The Software Defined Vehicle (SDV) has emerged as an area of priority investment for OEMs worldwide. While most new-generation OEMs are on an SDV-first approach, major legacy participants are adopting roadmaps to migrate from legacy to SDV architectures. The transition, by enabling a ‘continuous revenue and services delivery’ platform, is expected to drive significant value for OEMs, their suppliers, and end customers.
A single SDV in operation generates large volumes of data, which can be stored in a repository by the OEM, or an intermediatory, for analysis and use. The scenario becomes complex, as well as throws up immense possibilities, when we consider the millions of SDVs that are set to become operational over the next three to five years. The potential use cases that can be built on this huge dataset available is a long list, and can provide valuable insights for OEMs, vehicle owners, and other participants.
From a technology perspective, for some time now, the rising SDV ecosystem has emerged as an obvious target for AI intervention, given the size of the data and the use cases possibilities.
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