Context-aware similarity measurement of lane-changing trajectories

Abstract

Lane change (LC), which is an essential behavior in driving, has an important role in traffic efficiency and safety. Examining the spatial lane change behavior of drivers and determining their movement patterns through investigation of the similarities of lane change trajectories is crucial in transportation systems. The moving objectives’ trajectories are a function of their contexts. Thus, in order to consider these contexts in studies of movements, acquiring a deeper understanding of them is necessary. This paper takes advantage of internal/external contexts and spatial footprints in order to contextualize a measure for lane change trajectory similarities. By employing dynamic time warping (DTW), the similarities of multi-dimensional trajectories were estimated, and through the Next Generation Simulation (NGSIM) dataset, the dynamic time warping approach was scrutinized. The results show that contextual information can limit and increase movements and must be utilized. The vehicles’ lane change trajectories were employed in order to present three novel prediction models for trajectory-grounded position. The historical data underwent similarity evaluations for the prediction of positions. According to the results, the suggested models were applicable in precise anticipation of the positions of LC vehicles. As far as the authors are aware, this research is the first one to employ a similarity-based framework to present a thorough analysis of position simulation of LC vehicles and similarities of lane change trajectories.