can drive without a human is a unique challenge. There are many kinds of events that fully autonomous vehicles have to be prepared to handle in milliseconds, and mistakes can have serious consequences. Solving these problems requires innovation across a number of fields, such as AI and machine learning, advanced sensors, simulation software that can mimic real-world driving, and computing frameworks to evaluate the system’s performance.
In 2007, I joined the Urban Challenge that was run by the US Defense Advanced Research Project Agency to test and develop autonomous vehicles . I vividly remember the first moment our car, Junior, drove by itself in the parking lot using software I was working on just hours before. That was a watershed moment for me. It became clear that this was the most impactful and interesting engineering problem of our age, one I’ve dedicated all my time to ever since.
Over the last decade, the autonomous vehicle industry has solved many technical challenges. For instance, since 2020, residents in the East Valley of Phoenix, in Arizona, have been able to open the Waymo One app, hail a ride, and get where they need to go in a vehicle without a human driver. It’s hard to overstate how significant that breakthrough is.
The progress the industry will make in 2023 will be the result of years of testing and deploying AVs across different geographies. As a result, the AV industry is now focusing on mastering generalizable driving technology as it moves toward scaling up commercial deployments. This is important, because AVs don’t make commercial sense if they can’t easily operate in different places.
In 2023, this will lead to AV deployments across multiple markets. Over the years, many AV companies—we at Waymo, and others at Aurora, Cruise, Motional, Nuro, and Oxbotica to name just a few—have been making tremendous progress in cities as diverse as