CN108491071B - Brain-controlled vehicle sharing control method based on fuzzy control - Google Patents
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- 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
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- 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
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- 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
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Abstract
The invention relates to a brain-controlled vehicle sharing control method based on fuzzy control.A vehicle enters a fault-tolerant mechanism based on fuzzy control when an electroencephalogram command with motor imagery is identified on line in the running process of the vehicle, and enters an intelligent driving mechanism based on fuzzy control when the electroencephalogram command with motor imagery is not identified; the invention can correct the error brain electrical signal and automatically supervise the vehicle without brain electrical instruction, thereby making up the problems of high error rate, poor real-time performance, limited command number and the like of brain-computer interface recognition and greatly improving the safety of the brain-controlled vehicle in unknown environment.
Description
Technical Field
The invention relates to a brain-controlled vehicle sharing control method based on fuzzy control, and belongs to the field of BCI and the technical field of vehicle engineering.
Background
With the intensive research of the BCI (brain-computer interface) technology, the application objects thereof are increasingly wide, and a brain-controlled vehicle is one of the research hotspots therein. The brain-controlled vehicle combines the BCI technology and the vehicle technology, and controls the running of the vehicle by identifying the electroencephalogram signals. The brain-controlled vehicle has great application value in military, civil and entertainment aspects, not only improves the mobility of the disabled, but also provides a new idea for unmanned auxiliary control.
The search of the prior art documents shows that the research on the brain-controlled vehicle at home and abroad is still in a preliminary stage at present, most vehicles are directly controlled by using a brain-computer interface, and the feasibility of the brain-controlled vehicle is only verified. However, the existing brain-computer interface has many problems, such as low accuracy of brain-computer signal identification, poor real-time performance, limited command number, serious instruction delay, low transmission rate and easy error in intention interpretation for vehicles with extremely high safety requirements, so that the safety performance of the vehicles is difficult to guarantee by directly controlling the vehicles by using the brain-computer instructions. A Beijing university of finishing workers Bilu rescue topic group provides a model predictive control method for brain-controlled vehicles, and the stability of the brain-controlled vehicles is kept under the condition of ensuring the safety. However, the correction of wrong electroencephalogram instructions is still lacked, the supervision and the guarantee of brain-controlled vehicles are lacked under the condition of no electroencephalogram instructions, how to establish an auxiliary shared control method between the human brain and the vehicles to ensure the driving safety of the vehicles to the greatest extent is a technical problem to be solved urgently.
Disclosure of Invention
The brain-controlled vehicle sharing control method based on fuzzy control can correct wrong electroencephalogram signals and can automatically supervise the vehicle under the condition of no electroencephalogram instruction, the problems of high error rate of brain-computer interface identification, poor real-time performance, limited command number and the like are solved, and the safety of the brain-controlled vehicle in an unknown environment is greatly improved.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a brain-controlled vehicle sharing control method based on fuzzy control, in the vehicle driving process, when the electroencephalogram command with motor imagery is identified to be generated on line, the vehicle enters a fault-tolerant mechanism based on fuzzy control, and when the electroencephalogram command with motor imagery is not identified, the vehicle enters an intelligent driving mechanism based on fuzzy control;
as a further preferred aspect of the present invention, the method specifically comprises the following steps:
initializing brain electricity acquisition equipment and a vehicle system, acquiring brain electricity signals of a vehicle under a motor imagery task in real time by using the brain electricity acquisition equipment, carrying out feature extraction and classification on the brain electricity signals on line, and taking a classification result as brain control instructions for accelerating, decelerating, turning left and turning right applied to the vehicle;
step two, judging whether an electroencephalogram command is generated, if so, entering the step three, otherwise, entering the step four;
thirdly, entering a fault-tolerant mechanism based on fuzzy control, judging whether the electroencephalogram command is safe through a camera and an ultrasonic sensor, executing the electroencephalogram command if the vehicle is safe to run, and correcting the wrong electroencephalogram command if the vehicle is unsafe to run;
fourthly, entering an intelligent driving mechanism based on fuzzy control, judging the type of the environment where the vehicle is located when the vehicle runs through a camera and an ultrasonic sensor, if the vehicle is safe, not performing any control, and if the vehicle is not safe, selecting behaviors according to the environment where the vehicle is located;
as a further preferred aspect of the present invention, the electroencephalogram signals under the motor imagery task in the first step include three types, which are respectively a P300 evoked potential, a steady state visual evoked potential and an ERD/ERS;
as a further preferred embodiment of the present invention, in the third step, based on a fault-tolerant mechanism of fuzzy control, the distance between the vehicle and the lane lines on both sides of the vehicle is obtained through the camera, the distances between the vehicle and the obstacles in front of, on the left side of, on the right side of, and on the right side of the vehicle are obtained through the five-way ultrasonic sensor, and whether the received electroencephalogram command is safe or not is judged by combining the current vehicle speed, if so, the electroencephalogram command is executed, and if not, the wrong electroencephalogram command is corrected; the input of the fuzzy controller is received brain electrical instructions, including the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the barriers in front of, on the left side of, on the left front of, on the right side of and on the right front of the vehicle and the running speed of the current vehicle, and the actual control instructions output by the fuzzy control rule include acceleration, deceleration, left turning, right turning and stopping;
as a further preferable mode of the present invention, in the fourth step, based on the intelligent driving mechanism of fuzzy control, the distance between the vehicle and the lane lines on both sides of the vehicle is obtained through the camera, the distances between the vehicle and the obstacles in front of, on the left side of, on the front of, on the right side of, and on the right side of the vehicle are obtained through the five ultrasonic sensors, and whether the current environment is safe or not is judged by combining the current vehicle speed, if the vehicle is safe to run, no control is performed, and if the vehicle is unsafe to run, the behavior is selected according to the fuzzy control rule; the driving mechanism is the condition that no electroencephalogram command is received, the input of the fuzzy controller is the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the obstacles in front of the vehicle, on the left side of the vehicle, on the left front of the vehicle, on the right side of the vehicle and on the right front of the vehicle, and the current driving speed of the vehicle, and the actual control behaviors output by a series of fuzzy control rules comprise the following of the left lane line, the following of the right lane line, the deceleration, the left turn, the right turn and the stop.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
the invention provides a brain-controlled vehicle sharing control method based on fuzzy control, which enters a fault-tolerant mechanism based on fuzzy control when an electroencephalogram command with motor imagery is identified on line, enters an intelligent driving mechanism based on fuzzy control when the electroencephalogram command with motor imagery is not identified, can correct wrong electroencephalogram signals and automatically supervise a vehicle under the condition of no electroencephalogram command, ensures that the vehicle runs in a lane line without obstacles, and automatically avoids obstacles when the obstacles exist, solves the problems of high error rate, poor real-time performance, low transmission rate and the like of a brain-computer interface identification, greatly improves the safety of the brain-controlled vehicle under an unknown environment, and has certain practical significance and wide application prospect.
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The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a block diagram of a system for brain-controlled vehicles according to the present invention;
fig. 2 is a flowchart of a brain-controlled vehicle sharing control method according to the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic views illustrating only the basic structure of the present invention in a schematic manner, and thus show only the constitution related to the present invention.
According to the brain-controlled vehicle sharing control method based on the fuzzy control, in the vehicle driving process, when the electroencephalogram command with the motor imagery is identified to be generated on line, the vehicle enters a fault-tolerant mechanism based on the fuzzy control, and when the electroencephalogram command with the motor imagery is not identified, the vehicle enters an intelligent driving mechanism based on the fuzzy control;
as shown in fig. 1, the brain-controlled vehicle system mainly comprises an electroencephalogram signal acquisition module, a signal processing module, a shared control module and a vehicle; the specific work engineering is as follows:
firstly, electroencephalogram signals under motor imagery tasks are collected in real time by electroencephalogram collection equipment, and electroencephalogram signals of a frontal area, a central area and a parietal area of a brain of a subject under different motor imagery actions are collected by an EPOC + headgear of an EMOTIV company; the motor imagery actions are respectively imagination of left hand, imagination of right hand, imagination of feet and imagination of tongue movement, and respectively correspond to left turn, right turn, acceleration and deceleration instructions of the vehicle;
secondly, carrying out on-line processing on the acquired electroencephalogram signals, wherein the processing comprises preprocessing, feature extraction and classification, and the classification result is used as brain control instructions for left turning, right turning, acceleration and deceleration applied to the vehicle;
and thirdly, realizing sharing control based on fuzzy control, entering a fault-tolerant mechanism based on fuzzy control when the electroencephalogram command with motor imagery is identified on line, entering an intelligent driving mechanism based on fuzzy control when the electroencephalogram command with motor imagery is not identified, and outputting a final actual control command to a vehicle.
As a further preferred aspect of the present invention, as shown in fig. 2, a brain-controlled vehicle sharing control method based on fuzzy control specifically includes the steps of:
initializing brain electricity acquisition equipment and a vehicle system, acquiring brain electricity signals of a vehicle under a motor imagery task in real time by using the brain electricity acquisition equipment, carrying out feature extraction and classification on the brain electricity signals on line, and taking a classification result as brain control instructions for accelerating, decelerating, turning left and turning right applied to the vehicle;
step two, judging whether an electroencephalogram command is generated, if so, entering the step three, otherwise, entering the step four;
thirdly, entering a fault-tolerant mechanism based on fuzzy control, judging whether the electroencephalogram command is safe through a camera and an ultrasonic sensor, executing the electroencephalogram command if the vehicle is safe to run, and correcting the wrong electroencephalogram command if the vehicle is unsafe to run;
fourthly, entering an intelligent driving mechanism based on fuzzy control, judging the type of the environment where the vehicle is located when the vehicle runs through a camera and an ultrasonic sensor, if the vehicle is safe, not performing any control, and if the vehicle is not safe, selecting behaviors according to the environment where the vehicle is located;
as a further preferred aspect of the present invention, the electroencephalogram signals under the motor imagery task in the first step include three types, which are respectively a P300 evoked potential, a steady state visual evoked potential and an ERD/ERS; the electroencephalogram signals under the motor imagery task in the first step are numerous in brain-computer interface types, and the electroencephalogram signals which are widely applied are mainly three types: p300 evoked potential, steady state visual evoked potential, ERD/ERS (event related desynchronization potential/event related synchrony); considering that an application object is a vehicle and a brain-controlled driver needs to observe the surrounding environment by eyes, the motor imagery electroencephalogram signal in the ERD/ERS is most suitable;
as a further preferred embodiment of the present invention, in the third step, based on a fault-tolerant mechanism of fuzzy control, the distance between the vehicle and the lane lines on both sides of the vehicle is obtained through the camera, the distances between the vehicle and the obstacles in front of, on the left side of, on the right side of, and on the right side of the vehicle are obtained through the five-way ultrasonic sensor, and whether the received electroencephalogram command is safe or not is judged by combining the current vehicle speed, if so, the electroencephalogram command is executed, and if not, the wrong electroencephalogram command is corrected; the input of the fuzzy controller is received brain electrical instructions, including the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the barriers in front of, on the left side of, on the left front of, on the right side of and on the right front of the vehicle and the running speed of the current vehicle, and the actual control instructions output by the fuzzy control rule include acceleration, deceleration, left turning, right turning and stopping;
specifically, the fuzzy subsets of the input and output of the fuzzy controller are as follows:
inputting: brain electrical instruction fuzzy subset B: { a, D, L, R }, corresponding to { acceleration, deceleration, left turn, right turn };
the distance between the vehicle and the lane lines on the two sides of the vehicle, namely the distance between the vehicle and the left and right lane lines, fuzzy subsets CL and CR: { F, M, N }, corresponding to { far, medium, near };
distance from front, left side, front left, right side, front right obstacle fuzzy subsets H, L, LH, R, RH: { F, M, N }, corresponding to { far, medium, near };
current vehicle speed fuzzy subset V: { Q, S, Z }, corresponding to { fast, slow, zero }.
And (3) outputting: actual control instruction fuzzy subset O: { A, D, L, R, P }, corresponding to { accelerate, decelerate, turn left, turn right, stop }.
If the fuzzy control rules are directly formulated, the number is huge, the control rules need to be optimized, the problems of difference between a brain control vehicle and a human driving vehicle, low identification accuracy of brain control instructions and the like are considered in the optimization process, and the optimization rules are as follows:
(1) when the vehicle is close to the obstacle or lane line, the output O is stopped no matter B, V at this time;
(2) when the distance between the vehicle and the obstacle or the lane line is medium, if V is fast, no matter how B is, the output O is deceleration;
(3) when the vehicle speed is zero, the output is consistent with B regardless of other inputs;
(4) when the distance between the vehicle and the left side or the left front obstacle is middle, the distance between the vehicle and the left lane line is middle, and the distance between the vehicle and the left lane line is slow, if B is left turn, the output is right turn; the right side is also true.
After outputting the actual control command, the system judges whether the control command is finished or not, if so, the step of the second step is carried out, otherwise, the system waits for the control command to be finished.
As a further preferable mode of the present invention, in the fourth step, based on the intelligent driving mechanism of fuzzy control, the distance between the vehicle and the lane lines on both sides of the vehicle is obtained through the camera, the distances between the vehicle and the obstacles in front of, on the left side of, on the front of, on the right side of, and on the right side of the vehicle are obtained through the five ultrasonic sensors, and whether the current environment is safe or not is judged by combining the current vehicle speed, if the vehicle is safe to run, no control is performed, and if the vehicle is unsafe to run, the behavior is selected according to the fuzzy control rule; the driving mechanism is the condition that no electroencephalogram instruction is received, the input of the fuzzy controller is the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the obstacles in front of the vehicle, on the left side of the vehicle, on the left front of the vehicle, on the right side of the vehicle and on the right front of the vehicle, and the driving speed of the current vehicle, and the actual control behaviors output by a series of fuzzy control rules comprise the following of the left lane line, the following of the right lane line, the deceleration, the left turn, the right turn and the stop;
specifically, the fuzzy subsets of the fuzzy controller input and output are as follows:
inputting: the distance between the vehicle and the lane lines on the two sides of the vehicle, namely the distance between the vehicle and the left and right lane lines, fuzzy subsets CL and CR: { F, M, N }, corresponding to { far, medium, near };
distance from front, left side, front left, right side, front right obstacle fuzzy subsets H, L, LH, R, RH: { F, M, N }, corresponding to { far, medium, near };
current vehicle speed fuzzy subset V: q, S, corresponding to fast, slow.
And (3) outputting: actual control instruction fuzzy subset O: { KL, KR, D, L, R, P }, corresponding to { along the left lane line, along the right lane line, slow down, turn left, turn right, stop }.
If the number is huge if the number is directly set, the control rule needs to be optimized, the difference between a brain control vehicle and a human driving vehicle is considered in the optimization process, and the optimization rule is as follows:
(1) when the vehicle speed is high, the output is D;
(2) when the vehicle speed is slow, the distance between the vehicle speed and the lane line is far or medium, the current environment is judged to be safe, and no control is performed;
(3) when the speed is slow, the distance from the left lane line is close, and no barrier exists on the left side, the left front and the front, the output is KL; and vice versa;
(4) when the speed is slow, the distance from the left lane line is middle, and no barrier exists at the right side and the front right, the output is R; and vice versa;
(5) when the speed is slow, the vehicle is close to the left lane line, at least one distance between the front, the left side and the left front is middle or near, and at least one distance between the right side and the right front is middle or near, the output is P; the right side is reversed.
After outputting the actual control command, the system judges whether the control command is finished or not, if so, the step of the second step is carried out, otherwise, the system waits for the control command to be finished.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The meaning of "and/or" as used herein is intended to include both the individual components or both.
The term "connected" as used herein may mean either a direct connection between components or an indirect connection between components via other components.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.
Claims (3)
1. A brain-controlled vehicle sharing control method based on fuzzy control is characterized in that: in the running process of the vehicle, when the generation of an electroencephalogram command with motor imagery is identified on line, the vehicle enters a fault-tolerant mechanism based on fuzzy control, and when the electroencephalogram command with motor imagery is not identified, the vehicle enters an intelligent driving mechanism based on fuzzy control;
the method specifically comprises the following steps:
initializing brain electricity acquisition equipment and a vehicle system, acquiring brain electricity signals of a vehicle under a motor imagery task in real time by using the brain electricity acquisition equipment, carrying out feature extraction and classification on the brain electricity signals on line, and taking a classification result as brain control instructions for accelerating, decelerating, turning left and turning right applied to the vehicle;
step two, judging whether an electroencephalogram command is generated, if so, entering the step three, otherwise, entering the step four;
thirdly, entering a fault-tolerant mechanism based on fuzzy control, judging whether the electroencephalogram command is safe through a camera and an ultrasonic sensor, executing the electroencephalogram command if the vehicle is safe to run, and correcting the wrong electroencephalogram command if the vehicle is unsafe to run;
fourthly, entering an intelligent driving mechanism based on fuzzy control, judging the type of the environment where the vehicle is located when the vehicle runs through a camera and an ultrasonic sensor, if the vehicle is safe, not performing any control, and if the vehicle is not safe, selecting behaviors according to the environment where the vehicle is located;
in the third step, based on a fault-tolerant mechanism of fuzzy control, the distance between the vehicle and lane lines on two sides of the vehicle is acquired through a camera, the distance between the vehicle and obstacles in front of the vehicle, on the left side of the vehicle, on the left front of the vehicle, on the right side of the vehicle and on the right front of the vehicle are acquired through five ultrasonic sensors, whether the received electroencephalogram instruction is safe or not is judged by combining the current vehicle speed, if so, the electroencephalogram instruction is executed, and if not, the wrong electroencephalogram instruction is corrected; the input of the fuzzy controller is received brain electrical instructions, including the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the barriers in front of, on the left side of, on the left front of, on the right side of and on the right front of the vehicle and the running speed of the current vehicle, and the actual control instructions output by the fuzzy control rule include acceleration, deceleration, left turning, right turning and stopping;
specifically, the fuzzy subsets of the input and output of the fuzzy controller are as follows:
inputting: brain electrical instruction fuzzy subset B: { a, D, L, R }, corresponding to { acceleration, deceleration, left turn, right turn };
the distance between the vehicle and the lane lines on the two sides of the vehicle, namely the distance between the vehicle and the left and right lane lines, fuzzy subsets CL and CR: { F, M, N }, corresponding to { far, medium, near };
distance from front, left side, front left, right side, front right obstacle fuzzy subsets H, L, LH, R, RH: { F, M, N }, corresponding to { far, medium, near };
current vehicle speed fuzzy subset V: { Q, S, Z }, corresponding to { fast, slow, zero };
and (3) outputting: actual control instruction fuzzy subset O: { a, D, L, R, P }, corresponding to { acceleration, deceleration, left turn, right turn, stop };
if the fuzzy control rules are directly formulated, the number is huge, the control rules need to be optimized, the problems of difference between a brain control vehicle and a human driving vehicle, low identification accuracy of brain control instructions and the like are considered in the optimization process, and the optimization rules are as follows:
(1) when the vehicle is close to the obstacle or lane line, the output O is stopped no matter B, V at this time;
(2) when the distance between the vehicle and the obstacle or the lane line is medium, if V is fast, no matter how B is, the output O is deceleration;
(3) when the vehicle speed is zero, the output is consistent with B regardless of other inputs;
(4) when the distance between the vehicle and the left side or the left front obstacle is middle, the distance between the vehicle and the left lane line is middle, and the distance between the vehicle and the left lane line is slow, if B is left turn, the output is right turn; the right side is vice versa;
after outputting the actual control command, the system judges whether the control command is finished or not, if so, the step of the second step is carried out, otherwise, the system waits for the control command to be finished.
2. The brain-controlled vehicle sharing control method based on fuzzy control according to claim 1, wherein: the electroencephalogram signals under the motor imagery task in the first step comprise three types, namely a P300 evoked potential, a steady state visual evoked potential and an ERD/ERS.
3. The brain-controlled vehicle sharing control method based on fuzzy control according to claim 1, wherein: the distance between the vehicle and lane lines on two sides of the vehicle is acquired through a camera based on an intelligent driving mechanism of fuzzy control in the fourth step, the distance between the vehicle and obstacles in front of the vehicle, on the left side of the vehicle, on the left front of the vehicle, on the right side of the vehicle and on the right front of the vehicle are acquired through five ultrasonic sensors, whether the current environment is safe or not is judged by combining the current vehicle speed, if the vehicle is safe to run, no control is performed, and if the vehicle is unsafe to run, behaviors are selected through fuzzy control rules; the driving mechanism is the condition that no electroencephalogram command is received, the input of the fuzzy controller is the distance between the vehicle and the lane lines on the two sides of the vehicle, the distance between the vehicle and the obstacles in front of the vehicle, on the left side of the vehicle, on the left front of the vehicle, on the right side of the vehicle and on the right front of the vehicle, and the current driving speed of the vehicle, and the actual control behaviors output by a series of fuzzy control rules comprise the following of the left lane line, the following of the right lane line, the deceleration, the left turn, the right turn and the stop.
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| CN109658503B (en) * | 2018-12-29 | 2020-07-28 | 北京理工大学 | Driver behavior intention detection method fused with electroencephalogram 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 |
| US11780445B2 (en) * | 2020-01-13 | 2023-10-10 | Ford Global Technologies, Llc | Vehicle computer command system with a brain machine interface |
| CN114089628B (en) * | 2021-10-25 | 2022-11-04 | 西北工业大学 | Brain-driven mobile robot control system and method based on steady-state visual stimulation |
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| 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 |
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