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Title: Driver-Vehicle Interaction: The Effects of Physical Exercise and Takeover Request Modality on Automated Vehicle Takeover Performance between Younger and Older Drivers
Award ID(s):
1755746
NSF-PAR ID:
10381391
Author(s) / Creator(s):
;
Date Published:
Journal Name:
2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS)
Page Range / eLocation ID:
1 to 4
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. 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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