2021-06-23 14:00:00 2021-06-23 15:00:00 US/East-Indiana Aging and automation: non-chronological age factors and takeover request modality predict transition to manual control performance during automated driving Gaojian Huang, Ph.D. Candidate https://purdue-edu.zoom.us/j/99727133820?pwd=ZndJTEhmZDVoOXJmeVlLR1ZKNS9tQT09

June 23, 2021

Aging and automation: non-chronological age factors and takeover request modality predict transition to manual control performance during automated driving

Event Date: June 23, 2021
Sponsor: Dr. Brandon Pitts
Time: 1:00 pm EDT
Location: https://purdue-edu.zoom.us/j/99727133820?pwd=ZndJTEhmZDVoOXJmeVlLR1ZKNS9tQT09
Priority: No
School or Program: Industrial Engineering
College Calendar: Show
Gaojian Huang, Ph.D. Candidate
Gaojian Huang, Ph.D. Candidate

 

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 resulted in faster response times for both age groups across driving conditions, but was more pronounced for older adults.

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, age was not included as a factor in this study. 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 led to the faster takeover and information processing times. The shorter lead time resulted in poorer takeover quality. 

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. Overall, no difference between informative and instructional tactile signals was found, but signals presented in the seat back were associated with better takeover performance. 

Findings from this research can inform the development of next-generation HMIs that account for differences in various demographic factors. 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.