Conference:  Transform X 2022

Authors: Neda Cvijetic, Russell Kaplan

2022-10-19

Neda Cvijetic, Senior Vice President of Autonomous Driving at Stellantis, joined Russell Kaplan, Scale’s Director of Engineering, for a fireside chat. The two will discussed the role of data diversity in building safer autonomy, and Cvijetic shared highlights from her career building autonomous vehicles at Tesla, NVIDIA, and now Stellantis, the parent company of Dodge, Fiat, and Chrysler.Cvijetic explained the opportunities that arise while building an entirely new infrastructure for training models for autonomous vehicles at a large automotive OEM, without the hindrance of having to support legacy systems. She also discussed how she plans to achieve Stellantis’s publicly shared goals around Level 3 autonomy in 2024. The Stellantis portfolio includes Jeep, meaning its systems will also handle off-road scenarios. Kaplan and Cvijetic also covered core paradigms in autonomous vehicles, from mapping to deploying to a million-vehicle fleet, to large language models, with more than a few surprising real-world anecdotes.Prior to Stellantis and NVIDIA, Cvijetic worked on autopilot and infotainment systems at Tesla, served on the adjunct faculty of Columbia University, and held senior research positions at NEC Labs America. She holds more than 20 U.S. patents.

Conference:  Transform X 2021

Authors: Dragomir Anguelov

2021-10-07

In this keynote, Drago Anguelov, Head of Research at Waymo, discusses Waymo’s progress towards building a scalable technology stack for autonomous driving vehicles. With more than a decade of experience in solving autonomous driving, Waymo is now operating the world’s first commercial ride-hailing service Waymo One in Phoenix and has recently welcomed its first riders in San Francisco by kicking off the Trusted Tester program. Drago will give an overview of the key autonomous driving challenges and describe how Waymo is leveraging the cutting edge ML systems across the stack to handle them. He will also outline promising avenues to keep expanding the scope of ML in the stack in the future and showcase some of Waymo’s work in the space.

Conference:  Transform X 2021

Authors: Dmitri Dolgov

2021-10-07

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