Lex Fridman Podcast

Dmitri Dolgov: Waymo and the Future of Self-Driving Cars | Lex Fridman Podcast #147

Dmitri Dolgov is the CTO of Waymo, an autonomous vehicle company. Please support this podcast by checking out our sponsors: - Tryolabs: https://tryolabs.com/lex - Blinkist: https://blinkist.com/lex and use code LEX to get 25% off premium - BetterHelp: https://betterhelp.com/lex to get 10% off - Cash App: https://cash.app/ and use code LexPodcast to get $10 EPISODE LINKS: Waymo's Twitter: https://twitter.com/waymo Waymo's Website: https://waymo.com PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 OUTLINE: 0:00 - Introduction 2:16 - Computer games 7:23 - Childhood 9:55 - Robotics 10:44 - Moscow Institute of Physics and Technology 12:56 - DARPA Urban Challenge 23:16 - Waymo origin story 38:58 - Waymo self-driving hardware 47:31 - Connected cars 53:23 - Waymo fully driverless service in Phoenix 57:45 - Getting feedback from riders 1:05:58 - Creating a product that people love 1:11:49 - Do self-driving cars need to break the rules like humans do? 1:18:33 - Waymo Trucks 1:24:11 - Future of Waymo 1:37:23 - Role of lidar in autonomous driving 1:50:23 - Machine learning is essential for autonomous driving 1:54:25 - Pedestrians 2:01:02 - Trolley problem 2:05:30 - Book recommendations 2:16:56 - Meaning of life CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman

Lex FridmanhostDmitri Dolgovguest

Dec 20, 20202h 23mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Waymo CTO reveals hard-won lessons building fully driverless cars

Dmitri Dolgov, CTO of Waymo, walks through the technical, historical, and product journey from the DARPA Urban Challenge to Waymo’s fully driverless ride-hailing service in Phoenix. He explains why Waymo pivoted from driver-assist to building a full replacement “driver,” and how custom hardware (LIDAR, radar, cameras) and large-scale machine learning underpin the system. Dolgov details the challenges of safety, perception, prediction, and mapping, plus the operational realities of running a commercial driverless service and trucking program. The conversation also touches on regulation, ethics (including the trolley problem), user experience design, and how advances like transformers in AI are reshaping autonomous driving.

IDEAS WORTH REMEMBERING

5 ideas

Proving full autonomy once is far easier than productizing it.

Dolgov contrasts early Google milestones—like 10 challenging 100‑mile routes with zero interventions—with the much harder task of engineering a reliable, repeatable, safety‑validated product that can run continuously for paying customers.

Waymo’s core bet is a full replacement driver, not driver assist.

Around 2013, the team deliberately pivoted away from freeway driver‑assist systems to focus on vehicles that can operate with no human in the loop, which fundamentally changes system design, safety requirements, and business model.

Sensor diversity (LIDAR, radar, cameras) is treated as a safety and capability multiplier.

Dolgov rejects the idea that LIDAR is a “crutch,” arguing that active sensors plus cameras offer complementary physics and richer raw data, improving robustness in low light, complex scenes, and rare edge cases.

Machine learning now permeates the entire stack, not just object detection.

While ConvNets handle classic perception tasks, Waymo increasingly uses data‑hungry models and ideas from language transformers for semantic scene understanding, behavior prediction, planning, mapping, and simulation.

Phoenix is a full-stack template for scaling, not just a demo city.

Waymo uses Phoenix to learn end‑to‑end: hardware/software performance, evaluation and release processes, rider UX, mapping, operations, and regulatory engagement, with the goal of making this pipeline copy‑and‑pasteable to new cities.

WORDS WORTH SAVING

5 quotes

There’s something magical about there being nobody in the driver’s seat.

— Lex Fridman

The most important lesson was that we believed it’s doable.

— Dmitri Dolgov

Why would you handicap yourself and not use one or more of those sensing modalities when they undoubtedly make your system more capable and safer?

— Dmitri Dolgov

Truly good driving gives you both efficiency and assertiveness, but also comfort, predictability, and safety.

— Dmitri Dolgov

There’s no substitute for actually doing the real thing—having a fully driverless product out there with users paying you money to get from point A to point B.

— Dmitri Dolgov

Dmitri Dolgov’s background and path from early programming to robotics and Stanford’s DARPA Urban Challenge teamThe birth and evolution of Google’s self-driving car project into WaymoTechnical architecture: custom sensors (LIDAR, radar, cameras), onboard compute, and large-scale MLFrom prototypes to product: milestones leading to fully driverless Waymo One rides in PhoenixSafety, perception, prediction, and interaction with pedestrians, cyclists, and other vehiclesScaling challenges: mapping, evaluation, deployment, and city-by-city commercializationWaymo Via and autonomous trucking, plus broader AI/ML trends (end-to-end learning, transformers, simulation)Ethics, regulation, user experience, and public reception of driverless cars

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