Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Abstract

Complex road sections without lane markings cannot constrain vehicles to follow the lane disciplines. As a result, vehicles often exhibit more disorderly rapid lateral movements (RLMs) in these areas, making it difficult to accurately predict vehicle trajectories. This study takes toll plaza diverging area as an example to propose a framework incorporated the Hidden Markov Model (HMM) and Temporal Fusion Transformer (TFT) for vehicle trajectory prediction in non-lane based complex road sections. The results demonstrate that the vehicles exhibit more RLMs when there are more toll lanes matching their toll collection types. Validated on two toll plaza diverging areas with different structures, the proposed framework achieves higher prediction accuracy than other state-of-the-art predictive methods, particularly in long prediction horizons. In addition, the interpretability of TFT suggests that incorporating RLM intention prediction and environmental factors specific to non-lane based areas into trajectory prediction is of great importance.