Analysis and modeling of human driving behaviors using adaptive cruise control

This paper describes a driver model based on the feedback-error learning scheme for adaptive cruise control (ACC) use on driver behaviors. The driver model for simulations is implemented by using a neural network The focus of the study is on the adaptation process of driving behaviors using ACC. The developed simulation model is used for predicting control performance of a skilled driver using ACC In the experiments, we used a fixed-base driving simulator (DS) installed ACC system for collecting driver´s data Headway time when lane-changing in a row, FFT analysis of steering angle, and lateral deviation from the road center were investigated as driver behavior characteristics during ACC use and manual driving, respectively. The simulation results for the lateral deviation were compared with the experimental results and showed that control performance with ACC use will be better than that of manual driving. Furthermore, it was found that human error occurred during the ACC use in the DS experiments