Analysis and modeling of human driving behaviors using adaptive cruise control

We describe a driver model based on the feedback-error learning scheme using neural network (NN) for adaptive cruise control (ACC) use in driving and show the applicability of the feedback-error learning scheme as a behavior model of adaptability of human. The focus of the study is on the adaptation process of driving behaviors using ACC. The driver model for computer simulations was implemented by using a NN. The developed simulation model is used for predicting control performance of 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 and lateral deviation from road center were investigated as driver behavior characteristics during ACC use and manual driving, respectively. The simulation results of the lateral deviation were compared with the experimental results and showed that control performance with ACC use would be better than that of manual driving. Furthermore, it was found that human error occurred during ACC use in the DS experiments and provided an explanation of the cause in terms of Neisser’s perceptual cycle model.