A feature-driven approach for ADAS using real-time smartphone inertial sensor data

With rapid advancement of advanced driver assistance systems (ADAS) and autonomous vehicles, ensuring safe interaction between human drivers and ADAS system has become a critical challenge for accurately predicting driver behaviour. The study addresses the problem of developing robust systems, with limited raw features from smartphone inertial measurement unit (IMU) sensors. This study proposes a feature-driven approach to predict driver behaviour using smartphone IMU data. The objective is to enhance prediction accuracy by utilising two novel features namely, statistical deep features and cross-correlated features. These features capture more intrinsic patterns in driver behaviour and improve the performance. The proposed methodology was experimented on three benchmark datasets with machine learning models like random forest (RF) and extreme gradient boosting (XGBoost). Using cross-correlated features, accuracies of 99%, 100%, and 99% were obtained. This demonstrates that this approach outperforms existing methods, capturing detailed patterns and providing more reliable predictions in complex traffic.