Everyday AI Podcast – An AI and ChatGPT Podcast - EP 486: Autonomous Driving: How new NVIDIA tech will make it a reality

Episode Date: March 20, 2025

You ever autonomously land a robot on Mars? 🪐Marco Pavone has helped do just that. Marco is NVIDIA's Director, Autonomous Vehicle Research. His next challenge? Bring true autonomy to vehicles on the road. 🚘Yeah, yeah, yeah. We’ve been hearing that autonomous vehicles are coming for like a decade. But new announcements at NVIDIA GTC are making that a reality. Like…. This year. Join us as we dive in: Autonomous Driving: How new NVIDIA tech will make it a reality -- An Everyday AI Chat with Jordan Wilson and Marco Pavone.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on these stories? Join the conversationUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:NVIDIA's GTC Conference and Autonomous Vehicle AnnouncementsMarco Pavone's Background and Role at NVIDIAIntroduction to Helios Full Stack SystemOverview of Current State of Autonomous VehiclesNVIDIA's Role in the Auto and Autonomous Vehicle IndustryNVIDIA and General Motors PartnershipChanges in Autonomous Driving TechnologiesGenerative AI and Simulation in Autonomous DrivingChallenges in Scaling Autonomous Vehicle DeploymentsHandling Location-Specific Driving Behaviors with Data-Driven ParadigmsNVIDIA's Use of Foundation Models and Simulation TechnologiesTimestamps:00:00 "Autonomous Vehicles: A Reality?"04:52 Autonomous Vehicles: Emerging Consumer Reality08:53 NVIDIA Expands Partnership in Physical AI13:00 Advancements in AI and Simulation15:40 Generative Simulation for Autonomous Vehicles17:15 Autonomous Vehicle Technology Challenges23:17 Adapting to Autonomous Vehicle Data24:06 "Data-Driven Bias in Autonomous Vehicles"27:53 Key Announcements in Vehicle AutonomyKeywords:Everyday AI Show, autonomous vehicles, NVIDIA GTC conference, AI advancements, autonomous drivingSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

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Listen daily for practical advice to boost your career, business, and everyday life. Could this year be the year that autonomous vehicles become the norm, right? You know, I know we've been hearing about how AI is going to, you know, improve. autonomous driving and it's going to bring us, you know, fully autonomous vehicles. We've been hearing this for many years. But I think today's guest is going to help really open our eyes and our ears to maybe some of these new technological breakthroughs that have actually changed the research going into this and might make this a possibility. And, you know, we are here at NVIDIA's GTC conference. A lot of new announcements when it comes to autonomous vehicles.

and we're going over those and, you know, talking about how this might impact all of us, right? The roads we drive, the safety of the future of autonomous vehicles. And I'm very excited for today's conversation. I hope you are too. So, hey, what's going on, y'all? My name's Jordan Wilson and welcome to Everyday AI. This is your daily live stream podcast and free daily newsletter, helping us all keep up with the ever-changing world of AI and how we can actually learn from the experts, you know,

helping build it all so we can grow our companies and our careers. That's what you're trying to do. You're definitely in the right place. Make sure if you haven't already, please go to your Everyday AI.com. Sign up for the free daily newsletter. We're going to be recapping all of the important insights from today's interview there, as well as everything else that you need to stay ahead. All right.

So enough chit chat. I'm excited for today's guests and talk about everything new that Nvidia is working on in the autonomous vehicle space. It is a lot. So please help me welcome to the Everyday AI. Marco Pavone, an associate professor at Stanford University, and the lead autonomous vehicle research at NVIDIA. Marco, thank you so much for joining the Everyday AI show.

Thank you very much for having me. All right. I'm excited for this conversation. This is, I think, one of those hot topics that people love talking about. But before we dive in, can you just tell everyone a little bit about your background and what you do here at NVIDIA? Yeah, so I'm an adibologist. So I'm a faculty at Stanford, and I also lead to research at the EBIA.

My work is in the field of Alhano's robotics, so how do we make robotic systems capable of making decisions on their own, especially in high-stake applications, like, for example, self-driving vehicles or aerospace vehicles. And prior to joining Stanford, I was a robotist at another chat proportion lab, so still working on self-driving vehicles, but on Mars, instead of Earth. Yeah, yeah, it's not every day you get to talk to someone that's, you know, you know, helped the autonomous, you know, which project was that on Mars that it was? It was one on the Mars-lamming mission. There we go.

NASA sent a rover to Mars, and the mission was a success. Love that. So yeah, we're bringing, you know, some research from Mars to, you know, your streets here. So, you know, there was a lot that's been announced so far at NVIDIA GTC when it comes to autonomous vehicles. But, you know, one of the things I wanted to talk about is Halos. So can you tell our audience what that is and how it's ultimately, you know, going to impact everyone else on the roads. Yeah, sure. So HALOS is a full stack system that comprises hardware, software, tools, and safety principles to combine all of these elements into a safe driving stack. And it's exactly because it's basically unifying all the investments that Nvidia has been

made the past two years on the topic of automotive safety into unified program. And I believe this program is going to boost safety both with respect to the, you know, own ID program internal at the Nvidia, but also helping partners in making their program, the programs you can you just bring us up to speed, right? because, you know, even people, you know, who's listened to the show, we've talked about autonomous vehicles and in AI, you know, in years past. But bring us up to today. What, you know, what has been successful because there's obviously, you know, fully self-driving cars on the road, right, in certain states where it's allowed. But, you know, where are we at, you know,

in the, you know, fully self-driving autonomous vehicles, what's working, what's not? Well, autonomous vehicles are becoming a reality. And I'm sure that you have heard these sentence many times in a few years. But like, you know, if you can to San Francisco, you will see robotaxis providing rights to customers without any safety driver on board. And a very advanced driver assistance systems, for example, the Tesla autopilot are becoming available on a massive scale. So my point is that the self-driving technology is now graduating into becoming.

consuming consumer technology. That's why I feel confident by saying that the autonomous vehicles are becoming a reality. We still have a challenge. So we have, you know, solved the problem yet is, I like to draw a parallelism with respect to aviation. It took us like a hundred years to get to an industry that is as safe as it is today and as efficient as it is today. So this is a marathon in it's not a spring. What are the key technological challenges? Well, in the context of a full set traveling vehicles like robot access systems, their deployments are still relatively limited in few cities in the world. And making, scaling up those deployments still represent significant technological challenges as requires scaling the algorithms, allowing the algorithm

to generalize to new situations in a much more effective way. So we need some innovation there throughout the development cycle, from simulation to training to algorithm design and so on. Same thing for semi-automated systems. The semi-automated systems are available worldwide, but of course we want to increase the availability. That's again that requires technology. And can you maybe just help our audience better understand

and in video's footprint right now in the, you know, not even just the autonomous vehicle industry, but just the auto industry in general, because, you know, it's something I've found out through the years by getting to talk to, you know, really smart people such as yourself, but, you know, people don't know, you know, you probably have, you know, or might have Nvidia, you know, hardware in your car, right?

Like Tesla, you know, is probably using, right? Invidia's data, you know, their data centers. But can you just bring us up to speed? What is in video's footprint right now? the auto industry. Yeah, absolutely. So, invidia, first of all, is both a product company

and an inconsistent company. So, Nvidia has a substantial investment in developing its own autonomous vehicle solution, which we call Nvidia pride. And it is doing so in collaboration with partners, such as, for example,

Mercedes. And it's also helping, since it's also an inconsistent company, it's also helping other AV companies to develop their own. They be problems. In many different ways, there is not like a unique recipe.

It could be by providing the Nvidia automatic-grade chip. Many of the autonomous vehicle companies out there are using indeed Nvidia hardware. It may be by providing data centers to train the AI. It could be by providing simulation technologies and so on and so forth. Every company is a bit different, but this is also what, a researcher like myself makes it exciting because I have an opportunity to really scale up my contributions even beyond the confines of Nvidia to really the entire ecosystem.

So, you know, speaking of different brands or different companies, also some exciting news, Nvidia and GM, talk about this partnership a little bit and, you know, when we might see, you know, that partnership actually out there on the roads, right? I'm not going to hold you to it, right? But like, what what's coming in this partnership? Well, in terms of timing, it's a little bit of sensitivity. Of course. I would say, though, that is a super exciting partnership.

I was also imparting involved in the discussion, so I'm really happy to see that coming to fruition. One of the interesting aspects of it is that there's a partnership regarding automotive, but also manufacturing and potentially in-robotics. It's a very broad partnership. that also plays well with invidia's broader ambitions. Of course, as we said before, invidia has a very strong autonomous vehicle program,

but indeed is also scaling up this problem to what we refer to as a physical AI program, whereby cars are just one instantization, one embodiment of a broader concept that is that of physical AI. In addition, for example, to humanoid or autonomous mobile robots and so on. And so this collaboration with General Motors can also involve this kind of broader vision. So simply like, does this just mean in the future, you know, are we going to see, you know,

different versions of GM's vehicles, have autonomous capabilities or is the long-term goal with this partnership to have maybe most or all of GM's vehicles. Like, what's that going to look like? Is it just going to be kind of like certain vehicles in the future are going to, you know, kind of benefit from this partnership? Or is it just kind of all vehicles like longer down the line? Okay. No, no, no, that's fine.

That's fine. No, okay. All right. We'll just have to follow up on that when the news does come out. But, you know, so I'm curious. It seems like, you know, very fresh. I don't press. Yeah, it's extremely fresh, extremely fresh.

So yeah, we'll follow off with that once that news is officially released. But, you know, one thing that I do want to talk about is, you know, and we even mentioned this, right? Like there's been a lot of excitement, right, in this space for three, five, ten years. What specifically do you think has changed over the last couple of years that leads you and your team to believe that, you know, now is this time, you know, that, you know, we are kind of hitting that moment when this might become. much more common autonomous vehicles on the road.

So for, for the world, even more than 10 years, right? It's true. I was doing my PhD between 2006 and 2010 at MIT. It was the time where the autonomous driving technology was

starting with its fairly stats. I would say it's almost been 20 years. Okay, there we go. So the way we build autonomous system today is very different from what we were used to do 20 years ago, of course. there have been, of course, a lot of lessons learned,

but most importantly, the technology has changed. And there is not like a single technology that has really, you know, changed the game completely, but a convergence of technologies. All the way from Hubbard, so basically having dedicated chips and dedicated sensors, all the way to, as you can imagine, AI becoming pervasive in the design of autonomous systems.

And in that context, I think one of the most exciting opportunities, clearly still a little bit debated here in the community, is the opportunity of leveraging so-called internet-train models. You might be familiar with GTPT, for example, with the idea that with this type of models, we have an opportunity to bring internet-scale knowledge to the task of driving. Think about how you learned about driving. It took you a few hours.

probably to learn how to drive a car simply because you brought a lifetime of experiences to the task of driving. Well, that's a hypothesis about behind using this kind of interoperative models to bring multiple lifetime of experiences of general knowledge to the task of driving. So that's another AI in general and potentially interoperative models in particular

provide opportunities to improve the technology. And another, I would say, big technology that has made an amazing process in the past two, three years is the simulation technology. And in Vida, actually, had a number of announcements related to simulation. Simulation is always been a holy grail in robotics.

And now, finally, we are simulators that we can use throughout the development lifecycle from the training of the vehicle, the training of the AI, or the way to the testing of AI. So the key, the challenge with simulation historically has been the so-called simulation to realism gap. And this gap has become an increasingly closer along a number of dimensions in terms of visual realism, in terms of behavioral realism, how faithfully we replicate the behaviors of humans in the road and so on and so forth.

So long story, sure, I wouldn't say it's a single technology that is really pushing this industry forward, is really a convergence of technologies from the chip all the way to the algorithm or the simulation that all of this, you know, now I finally coming together. Yeah, and I do want to talk about that a little bit more. So kind of this concept of using, you know, Nvidia's new generative AI technology, cosmos, correct? So like, walk us through that. And, you know, I know this might be difficult, you know, to imagine.

on the podcast. So in the newsletter, we'll link to some of these videos, you know, and how Cosmos helps. But walk us through these simulations and how specifically, right, maybe, you know, since we've hit this generative AI wave, how does that help with, you know, Nvidia's ability to use more diverse simulations? And how does that make ultimately the autonomous vehicle sector safer? Yeah, that's a great question. So typically simulation is restricted to the scenarios that are authored by a human. So a human saying, okay, I want to test a vehicle with a particular intersection. So I'm going to draw a map with respect to which a vehicle has to drive. It's fine.

Of course, it doesn't scale to millions of cases, right? Or there are new technologies referred to as neuroreconstruction technologies that allow you to reconstruct in 3D a scenario out of drive. that are recorded. This is all fine, but for autonomous vehicles is really a game of the last 5% or 1%. It's all about thinking about very complicated coronary cases. And that's where generative simulation comes in. So this new technology and the cosmos is one of the prominent examples, allow you to simulate, allow you to generate a simulation out of textual prompts or images. So this allows you to create a completely new simulation scenarios to, for example,

stress tests your vehicle. So it really allows you to automatically and in a way it is highly scalable, generate a plethora of a corner cases that can allow you to better, more robust systems and also test those systems. Now, it's still a technology in a development, so there are still challenges, like for example, physics realism is a challenge to what extent is generated simulations, for example, obey the law of physics. But there's quite a bit of progress in improving the physics realism. So I'm very hopeful that this technology will be yet another tool that has no time engineering I can leverage in order to build a more capable and safer autonomous vehicles.

And it's not just for Nvidia. I believe that the customers and broadly generally simulation is going to have a significant impact in the whole industry, but more broadly in the whole robotics industry. Yeah, and I think that's really important to bring up, because, you know, especially if you've been to a city like San Francisco or, you know, I've seen, you know, Waymo's and in Austin, Texas, right? So these vehicles and this technology has, you know, it's been out on the road. roads, right? It's out there in the wild, which, you know, allows Nvidia and other players in the space to gather that actual real life data, right? So, you know, I'm wondering, you know, if the simulation side is improving and you're able to, you know, simulate more scenarios with the cosmos platform, you know, what are still some of those bigger hurdles, right? Aside from, you know, just more time and, you know, more data from the real world from the cars, right? What are some of those other big hurdles that the

space is still looking to maybe overcome? Well, data is a big one. So simulation is going to help, but you still need to have real data to ground your system. So the hurdle is to make technologies, AI technologies that can adapt with an increasingly lower amount of data to new areas. And it is a problem that makes it difficult to appreciate, but it's crucial. transportation is a very location-specific phenomenon. Like, I don't know if you have Italian followers.

I'd like to give an example that actually this is also tip for you. I love it. Please, please. I have to. I'd have to soon. If you go to Italy, as someone blinks the lights at you, typically that is a kind sign. It means you can cross in front of me and eating at you.

If you go to the south of Italy, typically blinking is an aggressive sign. It means don't you dare. cross in front of me because I'm not going to stop. And if you cross me, we're going to crash. Right. So in the same country, a few hundreds kilometers apart, with two completely different behaviors. So this is what I'm saying.

You still need to have some location-specific data and train your AI. So then the game is, but that is expensive to acquire. Sure. So how through better simulation and better algorithms, we can decrease the reliance on real data. There always will be some need for real data.

The question is how we can reduce it so that we can really quickly expand to new domains. Technically, we refer to those as operational design domains to really make this technology financially viable. We know it's technologically feasible. Waymo is developing robot

accidents in San Francisco. But to make it financially viable, we have to be able to scale it up. Ski it up means to be able to kick it at a flywheel that is not to honor us in so how much data we need. Simulation is one tool, better algorithm design is another tool

driving down the cost of some key sensors. Like, for example, as you might have heard, there's a quite a bit of discussion in the community in terms of to what extent you want to have a sensor system that is very much camera-centric, or maybe also relying on ladders and so on and so forth. So these are additional discussion. Again, it's not just a single technology,

is a combination of technologies, but I would say the capability of scaling operational design domains more seamlessly is the major challenge, which would be solved through a combination of a number of technologies from redundancy at the sensor level to simulation to algorithms that adapt more quickly to new scenarios with the best. That's a fascinating example, right? Because I never thought about that, that, you know, it's not just a one-size-fits-all approach for autonomous vehicles. Yeah, yeah. You're like definitely think about that. So like as an example, right, like even I'm thinking in the United States, people have different driving styles, right, from state to

state, city, you know, big city urban areas, right? So, you know, I'm curious, you know, what are some maybe successes that you found in addressing those or how do you even go about knowing those things, right? Aside from like, you know, you, you know, you have lived there, you know, but for everyone else and for all these other challenges that maybe the industry hasn't thought about. I mean, how do you, you know, start to tackle these issues? Is it just, you know, maybe, oh, there was an accident and we don't know why because, you know, all of our data was right. And, you know, is that kind of how you discover these things?

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where we let the autonomous vehicle learn from the experience basically from demonstrations because that is more scalable as a technology. So all these new behaviors essentially are to a large extent and learned from data that is acquired through other test vehicles, but even maybe dash cam videos. The good thing is that videos are bound on the internet. That's yet another opportunity to more seamlessly skill up the AI to new operational design domain. So bottom line is that these days many of those behavioral nuances are learned through data, which again means that we need to have technologies that allow us to make as much use, as much efficient use of this data as possible. The good thing is that data abounds soon. We do need data acquire from a test fleet,

for sure. But one thing that abounds on the internet is videos, and basically driving videos. And that is again a new modality that as an autonomy engineers we have at our disposal, to allow the skill of this technology and even more efficient. You know, and I'm curious because on the data point, I may be wrong in this assumption, but I'm guessing very early on, a lot of the data that you might get, you know, from those driving videos, let's say,

if there was a bunch from five years ago, I'm guessing that the other cars, for the most part, were not autonomous, right? They were driven by humans. So, you know, I'm curious, like, what are you all at Nvidia? And again, maybe just the broader industry doing to account for that, right? Because what if in five years, it's 10% autonomous vehicles and, you know,

how can you say, oh, in the training data, this was an autonomous vehicle versus this as a human? And, you know, what challenges does that, you know, bring in the future? Will it just be, you know, autonomous vehicles, be able to more communicate with each other so they know, okay, you're an autonomous vehicle, you know, you have the halo system, so this is what you're going to do? That's a great question. I have a lot of some questions. So, first of all, as you alluded to, now that we're moving toward a more data-driven paradigm, then the brain of the autonomous vehicles becomes very much dependent on the data that you use to train the brain. So it becomes imperative, and this is one of the hallmarks of kilos,

to develop AI-cudation workflows that allow you to remove unsafe behaviors or biases from your training set. And interestingly, this is another domain where we use internet pre-traded models now not as drivers, but as judges that allow us to judge whether a given demonstration is a demonstration of safe driving or not. And of course, there are humans in the loop to align the judgment of these AI models. So we, as humans, we think it's acceptable driving. But we train this basically AI models to serve as judges at scale to remove all those biases that you were matching.

Now, moving forward, ADE is a very weird technology because we are solving the hardest problem first. Going back again to aviation, you know, I have this kind of dual aerospace and earth-based background. It's like as if the BRB brothers at the first problem they wanted to solve was a supersonic flag. That's the state of the AV industry. solving the hardest problem first because we're solving the problem where there are only few automated vehicles everybody else is a human like you have a human driven vehicles there is no dedicated infrastructure but is this basically what it is now in the future there will be a higher penetration of autonomous vehicle so your question is how that will change the technology

definitely provides an opportunity to make the technology even safer But as with everything, you have an opportunity, but you also have a challenge. What is the challenge? Well, there are multiple. So let's assume that, for example, autonomous vehicles could communicate with each other. In principle, that is great because it allows some level of coordination, which has clearly an immediate impact on safety. But it exposes your decision-making capabilities to external interference.

So, for example, cyber security becomes much more of a threat than it is now where the system is basically very much confined within the vehicle. It might introduce latencies. You know, sometimes when you have a call on your phone, you don't care if your communication drops for a little bit. That could be fatal in the case of an automated vehicles. So not to say that these are impossible challenges. I'm just saying that it's not as simple as people might think that vehicle to vehicle or vehicle to infrastructure might simplify the problem. Let alone the challenge of who is going to place that infrastructure,

how you're going to standardize that infrastructure. So, yes, when there will be higher penetration autonomous vehicles, there will be opportunities to make this technology even safer. But the exact mechanics about how we will do it is still subject to all the discussion. So, Marco, we've covered a lot in this conversation. you know, as we wrap up, what do you think is maybe the one most important thing for our viewers and listeners to know about, you know, specifically even new advancements that were announced here at GTC and how that is seemingly going to quickly change the future of autonomous driving on our roads? I think there were two announcements broadly that are going to have a significant impact in the field of vehicle autonomy.

First, all the announcements related to simulation, and seven, the announcements related to foundation models, internet training models that would be used in the context of physical AI. One of the big announcements, actually, this is a month that was made as CS and then it has been the finite GTC, is that of cosmos, which is by now a sort of an umbrella term, where we cover both video generation models. particularly useful for simulation and reasoning models, particularly useful for economists driving in the real port.

So these are definitely technologies that are worthwhile to keep in mind if you are an economy researchers. Many of these technologies are available open source on the Alpine face. And at Nvidia, that's one of the reasons why I'm excited about being at Nvidia, we publish a lot, so we share a lot of our knowledge. And again, this is because Nvidia is both a product company and an ecosystem company. So we want to make sure that as we grow, the entire ecosystem really grows. I love it. I think I just became so much more informed on everything, you know, autonomous vehicles, what Nvidia is working on. And I really hope that

our audience did as well. So Marco, thank you so much for your time and coming on the everyday AI show to share with us. We appreciate it. Likewise, and if you want to become an autonomy engineer, you're welcome to take someone with horses. There we go. Hey, at least I'm part of the way there, right? I went from zero to one, I think. So, hey, that was a lot of fantastic information. InVidia is working on a ton of advancement. So if you missed anything in there, if you want to know more, we're to be recapping today's conversation in the newsletter. So thank you for joining us. If you haven't already, go sign up for that newsletter. Read it today. It's got to be a great one at your everyday AI.com. So thank you so much for tuning in. Hope to see you back tomorrow and every day for more

Everyday AI. Thanks y'all. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit Your EverydayAI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time. Meet Firefly AI Assistant. an Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface.

You direct the outcome while the assistant accelerates execution. Stand control with the ability to step in and refine at any time. See it today at firefly.adop.com. And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit your everyday AI.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.