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

A thorough understanding of the interaction between the target agent and surrounding agents is a prerequisite for accurate trajectory prediction. Although many methods have been explored, they all assign correlation coefficients to surrounding agents in a purely learning-based manner. In this study,...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Huang, Shiji, Ye, Lei, Chen, Min, Luo, Wenhai, Wang, Dihong, Xu, Chenqi, Liang, Deyuan
Format: Article
Language:English
Subjects:
Correlation coefficients
Feature extraction
Graph neural networks
Interaction models
Neural networks
Online Access:Get full text
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Summary:A thorough understanding of the interaction between the target agent and surrounding agents is a prerequisite for accurate trajectory prediction. Although many methods have been explored, they all assign correlation coefficients to surrounding agents in a purely learning-based manner. In this study, we present ASPILin, which manually selects interacting agents and calculates their correlations instead of attention scores. Surprisingly, these simple modifications can significantly improve prediction performance and substantially reduce computational costs. Additionally, ASPILin models the interacting agents at each past time step separately, rather than only modeling the interacting agents at the current time step. This clarifies the causal chain of the target agent's historical trajectory and helps the model better understand dynamic interactions. We intentionally simplified our model in other aspects, such as map encoding. Remarkably, experiments conducted on the INTERACTION, highD, and CitySim datasets demonstrate that our method is efficient and straightforward, outperforming other state-of-the-art methods.
ISSN:2331-8422