Authors:
Oussama Mazari Abdessameud
and
Walid Cherifi
Affiliation:
Dept. Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria
Keyword(s):
Road Anomaly Detection, Smartphone, GPS Turn-by-Turn Navigation, Federated Learning.
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.