(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
The increasing number of road accidents involving two-wheelers has become a major concern due to factors such as overspeeding, drunk driving, poor road conditions, and lack of real-time hazard awareness. Existing safety systems mainly focus on post-accident response rather than prevention. This project proposes an intelligent Accident Prevention System for Two- Wheelers using embedded and sensor-based technologies to enhance rider safety through proactive monitoring and control. The system integrates multiple sensors, including LiDAR for front obstacle detection, ultrasonic sensors for rear vehicle monitoring and pothole detection, an alcohol sensor for detecting intoxicated driving, and an MPU6050 sensor for monitoring tilt and sudden movements. An ESP32 microcontroller processes real-time sensor data and determines unsafe conditions based on predefined thresholds. When a potential risk is detected, the system provides immediate alerts through an LCD display and buzzer, and automatically limits or cuts off motor operation using a relay module. By combining real-time sensing, intelligent decision-making, and automated control mechanisms, the system ensures early detection of hazards and reduces dependency on human reaction time. The proposed solution is cost-effective, reliable, and scalable, making it suitable for practical implementation in two- wheelers. Overall, the system significantly improves road safety by preventing accidents and promoting responsible riding behavior.