  Enabling resilient autonomous motion planning requires robust predictions of
surrounding road users' future behavior. In response to this need and the
associated challenges, we introduce our model titled MTP-GO. The model encodes
the scene using temporal graph neural networks to produce the inputs to an
underlying motion model. The motion model is implemented using neural ordinary
differential equations where the state-transition functions are learned with
the rest of the model. Multimodal probabilistic predictions are obtained by
combining the concept of mixture density networks and Kalman filtering. The
results illustrate the predictive capabilities of the proposed model across
various data sets, outperforming several state-of-the-art methods on a number
of metrics.
