Improving behavior profile discovery for vehicles

TL;DR

This paper introduces a novel method for discovering vehicle behavior profiles by analyzing intersection observations, clustering trajectories with EKF and EM, and without relying on environmental map data, highlighting assertiveness and interaction as key factors.

Contribution

It presents a new approach to behavior profile discovery based solely on intersection observations, using EKF, EM, and KL divergence for clustering without environmental map data.

Findings

Behavior profiles can be effectively discovered from intersection data.

Clustering reveals key driver behavior factors like assertiveness.

Method is dynamically consistent with vehicle motion.

Abstract

Multiple approaches have already been proposed to mimic real driver behaviors in simulation. This article proposes a new one, based solely on the exploration of undisturbed observation of intersections. From them, the behavior profiles for each macro-maneuver will be discovered. Using the macro-maneuvers already identified in previous works, a comparison method between trajectories with different lengths using an Extended Kalman Filter (EKF) is proposed, which combined with an Expectation-Maximization (EM) inspired method, defines the different clusters that represent the behaviors observed. This is also paired with a Kullback-Liebler divergent (KL) criteria to define when the clusters need to be split or merged. Finally, the behaviors for each macro-maneuver are determined by each cluster discovered, without using any map information about the environment and being dynamically consistent with vehicle motion. By observation it becomes clear that the two main factors for driver's behavior are their assertiveness and interaction with other road users.