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

Conference:  Transform X 2021


Authors:   Dmitri Dolgov


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.