  Autonomous vehicles should be able to predict the future states of its
environment and respond appropriately. Specifically, predicting the behavior of
surrounding human drivers is vital for such platforms to share the same road
with humans. Behavior of each of the surrounding vehicles is governed by the
motion of its neighbor vehicles. This paper focuses on predicting the behavior
of the surrounding vehicles of an autonomous vehicle on highways. We are
motivated by improving the prediction accuracy when a surrounding vehicle
performs lane change and highway merging maneuvers. We propose a novel pooling
strategy to capture the inter-dependencies between the neighbor vehicles.
Depending solely on Euclidean trajectory representation, the existing pooling
strategies do not model the context information of the maneuvers intended by a
surrounding vehicle. In contrast, our pooling mechanism employs polar
trajectory representation, vehicles orientation and radial velocity. This
results in an implicitly maneuver-aware pooling operation. We incorporated the
proposed pooling mechanism into a generative encoder-decoder model, and
evaluated our method on the public NGSIM dataset. The results of maneuver-based
trajectory predictions demonstrate the effectiveness of the proposed method
compared with the state-of-the-art approaches. Our "Pooling Toolbox" code is
available at https://github.com/m-hasan-n/pooling.
