A Hybrid Dynamic Model for Predicting Human Cognition and Reliance during Automated Driving

TL;DR

This paper introduces a simple hybrid dynamic model that predicts human cognitive states and reliance behavior during automated driving, enabling personalized and interpretable insights for vehicle automation design.

Contribution

A novel 12-parameter hybrid dynamic model that captures continuous cognitive states and discrete reliance transitions, suitable for online adaptation and personalized analysis.

Findings

Model fits observed reliance trajectories well.

Reliance is primarily influenced by trust and perceived risk.

Model parameters reveal individual differences in cognitive dynamics.

Abstract

We propose a simple (12 parameter) hybrid dynamic model that simultaneously captures the continuous-valued dynamics of three human cognitive states-trust, perceived risk, and mental workload-as well as discrete transitions in reliance on the automation. The discrete-time dynamic evolution of each cognitive state is modeled using a first-order affine difference equation. Reliance is defined as a single discrete-valued state, whose evolution at each time step depends on the cognitive states satisfying certain threshold conditions. Using data collected from 16 participants, we estimate participant-specific model parameters based on their reliance on the automation and intermittently self-reported cognitive states during a continuous drive in a vehicle simulator. The model can be estimated using a single user's trajectory data (e.g. 8 minutes of driving), making it suitable for online parameter adaptation methods. Our results show that the model fits the observed trajectories well for several participants, with their reliance behavior primarily influenced by trust, perceived risk, or both. Importantly, the model is interpretable, such that the variations in model parameters across participants provide insights into differences in the time scales over which cognitive states evolve, and how these states are influenced by task complexity. Implications on the design of human-centric vehicle automation design are discussed.