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1. Takeover requests for automated driving: The effects of signal direction, lead time, and modality on takeover performance

   https://doi.org/10.1016/j.aap.2021.106534  

   Huang, Gaojian; Pitts, Brandon J. (February 2022, Accident Analysis & Prevention)
2. Driver-Vehicle Interaction: The Effects of Physical Exercise and Takeover Request Modality on Automated Vehicle Takeover Performance between Younger and Older Drivers

   https://doi.org/10.1109/ICHMS53169.2021.9582642  

   Huang, Gaojian; Pitts, Brandon (October 2021, 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS))
3. Age and gender differences in emergency takeover from automated to manual driving on simulator

   https://doi.org/10.1080/15389588.2019.1661677  

   Loeb, Helen; Belwadia, Aditya; Maheshwaria, Jalaj; Shaikhb, Saniyah (October 2019, Traffic injury prevention)

   null (Ed.)

   Objective: The objective of this study was to explore how age and sex impact the ability to respond to an emergency when in a self-driving vehicle. Methods: For this study, 60 drivers (male: 48%, female: 52%) of different age groups (teens: aged 16–19, 32%, adults: aged 35–54, 37%, seniors: aged 65þ, 32%) were recruited to share their perspectives on self-driving technology. They were invited to ride in a driving simulator that mimicked a vehicle in autopilot mode (longitudinal and lateral control). Results: In a scenario where the automated vehicle unexpectedly drives toward a closed highway exit, 21% of drivers did not react at all. For this event, where drivers had 6.2 s to avoid a crash, 40% of drivers crashed. Adults aged 35–54 crashed less than other age groups (33% crash rate), whereas teens crashed more (47% crash rate). Seniors had the highest crash rate (50% crash rate). Males (38% crash rate) crashed less than females (43% crash rate). All participants with a reaction time less than 4 s were able to avoid the crash. Conclusions: The results from the simulation drives show that humans lose focus when they do not actively drive so that their response in an emergency does not allow them to reclaim control quickly enough to avoid a crash. 

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4. A Survey on Perceptions of Smartwatch Haptic Feedback for Enhancing Automated Vehicle Takeover Decisions

   https://doi.org/10.1177/10711813241275929  

   Lo, Wei-Hsiang; Huang, Gaojian (August 2024, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The imperfections in the driving automation system have challenged older adults because the takeover process is cognitively and physically demanding. Due to the wrist being more vibration-sensitive, the haptic display on the smartwatch could be a good option to warn the driver. However, the preference between two vibrotactile patterns, dynamic patterns (vibrating sequences at different locations on the smartwatch) and static patterns (vibrating at certain locations on the smartwatch), is still unclear. Therefore, this study examined the effects of vibrotactile patterns between younger (mean age = 30.97) and older adults (mean age = 69.45) using a national survey. Three hundred forty respondents’ data were collected. The results showed that static patterns received higher usefulness and satisfaction scores than dynamic patterns. However, no age differences were found. These findings provide a potential guide for the next-generation takeover warning system on wrist-wearable devices in the automated system. 

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5. Evaluating Login Challenges as aDefense Against Account Takeover

   https://doi.org/10.1145/3308558.3313481  

   Doerfler, Periwinkle; Thomas, Kurt; Marincenko, Maija; Ranieri, Juri; Jiang, Yu; Moscicki, Angelika; McCoy, Damon (January 2019, The World Wide Web Conference)