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Automatic driving control method based on CNN-LSTM

A technology of automatic driving control and driving platform, applied in neural learning methods, special data processing applications, instruments, etc., can solve problems such as not covering all road conditions, complex and changeable road conditions, etc., to achieve accurate and reliable judgment, comprehensive road condition information, source wide range of effects

Active Publication Date: 2019-08-30
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • 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

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  • Automatic driving control method based on CNN-LSTM
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  • Automatic driving control method based on CNN-LSTM

Examples

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Embodiment 1

[0086] like 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, sam...

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...

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Abstract

The invention discloses an automatic driving control method based on CNN-LSTM. The automatic driving control method comprises the following steps: S1, acquiring image data and driving information of adriver at the same sampling frequency in a manner of combining a real vehicle driving or driving video recording or driving simulation system with a driving platform frame; and S2, based on a c++ language and a python language, in combination with an openCV library, automatically extracting image data to a region of interest by utilizing a Tensorflow framework, standardizing the image data into auniform size, arranging driving image data into an N * 4 two-dimensional tensor, and taking the N * 4 two-dimensional tensor as a training label Label. The automatic driving control method based on CNN-LSTM solves the problem that all road conditions cannot be covered by means of a traditional exhaustion type automatic driving algorithm due to the fact that the road conditions are complex and changeable in the prior art.

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

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06N3/04G06N3/08G06F17/50
CPCG06N3/08G06V20/56G06V10/25G06F30/20G06N3/044G06N3/045
Inventor 王文伟张志鹏林程李宜丁
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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