<p>Adults aged 65 years and older have become the fastest-growing
age group worldwide and are known to face perceptual, cognitive, and physical
challenges in later stages of life. Automation may help to support these
various age-related declines. However, many current automated systems often
suffer from design limitations and occasionally require human intervention. To
date, there is little guidance on how to design human-machine interfaces (HMIs)
to help a wide range of users, especially older adults, transition to manual
control. Multimodal interfaces, which present information in the visual,
auditory, and/or tactile sensory channels, may be one viable option to
communicate roles in human-automation systems, but insufficient empirical
evidence is available for this approach. Also, the aging process is not
homogenous across individuals, and physical and cognitive factors may better
indicate one’s aging trajectory. Yet, the benefits that such individual
differences have on task performance in human-automation systems are not well
understood. Thus, the purpose of this dissertation work was to examine the
effects of 1) multimodal interfaces and 2) one particular non-chronological age
factor, engagement in physical exercise, on transitioning from automated to
manual control dynamic automated environments. Automated driving was used as
the testbed. The work was completed in three phases. </p><p><br></p>
<p>The vehicle takeover process involves 1) the perception of
takeover requests (TORs), 2) action selection from possible maneuvers that can
be performed in response to the TOR, and 3) the execution of selected actions.
The first phase focused on differences in the detection of multimodal TORs
between younger and older drivers during the initial phase of the vehicle
takeover process. Participants were asked to notice and respond to uni-, bi-
and trimodal combinations of visual, auditory, and tactile TORs. Dependent
measures were brake response time and maximum brake force. Overall, bi- and
trimodal warnings were associated with faster responses for both age groups
across driving conditions, but was more pronounced for older adults. Also,
engaging in physical exercise was found to be correlated with smaller maximum
brake force. </p><p><br></p>
<p>The second phase aimed to quantify the effects of age and
physical exercise on takeover task performance as a function of modality type
and lead time (i.e., the amount of time given to make decisions about which
action to employ). However, due to COVID-19 restrictions, the study could not
be completed, thus only pilot data was collected. Dependent measures included
decision making time and maximum resulting jerk. Preliminary results indicated
that older adults had a higher maximum resulting jerk compared to younger
adults. However, the differences in decision-making time and maximum resulting
jerk were narrower for the exercise group (compared to the non-exercise group)
between the two age groups. </p><p><br></p>
<p>Given COVID-19 restrictions, the objective of phase two
shifted to focus on other (non-age-related) gaps in the multimodal literature.
Specifically, the new phase examined the effects of signal direction, lead
time, and modality on takeover performance. Dependent measures included
pre-takeover metrics, e.g., takeover and information processing time, as well
as a host of post-takeover variables, i.e., maximum resulting acceleration.
Takeover requests with a tactile component were associated with the faster
takeover and information processing times. The shorter lead time was correlated
with poorer takeover quality.</p><p><br></p>
<p>The third, and final, phase used knowledge from phases one and
two to investigate the effectiveness of meaningful tactile signal patterns to
improve takeover performance. Structured and graded tactile signal patterns
were embedded into the vehicle’s seat pan and back. Dependent measures were
response and information processing times, and maximum resulting acceleration. Overall,
in only instructional signal group, meaningful tactile patterns (either in the
seat back or seat pan) had worse takeover performance in terms of response time
and maximum resulting acceleration compared to signals without patterns.
Additionally, tactile information presented in the seat back was perceived as
most useful and satisfying.</p><p><br></p>
<p>Findings from this research can inform the development of
next-generation HMIs that account for differences in various demographic
factors, as well as advance our knowledge of the aging process. In addition,
this work may contribute to improved safety across many complex domains that
contain different types and forms of automation, such as aviation,
manufacturing, and healthcare.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/14879706 |
| Date | 30 June 2021 |
| Creators | Gaojian Huang (11037906) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Aging_and_Automation_Non-chronological_Age_Factors_and_Takeover_Request_Modality_Predict_Transition_to_Manual_Control_Performance_during_Automated_Driving/14879706 |
Page generated in 0.0026 seconds