CN108491071A - A kind of brain control vehicle Compliance control method based on fuzzy control - Google Patents
A kind of brain control vehicle Compliance control method based on fuzzy control Download PDFInfo
- Publication number
- CN108491071A CN108491071A CN201810178142.5A CN201810178142A CN108491071A CN 108491071 A CN108491071 A CN 108491071A CN 201810178142 A CN201810178142 A CN 201810178142A CN 108491071 A CN108491071 A CN 108491071A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- control
- brain
- instruction
- fuzzy control
- 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.)
- Granted
Links
Classifications
-
- 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
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Automation & Control Theory (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Neurosurgery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Neurology (AREA)
- Dermatology (AREA)
- Biomedical Technology (AREA)
- Human Computer Interaction (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Multimedia (AREA)
- Traffic Control Systems (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Feedback Control In General (AREA)
Abstract
The brain control vehicle Compliance control method based on fuzzy control that the present invention relates to a kind of, in vehicle travel process, when online recognition to the brain electricity for having Mental imagery, which instructs, to be generated, vehicle enters the fault tolerant mechanism based on fuzzy control, when the brain electricity for not recognizing Mental imagery instructs, vehicle then enters the intelligent driving mechanism based on fuzzy control；The present invention can just correct the EEG signals of mistake, it also can be in the case where no brain electricity be instructed from provost's vehicle, the problems such as brain-computer interface identification error rate is high, real-time is poor, command number is limited is compensated for, safety of the brain control vehicle under circumstances not known is substantially increased.
Description
Technical field
The brain control vehicle Compliance control method based on fuzzy control that the present invention relates to a kind of, belongs to the fields BCI and vehicle
Field of engineering technology.
Background technology
With BCI（Brain-computer interface）The further investigation of technology, application is also increasingly extensive, and brain control vehicle is exactly wherein
One of research hotspot.BCI technologies are combined by brain control vehicle with vehicle technology, and vehicle is controlled by the identification to EEG signals
Traveling.Brain control vehicle has major application value in terms of military, civilian, amusement, not only increases the action of disabled person
Ability, while also new approaches are provided for the control of unmanned auxiliary.
It finds by prior art documents, is both at home and abroad still in the research of brain control vehicle the elementary step at present,
All it is mostly to directly control vehicle using brain-computer interface, only demonstrates the feasibility of brain control vehicle.But current brain-computer interface
There are many problems, EEG's Recognition accuracy rate is low, real-time is poor, command number is limited, for the high vehicle of security requirement
Instruction delay is serious for, transmission rate is low and is intended to understand easy error, therefore application brain electricity instruction directly controls vehicle
It is difficult to ensure its security performance.Beijing Institute of Technology Bi Lu saves seminar and proposes a kind of model prediction control towards brain control vehicle
Method processed keeps the stationarity of brain control vehicle under conditions of ensureing safety.But still lacks and wrong brain electricity instruction is entangled
Just, the supervision and guarantee to brain control vehicle are lacked in the case where no brain electricity instructs, how to be established between human brain and vehicle
A kind of Compliance control method of auxiliary farthest ensures the safety of vehicle traveling, becomes technical problem urgently to be resolved hurrily.
Invention content
The present invention provides a kind of brain control vehicle Compliance control method based on fuzzy control, can be to the EEG signals of mistake
Just correct, also can in the case where no brain electricity is instructed from provost's vehicle, compensate for brain-computer interface identification error rate it is high,
The problems such as real-time is poor, command number is limited, substantially increases safety of the brain control vehicle under circumstances not known.
The technical solution adopted by the present invention to solve the technical problems is：
A kind of brain control vehicle Compliance control method based on fuzzy control, in vehicle travel process, when online recognition is to there is movement
When the brain electricity instruction of the imagination generates, vehicle enters the fault tolerant mechanism based on fuzzy control, when the brain for not recognizing Mental imagery
When electricity instruction, vehicle then enters the intelligent driving mechanism based on fuzzy control；
As present invention further optimization, specifically comprise the following steps：
The first step is initialized brain wave acquisition equipment and Vehicular system, is thought in movement using the real-time collection vehicle of brain wave acquisition equipment
As the EEG signals under task, to its it is online carry out feature extraction and classifying, the result of classification is as applying vehicle
The brain control instruction for accelerating, slowing down, turn left and turning right；
Second step judges whether there is the instruction of brain electricity and generates, third is entered if having and is walked, the 4th step is otherwise entered；
Third walks, and into the fault tolerant mechanism based on fuzzy control, judges that brain electricity instruction is by camera, ultrasonic sensor
No safety executes the instruction of brain electricity if vehicle driving safety, if vehicle, which travels the dangerous brain electricity to its mistake, instructs progress
It corrects；
4th step judges that vehicle travels into the intelligent driving mechanism based on fuzzy control by camera, ultrasonic sensor
When residing environment category, do not make any control if safety, if dangerous according to local environment housing choice behavior；
As present invention further optimization, the EEG signals in aforementioned first step step under Mental imagery task, including three kinds,
Respectively P300 Evoked ptentials, Steady State Visual Evoked Potential and ERD/ERS；
As present invention further optimization, the fault tolerant mechanism based on fuzzy control during aforementioned third is step by step rapid passes through camera
Obtain the distance between vehicle and its both sides lane line, it is No. five ultrasonic sensors acquisition vehicles and its front, left side, left front
The distance between side, right side, right front barrier judge that the brain electricity received instruction judges whether safety in conjunction with current vehicle speed,
The instruction of brain electricity is executed if safety, if the dangerous brain electricity instruction to its mistake is corrected；Fuzzy controller is defeated at this time
It is the brain electricity instruction received, including the distance between vehicle and its both sides lane line to enter, with its front, left side, left front, the right side
The speed of operation of the distance between side, right front barrier and current vehicle, the practical control exported by fuzzy control rule
Instruction includes accelerating, slow down, turn left, turn right and stopping；
As present invention further optimization, the aforementioned 4th it is step by step rapid in the intelligent driving mechanism based on fuzzy control, by taking the photograph
As head obtains the distance between vehicle and its both sides lane line, No. five ultrasonic sensors acquisition vehicles and its front, left side, left side
The distance between front, right side, right front barrier judge whether current environment is safe, if vehicle is travelled in conjunction with current vehicle speed
It is safe then do not do any control, pass through fuzzy control rule housing choice behavior if vehicle traveling is dangerous；Driving mechanism at this time is
The input of the case where being not received by the instruction of brain electricity, fuzzy controller is the distance between vehicle and its both sides lane line, with it
The speed of operation in the distance between front, left side, left front, right side, right front barrier and current vehicle, passes through a series of moulds
The practical controlling behavior of paste control rule output includes along left-lane line, along right-lane line, deceleration, left-hand rotation, right-hand rotation and stopping.
By above technical scheme, compared with the existing technology, the invention has the advantages that：
The present invention provides a kind of brain control vehicle Compliance control method based on fuzzy control, when online recognition is to there is Mental imagery
When the instruction of brain electricity generates, then enter the fault tolerant mechanism based on fuzzy control, when the brain electricity for not recognizing Mental imagery instructs
Then enter the intelligent driving mechanism based on fuzzy control, the EEG signals of mistake can just be corrected, it also can be in no brain
From provost's vehicle in the case of electricity instruction, it is ensured that vehicle, in being travelled in lane line, is carried out in clear when having barrier
Automatic obstacle avoiding compensates for the problems such as brain-computer interface identification error rate is high, real-time is poor, transmission rate is low, substantially increases brain control
Safety of the vehicle under circumstances not known has certain realistic meaning and is widely applied foreground.
Description of the drawings
Present invention will be further explained below with reference to the attached drawings and examples.
Fig. 1 is the system block diagram of midbrain control vehicle of the present invention；
Fig. 2 is the flow chart of midbrain control vehicle Compliance control method of the present invention.
Specific implementation mode
In conjunction with the accompanying drawings, the present invention is further explained in detail.These attached drawings are simplified schematic diagram, only with
Illustration illustrates the basic structure of the present invention, therefore it only shows the composition relevant to the invention.
A kind of brain control vehicle Compliance control method based on fuzzy control of the present invention, in vehicle travel process, when online
When recognizing the brain electricity instruction of Mental imagery and generating, vehicle enters the fault tolerant mechanism based on fuzzy control, when not recognizing
When the brain electricity instruction of Mental imagery, vehicle then enters the intelligent driving mechanism based on fuzzy control；
As shown in Figure 1, brain control Vehicular system includes mainly eeg signal acquisition, signal processing, Compliance control module and vehicle；Tool
Body running engineering is as follows：
The first step acquires the EEG signals under Mental imagery task, using EMOTIV companies in real time using brain wave acquisition equipment
EPOC+ headgears acquire brain frontal cortex region of the subject under different motion Imaginary Movement, middle section and top region brain
Electric signal；The action of its Mental imagery is respectively imagination left hand, the imagination right hand, imagination foot, imagination tongue movements, corresponds to vehicle respectively
Left-hand rotation, right-hand rotation, acceleration, deceleration instruction；
Second step carries out online processing, including pretreatment, feature extraction and classification, classification results to collected EEG signals
Brain control as left-hand rotation, right-hand rotation, acceleration and the deceleration applied to vehicle instructs；
Third walks, and realizes the Compliance control based on fuzzy control, when online recognition to the brain electricity for having Mental imagery, which instructs, to be generated,
Then enter the fault tolerant mechanism based on fuzzy control, then enters when the brain electricity for not recognizing Mental imagery instructs and be based on Fuzzy Control
The intelligent driving mechanism of system exports final practical control instruction to vehicle.
As present invention further optimization, as shown in Fig. 2, a kind of brain control vehicle Compliance control side based on fuzzy control
Method specifically comprises the following steps：
The first step is initialized brain wave acquisition equipment and Vehicular system, is thought in movement using the real-time collection vehicle of brain wave acquisition equipment
As the EEG signals under task, to its it is online carry out feature extraction and classifying, the result of classification is as applying vehicle
The brain control instruction for accelerating, slowing down, turn left and turning right；
Second step judges whether there is the instruction of brain electricity and generates, third is entered if having and is walked, the 4th step is otherwise entered；
Third walks, and into the fault tolerant mechanism based on fuzzy control, judges that brain electricity instruction is by camera, ultrasonic sensor
No safety executes the instruction of brain electricity if vehicle driving safety, if vehicle, which travels the dangerous brain electricity to its mistake, instructs progress
It corrects；
4th step judges that vehicle travels into the intelligent driving mechanism based on fuzzy control by camera, ultrasonic sensor
When residing environment category, do not make any control if safety, if dangerous according to local environment housing choice behavior；
As present invention further optimization, the EEG signals in aforementioned first step step under Mental imagery task, including three kinds,
Respectively P300 Evoked ptentials, Steady State Visual Evoked Potential and ERD/ERS；Wherein, in first step step under Mental imagery task
EEG signals, brain-computer interface numerous types, there are mainly three types of widely applied EEG signals：P300 Evoked ptentials, stable state regard
Feel Evoked ptential, ERD/ERS（Event-related desynchronization current potential/event-related design current potential）；In view of application is vehicle,
Brain control driver needs to observe ambient enviroment with eyes, therefore Mental imagery EEG signals are suitble to the most in ERD/ERS；
As present invention further optimization, the fault tolerant mechanism based on fuzzy control during aforementioned third is step by step rapid passes through camera
Obtain the distance between vehicle and its both sides lane line, it is No. five ultrasonic sensors acquisition vehicles and its front, left side, left front
The distance between side, right side, right front barrier judge that the brain electricity received instruction judges whether safety in conjunction with current vehicle speed,
The instruction of brain electricity is executed if safety, if the dangerous brain electricity instruction to its mistake is corrected；Fuzzy controller is defeated at this time
It is the brain electricity instruction received, including the distance between vehicle and its both sides lane line to enter, with its front, left side, left front, the right side
The speed of operation of the distance between side, right front barrier and current vehicle, the practical control exported by fuzzy control rule
Instruction includes accelerating, slow down, turn left, turn right and stopping；
Specifically, fuzzy controller its input, output fuzzy subset it is as follows：
Input：Brain electricity instructs fuzzy subset B：{ A, D, L, R } corresponds to { accelerate, slow down, turn left, turn right }；
The distance between vehicle and its both sides lane line, i.e. range ambiguity subset CL, CR away from left and right lane line：{ F, M, N },
Corresponding to it is remote, in, it is close }；
Away from front, left side, left front, right side, right front obstacle distance fuzzy subset H, L, LH, R, RH：{ F, M, N }, it is corresponding
In it is remote, in, it is close }；
Current vehicle speed fuzzy subset V：{ Q, S, Z }, correspond to fast, slowly, zero }.
Output：Practical control instruction fuzzy subset O：{ A, D, L, R, P } corresponds to and { accelerates, slow down, turn left, turn right, stop
Only }.
The huge amount if directly formulating fuzzy control rule needs to optimize control rule, and optimization process considers
The problems such as brain control vehicle drives the otherness of vehicle with people, the recognition accuracy of brain control instruction is low, the principle of optimality is as follows：
（1）When vehicle distances barrier or lane line distance are close, regardless of B, V at this time, output O is to stop；
（2）When vehicle distances barrier or lane line distance are middle, if V is fast at this time, regardless of B, output O is to subtract
Speed；
（3）It when car speed is zero, is then inputted regardless of other, output is all consistent with B；
（4）On the left of the vehicle distances or left front obstacle distance be in and in being away from left-lane line distance, V is when being slow, if B is
Turn left, then output is right-hand rotation；Right side is as the same.
After the practical control instruction of output, whether system judges the end of control command, terminate then to enter second step step, it is no
Then control command is waited for terminate.
As present invention further optimization, the aforementioned 4th it is step by step rapid in the intelligent driving mechanism based on fuzzy control, lead to
It crosses camera and obtains the distance between vehicle and its both sides lane line, No. five ultrasonic sensors obtain vehicle and its front, a left side
The distance between side, left front, right side, right front barrier judge whether current environment is safe, if vehicle in conjunction with current vehicle speed
Driving safety does not do any control then, passes through fuzzy control rule housing choice behavior if vehicle traveling is dangerous；Machine is driven at this time
The case where being made as being not received by the instruction of brain electricity, the input of fuzzy controller are the distance between vehicle and its both sides lane line,
With the speed of operation in the distance between its front, left side, left front, right side, right front barrier and current vehicle, pass through a system
The practical controlling behavior of row fuzzy control rule output include along left-lane line, along right-lane line, deceleration, left-hand rotation, turn right and stop
Only；
Specifically, fuzzy controller inputs, the fuzzy subset of output is as follows：
Input：The distance between vehicle and its both sides lane line, i.e. range ambiguity subset CL, CR away from left and right lane line：F,
M, N }, correspond to remote, in, it is close }；
Away from front, left side, left front, right side, right front obstacle distance fuzzy subset H, L, LH, R, RH：{ F, M, N }, it is corresponding
In it is remote, in, it is close }；
Current vehicle speed fuzzy subset V：{ Q, S } corresponds to { fast, slow }.
Output：Practical control instruction fuzzy subset O：{ KL, KR, D, L, R, P } corresponds to { along left-lane line, along right lane
Line slows down, and turns left, and turns right, and stops }.
If directly formulate if huge amount, need to control rule optimize, optimization process consider brain control vehicle with
People drives the otherness of vehicle, and the principle of optimality is as follows：
（1）When speed is fast, export as D；
（2）When speed is slow, away from lane line distance and away from obstacle distance be remote or middle when, judge current environment safety, no
Do any control；
（3）When speed is slow, is close away from left-lane line and left side, left front and front are without barrier, then output is KL；Instead
It is as the same；
（4）In speed is slow, is away from left-lane line and right side, right front are without barrier, export as R；Vice versa；
（5）Away from left-lane line it is close when speed is slow, vehicle, it is during front, left side, at least one distance of left front are or close same
When right side, right front at least one be in or it is close when, export as P；Right side is on the contrary.
After the practical control instruction of output, whether system judges the end of control command, terminate then to enter second step step, it is no
Then control command is waited for terminate.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein（Including technology art
Language and scientific terminology）With meaning identical with the general understanding of the those of ordinary skill in the application fields.Should also
Understand, those terms such as defined in the general dictionary, which should be understood that, to be had and the meaning in the context of the prior art
The consistent meaning of justice, and unless defined as here, will not be with idealizing or the meaning of too formal be explained.
The meaning of "and/or" described herein refers to that the case where respective individualism or both exists simultaneously wraps
Including including.
The meaning of " connection " described herein can be directly connected to can also be to pass through between component between component
Other components are indirectly connected with.
It is enlightenment with above-mentioned desirable embodiment according to the present invention, through the above description, relevant staff is complete
Various changes and amendments can be carried out without departing from the scope of the technological thought of the present invention' entirely.The technology of this invention
Property range is not limited to the contents of the specification, it is necessary to determine its technical scope according to right.
Claims (5)
1. a kind of brain control vehicle Compliance control method based on fuzzy control, it is characterised in that：In vehicle travel process, when online
When recognizing the brain electricity instruction of Mental imagery and generating, vehicle enters the fault tolerant mechanism based on fuzzy control, when not recognizing
When the brain electricity instruction of Mental imagery, vehicle then enters the intelligent driving mechanism based on fuzzy control.
2. the brain control vehicle Compliance control method according to claim 1 based on fuzzy control, it is characterised in that：Specific packet
Include following steps：
The first step is initialized brain wave acquisition equipment and Vehicular system, is thought in movement using the real-time collection vehicle of brain wave acquisition equipment
As the EEG signals under task, to its it is online carry out feature extraction and classifying, the result of classification is as applying vehicle
The brain control instruction for accelerating, slowing down, turn left and turning right；
Second step judges whether there is the instruction of brain electricity and generates, third is entered if having and is walked, the 4th step is otherwise entered；
Third walks, and into the fault tolerant mechanism based on fuzzy control, judges that brain electricity instruction is by camera, ultrasonic sensor
No safety executes the instruction of brain electricity if vehicle driving safety, if vehicle, which travels the dangerous brain electricity to its mistake, instructs progress
It corrects；
4th step judges that vehicle travels into the intelligent driving mechanism based on fuzzy control by camera, ultrasonic sensor
When residing environment category, do not make any control if safety, if dangerous according to local environment housing choice behavior.
3. the brain control vehicle Compliance control method according to claim 2 based on fuzzy control, it is characterised in that：Aforementioned
EEG signals in one step under Mental imagery task, including three kinds, respectively P300 Evoked ptentials, stable state vision inducting is electric
Position and ERD/ERS.
4. the brain control vehicle Compliance control method according to claim 2 based on fuzzy control, it is characterised in that：Aforementioned
Three it is step by step rapid in the fault tolerant mechanism based on fuzzy control, the distance between vehicle and its both sides lane line are obtained by camera,
No. five ultrasonic sensors obtain the distance between vehicle and its front, left side, left front, right side, right front barrier, in conjunction with
Current vehicle speed judges that the brain electricity received instruction judges whether safety, the instruction of brain electricity is executed if safety, if dangerous to it
The brain electricity instruction of mistake is corrected；The input of fuzzy controller at this time be receive brain electricity instruction, including vehicle with its two
The distance between side lane line, with the distance between its front, left side, left front, right side, right front barrier and current vehicle
Speed of operation, by fuzzy control rule export practical control instruction include accelerate, slow down, turn left, turn right and stop.
5. the brain control vehicle Compliance control method according to claim 2 based on fuzzy control, it is characterised in that：Aforementioned
Four it is step by step rapid in the intelligent driving mechanism based on fuzzy control, by camera obtain between vehicle and its both sides lane line away from
From, No. five ultrasonic sensors obtain the distance between vehicle and its front, left side, left front, right side, right front barrier,
Judge whether current environment is safe, does not do any control if vehicle driving safety in conjunction with current vehicle speed, if vehicle traveling is uneasy
It is complete then pass through fuzzy control rule housing choice behavior；It is fuzzy control the case where being not received by the instruction of brain electricity to drive mechanism at this time
The input of device is the distance between vehicle and its both sides lane line, with its front, left side, left front, right side, right front barrier
The distance between and current vehicle speed of operation, include edge by a series of practical controlling behavior that fuzzy control rules export
Left-lane line, along right-lane line, deceleration, left-hand rotation, right-hand rotation and stopping.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810178142.5A CN108491071B (en) | 2018-03-05 | 2018-03-05 | Brain-controlled vehicle sharing control method based on fuzzy control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810178142.5A CN108491071B (en) | 2018-03-05 | 2018-03-05 | Brain-controlled vehicle sharing control method based on fuzzy control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108491071A true CN108491071A (en) | 2018-09-04 |
| CN108491071B CN108491071B (en) | 2021-05-04 |
Family
ID=63341565
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810178142.5A Active CN108491071B (en) | 2018-03-05 | 2018-03-05 | Brain-controlled vehicle sharing control method based on fuzzy control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108491071B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109460145A (en) * | 2018-10-16 | 2019-03-12 | 北京机械设备研究所 | A kind of quick interference method of automated system decision based on brain understanding |
| CN109658503A (en) * | 2018-12-29 | 2019-04-19 | 北京理工大学 | A kind of driving behavior intention detection method merging EEG signals |
| CN110297489A (en) * | 2019-05-14 | 2019-10-01 | 西安工程大学 | The control method and control system of automatic driving vehicle |
| US20210213958A1 (en) * | 2020-01-13 | 2021-07-15 | Ford Global Technologies, Llc | Vehicle computer command system with a brain machine interface |
| CN114089628A (en) * | 2021-10-25 | 2022-02-25 | 西北工业大学 | Brain-driven mobile robot control system and method based on steady-state visual stimulation |
| CN114103974A (en) * | 2021-11-01 | 2022-03-01 | 北京理工大学 | Brain-computer interface method for vehicle continuous control |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102350990A (en) * | 2011-06-29 | 2012-02-15 | 北京理工大学 | Comparison model for obstacle avoidance behaviors of vehicle under manned and unmanned conditions |
| CN103083014A (en) * | 2013-01-08 | 2013-05-08 | 北京理工大学 | Method controlling vehicle by electroencephalogram and intelligent vehicle using method |
| 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 |
| CN107015632A (en) * | 2016-01-28 | 2017-08-04 | 南开大学 | Control method for vehicle, system based on brain electricity driving |
| CN107065850A (en) * | 2016-12-12 | 2017-08-18 | 东南大学 | The four motorized wheels intelligent car systems and its control method of a kind of brain wave control |
-
2018
- 2018-03-05 CN CN201810178142.5A patent/CN108491071B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102350990A (en) * | 2011-06-29 | 2012-02-15 | 北京理工大学 | Comparison model for obstacle avoidance behaviors of vehicle under manned and unmanned conditions |
| CN103083014A (en) * | 2013-01-08 | 2013-05-08 | 北京理工大学 | Method controlling vehicle by electroencephalogram and intelligent vehicle using method |
| 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 |
| CN107015632A (en) * | 2016-01-28 | 2017-08-04 | 南开大学 | Control method for vehicle, system based on brain electricity driving |
| CN107065850A (en) * | 2016-12-12 | 2017-08-18 | 东南大学 | The four motorized wheels intelligent car systems and its control method of a kind of brain wave control |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109460145A (en) * | 2018-10-16 | 2019-03-12 | 北京机械设备研究所 | A kind of quick interference method of automated system decision based on brain understanding |
| CN109460145B (en) * | 2018-10-16 | 2021-09-07 | 北京机械设备研究所 | Automatic system decision rapid intervention method based on brain recognition |
| CN109658503A (en) * | 2018-12-29 | 2019-04-19 | 北京理工大学 | A kind of driving behavior intention detection method merging EEG signals |
| CN110297489A (en) * | 2019-05-14 | 2019-10-01 | 西安工程大学 | The control method and control system of automatic driving vehicle |
| US20210213958A1 (en) * | 2020-01-13 | 2021-07-15 | Ford Global Technologies, Llc | Vehicle computer command system with a brain machine interface |
| US11780445B2 (en) * | 2020-01-13 | 2023-10-10 | Ford Global Technologies, Llc | Vehicle computer command system with a brain machine interface |
| CN114089628A (en) * | 2021-10-25 | 2022-02-25 | 西北工业大学 | Brain-driven mobile robot control system and method based on steady-state visual stimulation |
| CN114103974A (en) * | 2021-11-01 | 2022-03-01 | 北京理工大学 | Brain-computer interface method for vehicle continuous control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108491071B (en) | 2021-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108491071A (en) | A kind of brain control vehicle Compliance control method based on fuzzy control | |
| CN104083258B (en) | A kind of method for controlling intelligent wheelchair based on brain-computer interface and automatic Pilot technology | |
| CN109901595A (en) | A kind of automated driving system and method based on monocular cam and raspberry pie | |
| CN105329238B (en) | A kind of autonomous driving vehicle lane-change control method based on monocular vision | |
| CN110077414A (en) | A kind of vehicle driving safety support method and system based on driver status monitoring | |
| CN106184223A (en) | A kind of automatic Pilot control method, device and automobile | |
| DE102014224645A1 (en) | Control of a vehicle | |
| DE102012006207A1 (en) | Assistance devices and method for operating an assistance device for driving control of a towing vehicle with trailer | |
| CN104898524A (en) | Unmanned plane remote control system based on gesture | |
| CN105584479B (en) | A kind of model predictive control method towards brain control vehicle and the brain control vehicle using this method | |
| CN209258112U (en) | Vehicle DAS (Driver Assistant System) and vehicle | |
| CN107065850A (en) | The four motorized wheels intelligent car systems and its control method of a kind of brain wave control | |
| CN109521880B (en) | Teleoperation robot system and method based on mixed bioelectricity signal driving | |
| CN112000087A (en) | Intent priority fuzzy fusion control method for brain-controlled vehicle | |
| CN112631173A (en) | Brain-controlled unmanned platform cooperative control system | |
| CN109116855A (en) | A kind of moving trolley tracking control method and system | |
| CN103745241A (en) | Intelligent driving method based on self-learning algorithm | |
| CN206475931U (en) | A kind of personalized electric boosting steering system | |
| CN112171669A (en) | Brain-computer cooperation digital twin reinforcement learning control method and system | |
| EP3904186A1 (en) | Vehicle travel control device | |
| CN110716578A (en) | Aircraft control system based on hybrid brain-computer interface and control method thereof | |
| CN106985818A (en) | A kind of motor vehicle intelligent drive assist system based on cloud computing | |
| CN111399636A (en) | Unmanned vehicle guiding method, system and device based on limb action instruction | |
| Wang et al. | Research on application in intelligent vehicle automatic control system | |
| CN203472854U (en) | Video monitoring trolley |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |