Abstract: This project presents the design and implementation of an intelligent car crash detection system aimed at enhancing vehicle safety and emergency response efficiency. The system utilizes data collected from multiple sensors, including accelerometers, gyroscopes, and GPS modules, to continuously monitor vehicle dynamics and detect abnormal patterns indicative of collisions. By applying advanced signal processing techniques and machine learning algorithms, such as decision trees or neural networks, the system can distinguish between normal driving maneuvers and actual crash events with high accuracy. Once a collision is detected, the system automatically triggers an alert mechanism that sends critical information—such as location, impact severity, and time—to emergency services via wireless communication modules. This rapid alert reduces emergency response times and potentially lowers fatalities and injuries associated with road accidents. The system is designed to operate in real-time with minimal latency and to be robust under varying road conditions and crash scenarios. Testing and evaluation on simulated and real-world datasets demonstrate the effectiveness and reliability of the proposed solution, making it a promising addition to intelligent transportation systems and future smart vehicles.

Keywords: Car Crash Detection

| DOI: 10.17148/IARJSET.2025.125352