  It is desirable to predict the behavior of traffic participants conditioned
on different planned trajectories of the autonomous vehicle. This allows the
downstream planner to estimate the impact of its decisions. Recent approaches
for conditional behavior prediction rely on a regression decoder, meaning that
coordinates or polynomial coefficients are regressed. In this work we revisit
set-based trajectory prediction, where the probability of each trajectory in a
predefined trajectory set is determined by a classification model, and
first-time employ it to the task of conditional behavior prediction. We propose
RESET, which combines a new metric-driven algorithm for trajectory set
generation with a graph-based encoder. For unconditional prediction, RESET
achieves comparable performance to a regression-based approach. Due to the
nature of set-based approaches, it has the advantageous property of being able
to predict a flexible number of trajectories without influencing runtime or
complexity. For conditional prediction, RESET achieves reasonable results with
late fusion of the planned trajectory, which was not observed for
regression-based approaches before. This means that RESET is computationally
lightweight to combine with a planner that proposes multiple future plans of
the autonomous vehicle, as large parts of the forward pass can be reused.
