SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving

Jul 14, 2026

A new framework called SWIFT integrates small-world network principles and traffic flow theory to improve trajectory prediction for autonomous driving. SWIFT introduces structural inductive biases and a flow regime encoder, enabling the model to adapt interactions based on traffic states. Experiments on three real-world datasets demonstrate that SWIFT outperforms strong baselines in prediction accuracy, generalization to new locations and regimes, and robustness to noisy data.

Why it matters: This work shows that leveraging structural priors from traffic networks can meaningfully enhance the reliability and adaptability of trajectory prediction systems, which are essential for safe autonomous driving.

Full story at: arXiv Robotics ↗