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

• URL: http://arxiv.org/abs/2608.14349v1
• Date: Fri, 14 Aug 2026 14:42:21 GMT
• Title: Non-Parametric Spatiotemporal Trajectory Prediction via State-Conditioned Transition Sampling
• Abstract summary: We present a training-free method for multi-modal trajectory prediction.<n>The method builds a transition table of historical state-to-next-position pairs.<n>It retrieves neighbors using a product kernel over spatial proximity, bearing, speed, and temporal context.
• Score: 1.8377229717030115
• License: http://creativecommons.org/licenses/by/4.0/
• 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. The method builds a transition table of historical state-to-next-position pairs and retrieves neighbors using a product kernel over spatial proximity, bearing, speed, and temporal context. Two inference modes operate over this shared representation: diversity-penalized sampling produces trajectories covering distinct plausible routes, while beam search finds the highest-likelihood path. On the TrAISformer benchmark (Danish Maritime AIS), our method achieves competitive accuracy at full data availability and dramatically outperforms the transformer in data-scarce regimes---remaining stable down to 10% of training data where TrAISformer degrades catastrophically. This enables deployment in new geographic regions from an order of magnitude less historical data, and with no GPU training.

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