  Studies have shown that autonomous vehicles (AVs) behave conservatively in a
traffic environment composed of human drivers and do not adapt to local
conditions and socio-cultural norms. It is known that socially aware AVs can be
designed if there exist a mechanism to understand the behaviors of human
drivers. We present a notion of Machine Theory of Mind (M-ToM) to infer the
behaviors of human drivers by observing the trajectory of their vehicles. Our
M-ToM approach, called StylePredict, is based on trajectory analysis of
vehicles, which has been investigated in robotics and computer vision.
StylePredict mimics human ToM to infer driver behaviors, or styles, using a
computational mapping between the extracted trajectory of a vehicle in traffic
and the driver behaviors using graph-theoretic techniques, including spectral
analysis and centrality functions. We use StylePredict to analyze driver
behavior in different cultures in the USA, China, India, and Singapore, based
on traffic density, heterogeneity, and conformity to traffic rules and observe
an inverse correlation between longitudinal (overspeeding) and lateral
(overtaking, lane-changes) driving styles.
