arXiv Machine Learning August 17, 2026 By Michael Fore, Akshay Jain, Justin Downes, Rohan Pradhan, Duncan Botti

Non-Parametric Spatiotemporal Trajectory Prediction via State-Conditioned Transition Sampling

Read the original on arXiv Machine Learning →

arXiv:2608. 14349v1 Announce Type: new Abstract: We present a training-free method for multi-modal trajectory prediction that achieves comparable accuracy to a 57M-parameter transformer while requiring no GPU and zero learned parameters.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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CRHT: A Continuous Regression Hybrid Transformer for Vessel Trajectory Prediction with Online Cluster Sampling

arXiv:2608. 10256v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is critical for maritime safety and anomaly detection, yet existing models often struggle with geographic bias and navigational realism.

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SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

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EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting

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EnvShip-Bench: An Environment-Enhanced Benchmark for Short-Term Vessel Trajectory Prediction

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arXiv:2606. 15244v1 Announce Type: new Abstract: Modern trajectory predictors increasingly condition on external spatial context, such as map geometry, signed distance fields (SDFs), and nearby moving agents.

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CRHT: A Continuous Regression Hybrid Transformer for Vessel Trajectory Prediction with Online Cluster Sampling

SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

EnvShip: A Unified Framework for Context-Aware and Cross-Region Vessel Trajectory Forecasting

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