Driver trust has a great impact on the intention to accept, use, and adapt to automated vehicles. To date, driver trust in automated vehicle technologies has mostly been estimated by subjective data. Currently available objective measures of driver trust primarily rely on self-reported ratings as the ground truth, while the largely reported inconsistency between drivers’ self-reported trust levels and observed actual behavior suggests that drivers’ trust measures shall not solely rely on subjective reporting values. To address the issue, this study proposes an objective method to assess and predict driver trust in automated vehicle technologies, by using the transition probability matrix of drivers’ hand positions during automated vehicle system engagement. An on-road experiment was conducted on public roadways in real traffic. Data on use frequencies of advanced driver assistance systems (ADAS) and self-reported trust ratings were collected and combined in classifying driver trust levels: lower, medium, and higher based on the K-means clustering results. Drivers’ hand positions during ADAS engagement (i.e., during lane-departure warning and lane-keeping assist system uses) were then found closely associated with their trust levels. Differences of frequencies and transition probabilities for hand positions were further compared within and across the three trust groups. Results showed that drivers from the lower, medium, and higher trust groups were more likely to keep hands on the top, mid, and low positions of the wheel, respectively. Factors affecting driver trust in automated vehicle technologies were also explored through mixed model analyses. Middle-aged drivers placed more trust in ADAS than younger drivers, while female drivers exhibited greater trust than male drivers. The Random Forests algorithm was applied to build a prediction model for driver trust in automated vehicle technologies, by inputting the hand position transition probability matrix, age, gender, and ADAS types as independent variables. The overall prediction accuracy was approximately 80%. Findings in this study could contribute to the objective and real-time estimations of driver trust in automated vehicle technologies.

驾驶员的信任对接受，使用和适应自动驾驶的意图有很大的影响。迄今为止，驾驶员对自动驾驶汽车技术的信任度大多是通过主观数据来估计的。当前可用的驾驶员信任客观度量主要依靠自我报告的评级作为基本事实，而大量报告的驾驶员自我报告的信任水平与观察到的实际行为之间的不一致表明驾驶员的信任度量不应仅依赖于主观报告值。为了解决这个问题，本研究提出了一种客观的方法，通过使用自动驾驶汽车系统接合过程中驾驶员手部位置的转变概率矩阵来评估和预测驾驶员对自动驾驶汽车技术的信任度。在实际交通中在公共道路上进行了道路实验。收集有关高级驾驶员辅助系统（ADAS）使用频率和自我报告的信任等级的数据，并根据K-means聚类结果对驾驶员信任级别进行分类：低，中和较高。然后发现驾驶员在ADAS接合期间（即在车道偏离警告和车道保持辅助系统使用期间）的手部位置与他们的信任度紧密相关。在三个信任组之内和之间进一步比较了手位置的频率和转移概率的差异。结果表明，来自较低，中和较高信任组的驾驶员更有可能分别将手放在方向盘的顶部，中间和较低位置。通过混合模型分析，还探讨了影响驾驶员对自动驾驶技术的信任的因素。中年驾驶员对ADAS的信任度高于年轻驾驶员，而女性驾驶员比男性驾驶员表现出更大的信任度。通过输入手位置转换概率矩阵，年龄，性别和ADAS类型作为自变量，应用随机森林算法构建自动驾驶技术中驾驶员信任的预测模型。总体预测准确性约为80％。这项研究的结果可能有助于客观和实时地评估驾驶员对自动驾驶汽车技术的信任度。性别和ADAS类型作为自变量。总体预测准确性约为80％。这项研究的结果可能有助于客观和实时地评估驾驶员对自动驾驶汽车技术的信任度。性别和ADAS类型作为自变量。总体预测准确性约为80％。这项研究的结果可能有助于客观和实时地评估驾驶员对自动驾驶汽车技术的信任度。