Toward Adaptive Trust Calibration for Level 2 Driving Automation

TL;DR

This paper introduces a probabilistic framework using POMDPs to optimize transparency in Level 2 driving automation, balancing trust calibration and workload in complex city environments.

Contribution

It presents a novel POMDP-based model for dynamic trust and workload management in human-automation interaction during autonomous driving.

Findings

Model effectively varies transparency based on trust and workload estimates.

Framework improves trust calibration in complex driving scenarios.

Demonstrates potential for real-time adaptive automation transparency.

Abstract

Properly calibrated human trust is essential for successful interaction between humans and automation. However, while human trust calibration can be improved by increased automation transparency, too much transparency can overwhelm human workload. To address this tradeoff, we present a probabilistic framework using a partially observable Markov decision process (POMDP) for modeling the coupled trust-workload dynamics of human behavior in an action-automation context. We specifically consider hands-off Level 2 driving automation in a city environment involving multiple intersections where the human chooses whether or not to rely on the automation. We consider automation reliability, automation transparency, and scene complexity, along with human reliance and eye-gaze behavior, to model the dynamics of human trust and workload. We demonstrate that our model framework can appropriately vary automation transparency based on real-time human trust and workload belief estimates to achieve trust calibration.