the driving proficiency of the current driver is recognized by using the driving proficiency recognition model, determining a preset distance value corresponding to the driving proficiency of the current driver according to the pre-established corresponding relation, and taking the determined preset distance value as the alarm distance value.
Preferably, the driving proficiency level is classified into general proficiency, relatively proficiency and proficiency, wherein the pre-established correspondence between the driving proficiency level and the preset distance value includes: presetting a corresponding preset first distance value when the driving proficiency is general proficiency; presetting a second distance value corresponding to the driving proficiency level when the driving proficiency level is relatively proficient; when the driving proficiency is preset to be proficiency, corresponding to a preset third distance value; wherein the first distance value is greater than the second distance value, which is greater than the third distance value.
Preferably, the triggering of the warning mechanism according to the driving proficiency of the current driver further includes:
detecting the road condition in front of the vehicle in real time;
determining whether to give a prompt or not according to the road condition ahead and the driving proficiency of the current driver, and prompting the current driver to replan a traveling route; and
and when the re-planning travel route is determined, re-planning the route according to the driving proficiency of the current driver.
Preferably, the front road condition includes: the number of lanes, the degree of traffic congestion, whether to school road sections or not, and the visibility, wherein the front road condition refers to the road condition of the front road which is a preset distance away from the vehicle.
A second aspect of the invention provides a vehicle comprising a processor and a memory, the memory being configured to store at least one instruction, the processor being configured to execute the at least one instruction to implement the driving safety assistance method.
A third aspect of the present invention provides a computer-readable storage medium storing at least one instruction which, when executed by a processor, implements the driving safety assistance method.
A fourth aspect of the present invention provides a driving safety assistance device, including:
the acquisition module is used for 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;
the execution module is used for calling a driving proficiency recognition model generated by pre-training and recognizing the driving proficiency of the current driver according to the driving record corresponding to the current driver; and
the execution module is also used for triggering a warning mechanism according to the driving proficiency of the current driver in the driving process of the vehicle,
wherein the triggering of the warning mechanism according to the driving proficiency of the current driver comprises:
determining an alarm distance value according to the driving proficiency of the current driver;
detecting a distance value between the vehicle and an obstacle in the driving process of the vehicle; and
and when the detected distance value is smaller than the determined alarm distance value, triggering an alarm mechanism.
According to the driving safety assisting method, the driving safety assisting device, the vehicle and the readable storage medium, the identity information of the current driver of the vehicle is obtained, and the driving record corresponding to the current driver is obtained according to the identity information of the current driver; calling a driving proficiency recognition model generated by pre-training, and recognizing 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, and triggering the warning mechanism according to the driving proficiency program of the driver, so that the driving safety is effectively improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a driving safety assisting method according to a preferred embodiment of the present invention.
Fig. 2 is a structural diagram of a driving safety assisting device according to a preferred embodiment of the present invention.
FIG. 3 is a schematic diagram of a vehicle according to a preferred embodiment of the present invention.
The following detailed description will further illustrate the invention in conjunction with the above-described figures.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a detailed description of the present invention will be given below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention, and the described embodiments are merely a subset of the embodiments of the present invention, rather than a complete embodiment. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Fig. 1 is a flowchart of a driving safety assistance method according to a preferred embodiment of the present invention.
In this embodiment, the driving safety assistance method may be applied to a vehicle, and for a vehicle that needs driving safety assistance, the driving safety assistance function provided by the method of the present invention may be directly integrated on the vehicle, or may be run on the vehicle in a form of a Software Development Kit (SDK).
As shown in fig. 1, the driving safety assistance method specifically includes the following steps, and according to different requirements, the order of the steps in the flowchart may be changed, and some steps may be omitted.
The method comprises the steps of S1, obtaining identity information of a current driver of a vehicle, and obtaining a 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 driver gets the driving license and the vehicle insurance claim settlement record. In one embodiment, the vehicle insurance claim records include, but are not limited to, number of steps taken, frequency of steps taken, extent of damage, amount of claims, and the like.
The vehicle in the embodiment of the invention establishes communication connection with the server through a network (such as WIFI, radio and the like). The server stores a driving record for each driver. The servers may be affiliated with different insurance companies.
In one embodiment, a user interface may be displayed on the display screen of the vehicle for the driver to enter identification information. The identity information may be a driver's fingerprint, identification number, or other information that can verify the identity of the driver.
And S2, calling a driving proficiency recognition model generated by pre-training, and recognizing 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 to the driving proficiency recognition model generated by the pre-training, and the driving proficiency of the current driver is obtained.
In this embodiment, the driving proficiency may be classified into general proficiency, relatively proficiency, and proficiency.
In the present embodiment, the driving record corresponding to the driving proficiency level being general proficiency belongs to the first parameter range, the driving record corresponding to the driving proficiency level being relatively proficiency belongs to the second parameter range, and the driving record corresponding to the driving proficiency level being proficiency 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 of training the driving proficiency recognition model includes:
1) And acquiring a preset number of driving records respectively corresponding to the different driving proficiencies, and labeling the category of the driving record corresponding to each driving proficiency, so that the driving record corresponding to each driving proficiency carries a category label, and taking the preset number of driving records respectively corresponding to the different driving proficiencies after category labeling as training samples.
For example, 500 driving records corresponding to the case where the driving proficiency is general proficiency are selected, and the 500 driving records are respectively marked with "1", that is, "1" is used as a label. Similarly, 500 driving records corresponding to the driving proficiency level as the driving proficiency level are selected, and the 500 driving records are respectively marked with '2', namely '2' is used as a label. 500 driving records corresponding to the driving proficiency are selected, and the 500 driving records are respectively marked with '3', namely '3' is used as a label.
2) Randomly dividing the training sample into a training set with a first preset proportion and a verification set with a second preset proportion, training the deep neural network by using the training set to obtain the driving proficiency recognition model, and verifying the accuracy of the trained driving proficiency recognition model by using the verification set.
For example, driving records corresponding to different driving proficiencies may first be distributed into different folders according to the labeled categories. For example, the driving record corresponding to the driving proficiency level being general proficiency level is distributed into the first folder, the driving record corresponding to the driving proficiency level being relatively proficiency level is distributed into the second folder, and the driving record corresponding to the driving proficiency level being proficiency level is distributed into the third folder. Then, driving records of a first preset proportion (for example, 70%) are respectively extracted from different folders to serve as training sets to train the deep neural network to obtain the driving proficiency recognition model, the remaining driving records of a second preset proportion (for example, 30%) are respectively taken from the different folders to serve as verification sets, and the driving proficiency recognition model obtained through training is verified in accuracy through the verification sets.
3) And if the accuracy is greater than or equal to the preset accuracy, ending the training.
If the accuracy is smaller than the preset accuracy, increasing the number of training samples in the step 1), namely obtaining more training samples, and retraining the deep neural network by using the more training samples according to the step 2) until the accuracy of the driving proficiency recognition model obtained again is larger than or equal to the preset accuracy.
And S3, triggering a warning mechanism according to the driving proficiency of the current driver in the driving process of the vehicle.
Preferably, the triggering of the warning mechanism according to the driving proficiency of the current driver includes steps (y 1) to (y 3):
and (y 1) determining a warning distance value according to the driving proficiency of the current driver.
In one embodiment, the determining a warning distance value according to the driving proficiency of the current driver includes: the method comprises the steps of establishing a corresponding relation between driving proficiency and preset distance values in advance, wherein different driving proficiency correspond to different preset distance values; and when the driving proficiency of the current driver is recognized by using the driving proficiency recognition model, determining a preset distance value corresponding to the driving proficiency of the current driver according to the pre-established corresponding relation, and taking the determined preset distance value as the alarm distance value.
Taking the driving proficiency as general proficiency, more proficiency and proficiency as examples, in the embodiment, when the driving proficiency is general proficiency, the driving proficiency can be preset to correspond to a preset first distance value; presetting a second distance value corresponding to the driving proficiency level when the driving proficiency level is relatively proficient; and presetting a third distance value corresponding to the preset driving proficiency level when the driving proficiency level is proficiency. Therefore, when the driving proficiency level of the current driver is recognized by the driving proficiency level recognition model, the warning distance value corresponding to the driving proficiency level of the current driver can be determined according to the pre-established corresponding relation.
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.
And (y 2) detecting a distance value between the vehicle and the obstacle during the running process of the vehicle.
Preferably, the distance between the vehicle and the obstacle may be detected when the vehicle is reversing.
Preferably, the distance between the vehicle and the obstacle may be detected while the vehicle is traveling.
In one embodiment, the obstacle may refer to an object in a stationary state or a pedestrian or a vehicle in a dynamic state.
In one embodiment, the distance between the vehicle and the obstacle may refer to a distance between the vehicle and an obstacle located in front of, behind, to the left of, or to the right of the vehicle.
Specifically, a distance value between the vehicle and an obstacle may be detected using a radar mounted on the vehicle.
And (y 3) triggering an alarm mechanism when the detected distance value is smaller than the determined alarm distance value.
In one embodiment, the triggering of the warning mechanism may be controlling a buzzer of the vehicle to emit a warning sound effect, and/or displaying a text message on a display screen of the vehicle to prompt the current driver.
In other embodiments, the vehicle may also be controlled to decelerate when the detected distance value is smaller than the determined warning distance value.
Preferably, the triggering of the warning mechanism according to the driving proficiency of the current driver comprises:
step S41, detecting the road condition in front of the vehicle in real time, wherein the road condition in front comprises, but is not limited to: number of lanes, degree of traffic congestion, whether school road segments, visibility, etc.
In one embodiment, the front road condition may refer to a road condition of a front road that is a preset distance (e.g., 1 km) away from the vehicle.
Specifically, a preset map (e.g., google map, baidu map) may be called to obtain the number of lanes, the degree of traffic congestion, whether the road ahead includes school road segments, and the like included in the road ahead, and preset weather preset software may be called to obtain the visibility index, and the like.
And S42, determining whether to give a prompt according to the road condition ahead and the driving proficiency of the current driver, and prompting the current driver to replan the traveling route.
Since the same road condition is different for drivers with different driving proficiency degrees, the driving difficulty degree is also different, and therefore a prompt for replanning the driving route for the driver with the driving proficiency degree in the front road condition can be determined by setting a rule.
For example, for a school road segment, the driver may need to stop at any time to wait for students to pass, so the driving proficiency level of the driver is high. It is thus possible to set in the rule: and if the front road comprises the school road section and the driving proficiency of the current driver is general proficiency, the prompt is sent out to prompt the current driver to select whether to replan the traveling route.
The above description is merely illustrative, and should not be construed as limiting the technical solutions for determining whether to issue a prompt according to the road condition ahead and the driving proficiency of the current driver.
And S43, when the re-planning of the traveling route is determined, re-planning the route according to the driving proficiency of the current driver.
Still as in the above example, assuming that the road ahead includes school segments and the current driver's driving proficiency is generally skilled, a new travel route that avoids the school segments may be re-planned.
In summary, in the driving safety assistance method in the embodiment of the present invention, by acquiring the identity information of the current driver of the vehicle, the driving record corresponding to the current driver is acquired according to the identity information of the current driver; calling a driving proficiency recognition model generated by pre-training, and recognizing 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, and triggering the warning mechanism according to the driving proficiency program of the driver, so that the driving safety is effectively improved.
Fig. 1 above describes the driving safety assistance method of the present invention in detail, and functional modules of a software device for implementing the driving safety assistance method and a hardware device architecture for implementing the driving safety assistance method are described below with reference to fig. 2 to 3.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
Fig. 2 is a structural diagram of a driving safety assisting device according to a preferred embodiment of the present invention.
In some embodiments, the driving safety assistance device 30 is operated in a vehicle. The vehicle is connected to an external device through a network. The driving safety assistance device 30 may comprise a plurality of functional modules consisting of program code segments. The program codes of the various program segments in the driving safety assistance device 30 may be stored in the memory of the vehicle and executed by the at least one processor to implement the driving safety assistance function (described in detail in fig. 2).
In this embodiment, the driving safety assistance device 30 may be divided into a plurality of functional modules according to the functions performed by the driving safety assistance device. The functional module may include: an acquisition module 301 and an execution module 302. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.
The obtaining module 301 obtains identity information of a current driver of a vehicle, and obtains a 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 driver gets the driving license and the vehicle insurance claim record. In one embodiment, the vehicle insurance claim records include, but are not limited to, number of times to make an insurance, frequency of making an insurance claim, extent of damage, amount of claim, and the like.
The vehicle in the embodiment of the invention establishes communication connection with the server through the network. The server stores a driving record for each driver. The servers may be affiliated with different insurance companies.
In one embodiment, the vehicle and server may be communicatively coupled via a network via any conventional wireless network. The Wireless network may be of any type of conventional Wireless communication, such as radio, wireless Fidelity (WIFI), cellular, satellite, broadcast, etc. The wireless communication technology may include, but is 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 rate GSM Evolution (Enhanced Data Rates for GSM Evolution, EDGE), long-Term Evolution (Long-Term Evolution, LTE), advanced Long-Term Evolution (LTE), time-Division Long-Term Evolution (TD-LTE), high Performance Radio Local Area Network (High lan), high Performance Wide Area Radio Network (High-Term Area Network), high WAN), local Multipoint Distribution Service (LMDS), worldwide Interoperability for Microwave Access (WiMAX), zigBee protocol (ZigBee), bluetooth, orthogonal Frequency Division Multiplexing (Flash-OFDM), high Capacity space Division Multiple Access (HC-SDMA), universal Mobile Telecommunications System (Universal Mobile Telecommunications System, UMTS), universal Mobile Telecommunications System Time Division Duplex (UMTS-Time Division Multiplexing, UMTS-TDD), evolved High Speed Packet Access (Evolved High Speed Packet Access, HSPA +), time Division Synchronous Code Division Multiple Access (Time Division Multiplexing, TD), evolution-Data Optimized (EV-DO), digital Enhanced Cordless Telecommunications (DECT), and others.
In one embodiment, the obtaining module 301 may display a user interface on a display screen of the vehicle for the driver to input the identity information. The identity information may be a driver's fingerprint, identification number, or other information that can verify the identity of the driver.
The execution module 302 is configured to invoke a 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.
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, so as to obtain the driving proficiency of the current driver.
In this embodiment, the driving proficiency may be classified into general proficiency, relatively proficiency, and proficiency.
In the present embodiment, the driving record corresponding to the driving proficiency level being general proficiency belongs to the first parameter range, the driving record corresponding to the driving proficiency level being relatively proficiency belongs to the second parameter range, and the driving record corresponding to the driving proficiency level being proficiency 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 further configured to train the driving proficiency recognition model.
Specifically, the executing module 302 obtains a preset number of driving records respectively corresponding to the different driving proficiencies, and labels the category of the driving record corresponding to each driving proficiency, so that the driving record corresponding to each driving proficiency carries a category label, and the preset number of driving records respectively corresponding to the different driving proficiencies after category labeling are used as training samples.
For example, 500 driving records corresponding to the driving proficiency level being general proficiency are selected, and the 500 driving records are respectively marked with "1", namely "1" is used as a label. Similarly, 500 driving records corresponding to the driving proficiency level being more proficient are selected, and the 500 driving records are respectively marked with "2", namely the "2" is used as a label. 500 driving records corresponding to the driving proficiency level are selected, and the 500 driving records are respectively marked with 3, namely 3 is used as a label.
The execution module 302 randomly divides the training samples into a training set with a first preset proportion and a verification set with a second preset proportion, trains the deep neural network by using the training set to obtain the driving proficiency recognition model, and verifies the accuracy of the trained driving proficiency recognition model by using the verification set.
For example, driving records corresponding to different driving proficiencies may first be distributed into different folders according to the labeled categories. For example, the driving record corresponding to the driving proficiency level being general proficiency level is distributed into the first folder, the driving record corresponding to the driving proficiency level being relatively proficiency level is distributed into the second folder, and the driving record corresponding to the driving proficiency level being proficiency level is distributed into the third folder. Then, the driving proficiency recognition models are obtained by respectively extracting driving records with a first preset proportion (for example, 70%) from different folders as training sets to train the deep neural network, the remaining driving records with a second preset proportion (for example, 30%) are respectively taken from the different folders as verification sets, and the driving proficiency recognition models obtained through training are verified in accuracy by using the verification sets.
If the accuracy is greater than or equal to the predetermined accuracy, the executing module 302 ends the training.
If the accuracy is smaller than the preset accuracy, the execution module 302 increases the number of training samples, that is, obtains more training samples, and retrains the deep neural network using the more training samples until the obtained accuracy of the driving proficiency recognition model is greater than or equal to the preset accuracy.
The execution module 302 is further configured to trigger a warning mechanism according to the driving proficiency level of the current driver during the vehicle driving process.
In a preferred embodiment, the executing module 302 for triggering the warning mechanism according to the driving proficiency of the current driver includes:
the execution module 302 determines a warning distance value according to the driving proficiency of the current driver.
In one embodiment, the determining a warning distance value according to the driving proficiency of the current driver includes: the method comprises the steps of establishing a corresponding relation between driving proficiency and preset distance values in advance, wherein different driving proficiency correspond to different preset distance values; and when the driving proficiency of the current driver is recognized by using the driving proficiency recognition model, determining a preset distance value corresponding to the driving proficiency of the current driver according to the pre-established corresponding relation, and taking the determined preset distance value as the alarm distance value.
Taking the driving proficiency as general proficiency, more proficiency and proficiency as examples, in the embodiment, when the driving proficiency is general proficiency, the driving proficiency can be preset to correspond to a preset first distance value; presetting a second distance value corresponding to the driving proficiency level when the driving proficiency level is relatively proficient; and presetting a third distance value corresponding to the preset driving proficiency level when the driving proficiency level is proficiency. Therefore, when the driving proficiency of the current driver is recognized by using the driving proficiency recognition model, the warning distance value corresponding to the driving proficiency of the current driver can be determined according to the pre-established corresponding relation.
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 further detects a distance value between the vehicle and an obstacle during the driving of the vehicle.
Preferably, the distance between the vehicle and the obstacle may be detected when the vehicle is reversing.
Preferably, the distance between the vehicle and the obstacle may be detected while the vehicle is traveling.
In one embodiment, the obstacle may refer to an object in a stationary state or a pedestrian or a vehicle in a dynamic state.
In one embodiment, the distance between the vehicle and the obstacle may refer to a distance between the vehicle and an obstacle located in front of, behind, to the left of, or to the right of the vehicle.
Specifically, a distance value between the vehicle and an obstacle may be detected using a radar mounted on the vehicle.
When the detected distance value is smaller than the determined alarm distance value, the execution module 302 triggers an alarm mechanism.
In one embodiment, the triggering of the warning mechanism may be controlling a buzzer of the vehicle to emit a warning sound effect, and/or displaying a text message on a 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 smaller than the determined warning distance value.
In another preferred embodiment, the executing module 302 triggering the warning mechanism according to the driving proficiency of the current driver includes:
the execution module 302 detects the road condition ahead of the vehicle in real time, wherein the road condition ahead includes, but is not limited to: number of lanes, degree of traffic congestion, whether school road segments, visibility, etc.
In one embodiment, the front road condition may refer to a road condition of a front road that is a preset distance (e.g., 1 km) away from the vehicle.
Specifically, a preset map (e.g., google map or Baidu map) may be called to obtain the number of lanes included in the front road condition, the traffic congestion degree, whether the front road includes a school road segment, and the like, and preset weather preset software may be called to obtain the visibility index, and the like.
The execution module 302 determines whether to send a prompt according to the road condition ahead and the driving proficiency of the current driver, and prompts the current driver to re-plan a traveling route.
Since the same road condition is different for drivers with different driving proficiency degrees, the driving difficulty degree is also different, and therefore a prompt for replanning the driving route for the driver with the driving proficiency degree in the front road condition can be determined by setting a rule.
For example, for a school road segment, the driver may need to stop at any time to wait for students to pass, so the driving proficiency level of the driver is high. It is thus possible to set in the rule: and if the road in front comprises the school road section and the driving proficiency of the current driver is determined to be general proficiency, sending the prompt to prompt the current driver to select whether to re-plan the travel route.
The above description is only for illustration and should not be construed as a limitation on the technical solution of determining whether to issue a prompt according to the road condition ahead 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 as in the above example, assuming that the road ahead includes school segments and the current driver's driving proficiency is generally skilled, a new travel route that avoids the school segments may be re-planned.
In summary, the driving safety assistance device in the embodiment of the present invention obtains the driving record corresponding to the current driver according to the identity information of the current driver by obtaining the identity information of the current driver of the vehicle; calling a driving proficiency recognition model generated by pre-training, and recognizing 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, and triggering the warning mechanism according to the driving proficiency program of the driver, so that the driving safety is effectively improved.
Fig. 3 is a schematic structural diagram of a vehicle according to a preferred embodiment of the invention. In the preferred embodiment of the present invention, the vehicle 3 includes a memory 31, at least one processor 32, and at least one communication bus 33. It will be appreciated by those skilled in the art that the configuration of the vehicle shown in fig. 3 is not a limitation of the embodiments of the present invention, and may be a bus-type configuration or a star-type configuration, and that the vehicle 3 may include more or less hardware or software than shown, or a different arrangement of components.
In some embodiments, the vehicle 3 includes a terminal capable of automatically performing numerical calculations and/or information processing according to instructions set in advance or stored in advance, and the hardware includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like.
It should be noted that the vehicle 3 is only an example, and other existing or future electronic products, such as those that may be adapted to the present invention, are also included in the scope of the present invention and are incorporated herein by reference.
In some embodiments, the memory 31 is used for storing program codes and various data, such as the driving safety assistance device 30 installed in the vehicle 3, and realizes high-speed and automatic access of programs or data during the operation of the vehicle 3. The Memory 31 includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable rewritable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical Disc Memory, a magnetic disk Memory, a tape Memory, or any other storage medium readable by a computer capable of carrying or storing data.
In some embodiments, the at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The at least one processor 32 is a Control Unit (Control Unit) of the vehicle 3, connects various components of the entire vehicle 3 by using various interfaces and lines, and executes various functions of the vehicle 3 and processes data, for example, functions of driving safety assistance, by operating or executing programs or modules stored in the memory 31 and calling data stored in the memory 31.
In some embodiments, the at least one communication bus 33 is arranged to enable connectivity communication between the memory 31 and the at least one processor 32, and/or the like.
Although not shown, the vehicle 3 may further include a power source (such as a battery) for supplying power to various components, and preferably, the power source may be logically connected to the at least one processor 32 through a power management device, so as to achieve functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The vehicle 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes instructions for causing a vehicle (which may be a server, a personal computer, etc.) or a processor (processor) to perform parts of the methods according to the embodiments of the present invention.
In a further embodiment, in conjunction with fig. 2, the at least one processor 32 may execute operating devices of the vehicle 3 and various installed applications (such as the driving safety assistance device 30), program code, and the like, such as the various modules described above.
The memory 31 has program code stored therein, and the at least one processor 32 can call the program code stored in the memory 31 to perform related functions. For example, the modules shown in fig. 2 are program codes stored in the memory 31 and executed by the at least one processor 32, so as to implement the functions of the modules for the purpose of driving safety assistance.
In one embodiment of the present invention, the memory 31 stores a plurality of instructions that are executed by the at least one processor 32 for the purpose of driving safety assistance.
Specifically, the at least one processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, and details are not repeated here.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, vehicle, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not to denote any particular order.
Finally, it should be noted that the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the same, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A driving safety assistance method is characterized by comprising the following steps:
acquiring identity information of a current driver of a vehicle, and acquiring a driving record corresponding to the current driver according to the identity information of the current driver, wherein the driving record comprises time for the current driver to take a driving license and an insurance claim settlement record, and the insurance claim settlement record comprises insurance times, insurance frequency, damage degree and claim settlement amount;
calling a driving proficiency recognition model generated by pre-training, and recognizing 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 in the driving process of the vehicle,
wherein the triggering of the warning mechanism according to the driving proficiency of the current driver comprises:
determining an alarm distance value according to the driving proficiency of the current driver;
detecting a distance value between the vehicle and an obstacle in the driving process of the vehicle; and
when the detected distance value is smaller than the determined alarm distance value, triggering an alarm mechanism;
the triggering of the warning mechanism according to the driving proficiency of the current driver further comprises:
detecting a road condition ahead of the vehicle in real time, the road condition ahead comprising: whether a school road section exists;
the road of the current side comprises a school road section, when the driving proficiency of the current driver is general proficiency, a prompt for replanning a traveling route is sent, and when the replanning traveling route is determined, a new traveling route avoiding the school road section is planned according to the driving proficiency of the current driver.
2. A driving safety assistance method according to claim 1, wherein the method of training the driving proficiency recognition model comprises:
acquiring a preset number of driving records respectively corresponding to different driving proficiency degrees, labeling the type of the driving record corresponding to each driving proficiency degree, enabling the driving record corresponding to each driving proficiency degree to carry a type label, and taking the preset number of driving records respectively corresponding to different driving proficiency degrees after type labeling as training samples;
randomly dividing the training sample into a training set with a first preset proportion and a verification set with a second preset proportion, training the deep neural network by using the training set to obtain the driving proficiency recognition model, and verifying the accuracy of the driving proficiency recognition model by using the verification set; and
if the accuracy is greater than or equal to the preset accuracy, ending the training; and if the accuracy is smaller than the preset accuracy, increasing the number of samples of training samples to retrain the deep neural network until the accuracy of the driving proficiency recognition model obtained again is larger than or equal to the preset accuracy.
3. A driving safety assistance method according to claim 1, wherein the method further comprises:
and when the detected distance value is smaller than the determined alarm distance value, controlling the vehicle to decelerate.
4. The driving safety assistance method according to claim 1, wherein the determining of the warning distance value according to the driving proficiency of the current driver includes:
the method comprises the steps of establishing a corresponding relation between driving proficiency and preset distance values in advance, wherein different driving proficiency correspond to different preset distance values; and
and when the driving proficiency level of the current driver is identified by using the driving proficiency level identification model, determining a preset distance value corresponding to the driving proficiency level of the current driver according to the pre-established corresponding relation, and taking the determined preset distance value as the alarm distance value.
5. The driving safety assistance method according to claim 4, wherein the driving proficiency level is classified as general proficiency, relatively proficiency, and proficiency, wherein the previously established correspondence between the driving proficiency level and the preset distance value includes: presetting a corresponding preset first distance value when the driving proficiency is general proficiency; presetting a corresponding preset second distance value when the driving proficiency level is more proficiency; presetting a third distance value corresponding to the driving proficiency level when the driving proficiency level is proficiency;
wherein the first distance value is greater than the second distance value, which is greater than the third distance value.
6. The driving safety assistance method according to claim 1, wherein the front road condition further includes: the number of lanes, the degree of traffic congestion and the visibility are determined, wherein the front road condition refers to the road condition of a front road which is a preset distance away from the vehicle.
7. A vehicle comprising a processor and a memory, the memory configured to store at least one instruction, the processor configured to execute the at least one instruction to implement the driving safety assistance method of any one of claims 1 to 6.
8. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and the at least one instruction when executed by a processor implements the driving safety assistance method according to any one of claims 1 to 6.
9. A driving safety assistance apparatus, characterized in that the apparatus comprises:
the vehicle insurance system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring the identity information of the current driver of a vehicle and acquiring a driving record corresponding to the current driver according to the identity information of the current driver, and the driving record comprises the time for the current driver to obtain a driving license and a vehicle insurance claim record, wherein the vehicle insurance claim record comprises the number of times of taking out insurance, the frequency of taking out insurance, the degree of damage and the amount of claim money;
the execution module is used for calling a driving proficiency recognition model generated by pre-training and recognizing the driving proficiency of the current driver according to the driving record corresponding to the current driver; and
the execution module is also used for triggering a warning mechanism according to the driving proficiency of the current driver in the driving process of the vehicle,
wherein the triggering of the warning mechanism according to the driving proficiency of the current driver comprises:
determining an alarm distance value according to the driving proficiency of the current driver;
detecting a distance value between the vehicle and an obstacle in the driving process of the vehicle; and
when the detected distance value is smaller than the determined alarm distance value, triggering an alarm mechanism;
the triggering of the warning mechanism according to the driving proficiency of the current driver further comprises:
real-time detection the road conditions in front of the vehicle, the road conditions in front include: whether a school road section exists;
the road of the current side comprises a school road section, when the driving proficiency of the current driver is general proficiency, a prompt for replanning a traveling route is sent, and when the replanning traveling route is determined, a new traveling route avoiding the school road section is planned according to the driving proficiency of the current driver.
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| CN201910679052.9A CN110525451B (en) | 2019-07-25 | 2019-07-25 | Driving safety assisting method and device, vehicle and readable storage medium |
| PCT/CN2019/118608 WO2021012528A1 (en) | 2019-07-25 | 2019-11-14 | Driving safety assistance method and apparatus, vehicle, and readable storage medium |
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| CN201910679052.9A CN110525451B (en) | 2019-07-25 | 2019-07-25 | Driving safety assisting method and device, vehicle and readable storage medium |
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| CN111038518B (en) * | 2019-12-23 | 2022-02-18 | 北京梧桐车联科技有限责任公司 | Driving control method and device, electronic equipment and storage medium |
| CN113051969A (en) * | 2019-12-26 | 2021-06-29 | 深圳市超捷通讯有限公司 | Object recognition model training method and vehicle-mounted device |
| CN113859246B (en) * | 2020-06-30 | 2023-09-08 | 广州汽车集团股份有限公司 | Vehicle control method and device |
| CN112183457A (en) * | 2020-10-19 | 2021-01-05 | 上海汽车集团股份有限公司 | Control method, device and equipment for atmosphere lamp in vehicle and readable storage medium |
| CN112644514B (en) * | 2020-12-31 | 2022-05-10 | 上海商汤临港智能科技有限公司 | Driving data processing method, device, equipment, storage medium and program product |
| CN112721935A (en) * | 2021-01-19 | 2021-04-30 | 西人马帝言(北京)科技有限公司 | Vehicle control model training method, vehicle control method and device |
| CN113401135B (en) * | 2021-06-30 | 2024-01-16 | 岚图汽车科技有限公司 | Driving function intelligent configuration pushing method, device, equipment and storage medium |
| CN115240393A (en) * | 2021-07-15 | 2022-10-25 | 广州汽车集团股份有限公司 | Collision early warning method and device based on driver driving experience and automobile |
| CN113790905B (en) * | 2021-10-13 | 2023-08-22 | 安徽光阵光电科技有限公司 | Intelligent automobile ADAS function detection device |
| CN116279519B (en) * | 2023-05-17 | 2023-07-21 | 山东新凌志检测技术有限公司 | Active safety early warning system for automobile |
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| CN106935027B (en) * | 2015-12-30 | 2020-07-07 | 沈阳美行科技有限公司 | Traffic information prediction method and device based on driving data |
| CN106428015B (en) * | 2016-09-12 | 2019-07-19 | 惠州Tcl移动通信有限公司 | A kind of intelligent travelling crane householder method and device |
| CN107016193B (en) * | 2017-04-06 | 2020-02-14 | 中国科学院自动化研究所 | Expected following distance calculation method in driver following behavior analysis |
| CN107490384B (en) * | 2017-08-17 | 2020-06-23 | 湖北文理学院 | Optimal static path selection method based on urban road network |
| CN108891411B (en) * | 2018-05-21 | 2020-10-02 | 西藏帝亚一维新能源汽车有限公司 | Automatic passenger-riding-substituting parking control method |
| CN108891350B (en) * | 2018-07-27 | 2021-08-24 | 武汉理工大学 | Front-vehicle driver braking habit based rear-end collision prevention early warning system and method |
| CN112927511A (en) * | 2018-08-06 | 2021-06-08 | 江苏师范大学 | Vehicle early warning method based on driver age and gender identification |
| CN109398367A (en) * | 2018-11-19 | 2019-03-01 | 江苏师范大学 | A kind of Vehicular intelligent anti-collision early warning system based on ultrasound |
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