Multimodal Driver State Modeling through Unsupervised Learning

TL;DR

This paper introduces an unsupervised learning framework combining Bayesian change point detection and Latent Dirichlet Allocation to analyze driver physiological and behavioral patterns from naturalistic driving data, revealing insights into driver states.

Contribution

It presents a novel methodology for automatically detecting driver behavioral and physiological patterns without manual labeling, enhancing understanding of driver states in various scenarios.

Findings

Drivers' heart rate patterns correlate with driving behaviors like harsh braking.

High gaze entropy is associated with aggressive driving maneuvers.

Physiological responses vary significantly across different driving scenarios.

Abstract

Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral patterns. Unsupervised analysis of NDD can be used to automatically detect different patterns from the driver and vehicle data. In this paper, we propose a methodology to understand changes in driver's physiological responses within different driving patterns. Our methodology first decomposes a driving scenario by using a Bayesian Change Point detection model. We then apply the Latent Dirichlet Allocation method on both driver state and behavior data to detect patterns. We present two case studies in which vehicles were equipped to collect exterior, interior, and driver behavioral data. Four patterns of driving behaviors (i.e., harsh brake, normal brake, curved driving, and highway driving), as well as two patterns of driver's heart rate (HR) (i.e., normal vs. abnormal high HR), and gaze entropy (i.e., low versus high), were detected in these two case studies. The findings of these case studies indicated that among our participants, the drivers' HR had a higher fraction of abnormal patterns during harsh brakes, accelerating and curved driving. Additionally, free-flow driving with close to zero accelerations on the highway was accompanied by more fraction of normal HR as well as a lower gaze entropy pattern. With the proposed methodology we can better understand variations in driver's psychophysiological states within different driving scenarios. The findings of this work, has the potential to guide future autonomous vehicles to take actions that are fit to each specific driver.