  Identification of high-risk driving situations is generally approached
through collision risk estimation or accident pattern recognition. In this
work, we approach the problem from the perspective of subjective risk. We
operationalize subjective risk assessment by predicting driver behavior changes
and identifying the cause of changes. To this end, we introduce a new task
called driver-centric risk object identification (DROID), which uses egocentric
video to identify object(s) influencing a driver's behavior, given only the
driver's response as the supervision signal. We formulate the task as a
cause-effect problem and present a novel two-stage DROID framework, taking
inspiration from models of situation awareness and causal inference. A subset
of data constructed from the Honda Research Institute Driving Dataset (HDD) is
used to evaluate DROID. We demonstrate state-of-the-art DROID performance, even
compared with strong baseline models using this dataset. Additionally, we
conduct extensive ablative studies to justify our design choices. Moreover, we
demonstrate the applicability of DROID for risk assessment.
