skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Directional vibrotactile takeover requests on a wrist-worn device: effects of age, pattern type, and urgency in automated driving

This content will become publicly available on September 1, 2026

Title: Directional vibrotactile takeover requests on a wrist-worn device: effects of age, pattern type, and urgency in automated driving
Drivers are still required to perform the takeover task in highly automated vehicles. This task, which is cognitively and physically demanding, may present challenges for older adults due to general age-related declines in perception and cognition. Tactile modalities that may not be occupied by many non-driving-related tasks could serve as a potential solution for delivering takeover requests. Among these, directional vibrotactile stimuli presented via a wrist-worn device represent a promising approach. However, the effects of the two common types of directional vibrotactile patterns, dynamic patterns that vibrate sequentially at different locations and static patterns that vibrate at fixed locations, are still unknown. Therefore, this study aimed to investigate the effect of age (younger and older adults), vibrotactile pattern types (Baseline, Full-Dynamic, Semi-Dynamic, and Static), and interpulse interval (shorter (300 ms) and longer (800 ms)) on takeover performance. Forty participants (20 younger and 20 older adults) were engaged in the SAE Level 3 driving simulator study. Overall, Static and Baseline patterns were associated with faster reaction and takeover times and were perceived as more useful and satisfactory compared to the Full-Dynamic and Semi-Dynamic patterns. Shorter interpulse intervals (300 ms) for vibrotactile takeover requests resulted in better takeover performance, as indicated by shorter reaction and takeover times compared to longer interpulse intervals (800 ms). Finally, younger adults reacted faster to vibrotactile takeover requests than older adults did. The findings from the current study may inform the design of human–machine interfaces on wearable devices for next-generation automated vehicles.  more » « less
Award ID(s):
2153504
PAR ID:
10652023
Author(s) / Creator(s):
;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Accident Analysis & Prevention
Volume:
220
Issue:
C
ISSN:
0001-4575
Page Range / eLocation ID:
108093
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Higher levels of driving automation make effective takeover requests critical. The wrist’s sensitivity to vibration makes wristband devices a potential carrier for sending these requests. However, the impacts of conveying takeover requests through directional vibrotactile patterns such as dynamic patterns (sequential stimuli occurring at different locations on the wrist) and static patterns (fixed stimuli at the same locations on the wrist) are unclear. Therefore, this study examined the effects of directional vibrotactile patterns on takeover performance among younger and older adults. Participants responded to four patterns (two dynamic, one static, and one baseline) in a simulated SAE Level 3 automated vehicle. Takeover performance was evaluated using reaction time and takeover time. The results show that the static and baseline patterns had shorter reaction and takeover times compared to the dynamic patterns. In addition, younger adults react faster to takeover requests compared to older adults. Findings provide important insights for the future design of human-machine interfaces via wristband devices for automated vehicles. 
    more » « less
  2. ; (, 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. 
    more » « less
  3. ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Automated vehicles may enhance the quality of life by empowering individuals and promoting independent mobility, especially for older adults. However, takeovers are still required occasionally. To ensure a safe transition during the takeover, meaningful tactile displays as takeover requests could be a good option to improve takeover efficiency. Previous studies have examined the effects of tactile takeover request more from an objective perspective, however, subjective evaluations that could reach larger and more diverse sample sizes have not been extensively conducted yet. Therefore, the goal of this study was to investigate drivers of different age groups (younger and older adults) on their preference of vibrotactile displays, varied in information formats (instructional and informative) and locations (seat back and seat pan), on a large population scale ( n = 353) through a national survey. The results indicated a preference for informative patterns over instructional patterns and displays on the seat back over the seat pan. Also, a higher level of perceived urgency was noted in older participants (age ≥ 65) compared with younger participants. These findings may inform the design of next-generation tactile interfaces in automated systems. 
    more » « less
  4. (, International Conference on Human-Computer Interaction)
    Adults aged 65 years and older are the fastest growing age group worldwide. Future autonomous vehicles may help to support the mobility of older individuals; however, these cars will not be widely available for several decades and current semi-autonomous vehicles often require manual takeover in unusual driving conditions. In these situations, the vehicle issues a takeover request in any uni-, bi- or trimodal combination of visual, auditory, or tactile alerts to signify the need for manual intervention. However, to date, it is not clear whether age-related differences exist in the perceived ease of detecting these alerts. Also, the extent to which engagement in non-driving-related tasks affects this perception in younger and older drivers is not known. Therefore, the goal of this study was to examine the effects of age on the ease of perceiving takeover requests in different sensory channels and on attention allocation during conditional driving automation. Twenty-four younger and 24 older adults drove a simulated SAE Level 3 vehicle under three conditions: baseline, while performing a non-driving-related task, and while engaged in a driving-related task, and were asked to rate the ease of detecting uni-, bi- or trimodal combinations of visual, auditory, or tactile signals. Both age groups found the trimodal alert to be the easiest to detect. Also, older adults focused more on the road than the secondary task compared to younger drivers. Findings may inform the development of next-generation of autonomous vehicle systems to be safe for a wide range of age groups. 
    more » « less
  5. ; ; ; ; ; (, 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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email