  To plan safe maneuvers and act with foresight, autonomous vehicles must be
capable of accurately predicting the uncertain future. In the context of
autonomous driving, deep neural networks have been successfully applied to
learning predictive models of human driving behavior from data. However, the
predictions suffer from cascading errors, resulting in large inaccuracies over
long time horizons. Furthermore, the learned models are black boxes, and thus
it is often unclear how they arrive at their predictions. In contrast,
rule-based models, which are informed by human experts, maintain long-term
coherence in their predictions and are human-interpretable. However, such
models often lack the sufficient expressiveness needed to capture complex
real-world dynamics. In this work, we begin to close this gap by embedding the
Intelligent Driver Model, a popular hand-crafted driver model, into deep neural
networks. Our model's transparency can offer considerable advantages, e.g., in
debugging the model and more easily interpreting its predictions. We evaluate
our approach on a simulated merging scenario, showing that it yields a robust
model that is end-to-end trainable and provides greater transparency at no cost
to the model's predictive accuracy.
