Advanced International Journal for Research

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Volume 7 Issue 3
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Accident Prediction and Mitigation System for Two-Wheelers Using Portable Airbag Deployment

Author(s) Ms. Ankita B Baddoli, Ms. Radhika E Shanmukh, Ms. Tarabai R Heroor, Prof. Supriya Bagewadi
Country India
Abstract Two-wheelers constitute a major portion of road traffic worldwide and account for a significant percentage of accident-related injuries and fatalities. Traditional safety mechanisms such as helmets and reactive airbag systems provide protection only after an impact occurs, offering limited preventive capability. This paper presents an AI-enabled accident prediction and mitigation system specifically designed for two-wheelers by integrating Internet of Things (IoT), TinyML, and sensor fusion technologies to predict high-risk situations before a crash occurs. The proposed system utilizes an ESP32 microcontroller, a 9-axis IMU sensor, gyroscope, and speed sensor to continuously monitor real-time vehicle motion characteristics. A TinyML-based classification model categorizes rider behaviour into normal, risky, and pre-crash states. Upon detecting abnormal or high-risk patterns, the system generates instant alerts, activates a pre-arm safety mode, and deploys a portable airbag module during severe crash conditions to minimize impact force and rider injuries. A Wi-Fi-based dashboard displays real-time parameters including tilt angle, acceleration, crash status, airbag deployment state, GPS location, and risk score, while an I2C LCD provides immediate rider feedback. Additionally, GPS and GSM modules enable emergency SMS alerts with live location details during critical accidents. The proposed system offers a proactive, low-cost, scalable, and intelligent safety solution suitable for integration into motorcycles, smart helmets, and rider safety jackets.
Keywords Accident Detection, TinyML, IoT, ESP32, Airbag Deployment, Sensor Fusion, Two-Wheeler Safety, Embedded Systems.
Field Engineering
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-06-21
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E-ISSN 3048-7641
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