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

StylePredict: Machine Theory of Mind for Human Driver Behavior From Trajectories