MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs
This work addresses the need for robust multi-agent trajectory prediction in autonomous driving, representing an incremental improvement over existing methods.
The paper tackles the problem of predicting future trajectories of multiple road users for autonomous motion planning by introducing MTP-GO, a model that combines temporal graph neural networks, neural ODEs, and mixture density networks with Kalman filtering, resulting in outperforming state-of-the-art methods on various metrics.
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.