  Integrating driver, in-cabin, and outside environment's contextual cues into
the vehicle's decision making is the centerpiece of semi-automated vehicle
safety. Multiple systems have been developed for providing context to the
vehicle, which often rely on video streams capturing drivers' physical and
environmental states. While video streams are a rich source of information,
their ability in providing context can be challenging in certain situations,
such as low illuminance environments (e.g., night driving), and they are highly
privacy-intrusive. In this study, we leverage passive sensing through
smartwatches for classifying elements of driving context. Specifically, through
using the data collected from 15 participants in a naturalistic driving study,
and by using multiple machine learning algorithms such as random forest, we
classify driver's activities (e.g., using phone and eating), outside events
(e.g., passing intersection and changing lane), and outside road attributes
(e.g., driving in a city versus a highway) with an average F1 score of 94.55,
98.27, and 97.86 % respectively, through 10-fold cross-validation. Our results
show the applicability of multimodal data retrieved through smart wearable
devices in providing context in real-world driving scenarios and pave the way
for a better shared autonomy and privacy-aware driving data-collection,
analysis, and feedback for future autonomous vehicles.
