  Anticipating driver intention is an important task when vehicles of mixed and
varying levels of human/machine autonomy share roadways. Driver intention can
be leveraged to improve road safety, such as warning surrounding vehicles in
the event the driver is attempting a dangerous maneuver. In this work, we
propose a novel method of utilizing in-cabin and external camera data to
improve state-of-the-art (SOTA) performance in predicting future driver
actions. Compared to existing methods, our approach explicitly extracts object
and road-level features from external camera data, which we demonstrate are
important features for predicting driver intention. Using our handcrafted
features as inputs for both a transformer and an LSTM-based architecture, we
empirically show that jointly utilizing in-cabin and external features improves
performance compared to using in-cabin features alone. Furthermore, our models
predict driver maneuvers more accurately and earlier than existing approaches,
with an accuracy of 87.5% and an average prediction time of 4.35 seconds before
the maneuver takes place. We release our model configurations and training
scripts on https://github.com/ykung83/Driver-Intent-Prediction
