Smart Accident Detection and Alert System

Abstract: This paper presents an IoT-based real-time accident detection and alert system using Raspberry Pi. The system utilizes an accelerometertodetectsuddenimpacts,analcoholsensortomonitorthedriver'scondition,andacameramoduletocapture the driver's image upon accident detection. The geographical location is retrieved using the IPStack API instead of a traditional GPS module. A machine learning model processes real-time video input to identify accidents visually. When an accidentisdetectedbyeithertheaccelerometerortheMLmodel,relevantdataincludingdriverimages,accidentframes,and locationare sentviaemailusingSMTPprotocol.Additionally,allsystemdataisuploadedandvisualizedinreal-timeonthe ThingSpeak cloud platform, offering graphical insights for monitoring. This integrated solution enhances post-accident response time, driver condition analysis, and centralized cloud-based monitoring.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-05-18
DOI
https://doi.org/10.22214/ijraset.2025.71045
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Smart Accident Detection and Alert System

Mrs. Chetali Mhetre
International Journal for Research in Applied Science and Engineering Technology
IoT and GPS-based Vehicle Safety Systems
article

Smart Accident Detection and Alert System

Mrs. Chetali Mhetre
article en

Abstract

Abstract: This paper presents an IoT-based real-time accident detection and alert system using Raspberry Pi. The system utilizes an accelerometertodetectsuddenimpacts,analcoholsensortomonitorthedriver'scondition,andacameramoduletocapture the driver's image upon accident detection. The geographical location is retrieved using the IPStack API instead of a traditional GPS module. A machine learning model processes real-time video input to identify accidents visually. When an accidentisdetectedbyeithertheaccelerometerortheMLmodel,relevantdataincludingdriverimages,accidentframes,and locationare sentviaemailusingSMTPprotocol.Additionally,allsystemdataisuploadedandvisualizedinreal-timeonthe ThingSpeak cloud platform, offering graphical insights for monitoring. This integrated solution enhances post-accident response time, driver condition analysis, and centralized cloud-based monitoring.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(5)
Openalex Percentile: Top 90%
IoT and GPS-based Vehicle Safety Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.