Maintaining adequate situation awareness (SA) is crucial for the safe operation of conditionally automated vehicles (AVs), which requires drivers to regain control during takeover (TOR) events. This study developed a predictive model for real-time assessment of driver SA using multimodal data (e.g., galvanic skin response, heart rate and eye tracking data, and driver characteristics) collected in a simulated driving environment. Sixty-seven participants experienced automated driving scenarios with TORs, with conditions varying in risk perception and the presence of automation errors. Using data from forty-four participants (twenty-three of those had invalid data) a LightGBM (Light Gradient Boosting Machine) model trained on the top 12 predictors identified by SHAP (SHapley Additive exPlanations) achieved promising performance with RMSE = 0.89, M AE = 0.71, and Corr = 0.78. These findings have implications towards context-aware modeling of SA in conditionally automated driving, paving the way for safer and more seamless driver–AV interactions.

In mixed traffic environments that involve conventional vehicles (CVs) and autonomous vehicles (AVs), it is essential for CV drivers to maintain an appropriate level of situation awareness (SA) to ensure safe and efficient interactions with AVs. While previous research has established the benefits of external human–machine interfaces (HMIs) for communicating AV intent, this study extended this knowledge by focusing on the vital but underexplored interaction with CV drivers. Specifically, we investigated how AV communication through HMIs affected CV drivers by systematically comparing internal (iHMI) and external (eHMI) interfaces, and examined their impact on CV driver awareness, cognitive load, and behavior. Initially, we designed eight HMI concepts through a human-centered design process. The two highest-rated concepts were selected for implementation as eHMIs and iHMIs. Subsequently, we designed a within-subjects experiment with three conditions: a control condition without any communication HMI, and two treatment conditions using eHMIs and iHMIs as communication means. We investigated the effects of these conditions on 50 participants in a virtual environment (VR) driving simulator. Self-reported assessments and eye-tracking measures were employed to evaluate participants’ SA, trust, acceptance, and mental workload. Results indicated that the iHMI condition resulted in superior SA among participants and improved trust in the AV compared to the control and eHMI conditions. Additionally, iHMI led to a comparatively lower increase in mental workload compared to the other two conditions. Our study contributes to the development of effective AV-CV communications and has the potential to inform the design of future AV systems.

Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e.,confident copilots,myopic pragmatists, andreluctant automators) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving.

Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. 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, and a miss condition. Each condition had 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% compared to a baseline model of$K$-nearest neighbor classifier of 84.5%. 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.

Understanding how trust is built over time is essential, as trust plays an important role in the acceptance and adoption of automated vehicles (AVs). This study aims to investigate the effects of system performance and participants’ trust preconditions on dynamic situational trust during takeover transitions. We evaluate the dynamic situational trust of 42 participants using both self-reported and behavioral measures while watching 30 videos with takeover scenarios. The study is a 3 by 2 mixed-subjects design, where the within-subjects variable is the system performance (i.e., accuracy levels of 95%, 80%, and 70%) and the between-subjects variable is the preconditions of the participants’ trust (i.e., overtrust and undertrust). Our results showed that participants quickly adjusted their self-reported situational trust levels, which were consistent with different accuracy levels of system performance in both trust preconditions. However, participants’ behavioral situational trust was affected by their trust preconditions across different accuracy levels. For instance, the overtrust precondition significantly increased the agreement fraction compared to the undertrust precondition. The undertrust precondition significantly decreased the switch fraction compared to the overtrust precondition. These results have important implications for designing an invehicle trust calibration system for conditional AVs.