Unlock instant, AI-driven research and patent intelligence for your innovation.

A method of automatic driving control based on cnn-lstm

A technology of automatic driving control and driving platform, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of complex and changeable road conditions, unable to cover all road conditions, and achieve comprehensive road condition information, accurate and reliable judgment, easy to use. obtained effect

Active Publication Date: 2020-06-16
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problems existing in the prior art, the present invention provides a CNN-LSTM-based automatic driving control method, which solves the problem that the previous methods cannot cover all road conditions due to the complex and changeable road conditions and relying on the traditional exhaustive automatic driving algorithm. The problem

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A method of automatic driving control based on cnn-lstm
  • A method of automatic driving control based on cnn-lstm
  • A method of automatic driving control based on cnn-lstm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0086] Such as Figure 5 to Figure 6 As shown, S11: Collect the image data (pictures in units of frames) from the driving video at a frequency of 10 Hz, and use the simulation driving platform to perform corresponding operations to collect the opening of the accelerator pedal, the opening of the brake pedal, the steering wheel Corner and gear information.

[0087] S12: Use the Tensorflow framework to automatically extract the area of ​​interest from the image data, and standardize it into a unified size of 200×200×3 (3 is the number of channels, indicating the RGB color space), and organize the driving data into a two-dimensional sheet of [1,4] quantity.

[0088] S13: Build a CNN-LSTM neural network. The CNN part consists of 5 convolutional layers, 4 pooling layers, and 2 fully connected layers. The LSTM part consists of 100 LSTM units. The specific parameters are as follows:

[0089] Input image: 200×200×3

[0090] Convolution layer 1: Convolution kernel 3×3, step size 2, ...

Embodiment 2

[0107] S21: Collect the image data (pictures in units of frames) from the CARLA simulation environment at a frequency of 20 Hz, and use the simulation driving platform to perform corresponding operations to collect the opening of the accelerator pedal, the opening of the brake pedal, the steering wheel angle and gear information.

[0108] S22: Use the Tensorflow framework to automatically extract the area of ​​interest from the image data, and standardize it into a unified size of 300×300×3 (3 is the number of channels, indicating the RGB color space), and organize the driving data into a two-dimensional sheet of [1,4] quantity.

[0109] S23: Build a CNN-LSTM neural network. The CNN part consists of 5 convolutional layers, 4 pooling layers, and 2 fully connected layers. The LSTM part consists of 150 LSTM units. The specific parameters are as follows:

[0110] Input image: 300×300×3

[0111] Convolution layer 1: Convolution kernel 3×3, step size 2, same padding, ReLU activati...

Embodiment 3

[0128] S31: Drive the real vehicle and use the frequency of 30Hz to collect the image data (pictures in frames) by the driving recording device, and record the corresponding operation of the driver at the same time, collecting the opening of the accelerator pedal, the opening of the brake pedal, the steering wheel angle and the gear. bit information.

[0129] S32: Use the Tensorflow framework to automatically extract the area of ​​interest from the image data, and standardize it into a unified size of 400×400×3 (3 is the number of channels, indicating the RGB color space), and organize the driving data into two-dimensional sheets of [1,4] quantity.

[0130] S33: Build a CNN-LSTM neural network. The CNN part consists of 5 convolutional layers, 4 pooling layers, and 2 fully connected layers. The LSTM part consists of 200 LSTM units. The specific parameters are as follows:

[0131] Input image: 400×400×3

[0132] Convolution layer 1: Convolution kernel 3×3, step size 2, same pa...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a CNN‑LSTM-based automatic driving control method, comprising the following steps: S1: Collecting image data at the same sampling frequency by combining real vehicle driving or driving video recording or simulated driving system with the driving platform and driver’s driving information; S2: Based on c++, python language, combined with openCV library, using Tensorflow framework to automatically extract image data to the area of ​​interest, and standardize it into a uniform size, organize the driving image data into N×4 two-dimensional sheets The amount is used as a training label Label; it solves the problem that the previous methods cannot cover all road conditions due to the complex and changeable road conditions, relying on the traditional exhaustive automatic driving algorithm.

Description

technical field [0001] The invention relates to the field of automatic driving control, in particular to a CNN-LSTM-based automatic driving control method. Background technique [0002] With the rise of the electric vehicle industry and the hot development of artificial intelligence technology, AI technology has shown great value and potential in the direction of intelligent driving, and the neural network, which is designed to imitate the working mode of the human brain, relies on its powerful Excellent performance and wide applicability are sought after by all walks of life. [0003] Convolutional Neural Networks (CNN) is a type of feed-forward neural network that includes convolution calculations and has a deep structure. It imitates the biological visual perception mechanism and can perform supervised learning and unsupervised learning. The convolution kernel parameter sharing and the sparsity of inter-layer connections enable the convolutional neural network to learn g...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/32G06N3/04G06N3/08G06F30/20
CPCG06N3/08G06V20/56G06V10/25G06F30/20G06N3/044G06N3/045
Inventor 王文伟张志鹏林程李宜丁
Owner BEIJING INSTITUTE OF TECHNOLOGYGY