The increasing prevalence of road accidents necessitates innovative solutions to enhance rider safety and reduce fatalities. This paper presents an AI-based smart helmet that integrates multi-sensor fusion, machine learning, and IoT connectivity to enable real-time accident detection and emergency alerting. The system employs a 6-axis IMU (accelerometer and gyroscope), GPS, and environmental sensors to monitor rider dynamics and detect collision events with 97.9% accuracy in controlled tests. A lightweight CNN model deployed on an STM32 microcontroller processes sensor data in real-time, distinguishing between normal riding and crash scenarios while minimizing false positives. Upon impact detection, the helmet automatically triggers GSM-based emergency alerts, transmitting the rider's GPS coordinates to predefined contacts and emergency services within <500ms.

Field tests involving 50 riders over 15,000 km demonstrated 100% detection of actual accidents, with a false positive rate of just 0.5%. The system's dual-network communication (GSM + Wi-Fi/Bluetooth) ensures reliable alert transmission even in low-coverage areas. Power optimization techniques, including dynamic voltage scaling and solar charging, enable 48–72 hours of operation per charge. Beyond collision detection, the helmet provides near-miss incident logging and supports preventive safety analytics.

This work advances smart helmet technology by combining edge AI processing, robust sensor fusion, and adaptive power management in a lightweight (<300g added weight), IP67-rated design that maintains standard helmet safety certifications. The results confirm that AI-enhanced smart helmets can significantly reduce emergency response times, potentially saving lives through automated crash reporting and precise location tracking. Future directions include V2X integration and drone-assisted emergency response.