As cars increasingly become another “thing” in the internet of things, an ocean of data will inundate companies, pose engineering challenges, and provide opportunities for manufacturers.
A single autonomous car, with all of its sensors, cameras, and LiDAR, could generate as much as 100 gigbatyes of data every second, said Barclays analyst Brian Johnson, in a note published Wednesday.
Extrapolating this, Johnson said:
Assuming the entire US fleet of vehicles (260mn vehicles) has a similar data generation, it would create an ocean of data. To put it in context, one hour’s worth of raw data across the entire US fleet would be ~5,800 exabytes in size.
To visualize this, Johnson noted that Amazon recently launched a service where refrigerated semi trucks carry 100 petabytes of data from clients to Amazon storage centers.
Or on a daily basis, there would be enough raw data to fill 1.4mn Amazon AWS “Snowmobile” data tractor trailer trucks (which each hold 100 petabytes) – at 45 feet per truck, a convoy of trucks would be 11,000 miles long. Even with data compression of 10,000x, that would still be a one mile convoy!
The sheer volume of this data will create new challenges for storage, management, and analysis, he noted. Even with 5G wireless technology, companies will need strategies for extracting important or useful information from the total amount of data collected. Johnson noted in particular that “edge analytics,” where information is analyzed close to the sensors themselves (rather than sent through the cloud) may end up being important tools for managing this massive volume.
So what would companies do with all of this information? Much of the conversation around using or monetizing data from drivers revolves around location-based marketing, but Johnson says cars will likely face competition from smartphones in this area.
Rather, he envisions companies taking the opportunity a step further, by offering more vehicle-related services, such as identifying warranty issues, predicting vehicle maintenance needs, and of course, developing fully autonomous cars.
Several companies are poised to take advantage of this push, he said.
Tesla has already made strides in developing an in-car system, and already has a stockpile of driver data. Johnson is generally bearish on Tesla, but he ascribes high value to its advancements in software, saying in his note “if we could buy just Tesla Software and not Tesla Auto, we would.”
Delphi can become a potentially valuable parts supplier to OEMs, providing traditional manufacturers with the tools needed to catch up to Tesla, Johnson said. Delphi has made several investments and alliances since 2015 — including a venture with Intel/Mobileye — that gives it a position in every level of the auto big data stack Johnson outlines, from sensors all the way up to cloud analytics.
Intel recently announced its acquisition of Mobileye, which has a “strong lead in mapping and camera sensing,” Johnson said. For example, he said, Intel/Mobileye’s Road Experience Management technology, combined with local sensor analytics is a good early example of the kind of edge analytics that could become indispensible. It analyzes data from LiDAR, sensors and cameras locally, while uploading map data to the cloud.
In particular, Ford is trying to combine data collected from vehicles with information from other places. For example data from a vehicle indicating a faulty part could be combined with data from sensors in Ford plants.
In GM’s case, its advantages may come through its investments in OnStar, Cruise Automation and 4G.
Watch: Hit the brakes on autos?