Introduction
The
(RNN). It is able to classify the car state:
•
car parked
•
car driving on a normal condition road
•
car driving on a bumpy road
•
car skidding or swerving
The main idea is to define an AI-car sensing node ECU with an embedded artificial intelligence processing.
The system hosts an SPC58EC Chorus 4M microcontroller, which is able to acquire discrete acceleration variations on a
three-axis reference system.
The
board retrieves inertial data. The acquired data are
transmitted to the LSTM RNN, which classifies the car state. The classification result is shown on the
touch display.
The LSTM RNN has been implemented and trained using the TensorFlow 2.4.0 framework (Keras) in the Google Colab
environment. The AI-SPC5Studio plug-in has been used to convert the resulting trained neural network into an optimized C
code library, which can run on an MCU with limited power computing resources.
Figure 1.
AEKD-AICAR1 evaluation kit
The LSTM RNN training has been performed with several time-series acceleration waveforms recorded on a real vehicle in
motion. The resulting prediction accuracy, calculated by the confusion matrix, is about 93%. Field tests carried-out under all road
conditions with a sedan confirm the adherence of the computed results compared with the real road conditions.
Note:
The
is an evaluation tool for R&D laboratory use only. It is not destined for use inside a vehicle.
Getting started with the AEKD-AICAR1 evaluation kit for car state classification
UM3053
User manual
UM3053
-
Rev 1
-
September 2022
For further information contact your local STMicroelectronics sales office.
www.st.com