  Predicting the motion of a driver's vehicle is crucial for advanced driving
systems, enabling detection of potential risks towards shared control between
the driver and automation systems. In this paper, we propose a variational
neural network approach that predicts future driver trajectory distributions
for the vehicle based on multiple sensors. Our predictor generates both a
conditional variational distribution of future trajectories, as well as a
confidence estimate for different time horizons. Our approach allows us to
handle inherently uncertain situations, and reason about information gain from
each input, as well as combine our model with additional predictors, creating a
mixture of experts. We show how to augment the variational predictor with a
physics-based predictor, and based on their confidence estimations, improve
overall system performance. The resulting combined model is aware of the
uncertainty associated with its predictions, which can help the vehicle
autonomy to make decisions with more confidence. The model is validated on
real-world urban driving data collected in multiple locations. This validation
demonstrates that our approach improves the prediction error of a physics-based
model by 25% while successfully identifying the uncertain cases with 82%
accuracy.
