  As autonomous driving systems prevail, it is becoming increasingly critical
that the systems learn from databases containing fine-grained driving
scenarios. Most databases currently available are human-annotated; they are
expensive, time-consuming, and subject to behavioral biases. In this paper, we
provide initial evidence supporting a novel technique utilizing drivers'
electroencephalography (EEG) signals to implicitly label hazardous driving
scenarios while passively viewing recordings of real-road driving, thus sparing
the need for manual annotation and avoiding human annotators' behavioral biases
during explicit report. We conducted an EEG experiment using real-life and
animated recordings of driving scenarios and asked participants to report
danger explicitly whenever necessary. Behavioral results showed the
participants tended to report danger only when overt hazards (e.g., a vehicle
or a pedestrian appearing unexpectedly from behind an occlusion) were in view.
By contrast, their EEG signals were enhanced at the sight of both an overt
hazard and a covert hazard (e.g., an occlusion signalling possible appearance
of a vehicle or a pedestrian from behind). Thus, EEG signals were more
sensitive to driving hazards than explicit reports. Further, the Time-Series AI
(TSAI) successfully classified EEG signals corresponding to overt and covert
hazards. We discuss future steps necessary to materialize the technique in real
life.
