Driver State and Behavior Detection Through Smart Wearables

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 multi-modal 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.

Automated summary

Integrating contextual cues from drivers, in-cabin environment, and outside surroundings is vital for the safety of semi-automated vehicles. This study demonstrates the feasibility of using smartwatches for passive sensing to classify driving context elements with high accuracy. The results highlight the potential of utilizing multi-modal data from smart wearables for context-aware driving scenarios and improving privacy-aware data collection and analysis for future autonomous vehicles.