ST AEKD-AICAR1, User Manual, Page: 6 / 39

• Random
This module implements pseudo-random number generators for various distributions.
import random
• Seaborn
Seaborn is a Python data visualization library based on Matplotlib . It provides a high-level interface to draw
attractive and informative statistical graphics.
import seaborn as sn
• Matplot
Matplotlib is a comprehensive library to create static, animated, and interactive visualizations in Python.
import matplotlib.pyplot as plt
2.4 AI-car sensing node life cycle
The following steps define the life cycle of the AI-car sensing node implementation as a deep learning model for
classification:
1.
Model definition
2. Model training
3. Model fitting and compilation
4. Model evaluation
2.4.1 Model definition
To define the model, we have chosen the topology of the LSTM network. The AI-car sensing node network is
based on the same network architecture of a speech recognition system. It consists of a neural convolution kernel
as an input layer, able to elaborate a convolution function with the input vector over a temporal dimension.
The input vector is a time sequence of
TIMESERIES_LEN
number of samples, which consist of discrete
acceleration variations on a three-axis (x, y, z) reference system: Δax, Δay, and Δaz.
TIMESERIES_LEN
represents the minimum size of the temporal window for the car state classification. With an
acquisition sampling time equal to 100 msec, the value of
TIMESERIES_LEN
has been fixed to 50 samples (5
seconds per acquisition).
A dense function implements the output layer. This function can provide an output shape of four-dimensional
vectors (one for each expected status: parking, normal, bumpy, skid). The
Softmax
function activates this layer
and converts the output vector values to a probability distribution.
From an implementation perspective, this involves a model layer architecture built with a connection topology into
a cohesive model:
UM3053
AI-car sensing node life cycle
UM3053 - Rev 1
page 6/39