Current Models for Implementing Crash Detection in Digital Mobile Platforms

Yaryha Andrei

Citation: Yaryha Andrei, "Current Models for Implementing Crash Detection in Digital Mobile Platforms", Universal Library of Engineering Technology, Volume 02, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article provides an in-depth examination of contemporary crash detection models integrated into digital mobile platforms and Internet of Vehicles (IoV) infrastructures. The study first discusses hardware configurations—including accelerometers, GPS modules, and dashcams—and explores how these sensors interact in real-time telematics environments. It then delineates the algorithmic frameworks underlying crash detection, contrasting threshold-based heuristics with machine learning and deep learning approaches. Further, practical facets such as multi-sensor calibration, federated learning for privacy preservation, and adaptable data communication strategies are critically analyzed. The article also highlights integration with external service ecosystems (e.g., emergency assistance, insurance) and addresses legal, privacy, and standardization challenges surrounding crash detection deployment. Taken together, these investigations elucidate the potential of holistic, data-driven systems for reducing false alarms and accelerating life-saving interventions in traffic accidents.


Keywords: Crash Detection; Internet of Vehicles (IoV); Federated Learning; Sensor Fusion; Mobile Platforms; Telematics; Road Safety; Emergency Notification.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0203003