Prototype Proposal for Quick Accident Detection and Response System

Rana, Sonjoy; Sengupta, Shounak; Jana, Sourav; Dan, Rahul; Sultana, Mahamuda; Sengupta, Diganta

doi:10.1109/icrcicn50933.2020.9296153

Cited by 8 publications

(1 citation statement)

References 11 publications

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“…Currently, researchers are accelerating research in this area, as is the case with the authors of [4,5]. One of the authors proposes the implementation of the Internet of Things (IoT) in the QARS (Quick Accident Response System) that helps to detect an accident using a multifunctional accelerometer and ultrasonic/proximity sensors, while the other author proposes an emergency alert system based on computer vision which is built using object tracking techniques to detect the occurrence of accidents.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Wireless Sensor System for Emergency Monitoring Roads with Low Communication Coverage

Postigo-Malaga,

Jimenez-Caceres,

Pelegri-Sebastia

et al. 2023

Electronics

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Rural areas often face communication challenges due to limited mobile coverage on remote roads, posing significant difficulties in reporting emergencies and accidents. This study presented an autonomous vehicle tracking system using low-cost radar sensors to detect possible emergencies in the sections of roads with low cell coverage. The radar sensor system could determine the number of vehicles that passed through the nodes and classify them based on the vehicle type. Each node within the system is equipped with ten radars, a processor unit, and a radio transmitter to communicate with the network in real-time, achieving a rapid response time of just 0.2 s. To ensure seamless connectivity, two distinct wireless communication networks are employed, one for the connection between the towers in the same node and the other for the connection between nodes and a center with cellular coverage. The results of this study can be useful in conveying emergency messages, as well as traffic management.

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Section: Introductionmentioning

confidence: 99%