Evidence map›Paper›PMID 40845037›Full record

ArticlePloS one2025

Exploring the impact of display types of information about autonomous driving in semi-autonomous vehicles on drivers' situation awareness and take-over performance under different driving scenarios.

Chengmin Zhou, Yuxuan Luo, Jake Kaner

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Chengmin ZhouCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, Jiangsu, China.ORCID https://orcid.org/0000-0002-4988-5896
Yuxuan LuoCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, Jiangsu, China.
Jake KanerSchool of Art and Design, Nottingham Trent University, Nottingham, United Kingdom.ORCID https://orcid.org/0000-0002-7946-7433

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the advent of the era of autonomous driving, designing an effective and appropriate autonomous driving information display is crucial for ensuring driving safety. Head-up Display (HUD) is regarded as a promising way for presenting in-vehicle information in the future. This study conducted a simulation experiment to explore the impacts of three types of autonomous driving information displays on HUD on Situation Awareness (SA) and take-over performance, while considering the complexity of different driving scenarios. The experiment used in this study adopted a Latin square experimental design and employed an integrated eye-tracking technology with self-reporting and the Situation Awareness Global Assessment Technique (SAGAT). The results show that although young drivers perform better with the Augmented Reality (AR) display in various complex scenarios, particularly in high-complexity scenarios (the fixation duration with AR display was significantly shorter than that with Pseudo-3D (P3D) display; P = 0.012). However, the advantages of the AR display will weaken as the complexity of the scenarios decreases. Additionally, the Surround Recognition (SR) display is more likely to reduce drivers' SA (the fixation counts on the SR display was significantly higher than that on the P3D and AR displays; P < 0.001) and take-over efficiency (the take-over reaction time for the SR display was significantly longer than that for the AR display; P = 0.09), especially in medium-complexity scenarios. Meanwhile, male participants pay more attention to the autonomous driving information on HUD. Nevertheless, there is no obvious difference between males and females in terms of specific preferences for the types of displays. The results of this study are expected to provide some inspiration for the design of autonomous driving information on HUD.

Indexed as

Augmented RealityAutomobile DrivingAwarenessAdultAttentionEye-Tracking TechnologyFemaleHumansMaleYoung Adult

Identifiers

PMID40845037
PMCPMC12373254

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Registered trials

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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.