A Study on Driver’s Deceleration Prediction Accuracy of ELM Model Depending on the Amount of Learning Data

To make advanced driver assistance systems (ADASs) more accessible, it is essential to ensure their response is more approximated to the driver’s operating behavior. In this study, we proposed a method to adapt the ADAS to the driver’s behavior with high accuracy using an extreme learning machine (ELM) model, which enabled fast learning of the vehicle response. The learning object was the driver’s vehicle velocity control during deceleration to predict the future velocity. The future velocity could be used for model predictive control (MPC) of the reference velocity to achieve the desired vehicle behavior and guarantee safety and efficiency. The proposed ELM model consisted of a series of serially connected velocity predictors that covered multiple time step horizons for MPC. We developed a system where the ELM model learned the driver’s deceleration trajectory and the effectiveness of this system was assessed by applying it to an actual test vehicle. The results showed that the proposed ELM model could predict the velocity in real-time.