Again, I’m not suggesting this. Bottleneck has a specific meaning. It means Waymo is limited by not having the ability to collect data. Well, clearly that’s not true because Waymo already has a reasonably scaled deployment across a dozen cities that no one else has and can handle millions of scenarios.
Real world data is absolutely required, but more of it doesn’t give you magical self driving ability as Uber’s CTO suggests. If it were the case, you’d see Tesla achieve fully autonomous driving years ago.
Mapping and simulation have very different purposes. Doesn’t look like you’re familiar with the basics of AV technology. Explains a lot why you’re confused about how real world and simulated data is used.
Most AVs, definitely Waymo vehicles, are self mapping. They can detect environment changes and relay it to the entire fleet. That's because they map using the same vehicles as the fleet.
> Waymo might very well be missing specific kinds of data (e.g more incidents/accidents, near-collisions etc)
Accidents and near-collisions are exactly the kind of scenarios perfect for simulation. You don't test them out in the real world and risk injuries/deaths. You need to have confidence they're handled before you deploy.
I agree real world data is important for Waymo. I didn't mean to say it wasn't, so I've edited my comment to reflect that. It's just that data is not some magic bullet to achieve self driving like Tesla and others suggest.
Of course, Waymo still has much more room for improvement. But it's much more efficient to supplement less but higher quality IRL data with large amounts of synthetic data, than to run a million data collection vehicles 24x7 because most IRL data is boring and useless.
Waymo said 6 years ago they simulate 20 million miles every single day [1]. Clearly, it's working for them given their scale of deployment right now.
> The insight driving the program, Naga said, is that the limiting factor for AV development is no longer the underlying technology. “The bottleneck is data,” he said. “[Companies like Waymo] need to go around and collect the data, collect different scenarios. You may be able to say: in San Francisco, ‘At this school intersection, I want some data at this time of day so I can train my models.’ The problem for all these companies is access to that data, because they don’t have the capital to deploy the cars and go collect all this information.”
You can’t be the CTO of Uber wanting to do AVs, and get the data collection requirement shockingly wrong.
Waymo is able to deploy with less (but targeted and high quality) data collection by having world class simulation capabilities. Not that they haven't collected huge amounts of data as it's no doubt important (I've heard their onboard storage is transferred and emptied every few days), it's just not a bottleneck. They have the most efficient operation in the AV industry.
The best example of why data collection isn’t the bottleneck is Tesla. They boast about billions of miles of data, yet they’re struggling to put out fully autonomous vehicles.
This is fascinating. I feel like this is converging into the concept of a traditional "IDE". So much of your setup reminds me of IDEs indexing, doing static analysis, building ASTs, etc. before a developer starts writing code.
> Before Waymo deploys in a new city, it deploys a huge fleet of cars that spend months of driving completely supervised, presumably to construct a detailed LIDAR map of the city.
Not entirely true. From their recent "road trips" last year, the trend is they just deploy less than 10 cars in a city for a few weeks (3-4 weeks from what I recall) for mapping and validating. Then they come back after a few months to setup infrastructure for ride hailing (depot, charging, maintenance, etc.) and start service.
We all know Tesla likes to play smoke and mirrors game with vehicle numbers — they have 300+ "robotaxis" but only 7 of them are unsupervised [1], and they shut down when it rains [2].
So let's use a metric that unequivocally shows who is 'winning'. I'm confident Waymo will have more paid rides per week than Tesla at the start of 2027 (I'll give you 2028 if you want). No other metric indicates scale better than passenger trips. If you have more robotaxis or you are in more cities, it will show up in the trip count.
I'll give $1000 to a charity of your choosing if Tesla beats Waymo in this metric. Fully unsupervised trips only, does not include trips with a safety driver or a monitor in a passenger seat, none of the usual games they like to play.
Waymo drives 4 million miles every week (500k+ miles each day). Vast majority of those collisions are when Waymos were stationary (they don’t redact narrative in crash reports like Tesla does, so you know what happened). That is an incredible safety record.
> Tesla is executing the strategy that most quickly scales to 100% of the population.
So, uh… where is this “scale” then? This “strategy” has been bandied about for better part of a decade. Why are they still in a tiny geofence in Austin with chase cars?
Waymo is doing it right now. Half a million rides every week, expansion to a dozen new cities. Tesla does a few hundred in a tiny area.
Scale is assessed by looking at concrete numbers, not by “strategies” that haven’t materialized for a decade.
Again, I’m not suggesting this. Bottleneck has a specific meaning. It means Waymo is limited by not having the ability to collect data. Well, clearly that’s not true because Waymo already has a reasonably scaled deployment across a dozen cities that no one else has and can handle millions of scenarios.
Real world data is absolutely required, but more of it doesn’t give you magical self driving ability as Uber’s CTO suggests. If it were the case, you’d see Tesla achieve fully autonomous driving years ago.