Take over request design for older drivers in Level 3 automated cars: the interplay of age, non-driving related tasks and culture

Srour Zreik, Rawan (2026) Take over request design for older drivers in Level 3 automated cars: the interplay of age, non-driving related tasks and culture. PhD thesis, University of Glasgow.

Abstract

Level 3 automated vehicles allow drivers to disengage from active driving and engage in non-driving-related tasks (NDRTs), while still requiring them to resume control when a Take-Over Request (TOR) is issued. This transfer of control presents a major human–machine interaction challenge, particularly for older drivers, whose cognitive, perceptual, and motor capabilities may affect takeover readiness. Existing research has largely focused on younger populations and has often treated older drivers as a homogeneous group, while giving limited attention to the effects of NDRT characteristics and cultural context on takeover behaviour. This thesis investigates how TORs can be designed to better support older drivers in Level 3 automated vehicles. The first contribution examines how older drivers perceive Level 3 automation and demonstrates that older adults should not be treated as a homogeneous group. Through interviews and participatory investigation, the findings reveal meaningful differences between drivers aged 60–69 and those aged 70+ in their expectations, concerns, and perceived benefits of automation. Building on these findings, the second contribution investigates the effect of TOR source on older drivers’ control transitions. A simulator-based study demonstrated that presenting TORs simultaneously on the in-vehicle display and the NDRT device significantly improved takeover reaction times, hazard perception, and TOR usability during lower-demand NDRTs, with the greatest benefits observed in drivers aged 70+. The third contribution presents a cross-cultural and age-sensitive participatory design study exploring expectations and preferences for TOR interfaces among younger and older drivers from two cultural contexts, Israel (low traffic-laws compliance) and the UK (high traffic-laws compliance). The findings reveal differences in communication preferences, modality expectations, and perceptions of automation across both age and cultural groups, highlighting the importance of culturally aware and age-sensitive TOR design. The fourth contribution investigates the role of complete NDRT suspension at TOR onset. A simulator-based study demonstrated that suspension significantly reduced takeover reaction times across both low- and high-demand tasks and across all three age groups (22–32, 60–69, and 70+). Importantly, suspension eliminated age-related disparities observed in the non-suspended condition, with the greatest benefits occurring in drivers aged 70+.

Overall, this thesis contributes a more nuanced understanding of takeover support in Level 3 automated vehicles and argues for adaptive, context-aware TOR systems that better accommodate age-related variability and cognitive demands.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Additional Information: This research was supported by the 'UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents', Grant Number EP/S02266X/1.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Funder's Name: Engineering and Physical Sciences Research Council (EPSRC)
• Supervisor's Name: Brewster, Professor Stephen and Harvey, Professor Monika
• Date of Award: 2026
• Depositing User: Theses Team
• Unique ID: glathesis:2026-86302
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 06 Nov 2026 11:34
• Last Modified: 06 Nov 2026 11:38
• Thesis DOI: 10.5525/gla.thesis.86302
• URI: https://theses.gla.ac.uk/id/eprint/86302

Related URLs

• Conference proceeding
• Enlighten Publications Record
• Conference proceeding
• Conference proceeding
• Conference proceeding
• Conference proceeding