Connected Car: So nutzt Tesla Artificial Intelligence und Big Data

Sehr interessanter Artikel über die Anwendung von Artificial Intelligence und Big Data bei Tesla. Das war bisher ein wohlgehütetes Geheimnis!

In fact, all Tesla vehicles – whether or not they are Autopilot enabled – send data directly to the cloud. A problem with the engine operation meaning that components were occasionally overheating was diagnosed in 2014 by monitoring this data and every vehicle was automatically “repaired” by software patch thanks to this.

Tesla effectively crowdsources its data from all of its vehicles as well as their drivers, with internal as well as external sensors which can pick up information about a driver’s hand placement on the instruments and how they are operating them. As well as helping Tesla to refine its systems, this data holds tremendous value in its own right. Researchers at McKinsey and Co estimate that the market for vehicle-gathered data will be worth $750 billion a year by 2030.

The data is used to generate highly data-dense maps showing everything from the average increase in traffic speed over a stretch of road to the location of hazards which cause drivers to take action. Machine learning in the cloud takes care of educating the entire fleet, while at an individual car level, edge computing decides what action the car needs to take right now. A third level of decision-making also exists, with cars able to form networks with other Tesla vehicles nearby in order to share local information and insights. In a near future scenario where autonomous cars are widespread, these networks will most likely also interface with cars from other manufacturers as well as other systems such as traffic cameras, road-based sensors or mobile phones.

Although details are scarce on the new AI technology that Tesla was creating, its current AI – driven by a partnership with hardware manufacturer Nvidia – is largely based on an unsupervised learning model of machine learning.

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