If you search YouTube for “fully autonomous vehicles,” you’d swear the long-promised future of self-driving cars, a la Total Recall or Batman, was already here. Tesla proclaims we are seeing “the beginning of the end for human-driven cars.” Google-owned Waymo released a video showing that “fully autonomous driving technology is here.”
Surely, after so many years of development, the long-awaited goal of fully automated vehicles must be arriving very soon.
In fact, maybe not even in this decade. Reality is always harder than hype.
How Self-Driving Cars Use Big Data and AI
Fully driverless cars require sophisticated artificial intelligence (AI), all of which is based on massive training efforts with potentially petabytes of source data. Driving around cones on an empty racetrack is one thing; navigating through dense urban streets with random pedestrian traffic and distracted adjacent drivers is something else.
Getting the autonomous driving industry from the former to the latter poses an intellectual and computing challenge spanning from datacenters to edge nodes (cars).
Will we get to fully autonomous driving? Almost certainly. But it’ll likely require years of working through the stages of autonomous driving, refining technology, and adapting infrastructure as we go. Much of making this future come to pass will depend on ever-improving ML training and efficiently handling the mountains of data that requires.
Leveling Up – 5 Levels of Self-Driving Cars
On the road from fully manual cars to Total Recall’s Johnny Cabs, the Society of Automotive Engineers has defined six levels:
Intel noted, “Every autonomous car will generate the data equivalent of almost 3,000 people. Extrapolate this further and think about how many cars are on the road.” One estimate from AAA noted that a single car could generate up to 5100TB of data annually.