# GraphRQI: Classifying Driver Behaviors Using Graph Spectrums

arXiv: https://arxiv.org/abs/1910.00049 · HTML: https://mlsanity.com/paper/1910.00049/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [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)
- [iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2306.06236/) (2023)
- [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)
- [Transfer-LMR: Heavy-Tail Driving Behavior Recognition in Diverse Traffic Scenarios](https://mlsanity.com/paper/2405.05354/) (2024)
- [Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL](https://mlsanity.com/paper/2511.12755/) (2025)
- [Controllable Generative Trajectory Prediction via Weak Preference Alignment](https://mlsanity.com/paper/2510.10731/) (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 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.