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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. 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. 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. The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks

   https://doi.org/10.3390/safety9010005  

   Zhang, Fangda ; Wang, Meng ; Parker, Jah’inaya ; Roberts, Shannon C ( March 2023 , Safety)

   Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks.

    

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5. How Will Drivers Take Back Control in Automated Vehicles? A Driving Simulator Test of an Interleaving Framework

   https://doi.org/10.1145/3409118.3475128  

   Nagaraju, Divyabharathi ; Ansah, Alberta ; Ch, Nabil Al ; Mills, Caitlin ; Janssen, Christian P. ; Shaer, Orit ; Kun, Andrew L ( September 2021 , AutomotiveUI 2021)

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   We explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of con- trol: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation. 

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