  In highway scenarios, an alert human driver will typically anticipate early
cut-in/cut-out maneuvers of surrounding vehicles using visual cues mainly.
Autonomous vehicles must anticipate these situations at an early stage too, to
increase their safety and efficiency. In this work, lane-change recognition and
prediction tasks are posed as video action recognition problems. Up to four
different two-stream-based approaches, that have been successfully applied to
address human action recognition, are adapted here by stacking visual cues from
forward-looking video cameras to recognize and anticipate lane-changes of
target vehicles. We study the influence of context and observation horizons on
performance, and different prediction horizons are analyzed. The different
models are trained and evaluated using the PREVENTION dataset. The obtained
results clearly demonstrate the potential of these methodologies to serve as
robust predictors of future lane-changes of surrounding vehicles proving an
accuracy higher than 90% in time horizons of between 1-2 seconds.
