  Numerous car accidents are caused by improper driving maneuvers. Serious
injuries are however avoidable if such driving maneuvers are detected
beforehand and the driver is assisted accordingly. In fact, various recent
research has focused on the automated prediction of driving maneuver based on
hand-crafted features extracted mainly from in-cabin driver videos. Since the
outside view from the traffic scene may also contain informative features for
driving maneuver prediction, we present a framework for the detection of the
drivers' intention based on both in-cabin and traffic scene videos. More
specifically, we (1) propose a Convolutional-LSTM (ConvLSTM)-based auto-encoder
to extract motion features from the out-cabin traffic, (2) train a classifier
which considers motions from both in- and outside of the cabin jointly for
maneuver intention anticipation, (3) experimentally prove that the in- and
outside image features have complementary information. Our evaluation based on
the publicly available dataset Brain4cars shows that our framework achieves a
prediction with the accuracy of 83.98% and F1-score of 84.3%.
