AI-PaaS Towards the Development of an AI-Powered Accident Alert System

Asani, E. Oluwatobi and Akande, Oladapo Daniel and Okosun, Esther Edeghogho and Olowe, Oluwambo Tolulope and Ogundokun, Roseline Oluwaseun and Okeyinka, A.E. (2023) AI-PaaS Towards the Development of an AI-Powered Accident Alert System. International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), Landmark University. pp. 1-8.

[img] Text
AI-PaaS.pdf
Download (926kB)
[img] Text (AI-PaaS Towards the Development of an AI-Powered Accident Alert System)
AI-PaaS.pdf - Published Version
Download (926kB)
Official URL: https://ieeexplore.ieee.org/

Abstract

The development of an accident detection system is a crucial step towards improving emergency response times, saving lives and achieving the ambitious projection of the United Nations General Assembly to drastically reduce the global fatality rate of road traffic crashes by half by the year 2030. It is also cardinal to the attainment of the United Nation’s SDG 11 goal of making cities and human settlements inclusive, safe, resilient and sustainable. In this study we present a preliminary development of an AI-powered Accident Alert System (AI-PaaS). The system has four modules namely, sensors module, detection module, registration module and messaging module. The detection module is powered by sensing technology and the Hidden Markov Model to intelligently and correctly detect that an accidents sound. The MPU 6050 containing both accelerometer and gyroscope is also integrated to detect any sharp variation in the acceleration and angular vis-à-vis a predefined threshold value. Once an accident has been detected, the messaging module is triggered to communicate first responders and the victims’ pre-registered kin. Preliminary results are presented. The system can potentially reduce road accident fatality by providing accurate and timely location-based information to emergency service providers.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: EMMANUEL ASANI
Date Deposited: 15 Jan 2024 07:55
Last Modified: 15 Jan 2024 07:55
URI: https://eprints.lmu.edu.ng/id/eprint/4526

Actions (login required)

View Item View Item
Landmark University Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.