  Intelligent vehicle anticipation of the movement intentions of other drivers
can reduce collisions. Typically, when a human driver of another vehicle
(referred to as the target vehicle) engages in specific behaviors such as
checking the rearview mirror prior to lane change, a valuable clue is therein
provided on the intentions of the target vehicle's driver. Furthermore, the
target driver's intentions can be influenced and shaped by their driving
environment. For example, if the target vehicle is too close to a leading
vehicle, it may renege the lane change decision. On the other hand, a following
vehicle in the target lane is too close to the target vehicle could lead to its
reversal of the decision to change lanes. Knowledge of such intentions of all
vehicles in a traffic stream can help enhance traffic safety. Unfortunately,
such information is often captured in the form of images/videos. Utilization of
personally identifiable data to train a general model could violate user
privacy. Federated Learning (FL) is a promising tool to resolve this conundrum.
FL efficiently trains models without exposing the underlying data. This paper
introduces a Personalized Federated Learning (PFL) model embedded a long
short-term transformer (LSTR) framework. The framework predicts drivers'
intentions by leveraging in-vehicle videos (of driver movement, gestures, and
expressions) and out-of-vehicle videos (of the vehicle's surroundings -
frontal/rear areas). The proposed PFL-LSTR framework is trained and tested
through real-world driving data collected from human drivers at Interstate 65
in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability
and high precision, and that out-of-vehicle information (particularly, the
driver's rear-mirror viewing actions) is important because it helps reduce
false positives and thereby enhances the precision of driver intention
inference.
