Evidence map›Paper›PMID 42218174›Full record

ArticleScientific reports2026

Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation.

Eman S Sabry, Kareem E Wagdy, Yahya M Elkholy, Mohamed A Farid, Farouk M Domdom, Mohamed A Mohamed, Ahmed A Abdelkhalik, Samah Alshathri, Walid El-Shafai

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Article in Scientific reports, 2026. 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

9 authors.

Eman S SabryDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Kareem E WagdyDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Yahya M ElkholyDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Mohamed A FaridDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Farouk M DomdomDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Mohamed A MohamedDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Ahmed A AbdelkhalikDepartment of Communications and Computers Engineering, Higher Institute of Engineering, El- Shorouk Academy, El-Shorouk City, Egypt.
Samah AlshathriDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Walid El-ShafaiCollege of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia. eng.waled.elshafai@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Evaluating driver proficiency remains a fundamental determinant of road safety, regulatory adherence, and public confidence in licensing systems. This paper presents the Smart Driving Test System (SDTS), a fully integrated, sensor-based, microcontroller-driven framework designed to automate and standardize the assessment of driving skills. A functional prototype demonstrates SDTS capability to replace subjective human judgment with objective, repeatable, and data-centric performance metrics. The work includes a comprehensive feasibility analysis encompassing technological maturity, economic viability, operational deployment, and legal compliance, further supported by a detailed cost estimation model and implementation strategy. A comparative assessment with established international smart driving evaluation platforms underscores SDTS's distinctive contributions in communication architecture, modular hardware configuration, graphical user interface functionalities, and scalable system design. The paper also outlines prospective enhancements, including the incorporation of machine learning for predictive performance analysis, adoption of low-power wireless communication protocols such as ZigBee and LoRa to simplify installation, utilization of cloud-based analytics for centralized monitoring, and employment of multimodal sensor fusion to improve environmental perception. By bridging the gap between conventional manual testing procedures and next-generation automated evaluation, SDTS significantly enhances the transparency, consistency, and reliability of driver certification processes. Furthermore, it lays the groundwork for the digital transformation of transportation licensing infrastructure and aligns with broader advancements toward intelligent transportation ecosystems, promoting safer roads, harmonized assessment standards, and strengthened public trust in driver qualification frameworks.

Indexed as

Automobile Driver ExaminationAutomobile DrivingAutomationHumansMachine LearningAutomated driving assessmentDriver evaluationDriving license automationEmbedded systemsIntelligent traffic managementIntelligent transportation systemsMicrocontroller-based controlReal-time monitoringRoad safetyRS485 communicationSensor fusionSmart driving test systems

Identifiers

PMID42218174
PMCPMC13222364

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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.