CN107176098B - Automatic monitoring and early warning device for inner wheel difference blind area and control method - Google Patents
Automatic monitoring and early warning device for inner wheel difference blind area and control method Download PDFInfo
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- CN107176098B CN107176098B CN201710557434.5A CN201710557434A CN107176098B CN 107176098 B CN107176098 B CN 107176098B CN 201710557434 A CN201710557434 A CN 201710557434A CN 107176098 B CN107176098 B CN 107176098B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60Q—ARRANGEMENT OF SIGNALLING OR LIGHTING DEVICES, THE MOUNTING OR SUPPORTING THEREOF OR CIRCUITS THEREFOR, FOR VEHICLES IN GENERAL
- B60Q9/00—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling
- B60Q9/008—Arrangement or adaptation of signal devices not provided for in one of main groups B60Q1/00 - B60Q7/00, e.g. haptic signalling for anti-collision purposes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R16/00—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for
- B60R16/02—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements
- B60R16/023—Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric constitutive elements for transmission of signals between vehicle parts or subsystems
- B60R16/0231—Circuits relating to the driving or the functioning of the vehicle
- B60R16/0232—Circuits relating to the driving or the functioning of the vehicle for measuring vehicle parameters and indicating critical, abnormal or dangerous conditions
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Abstract
The invention discloses an automatic monitoring and early warning device for an inner wheel difference blind area, which comprises: the driving motor is arranged above the front wheel and the rear wheel on the right side of the vehicle body and is provided with a transmission mechanism; the rear end of the sensor arm is connected with the transmission mechanism, and the front end of the sensor arm is hinged with the surface of the vehicle body; the infrared light sensor is used for detecting the infrared light, which is fixedly connected with the front end of the sensor arm; and the microcontroller is connected with the driving motor through a control circuit. The automatic monitoring and early warning device for the dead zone of the inner wheel difference can monitor the dead zone information of the inner wheel difference in real time when a vehicle turns rightwards and send early warning information when danger is found. Meanwhile, the invention also provides a control method based on the BP neural network, which can adjust the angle of the infrared sensor according to different dead zones of the inner wheel difference of each right turn of the vehicle, so that the acquisition range of the infrared sensor is covered to the whole dead zone of the inner wheel difference, and more accurate early warning information is provided.
Description
Technical Field
The invention belongs to the technical field of automobile electronics, and particularly relates to an automatic monitoring and early warning device for an inner wheel difference blind area and a control method.
Background
In recent years, with the rapid development of economy in China, china has become a very important automobile country. However, the increase of automobiles brings about more traffic accidents, wherein the accident of large vehicles is one of the main reasons for causing major traffic accidents. The inner wheel difference is the difference between the turning radius of the front inner wheel and the turning radius of the rear inner wheel when the vehicle turns. Due to the existence of the inner wheel difference, when the vehicle turns, the movement tracks of the front wheel and the rear wheel are not coincident. If only the front wheel passes through the bicycle and forgets the difference of the inner wheel, the rear inner wheel can be driven out of the road or collide with other objects during the driving.
Because the large truck has a higher truck body, a driver sits on a left driver seat, a visual blind area is easy to generate when the truck turns right, the rear right is observed only by a rearview mirror, the visual range is limited, and the truck body is easy to collide with the truck running at high speed on an inner lane; because the truck has a large inner wheel difference in the right steering, and the driver sometimes judges that there is error or negligence, the truck is highly likely to collide with the non-vehicle. Therefore, when the vehicle turns right, the early warning of the dead zone of the inner wheel difference is necessary for the driver. Most of the existing internal wheel difference early warning devices adopt cameras, sensors and the like collect information of whether obstacles such as vehicles and pedestrians exist in internal wheel difference dead zones or not, and remind drivers. However, the blind area position of the inner wheel difference can be correspondingly changed due to different steering angles of the vehicle, because the radiation range of the sensor is limited, if the sensor is arranged at a fixed position, some positions cannot be radiated, and the acquired information is deviated. In addition, certain errors can occur in the theoretical inner wheel difference deduced by adopting the inner wheel difference geometric model, so that the angle of the infrared sensor is controlled in real time, and the whole inner wheel difference blind area is covered.
Disclosure of Invention
The invention aims to provide an automatic monitoring and early warning device for an inner wheel difference blind area, which can monitor inner wheel difference blind area information in real time when a vehicle turns rightwards and send early warning information when danger is found.
The invention further aims to provide a control method based on the BP neural network, which can adjust the angle of the infrared sensor according to different dead zones of the inner wheel difference of each right turn of the vehicle, so that the acquisition range of the infrared sensor is covered to the whole dead zone of the inner wheel difference, and more accurate early warning information is provided.
The technical scheme provided by the invention is as follows:
steering wheel angle sensor;
an infrared photoelectric sensor rotating mechanism, comprising:
the driving motor is arranged above the front wheel and the rear wheel on the right side of the vehicle body and is provided with a transmission mechanism;
the rear end of the sensor arm is connected with the transmission mechanism, and the front end of the sensor arm is hinged with the surface of the vehicle body;
an infrared photoelectric sensor fixedly connected with the front end of the sensor arm;
and the microcontroller is connected with the steering wheel angle sensor and is connected with the driving motor through a control circuit.
Preferably, the transmission mechanism adopts a gear rack structure.
Preferably, the rear end of the sensor arm is fixedly connected with a rack of the transmission mechanism.
Preferably, the automatic monitoring and early warning device for the inner wheel difference blind area further comprises an alarm module, wherein the alarm module is close to an instrument panel and is connected with the microprocessor, and the alarm module comprises:
the Chinese liquid crystal display module adopts 12864 liquid crystal display screens with Chinese character libraries;
the voice warning module adopts ISD0 series record-replay chip;
and the photoelectric warning module adopts a high-brightness light emitting diode as a warning lamp.
Preferably, the infrared photoelectric sensor is a reflective photoelectric sensor.
Preferably, the microcontroller adopts an STM32F103RCT6 type singlechip.
Preferably, the automatic monitoring and early warning device for the inner wheel difference dead zone further comprises a power supply module, and the power supply module adopts AMS1117-5.0 and L6932-3.3 chips to stabilize the voltage required by the two systems, namely 5V and 3.3V.
The control method of the automatic monitoring and early warning device for the dead zone of the inner wheel difference is characterized in that when a vehicle turns right, according to angle information transmitted by a steering wheel angle sensor, the minimum turning radius of the vehicle, the wheel base of the vehicle, based on BP neural network, regulates and controls the angles of infrared photoelectric sensors at the front side and the rear side of the vehicle, and the control method comprises the following steps:
step one, normalizing the front and rear wheel distance l, the wheel distance s, the minimum turning radius r and the steering angle theta of a steering wheel sensor of a vehicle in sequence, and determining an input layer vector x= { x of a three-layer BP neural network 1 ,x 2 ,x 3 ,x 4 -a }; wherein x is 1 Is the front and rear track coefficient of the vehicle, x 2 Is the wheel base coefficient of the vehicle, x 3 Minimum turning radius coefficient of vehicle, x 4 A steering angle coefficient for a steering wheel sensor;
step two, mapping the input layer vector to an intermediate layer, wherein the intermediate layer vector y= { y 1 ,y 2 ,…,y m -a }; m is the number of intermediate layer nodes;
step three, obtaining an output layer vector z= { z 1 ,z 2 -a }; wherein z is 1 For the angle adjusting coefficient, z of the infrared photoelectric sensor at the front side of the vehicle 2 Angle adjustment coefficient of rear-side infrared photoelectric sensor of vehicle to make
α i+1 ＝z 1 i α max ，
β i+1 ＝z 2 i β max ，
Wherein z is 1 i 、z 2 i Layer vector parameters, alpha, are output for the ith sampling period respectively max 、β max Respectively setting the maximum angle of the front-side infrared photoelectric sensor of the vehicle and the maximum angle of the rear-side infrared photoelectric sensor of the vehicle, alpha i+1 、β i+1 The angle of the vehicle front side infrared photoelectric sensor and the angle of the vehicle rear side infrared photoelectric sensor in the (i+1) th sampling period are respectively; and
in the first step, the front-rear wheel distance l, the wheel base s, the minimum turning radius r and the steering angle theta of the steering wheel sensor are normalized to form a normalization formula:
wherein x is j To input parameters in layer vectors, X j The parameters l, s, r, θ, j=1, 2,3,4; x is X jmax And X jmin Respectively, the maximum and minimum of the corresponding parameters.
Preferably, in the third step, in the initial running state, the angle of the vehicle front side infrared photoelectric sensor and the angle of the vehicle rear side infrared photoelectric sensor satisfy empirical values:
α 0 ＝0.85α max ，
β 0 ＝0.67β max ，
wherein alpha is 0 、β 0 The initial angles of the front-side infrared photoelectric sensor and the rear-side infrared photoelectric sensor of the vehicle are respectively; alpha max 、β max The maximum angles of the front-side infrared photoelectric sensor and the rear-side infrared photoelectric sensor of the vehicle are respectively set.
The beneficial effects of the invention are as follows: the automatic monitoring and early warning device for the dead zone of the inner wheel difference can monitor the dead zone information of the inner wheel difference in real time when a vehicle turns rightwards and send early warning information when danger is found. Meanwhile, the invention also provides a control method based on the BP neural network, which can adjust the angle of the infrared sensor according to different dead zones of the inner wheel difference of each right turn of the vehicle, so that the acquisition range of the infrared sensor is covered to the whole dead zone of the inner wheel difference, and more accurate early warning information is provided.
Drawings
Fig. 1 is a schematic diagram of an infrared photoelectric sensor rotating mechanism of the automatic monitoring and early warning device for the inner wheel difference blind area.
Fig. 2 is a schematic connection diagram of an automatic monitoring and early warning device module for the inner wheel difference blind area.
Fig. 3 is a schematic workflow diagram of the automatic monitoring and early warning device for the inner wheel difference blind area.
Fig. 4 is a schematic diagram of pin distribution of a microcontroller according to the present invention.
Fig. 5 is a general framework of the automatic monitoring and early warning device for the inner wheel difference blind area.
Detailed Description
The present invention is described in further detail below with reference to the drawings to enable those skilled in the art to practice the invention by referring to the description.
As shown in figures 1-4, the invention provides an automatic monitoring and early warning device for an inner wheel difference blind area, which can adjust the angle of an infrared sensor according to different steering angles of a vehicle. Meanwhile, the infrared sensor is used for acquiring early warning information and displaying the early warning information on a Chinese liquid crystal display screen, and carrying out voice and lamplight flickering warning, and when an accident occurs in a vehicle turning, the information such as the geographic position and time of a user is sent to a traffic police monitoring center in a short message mode through a GSM network. The automatic monitoring and early warning device for the dead zone of the inner wheel difference comprises a steering wheel angle sensor which is connected with the microcontroller 110, wherein the steering wheel angle sensor transmits a steering wheel steering angle signal to the microcontroller 110 when a vehicle turns; the microcontroller 110 employs an STM32F103RCT6 single-chip microcomputer. The driving motor 120 is arranged above the front wheel and the rear wheel on the right side of the vehicle body and is connected with the microcontroller 110 through a control circuit, and the driving motor 120 is provided with a transmission mechanism which comprises a cylindrical gear 121 and a rack 122; a sensor arm 130, the rear end of which is fixedly connected to the rack 122, and the front end of which is hinged to the surface of the vehicle body; and an infrared photoelectric sensor 140 fixedly connected to the front end of the sensor arm 130. When the vehicle turns right, the steering wheel angle sensor transmits a steering wheel angle signal to the microcontroller 110, the microcontroller 110 sends a signal to the driving motor control circuit, and the driving motor 120 is started and controlled to drive the rack 122 meshed with the cylindrical gear 121 to do transverse movement, so that the front end of the sensor arm 130 fastened with the rack 122 is driven to rotate around the hinge base point of the sensor arm and the surface of the vehicle body, and the infrared photoelectric sensor 140 is driven to rotate around the base point. The sensor arm 130 is of a telescopic structure, when the angle of rotation of the infrared photoelectric sensor 140 is large, the sensor arm 130 is extended, and when the angle of rotation of the infrared photoelectric sensor 140 is small, the sensor arm 130 is shortened.
The infrared photoelectric sensor 140 mainly functions to generate a sensing signal for an obstacle (pedestrian, vehicle, etc.) in a 'blind view' area when the truck turns, and then sends the signal to the microcontroller 110 for judgment processing. The infrared photoelectric sensor 140 adopts a reflective photoelectric sensor, which has a pair of infrared emitting and receiving tubes, the emitting tubes emit infrared rays with a certain frequency, when the detection direction encounters an obstacle (reflecting surface), the infrared rays are reflected back to be received by the receiving tubes, after being processed by a comparator circuit, a green indicator light is turned on, and meanwhile, a signal output interface outputs a digital signal (a low level signal). The data pin of the infrared photoelectric tube at the front side of the truck is connected with the PC0 pin of the microcontroller and is arranged to jump and interrupt along the outside; the data pin of the infrared photoelectric tube at the rear side of the truck is connected with the PC1 pin of the microcontroller and is arranged to jump along the external interruption, when obstacles such as pedestrians, vehicles and the like are located in the detection range of the sensor, a jump signal can be output to trigger the microcontroller 110 to receive the sensor data.
The microcontroller 110 is connected with the Chinese liquid crystal display 150 in an interrupted manner, the microcontroller 110 collects and processes the signals reflected by the infrared sensor 140, and transmits alarm information to the Chinese liquid crystal display 150. The Chinese liquid crystal display module 150 adopts 12864 liquid crystal display with Chinese character library, and can display the safety condition of the front side visual field blind area of the truck and the safety condition of the rear side visual field blind area of the truck in a split way. If obstacles such as pedestrians and the like enter the blind area with the inner wheel difference in the turning process of the truck, the system can display alarm information on the liquid crystal screen.
The voice warning module 160 employs an ISD0 series recording and playback chip. The multi-section recording device can record a plurality of sections, the sampling rate can be adjusted between 4K and 12K, and the power supply range can be between 2.4V and 5.5V. The microcontroller 110 interrupts the data pins connected to the voice alert module 160, and enables the system to prompt the driver with an on-site voice alert in time when a pedestrian is detected in the blind zone of the truck turning vision: "Note that truck turning danger-! "
The photoelectric warning module 170 is connected with the microcontroller 110, and adopts a high-brightness light emitting diode as a warning light, and further prompts a driver that the turn is dangerous at the moment through photoelectric flashing.
The power module 180 uses AMS1117-5.0 and L6932-3.3 chips to regulate the voltage required by both systems, 5V and 3.3V.
The automatic monitoring and early warning device for the inner wheel difference blind area further comprises a GMS automatic alarm module 190 based on GPS positioning, and the GMS automatic alarm module adopts a SIM300GSM module as wireless remote communication interface equipment. The standard SIM card holder is integrated in the module, so that a user can conveniently use the SIM card to access a network, and the GSM module is controlled to communicate with a mobile phone of a vehicle owner by using an AT instruction set through a computer or a singlechip. The microcontroller 110 uses the serial port to drive the GSM automatic alarm module 190. The GMS automatic alarm module 190 is connected to a vibration sensor mounted in the truck, which outputs a digital signal through a not gate. When no vibration signal exists, the sensor is conducted, the input end of the NOT gate is at a high level, the output end is at a low level after passing through the TTL NOT gate, when the vibration signal exists, the sensor is cut off, the input end of the NOT gate is at a low level, the output end is at a high level after passing through the NOT gate, and the cut-off time of the sensor is increased along with the vibration time. The vibration sensor is connected with the PA2 interface of the singlechip and inputs data into the microcontroller 110. The P1 port of the microcontroller 110 is used as an output end of the alarm signal, and the P1 port has an output latch function through an I/0 bidirectional static interface, so that the control of the alarm can be conveniently realized through software.
When the vibration monitor does not output a digital pulse signal, the microcontroller 110 always loops through the main program. When the vibration monitor outputs a digital pulse signal, the microcontroller 110 program jumps to the interrupt subroutine EXT0 to execute, triggers the GPS positioning system to detect the alarm position, and the current position information output by the GPS positioning system is sent to the traffic department in an information mode through the GMS automatic alarm module 190 to alarm.
The automatic monitoring and early warning device for the inner wheel difference blind area further comprises a serial port debugging module circuit 210, variables to be observed in the software program, a GSM module return result, an execution result and the like can be printed on the upper computer software through the serial port, so that the software execution flow can be more fully known, and the writing and the stabilization of the software are accelerated.
As shown in fig. 5, the invention provides an automatic monitoring and early warning device for blind areas of inner wheel differences, which takes a microcontroller (STM 32 single chip microcomputer) as a core, uses an infrared photoelectric sensor to collect front end signals in real time, and because a truck turns on the right side, the front and rear positions on the right side of the truck are respectively provided with the infrared photoelectric sensor, when a truck or a pedestrian enters a driver 'blind area' caused by the inner wheel differences, 2 infrared photoelectric sensors collect reflected signals and send the signals to the microcontroller to carry out operation and processing. The alarm information is displayed on a Chinese liquid crystal display screen, and the voice and lamplight flash warning is carried out to remind a driver to judge dangerous conditions in advance. If the vehicle turns and accidents occur, a vibration sensor arranged on the vehicle can transmit collision signals to a microcontroller, a GPS module receives satellite signals from a GPS antenna unit, information such as the geographic position and time of a user is obtained through processing and is sent to a GSM module, and the information such as the geographic position and time of the user is sent to a traffic police monitoring center in a short message mode through a GSM wireless communication network by a GSM antenna.
The invention also provides a control method of the automatic monitoring and early warning device for the dead zone of the inner wheel difference, when the vehicle turns right, according to the angle information transmitted by the steering wheel angle sensor, the minimum turning radius of the vehicle, the wheel base of the vehicle, the vehicle wheel base regulates and controls the angles of the infrared photoelectric sensors at the front side and the rear side of the vehicle based on the BP neural network, and the method specifically comprises the following steps:
step one, establishing a BP neural network model;
the BP network system structure adopted by the invention is composed of three layers, the first layer is an input layer, n nodes are used as the first layer, n detection signals representing the working state of equipment are corresponding to the first layer, and the signal parameters are given by a data preprocessing module. The second layer is a hidden layer, and m nodes are determined in an adaptive manner by the training process of the network. The third layer is an output layer, and p nodes are totally determined by the response which is actually required to be output by the system.
The mathematical model of the network is:
input layer vector: x= (x 1 ,x 2 ,…,x n ) T
Intermediate layer vector: y= (y) 1 ,y 2 ,…,y m ) T
Outputting layer vectors: z= (z) 1 ,z 2 ,…,z p ) T
In the present invention, the number of input layer nodes is n=4, and the number of output layer nodes is p=2. The number of hidden layer nodes m is estimated by:
according to the sampling period, the input 4 parameters are x 1 Is the front and rear track coefficient of the vehicle, x 2 Is the wheel base coefficient of the vehicle, x 3 Minimum turning radius coefficient of vehicle, x 4 A steering angle coefficient for a steering wheel sensor;
the 4 factors for determining the dead zone of the vehicle internal wheel difference belong to different physical quantities, and the dimensions of the factors are different. Therefore, the data needs to be normalized to a number between 0 and 1 before the data is input into the neural network.
Specifically, the vehicle front-rear wheel track factor x is obtained by normalizing the vehicle front-rear wheel track l 1 ：
Wherein l min And l max Respectively, the minimum and maximum values of the front and rear wheel tracks of the vehicle.
Similarly, the vehicle wheelbase coefficient x is obtained after normalizing the vehicle wheelbase s 2 ：
Wherein s is min Sum s max Respectively minimum and maximum values of the vehicle wheelbase.
Gauge for minimum turning radius r of vehicleAfter the meshing, the minimum turning radius coefficient x of the vehicle is obtained 3 ：
Wherein r is min And r max Respectively minimum and maximum of minimum turning radius of the vehicle.
Normalizing the steering angle theta of the steering wheel sensor to obtain a steering angle coefficient x of the steering wheel sensor 4 ：
Wherein θ min And theta max Steering to the disk sensor is at a minimum angle and at a maximum angle, respectively.
The 2 parameters of the output signal are expressed as: z 1 For the angle adjusting coefficient, z of the infrared photoelectric sensor at the front side of the vehicle 2 The angle adjustment coefficient of the rear infrared photoelectric sensor of the vehicle;
angle adjusting coefficient z of front infrared photoelectric sensor of vehicle 1 Expressed as the ratio of the angle of the front-side infrared photoelectric sensor of the vehicle to the maximum angle set in the current sampling period in the next sampling period, namely, the acquired angle is alpha in the ith sampling period i Outputting an angle adjustment coefficient z of the ith sampling period through the BP neural network 1 i Thereafter, the angle alpha in the (i+1) th sampling period is controlled i+1 So that it meets alpha i+1 ＝z 1 i α max ；
Angle adjusting coefficient z of rear infrared photoelectric sensor of vehicle 2 Expressed as the ratio of the angle of the rear infrared photosensor of the vehicle in the next sampling period to the maximum angle set in the current sampling period, i.e. the acquired nozzle angle in the ith sampling period is beta i Outputting a nozzle angle adjustment coefficient z of the ith sampling period through the BP neural network 2 i Thereafter, the nozzle angle in the (i+1) th sampling period is controlled to beβ i+1 Make it satisfy beta i+1 ＝z 2 i β max ；
Step two: training of the BP neural network is performed.
After the BP neural network node model is established, the BP neural network can be trained. Obtaining training samples according to experience data of products, and giving connection weight w between input node i and hidden layer node j ij Connection weight w between hidden layer node j and output layer node k jk Threshold θ of hidden node j j The threshold w of the output layer node k ij 、w jk 、θ j 、θ k Are random numbers between-1 and 1.
In the training process, continuously correcting w ij And w jk And (3) completing the training process of the neural network until the systematic error is less than or equal to the expected error.
As shown in table 1, a set of training samples and the values of the nodes during training are given.
Table 1 training process node values
Step three, acquiring data operation parameters and inputting the data operation parameters into a neural network to obtain a regulation and control coefficient;
the trained artificial neural network is solidified in the chip, so that the hardware circuit has the functions of prediction and intelligent decision making, and intelligent hardware is formed. After intelligent hardware is powered on and started, angle alpha of infrared photoelectric sensor at front side of vehicle 0 ＝0.85α max Angle beta of rear infrared photoelectric sensor of vehicle 0 ＝0.67β max ；
At the same time, the initial data of the front and rear wheel distance l, the wheel distance s, the minimum turning radius r and the steering angle theta of the steering wheel sensor are normalizedObtaining initial input vector of BP neural network
Obtaining an initial output vector by the operation of the BP neural network>
Step four: obtaining initial output vector
The angle of the front-side infrared photoelectric sensor and the angle of the rear-side infrared photoelectric sensor of the vehicle can be adjusted, and the angle of the front-side infrared photoelectric sensor and the angle of the rear-side infrared photoelectric sensor of the vehicle are respectively:
α 1 ＝z 1 0 α max
β 1 ＝z 2 0 β max
the sensor is used for acquiring the front and rear wheel distance l, the wheel base s, the minimum turning radius r and the steering angle theta of the steering wheel sensor in the ith sampling period, and the input vector x of the ith sampling period is obtained by normalization i ＝(x 1 i ,x 2 i ,x 3 i ,x 4 i ) Obtaining an output vector z of the ith sampling period through the operation of the BP neural network i ＝(z 1 i ,z 2 i ) Then controlling and adjusting the angle of the front-side infrared photoelectric sensor and the angle of the rear-side infrared photoelectric sensor of the vehicle so that the angle of the front-side infrared photoelectric sensor of the vehicle and the angle of the rear-side infrared photoelectric sensor of the vehicle in the (i+1) th sampling period are respectively as follows:
α i+1 ＝z 1 i α max ，
β i+1 ＝z 2 i β max ，
through the arrangement, the angle of the front infrared photoelectric sensor of the vehicle and the angle of the rear infrared photoelectric sensor of the vehicle are regulated and controlled by adopting the BP neural network algorithm, so that the vehicle reaches the optimal running state, and the accuracy of early warning information is improved.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use for which the invention would be readily apparent to those skilled in the art, and accordingly, the invention is not limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (7)
1. An interior poor blind area automatic monitoring early warning device of wheel includes:
steering wheel angle sensor;
an infrared photoelectric sensor rotating mechanism, comprising:
the driving motor is arranged above the front wheel and the rear wheel on the right side of the vehicle body and is provided with a transmission mechanism;
the rear end of the sensor arm is connected with the transmission mechanism, and the front end of the sensor arm is hinged with the surface of the vehicle body;
an infrared photoelectric sensor fixedly connected with the front end of the sensor arm;
the microcontroller is connected with the steering wheel angle sensor and is connected with the driving motor through a control circuit;
the control method is characterized in that when a vehicle turns right, according to angle information transmitted by a steering wheel angle sensor, the minimum turning radius of the vehicle, the wheel base of the vehicle, based on BP neural network, regulates and controls the angles of infrared photoelectric sensors at the front side and the rear side of the vehicle, and the control method comprises the following steps:
step one, normalizing the front and rear wheel distance l, the wheel distance s, the minimum turning radius r and the steering angle theta of a steering wheel sensor of a vehicle in sequence, and determining an input layer vector x= { x of a three-layer BP neural network 1 ,x 2 ,x 3 ,x 4 -a }; wherein x is 1 Is the front and rear track coefficient of the vehicle, x 2 Is the wheel base coefficient of the vehicle, x 3 Minimum turning radius coefficient of vehicle, x 4 A steering angle coefficient for a steering wheel sensor;
step two, mapping the input layer vector to an intermediate layer, wherein the intermediate layer vector y= { y 1 ,y 2 ,...,y m -a }; m is the number of intermediate layer nodes;
step three, obtaining an output layer vector z= { z 1 ,z 2 -a }; wherein z is 1 For the angle adjusting coefficient, z of the infrared photoelectric sensor at the front side of the vehicle 2 Angle adjustment coefficient of rear-side infrared photoelectric sensor of vehicle to make
α i+1 ＝z 1 i α max ，
β i+1 ＝z 2 i β max ，
Wherein z is 1 i 、z 2 i Layer vector parameters, alpha, are output for the ith sampling period respectively max 、β max Respectively setting the maximum angle of the front-side infrared photoelectric sensor of the vehicle and the maximum angle of the rear-side infrared photoelectric sensor of the vehicle, alpha i+1 、β i+1 The angle of the vehicle front side infrared photoelectric sensor and the angle of the vehicle rear side infrared photoelectric sensor in the (i+1) th sampling period are respectively; and
in the first step, the front-rear wheel distance l, the wheel base s, the minimum turning radius r and the steering angle theta of the steering wheel sensor are normalized to form a normalization formula:
wherein x is j To input parameters in layer vectors, X j The parameters l, s, r, θ, j=1, 2,3,4; x is X jmax And X jmin Respectively the maximum value and the minimum value of the corresponding parameters;
in the third step, in the initial running state, the angle of the vehicle front side infrared photoelectric sensor and the angle of the vehicle rear side infrared photoelectric sensor satisfy the empirical values:
α 0 ＝0.85α max ，
β 0 ＝0.67β max ，
wherein alpha is 0 、β 0 The initial angles of the front-side infrared photoelectric sensor and the rear-side infrared photoelectric sensor of the vehicle are respectively; alpha max 、β max The maximum angles of the front-side infrared photoelectric sensor and the rear-side infrared photoelectric sensor of the vehicle are respectively set.
2. The automatic monitoring and early warning device for the inner wheel difference blind area according to claim 1, wherein the transmission mechanism adopts a gear rack structure.
3. The automatic monitoring and early warning device for the inner wheel difference blind area according to claim 1, wherein the rear end of the sensor arm is fixedly connected with a rack of the transmission mechanism.
4. The automatic monitoring and early warning device for the blind area of the inner wheel difference according to claim 1, further comprising an alarm module which is arranged close to an instrument panel and is connected with the microcontroller, wherein the alarm module comprises:
the Chinese liquid crystal display module adopts 12864 liquid crystal display screens with Chinese character libraries;
the voice warning module adopts an ISD0 series record-replay chip;
and the photoelectric warning module adopts a high-brightness light emitting diode as a warning lamp.
5. The automatic monitoring and early warning device for the inner wheel difference blind area according to claim 1, wherein the infrared photoelectric sensor is a reflection type photoelectric sensor.
6. The automatic monitoring and early warning device for the inner wheel difference blind area according to claim 1, wherein the microcontroller adopts an STM32F103RCT6 type singlechip.
7. The automatic monitoring and early warning device for the inner wheel difference blind area according to claim 1, further comprising a power module, wherein the power module adopts AMS1117-5.0 and L6932-3.3 chips to stabilize the voltage required by the two systems, namely 5V and 3.3V.
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| CN201710557434.5A CN107176098B (en) | 2017-07-10 | 2017-07-10 | Automatic monitoring and early warning device for inner wheel difference blind area and control method |
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| CN109017602B (en) * | 2018-07-13 | 2023-08-18 | 吉林大学 | Self-adaptive center console based on human body gesture recognition and control method thereof |
| CN110065450A (en) * | 2019-04-18 | 2019-07-30 | 深圳技术大学 | Lubrication groove difference blind area detection system and its lorry blind area monitoring method |
| CN114049773B (en) * | 2021-11-04 | 2022-10-25 | 哈尔滨工业大学 | Constructor safety risk assessment early warning method and system |
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