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1. Automated Vehicle Takeover: A Pilot Study on the Effects of Age, Physical exercise, and Takeover Request Modality on Post-Takeover Performance

   https://doi.org/10.1177/1071181321651012  

   Huang, Gaojian; Pitts, Brandon J. (September 2021, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
2. Assessing Meaningful Visual and Tactile Feedback for Effective Automated Vehicle Takeover by Hearing and Non-hearing Drivers

   https://doi.org/10.1177/10711813241268844  

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

   With SAE Level 3 automated vehicles handling most driving tasks, there are still situations when the driver needs to take over. Multimodal displays have been introduced to inform drivers of the need to take over for critical scenarios (e.g., in construction zones) in instructional or informative formats. However, the effects of multimodal displays on takeover performance for drivers with hearing impairments are still unclear. Therefore, the goal of this study was to investigate how signal type (single tactile (T), single visual (V), and visual and tactile combined (VT)), information type (instructional, informative, and baseline), and hearing impairment (hearing-impaired and non-hearing-impaired drivers) affect drivers’ takeover performance. Findings show that signal type significantly influenced reaction and takeover times, with multimodal signals (VT) resulting in faster reactions compared to single modal signals. Additionally, the baseline condition yielded the faster reaction times compared to both instructional and informative formats. Hearing impairment, however, did not significantly affect reaction and takeover times. Findings may inform the development of future vehicle interfaces to assist drivers with hearing impairments. 

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3. 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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4. The effects of age and physical exercise on multimodal signal responses: Implications for semi-autonomous vehicle takeover requests

   https://doi.org/10.1016/j.apergo.2021.103595  

   Huang, Gaojian; Pitts, Brandon J. (January 2022, Applied Ergonomics)
5. Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving

   https://doi.org/10.1177/1071181319631053  

   Huang, Gaojian; Steele, Clayton; Zhang, Xinrui; Pitts, Brandon J. (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   The rapid growth of autonomous vehicles is expected to improve roadway safety. However, certain levels of vehicle automation will still require drivers to ‘takeover’ during abnormal situations, which may lead to breakdowns in driver-vehicle interactions. To date, there is no agreement on how to best support drivers in accomplishing a takeover task. Therefore, the goal of this study was to investigate the effectiveness of multimodal alerts as a feasible approach. In particular, we examined the effects of uni-, bi-, and trimodal combinations of visual, auditory, and tactile cues on response times to takeover alerts. Sixteen participants were asked to detect 7 multimodal signals (i.e., visual, auditory, tactile, visual-auditory, visual-tactile, auditory-tactile, and visual-auditory-tactile) while driving under two conditions: with SAE Level 3 automation only or with SAE Level 3 automation in addition to performing a road sign detection task. Performance on the signal and road sign detection tasks, pupil size, and perceived workload were measured. Findings indicate that trimodal combinations result in the shortest response time. Also, response times were longer and perceived workload was higher when participants were engaged in a secondary task. Findings may contribute to the development of theory regarding the design of takeover request alert systems within (semi) autonomous vehicles. 

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