Can-Based Intelligent Vehicle Automation And Real-Time Accident Detection With Automatic Alert System

Modern vehicular systems increasingly rely on automation and intelligent monitoring to enhance safety, reduce human error, and ensure rapid emergency response. Conventional accident detection mechanisms depend on manual reporting, resulting in delayed assistance and increased fatalities. This research presents a Control Area Network (CAN)–based vehicle automation and accident alert system that integrates sensors, microcontrollers, and communication modules to detect collisions, monitor vehicle parameters, and automatically transmit emergency notifications. The system utilizes accelerometers, vibration sensors, and onboard diagnostics to detect abnormalities, while CAN provides reliable, high-speed, fault-tolerant communication within the vehicle. Upon detecting an accident, the system triggers automatic alerts containing vehicle ID, GPS location, and status information to emergency contacts or authorities. Additionally, automation features such as speed monitoring, obstacle detection, and automatic braking enhance driver safety. The integration of CAN ensures robustness, low latency, and reduced wiring complexity. The proposed system offers a cost-effective, scalable solution for intelligent transportation systems and next-generation automotive safety design.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-31
DOI
https://doi.org/10.5281/zenodo.19356205
Primary Topic
IoT and GPS-based Vehicle Safety Systems
Type
article
Field-Weighted Citation Impact
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article

Can-Based Intelligent Vehicle Automation And Real-Time Accident Detection With Automatic Alert System

IJESAT
Zenodo (CERN European Organization for Nuclear Research)
IoT and GPS-based Vehicle Safety Systems
article

Can-Based Intelligent Vehicle Automation And Real-Time Accident Detection With Automatic Alert System

IJESAT
article en

Abstract

Modern vehicular systems increasingly rely on automation and intelligent monitoring to enhance safety, reduce human error, and ensure rapid emergency response. Conventional accident detection mechanisms depend on manual reporting, resulting in delayed assistance and increased fatalities. This research presents a Control Area Network (CAN)–based vehicle automation and accident alert system that integrates sensors, microcontrollers, and communication modules to detect collisions, monitor vehicle parameters, and automatically transmit emergency notifications. The system utilizes accelerometers, vibration sensors, and onboard diagnostics to detect abnormalities, while CAN provides reliable, high-speed, fault-tolerant communication within the vehicle. Upon detecting an accident, the system triggers automatic alerts containing vehicle ID, GPS location, and status information to emergency contacts or authorities. Additionally, automation features such as speed monitoring, obstacle detection, and automatic braking enhance driver safety. The integration of CAN ensures robustness, low latency, and reduced wiring complexity. The proposed system offers a cost-effective, scalable solution for intelligent transportation systems and next-generation automotive safety design.

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 7%
IoT and GPS-based Vehicle Safety Systems
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