Real-Time Vehicle Emergency Braking Detection with Moving Average Method Based on Accelerometer and Gyroscope Data

Emergency braking detection plays a vital role in enhancing road safety by identifying potentially hazardous driving behaviors. While existing methods rely heavily on artificial intelligence and computationally intensive algorithms, this paper proposes a lightweight, real-time algorithm for distinguishing emergency braking from non-emergency events using accelerometer and gyroscope signals. The proposed approach applies magnitude calculations and a moving average filters algorithm to preprocess inertial data collected from a six-axis IMU sensor. By analyzing peak values of acceleration and angular velocity, the algorithm successfully separates emergency braking from other events such as regular braking, passing over speed bumps, or traversing damaged roads. The results demonstrate that emergency braking exhibits a unique short-pulse pattern in acceleration and low angular velocity, distinguishing it from other high-oscillation disturbances. Furthermore, varying the window length of the moving average impacts classification accuracy and computational cost. The proposed method avoids the complexity of neural networks while retaining high detection accuracy, making it suitable for embedded and real-time vehicular systems, such as early warning applications for fleet management.