Multi-Sensor-Based Blind-Spot Reduction Technology and a Data-Logging Method Using a Gesture Recognition Algorithm...

Area of Science:

• Engineering
• Computer Science
• Artificial Intelligence

Background:

• Autonomous driving technology is advancing rapidly, driven by the fourth industrial revolution.
• Object recognition and multi-sensor integration are key to enhancing vehicle safety and functionality.

Purpose of the Study:

• To propose a novel system for micro e-mobility vehicles (MEVs) integrating data logging and gesture recognition.
• To build a database for autonomous driving using an Internet of Things (IoT) environment.
• To enhance safety by removing blind spots and recognizing pedestrians.

Main Methods:

• Utilizing multiple sensors installed in MEVs for data logging and object recognition.
• Implementing a gesture recognition algorithm for MEV control and safety information dissemination.
• Developing a system for multi-sensor control and data logging within an IoT framework.

Main Results:

• The proposed system effectively logs driving data and recognizes surrounding objects to eliminate blind spots.
• Gesture recognition enables MEV control and provides crucial safety information by identifying pedestrians.
• The system demonstrates capability in multi-sensor control and data logging.

Conclusions:

• The developed system enhances MEV safety and functionality through advanced sensor integration and gesture recognition.
• This technology contributes to building a robust database for autonomous driving and AI development.
• The system has broad applicability in 5G communication, autonomous driving, and AI sectors.