Abstract: The Automatic Accident Detector and Rescue System (AADRS) is an innovative approach designed to enhance road safety and improve emergency response efficiency. This system integrates advanced technologies such as sensors, GPS, communication networks, and artificial intelligence to promptly detect vehicle accidents and initiate timely rescue operations. The core components of the AADRS include accelerometers, gyroscopes, pressure sensors, cameras, and radar for accurate accident detection, coupled with GPS for precise location tracking. Communication is facilitated through cellular networks and Vehicle-to-Everything (V2X) technology, ensuring swift alerts to emergency services. AI algorithms and machine learning models analyze sensor data to confirm accidents and assess their severity, enabling a rapid and appropriate response. Upon detecting a collision, the system automatically transmits critical information, including the vehicle's location and the nature of the incident, to emergency responders. This immediate notification significantly reduces the time taken for rescue teams to arrive at the scene, potentially lowering the severity of injuries sustained. The AADRS offers numerous benefits, such as faster emergency response times, enhanced safety through early alerts to other vehicles, and precise location data aiding efficient rescue operations. However, challenges such as technical reliability, privacy concerns, integration with existing systems, and cost barriers need to be addressed for widespread adoption. Future developments in this field may include more advanced AI models, integration with autonomous vehicles, collaboration with smart city infrastructure, and the establishment of global standards for interoperability. The AADRS represents a substantial advancement in automotive safety, promising to save lives and mitigate the impact of road accidents through the effective use of modern technology.

Keywords: Automatic Accident Detection, Road Safety, LM35, Power cut-off, Arduino UNO, GSM module.

| DOI: 10.17148/IARJSET.2024.11607