Trustworthy Pedestrian Trajectory Prediction via Pattern-Aware Interaction Modeling
This work addresses the need for reliable and transparent pedestrian trajectory prediction for intelligent applications, offering an incremental improvement over existing methods.
The paper tackles the problem of achieving trustworthy pedestrian trajectory prediction by proposing a Transformer-based model that explicitly captures diverse interaction patterns and introduces a training strategy to reduce initial-step divergence. The model outperforms black-box baselines in accuracy, especially in high-density scenarios, and reduces initial-step prediction error by approximately 6.58%.
Accurate and reliable pedestrian trajectory prediction is critical for the application of intelligent applications, yet achieving trustworthy prediction remains highly challenging due to the complexity of interactions among pedestrians. Previous methods often adopt black-box modeling of pedestrian interactions. Despite their strong performance, such opaque modeling limits the reliability of predictions in real-world deployments. To address this issue, we propose InSyn (Interaction-Synchronization Network), a novel Transformer-based model that explicitly captures diverse interaction patterns (e.g., walking in sync or conflicting) while effectively modeling direction-sensitive social behaviors. Additionally, we introduce a training strategy, termed Seq-Start of Seq (SSOS), designed to alleviate the common issue of initial-step divergence in numerical time-series prediction. Experiments on the ETH and UCY datasets demonstrate that our model not only outperforms recent black-box baselines in prediction accuracy, especially under high-density scenarios, but also provides transparent interaction modeling, as shown in the case study. Furthermore, the SSOS strategy proves to be effective in improving sequential prediction performance, reducing the initial-step prediction error by approximately 6.58%. Code is avaliable at https://github.com/rickzky1001/InSyn