Human-like speed modeling for autonomous vehicles during car-following at intersections

Abstract

This study aims to model drivers’ speed in car-following during braking situations at intersections to estimate a safe comfortable human-like speed at the minimum car-following distance for autonomous vehicles (AV). Several car-following behavioral measures were extracted at different times before reaching the minimum following distance and the intersection control type (signalized or unsignalized) was recorded to train the model using three machine-learning techniques. The results showed that the XGBoost model is superior to other techniques with R2 values of 0.99 and 0.97 for training and testing datasets, respectively. The results also indicated that the control type impacts driver speed at the minimum following distance. The modeled speed will provide a more comfortable experience for AV riders and will not violate the expectations of the surrounding traditional vehicle drivers. Also, the proposed model can be adopted to enhance current car-following models by considering the effect of intersections and their control type.