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.
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction With Neural ODEs
IEEE Transactions on Intelligent Vehicles ; 8 , 9 ; 4223-4236
2023-09-01
2170951 byte
Article (Journal)
Electronic Resource
English
MULTI-HEAD ATTENTION BASED PROBABILISTIC VEHICLE TRAJECTORY PREDICTION
British Library Conference Proceedings | 2020
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