# CMetric: A Driving Behavior Measure Using Centrality Functions

arXiv: https://arxiv.org/abs/2003.04424 · HTML: https://mlsanity.com/paper/2003.04424/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [Data-driven Traffic Simulation: A Comprehensive Review](https://mlsanity.com/paper/2310.15975/) (2023)
- [BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving](https://mlsanity.com/paper/2312.06371/) (2023)
- [Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Traffic](https://mlsanity.com/paper/2202.00881/) (2022)
- [D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights](https://mlsanity.com/paper/2207.10398/) (2022)
- [Minds on the Move: Decoding Trajectory Prediction in Autonomous Driving with Cognitive Insights](https://mlsanity.com/paper/2502.20084/) (2025)
- [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2303.00977/) (2023)
- [Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human Drivers](https://mlsanity.com/paper/2205.00562/) (2022)
- [Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving](https://mlsanity.com/paper/2211.10585/) (2022)
- [SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces](https://mlsanity.com/paper/2303.05584/) (2023)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge](https://mlsanity.com/paper/2103.10245/) (2021)
- [SG-LSTM: Social Group LSTM for Robot Navigation Through Dense Crowds](https://mlsanity.com/paper/2303.04320/) (2023)
- [Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization](https://mlsanity.com/paper/2306.08815/) (2023)
- [Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic](https://mlsanity.com/paper/2211.11963/) (2022)
- [Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios](https://mlsanity.com/paper/2405.05354/) (2024)
- [Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2406.09755/) (2024)
- [Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL](https://mlsanity.com/paper/2511.12755/) (2025)
- [Towards Human-Like Trajectory Prediction for Autonomous Driving: A Behavior-Centric Approach](https://mlsanity.com/paper/2505.21565/) (2025)
- [Are LLMs The Way Forward? A Case Study on LLM-Guided Reinforcement Learning for Decentralized Autonomous Driving](https://mlsanity.com/paper/2511.12751/) (2025)

## Abstract (arXiv)

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.