  In this study, we address the limitations inherent in most existing vehicle
trajectory prediction methodologies that indiscriminately incorporate all
agents within a predetermined proximity when accounting for inter-agent
interactions. These approaches commonly employ attention-based architecture or
graph neural networks for encoding interactions, which introduces three
challenges: (i) The indiscriminate selection of all nearby agents substantially
escalates the computational demands of the model, particularly in those
interaction-rich scenarios. (ii) Moreover, the simplistic feature extraction of
current time agents falls short of adequately capturing the nuanced dynamics of
interactions. (iii) Compounded by the inherently low interpretability of
attention mechanism and graph neural networks, there is a propensity for the
model to allocate unreliable correlation coefficients to certain agents,
adversely impacting the accuracy of trajectory predictions. To mitigate these
issues, we introduce ASPILin, a novel approach that enhances the selection of
interacting agents by considering their current and future lanes, extending
this consideration across all historical frames. Utilizing the states of the
agents, we estimate the nearest future distance between agents and the time
needed to reach this distance. Then, combine these with their current distances
to derive a physical correlation coefficient to encode interactions.
Experiments conducted on popular trajectory prediction datasets demonstrate
that our method is efficient and straightforward, outperforming other
state-of-the-art methods.
