CN107168313A - Control the method and device of vehicle drive - Google Patents
Control the method and device of vehicle drive Download PDFInfo
- Publication number
- CN107168313A CN107168313A CN201710349318.4A CN201710349318A CN107168313A CN 107168313 A CN107168313 A CN 107168313A CN 201710349318 A CN201710349318 A CN 201710349318A CN 107168313 A CN107168313 A CN 107168313A
- Authority
- CN
- China
- Prior art keywords
- eeg signals
- vehicle
- mental imagery
- imagery eeg
- driver
- 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
- 238000000034 method Methods 0.000 title claims abstract description 47
- 230000003340 mental effect Effects 0.000 claims abstract description 143
- 238000012549 training Methods 0.000 claims description 24
- 210000004556 brain Anatomy 0.000 claims description 22
- 230000033001 locomotion Effects 0.000 claims description 20
- 210000001699 lower leg Anatomy 0.000 claims description 12
- 238000000605 extraction Methods 0.000 claims description 10
- 230000000007 visual effect Effects 0.000 abstract description 7
- 208000003464 asthenopia Diseases 0.000 abstract description 5
- 238000000537 electroencephalography Methods 0.000 description 124
- 230000008569 process Effects 0.000 description 11
- 230000005611 electricity Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 238000005457 optimization Methods 0.000 description 5
- 230000007613 environmental effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 210000004761 scalp Anatomy 0.000 description 3
- 230000002269 spontaneous effect Effects 0.000 description 3
- 210000003710 cerebral cortex Anatomy 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000763 evoking effect Effects 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000036624 brainpower Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004424 eye movement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 230000003387 muscular Effects 0.000 description 1
- 210000000653 nervous system Anatomy 0.000 description 1
- 230000007935 neutral effect Effects 0.000 description 1
- 210000000578 peripheral nerve Anatomy 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000004936 stimulating effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Aviation & Aerospace Engineering (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Human Computer Interaction (AREA)
- Neurosurgery (AREA)
- Neurology (AREA)
- Health & Medical Sciences (AREA)
- Dermatology (AREA)
- Biomedical Technology (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
This disclosure relates to which a kind of method and device of control vehicle drive, this method includes:Gather the first Mental imagery EEG signals of driver;Vehicle operation is controlled according to the first Mental imagery EEG signals of the current running status of vehicle and the driver collected.This method avoid driver due to visual fatigue caused by extraneous visual stimulus, the security in vehicle operation is improved.
Description
Technical field
This disclosure relates to field of automobile control, in particular it relates to a kind of method and device of control vehicle drive.
Background technology
Brain-computer interface technology refers to independent of traditional peripheral nerve and sarcous motion control path, in brain
The man-machine interaction mode of connection is directly set up between external equipment.Brain-computer interface technology is applied to intelligent driving field, can be
Human brain is realized to be set up directly " link " between automotive system, realizes " idea " control automobile.
The EEG (Electro encephalo graphy, EEG signals) for being presently used for realizing brain-computer interface is usually
SSVEP (steady-state visual evoked potential, Steady State Visual Evoked Potential), P300 etc..Existing " brain control
Automobile " scheme is mainly based upon SSVEP and realizes that auxiliary drives function, for example, navigation and music control etc..Or pass through brain telecommunications
Number judge driver fatigue state, the corresponding control of triggering or warning function.But due to brain-computer interface technology recognition accuracy,
There is certain limitation in transmission rate and stability, it is necessary to rely on extraneous visual stimulus system, can not only embody driver
Subjective consciousness, and road conditions and ambient condition information can not be paid close attention in real time due to driver eye fixation's stimulating system, caused
Security is substantially reduced, and the visual fatigue of driver is easily caused during driver's long-duration driving.
There is drawbacks described above to the mode that automobile is controlled due to being currently based on EEG signals, therefore one kind is effectively solved
Measure needs to be suggested.
The content of the invention
The purpose of the disclosure is to provide a kind of method and device of control vehicle drive, to solve to be based in the prior art
EEG signals are controlled the problem of security is relatively low to automobile.
To achieve these goals, the disclosure provides a kind of method for controlling vehicle drive, the first fortune of collection driver
Dynamic imagination EEG signals;According to the current running status of vehicle and the first Mental imagery EEG signals collected control vehicle
Operation.
Optionally, the above method also includes:Before the first Mental imagery EEG signals of collection driver, when default
Between in section multi collect driver the second Mental imagery EEG signals;Extract the second Mental imagery EEG signals of multi collect
Feature;Learning training is carried out to the feature extracted using grader, the spy of at least two Mental imagery EEG signals is obtained
Levy;Operate foundation are corresponding to close with least two of vehicle respectively the feature of at least two Mental imagery EEG signals extracted
System.
Optionally, vehicle is controlled according to the current running status of vehicle and the first Mental imagery EEG signals collected
Operation, including:The feature of the first Mental imagery EEG signals is extracted, according to the feature of the Mental imagery EEG signals pre-established
Corresponding relation between the operation of vehicle, it is determined that object run corresponding with the first Mental imagery EEG signals collected,
Control the operation of vehicle performance objective.
Optionally, above-mentioned at least two Mental imageries EEG signals, including:Driver's imagination controls its body different parts
At least two produced Mental imagery EEG signals.
Optionally, above-mentioned at least two operation includes:Vehicle left/right steering operation, the forward/backward operation of vehicle and car
Halt/back operation.
Optionally, the first Mental imagery EEG signals of driver are gathered, including:With predetermined period taken at regular intervals driver
The first electric imaginary signals of motion brain.
Optionally, vehicle is controlled according to the current running status of vehicle and the first Mental imagery EEG signals collected
Operation, including:If the first Mental imagery EEG signals currently collected are to be controlled its left hand by driver's imagination and produced
Mental imagery EEG signals, then control vehicle turn left the first predetermined angle;If the first Mental imagery brain currently collected
Electric signal is to imagine the Mental imagery EEG signals for controlling its right hand and producing by driver, then controls vehicle right-hand rotation second to preset
Angle;When the travel speed of vehicle is more than 0, if the first Mental imagery EEG signals currently collected are thought by driver
As the Mental imagery EEG signals for controlling its left foot and/right crus of diaphragm and producing, then vehicle brake is controlled;When travel speed of vehicle etc.
When 0, if the first Mental imagery EEG signals currently collected are to be controlled its left foot and/right crus of diaphragm by driver's imagination and produced
Raw Mental imagery EEG signals, then control vehicle at the uniform velocity to travel.
To achieve these goals, the disclosure also provides a kind of device of control vehicle drive, including:First collection mould
Block, the first Mental imagery EEG signals for gathering driver;Control module, for according to the current running status of vehicle with
And the first Mental imagery EEG signals control vehicle operation collected.
Optionally, said apparatus also includes:Second acquisition module, for the first Mental imagery brain electricity in collection driver
Before signal, the second Mental imagery EEG signals of multi collect driver in preset time period;Extraction module, for extracting
The feature of second Mental imagery EEG signals of multi collect;Sort module, for being entered using grader to the feature extracted
Row learning training, obtains the feature of at least two Mental imagery EEG signals;Module is set up, for by extract at least two
The feature of Mental imagery EEG signals sets up corresponding relation with least two operations of vehicle respectively.
Optionally, above-mentioned control module is used for:The feature of the first Mental imagery EEG signals is extracted, according to what is pre-established
Corresponding relation between the feature of Mental imagery EEG signals and the operation of vehicle, it is determined that with the first Mental imagery brain for collecting
The corresponding object run of electric signal, the performance objective operation of control vehicle.
Optionally, above-mentioned at least two Mental imageries EEG signals include:Driver's imagination controls its body different parts
At least two produced Mental imagery EEG signals.
Optionally, above-mentioned at least two operation includes:Vehicle left/right steering operation, the forward/backward operation of vehicle and car
Halt/back operation.
Optionally, above-mentioned first acquisition module is used for:Thought with the motion brain electricity of predetermined period taken at regular intervals driver first
Picture signals.
Optionally, above-mentioned control module, including:First control unit, if thought for the first motion currently collected
As EEG signals are to imagine the Mental imagery EEG signals for controlling its left hand and producing by driver, then vehicle left-hand rotation first is controlled
Predetermined angle;Second control unit, if for the first Mental imagery EEG signals currently collected being imagined by driver
The Mental imagery EEG signals for controlling its right hand and producing, then control vehicle the second predetermined angle of right-hand rotation;3rd control unit, is used
In when the travel speed of vehicle is more than 0, if the first Mental imagery EEG signals currently collected are imagined by driver
The Mental imagery EEG signals for controlling its left foot and/right crus of diaphragm and producing, then control vehicle brake;4th control unit, for working as
When the travel speed of vehicle is equal to 0, if the first Mental imagery EEG signals currently collected are to be imagined to control by driver
Its left foot and/right crus of diaphragm and the Mental imagery EEG signals that produce, then control vehicle at the uniform velocity to travel.
The scheme that the present embodiment is provided, controls car steering function, due to Mental imagery by Mental imagery EEG signals
EEG signals are spontaneous EEG signals, independent of environmental stimuli, it is to avoid caused by driver is due to extraneous visual stimulus
Visual fatigue, improves the security of driving procedure.
Other feature and advantage of the disclosure will be described in detail in subsequent embodiment part.
Brief description of the drawings
Accompanying drawing is, for providing further understanding of the disclosure, and to constitute a part for specification, with following tool
Body embodiment is used to explain the disclosure together, but does not constitute limitation of this disclosure.In the accompanying drawings:
Fig. 1 be this disclosure relates to control vehicle drive method flow chart;
Fig. 2 be this disclosure relates to vehicle control system structured flowchart;
Fig. 3 be this disclosure relates to training system realize the flow chart of training process;
Fig. 4 be this disclosure relates to the flow chart that EEG signals are identified;
Fig. 5 be this disclosure relates to control vehicle drive device structured flowchart.
Embodiment
It is described in detail below in conjunction with accompanying drawing embodiment of this disclosure.It should be appreciated that this place is retouched
The embodiment stated is merely to illustrate and explained the disclosure, is not limited to the disclosure.
Embodiment of the disclosure provides a kind of method for controlling vehicle drive, and Fig. 1 is the flow chart of this method, such as Fig. 1
Shown, this method includes following processing:
Step 101:Gather the first Mental imagery EEG signals of driver;
In the present embodiment, the Mental imagery EEG signals of driver, Ke Yishe can be regularly gathered with predetermined period
The predetermined period is put for several seconds, based on this, the Mental imagery EEG signals that can be arrived according to continuous collecting in a period of time come
The control to a certain complete operation of vehicle is realized, for example, it is assumed that collection period is 1 second, when being collected in 10 seconds 10 times
Mental imagery EEG signals, then performed 10 corresponding control operations to vehicle of the EEG signals in this 10 seconds.
The method that the present embodiment is provided can also include the operation foundation pair that need to perform specified EEG signals and vehicle
The step of should being related to, based on this, before step 101, the method that the present embodiment is provided can also include:Collection driver's
Before first Mental imagery EEG signals, the second Mental imagery EEG signals of multi collect driver in preset time period;
Extract the feature to the second Mental imagery EEG signals of multi collect;Study instruction is carried out to the feature extracted using grader
Practice, obtain the feature of at least two Mental imagery EEG signals;By the spy of at least two Mental imagery EEG signals extracted
Levy at least two operations respectively with vehicle and set up corresponding relation.Wherein, at least two operations of vehicle can specifically include:Car
The forward/backward operation of left/right steering operation, vehicle and vehicle halt/back operation.
It should be noted that in the present embodiment, the behaviour of learning training is carried out to the Mental imagery EEG signals of driver
Be required for performing when making not in user every time using vehicle, for same user, its it is first using automobile when perform
A complete learning training.If other users need to use Current vehicle, need to re-start it is complete once
Learning training.
Step 102:According to the current running status of vehicle and the first Mental imagery EEG signals collected control car
Operation.
After the first Mental imagery EEG signals of driver are collected, the first Mental imagery EEG signals are extracted
Feature, according to the corresponding relation between the feature of Mental imagery EEG signals and the operation of vehicle pre-established, it is determined that with working as
Before the corresponding object run of Mental imagery EEG signals that collects, the performance objective operation of control vehicle.
Wherein, the running status of vehicle can specifically include the speed that vehicle is travelled, and can pass through CAN (Controller
Area Network, controller local area network) bus obtains the speed information of automobile, based on this, according to the operation of vehicle currently
State and the Mental imagery EEG signals control vehicle operation collected, include but is not limited to following several control modes:
If the first Mental imagery EEG signals currently collected are controlled its left hand by driver's imagination and produced
Mental imagery EEG signals, then control vehicle the first predetermined angle of left-hand rotation;
If the first Mental imagery EEG signals currently collected are controlled its right hand by driver's imagination and produced
Mental imagery EEG signals, then control vehicle the second predetermined angle of right-hand rotation;
When the travel speed of vehicle is more than 0, if the first Mental imagery EEG signals currently collected are by driving
The Mental imagery EEG signals that member's imagination controls its left foot and/right crus of diaphragm and produced, then control vehicle brake;
When the travel speed of vehicle is equal to 0, if the first Mental imagery EEG signals currently collected are by driving
The Mental imagery EEG signals that member's imagination controls its left foot and/right crus of diaphragm and produced, then control vehicle at the uniform velocity to travel.
Each step and realization principle of the method provided above the present embodiment have carried out brief elaboration, below then to reality
The system of existing this method is introduced.
In the present embodiment, eeg signal acquisition device, electroencephalogramsignal signal analyzing device and the part structure of actuator three can be used
Into vehicle control system realize method that the present embodiment is provided, the structured flowchart of the system is as shown in Figure 2.
Eeg signal acquisition device is used for the Mental imagery EEG signals of collection vehicle driver.The eeg signal acquisition device bag
Include electrode for encephalograms, amplifier and transmitting element.Wherein, electrode for encephalograms is contacted with driver's scalp, for picking up the brain electricity of user
Signal, multiple electrode for encephalograms of diverse location can make the electric cap of brain and be worn on in account.Each electrode for encephalograms is sequentially through electricity
Polar curve is connected on amplifier, and electroencephalogramsignal signal analyzing device is sent to after the processing such as amplifier is amplified to EEG signals, filtered.
When user carries out Mental imagery, mainly the scalp EEG signals in its sensorimotor area change.Therefore, in the present embodiment
In, electrode for encephalograms is attached to the scalp sensorimotor area of driver, imaginary signals can be measured by the electrode for encephalograms in motion
The electric current generated when being transmitted between nervous system.
In the present embodiment, after the Mental imagery EEG signals of driver are collected, it is necessary to analyze the signal,
It can specifically be realized using electroencephalogramsignal signal analyzing device.The electroencephalogramsignal signal analyzing device is by wired or wireless communication mode with being connected
There is the amplifier connection of each electrode for encephalograms connection.If the connected mode of amplifier and electroencephalogramsignal signal analyzing device is wire communication,
Then the transmitting element of eeg signal acquisition device is connection amplifier and the cable of electroencephalogramsignal signal analyzing device.Brain is electric in this case
The receiving unit of signal analyzer is the port being connected with cable, such as USB (USB, Universal Serial
Bus) mouth or serial ports.If the connected mode of amplifier and electroencephalogramsignal signal analyzing device is radio communication, eeg signal acquisition device
Transmitting element and the receiving unit of electroencephalogramsignal signal analyzing device can be wireless communication module, such as bluetooth, zigbee, wifi etc..
Amplifier and electrode for encephalograms can be fixed on the electric cap of brain using communication, it is allowed to the complete free movement of tester,
Traditional cable movement is avoided to disturb, to obtain high-quality electroencephalographic record.Wherein, amplifier can built-in chargeable electricity
Pond, it is allowed to the continuous record of a few hours.
The hardware of electroencephalogramsignal signal analyzing device is the complete industrial control board of interface;Its software systems includes receiving unit, training system
System, identifying system and control unit.Receiving unit receives the Mental imagery EEG signals of driver from eeg signal acquisition device;
Training system being capable of the Sample Establishing eeg signal classification device mould based on a number of Mental imagery EEG signals collected
Type, and model parameter is passed into identifying system;Identifying system can realize the Mental imagery position of ONLINE RECOGNITION user, be sent to
Control unit;Control unit is controlled after optimization generation control instruction and is transferred to executing agency.
Wherein, training system, which is used to realize, is based on sufficient amount of known sample, is found most according to certain learning rules
The process of excellent grader.Fig. 3 is the flow chart that training system realizes training process.The training process is located in advance including EEG signals
Reason, feature extraction and classifier training.
Pretreatment:Because EEG signals are very faint, the interference of noise, main noise are highly susceptible in its gatherer process
Have:50Hz/60Hz power supply disturbance artefact, eye movement artefact and muscular movement artefact.It can reduce and make an uproar after pretreatment
Sound shadow is rung, and obtains more pure EEG signals.Preprocess method can include band filter (high pass, low pass and band logical
Deng), spatial filter (Laplace filter and altogether average reference etc.) and burbling noise component algorithms (isolated component point
Analysis and principal component analysis) etc..
Feature extraction:The purpose of feature extraction is to find out to be best able to explicitly indicate that user's difference consciousness letter in EEG signals
The feature of breath, various parameters is determined by these features, and constitute the characteristic vector for having maximum relation degree with Tasks
Process.Mental imagery EEG feature extraction method can use ERD (Event related desynchronization,
Event-related desynchronization), ERS (Event related desynchronization, event-related design) and CSP
Modes such as (Common special pattern, feature and cospace patterns) is carried out.
When some corticocerebral region is activated, the metabolism in the region and CBF increase, while brain information is processed
The amplitude for causing alpha (8-13Hz) and beta (13-30Hz) frequency range to be shaken lowers or blocked, and this electrophysiological phenomena is claimed
For ERD.Alpha and beta frequency spectrums show the electrical activity that obvious wave amplitude increases under brain tranquillization or inert condition, and this is
ERS.User is when imagining left hand motion, and ERD phenomenons will occur in sensorimotor region on the right side of cerebral cortex;In imagination right hand fortune
When dynamic, ERD phenomenons will occur in sensorimotor region on the left of cerebral cortex;In imagination both feet motion, the sense of cerebral cortex center
Feel that ERD phenomenons will occur in moving region.Therefore corresponding brain electricity can be extracted according to the difference of these region electrical energy of brain
Feature, identifies all kinds of brain power modes, i.e. various EEG signals eventually through suitable grader.
The principle of cospace pattern is to seek taking optimal spatial mapping direction by building a common spatial filter,
So that a class signal is maximum in the energy value that this side up, and another kind of signal is minimum in the energy value that this side up.
Classifier training:Each training sample is carried out after feature extraction, composing training sample set feeding grader;According to
Classification accuracy is iterated, and constantly adjusts classifier parameters, finally gives optimal grader.Sorting technique mainly has LDA
(Linear discriminant analysis, linear discriminant analysis), NN (Neural network, neutral net), ELM
(Extreme learning machine, extreme learning machine), SVM (Support Vector Machines, SVMs)
Deng.
Identifying system can be realized to be handled the EEG signals collected in real time, draws the purpose of recognition result, is used
Automobile corresponding function is controlled with realizing.Fig. 4 is the flow chart that EEG signals are identified.It is pre- that the process includes EEG signals
Processing, feature extraction and Classification and Identification.Pretreatment and pretreatment and the feature extracting method of feature extracting method and training system
Unanimously, it will not be repeated here;Classification and Identification be based on training after optimum classifier identify user movement be intended to, that is, identify
Vehicle corresponding to the Mental imagery EEG signals of user needs operation to be performed.
Control unit:Can be total by CAN between the control unit of electroencephalogramsignal signal analyzing device and the electronic controller of actuator
Line is communicated, therefore it is required that the control unit of electroencephalogramsignal signal analyzing device will have CAN interface.The control list of electroencephalogramsignal signal analyzing device
Member obtains the speed information (at least one in the current running status of vehicle i.e. in above-mentioned steps 102 of automobile by CAN
Kind of information), control unit according to speed and EEG's Recognition result formation control instruction, instruct specific forming process include but
It is not limited to following several:
1st, when speed is 0km/h:
EEG's Recognition result is that driver imagines that its left hand is moved, then control instruction is:Automobile turns left 10 ° (i.e. pair
Above-mentioned predetermined angle is answered, can also be specifically other number of degrees, 10 ° herein are merely illustrative);
EEG's Recognition result is that driver imagines that its right hand is moved, then control instruction is:Automobile is turned right 10 °;
EEG's Recognition result is that driver imagines its both feet motion (double-legged or any one pin), then control instruction
For:Automobile at the uniform velocity advances, and speed is 10km/h.
2nd, when speed is more than 0km/h:
Electroencephalogramrecognition recognition result is that driver imagines that its left hand is moved, then control instruction is:Automobile turns left 10 °;
Electroencephalogramrecognition recognition result is that driver imagines that its right hand is moved, then control instruction is:Automobile is turned right 10 °;
Electroencephalogramrecognition recognition result is that driver imagines its both feet motion, then control instruction is:Brake to speed is 0km/h.
Above control instruction is sent to the electronic controller of executing agency by CAN after control optimization.
Pass through the control signal, it is possible to achieve the optimization controlled automobile, control optimisation strategy can specifically include:Start
Optimization, solves that stiction is larger the problem of cause when automobile starting advances;Optimization is turned to, automobile is solved and is turned to suddenly by straight trip
When, the problem of turning to not in place;Security performance optimizes, and solves to run into barrier, recognition result mistake etc. in vehicle traveling process
The safety problem brought.
Actuator in the present embodiment includes electronic controller and automobile bottom executing agency.Electronic controller passes through CAN
Bus receives the control instruction that brain electricity analytical device is transmitted, and drives the bottom executing agency of automobile to complete corresponding according to control instruction
Action.Wherein, automobile bottom executing agency includes but is not limited to steering wheel, wheel, indicator lamp and engine etc..
The method that the present embodiment is provided, car steering function is controlled by Mental imagery EEG signals.Due to Mental imagery
EEG signals are spontaneous EEG signals, independent of environmental stimuli, it is to avoid caused by driver is due to extraneous visual stimulus
Visual fatigue, improves user's initiative;Meanwhile, this method imagines EEG signals as the control of automobile using self-motion
Instruction, can bring new experience for driver, liberate the both feet and both hands of driver, while being also physical handicaps patient
Manipulation automobile offers convenience;In person, this method is further increased by Mental imagery training system and rational control strategy
The security of automobile control process.
Embodiment of the disclosure additionally provides a kind of device of control vehicle drive, and the device is used to realize above-mentioned control car
The method driven, the device can be set and in vehicle or mobile unit, Fig. 5 is the structured flowchart of the device, such as
Shown in Fig. 5, the device 50 includes following part:
First acquisition module 51, the first Mental imagery EEG signals for gathering driver;
First acquisition module 51 can be used for:With the first motion brain electricity imagination letter of predetermined period taken at regular intervals driver
Number.
Control module 52, for according to the current running status of vehicle and the first Mental imagery EEG signals collected
Control vehicle operation.
Further, said apparatus 50 can also include:Second acquisition module, for the first motion in collection driver
Before imagination EEG signals, the second Mental imagery EEG signals of multi collect driver in preset time period;Extraction module,
For extracting the feature to the second Mental imagery EEG signals of multi collect;Sort module, for using grader to extracting
The feature arrived carries out learning training, obtains the feature of at least two Mental imagery EEG signals;Module is set up, for that will extract
At least two operations of the feature respectively with vehicle of at least two Mental imagery EEG signals set up corresponding relation.
Wherein, at least two Mental imagery EEG signals can specifically include:Driver's imagination controls its body difference portion
At least two Mental imagery EEG signals produced by position.And, at least two operations can specifically include:Vehicle left/right turns
Halted/back operation to operation, the forward/backward operation of vehicle and vehicle.
Based on above-mentioned second acquisition module, extraction module, sort module and the function that module is realized is set up, this implementation
Control module 52 in example specifically can be used for:After the first Mental imagery EEG signals of collection driver, extract this
The feature of one Mental imagery EEG signals, according between the feature of Mental imagery EEG signals and the operation of vehicle pre-established
Corresponding relation, it is determined that object run corresponding with the first Mental imagery EEG signals currently collected, control vehicle is performed
Object run.
Above-mentioned control module can specifically include following part:First control unit, if for currently collecting
The first Mental imagery EEG signals be to be imagined to control its left hand and the Mental imagery EEG signals that produce by driver, then control
Vehicle the first predetermined angle of left-hand rotation;Second control unit, if being for the first Mental imagery EEG signals currently collected
The Mental imagery EEG signals for controlling its right hand and producing are imagined by driver, then control vehicle the second predetermined angle of right-hand rotation;The
Three control units, for when the travel speed of vehicle is more than 0, if the first Mental imagery EEG signals currently collected are
The Mental imagery EEG signals for controlling its left foot and/right crus of diaphragm and producing are imagined by driver, then control vehicle brake;4th control
Unit, for when the travel speed of vehicle is equal to 0, if the first Mental imagery EEG signals currently collected are by driving
The Mental imagery EEG signals that member's imagination controls its left foot and/right crus of diaphragm and produced, then control vehicle at the uniform velocity to travel.
The device that the present embodiment is provided, car steering function is controlled by Mental imagery EEG signals.Due to Mental imagery
EEG signals are spontaneous EEG signals, independent of environmental stimuli, it is to avoid caused by driver is due to extraneous visual stimulus
Visual fatigue, improves user's initiative;Meanwhile, the present apparatus is referred to using self-motion imagination EEG signals as the control of automobile
Order, can bring new experience for driver, liberate the both feet and both hands of driver, while also being grasped for physical handicaps patient
Control automobile offers convenience;In person, the present apparatus further increases vapour by Mental imagery training system and rational control strategy
The security of car control process.
The preferred embodiment of the disclosure is described in detail above in association with accompanying drawing, still, the disclosure is not limited to above-mentioned reality
The detail in mode is applied, in the range of the technology design of the disclosure, a variety of letters can be carried out with technical scheme of this disclosure
Monotropic type, these simple variants belong to the protection domain of the disclosure.
It is further to note that each particular technique feature described in above-mentioned embodiment, in not lance
In the case of shield, it can be combined by any suitable means.In order to avoid unnecessary repetition, the disclosure to it is various can
The combination of energy no longer separately illustrates.
In addition, can also be combined between a variety of embodiments of the disclosure, as long as it is without prejudice to originally
Disclosed thought, it should equally be considered as disclosure disclosure of that.
Claims (14)
1. a kind of method for controlling vehicle drive, it is characterised in that including:
Gather the first Mental imagery EEG signals of driver;
The vehicle fortune is controlled according to the current running status of vehicle and the first Mental imagery EEG signals collected
OK.
2. according to the method described in claim 1, it is characterised in that methods described also includes:
Before the first Mental imagery EEG signals of collection driver, driven in preset time period described in multi collect
The second Mental imagery EEG signals of member;
Extract the feature of the second Mental imagery EEG signals of the multi collect;
Learning training is carried out to the feature extracted using grader, the spy of at least two Mental imagery EEG signals is obtained
Levy;
By at least two operations of the feature of at least two Mental imageries EEG signals extracted respectively with the vehicle
Set up corresponding relation.
3. method according to claim 2, it is characterised in that the running status current according to vehicle and collect
The first Mental imagery EEG signals control vehicle operation, including:
Extract the feature of the first Mental imagery EEG signals, according to the feature of the Mental imagery EEG signals pre-established with
Corresponding relation between the operation of vehicle, it is determined that target corresponding with the first Mental imagery EEG signals collected is grasped
Make, control the vehicle to perform the object run.
4. method according to claim 2, it is characterised in that at least two Mental imageries EEG signals, including:
Driver's imagination controls at least two Mental imagery EEG signals produced by its body different parts.
5. method according to claim 2, it is characterised in that at least two operation includes:
The forward/backward operation of vehicle left/right steering operation, vehicle and vehicle halt/back operation.
6. according to the method described in claim 1, it is characterised in that the first Mental imagery brain telecommunications of the collection driver
Number, including:
With the first electric imaginary signals of motion brain of driver described in predetermined period taken at regular intervals.
7. the method according to claim 1 to 6 any one, it is characterised in that the operation shape according to vehicle currently
State and the first Mental imagery EEG signals collected control the vehicle operation, including:
If the first Mental imagery EEG signals currently collected are to be controlled its left hand by the driver imagination and produced
Raw Mental imagery EEG signals, then control the first predetermined angle of the vehicle left-hand rotation;
If the first Mental imagery EEG signals currently collected are to be controlled its right hand by the driver imagination and produced
Raw Mental imagery EEG signals, then control the second predetermined angle of the vehicle right-hand rotation;
When the vehicle travel speed be more than 0 when, if the first Mental imagery EEG signals currently collected be by
The Mental imagery EEG signals that driver's imagination controls its left foot and/right crus of diaphragm and produced, then control the vehicle brake;
When the vehicle travel speed be equal to 0 when, if the first Mental imagery EEG signals currently collected be by
The Mental imagery EEG signals that driver's imagination controls its left foot and/right crus of diaphragm and produced, then control the vehicle at the uniform velocity to go
Sail.
8. a kind of device of control vehicle drive, it is characterised in that including:
First acquisition module, the first Mental imagery EEG signals for gathering driver;
Control module, for according to the current running status of vehicle and the first Mental imagery EEG signals control collected
Make the vehicle operation.
9. device according to claim 8, it is characterised in that described device also includes:
Second acquisition module, for before the first Mental imagery EEG signals of collection driver, in preset time period
The second Mental imagery EEG signals of driver described in interior multi collect;
Extraction module, the feature of the second Mental imagery EEG signals for extracting the multi collect;
Sort module, for carrying out learning training to the feature extracted using grader, obtains at least two motions and thinks
As the feature of EEG signals;
Set up module, for by the feature of at least two Mental imageries EEG signals extracted respectively with the vehicle
Corresponding relation is set up at least two operations.
10. device according to claim 9, it is characterised in that the control module is used for:
Extract the feature of the first Mental imagery EEG signals, according to the feature of the Mental imagery EEG signals pre-established with
Corresponding relation between the operation of vehicle, it is determined that target corresponding with the first Mental imagery EEG signals collected is grasped
Make, control the vehicle to perform the object run.
11. device according to claim 9, it is characterised in that at least two Mental imageries EEG signals, including:
Driver's imagination controls at least two Mental imagery EEG signals produced by its body different parts.
12. device according to claim 9, it is characterised in that at least two operation includes:
The forward/backward operation of vehicle left/right steering operation, vehicle and vehicle halt/back operation.
13. device according to claim 8, it is characterised in that first acquisition module is used for:
With the electric imaginary signals of the motion brain of driver first described in predetermined period taken at regular intervals.
14. the device according to claim 8 to 13 any one, it is characterised in that the control module, including:
First control unit, if for the first Mental imagery EEG signals currently collected being thought by the driver
As the Mental imagery EEG signals for controlling its left hand and producing, then the first predetermined angle of the vehicle left-hand rotation is controlled;
Second control unit, if for the first Mental imagery EEG signals currently collected being thought by the driver
As the Mental imagery EEG signals for controlling its right hand and producing, then the second predetermined angle of the vehicle right-hand rotation is controlled;
3rd control unit, for when the travel speed of the vehicle is more than 0, if first motion currently collected
Imagination EEG signals are that the Mental imagery EEG signals for controlling its left foot and/right crus of diaphragm and producing are imagined by the driver, then control
Make the vehicle brake;
4th control unit, for when the travel speed of the vehicle is equal to 0, if first motion currently collected
Imagination EEG signals are that the Mental imagery EEG signals for controlling its left foot and/right crus of diaphragm and producing are imagined by the driver, then control
The vehicle is made at the uniform velocity to travel.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710349318.4A CN107168313A (en) | 2017-05-17 | 2017-05-17 | Control the method and device of vehicle drive |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710349318.4A CN107168313A (en) | 2017-05-17 | 2017-05-17 | Control the method and device of vehicle drive |
Publications (1)
Publication Number | Publication Date |
---|---|
CN107168313A true CN107168313A (en) | 2017-09-15 |
Family
ID=59815260
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201710349318.4A Pending CN107168313A (en) | 2017-05-17 | 2017-05-17 | Control the method and device of vehicle drive |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107168313A (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108536154A (en) * | 2018-05-14 | 2018-09-14 | 重庆师范大学 | Low speed automatic Pilot intelligent wheel chair construction method based on bioelectrical signals control |
CN110562120A (en) * | 2019-08-22 | 2019-12-13 | 中国第一汽车股份有限公司 | Vehicle light control method and system and vehicle |
CN112947455A (en) * | 2021-02-25 | 2021-06-11 | 复旦大学 | Expressway automatic driving system and method based on visual brain-computer interaction |
CN113002558A (en) * | 2021-03-30 | 2021-06-22 | 复旦大学 | Intelligent driving system and method for assisting disabled person based on electroencephalogram signals |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1498595A (en) * | 2002-10-30 | 2004-05-26 | ������������ʽ���� | Controller utilizing brain wave signal |
CN101716079A (en) * | 2009-12-23 | 2010-06-02 | 江西蓝天学院 | Brainprint identity identification authentication method based on multi-characteristics algorithm |
KR20120098289A (en) * | 2011-02-28 | 2012-09-05 | 포항공과대학교 산학협력단 | An eeg-based tongue-machine interface apparatus, an eeg-based tongue-machine interface method, driving apparatus using an eeg-based tongue-machine interface and operation method of driving apparatus using an eeg-based tongue-machine interface |
CN103425249A (en) * | 2013-09-06 | 2013-12-04 | 西安电子科技大学 | Electroencephalogram signal classifying and recognizing method based on regularized CSP and regularized SRC and electroencephalogram signal remote control system |
CN105468138A (en) * | 2015-07-15 | 2016-04-06 | 武汉理工大学 | Intelligent vehicle obstacle avoidance and navigation method based on brain-computer interface technology and lidar |
WO2016186381A1 (en) * | 2015-05-15 | 2016-11-24 | 주식회사 한글과컴퓨터 | Method and apparatus for operating unmanned smart car using biometric signal analysis |
CN106292705A (en) * | 2016-09-14 | 2017-01-04 | 东南大学 | Many rotor wing unmanned aerial vehicles idea remote control system based on Bluetooth brain wave earphone and operational approach |
CN106419909A (en) * | 2016-09-12 | 2017-02-22 | 西安电子科技大学 | Multi-class motion imagination EEG signal classification method based on characteristic recombination and wavelet transformation |
-
2017
- 2017-05-17 CN CN201710349318.4A patent/CN107168313A/en active Pending
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1498595A (en) * | 2002-10-30 | 2004-05-26 | ������������ʽ���� | Controller utilizing brain wave signal |
CN101716079A (en) * | 2009-12-23 | 2010-06-02 | 江西蓝天学院 | Brainprint identity identification authentication method based on multi-characteristics algorithm |
KR20120098289A (en) * | 2011-02-28 | 2012-09-05 | 포항공과대학교 산학협력단 | An eeg-based tongue-machine interface apparatus, an eeg-based tongue-machine interface method, driving apparatus using an eeg-based tongue-machine interface and operation method of driving apparatus using an eeg-based tongue-machine interface |
CN103425249A (en) * | 2013-09-06 | 2013-12-04 | 西安电子科技大学 | Electroencephalogram signal classifying and recognizing method based on regularized CSP and regularized SRC and electroencephalogram signal remote control system |
WO2016186381A1 (en) * | 2015-05-15 | 2016-11-24 | 주식회사 한글과컴퓨터 | Method and apparatus for operating unmanned smart car using biometric signal analysis |
CN105468138A (en) * | 2015-07-15 | 2016-04-06 | 武汉理工大学 | Intelligent vehicle obstacle avoidance and navigation method based on brain-computer interface technology and lidar |
CN106419909A (en) * | 2016-09-12 | 2017-02-22 | 西安电子科技大学 | Multi-class motion imagination EEG signal classification method based on characteristic recombination and wavelet transformation |
CN106292705A (en) * | 2016-09-14 | 2017-01-04 | 东南大学 | Many rotor wing unmanned aerial vehicles idea remote control system based on Bluetooth brain wave earphone and operational approach |
Non-Patent Citations (1)
Title |
---|
徐宝国 等: "在线脑机接口中脑电信号的特征提取与分类方法", 《电子学报》 * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108536154A (en) * | 2018-05-14 | 2018-09-14 | 重庆师范大学 | Low speed automatic Pilot intelligent wheel chair construction method based on bioelectrical signals control |
CN110562120A (en) * | 2019-08-22 | 2019-12-13 | 中国第一汽车股份有限公司 | Vehicle light control method and system and vehicle |
CN110562120B (en) * | 2019-08-22 | 2021-09-28 | 中国第一汽车股份有限公司 | Vehicle light control method and system and vehicle |
CN112947455A (en) * | 2021-02-25 | 2021-06-11 | 复旦大学 | Expressway automatic driving system and method based on visual brain-computer interaction |
CN113002558A (en) * | 2021-03-30 | 2021-06-22 | 复旦大学 | Intelligent driving system and method for assisting disabled person based on electroencephalogram signals |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lin et al. | EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment | |
CN107168313A (en) | Control the method and device of vehicle drive | |
Scherer et al. | Toward self-paced brain–computer communication: navigation through virtual worlds | |
Carrino et al. | A self-paced BCI system to control an electric wheelchair: Evaluation of a commercial, low-cost EEG device | |
KR102330385B1 (en) | Apparatus, system and method for recognizing emotion and controlling vehicle | |
Müller et al. | Using a SSVEP-BCI to command a robotic wheelchair | |
CN103349595A (en) | Intelligent brain-computer interface wheelchair based on multi-mode hierarchical control | |
CN104548347A (en) | Pure idea nerve muscle electrical stimulation control and nerve function evaluation system | |
CN103735262B (en) | Dual-tree complex wavelet and common spatial pattern combined electroencephalogram characteristic extraction method | |
CN112244774A (en) | Brain-computer interface rehabilitation training system and method | |
CN102331782B (en) | Automatic vehicle controlling method with multi-mode brain-computer interface | |
CN110495893B (en) | System and method for multi-level dynamic fusion recognition of continuous brain and muscle electricity of motor intention | |
CN106362287A (en) | Novel MI-SSSEP mixed brain-computer interface method and system thereof | |
Shende et al. | Literature review of brain computer interface (BCI) using Electroencephalogram signal | |
Gheorghe et al. | Steering timing prediction in a driving simulator task | |
CN106648087B (en) | Feature EEG processing method based on consciousness task | |
CN107961120A (en) | A kind of intelligent wheelchair control system based on brain electric control | |
Fatima et al. | Towards a low cost Brain-computer Interface for real time control of a 2 DOF robotic arm | |
Tariq et al. | Motor imagery based EEG features visualization for BCI applications | |
CN113616436A (en) | Intelligent wheelchair based on motor imagery electroencephalogram and head posture and control method | |
CN117918863A (en) | Method and system for processing brain electrical signal real-time artifacts and extracting features | |
CN115454238A (en) | Human-vehicle interaction control method and device based on SSVEP-MI fusion and automobile | |
CN114557708A (en) | Device and method for detecting somatosensory stimulation consciousness based on electroencephalogram dual-feature fusion | |
Yu et al. | A self-paced brain-computer interface speller by combining motor imagery and P300 potential | |
DE102006016716A1 (en) | Person e.g. vehicle user, training method involves evaluating physiological or technical data using algorithm of machine learning in feedback system for evaluation of feedback about physical and/or mental condition, behavior and/or effect |
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 |
Application publication date: 20170915 |
|
RJ01 | Rejection of invention patent application after publication |