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1. Modeling Driver Takeover Intention in Automated Vehicles With Attention-Based CNN Algorithm

   https://doi.org/10.1177/1071181322661303  

   Gupta, Shantanu; Mishra, Rohit; Chang, Yu-Hao; Ma, Zheng; Ma, Fenglong; Zhang, Yiqi (September 2022, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors. 

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2. Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

   https://doi.org/10.1109/TITS.2023.3295783  

   Ayoub, Jackie; Avetisian, Lilit; Yang, X. Jessie; Zhou, Feng (December 2023, IEEE Transactions on Intelligent Transportation Systems)

   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. 

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3. Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors

   https://doi.org/10.1177/1071181320641484  

   Ma, Zheng; Zhang, Yiqi (December 2020, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   null (Ed.)

   Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors. 

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4. A Computational Cognitive Model of Driver Response Time for Scheduled Freeway Exiting Takeovers in Conditionally Automated Vehicles

   https://doi.org/10.1177/00187208221143028  

   Tan, Xiaomei; Zhang, Yiqi (December 2022, Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This study develops a computational model to predict drivers’ response time and understand the underlying cognitive mechanism for freeway exiting takeovers in conditionally automated vehicles (AVs). Background Previous research has modeled drivers’ takeover response time in emergency scenarios that demand a quick response. However, existing models may not be applicable for scheduled, non-time-critical takeovers as drivers take longer to resume control when there is no time pressure. A model of driver response time in non-time-critical takeovers is lacking. Method A computational cognitive model of driver takeover response time is developed based on Queuing Network-Model Human Processor (QN-MHP) architecture. The model quantifies gaze redirection in response to takeover request (ToR), task prioritization, driver situation awareness, and driver trust to address the complexities of drivers' takeover strategies when sufficient time budget exists. Results Experimental data of a preliminary driving simulator study were used to validate the model. The model accounted for 97% of the experimental takeover response time for freeway exiting. Conclusion The current model can successfully predict drivers’ response time for scheduled, non-time-critical freeway exiting takeovers in conditionally AVs. Application This model can be applied to the human-machine interface design with respect to ToR lead time for enhancing safe freeway exiting takeovers in conditionally AVs. It also provides a foundation for future modeling work towards an integrated driver model of freeway exiting takeover performance. 

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5. Towards Adaptive Autonomous Vehicle Systems: Considering Trust and Risk Perception during Failures

   https://doi.org/10.1016/j.aap.2025.108267  

   Lim, Cherin; Rajivan, Prashanth (January 2026, Accident Analysis & Prevention)

   As autonomous vehicles (AVs) increasingly operate in unpredictable environments, their function is shifting from simply being modes of transportation to becoming active collaborators alongside human drivers. In turn, drivers must assume a cooperative role, working effectively with autonomous systems to achieve shared goals, most importantly, ensuring human safety. Despite considerable progress, real-world usage and testing of AVs continue to highlight vulnerabilities, particularly in safety-critical situations. This study examines how vehicle failure, the type of failure (security vs. mechanical), and the scenario context influence drivers’ trust and risk perception toward AVs. Initially, six categories of failure scenarios were identified using data from the California Department of Motor Vehicles’ disengagement reports. Subsequently, an online experiment was conducted, where participants experienced both baseline (normal operation) and failure drives. In the failure drive, participants encountered one specific failure type and scenario. The results revealed a substantial decrease in driver trust following the failure drive across all conditions. Higher perceived risk was associated with reduced trust and lower risk-taking behaviors. Scenarios were classified into high- and low-risk categories, with control-related issues having the most pronounced effect on increasing perceived risk. The experiment found no difference in trust or risk perception whether the failure was caused by a malicious intervention or a mechanical issue. These findings suggest that AV design should incorporate mechanisms for assessing and appropriately responding to varying risk levels in critical situations. Enhancing this capability will strengthen the collaborative relationship between humans and AVs, ultimately improving safety and reliability in human-machine teaming contexts. 

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