From consumers to the experts, there’s still a lot of uncertainty about self-driving cars.
Is the public ready for two-ton automobiles driven by software? Is Lidar — an expensive and often unwieldily technology used to calculate distances using lasers — even necessary? (Elon Musk says no.) And how will self-driving cars communicate with other cars and pedestrians?
This new self-driving startup — like many others before it — purports to have the answers. Drive.ai, co-founded by Carol Reiley, Sameep Tandon and a team of six other engineers, came out of stealth just last week with one basic premise: The answer to all these questions lies within deep learning.
What’s the difference between deep learning and other machine learning systems? At the risk of oversimplifying it, deep learning is a machine learning system that is mirrored closely after the human brain, whereas other machine learning systems often require rules and human programming.
So why do Reiley and Tandon think deep learning is the key to unlocking the true potential of self-driving cars?
Consider this: If you had to give a machine a set of rules that would help it recognize a chair, what would those rules be? Maybe: This chair stands on four legs and is brown.
But what happens when you flip that chair upside down? Or if you introduce a different colored chair? Based on the given rules, that machine may not be able to recognize the object.
But give a person a chair — just a chair — and they’ll be able to recognize chairs everywhere.
Unlike other computer vision and machine learning systems, deep learning systems can roughly think and make decisions like a human brain.
“Other systems use a lot of rules, but autonomous driving is a hard and complicated problem,” Tandon, who is also Drive.ai’s CEO, told Recode. “Deep learning systems are the most effective in a practical, real-world application because it can capture more nuance and different variations than other machine learning algorithms.”
After launching out of stealth early last week, Drive.ai joined the growing fray of self-driving startups with plans to integrate with commercial fleets in an effort to acquire more driving data. Eventually, like the self-driving startups before them, Reiley and Tandon hope to work directly with automakers and have already secured a few fleet partners, though they declined to say who they were.
Drive.ai isn’t alone in its ambitions. Many of the freshman class of self-driving startups that have recently launched fall into one of two buckets: They have either been created in the hope of eventually being acquired by an automaker or, like Drive.ai, they want to be vehicle-agnostic and hope to avoid being restricted to a single legacy automaker.
Think Cruise, which was acquired by General Motors, versus MobileEye, which has become an industry standard. But as the self-driving market gets more competitive, there may be a shift away from automakers working with technology companies that are standard across the industry in order to secure a competitive advantage over its rivals.
For now, the team is focused on its commercial fleet partnerships in order to ensure their technology is safe and reliable. Tandon, who was studying artificial intelligence at Stanford along with several other of the team’s engineers before halting his studies to start Drive.ai, and Johns Hopkins Ph.D. candidate Reiley have so far raised $12 million from undisclosed investors and have a total of 20 employees.
The company also announced that General Motors veteran Steve Girsky joined its board — a move that may bolster its relationships with carmakers (or even hint at an existing one).
Admittedly, Drive.ai isn’t the only company using deep learning algorithms in their self-driving cars, Tandon said. Mobileye, for example, recently announced they would use deep learning for their camera systems.
But Drive.ai is the only one using deep learning to drive the entire system, he said. That means deep learning is powering everything — from the sensors and cameras, to the vehicle’s decision-making, to the way the ca