| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01102 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/matecconf/202439201102 | |
| Published online | 18 March 2024 | |
Harnessing sensor fusion and AI for accurate accident detection and classification in the safety of smart cities
1 Department of CSE, KG Reddy College of Engineering and Technology, Telangana - 501504
2 Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad
3 Department of Computer Science and Engineering (Data Science), Vardhaman College of Engineering, Shamshabad, Hyderabad 501218
4 Department of Computer Science & Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha - 751024
5 Department of IT, GRIET, Hyderabad, Telangana, India
6 Lovely Professional University, Phagwara, Punjab, India.
7 Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur Andhra Pradesh, India
8 Rajeev Institute of Technology, Hassan
9 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh - 522302, India
* Corresponding author: krkreddy20@gmail.com
With the growing number of automobiles, traffic accidents are increasing daily. The World Health Organization (WHO) study reports that annually, 1.4 million individuals have died, and 50 million have been wounded globally. An advanced accident detection technique using cognitive agents will reduce rescue operational delays, perhaps saving several lives. Intelligent Transportation Systems (ITS) are gaining significant attention in academia and industry because of the increasing popularity of smart cities. They are seen to enhance road safety in these urban areas. Internet of Things (IoT) and Artificial Intelligence (AI) systems have been widely used to decrease the time needed for rescue operations after an accident. This study introduces an IoT-enabled Automotive Accident Detecting and Categorization (IoT-AADC) method that combines a smartphone's internal and external sensors to identify and categorize the kind of accident. This innovative method enhances the effectiveness of emergency support like fire departments, towing agencies, etc., by providing crucial information regarding the accident category for better planning and execution of rescuing and relief activities. Emergency support providers enhance their preparedness by assessing the injuries experienced by those injured and the damage to the automobiles.
© The Authors, published by EDP Sciences, 2024
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