Current EVP, Autonomous Driving at Lyft and Former VP Street View at Google
Luc Vincent is the Current Executive VP of Autonomous Driving at Lyft and responsible for building a Level 5 system for the ride hailer to bring an autonomous ride-hailing service to market. Luc previously spent over 12 years at Google where he started the Google Street View product which forms the foundations of the current maps product. Luc spearheaded the journey from collecting images and turning pixels into knowledge to map the world. Luc previously worked on image processing at Xerox and has a PhD in computer vision.Read more
Tell us about your role and responsibilities at Lyft today, as well as at your previous role at Google?
Today at Lyft I lead the team called Lyft Level 5 which is focused on building self-driving cars, focusing on the specific needs of ride-sharing. Three and a half years ago, I founded this group and have been leading it ever since. Prior to that, I was at Google for over 12 years. I came to Google to work on Google Books. I was intrigued by the ambition of Google to scan all the world's books. At the time, they encouraged engineers to have side projects called the 20% Project. I got interested in a project that was a collaboration between Stanford and Google, headed by Larry Page himself.
Larry Page is one of the co-founders of Google. The idea was to collect imagery at street level from vehicles driving around. His vision was, essentially, to not only bring the web to people, but the world. He wanted to collect data at street level and make it useful in some way. Digital was still a bit fuzzy, but aligned with Google's overall mission. Since Google was growing very fast, there was nobody at the company at the time who had spent any time on this project, so I took that on as my 20% Project. I got some interest from seven interns who helped me out over the first Summer.
We put together an end-to-end demo of what something like this could look like. We hacked a car together with a bunch of cameras, Lidar and hardware, and then got help from the Google security team to drive this car around collecting data. We established an early, though clunky, way of getting data from the cars to a hard disk plugged under somebody's desk. After uploading the data, we put together an end-to-end pipelined computer vision to make this data useful. A couple of weeks later, I gave a tech talk, followed by a review from the VP of engineering, who thought it was interesting and gave us the go-ahead to make this real and hire people.
We hired engineers to work on it, and launched in five cities about a year later. Why five? From day one, we wanted to show that the project was not just a California experiment, but an ambition to grow beyond a single city, so we chose five cities spread across the US. We only had a small amount of data for each city, but still useful. From there we wanted to see what happened. It was a brand-new product space and nobody had launched street level imagery before in the context of maps. We received record traffic and press interest in the product and, from then on, we knew there was demand and that it was going to work.
We grew this project from an early stage experiment to something that could be scalable. My focus over the next few years was that scale; to make it real, more robust, and to essentially rewrite everything about the software we had built because everything was clunky. It was all about moving fast and not about having something super reliable. Along the way, my career grew, with the team growing from a handful of people to over 100, with global operations. After that I also expanded my scope of interest to be involved in different kinds of imagery – captured from airplanes, satellites, even user's cell phones – with the mission to make sense of all this imagery in the context of maps; to drive data for maps. It was not only about presenting images to users through Street View or Google Earth, but to essentially go from raw pixels to knowledge and structured data.
Today, Google and other company's maps are built automatically by data mining imagery and extracting the corresponding street signs, street names and house numbers; all the information required to make a map. We pioneered this along the way and expanded the scope to derive knowledge from other kinds of imagery at Google.
After collecting data via Lidar or radar in the early days, you expanded to use many other sources?
The Street View car was primarily about imagery, but from the very beginning we felt there was going to be interesting information that we could not easily derive from the imagery, which was about 3D of the environment. We wanted to be able to give users a smooth transition between panoramas. Imagine your Street View Images are like a bubble; navigating from bubble to bubble can be very jarring if there is no transition. Context is lost and so, in order to create smooth transitions from one bubble to the next, the first bubble needs to be warped in a way that it moves to the second one, which involves an understanding of 3D. The imagery is projected onto a course 3D of the environment and the user is moved smoothly to the next bubble.
In theory, you can construct a 3D from the raw imagery itself through stereo, but it is complicated and does not always work. To aid us, we placed Lidars on these vehicles. In the early days of the project, our Lidar had a single line, but over time we moved to much more sophisticated puck-style Lidars that gave us more information. It was primarily about imagery but Lidar helped.