The Rise of Roadside Edge Analytics in ITS and Smart Cities
Artificial Intelligence (AI)-based analytics capabilities are increasingly embedded in roadside infrastructure like traffic cabinets, streetlights, and traffic and surveillance cameras, allowing local intelligence to be captured, interpreted, shared, and acted upon locally and in real time for low-latency use cases, such as pedestrian detection, traffic management, adaptive traffic lights, and advanced surveillance.
The prime example is smart traffic cameras monitoring traffic levels in real time to dynamically adapt signal phasing for optimizing traffic flow, maximizing throughput, and minimizing congestion. Advanced AI frameworks running on high-compute processors featuring hardware accelerators are capable of distinguishing between different types of traffic (passenger cars, trucks, emergency vehicles, two-wheel vehicles), a capability not available I legacy traffic sensors like magnetic loops.
A key benefit of roadside edge analytics is the ability to close the loop in real time between sensor data interpretation and emergency response; for example,…
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