Road Surface Type Classification Based on Inertial Sensors and Machine Learning: A Comparison Between Classical and Deep Machine Learning Approaches For Multi-Contextual Real-world Scenarios
This project contains the Artificial Intelligence models and experiments developed for the paper Road Surface Type Classification Based on Inertial Sensors and Machine Learning: A Comparison Between Classical and Deep Machine Learning Approaches For Multi-Contextual Real-world Scenarios. In this research, we applied classical techniques of K-Means Clustering (KMC), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN); and Deep Learning techniques based on Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and hybrid LSTM-CNN. We analyzed in the time domain the data collected near and below the suspension, in addition to the impact of 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 files Classical Methods - Processing.ipynb and 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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To cite this paper use:
Menegazzo, J., von Wangenheim, A. Road surface type classification based on inertial sensors and machine learning. Computing (2021). https://doi.org/10.1007/s00607-021-00914-0
@article{menegazzo_road_2021,
title = {Road surface type classification based on inertial sensors and machine learning},
issn = {1436-5057},
url = {https://doi.org/10.1007/s00607-021-00914-0},
doi = {10.1007/s00607-021-00914-0},
language = {en},
urldate = {2021-02-18},
journal = {Computing},
author = {Menegazzo, Jeferson and von Wangenheim, Aldo},
month = feb,
year = {2021}
}This project is under Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Please see License File for more information.