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What does a“ robust infrastructure” look like when it comes to enabling AI in transportation, and what foundational elements are often underestimated or overlooked? Underpinning a robust infrastructure will consist of effective digital infrastructure where attributes like a common data layer will allow information to flow and applications to be enabled. Some of these will utilize AI and in the most safety critical cases secure assurance will be required. Alongside the digital infrastructure, information from sensing networks and a variety of sources( for example traffic flow monitoring, weather data, event data etc.) will need to be reliably integrated. This implies secure and robust sensing elements on our physical infrastructure, and effective communications across the network – not always easy when considering the scale of the transportation network.
Data is not a big homogeneous thing – for example cybersecurity data is very sparse and signals can be hard to spot. Day-to-day operational data may be full of details that need to be parsed out for monitoring, and data to gather for training will likely need some preprocessing etc. It’ s important to have the right processes in place for acquisition, curation, pre-processing, protection and post-processing of data and ensure all the necessary data workflows are in place.
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