KoopCast: Trajectory Forecasting via Koopman Operators

• URL: http://arxiv.org/abs/2509.15513v1
• Date: Fri, 19 Sep 2025 01:27:53 GMT
• Title: KoopCast: Trajectory Forecasting via Koopman Operators
• Authors: Jungjin Lee, Jaeuk Shin, Gihwan Kim, Joonho Han, Insoon Yang,
• Abstract summary: We present KoopCast, a lightweight model for trajectory forecasting in general dynamic environments.<n>We use Koopman operator theory, which enables a linear representation of nonlinear dynamics by lifting trajectories into a higher-dimensional space.<n>Our model offers three key advantages: (i) competitive accuracy, (ii) interpretability grounded in Koopman theory, and (iii) low-latency deployment.
• Score: 12.103961182264037
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present KoopCast, a lightweight yet efficient model for trajectory forecasting in general dynamic environments. Our approach leverages Koopman operator theory, which enables a linear representation of nonlinear dynamics by lifting trajectories into a higher-dimensional space. The framework follows a two-stage design: first, a probabilistic neural goal estimator predicts plausible long-term targets, specifying where to go; second, a Koopman operator-based refinement module incorporates intention and history into a nonlinear feature space, enabling linear prediction that dictates how to go. This dual structure not only ensures strong predictive accuracy but also inherits the favorable properties of linear operators while faithfully capturing nonlinear dynamics. As a result, our model offers three key advantages: (i) competitive accuracy, (ii) interpretability grounded in Koopman spectral theory, and (iii) low-latency deployment. We validate these benefits on ETH/UCY, the Waymo Open Motion Dataset, and nuScenes, which feature rich multi-agent interactions and map-constrained nonlinear motion. Across benchmarks, KoopCast consistently delivers high predictive accuracy together with mode-level interpretability and practical efficiency.

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