Stochastic modeling of vehicle trajectory during lane-changing

摘要

A signal processing approach for modeling vehicle trajectory during lane changing driving is discussed. Because individual driving habits are not a deterministic process, we developed a stochastic method. The proposed model consists of two parts: a dynamic system represented by a hidden Markov model and a cognitive distance space derived from the range distance distribution. The first part models the local dynamics of vehicular movements and generates a set of probable trajectories. The second part selects an optimal trajectory by stochastically evaluating the distances from surrounding vehicles. From experimental evaluation, we show that the model can predict the vehicle trajectory at given traffic conditions with 17.6 m prediction error for two different drivers.