Evidence map›Paper›PMID 39893220›Full record

ArticleScientific reports2025

A preliminary simulator study on exploring responses of drivers to driving system reminders on four stimuli in vehicles.

Zhao Zou, Fady Alnajjar, Michael Lwin, Luqman Ali, Hamad Al Jassmi, Omar Mubin, Muhammad Swavaf

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
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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

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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

7 authors.

Zhao ZouSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia.
Fady AlnajjarCollege of Information Technology, United Arab Emirates University, Al Ain, UAE. fady.alnajjar@uaeu.ac.ae.
Michael LwinSchool of Business, Western Sydney University, Sydney, NSW, Australia.
Luqman AliEmirates Centre for Mobility Research, United Arab Emirates University, Al Ain, UAE.
Hamad Al JassmiEmirates Centre for Mobility Research, United Arab Emirates University, Al Ain, UAE.
Omar MubinSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, NSW, Australia.
Muhammad SwavafCollege of Information Technology, United Arab Emirates University, Al Ain, UAE.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the realm of autonomous vehicles, society is undergoing a transition from conventional human-driven vehicles to autonomous driving systems. Therefore, there is an increasing demand for vehicles integrated with assistive driving systems. This pilot study designed to explore which type of driving system reminders, namely Text display, Image display, alarm notification, or humanoid voice command, provokes stronger preferences and higher rates of cooperation from drivers. A high-fidelity driving simulator mainly consisting of a Logitech PlayStation driving system, a reminder playing system and an emotion-detecting model was developed in a lab-setting environment. A cohort of participants (N = 6) was recruited to participate in the experiment, where they were tasked with completing assignments across four driving sessions, followed by a subsequent questionnaire. During each driving session, the participants were exposed to six reminders designed for different driving conditions, including seatbelt check, fuel level check, rear mirror check, over speed reminder, obstacles reminder and drowsy driving reminder. Concurrently, the participants' driving performance was observed by the researcher, while changes in their emotional states were detected by the model. Subsequent to the driving sessions, participants were invited to complete a questionnaire for assessing the various formats of driving reminders presented by the four stimuli, utilizing a 5-level Likert scale. The results revealed that driving reminders with sounds (alarm notification and humanoid voice command) exhibited higher recognition and cooperation rates among drivers than the silent reminders (text display and image display). Participants demonstrated stronger preferences for Voice-based driving reminders, which aligns with the observed behaviours of drivers. Despite the limitations of a small sample size of participants, this within-subject study which collected data from 24 individual driving sessions (6 participants x 4 driving sessions) provides insights on enhancing communication between human drivers and computer-assisted driving systems by developing improved alert systems for drivers. It also seeks to enhance the field of automotive user interface design by developing more intuitive and responsive interactions between humans and humanoid-assistant in future autonomous vehicles.

Indexed as

Automobile DrivingAutomobilesComputer SimulationReminder SystemsSafetyAdolescentAdultEmotionsFemaleHumansMalePilot ProjectsReproducibility of ResultsSurveys and QuestionnairesYoung AdultAssistive driving systemAutonomous vehiclesEmotion detectingHuman-computer interactionHumanoid voice

Identifiers

PMID39893220
PMCPMC11787351

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.