CMetric: A Driving Behavior Measure using Centrality Functions

Overview of CMetric: (left) The sensors on autonomous vehicle observe the positions of other vehicles or road-agents; (middle) The positions and corresponding spatial distances between vehicles are represented through a graph, DGG; (right) Our CMetric uses the closeness and degree centrality functions to measure the style of each vehicle. These styles are used to classify a global driving behavior (such as aggressive or conservative) for each vehicle.

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 efficient techniques to compute CMetric and demonstrate its performance on wellknown autonomous driving datasets. We evaluate the accuracy of CMetric and compare with ground truth behavior labels and with that of a human observer by performing a user study over over a long vehicle trajectory.

@article{chandra2020cmetric,
  title={CMetric: A Driving Behavior Measure Using Centrality Functions},
  author={Chandra, Rohan and Bhattacharya, Uttaran and Mittal, Trisha and Bera, Aniket and Manocha, Dinesh},
  journal={arXiv preprint arXiv:2003.04424},
  year={2020}
}