CN109284699A - A kind of deep learning method being applicable in vehicle collision - Google Patents
A kind of deep learning method being applicable in vehicle collision Download PDFInfo
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Abstract
The present invention relates to car crass detection fields, and in particular to a kind of deep learning method suitable for vehicle collision includes the following steps: S1: recording the travelling data of current vehicle and aforementioned vehicle by sensor, and is uploaded to cloud platform；S2: the prediction model based on convolutional neural networks is established according to the data of S1；S3: obtaining the travelling data of current vehicle, and is uploaded to the prediction model based on convolutional neural networks；S4: judge whether to collide according to the prediction model based on convolutional neural networks that S2 is established.The present invention overcomes the technological deficiency that the data sampling frequency of existing method is high, computation complexity is high, provides a kind of deep learning method suitable for vehicle collision.
Description
Technical field
The present invention relates to car crass detection fields, and in particular to a kind of deep learning method suitable for vehicle collision.
Background technique
With the fast development of internet, car networking also becomes increasingly to weigh as a kind of important means of intelligent transportation
It wants.Car networking is the Internet being made of information such as vehicle location, speed and routes, and vehicle passes through GPS, RFID, sensor
The acquisition of itself environment and status information can be completed with camera etc., then by dividing information of vehicles computer technology
Analysis and processing, finally calculate the best route of different vehicle and report without delay road conditions.Car networking system is by vehicle instrument
Table platform installs vehicle-mounted terminal equipment, realizes to the acquisition of vehicle all working and quiet multidate information, storage and sends, car networking one
As all have the function of outdoor scene, can use mobile network realize people-car interaction.
Existing vehicle net system mainly passes through vehicle collision avoidance algorithm and adopts respectively in both the horizontal and vertical directions
Judge whether to collide with prediction algorithm prediction.But the data sampling frequency of existing method is high, computation complexity
It is high.
Summary of the invention
The present invention overcomes the data sampling frequency of the learning method of above-mentioned existing vehicle collision height, computation complexity are high
Technological deficiency provides a kind of deep learning method suitable for vehicle collision.
In order to solve the above technical problems, technical scheme is as follows:
A kind of deep learning method suitable for vehicle collision, includes the following steps:
S1: the travelling data of current vehicle and aforementioned vehicle is recorded by sensor, and is uploaded to cloud platform；
S2: the prediction model based on convolutional neural networks is established according to the data of S1；
S3: obtaining the travelling data of current vehicle, and is uploaded to the prediction model based on convolutional neural networks；
S4: judge whether to collide according to the prediction model based on convolutional neural networks that S2 is established.
S1 records the travelling data of current vehicle and aforementioned vehicle, and the travelling data for the aforementioned vehicle for needing to obtain is at least
The travelling data of 50 aforementioned vehicles, the G-sensor sensor that the sensor is 50 hertz, for recording vehicle in cross
To the stress in, longitudinal, vertical three directions.
The prediction model based on convolutional neural networks of S2, convolutional neural networks include four layers, respectively input layer, convolution
Layer, pond layer and output layer, the data that the data of the input layer are acquired from sensor；The convolutional layer is to input layer
Data carry out convolution；The pond layer screens to obtain matrix to the matrix that convolutional layer obtains.
S1 is the value that the stress in lateral, longitudinal, vertical three directions is obtained by sensor, wherein lateral stress value table
Show that the stress of vehicle horizontal direction, longitudinal value indicate that vehicle stress in the front-back direction, vertical value indicate vehicle up and down direction
Stress.
Prediction model of the S3 based on convolutional neural networks, which is characterized in that the first layer is the square that input layer is 50*4
Battle array；The second layer is convolutional layer, is multiplied using the convolution matrix of 5*5*4 with input layer, and the matrix of 5*10*1 is obtained；Third layer is pond
Change layer, the matrix that the second layer is obtained passes through maximum pond；4th layer is output layer, judges hair according to the matrix that third layer obtains
The probability of raw collision.
S4 judges the grade of collision according to the size of g value according to the prediction model of convolutional neural networks,
Output layer uses logistic function, is greater than threshold value for judging whether, if more than threshold value, then judgement collides
And triggering collision warning；Otherwise, judgement does not collide and does not trigger alarm.
Compared with prior art, the beneficial effect of technical solution of the present invention is: overcoming prior art data acquiring frequency
The technological deficiency of high, computation complexity height, the situation that needs to collide in view of different directions, improves the effect of vehicle collision prediction
Rate and accuracy.
Detailed description of the invention
Fig. 1 is flow chart when vehicle of the present invention collides；
Fig. 2 is convolutional neural networks prediction model of the present invention.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1:
As shown in Figure 1, a kind of deep learning method suitable for vehicle collision, includes the following steps:
S1: the travelling data of current vehicle and aforementioned vehicle is recorded by sensor, and is uploaded to cloud platform；
S2: the prediction model based on convolutional neural networks is established according to the data of S1；
S3: obtaining the travelling data of current vehicle, and is uploaded to the prediction model based on convolutional neural networks；
S4: judge whether to collide according to the prediction model based on convolutional neural networks that S2 is established.
S1 records the travelling data of current vehicle and aforementioned vehicle, and the travelling data for the aforementioned vehicle for needing to obtain is at least
The travelling data of 50 aforementioned vehicles, the G-sensor sensor that the sensor is 50 hertz, for recording vehicle in cross
To the stress in, longitudinal, vertical three directions.50 hertz of G-sensor sensor record in real time vehicle in the horizontal direction, front and back
The stress data in three direction, up and down direction directions, namely 150 data sample points of record.
Wherein, based on the prediction model of convolutional neural networks, convolutional neural networks include four layers, respectively input layer, volume
Lamination, pond layer and output layer, the data that the data of the input layer are acquired from sensor；The convolutional layer is to input layer
Data carry out convolution；The pond layer screens to obtain matrix to the matrix that convolutional layer obtains.Wherein, input layer uses 50 rows 4
The matrix of column, convolutional layer carry out convolution to the matrix of input layer 50*4, and Convolution uses the matrix of a 5*4, by first layer
The matrix multiple of the matrix of input layer 50*4 and the second convolutional layer 5*4, obtains the matrix of a 10*1 size.Use different 5*
4 convolution matrix, is repeated 5 times, and may finally obtain the matrix of 5*10*1, and wherein the parameter of convolution matrix can be by trained
It arrives；The pond layer size of third layer can amount to 5, each matrix by the matrix-split of 5*10*1 at the matrix of 10*1 with 10*1
A maximum value is selected, the matrix of second layer 5*10*1 can finally obtain the matrix of 5*1 by pond layer；It is defeated at the 4th layer
Layer out, by the matrix of third layer 5*1, to calculate threshold value.
S1 is the value that the stress in lateral, longitudinal, vertical three directions is obtained by sensor, wherein lateral stress value table
Show that the stress of vehicle horizontal direction, longitudinal value indicate that vehicle stress in the front-back direction, vertical value indicate vehicle up and down direction
Stress.
Prediction model of the S3 based on convolutional neural networks, which is characterized in that the first layer is the square that input layer is 50*4
Battle array；The second layer is convolutional layer, is multiplied using the convolution matrix of 5*5*4 with input layer, and the matrix of 5*10*1 is obtained；Third layer is pond
Change layer, the matrix that the second layer is obtained passes through maximum pond；4th layer is output layer, judges hair according to the matrix that third layer obtains
The probability of raw collision.
S4 judges the grade of collision according to the size of g value according to the prediction model of convolutional neural networks,The stress condition of level, front and back, up and down direction is measured using g value, with it is collected by
Whether the size of force value is more than the threshold value of sensor settings, to determine whether colliding, is reported to cloud platform.
Output layer uses logistic function, is greater than threshold value for judging whether, if more than threshold value, then judgement collides
And triggering collision warning；Otherwise, judgement does not collide and does not trigger alarm.
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (7)
1. a kind of deep learning method suitable for vehicle collision, which comprises the steps of:
S1: the travelling data of current vehicle and aforementioned vehicle is recorded by sensor, and is uploaded to cloud platform；
S2: the prediction model based on convolutional neural networks is established according to the data of S1；
S3: obtaining the travelling data of current vehicle, and is uploaded to the prediction model based on convolutional neural networks；
S4: judge whether to collide according to the prediction model based on convolutional neural networks that S2 is established.
2. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the S1 record is worked as
The travelling data of vehicle in front and aforementioned vehicle, the travelling data for the aforementioned vehicle for needing to obtain are at least the row of 50 aforementioned vehicles
Car data, the G-sensor sensor that the sensor is 50 hertz, for recording vehicle lateral, longitudinal, three vertical
The stress in direction.
3. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the S2 based on
The prediction model of convolutional neural networks, convolutional neural networks include four layers, respectively input layer, convolutional layer, pond layer and output
Layer, the data that the data of the input layer are acquired from sensor；The convolutional layer carries out convolution to the data of input layer；Institute
It states pond layer the matrix that convolutional layer obtains is screened to obtain matrix.
4. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the S1 is to pass through
Sensor obtains the value of the stress in lateral, longitudinal, vertical three directions, wherein lateral stress value indicates vehicle horizontal direction
Stress, longitudinal value indicate that vehicle stress in the front-back direction, vertical value indicate the stress of vehicle up and down direction.
5. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the S3 is based on volume
The prediction model of product neural network, which is characterized in that the first layer is the matrix that input layer is 50*4；The second layer is convolution
Layer, is multiplied with input layer using the convolution matrix of 5*5*4, obtains the matrix of 5*10*1；Third layer is pond layer, and the second layer is obtained
The matrix obtained passes through maximum pond；4th layer is output layer, and the matrix obtained according to third layer judges the probability to collide.
6. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the S4 is according to volume
The prediction model of product neural network, the grade of collision is judged according to the size of g value,
7. the deep learning method according to claim 1 suitable for vehicle collision, which is characterized in that the output layer is adopted
With logistic function, it is greater than threshold value for judging whether, if more than threshold value, then judgement collides and triggers collision warning；
Otherwise, judgement does not collide and does not trigger alarm.
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Application publication date: 20190129 |