This project contains the Artificial Intelligence models and experiments developed for the paper Speed Bump Detection Through Inertial Sensors and Deep Learning in a Multi-Contextual Analysis. In this research, we developed models for the speed bump detection in segments of cobblestone and asphalt pavements. We applied Deep Learning techniques based on Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Network (LSTM), Convolutional Neural Network Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM). We analyzed some aspects, such as the influence of the vehicle data collection placement and the data window size. We also evaluate the generability of model learning for unknown contexts, such as an unknown car, driver, or environment. The contents of each folder are described below.
The jupyter notebooks present in the folders correspond to the applied techniques. To run the models, just change the folders where the datasets are located (datasets_folder) and the work folder that has the experiments (work_folder). These parameters are in the file Deep Learning - Processing.ipynb. Other instructions are documented in the source code.
This folder contains all the experiments performed. Each folder stores the experiments of a technique, among the generated models and execution logs.
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TODOThis project is under Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Please see License File for more information.