Quantifying Uncertainty in Motion Prediction for Autonomous Vehicles using a Variational Bayesian Mixture Model

The proposed Sequential Neural Variational Agent (SeNeVA) model can accurately predict future trajectories of moving objects while quantifying the associated uncertainties, enabling safer and more robust autonomous vehicle operation.