Data-Driven AI for Autonomous Vehicles With Dmitri Dolgov of Waymo

Waymo's investment in data mining, training cycle, and automation of the feedback loop is key to building a robust and generalizable autonomous driving system.

• Investment in frameworks and infrastructure for closing the loop on data mining training cycle
• Investment in feedback loop as a first-class object in the development life cycle
• Automation and low human engineering cost in the ML infrastructure
• Discovery of interesting long-tail examples through data mining and hard data example mining strategy
• Challenges in optimizing for both long-tail and average case distributions
• Unification and simplification of technology development and team organization to build a robust and generalizable core stack

Waymo has encountered various interesting long-tail examples such as construction zones, vehicles running stop signs or red lights, jaywalking pedestrians or cyclists, and even a drunk cyclist weaving through traffic with a stop sign on his back. These examples are brought into their data sets and expanded into a family of similar scenarios that guide the training and evaluation of their system. Waymo's investment in a simulator enables them to expand these examples and build a more robust and generalizable core stack.

Dmitri Dolgov is the co-CEO of Waymo, an autonomous driving technology company. He is also one of the founders of the Google self-driving car project, which began in 2009 and became Waymo in 2016. He joins Scale AI CEO Alexandr Wang in a fireside chat to discuss the challenges of data-driven artificial intelligence in enabling safe and effective autonomous vehicles.