| Autor: |
Ramakrishna Reddy K., Sathish Kumar T., Gogula Sreenivasulu, Sethy Abhisek, Ammisetty Veeraswamy, Sharath M.N., Gurnadha Gupta Koppuravuri, Kiran Ravi, Kansal Lavish |
| Jazyk: |
English<br />French |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
MATEC Web of Conferences, Vol 392, p 01102 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2261-236X |
| DOI: |
10.1051/matecconf/202439201102 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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