Design of Auto-Braking System for Accident Prevention and Accident Detection System Using IoT

2021 ◽  
pp. 101-114
Author(s):  
Gitanjali Mehta ◽  
Manoj Singh ◽  
Shubham Dubey ◽  
Uzair ◽  
Yogesh Mishra
Keyword(s):  
Detection System ◽  
Accident Prevention ◽  
Braking System ◽  
Accident Detection

Accident prevention using an auto braking system and accident detection using internet of things

2021 ◽  
pp. 142-148
Author(s):  
Gitanjali Mehta ◽  
Yogesh Mishra ◽  
Uzair Ashraf ◽  
Shubham Dubey ◽  
Manoj Singh ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Accident Prevention ◽  
Braking System ◽  
Accident Detection

Implementation of Automatic vehicle accident detection system using Zigbee

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
Ginne Rani ◽  
◽  
Aman Dhingya ◽  
Ankur Gupta ◽  
Sagar Kumar ◽  
...  
Keyword(s):  
Detection System ◽  
Vehicle Accident ◽  
Accident Detection

Design of Internet of Things-Based Accident Detection System

2020 ◽  
pp. 35-44
Author(s):  
Satyam Tayal ◽  
Harsh Pallav Govind Rao ◽  
Suryansh Bhardwaj ◽  
Samyak Jain
Keyword(s):  
Internet Of Things ◽  
Detection System ◽  
Accident Detection

scholarly journals In-tunnel Accident Detection System based on the Learning of Accident Sound

2021 ◽  
Vol 15 (1) ◽  
pp. 81-92
Author(s):  
Linyang Yan ◽  
Sun-Woo Ko
Keyword(s):  
Neural Network ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Deep Neural Network ◽  
Detection System ◽  
Recall Rate ◽  
Vehicle Accident ◽  
Special Environment ◽  
Accident Detection ◽  
Sound Features

Introduction: Traffic accidents are easy to occur in the tunnel due to its special environment, and the consequences are very serious. The existing vehicle accident detection system and CCTV system have the issues of low detection rate. Methods: A method of using Mel Frequency Cepstrum Coefficient (MFCC) to extract sound features and using a deep neural network (DNN) to learn sound features is proposed to distinguish accident sound from the non-accident sound. Results and Discussion: The experimental results show that the method can effectively classify accident sound and non-accident sound, and the recall rate can reach more than 78% by setting appropriate neural network parameters. Conclusion: The method proposed in this research can be used to detect tunnel accidents and consequently, accidents can be detected in time and avoid greater disasters.

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