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1. Fostering Drivers’ Trust in Automated Driving Styles: The Role of Driver Perception of Automated Driving Maneuvers

   https://doi.org/10.1177/00187208231189661  

   Ma, Zheng ; Zhang, Yiqi ( July 2023 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers’ perception of automated driving maneuvers and quantified the relationships among drivers’ perception of AV maneuvers, driver trust, and acceptance of AVs.

   Previous studies on automated driving styles focused on the impact of AV’s global driving style on driver’s attitude and driving performance. However, research on drivers’ perception of automated driving maneuvers at the specific driving style level is still lacking.

   Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator.

   Results revealed that drivers’ trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn’t differ significantly in their perceived inappropriateness of these maneuvers with different driving styles.

   The driving styles of automated vehicles and drivers influenced drivers’ perception of automated driving maneuvers, which influence their trust and acceptance of AVs.

   This study suggested that the design of AVs should consider drivers’ perceptions of automated driving maneuvers to avoid undermining drivers’ trust and acceptance of AVs.

    

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2. Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

   https://doi.org/10.1177/00187208221088358  

   Ma, Zheng ; Zhang, Yiqi ( April 2022 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. 

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3. Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

   https://doi.org/10.1177/00187208221142126  

   Kamaraj, Amudha V. ; Lee, Joonbum ; Domeyer, Joshua E. ; Liu, Shu-Yuan ; Lee, John D. ( January 2023 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation.

   Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent’s actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences.

   A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity.

   Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure.

   Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver’s perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

    

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4. 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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5. 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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