  Recent advances have shown the capability of Fully Convolutional Neural
Networks (FCN) to model cost functions for motion planning in the context of
learning driving preferences purely based on demonstration data from human
drivers. While pure learning from demonstrations in the framework of Inverse
Reinforcement Learning (IRL) is a promising approach, we can benefit from well
informed human priors and incorporate them into the learning process. Our work
achieves this by pretraining a model to regress to a manual cost function and
refining it based on Maximum Entropy Deep Inverse Reinforcement Learning. When
injecting prior knowledge as pretraining for the network, we achieve higher
robustness, more visually distinct obstacle boundaries, and the ability to
capture instances of obstacles that elude models that purely learn from
demonstration data. Furthermore, by exploiting these human priors, the
resulting model can more accurately handle corner cases that are scarcely seen
in the demonstration data, such as stairs, slopes, and underpasses.
