Redempt India ( ADAS )

EasyChair Preprint no. 12564

Abstract

This project aimed to develop an Accident Detection and Alert System (ADAS) that could improve road safety by detecting accidents and alerting emergency services. The system was designed to be low-cost, easy to install and operate, and capable of working in different environments. The system used an array of sensors and cameras to detect and analyze data, including acceleration, speed, and direction. The collected data were processed by a microcontroller and transmitted to a central server using wireless communication. The server then analyzed the data and triggered an alarm to alert emergency services in case of an accident. The project involved a comprehensive analysis of the available sensors and communication technologies, followed by the design and development of the ADAS prototype. The results of the tests showed that the ADAS prototype could detect accidents accurately and reliably, with a high degree of precision. The system was capable of sending alerts to emergency services within seconds of an accident, which could potentially save lives. In conclusion, this project successfully developed an Accident Detection and Alert System (ADAS) that can improve road safety and save lives. The system’s low cost, ease of installation and operation, and accuracy make it suitable for use in various environments. Further development and testing of the system could enhance its capabilities and make it a valuable tool for preventing road accidents.

Keyphrases: Accident Detection, ADAS, Alert system, deep learning framework, feature selection, GPS, image processing, LSTM, machine learning

@Booklet{EasyChair:12564,
  author = {Venish Surani and Krushnal Snehi and Arman Shaikh and Gaurav Varshney},
  title = {Redempt India ( ADAS )},
  howpublished = {EasyChair Preprint no. 12564},

  year = {EasyChair, 2024}}