  We present a novel algorithm (GraphRQI) to identify driver behaviors from
road-agent trajectories. Our approach assumes that the road-agents exhibit a
range of driving traits, such as aggressive or conservative driving. Moreover,
these traits affect the trajectories of nearby road-agents as well as the
interactions between road-agents. We represent these inter-agent interactions
using unweighted and undirected traffic graphs. Our algorithm classifies the
driver behavior using a supervised learning algorithm by reducing the
computation to the spectral analysis of the traffic graph. Moreover, we present
a novel eigenvalue algorithm to compute the spectrum efficiently. We provide
theoretical guarantees for the running time complexity of our eigenvalue
algorithm and show that it is faster than previous methods by 2 times. We
evaluate the classification accuracy of our approach on traffic videos and
autonomous driving datasets corresponding to urban traffic. In practice,
GraphRQI achieves an accuracy improvement of up to 25% over prior driver
behavior classification algorithms. We also use our classification algorithm to
predict the future trajectories of road-agents.
