Evidence map›Paper›PMID 40075130›Full record

ArticleScientific reports2025

The distraction potential of driving a partially automated vehicle through a construction zone.

Francesco Biondi, Praneet Sahoo, Noor Jajo

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Francesco BiondiHuman Systems Lab, Department of Kinesiology, University of Windsor, Windsor, ON, Canada. francesco.biondi@uwindsor.ca.
Praneet SahooHuman Systems Lab, Department of Kinesiology, University of Windsor, Windsor, ON, Canada.
Noor JajoHuman Systems Lab, Department of Kinesiology, University of Windsor, Windsor, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Partial driving automation is designed to control the vehicle's speed and acceleration without input from the human driver on the condition that the driver maintains alertness. These systems are promised to make driving more convenient and safer, especially in increasingly demanding road conditions such as construction zones. Despite this, little knowledge is available on how these systems are used in these accident-prone areas and the effect they may have on drivers' workload and glance allocation. This study aims to fill this gap by having participants drive a vehicle in partially automated and manual mode through three road sections: pre-construction, construction, and post-construction. Results show no differences in cognitive workload by driving mode or construction zone. An increase in glances directed away from the forward roadway toward the vehicle's touchscreen was observed during partially-automated driving in the pre-construction zone, a pattern that, notably, continued on when driving throughout the construction zone. These findings adds to the literature on the human factors of partial automation. More importantly, because drivers failed to increase the amount of time looking at the forward roadway when entering the construction zone, they show the potential perniciousness of partially automated driving and the detrimental effect certain systems may have on safety risk.

Indexed as

AttentionAutomationAutomobile DrivingDistracted DrivingAccidents, TrafficAdultFemaleHumansMaleYoung AdultAutomationCognitive workloadDetection response taskGlancePartial automationPartially automated driving systemsRoad safetyVisual attention

Identifiers

PMID40075130
PMCPMC11903694

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.