Federated Road Surface Anomaly Detection Using Smartphone Accelerometer Data

Oussama Mazari Abdessameud, Walid Cherifi

2024

Abstract

Road surface conditions significantly impact traffic flow, vehicle integrity, and driver safety. This importance is magnified in the context of service vehicles, where speed is often the only recourse for saving lives. Detecting road surface anomalies, such as potholes, cracks, and speed bumps, is crucial for ensuring smooth and safe driving experiences. Taking advantage of the widespread use of smartphones, this paper introduces a turn-by-turn navigation system that utilizes machine learning to detect road surface anomalies using accelerometer data and promptly alerts drivers. The detection model is personalized for individual drivers and continuously enhanced through federated learning, ensuring both local and global model improvements without compromising user privacy. Experimental results showcase the detection performance of our model, which continually improves with cumulative user contributions.

Paper Citation

in Harvard Style

Mazari Abdessameud O. and Cherifi W. (2024). Federated Road Surface Anomaly Detection Using Smartphone Accelerometer Data. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 376-383. DOI: 10.5220/0012766200003756

in Bibtex Style

@conference{data24,
author={Oussama Mazari Abdessameud and Walid Cherifi},
title={Federated Road Surface Anomaly Detection Using Smartphone Accelerometer Data},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={376-383},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012766200003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Federated Road Surface Anomaly Detection Using Smartphone Accelerometer Data
SN - 978-989-758-707-8
AU - Mazari Abdessameud O.
AU - Cherifi W.
PY - 2024
SP - 376
EP - 383
DO - 10.5220/0012766200003756
PB - SciTePress