Detection of Driver's Emergency Braking Intention in Single-Pedal Control Systems Basing EEG Signals

In automotive safety and intelligent transport systems, detecting a driver's emergency brake intention is crucial for improving driving safety. Traditional methods rely heavily on driver actions, resulting in slow response times, low accuracy, and difficulty adapting to individual differences and complex driving environments. Recently, physiological signal detection and driver behavior analysis have gained attention for their ability to provide faster, more accurate responses and better adaptability. This study proposes an electroencephalogram (EEG) signal-based method for detecting emergency braking intentions. EEG signals were collected from 12 participants in a simulated driving environment. Features were extracted using Adaptive Power Spectral Density (APSD) and classified using Regular Linear Discriminant Analysis (RLDA) for real-time detection of drivers' emergency braking intentions. This approach enhances vehicle safety and response speed in critical situations and provides technical support and a theoretical basis for the practical application of emergency braking intention detection in single-pedal control systems.