标题： 基于生理指标的条件自动化驾驶中的实时信任预测 显示英文标题

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

摘要： 信任校准在驾驶员和自动驾驶车辆（AV）之间的交互中是一个主要挑战。 为了校准信任，实时测量驾驶员的信任非常重要。 一种可能的方法是通过使用机器学习模型和生理指标来建模其动态变化。 在本文中，我们提出了一种基于机器学习模型的技术，以实时预测条件性自动驾驶车辆中驾驶员的动态信任，该技术使用生理测量数据。 我们在驾驶模拟器中进行了研究，参与者被要求在三种条件下从自动化驾驶中接管控制，这三种条件包括一个控制条件、一个误报条件和一个漏报条件，在不同场景中有八次接管请求（TORs）。 在实验过程中记录了驾驶员的生理指标，包括皮肤电反应（GSR）、心率（HR）指标和眼动追踪指标。 使用五种机器学习模型，我们发现极端梯度提升（XGBoost）表现最好，并能够以89.1%的f1分数实时预测驾驶员的信任。 我们的研究结果为如何设计车载信任监测系统以实时校准驾驶员的信任提供了良好的启示，从而促进驾驶员与自动驾驶车辆之间的交互。 显示英文摘要

摘要： 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.