  Predicting future behavior of the surrounding vehicles is crucial for
self-driving platforms to safely navigate through other traffic. This is
critical when making decisions like crossing an unsignalized intersection. We
address the problem of vehicle motion prediction in a challenging roundabout
environment by learning from human driver data. We extend existing recurrent
encoder-decoder models to be advantageously combined with anchor trajectories
to predict vehicle behaviors on a roundabout. Drivers' intentions are encoded
by a set of maneuvers that correspond to semantic driving concepts.
Accordingly, our model employs a set of maneuver-specific anchor trajectories
that cover the space of possible outcomes at the roundabout. The proposed model
can output a multi-modal distribution over the predicted future trajectories
based on the maneuver-specific anchors. We evaluate our model using the public
RounD dataset and the experiment results show the effectiveness of the proposed
maneuver-based anchor regression in improving prediction accuracy, reducing the
average RMSE to 28% less than the best baseline. Our code is available at
https://github.com/m-hasan-n/roundabout.
