# Real-time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

arXiv: https://arxiv.org/abs/2212.00607 · HTML: https://mlsanity.com/paper/2212.00607/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2022.

## Built on by (most cited first, 5 of 5)

- [A Survey on Datasets for Decision-making of Autonomous Vehicle](https://mlsanity.com/paper/2306.16784/) (2023)
- [Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2312.10209/) (2023)
- [Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning](https://mlsanity.com/paper/2409.08980/) (2024)
- [A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Prediction](https://mlsanity.com/paper/2509.06976/) (2025)
- [Understanding Mental States in Active and Autonomous Driving with EEG](https://mlsanity.com/paper/2512.09190/) (2025)

## Abstract (arXiv)

Trust calibration presents a main challenge during the interaction between drivers and automated vehicles (AVs). In order to calibrate trust, it is important to measure drivers' trust in real time. One possible method is through modeling its dynamic changes using machine learning models and physiological measures. In this paper, we proposed a technique based on machine learning models to predict drivers' dynamic trust in conditional AVs using physiological measurements in real time. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, a miss condition with eight takeover requests (TORs) in different scenarios. Drivers' physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers' trust in real time with an f1-score of 89.1%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers' trust to facilitate interaction between the driver and the AV in real time.