d 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 car communicates with people and things around it.
Communication, like deep learning, is crucial, according to Reiley and Tandon.
Cars, regardless of what system they’re operating, will need to be able to communicate with one another just as people will need to be able to understand the cars’ intentions. As the self-driving space becomes increasingly crowded with the entrance of legacy and new transportation players, standardizing that communication becomes trickier.
Communication, according to Tandon, is also an important method of gaining the trust of the public. After all, consumer trust — like the reliability and legislative approval of the technology — can either be the ultimate impediment to the deployment of self-driving or it can be what expedites it.
“We believe [a standard] needs to exist,” Tandon said. “Cars today have break lights, horns, rear-view mirrors, side-view mirrors — but all those standards developed over time. Our belief with the self-driving car is that a similar transformation will have to occur. For example, we may have to replace the human hand signals, like the wave.”
Already, Tandon and his team have been discussing implementing a standard method of communication with his existing partners.
“We’re still trying to figure out what that standard even is,” he said. “But to me, it’s just a fundamental part of the problem of self-driving cars.”
To that end, the company’s “kit,” which can be retrofitted to existing vehicles, includes LED signs that sit atop the car and speakers that relay the car’s intentions to pedestrians and human drivers around it. (Naturally, the kit is also equipped with sensors like radar, GPS, cameras and Lidar, though Reiley said the team considers Lidar to be more of a redundancy than a necessary part of the system.)
The company is still testing different methods of communication to measure things like how quickly and how well people respond to the audio-visual cues. For example, do pedestrians notice the LED sign that says that it’s safe to cross, or do they need an