Modeling Anticipation and Relaxation of Lane Changing Behavior Using Deep Learning

Modeling lane changing driving behavior has attracted significant attention recently. Most of the existing models are homogeneous and do not recognize the anticipation and relaxation phenomena occurring during the maneuver. To fill this gap, we adopted long short-term memory (LSTM) network and used large quantities of trajectory data extracted from video footage collected by an unmanned automated vehicle in Nanjing, China. Then, we divided complete lane changing behavior into two stages, that is, anticipation and relaxation. Description analysis of lane changing behavior revealed that the factors affecting the two stages are significantly different. In this context, two LSTM models with different input variables were proposed to predict the anticipation and the relaxation during the lane changing activity, respectively. The vehicle trajectory data were further divided into an anticipation dataset and a relaxation dataset to train the two LSTM models. Then we applied numerical tests to compare our models with two baseline models using real trajectory data of lane changing behavior. The results suggest that our models achieved the best performance for trajectory prediction in both lateral and longitudinal positions. Moreover, the simulation results show that the proposed models can precisely replicate the impact of the anticipation phenomenon on the target lane, and the relationship between the speed and spacing of the lane changing vehicle during the relaxation process can be reproduced with reasonable accuracy.