Enhancing Interaction Modeling with Agent Selection and Physical Methods for Trajectory Prediction

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.