Autonomous Vehicle Research at Waymo
Waymo’s VP of Research, Drago Anguelov, joins Practical AI to explore how advances in autonomy, vision models, and large-scale testing are shaping the future of driverless technology. The conversation dives into the dual challenges of building an onboard driver and testing that driver (via large scale simulation). Drago also gives us an update on what Waymo is doing to achieve intelligent, real-time performance while ensuring proven safety and reliability.
Featuring:
Drago Anguelov – LinkedIn
Chris Benson – Website, LinkedIn, Bluesky, GitHub, X
Daniel Whitenack – Website, GitHub, X
Links:
Waymo Research
New Insights for Scaling Laws in Autonomous Driving
AI in Motion
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Waymo's Foundation Model for Autonomous Driving with Drago Anguelov - #725
Today, we're joined by Drago Anguelov, head of AI foundations at Waymo, for a deep dive into the role of foundation models in autonomous driving. Drago shares how Waymo is leveraging large-scale machine learning, including vision-language models and generative AI techniques to improve perception, planning, and simulation for its self-driving vehicles. The conversation explores the evolution of Waymo’s research stack, their custom “Waymo Foundation Model,” and how they’re incorporating multimodal sensor data like lidar, radar, and camera into advanced AI systems. Drago also discusses how Waymo ensures safety at scale with rigorous validation frameworks, predictive world models, and realistic simulation environments. Finally, we touch on the challenges of generalization across cities, freeway driving, end-to-end learning vs. modular architectures, and the future of AV testing through ML-powered simulation.
The complete show notes for this episode can be found at https://twimlai.com/go/725.
Drago Anguelov — Robustness, Safety, and Scalability at Waymo
Drago Anguelov is a Distinguished Scientist and Head of Research at Waymo, an autonomous driving technology company and subsidiary of Alphabet Inc.
We begin by discussing Drago's work on the original Inception architecture, winner of the 2014 ImageNet challenge and introduction of the inception module. Then, we explore milestones and current trends in autonomous driving, from Waymo's release of the Open Dataset to the trade-offs between modular and end-to-end systems.
Drago also shares his thoughts on finding rare examples, and the challenges of creating scalable and robust systems.
Show notes (transcript and links): http://wandb.me/gd-drago-anguelov
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⏳ Timestamps:
0:00 Intro
0:45 The story behind the Inception architecture
13:51 Trends and milestones in autonomous vehicles
23:52 The challenges of scalability and simulation
30:19 Why LiDar and mapping are useful
35:31 Waymo Via and autonomous trucking
37:31 Robustness and unsupervised domain adaptation
40:44 Why Waymo released the Waymo Open Dataset
49:02 The domain gap between simulation and the real world
56:40 Finding rare examples
1:04:34 The challenges of production requirements
1:08:36 Outro
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Connect with Drago & Waymo
📍 Drago on LinkedIn: https://www.linkedin.com/in/dragomiranguelov/
📍 Waymo on Twitter: https://twitter.com/waymo/
📍 Careers at Waymo: https://waymo.com/careers/
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Links:
📍 Inception v1: https://arxiv.org/abs/1409.4842
📍 "SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation", Qiangeng Xu et al. (2021), https://arxiv.org/abs/2108.06709
📍 "GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting", Zhao Chen et al. (2022), https://arxiv.org/abs/2201.05938
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💬 Host: Lukas Biewald
📹 Producers: Cayla Sharp, Angelica Pan, Lavanya Shukla
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System Design for Autonomous Vehicles with Drago Anguelov - #454
Today we’re joined by Drago Anguelov, Distinguished Scientist and Head of Research at Waymo.
In our conversation, we explore the state of the autonomous vehicles space broadly and at Waymo, including how AV has improved in the last few years, their focus on level 4 driving, and Drago’s thoughts on the direction of the industry going forward. Drago breaks down their core ML use cases, Perception, Prediction, Planning, and Simulation, and how their work has lead to a fully autonomous vehicle being deployed in Phoenix.
We also discuss the socioeconomic and environmental impact of self-driving cars, a few research papers submitted to NeurIPS 2020, and if the sophistication of AV systems will lend themselves to the development of tomorrow’s enterprise machine learning systems.
The complete show notes for this episode can be found at twimlai.com/go/454.
Getting Waymo into autonomous driving
Waymo’s mission is to make it safe and easy for people and things to get where they’re going.
After describing the state of the industry, Drago Anguelov - Principal Scientist and Head of Research at Waymo - takes us on a deep dive into the world of AI-powered autonomous driving. Starting with Waymo’s approach to autonomous driving, Drago then delights Daniel and Chris with a tour of the algorithmic tools in the autonomy toolbox.
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Featuring:
Drago Anguelov – LinkedIn
Chris Benson – Website, GitHub, LinkedIn, X
Daniel Whitenack – Website, GitHub, X
Show Notes:
Dragomir Anguelov - Google Scholar
Drago Anguelov – Machine Learning for Autonomous Driving at Scale
Waymo
Waymo - Twitter
Waymo - LinkedIn
Upcoming Events:
Register for upcoming webinars here!