Aging and Automation: Non-chronological Age Factors and Takeover Request Modality Predict Transition to Manual Control Performance During Automated Driving
Abstract
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. 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. 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 decisionmaking time and maximum resulting jerk were narrower for the exercise group (compared to the non-exercise group) between the two age groups. Given COVID-19 restrictions, the objective of phase two shifted to focus on other (nonage-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 posttakeover 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.
Degree
Ph.D.
Advisors
Pitts, Purdue University.
Subject Area
Cognitive psychology|Kinesiology|Psychology|Recreation
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