  Probabilistic vehicle trajectory prediction is essential for robust safety of
autonomous driving. Current methods for long-term trajectory prediction cannot
guarantee the physical feasibility of predicted distribution. Moreover, their
models cannot adapt to the driving policy of the predicted target human driver.
In this work, we propose to overcome these two shortcomings by a Bayesian
recurrent neural network model consisting of Bayesian-neural-network-based
policy model and known physical model of the scenario. Bayesian neural network
can ensemble complicated output distribution, enabling rich family of
trajectory distribution. The embedded physical model ensures feasibility of the
distribution. Moreover, the adopted gradient-based training method allows
direct optimization for better performance in long prediction horizon.
Furthermore, a particle-filter-based parameter adaptation algorithm is designed
to adapt the policy Bayesian neural network to the predicted target online.
Effectiveness of the proposed methods is verified with a toy example with
multi-modal stochastic feedback gain and naturalistic car following data.
