Survey of AI-Driven Adaptive Traffic Signal Detection Using Edge–IoT Architecture
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Abstract
The growth in urbanization and vehicular population over time has been alarming, and this has led to situations like traffic congestion, road accidents, and environmental pollution. Even situations like this have posed significant threats to modern-day transportation systems. However, this situation is to be addressed and solved through intelligent transportation systems, where cutting-edge communication and data processing technologies are utilized. In the intelligent transport system technologies, traffic signal detection is seen as one of the major factors through which intelligent driving and decision-making are made possible. In this field of intelligent transport systems, IoT is identified as a revolutionary technology through which connectivity among road signals and other platforms is made easy.
In the manuscript, the reader can find a thorough overview of the various methods of traffic signal detection using IoT technology, implemented in the wider range of Intelligent Transportation Systems. Techniques, like cameras, RFID, GPS, and various Vehicle-Infrastructure communication methods, are considered, along with data acquisition and communication mechanisms, and edge computing and cloud platforms, and AI analytics. In addition, important issues like latency, scalability, interoperability, security, privacy, and cost are addressed. Finally, open research directions to build reliable, energy-efficient, and intelligent traffic signal detection systems are identified. It is believed that this research would be highly instrumental for reference purposes to researchers, practitioners, and policy makers involved in the domain of IoT-based intelligent transportation systems.
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