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Enabling resilient autonomous motion planning requires robust predictions ofsurrounding road users' future behavior. In response to this need and theassociated challenges, we introduce our model titled MTP-GO. The model encodesthe scene using temporal graph neural networks to produce the inputs to anunderlying motion model. The motion model is implemented using neural ordinarydifferential equations where the state-transition functions are learned withthe rest of the model. Multimodal probabilistic predictions are obtained bycombining the concept of mixture density networks and Kalman filtering. Theresults illustrate the predictive capabilities of the proposed model acrossvarious data sets, outperforming several state-of-the-art methods on a numberof metrics.

MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs