Inspired with Alexa von Tobel
00:04:38 · Alexa von Tobel
[00:04:38] S1: That's incredible. I love that. I wanna talk a little bit about your time at Waymo, where, again, you really led their autonomous trucking initiative. I could geek out on this just because I'm so fascinated by how that could transform society, safer, all of the things. But can we just get a sense of, like, first of tell us what you did there. Let's start there. And then I wanna talk a little bit about kinda where it's headed. So let's start there.
00:05:05 · Boris Sofman
[00:05:05] S0: It was such a wonderful time. So I was at Waymo for about five years, so I was an executive leading autonomous trucking, but then I was also leading various technology teams that supported kind of all platforms, cars and trucks. And so I was leading a perception team and then various other teams. And so we helped launch the Jaguars into San Francisco. We unified the tech stack to where cars and trucks were as unified as possible in terms of the overall architecture, where you'd have differences in the platform, sensor layouts, you'd have an articulated trailer, but we were using almost every single part of the autonomy stack, which was very powerful. And so we ended up leading both freeway driverless for both cars and trucks. Waymo ended up focusing much more on the car side where there was like a really a pressure to commercialize and accelerate that. And so we actually launched driverless on freeways for the cars at the end of twenty twenty three. But in a lot of ways that was actually the trucking kind of like kind of program, kind of unified into the car platform. And it was a fascinating problem because with trucking you had this like 80,000 pound trucks with an articulated trailer. You had like crippled vehicle dynamics where you know, there's like, so had so little flexibility on acceleration and deceleration because of the mass of these vehicles. You had these interesting challenges that because of that, we had to push the perception range out from a few 100 meters, which is what the car is mostly utilized to close to a thousand meters, you know, using camera and radar to go far beyond LIDAR range. You started to think about these extremely rare long tail kind of challenges that appear so infrequently on a freeway and completely changes the way you do a safety framework. It was just amazing problems.
00:06:46 · Alexa von Tobel
[00:06:46] S1: Can you give us an example of like one of those sort of real long tail challenges that you had to think through?
00:06:53 · Boris Sofman
[00:06:53] S0: Well, let me share one that's like actually a big class of problems, reliability. So reliability is effectively how resilient are you to failures in pieces of your software and hardware. And so what's interesting about freeways is that the speed at which you drive means that you cannot stop in a lane. Like it's just like your budget for that is just like tiny, absolutely tiny. Because if you stop in a lane on a freeway, you actually have a meaningful risk of having a rear end collision with a really, really serious, kind of high severity of collision. And as I think like something like six to 8% that integrates over time, the longer you're sitting there. And so you have this situation where on a surface street, when you're driving in San Francisco, you're at 25, 30 miles an hour, almost always. If you stop, it's a bad product experience or kind of operationally a headache, but it's not a safety risk in the traditional sense of safety. Here it is. And so not only do you have to reinvent the platform of redundancy for brakes, steering, actuation, electrical kind of anything that mechanically could fail. But by percentages, the largest frequency of what you have to deal with is everything else. So a sensor failing, your computer crashing, a process hanging, your localization failing, your router, your perception behavior, almost anything. And individually, things might be rare, like tens of thousands or hundreds of thousands or millions of miles in between. But when you compound all of them, you can't go driverless on a freeway without a solution. And so you end up having to have this incredible safety net of redundancy across everything where you have like catchalls with software that's constantly changing every single release and thrashing the reliability rates. And you have to have like a space shuttle level of reliability with a system that's evolving all the time. And so you have layers of primary and secondary systems and catchalls and budgets that you have to manage to deal with things that you will never see in our lifetime. So for example, a computer will have a kernel panic that just crashes completely out of your control every 500,000 miles, that'll eat up, that's way more than your entire budget of these sort of failures.
00:09:04 · Boris Sofman, Alexa von Tobel
And so it becomes this interesting thing where when we went driverless on a freeway, very first time, the first 100 miles of driverless with the Jaguars, our reliability that we had to hit was 1,000 times higher than what was already a pretty sizable scale, already driverless in San Francisco. And so three orders of magnitude. And so these are sort of challenges that are just like absolutely incredible. And for things that you would never see on a typical, you know, five hour drive on a freeway. [00:09:33] S1: Wow. Let's geek out for a second. Let's talk a little bit about the future of autonomous trucking. What's your expectation for the next five to ten years? Kind of what's your mental model? What's probable?
00:09:43 · Boris Sofman
[00:09:43] S0: Trucking is an incredible space. Like it's such a deep passion of not just mine, but actually a lot of the team at Bedrock is from the trucking program, just kinda really loved working together on this. As an industry, it's astronomically large, so the entire ride hailing industry in The US, I believe is something like 50,000,000,000 in revenue. Trucking is a trillion. So it's like really, really significant. And it's the lifeblood of the entire economy. So you have, everything grinds to a halt, you burden the transport of goods, It's very dangerous. There's like, point an accident happens, it's catastrophic. There's over, I think it's over 5,000 people a year die from trucking accidents in The United States. Very difficult kind of lifestyle for long haul truckers. And what you end up having is this like really deep inefficiency in the entire network where you have by design like eleven hour driving limits, federally regulated to where you cannot exceed that because of fatigue. And so what you end up having is this fragmentation where you do a long haul route across the country, 55, 60% of your time is spent on overnights, kind of like resting, or you have to do dual drivers, which is incredibly expensive. You end up having huge amounts of, I think it's something like over 25 to 30% of trucks are almost empty or close to empty because you have, people having to like, they missed their pickup window and now they're not gonna be able to meet their shift. Right, they have to go back home. You have people in Chicago when you actually need the trucks in LA during the holidays. And so you have all of this inefficiency and there's studies that have been done where only about a third of that inefficiency is due to asymmetric demand. Like there's more trucks coming into The US from Mexico than going the other way. The rest of it is human factors. And so you have this like giant latent kind of capability to be able to like optimize across the whole network and move trucks around anywhere we need to demand, be able to drive 20 fourseven. When you do that, the pricing will go down because the price of a load is amortizing all these empty loads and all this inefficiency.