This work is presented and accepted for publication in IV 2020.
In this paper, we proposed the methodology of recognizing "smooth", "bumpy", and "rough" road surfaces by using a unified sensor fusion framework DeepSense [1]. The raw accelerometer data is collected from a mobile phone, and processed on a desktop afterwards. During the preprocessing stage, raw sensory data is divided into small chunks and preprocessed using tsfresh [2] to extract time-series features. We implemented the DeepSense deep learning framework for the first time and succeeded in deploying it for road surface recognition on the desktop. One can also make it possible on a real-time on-board application.
We are releasing our approach's source code for Road Surface Recognition based on accelerometer sensor to share with the scientific community and industry with the aim of the collaboration with people around the world to push the boundaries in the field of intelligent transportation systems.
The collected accelerometer data can be found here.
Each file collected is a session and stored in csv format with the following fields:
| Field Name | Data Type | Description |
|---|---|---|
| Timestamp | Int | recorded in milliseconds, starts from 00:00:00.0, 1st of January, 1970 UTC |
| X-raw | Float | Raw accelerometer data for X axis |
| Y-raw | Float | Raw accelerometer data for Y axis |
| Z-raw | Float | Raw accelerometer data for Z axis |
| X-axis | Float | Virtually-oriented accelerometer data for X axis. This accelerometer data is virtually transformed from the phone’s coordinate frame to the world coordinate frame |
| X-axis | Float | Virtually-oriented accelerometer data for Y axis |
| X-axis | Float | Virtually-oriented accelerometer data for Z axis |
| Road_type | String | One of the "Smooth", "Bumpy" or "Rough" road type |
For a list of all source code dependencies, please see here.
- Clone the repo to your local drive.
- Setup your own Python environment and install the
requirements.txtby commandpip3 install -r requirements.txt. - Due to the inconsistency of installing the
tensorflow, you may need to install it manually.
We organized the codes in a modular way to make it convenient for customized usages. Before start, make sure the folders in the root directory is organized in the following way:
- Data folder at
./data/ - Execution root at
./src/
In the main.py, there's steps to run cross validation.
cd src/
python main.pyOur model is released under a GPLv3.0 license.
For a closed-source version of the source code for commercial purposes, please contact the authors: Wu and Hadachi
Shan Wu; Amnir Hadachi.
If you use our implementation or our dataset in an academic work, please cite:
@inproceedings{Shan2020Road,
title={Road Surface Recognition Based on DeepSense Neural Network using Accelerometer Data},
author={Wu, Shan and Hadachi, Amnir},
booktitle={Proceedings of the IEEE Intelligent Vehicles Symposium 2020},
year={2020},
organization={IEEE}
}
Publicated in IEEE Explore. Preprint version of the paper is here.