Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition

Perrusquía, Adolfo; Wei, Zhuangkun; Guo, Weisi

Abstract

Proliferation of autonomous systems have increased the threat space and the economic risk in several national infrastructures, e.g., at airports. Therefore, reliable detection of their intention is paramount to ensure smooth operation of national services and societal safety. This article reports a data-driven trajectory intent prediction algorithm which is based on a linear model structure of the autonomous system dynamics obtained from a dynamic mode decomposition algorithm. The model computation is enhanced by two sources of physics informed knowledge associated to the energy functional. Two different prediction algorithms that consider fixed or time-varying references are designed in terms of the availability of control input measurements. Rigorous theoretical results are provided to support the approach using matrix decomposition and optimization techniques. Simulation and experimental studies are carried out to verify the effectiveness of the proposal.

Citation

Perrusquía, A., Wei, Z., & Guo, W. (2024). Trajectory Intent Prediction of Autonomous Systems Using Dynamic Mode Decomposition. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(12), 7897-7908. https://doi.org/10.1109/tsmc.2024.3462790

• Journal Article Type: Article
• Acceptance Date: Sep 15, 2024
• Online Publication Date: Sep 24, 2024
• Publication Date: 2024-12
• Deposit Date: Feb 12, 2025
• Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• Print ISSN: 2168-2216
• Electronic ISSN: 2168-2232
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 54
• Issue: 12
• Pages: 7897-7908
• DOI: https://doi.org/10.1109/tsmc.2024.3462790
• Public URL: https://durham-repository.worktribe.com/output/3479256