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

Area of Science:

• Transportation Engineering
• Automotive Technology
• Human Factors in Driving

Background:

• Driver proficiency assessment is crucial for road safety and public trust in licensing.
• Current manual testing methods are subjective and lack consistency.
• There is a need for objective, data-driven driver evaluation systems.

Purpose of the Study:

• To introduce the Smart Driving Test System (SDTS), an automated framework for standardized driver skill assessment.
• To demonstrate the feasibility and advantages of a sensor-based, microcontroller-driven driving test.
• To compare SDTS with existing international smart driving evaluation platforms.

Main Methods:

• Development of a functional prototype for the Smart Driving Test System (SDTS).
• Integration of sensors and microcontroller-driven technology for automated data collection.
• Feasibility analysis covering technology, economics, deployment, and legal aspects.
• Comparative assessment with international smart driving evaluation systems.

Main Results:

• SDTS successfully replaces subjective human judgment with objective, repeatable performance metrics.
• The system demonstrates technological maturity, economic viability, and operational feasibility.
• SDTS offers advantages in communication, modularity, user interface, and scalability compared to international platforms.

Conclusions:

• SDTS enhances the transparency, consistency, and reliability of driver certification.
• The system facilitates the digital transformation of transportation licensing infrastructure.
• SDTS promotes safer roads and strengthens public trust in driver qualification frameworks.