Measuring Surprise in the Wild
Azadeh Dinparastdjadid Isaac Supeene Johan Engstrom
The quantitative measurement of how and when we experience surprise has
mostly remained limited to laboratory studies, and its extension to
naturalistic settings has been challenging. Here we demonstrate, for the first
time, how computational models of surprise rooted in cognitive science and
neuroscience combined with state-of-the-art machine learned generative models
can be used to detect surprising human behavior in complex, dynamic
environments like road traffic. In traffic safety, such models can support the
identification of traffic conflicts, modeling of road user response time, and
driving behavior evaluation for both human and autonomous drivers. We also
present novel approaches to quantify surprise and use naturalistic driving
scenarios to demonstrate a number of advantages over existing surprise measures
from the literature. Modeling surprising behavior using learned generative
models is a novel concept that can be generalized beyond traffic safety to any
dynamic real-world environment.