T-Foresight: Interpret moving strategies based on context-aware trajectory prediction
Year: 2025
Type: article
Abstract: Trajectory prediction and interpretation are crucial in various domains for optimizing movements in complex environments. However, understanding how diverse contextual factors—environmental, physical, and social—influence moving strategies is challenging due to their multifaceted nature, which complicates quantification and the derivation of actionable insights. We introduce an interpretable analytics workflow that addresses these challenges by innovatively leveraging ensemble learning for context-aware trajectory prediction. Multiple base predictors simulate diverse moving strategies, while a decision-making model assesses the suitability of each predictor in specific contexts. This approach quantifies the impact of contextual factors by interpreting the decision-making model’s predictions and reveals possible moving strategies through the aggregation of base predictors’ outputs. The workflow comes with T-Foresight, an interactive visualization interface that empowers stakeholders to explore predictions, interpret contextual influences, and devise and compare moving strategies effectively. We evaluate our approach in the domain of eSports, specifically MOBA games. Through case studies with professional analysts, we demonstrate T-Foresight’s effectiveness in illustrating player moving strategies and providing insights into top-tier tactics. A user study further confirms its usefulness in helping average players uncover and understand advanced strategies.
Source: Visual Informatics
Authors: Yueqiao Chen, Jiang Wu, Yingcai Wu, Dongyu Liu
Institutions: University of California, Davis, Zhejiang University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
OpenAlex ID: W4413136536