  General-purpose trajectory planning algorithms for automated driving utilize
complex reward functions to perform a combined optimization of strategic,
behavioral, and kinematic features. The specification and tuning of a single
reward function is a tedious task and does not generalize over a large set of
traffic situations. Deep learning approaches based on path integral inverse
reinforcement learning have been successfully applied to predict local
situation-dependent reward functions using features of a set of sampled driving
policies. Sample-based trajectory planning algorithms are able to approximate a
spatio-temporal subspace of feasible driving policies that can be used to
encode the context of a situation. However, the interaction with dynamic
objects requires an extended planning horizon, which depends on sequential
context modeling. In this work, we are concerned with the sequential reward
prediction over an extended time horizon. We present a neural network
architecture that uses a policy attention mechanism to generate a
low-dimensional context vector by concentrating on trajectories with a
human-like driving style. Apart from this, we propose a temporal attention
mechanism to identify context switches and allow for stable adaptation of
rewards. We evaluate our results on complex simulated driving situations,
including other moving vehicles. Our evaluation shows that our policy attention
mechanism learns to focus on collision-free policies in the configuration
space. Furthermore, the temporal attention mechanism learns persistent
interaction with other vehicles over an extended planning horizon.
