  Anticipating possible behaviors of traffic participants is an essential
capability of autonomous vehicles. Many behavior detection and maneuver
recognition methods only have a very limited prediction horizon that leaves
inadequate time and space for planning. To avoid unsatisfactory reactive
decisions, it is essential to count long-term future rewards in planning, which
requires extending the prediction horizon. In this paper, we uncover that clues
to vehicle behaviors over an extended horizon can be found in vehicle
interaction, which makes it possible to anticipate the likelihood of a certain
behavior, even in the absence of any clear maneuver pattern. We adopt a
recurrent neural network (RNN) for observation encoding, and based on that, we
propose a novel vehicle behavior interaction network (VBIN) to capture the
vehicle interaction from the hidden states and connection feature of each
interaction pair. The output of our method is a probabilistic likelihood of
multiple behavior classes, which matches the multimodal and uncertain nature of
the distant future. A systematic comparison of our method against two
state-of-the-art methods and another two baseline methods on a publicly
available real highway dataset is provided, showing that our method has
superior accuracy and advanced capability for interaction modeling.
