  Forecasting driving behavior or other sensor measurements is an essential
component of autonomous driving systems. Often real-world multivariate time
series data is hard to model because the underlying dynamics are nonlinear and
the observations are noisy. In addition, driving data can often be multimodal
in distribution, meaning that there are distinct predictions that are likely,
but averaging can hurt model performance. To address this, we propose the
Switching Recurrent Kalman Network (SRKN) for efficient inference and
prediction on nonlinear and multi-modal time-series data. The model switches
among several Kalman filters that model different aspects of the dynamics in a
factorized latent state. We empirically test the resulting scalable and
interpretable deep state-space model on toy data sets and real driving data
from taxis in Porto. In all cases, the model can capture the multimodal nature
of the dynamics in the data.
