CMetric: A Driving Behavior Measure Using Centrality Functions
This work addresses the need for real-time behavior classification in autonomous driving applications, though it appears incremental as it builds on existing graph theory and psychology concepts.
The authors tackled the problem of classifying driver behaviors for autonomous driving by introducing CMetric, a measure based on centrality functions from graph theory, which achieved performance validated on real-world traffic datasets using a new evaluation protocol called Time Deviation Error.
We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. 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 proximity of the road-agents and centrality functions corresponding to closeness and degree. These functions are used to compute the CMetric based on style likelihood and style intensity estimates. Our approach is general and makes no assumption about traffic density, heterogeneity, or how driving behaviors change over time. We present an algorithm to compute CMetric and demonstrate its performance on real-world traffic datasets. To test the accuracy of CMetric, we introduce a new evaluation protocol (called "Time Deviation Error") that measures the difference between human prediction and the prediction made by CMetric.