Self-driving cars are getting more and more momentum. In 2014, Tesla introduced the “Autopilot” feature for it’s Model S, which allows autonomous driving. The technology for self-driving cars has been around for years though – there are other factors why it is still not here. It is mainly a legal question and not a technical one.
However, autonomous systems will be here in some years from now, and they will have a positive impact on cloud computing and big data. The use-cases were already described partially with smart cities in an earlier post. However, there are several other use-cases. Positive effects of self-driving cars are the advanced security: sensors need milliseconds to react to threads whereas humans need a second. This gives more time for better reactions. Autonomous systems can then also communicate with other cars and warn them in advance. This is called “Vehicle to Vehicle communication”. But communication is also done with infrastructure (which is called Vehicle to Infrastructure communication). A street for instance can warn the car that there are problems ahead – e.g. that the street itself is getting worse.
The car IT itself doesn’t need the cloud and big data – but services around that will heavily use cloud and big data services.
Self-driving cars also brings a side-effect: Smart Logistics. Smart Logistics are fully automated logistic devices that drive without the need of a driver and deliver goods to a destination. This can start in china with a truck that brings a container to a ship. This ship is also fully automated and works independent. The ship drives to New York, where the goods are picked up by a self-driving truck again. The truck brings the container to a distribution center, where robots unload the container and drones deliver the goods to the customers. All of that is handled by cloud and big data systems that often operate in real-time.
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My take is that this trend brings much needed attention to the maturing big data space. Organizations need this awareness if IT has any hope of gaining the type of support needed not only to invest in big data, but ultimately build the analytical culture that is often the core driver of big data success.
Peter Fretty, IDG blogger working on behalf of SAS