Takeover performance of older drivers in automated driving: A review
DOI: 10.1016/j.trf.2022.04.015
archive: archived pipeline: cataloged verified
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Summary
This review paper addresses the specific challenges older drivers face when operating Level 3 conditionally automated vehicles, focusing on their performance during take-over maneuvers. As the population ages, automated driving systems offer potential benefits for mobility and safety; however, Level 3 automation requires drivers to resume control when the system reaches its limits. The authors note that age-related declines in sensory, physical, and cognitive abilities—particularly executive function, attention, and processing speed—may impair an older driver’s ability to perceive take-over requests, disengage from non-driving related tasks (NDRTs), and execute safe vehicle control. The study aims to synthesize existing literature to determine the impact of aging on take-over performance and identify influencing factors. The authors conducted a systematic literature review using three databases: Web of Sciences, Scopus, and TRID. They searched for English-language articles and proceedings published between 2011 and 2022 that examined conditional automation and included groups of older adults. After screening 418 initial records for duplicates and relevance, two independent reviewers assessed full texts for eligibility. Fourteen studies were ultimately included, all utilizing driving simulators to assess take-over time and quality metrics such as steering angle, lane position, speed adaptation, and collision avoidance. The review extracted data on participant demographics, NDRT types, notification intervals, and driving conditions to analyze variations in performance. The findings reveal divergent results regarding age-related differences. Only five of the fourteen studies reported that older adults exhibited poorer take-over performance than younger adults, specifically in terms of longer take-over times and reduced maneuver quality. Other studies found no significant differences or mixed outcomes. The review identifies several critical factors influencing performance, including the type of NDRT, notification interval duration, vehicle speed, and the distribution of driving modes. For instance, some studies indicated that older drivers performed worse when disengaged from driving tasks compared to when monitoring the road, and that longer notification intervals could mitigate some performance deficits. Additionally, human-machine interface design, such as the modality of alerts (auditory vs. visual), significantly affected reaction times and workload. The authors conclude that there is no general consensus on the extent to which aging impairs take-over performance, likely due to methodological differences and varying demographic characteristics across studies. The review highlights the need for further research to clarify the specific cognitive and physical mechanisms affecting older drivers in automated environments. It suggests that future studies should standardize methodologies and consider individual differences in cognitive decline. The findings imply that designing automated vehicles for older adults requires careful consideration of notification systems and interface design to accommodate age-related changes in attention and processing speed, ensuring safety during the transition from automated to manual control.
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | semantic_scholar | — | — | 6 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | failed | — | — | — | 2 | 2026-08-23 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework