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.


Title: Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving
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.  more » « less
Award ID(s):
1755746
PAR ID:
10171814
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Volume:
63
Issue:
1
ISSN:
2169-5067
Page Range / eLocation ID:
1739 to 1743
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. (, 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
  2. ; ; (, 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. 
    more » « less
  3. ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    The introduction of advanced technologies has made driving a more automated activity. However, most vehicles are not designed with cybersecurity considerations and hence, they are susceptible to cyberattacks. When such incidents happen, it is critical for drivers to respond properly. The goal of this study was to observe drivers’ responses to unexpected vehicle cyberattacks while driving in a simulated environment and to gain deeper insights into their perceptions of vehicle cybersecurity. Ten participants completed the experiment and the results showed that they perceived and responded differently to each vehicle cyberattack. Participants correctly identified the cybersecurity issue and took according action when the issue caused a noticeable visual and auditory response. Participants preferred to be clearly informed about what happened and what to do through a combination of visual, tactile, and auditory warnings. The lack of knowledge of vehicle cybersecurity was obvious among participants. 
    more » « less
  4. ; (, Accident Analysis & Prevention)
    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
  5. ; ; ; (, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)
    Adaptive task allocation is used in many human-machine systems and has been proven to improve operators’ performance with automated systems. However, there has been limited knowledge surrounding the benefits of adaptive task allocation in automated vehicles. In this study, participants were presented with photos and videos depicting driving scenarios of low or high workloads at two levels of automation (SAE Levels 2 and 3). The participants reported which tasks they felt comfortable allocating to themselves or to the driving automation system (DAS) in each driving scenario, as well as whether they would conduct the task allocation manually or have the DAS automatically allocate the tasks. Our results showed that participants preferred conducting manual task allocation and preferred the system to complete more tasks when the perceived workload was high. There was no significant difference between the high and low workload scenarios in terms of whether participants chose to allocate tasks. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email