Designing An Ai-Car Sensing Node; Tool-Set Introduction; Creating A Google Colab Notebook; Figure 4. Ai-Car Sensing Node: Car State Classification - ST AEKD-AICAR1 User Manual

2

Designing an AI-car sensing node

2.1

Tool-set introduction

An AI-car sensing node is an AI-deep learning system based on an LSTM recurring neural network, which can
provide a car state classification: parking, driving on a normal condition road, driving on a bumpy road, and car
skidding or swerving.
The LSTM RNN has been modeled with TensorFlow (Keras framework). This is an open-source software library
for machine learning, which provides optimized modules to implement AI algorithms related to the classification
problem.
A significant amount of computing power is required to implement sufficiently robust and efficient models. For
initial tests, we can rely on a standard machine. As the dataset size increases, the execution of complex training
algorithms becomes rapidly prohibitive.
To address this issue, there are several cloud services that offer computing power. Google Colab is an alternative
platform, which allows running the code directly on the cloud, even if with some limitations.
2.2

Creating a Google Colab notebook

To use the Google Colab
notebook" project file.
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Figure 4. AI-car sensing node: car state classification
platform, you need a Google account to login. After logging in, create a new "Colab
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Designing an AI-car sensing node
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