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Car crashes, mostly due to careless driving, bring severe economic loss and human losses. Advanced crash detection algorithms are of paramount importance to enhance automotive safety. This paper introduces an advanced crash detection algorithm based on data from all three axes with time analysis for real-time scenarios. The algorithm successfully identifies the driving and parking modes and has a reduced false positive rate. The system is tested rigorously to integrate well with black box systems, thus optimizing the storage by focusing on vital crash event data. This solution seeks to expand the scope of collision detection capabilities, improve response times, and enhance overall vehicular safety. Future iterations will add gyroscopic sensors and machine learning models to better improve the detection accuracy.

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Real-Time Accident Detection and Alert System Using Accelerometer and GPS Module

  • 会议论文

  • 发表日期：

    2026年

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  • Tharun Alla,
  • Sai Govardhan,
  • Abhiram,
  • Meena Belwal

摘要

Car crashes, mostly due to careless driving, bring severe economic loss and human losses. Advanced crash detection algorithms are of paramount importance to enhance automotive safety. This paper introduces an advanced crash detection algorithm based on data from all three axes with time analysis for real-time scenarios. The algorithm successfully identifies the driving and parking modes and has a reduced false positive rate. The system is tested rigorously to integrate well with black box systems, thus optimizing the storage by focusing on vital crash event data. This solution seeks to expand the scope of collision detection capabilities, improve response times, and enhance overall vehicular safety. Future iterations will add gyroscopic sensors and machine learning models to better improve the detection accuracy.