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

Summary

Researchers propose SWIFT, a unified framework integrating small-world networks with traffic flow theory for trajectory prediction in autonomous driving. It introduces structural inductive biases via a Small-World Interaction Network and a Flow Regime Encoder, outperforming baselines on nuScenes, MoCAD, and NGSIM datasets, with improved generalization and robustness.

SourcearXiv RoboticsAuthor: Chengyue Wang, Bin Rao, Haicheng Liao, Bonan Wang, Chengzhong Xu, Zhenning Li
SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving
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[Submitted on 3 Jul 2026]

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

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Abstract:Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purely data-driven and lack structural priors, which limits their generalization under distribution shifts. In this work, interaction modeling is revisited through the structure and dynamics of traffic networks, and SWIFT (Small-World Interaction Framework for Trajectory prediction) is proposed as a unified framework that integrates small-world networks with traffic flow theory. SWIFT introduces structural inductive biases via a Small-World Interaction Network that captures both local and global dependencies, and a Flow Regime Encoder that adapts the interaction structure to scene-level traffic states. Interaction reasoning is further enhanced through a multi-relational graph module that explicitly encodes direct and higher-order agent relationships. Extensive experiments on three real-world datasets, nuScenes, MoCAD, and NGSIM, show that SWIFT consistently outperforms strong baselines in prediction accuracy across diverse traffic regimes. Beyond accuracy, SWIFT exhibits improved generalization to unseen locations and regimes, robustness under noisy observations, and strong performance with limited training data, supporting the effectiveness of its structure-aware design.

Comments: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence

Subjects:

Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

Cite as: arXiv:2607.09741 [cs.RO]

(or arXiv:2607.09741v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2607.09741

arXiv-issued DOI via DataCite

Submission history

From: Chengyue Wang [view email] [v1] Fri, 3 Jul 2026 04:00:33 UTC (16,396 KB)

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Key points and analysis

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Key points

  • SWIFT combines small-world networks and traffic flow theory for structured trajectory prediction.
  • The framework includes a Small-World Interaction Network and a Flow Regime Encoder for adaptive interactions.
  • Experiments on three real-world datasets show consistent improvements over strong baselines.
  • SWIFT demonstrates better generalization to unseen scenarios and robustness to noisy data.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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