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We present a new measure, CMetric, to classify driver behaviors usingcentrality functions. Our formulation combines concepts from computationalgraph theory and social traffic psychology to quantify and classify thebehavior of human drivers. CMetric is used to compute the probability of avehicle executing a driving style, as well as the intensity used to execute thestyle. Our approach is designed for realtime autonomous driving applications,where the trajectory of each vehicle or road-agent is extracted from a video.We compute a dynamic geometric graph (DGG) based on the positions and proximityof the road-agents and centrality functions corresponding to closeness anddegree. These functions are used to compute the CMetric based on stylelikelihood and style intensity estimates. Our approach is general and makes noassumption about traffic density, heterogeneity, or how driving behaviorschange over time. We present an algorithm to compute CMetric and demonstrateits performance on real-world traffic datasets. To test the accuracy ofCMetric, we introduce a new evaluation protocol (called "Time Deviation Error")that measures the difference between human prediction and the prediction madeby CMetric.

CMetric: A Driving Behavior Measure Using Centrality Functions