# Toward Adaptive Trust Calibration for Level 2 Driving Automation

arXiv: https://arxiv.org/abs/2009.11890 · HTML: https://mlsanity.com/paper/2009.11890/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 4 of 4)

- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles](https://mlsanity.com/paper/2209.10536/) (2022)
- [FollowMe: Vehicle Behaviour Prediction in Autonomous Vehicle Settings](https://mlsanity.com/paper/2304.06121/) (2023)
- [Toward Informed AV Decision-Making: Computational Model of Well-being and Trust in Mobility](https://mlsanity.com/paper/2505.14983/) (2025)

## Abstract (arXiv)

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.