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

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

Lex Fridman PodcastDec 20, 20202h 23m

Lex Fridman (host), Dmitri Dolgov (guest), Narrator, Narrator, Narrator, Narrator

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

In this episode of Lex Fridman Podcast, featuring Lex Fridman and Dmitri Dolgov, Dmitri Dolgov: Waymo and the Future of Self-Driving Cars | Lex Fridman Podcast #147 explores 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.

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.

Key Takeaways

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.

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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.

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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.

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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.

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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.

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Fleet connectivity improves performance, but each car must stand alone.

Vehicles can share information about crashes, construction, and changing road conditions, but all safety‑critical decisions are made onboard without relying on connectivity or teleoperation, which Waymo explicitly avoids.

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Real-world rider feedback deeply shapes product and behavior design.

From pickup/drop‑off locations and grocery workflows to biking with a disassembled bike, user studies and in‑ride feedback inform mapping details, UI, and driving style—aiming for an experience that is safe, efficient, predictable, and comfortable.

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Notable 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

Questions Answered in This Episode

How will advances in large language models and transformers concretely change prediction and planning for autonomous vehicles over the next decade?

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. ...

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What are the hardest remaining edge cases that prevent Waymo from rapidly deploying in dense, chaotic cities worldwide?

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How should regulators evaluate and certify driverless systems when their behavior emerges from complex machine learning rather than simple, auditable rules?

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In a future where fleets of driverless cars coordinate, how might traffic patterns, city design, and personal car ownership change?

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What ethics framework should guide autonomous vehicles in ambiguous situations where human drivers today behave inconsistently or illegally?

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Transcript Preview

Lex Fridman

The following is a conversation with Dmitry Dolgov, the CTO of Waymo, which is an autonomous driving company that started as Google Self-Driving Car Project in 2009, and became Waymo in 2016. Dmitry was there all along. Waymo's currently leading in the fully autonomous vehicle space, in that they actually have an at-scale deployment of publicly accessible autonomous vehicles driving passengers around with no safety driver, with nobody in the driver's seat. This, to me, is an incredible accomplishment of engineering on one of the most difficult and exciting artificial intelligence challenges of the 21st century. Quick mention of a sponsor, followed by some thoughts related to the episode. Thank you to Trial Labs, a company that helps businesses apply machine learning to solve real-world problems. Blinkist, an app I use for reading through summaries of books. BetterHelp, online therapy with a licensed professional. And Cash App, the app I use to send money to friends. Please check out these sponsors in the description to get a discount and to support this podcast. As a side note, let me say that autonomous and semi-autonomous driving was the focus of my work at MIT, and is a problem space that I find fascinating and full of open questions from both a robotics and a human psychology perspective. There's quite a bit that I could say here about my experiences in academia on this topic that revealed to me, let's say, the less admirable sides of human beings. But I choose to focus on the positive, on solutions, on brilliant engineers like Dmitry and the team at Waymo, who work tirelessly to innovate and to build amazing technology that will define our future. Because of Dmitry and others like him, I'm excited for this future. And who knows? Perhaps I, too, will help contribute something of value to it. If you enjoy this thing, subscribe on YouTube, review it with five stars on Apple Podcast, follow on Spotify, support on Patreon, or connect with me on Twitter @LexFridman. And now, here's my conversation with Dmitry Dolgov. When did you first fall in love with robotics, or even computer science, more in general?

Dmitri Dolgov

Computer science first, at a fairly young age. Robotics happened much later. Um, I, uh, I think my first interesting introduction to computers was in the late '80s, uh, when we got our first computer. I think it was an, uh, an IBM, I think. IBM AT, I think. Remember those things that had, like, a turbo button in the front?

Lex Fridman

Turbo button? Yeah.

Dmitri Dolgov

Where, where you would press it and, you know, make, make the thing goes faster.

Lex Fridman

Did they already have floppy disks?

Dmitri Dolgov

Yeah. Yeah, yeah. Yeah, like the, the 5.4-inch ones.

Lex Fridman

I think there was a bigger inch. So good- went something, then five inches, then three inches.

Dmitri Dolgov

Yeah, I think that was the five. I don't, I, maybe that was before that was the, the giant plates, and I didn't get that. Uh, but it was definitely not the, not the three-inch ones. Uh, anyway, so that, that, you know, we got that, uh, computer. I spent the first, uh, few months just, you know, playing video games, uh, as you would expect. I, uh, got bored of that, uh, so I, uh, started messing around and, uh, trying to figure out how to, you know, make the thing do other stuff. Got into, uh, exploring, you know, programming, and a couple of years later, it got to a point where, um, I actually wrote a game, uh, a, a little game.

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