Using Graph-Theoretic Machine Learning to Predict Human Driver Behavior

TL;DR

This paper introduces a graph-theoretic machine learning approach to predict human driver behavior from vehicle trajectories, enhancing autonomous vehicle adaptability across diverse traffic environments.

Contribution

It presents a novel, robust, and extendable method combining graph theory and machine learning to interpret driver behaviors from trajectory data.

Findings

Effective prediction of driver behavior across multiple countries

Robustness demonstrated on real-world and simulated data

Method outperforms prior approaches in adaptability

Abstract

Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic environment composed of human drivers and do not adapt to local conditions and socio-cultural norms. It is known that socially aware AVs can be designed if there exists a mechanism to understand the behaviors of human drivers. We present an approach that leverages machine learning to predict, the behaviors of human drivers. This is similar to how humans implicitly interpret the behaviors of drivers on the road, by only observing the trajectories of their vehicles. We use graph-theoretic tools to extract driver behavior features from the trajectories and machine learning to obtain a computational mapping between the extracted trajectory of a vehicle in traffic and the driver behaviors. Compared to prior approaches in this domain, we prove that our method is robust, general, and extendable to broad-ranging applications such as autonomous navigation. We evaluate our approach on real-world traffic datasets captured in the U.S., India, China, and Singapore, as well as in simulation.