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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 exists a mechanism to understand the behaviors of humandrivers. We present an approach that leverages machine learning to predict, thebehaviors of human drivers. This is similar to how humans implicitly interpretthe behaviors of drivers on the road, by only observing the trajectories oftheir vehicles. We use graph-theoretic tools to extract driver behaviorfeatures from the trajectories and machine learning to obtain a computationalmapping between the extracted trajectory of a vehicle in traffic and the driverbehaviors. Compared to prior approaches in this domain, we prove that ourmethod is robust, general, and extendable to broad-ranging applications such asautonomous navigation. We evaluate our approach on real-world traffic datasetscaptured in the U.S., India, China, and Singapore, as well as in simulation.

Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior