CN109664891A - Auxiliary driving method, device, equipment and storage medium - Google Patents
Auxiliary driving method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN109664891A CN109664891A CN201811614271.0A CN201811614271A CN109664891A CN 109664891 A CN109664891 A CN 109664891A CN 201811614271 A CN201811614271 A CN 201811614271A CN 109664891 A CN109664891 A CN 109664891A
- Authority
- CN
- China
- Prior art keywords
- driving
- driver
- auxiliary
- information
- driving condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
-
- 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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/182—Selecting between different operative modes, e.g. comfort and performance modes
Abstract
The embodiment of the invention discloses a kind of auxiliary driving method, device, equipment and storage mediums.It include: to judge that driver's watches whether information meets the trigger condition that auxiliary drives attentively；If satisfied, acquisition driver biological information, wherein the biological information include it is following at least one: eye movement information, face information and behavioural information；The driving condition that the driver is presently in is determined according to the biological information；It determines that target auxiliary drives grade according to the driving condition, and grade is driven using target auxiliary, auxiliary driving is carried out to vehicle.Auxiliary driving method provided in an embodiment of the present invention, driver watch attentively information meet auxiliary drive trigger condition after, the driving condition that driver is presently in is determined by biological information, so that it is determined that target auxiliary drives grade, the reliability that auxiliary drives can be improved.
Description
Technical field
The present embodiments relate to vehicle assistant drive technical field more particularly to a kind of auxiliary driving method, device, set
Standby and storage medium.
Background technique
With the fast development of automobile industry, automobile has become people and goes out indispensable walking-replacing tool, however because
Traffic accident caused by the subjective reasons such as driver experience is insufficient or absent minded is increasing.Simultaneously with sensor skill
The rapid development of art, car networking technology and artificial intelligence, driving assistance system are increasingly becoming the standard configuration of automobile.Auxiliary drives
System is greatly improved safe, comfortable and operation ease in vehicle driving.
In existing auxiliary driving technology, auxiliary, which drives, has multiple grades, and driver can select according to the demand of itself
It selects different auxiliary and drives grade, but sometimes user can not correctly select auxiliary to drive grade, so that auxiliary driving etc.
Grade is mismatched with current driving condition.
Summary of the invention
The embodiment of the present invention provides a kind of auxiliary driving method, device, equipment and storage medium, and auxiliary can be improved and drive
Reliability.
In a first aspect, the embodiment of the invention provides a kind of auxiliary driving methods, comprising:
Judge that driver's watches whether information meets the trigger condition that auxiliary drives attentively；
Acquire driver biological information, wherein the biological information include it is following at least one: eye movement information, face letter
Breath and behavioural information；
The driving condition that the driver is presently in is determined according to the biological information；
It determines that target auxiliary drives grade according to the driving condition, and grade is driven to vehicle using target auxiliary
Carry out auxiliary driving.
Further, judge that driver's watches whether information meets the trigger condition that auxiliary drives attentively, comprising:
The multiframe eyes image of driver is continuously acquired in predeterminable area, and judges whether the multiframe eyes image is complete
It is whole；Wherein, predeterminable area includes one or more of windshield region, rearview mirror or left and right visor；
If complete, judge whether the multiframe eyes image meets the trigger condition that auxiliary drives, trigger condition includes
It is not detected and watches information attentively.
Further, the multiframe eyes image of driver is continuously acquired in setting regions, and judges the multiframe eye figure
Seem it is no it is complete after, further includes:
If the multiframe eyes image is imperfect, meet the trigger condition that auxiliary drives.
Further, driving condition that the driver is presently in is determined according to the biological information, comprising:
The current state of attention and/or degree of fatigue of driver is determined according to the eye movement information；
Driving condition is determined according to the state of attention and/or degree of fatigue and face information and behavioural information.
Further, before determining that target auxiliary drives grade according to the driving condition, further includes:
It establishes driving condition and auxiliary drives the mapping ruler of grade；
Correspondingly, determining that target auxiliary drives grade according to the driving condition, comprising:
Determine that target auxiliary corresponding with the driving condition drives grade according to the mapping ruler.
Further, the driving condition for determining that the driver is presently according to the biological information, comprising:
The biological information is inputted to the disaggregated model pre-established, obtains the output result of the disaggregated model；
The output result is determined as the driving condition that driver is presently in.
Further, the biological information is being inputted to the disaggregated model pre-established, is obtaining the defeated of the disaggregated model
Out before result, comprising:
Obtain training sample set, wherein the sample set includes biological information and driving shape corresponding with biological information
State；
Based on the sample set, model training is carried out using deep neural network DNN algorithm, obtains disaggregated model.
Further, the driving condition includes: normal driving state, dabbles driving condition, fatigue driving state and note
Meaning power does not concentrate driving condition.
Further, auxiliary drive grade include: pilot steering, auxiliary drivings, semi-automatic driving, it is highly automated driving,
Hypervelocity automatic Pilot and full-automatic driving.
Second aspect, the embodiment of the invention also provides a kind of auxiliary driving devices, comprising:
Trigger condition judgment module, for judging that driver's watches whether information meets the trigger condition that auxiliary drives attentively；
Biological information acquisition module, for acquiring the biological information of driver, wherein the biological information include with down toward
It is one few: eye movement information, face information and behavioural information；
Driving condition determining module, for determining driving shape that the driver is presently according to the biological information
State；
Target auxiliary drives level determination module, for determining that target auxiliary drives grade according to the driving condition, and
Driving grade is assisted to carry out auxiliary driving to vehicle using the target.
The third aspect the embodiment of the invention also provides a kind of mobile device, including memory, processor and is stored in
On reservoir and the computer program that can run on a processor, the processor realize that the present invention such as is implemented when executing described program
Auxiliary driving method described in example.
Fourth aspect, the embodiment of the invention also provides a kind of computer readable storage mediums, are stored thereon with computer
Program, the program realize auxiliary driving method as described in the embodiments of the present invention when being executed by processor.
The embodiment of the present invention first determines whether that driver's watches whether information meets the trigger condition that auxiliary drives attentively, if full
Foot, the biological information of group acquisition driver, then determines driving condition that driver is presently in, last root according to biological information
It determines that target auxiliary drives grade according to driving condition, and grade is driven using target auxiliary, auxiliary driving is carried out to vehicle.This hair
The auxiliary driving method that bright embodiment provides passes through body in watching attentively after information meets the trigger condition that auxiliary drives for driver
Body information determines the driving condition that driver is presently in, so that it is determined that target auxiliary drives grade, auxiliary can be improved and drive
Reliability.
Detailed description of the invention
Fig. 1 is the flow chart of one of the embodiment of the present invention one auxiliary driving method；
Fig. 2 is the flow chart of one of the embodiment of the present invention two auxiliary driving method；
Fig. 3 is the structural schematic diagram of one of the embodiment of the present invention three auxiliary driving device；
Fig. 4 is the structural schematic diagram of one of the embodiment of the present invention four mobile device.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples.It is understood that this place is retouched
The specific embodiment stated is used only for explaining the present invention rather than limiting the invention.It also should be noted that in order to just
Only the parts related to the present invention are shown in description, attached drawing rather than entire infrastructure.
Embodiment one
Fig. 1 is the flow chart of a kind of auxiliary driving method that the embodiment of the present invention one provides, and the present embodiment is applicable to pair
Vehicle carries out the case where auxiliary drives, and this method can be executed by auxiliary driving device, which can be by hardware and/or software
Composition, and can generally be integrated in the mobile unit for driving function with auxiliary.As shown in Figure 1, this method specifically include it is as follows
Step:
Step 110, judge that driver's watches whether information meets the trigger condition that auxiliary drives attentively.
Wherein, watching information attentively may include blinkpunkt information (or direction of visual lines).Specifically, judging watching attentively for driver
Whether information meets the trigger condition that auxiliary drives, and can be implemented by following manner: continuously acquire driver's in predeterminable area
Multiframe eyes image, and whether judge multiframe eyes image complete, if completely, judging whether multiframe eyes image meets auxiliary
The trigger condition of driving.
Wherein, include pupil information in complete eyes image, i.e., when the eyes of driver, which are in, opens state, obtain
Eyes image be it is complete, if driver's eyes are in closed state, the eyes image obtained is imperfect.Setting regions can
To include one or more of windshield region, rearview mirror or left and right visor.Multiframe eyes image can be setting time
Interior continuous image, setting time can be the arbitrary value between 2-5 seconds.Trigger condition, which can be to be not detected, watches information attentively.
Detection watches can be for information attentively and detects blinkpunkt information, and specific mode can be to the multiframe eye obtained in setting regions
Image is analyzed using eyeball tracking technology, determines whether driver's blinkpunkt falls in setting regions, if falling in setting regions,
Information then is watched attentively to detect, if not falling within setting regions, watches information attentively to be not detected.In the present embodiment, setting
In all complete situation of the multiframe eyes image continuously acquired in region, can the eyes image judgement further according to acquisition be detected
Watch information attentively, if can't detect, show that current driver's are not driven attentively, then meet the trigger condition that auxiliary drives,
To start auxiliary driving device.Optionally, if multiframe eyes image is imperfect, meet the trigger condition that auxiliary drives.
Specifically, the eyes of driver are in closed state if the multiple image detected in setting regions is imperfect,
Then show that driver is in a state of fatigue, needs to start immediately auxiliary and drive the safety that just can guarantee driving.I.e. when
Setting regions continuously acquire multiframe eyes image it is imperfect when, and meet auxiliary drive trigger condition.In the present embodiment, lead to
That crosses driver watches information attentively to determine whether the trigger condition for meeting auxiliary driving, just opens in the case where meeting trigger condition
It is dynamic, if not satisfied, then being driven without starting auxiliary, to improve the reliability that auxiliary drives.
Step 120, the biological information of driver is acquired.
Wherein, biological information include it is following at least one: eye movement information, face information and behavioural information.Eye movement information can
To include blinkpunkt information (or direction of visual lines), blinkpunkt stay time, eyelid the closure degree and frequency of wink of driver
Deng；Face information may include expression information etc.；Behavioural information may include the behavioural information etc. of hand.
In the present embodiment, the mode for acquiring the biological information of driver, which can be, obtains driver using eyeball tracking technology
Eye movement information, the face information of driver is obtained using face recognition technology, driver is obtained using Activity recognition technology
Behavioural information.Specifically, eyeball tracking module, face recognition module and Activity recognition module can be installed in automobile, driving
On the run, eyeball tracking module obtains the eye movement information of driver to the person of sailing in real time, and face recognition module obtains in real time
The face information of driver, Activity recognition module obtain the behavioural information of driver in real time.
Step 130, driving condition that driver is presently in is determined according to biological information.
Wherein, driving condition may include normal driving state, dabble driving condition, fatigue driving state and attention not
Concentrate driving condition.Normal driving state can be driver in wholwe-hearted driving；Dabbling driving condition can be driver one
It is driving to peep (such as: some shop is found on driving side when driver) to roadside on one side；Fatigue driving state can be driving
Member drives in the state of in fatigue；Absent minded driving condition can be driver and not drive (such as: driving wholwe-hearted
The person of sailing is taking on the telephone, is smoking or absent-minded etc.).Specifically, in the eye movement information, face information and the behavioural information that obtain driver
After equal biological informations, analysis is carried out to the biological information of acquisition and obtains the driving condition that driver is presently in.
Optionally, driving condition that driver is presently in is determined according to biological information, can be implemented by following manner: root
The current state of attention and/or degree of fatigue of driver is determined according to eye movement information；According to state of attention and/or degree of fatigue
Driving condition is determined with face information and behavioural information.
Wherein, the acquisition modes of eye movement information can be the multiframe eye figure that driver is obtained using eyeball tracking module
Picture carries out signature analysis to each frame eyes image, obtains the eye feature in each frame eyes image, then comprehensive analysis institute
There is the eye feature in eyes image, obtains blinkpunkt information, blinkpunkt stay time, eyelid the closure degree of driver, blinks
The eye movements information such as eye frequency.Wherein, eye feature may include: pupil center location, pupil radium, eyelid position, in hot spot
Heart position etc..After obtaining eye movement information, the current state of attention and/or degree of fatigue of driver is determined according to eye movement information.
In the present embodiment, state of attention and/or degree of fatigue and eye movement information have certain mapping relations, are obtaining eye movement information
Afterwards, the state of attention and/or degree of fatigue of driver can be obtained according to mapping relations.Illustratively, if driver's eyelid closes
Conjunction degree is semi-closure conjunction state and frequency of wink is less than given threshold, then can be determined that user is currently at fatigue state；If driving
In the range of the person's of sailing blinkpunkt information is not in normal driving, and blinkpunkt is less than setting threshold in a position residence time
Value, and may determine that the current state of attention of driver is not focus on driving.
Specifically, after the state of attention and/or degree of fatigue that driver has been determined according to eye movement information, in conjunction with obtaining
The face information and behavioural information that take determine the driving condition of driver.Illustratively, it is assumed that driver in driving to
It peeps outside window, and shows worried expression, then can primarily determine that driver is in and dabble driving condition；Assuming that driver
In the range of blinkpunkt information is not in normal driving, and hand motion is to lift mobile phone, then can primarily determine at driver
In absent minded driving condition.
Optionally, driving condition that driver is presently in is determined according to biological information, comprising: input biological information pre-
The disaggregated model first established obtains the output result of disaggregated model；Result will be exported and be determined as the driving that driver is presently in
State.
Wherein, disaggregated model can be based on sample set, using deep neural network (Deep Neural Network,
DNN) algorithm carries out the model of model training acquisition.Sample set includes biological information and driving shape corresponding with biological information
State.In the present embodiment, the working principle of disaggregated model be can be biological informations such as eye movement information, face information and behavioural informations
After inputting disaggregated model, disaggregated model analyzes the biological information of input, obtains the output result of disaggregated model.Finally,
The output result of disaggregated model is determined as the driving condition that driver is presently in.
Optionally, in the present embodiment, determine that the mode for the driving condition that driver is presently in may be used also according to biological information
To be, biological information is analyzed in the way of mathematical modeling, to obtain the driving condition of driver, or mathematics is built
Mould technology is combined with deep neural network technology and is analyzed biological information, to obtain the driving condition of driver.
Step 140, it determines that target auxiliary drives grade according to driving condition, and grade is driven to vehicle using target auxiliary
Carry out auxiliary driving.
Wherein, auxiliary drives grade and may include pilot steering, auxiliary driving, semi-automatic driving, highly automated driving, surpasses
Highly automated driving and full-automatic driving.Grade is driven for different auxiliary, the work that driver and auxiliary system are responsible for is not
Together, table 1 shows a kind of each division of labor situation for assisting driving driver and auxiliary system under grade.
Table 1
In the present embodiment, after obtaining the driving condition that driver is presently in, corresponding mesh is determined according to driving condition
Mark auxiliary drives grade, and starts auxiliary system and assist driving grade to carry out auxiliary driving according to target.
Optionally, before determining that target auxiliary drives grade according to driving condition, further include following steps: establishing and drive
State and auxiliary drive the mapping ruler of grade.
In the present embodiment, the mode for establishing the mapping ruler of driving condition and auxiliary driving grade be can be according to driving shape
The practical driving situation of driver determines that corresponding auxiliary drives grade in state.Illustratively, table 2 be a kind of driving condition with it is auxiliary
Help the mapping ruler for driving grade.
Table 2
As shown in table 2, each driving condition can correspond to two kinds of auxiliary and drive grade, and vehicle can be according to current road
The selection one of which of condition or the qualification intelligence of driving of driver auxiliary drives grade.Illustratively, it is assumed that currently drive
The person of sailing, which is in, dabbles driving condition, and current road conditions are good (road vehicle is less), then the auxiliary selected drives grade as " auxiliary
Drive ", if current road conditions very congestion, the auxiliary selected, which drives grade, can be " semi-automatic driving ".Establish driving condition
The mapping ruler of grade is driven with auxiliary, the corresponding auxiliary of the driving condition that can quickly search drives grade, can be improved
Auxiliary drives the efficiency of grade switching, to improve the safety of driving.
Optionally, it determines that target auxiliary drives grade according to driving condition, can be implemented by following manner: be advised according to mapping
Then target auxiliary corresponding with driving condition drives grade.
Specifically, can directly be determined according to mapping ruler should after getting the driving condition that driver is presently in
The corresponding target auxiliary of driving condition drives grade.
The technical solution of the present embodiment first determines whether that driver's watches whether information meets the triggering item that auxiliary drives attentively
Then part determines driving shape that driver is presently according to biological information if satisfied, then acquire the biological information of driver
State finally determines that target auxiliary drives grade according to driving condition, and drives grade using target auxiliary and assist vehicle
It drives.Auxiliary driving method provided in an embodiment of the present invention meets the trigger condition that auxiliary drives in the information of watching attentively of driver
Afterwards, determine that the driving condition that driver is presently in can be improved so that it is determined that target auxiliary drives grade by biological information
Assist the reliability driven.
Embodiment two
Fig. 2 is a kind of flow chart of auxiliary driving method provided by Embodiment 2 of the present invention, as to above-described embodiment
It is explained further, as shown in Fig. 2, this method comprises the following steps:
Step 210, training sample set is obtained.
Wherein, sample set includes biological information and driving condition corresponding with biological information.Obtain the mode of sample set
It can be and acquire a large amount of drivers in biological informations such as eye movement information, face information and behavioural informations on the run, and root
According to the actual conditions of driver, corresponding driving condition is marked to these biological informations.
Step 220, it is based on sample set, model training is carried out using DNN algorithm, obtains disaggregated model.
Specifically, constantly carrying out model training using DNN algorithm, in the training process, constantly after obtaining sample set
The parameter in DNN algorithm is adjusted, until model has the ability of accurate output driving condition, to obtain disaggregated model.
Step 230, biological information is inputted to the disaggregated model pre-established, obtains the output result of disaggregated model.
Step 240, result will be exported and is determined as the driving condition that driver is presently in.
The technical solution of the present embodiment carries out model training using DNN algorithm based on sample set, obtains disaggregated model, can
The accuracy of driving condition is obtained to improve disaggregated model for biological information.
Embodiment three
Fig. 3 is a kind of structural schematic diagram for auxiliary driving device that the embodiment of the present invention three provides, as shown in figure 3, the dress
It sets and includes:
Trigger condition judgment module 310, for judging that driver's watches whether information meets the triggering item that auxiliary drives attentively
Part；
Biological information acquisition module 320, for acquiring the biological information of driver, wherein biological information include it is following at least
One: eye movement information, face information and behavioural information；
Driving condition determining module 330, for determining driving condition that driver is presently according to biological information；
Target auxiliary drives level determination module 340, for determining that target auxiliary drives grade according to driving condition, and adopts
Driving grade is assisted to carry out auxiliary driving to vehicle with target.
Optionally, trigger condition judgment module 310, is also used to:
The multiframe eyes image of driver is continuously acquired in predeterminable area, and judges whether multiframe eyes image is complete；Its
In, predeterminable area includes one or more of windshield region, rearview mirror or left and right visor；
If complete, judge whether multiframe eyes image meets the trigger condition that auxiliary drives, trigger condition includes not examining
It measures and watches information attentively.
Optionally, trigger condition judgment module 310, is also used to:
If multiframe eyes image is imperfect, meet the trigger condition that auxiliary drives.
Optionally, driving condition determining module 330, is also used to:
The current state of attention and/or degree of fatigue of driver is determined according to eye movement information；
Driving condition is determined according to state of attention and/or degree of fatigue and face information and behavioural information.
Optionally, further includes:
Mapping ruler establishes module, drives the mapping ruler of grade for establishing driving condition and auxiliary；
Target auxiliary drives level determination module 340, is also used to:
Determine that target auxiliary corresponding with driving condition drives grade according to mapping ruler.
Optionally, driving condition determining module 330, is also used to:
Biological information is inputted to the disaggregated model pre-established, obtains the output result of disaggregated model；
Result will be exported and be determined as the driving condition that driver is presently in.
Optionally, further includes:
Sample set obtains module, for obtaining training sample set, wherein sample set includes biological information and and body
The corresponding driving condition of information；
Disaggregated model obtains module, for being based on sample set, carries out model training using deep neural network DNN algorithm,
Obtain disaggregated model.
Optionally, driving condition includes: normal driving state, dabbles driving condition, fatigue driving state and attention not
Concentrate driving condition.
Optionally, auxiliary drives grade and includes: pilot steering, auxiliary driving, semi-automatic driving, highly automated driving, surpasses
Highly automated driving and full-automatic driving.
Method provided by the executable aforementioned all embodiments of the present invention of above-mentioned apparatus, it is corresponding to have the execution above method
Functional module and beneficial effect.The not technical detail of detailed description in the present embodiment, reference can be made to the aforementioned all implementations of the present invention
Method provided by example.
Example IV
Fig. 4 is a kind of structural schematic diagram for mobile device that the embodiment of the present invention four provides, as shown in figure 4, the present embodiment
A kind of mobile device provided, comprising: processor 41 and memory 42.Processor in the mobile device can be one or more
It is a, in Fig. 4 by taking a processor 41 as an example, processor 41 and memory 42 in the mobile device can by bus or its
He connects mode, in Fig. 4 for being connected by bus.
Auxiliary driving device provided by the above embodiment is integrated in the processor 41 of mobile device in the present embodiment.This
Outside, the memory 42 in the mobile device is used as a kind of computer readable storage medium, can be used for storing one or more programs,
Described program can be software program, computer executable program and module, such as auxiliary driving method in the embodiment of the present invention
Corresponding program instruction/module.Software program, instruction and the module that processor 41 is stored in memory 42 by operation,
Thereby executing the various function application and data processing of equipment, i.e. auxiliary driving method in realization above method embodiment.
Memory 42 may include storing program area and storage data area, wherein storing program area can storage program area, extremely
Application program needed for a few function；Storage data area, which can be stored, uses created data etc. according to equipment.In addition, depositing
Reservoir 42 may include high-speed random access memory, can also include nonvolatile memory, and a for example, at least disk is deposited
Memory device, flush memory device or other non-volatile solid state memory parts.In some instances, memory 42 can further comprise
The memory remotely located relative to processor 41, these remote memories can pass through network connection to equipment.Above-mentioned network
Example include but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
The program that processor 41 is stored in memory 42 by operation, at various function application and data
Reason realizes example auxiliary driving method provided in an embodiment of the present invention.
Embodiment five
The computer storage medium of the embodiment of the present invention, is stored thereon with computer program, which is filled by data backup
Such as auxiliary driving method provided in an embodiment of the present invention is realized when setting execution.
Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer can
Reading storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device
Or device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes:
Electrical connection, portable computer diskette, hard disk, random access memory (RAM), read-only storage with one or more conducting wires
Device (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer-readable storage
Medium can be any tangible medium for including or store program, which can be commanded execution system, device or device
Using or it is in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including but unlimited
In electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be that computer can
Any computer-readable medium other than storage medium is read, which can send, propagates or transmit and be used for
By the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
The computer for executing operation of the present invention can be write with one or more programming languages or combinations thereof
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
Further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
Be related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (LAN) or
Wide area network (WAN)-be connected to subscriber computer, or, it may be connected to outer computer (such as mentioned using Internet service
It is connected for quotient by internet).
Note that the above is only a better embodiment of the present invention and the applied technical principle.It will be appreciated by those skilled in the art that
The invention is not limited to the specific embodiments described herein, be able to carry out for a person skilled in the art it is various it is apparent variation,
It readjusts and substitutes without departing from protection scope of the present invention.Therefore, although being carried out by above embodiments to the present invention
It is described in further detail, but the present invention is not limited to the above embodiments only, without departing from the inventive concept, also
It may include more other equivalent embodiments, and the scope of the invention is determined by the scope of the appended claims.
Claims (12)
1. a kind of auxiliary driving method characterized by comprising
Judge that driver's watches whether information meets the trigger condition that auxiliary drives attentively,
If satisfied, then acquiring the biological information of driver, wherein the biological information include it is following at least one: eye movement information,
Face information and behavioural information；
The driving condition that the driver is presently in is determined according to the biological information；
It determines that target auxiliary drives grade according to the driving condition, and grade is driven using target auxiliary, vehicle is carried out
Auxiliary drives.
2. the method according to claim 1, wherein judging that driver's watches whether information meets auxiliary driving attentively
Trigger condition, comprising:
The multiframe eyes image of driver is continuously acquired in predeterminable area, and judges whether the multiframe eyes image is complete；Its
In, predeterminable area includes one or more of windshield region, rearview mirror or left and right visor；
If complete, judge whether the multiframe eyes image meets the trigger condition that auxiliary drives, trigger condition includes not examining
It measures and watches information attentively.
3. according to the method described in claim 2, it is characterized in that, continuously acquiring the multiframe eye figure of driver in setting regions
Picture, and after judging whether the multiframe eyes image complete, further includes:
If the multiframe eyes image is imperfect, meet the trigger condition that auxiliary drives.
4. the method according to claim 1, wherein determining the current institute of the driver according to the biological information
The driving condition at place, comprising:
The current state of attention and/or degree of fatigue of driver is determined according to the eye movement information；
Driving condition is determined according to the state of attention and/or degree of fatigue and face information and behavioural information.
5. the method according to claim 1, wherein determining that target auxiliary drives according to the driving condition
Before grade, further includes:
It establishes driving condition and auxiliary drives the mapping ruler of grade；
Correspondingly, determining that target auxiliary drives grade according to the driving condition, comprising:
Determine that target auxiliary corresponding with the driving condition drives grade according to the mapping ruler.
6. the method according to claim 1, wherein described determine that the driver works as according to the biological information
Preceding locating driving condition, comprising:
The biological information is inputted to the disaggregated model pre-established, obtains the output result of the disaggregated model；
The output result is determined as the driving condition that driver is presently in.
7. according to the method described in claim 6, it is characterized in that, the biological information to be inputted to the classification mould pre-established
Type, before the output result for obtaining the disaggregated model, comprising: obtain training sample set, wherein the sample set includes body
Information and driving condition corresponding with biological information；
Based on the sample set, model training is carried out using deep neural network DNN algorithm, obtains disaggregated model.
8. the method according to claim 1, wherein the driving condition includes: normal driving state, dabbles and drive
Sail state, fatigue driving state and absent minded driving condition.
9. -8 any method according to claim 1, which is characterized in that it includes: pilot steering, auxiliary that auxiliary, which drives grade,
Driving, semi-automatic driving, highly automated driving, hypervelocity automatic Pilot and full-automatic driving.
10. a kind of auxiliary driving device characterized by comprising
Trigger condition judgment module, for judging that driver's watches whether information meets the trigger condition that auxiliary drives attentively；
Biological information acquisition module, for acquiring the biological information of driver, wherein the biological information includes following at least one
It is a: eye movement information, face information and behavioural information；
Driving condition determining module, for determining driving condition that the driver is presently according to the biological information；
Target auxiliary drives level determination module, for determining that target auxiliary drives grade according to the driving condition, and uses
The target auxiliary drives grade and carries out auxiliary driving to vehicle.
11. a kind of mobile device including memory, processor and stores the calculating that can be run on a memory and on a processor
Machine program, which is characterized in that the processor realizes the method as described in any in claim 1-9 when executing described program.
12. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The method as described in any in claim 1-9 is realized when execution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811614271.0A CN109664891A (en) | 2018-12-27 | 2018-12-27 | Auxiliary driving method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811614271.0A CN109664891A (en) | 2018-12-27 | 2018-12-27 | Auxiliary driving method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109664891A true CN109664891A (en) | 2019-04-23 |
Family
ID=66147305
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811614271.0A Pending CN109664891A (en) | 2018-12-27 | 2018-12-27 | Auxiliary driving method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109664891A (en) |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110509928A (en) * | 2019-09-03 | 2019-11-29 | 北京蓦然认知科技有限公司 | A kind of auxiliary driving method, device |
| CN110745137A (en) * | 2019-09-30 | 2020-02-04 | 广州大道信息科技有限公司 | Automatic guidance system and method based on health information of driver |
| CN110766131A (en) * | 2019-05-14 | 2020-02-07 | 北京嘀嘀无限科技发展有限公司 | Data processing device and method and electronic equipment |
| CN111016913A (en) * | 2019-12-05 | 2020-04-17 | 马园 | Driver state control system and method based on image information |
| CN112550306A (en) * | 2019-09-10 | 2021-03-26 | 奥迪股份公司 | Vehicle driving assistance system, vehicle including the same, and corresponding method and medium |
| CN112960001A (en) * | 2021-04-19 | 2021-06-15 | 北京七鑫易维信息技术有限公司 | Driving mode switching method and device, vehicle and storage medium |
| CN113335300A (en) * | 2021-07-19 | 2021-09-03 | 中国第一汽车股份有限公司 | Man-vehicle takeover interaction method, device, equipment and storage medium |
| CN113942519A (en) * | 2020-06-30 | 2022-01-18 | 比亚迪股份有限公司 | Vehicle operation assisting method and system and vehicle |
| CN114030475A (en) * | 2021-12-22 | 2022-02-11 | 清华大学苏州汽车研究院（吴江） | Vehicle driving assisting method and device, vehicle and storage medium |
| CN114148342A (en) * | 2020-09-07 | 2022-03-08 | 奥迪股份公司 | Automatic driving judgment system, automatic driving control system and vehicle |
| CN114162130A (en) * | 2021-10-26 | 2022-03-11 | 东风柳州汽车有限公司 | Driving assistance mode switching method, device, equipment and storage medium |
| CN114373216A (en) * | 2021-12-07 | 2022-04-19 | 图湃(北京)医疗科技有限公司 | Eye movement tracking method, device, equipment and storage medium for anterior segment OCTA |
| CN114506332A (en) * | 2022-03-17 | 2022-05-17 | 上海安亭地平线智能交通技术有限公司 | Driving assistance method and device, electronic equipment and storage medium |
| CN115257806A (en) * | 2022-07-22 | 2022-11-01 | 重庆长安汽车股份有限公司 | Hierarchical assistance system, method, vehicle, and storage medium for automatic driving assistance system |
| CN115626171A (en) * | 2022-12-07 | 2023-01-20 | 小米汽车科技有限公司 | Automatic vehicle driving method and device, electronic equipment and storage medium |
| CN115257806B (en) * | 2022-07-22 | 2024-04-26 | 重庆长安汽车股份有限公司 | Hierarchical assistance system for automatic driving assistance system, hierarchical assistance method for automatic driving assistance system, vehicle, and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090027212A1 (en) * | 2007-07-26 | 2009-01-29 | Toyota Jidosha Kabushiki Kaisha | Drowsy state determination device and method |
| CN103318023A (en) * | 2013-06-21 | 2013-09-25 | 同济大学 | Vehicle-mounted real-time intelligent fatigue monitoring and auxiliary device |
| CN106462027A (en) * | 2014-06-23 | 2017-02-22 | 本田技研工业株式会社 | System and method for responding to driver state |
| CN106585624A (en) * | 2016-12-07 | 2017-04-26 | 深圳市元征科技股份有限公司 | Driver state monitoring method and device |
| CN108407813A (en) * | 2018-01-25 | 2018-08-17 | 惠州市德赛西威汽车电子股份有限公司 | A kind of antifatigue safe driving method of vehicle based on big data |
| CN109063545A (en) * | 2018-06-13 | 2018-12-21 | 五邑大学 | A kind of method for detecting fatigue driving and device |
-
2018
- 2018-12-27 CN CN201811614271.0A patent/CN109664891A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090027212A1 (en) * | 2007-07-26 | 2009-01-29 | Toyota Jidosha Kabushiki Kaisha | Drowsy state determination device and method |
| CN103318023A (en) * | 2013-06-21 | 2013-09-25 | 同济大学 | Vehicle-mounted real-time intelligent fatigue monitoring and auxiliary device |
| CN106462027A (en) * | 2014-06-23 | 2017-02-22 | 本田技研工业株式会社 | System and method for responding to driver state |
| CN106585624A (en) * | 2016-12-07 | 2017-04-26 | 深圳市元征科技股份有限公司 | Driver state monitoring method and device |
| CN108407813A (en) * | 2018-01-25 | 2018-08-17 | 惠州市德赛西威汽车电子股份有限公司 | A kind of antifatigue safe driving method of vehicle based on big data |
| CN109063545A (en) * | 2018-06-13 | 2018-12-21 | 五邑大学 | A kind of method for detecting fatigue driving and device |
Non-Patent Citations (3)
| Title |
|---|
| DWIVEDI等: "Drowsy driver detection using representation learning", 《ADVANCE COMPUTING CONFERENCE (IACC), 2014 IEEE INTERNATIONAL》 * |
| 王军等: "基于深度置信网络和生成模型的驾驶疲劳识别", 《湘潭大学自然科学学报》 * |
| 阿奇姆•伊斯坎达里安: "《智能车辆手册（卷II）》", 30 November 2016 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110766131A (en) * | 2019-05-14 | 2020-02-07 | 北京嘀嘀无限科技发展有限公司 | Data processing device and method and electronic equipment |
| CN110509928A (en) * | 2019-09-03 | 2019-11-29 | 北京蓦然认知科技有限公司 | A kind of auxiliary driving method, device |
| CN112550306A (en) * | 2019-09-10 | 2021-03-26 | 奥迪股份公司 | Vehicle driving assistance system, vehicle including the same, and corresponding method and medium |
| CN110745137A (en) * | 2019-09-30 | 2020-02-04 | 广州大道信息科技有限公司 | Automatic guidance system and method based on health information of driver |
| CN111016913A (en) * | 2019-12-05 | 2020-04-17 | 马园 | Driver state control system and method based on image information |
| CN113942519A (en) * | 2020-06-30 | 2022-01-18 | 比亚迪股份有限公司 | Vehicle operation assisting method and system and vehicle |
| CN113942519B (en) * | 2020-06-30 | 2023-08-08 | 比亚迪股份有限公司 | Vehicle operation assisting method and system and vehicle |
| CN114148342A (en) * | 2020-09-07 | 2022-03-08 | 奥迪股份公司 | Automatic driving judgment system, automatic driving control system and vehicle |
| CN112960001A (en) * | 2021-04-19 | 2021-06-15 | 北京七鑫易维信息技术有限公司 | Driving mode switching method and device, vehicle and storage medium |
| CN113335300A (en) * | 2021-07-19 | 2021-09-03 | 中国第一汽车股份有限公司 | Man-vehicle takeover interaction method, device, equipment and storage medium |
| CN114162130B (en) * | 2021-10-26 | 2023-06-20 | 东风柳州汽车有限公司 | Driving assistance mode switching method, device, equipment and storage medium |
| CN114162130A (en) * | 2021-10-26 | 2022-03-11 | 东风柳州汽车有限公司 | Driving assistance mode switching method, device, equipment and storage medium |
| CN114373216A (en) * | 2021-12-07 | 2022-04-19 | 图湃(北京)医疗科技有限公司 | Eye movement tracking method, device, equipment and storage medium for anterior segment OCTA |
| CN114030475A (en) * | 2021-12-22 | 2022-02-11 | 清华大学苏州汽车研究院（吴江） | Vehicle driving assisting method and device, vehicle and storage medium |
| CN114506332A (en) * | 2022-03-17 | 2022-05-17 | 上海安亭地平线智能交通技术有限公司 | Driving assistance method and device, electronic equipment and storage medium |
| CN115257806A (en) * | 2022-07-22 | 2022-11-01 | 重庆长安汽车股份有限公司 | Hierarchical assistance system, method, vehicle, and storage medium for automatic driving assistance system |
| CN115257806B (en) * | 2022-07-22 | 2024-04-26 | 重庆长安汽车股份有限公司 | Hierarchical assistance system for automatic driving assistance system, hierarchical assistance method for automatic driving assistance system, vehicle, and storage medium |
| CN115626171A (en) * | 2022-12-07 | 2023-01-20 | 小米汽车科技有限公司 | Automatic vehicle driving method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109664891A (en) | Auxiliary driving method, device, equipment and storage medium | |
| TWI741512B (en) | Method, device and electronic equipment for monitoring driver's attention | |
| US11685392B2 (en) | Apparatus, systems and methods for classifying digital images | |
| US11249544B2 (en) | Methods and systems for using artificial intelligence to evaluate, correct, and monitor user attentiveness | |
| EP2860664B1 (en) | Face detection apparatus | |
| US20190108407A1 (en) | Vehicle device | |
| WO2021016873A1 (en) | Cascaded neural network-based attention detection method, computer device, and computer-readable storage medium | |
| CN109177922A (en) | Vehicle starting method, device, equipment and storage medium | |
| JP2017007652A (en) | Method for recognizing a speech context for speech control, method for determining a speech control signal for speech control, and apparatus for executing the method | |
| CN103612632A (en) | Method and device for adjusting driving operating system | |
| CN103318023A (en) | Vehicle-mounted real-time intelligent fatigue monitoring and auxiliary device | |
| CN112289003A (en) | Method for monitoring end-of-life driving behavior of fatigue driving and active safe driving monitoring system | |
| CN109664889A (en) | A kind of control method for vehicle, device and system and storage medium | |
| CN114987500A (en) | Driver state monitoring method, terminal device and storage medium | |
| CN114735010A (en) | Intelligent vehicle driving control method and system based on emotion recognition and storage medium | |
| CN112083795A (en) | Object control method and device, storage medium and electronic equipment | |
| CN117095680A (en) | Vehicle control method, device, equipment and storage medium | |
| EP4029716A1 (en) | Vehicle interactive system and method, storage medium, and vehicle | |
| CN115649094A (en) | Intelligent control method for driving position, domain controller and related device | |
| CN114701503A (en) | Method, device and equipment for adjusting driving behavior of vehicle driver and storage medium | |
| CN113312958A (en) | Driver state-based order dispatching priority adjusting method and device | |
| CN111267865B (en) | Vision-based safe driving early warning method and system and storage medium | |
| CN113516015A (en) | Emotion recognition method, driving assistance device and application | |
| WO2021159269A1 (en) | Vision-based safe driving early warning method and system, and storage medium | |
| WO2021262166A1 (en) | Operator evaluation and vehicle control based on eyewear data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190423 |