Modeling vehicle U-turning behavior near intersections: A deep learning approach based on TCN and multi-head attention

Abstract

In U-turn bays near intersections, the conflict between U-turning vehicles and those going straight-ahead results in traffic accidents since straight-ahead vehicles cannot reliably anticipate the behavior of oncoming U-turning vehicles. However, previous studies on modeling U-turning behavior do not effectively capture the spatial–temporal interaction between the U-turning and surrounding vehicles. To address this issue, a deep-learning framework based on a temporal convolutional network (TCN) and multi-head attention mechanism is developed. The TCN is utilized to capture long-term dependencies of vehicles in the shared left- and U-turn lanes by extracting vehicle historical motion features. The self-attention mechanism extracts salient features related to the U-turn intentions, classifying the vehicles into left- and U-turning vehicles based on their driving intentions. A parallel TCN and spatial multi-head attention structure is constructed to model vehicle–vehicle interactions to further predict the future trajectory of U-turning vehicles. Finally, the obtained features are input into a Transformer-based decoder module and trajectory generator to predict the future displacement and body orientation of U-turning vehicles. The model is validated via comparison with state-of-the-art models and the observed trajectories under various scenarios. Ablation studies are conducted to quantify the efficacy of each module. Further, the effect of the surrounding homogenous and heterogeneous vehicles on U-turning vehicles in four different U-turn scenarios is quantified using spatial–temporal variation graphs and attention matrices.